arXiv subjects

Hao Fu

Publications and source records attributed to Hao Fu.

At least 37 records · Page 2Linked to original sources

arXiv2026

Enhancing User Fairness in Two-Layer RSMA: A Movable Antenna Approach

Enhancing user fairness in advanced multi-user systems like two-layer rate-splitting multiple access (RSMA) is a critical yet challenging task. This letter proposes a novel movable antenna (MA) approach to address this challenge. We formulate a max-min fairness problem, maximizing the minimum user rate, a key metric for fairness, through the joint optimization of the beamforming matrices, user clustering, common rate allocation, and the antenna position vector (APV). To solve this non-convex problem, we develop an efficient two-loop iterative algorithm. The outer-loop leverages the dynamic neighborhood pruning particle swarm optimization method to find a high-quality APV, while the inner-loop optimizes the remaining variables for a given APV. Simulation results validate our approach, demonstrating that the proposed scheme yields significant fairness gains over various benchmark schemes.

cs.IT↗

arXiv2026

Too many or too massive? Investigating the high-$z$ demography of active SMBHs from JWST

Recent JWST observations have unveiled a numerous population of low-luminosity active galactic nuclei (AGN) at $4< z<10$, with space densities roughly an order of magnitude above pre-JWST estimates, and many of these AGN have masses orders of magnitude above the local black hole mass-stellar mass ($M_{\rm BH}-M_{*}$) scaling relations. We investigate the consistency of these observations within a data-driven framework that links the galaxy stellar mass function to the supermassive black hole (SMBH) mass function and AGN luminosity functions using different $M_{\rm BH}-M_{*}$ relations and the observed Eddington-ratio distribution. By comparing our predictions against observed AGN luminosity functions at $z\sim 5.5$ we find that observations can be reproduced either by highly-elevated $M_{\rm BH}-M_{*}$ relations paired with low duty cycles, or moderate relations with higher duty cycles. Through the Soltan argument, we find that $M_{\rm BH}-M_{*}$ relations that are modestly above the local relation for AGN produce consistency between multiple tracers of the SMBH demography at $z\sim 5.5$, while more extreme normalisations would require a weakly-evolving luminosity function at $z> 5.5$. Continuity-equation modelling shows that initially high $M_{\rm BH}-M_{*}$ relations predict a strong two-phase evolutionary scenario and very steep low-mass SMBH mass functions in tension with several current estimates, while more moderate relations generate local SMBH mass functions in better agreement with present determinations and near-constant scaling relations. Our results favour a scenario where SMBHs at $z \sim 5$ on average lie modestly above local AGN scaling relations, with elevated but physically plausible duty cycles. Future wide-field clustering and demographic studies will help break the remaining degeneracies between SMBH scaling relations and AGN duty cycles at early cosmic times.

astro-ph.GA↗

arXiv2026

Gravity Wave Interactions in the Stratocumulus-Topped Boundary Layer

This work studies the breakup propensity of the stratocumulus-topped boundary layer (STBL) interacting with gravity waves using large-eddy simulation with a uniform vertical grid of $5$ m and horizontal spacing of $30$ m. A radiative-convective equilibrium (RCE) state is constructed to enforce stationarity in the STBL, and the gravity waves are introduced via a vertical momentum forcing mimicking a packet of plane waves. A nondimensionalization involving the inversion height and mean horizontal base wind as length and velocity scales is proposed to provide a framework to analyze the forcing parameter space. The magnitude of the scaled forcing amplitude ($\mathcal{A}$) is critical in understanding various STBL breakup conditions. Classification of breakup was based on the reduction of the liquid water path for each forced STBL case. We found that breakup did not occur for $\mathcal{A}<1$ and observed modest reductions in cloud for $1<\mathcal{A}<2$, but the deck recovered to the stationary state slowly after the single-period forcing ceased. Fixing $\mathcal{A}\sim 2$ showed that forcings with longer duration and wider locality promote breakup. However, when the forcing is a linear combination of waves of two different periods, the percentage of cleared cloud dramatically increases, though recovery of RCE is still observed in some cases. $\mathcal{A}\geq2.5$ marks a critical threshold by which the STBL breaks up entirely and remains patchy. We further explore the connection between these bulk breakup results and the turbulent state by examining energy budgets and the anisotropy induced by the forcing.

physics.flu-dyn↗

arXiv2026

Autonomous Driving in Unstructured Environments: How Far Have We Come?

Research on autonomous driving in unstructured outdoor environments is less advanced than in structured urban settings due to challenges like environmental diversities and scene complexity. These environments-such as rural areas and rugged terrains-pose unique obstacles that are not common in structured urban areas. Despite these difficulties, autonomous driving in unstructured outdoor environments is crucial for applications in agriculture, mining, and military operations. Our survey reviews over 250 papers for autonomous driving in unstructured outdoor environments, covering offline mapping, pose estimation, environmental perception, path planning, end-to-end autonomous driving, datasets, and relevant challenges. We also discuss emerging trends and future research directions. This review aims to consolidate knowledge and encourage further research for autonomous driving in unstructured environments. To support ongoing work, we maintain an active repository with up-to-date literature and open-source projects at: https://github.com/chaytonmin/Survey-Autonomous-Driving-in-Unstructured-Environments.

cs.RO↗

arXiv2026

SPAN: Benchmarking and Improving Cross-Calendar Temporal Reasoning of Large Language Models

We introduce SPAN, a cross-calendar temporal reasoning benchmark, which requires LLMs to perform intra-calendar temporal reasoning and inter-calendar temporal conversion. SPAN features ten cross-calendar temporal reasoning directions, two reasoning types, and two question formats across six calendars. To enable time-variant and contamination-free evaluation, we propose a template-driven protocol for dynamic instance generation that enables assessment on a user-specified Gregorian date. We conduct extensive experiments on both open- and closed-source state-of-the-art (SOTA) LLMs over a range of dates spanning 100 years from 1960 to 2060. Our evaluations show that these LLMs achieve an average accuracy of only 34.5%, with none exceeding 80%, indicating that this task remains challenging. Through in-depth analysis of reasoning types, question formats, and temporal reasoning directions, we identify two key obstacles for LLMs: Future-Date Degradation and Calendar Asymmetry Bias. To strengthen LLMs' cross-calendar temporal reasoning capability, we further develop an LLM-powered Time Agent that leverages tool-augmented code generation. Empirical results show that Time Agent achieves an average accuracy of 95.31%, outperforming several competitive baselines, highlighting the potential of tool-augmented code generation to advance cross-calendar temporal reasoning. We hope this work will inspire further efforts toward more temporally and culturally adaptive LLMs.

cs.AI↗

arXiv2025

Intrinsic-Motivation Multi-Robot Social Formation Navigation with Coordinated Exploration

This paper investigates the application of reinforcement learning (RL) to multi-robot social formation navigation, a critical capability for enabling seamless human-robot coexistence. While RL offers a promising paradigm, the inherent unpredictability and often uncooperative dynamics of pedestrian behavior pose substantial challenges, particularly concerning the efficiency of coordinated exploration among robots. To address this, we propose a novel coordinated-exploration multi-robot RL algorithm introducing an intrinsic motivation exploration. Its core component is a self-learning intrinsic reward mechanism designed to collectively alleviate policy conservatism. Moreover, this algorithm incorporates a dual-sampling mode within the centralized training and decentralized execution framework to enhance the representation of both the navigation policy and the intrinsic reward, leveraging a two-time-scale update rule to decouple parameter updates. Empirical results on social formation navigation benchmarks demonstrate the proposed algorithm's superior performance over existing state-of-the-art methods across crucial metrics. Our code and video demos are available at: https://github.com/czxhunzi/CEMRRL.

cs.RO↗

arXiv2025

The role of black hole feedback on galaxy star formation and the degeneracy with halo quenching

The interplay between the accretion of supermassive black holes (SMBHs) and the stellar mass growth of the host galaxies is still a matter of hot debate. The accretion of the SMBHs is expected to release energy under the form of AGNs. This energy is believed to impact the star formation activity and contribute to the quenching of galaxies. Here, we address this key unsolved issue with our cosmological semi-empirical model DECODE. In DECODE, we grow galaxies with their SFR linked to halo accretion rate distributions via abundance matching. SMBHs are evolved following the stellar mass growth of their host galaxies by assigning an accretion rate at each redshift from the empirical Eddington ratio distributions and duty cycles. We test the assumption that galaxies permanently quench when their central SMBHs approach the limit imposed by the observed $M_{\rm BH} - σ_\star$ relation, as a proxy of SMBH disruptive feedback. We find that simply imposing the $M_{\rm BH} - σ_\star$ condition is sufficient to generate a fraction of quenched galaxies consistent with current data, including the newest ones from Euclid. In addition, our minimal, data-driven model, also predicts SMBH scaling relations consistent in slope and normalisation with those observed, and an $M_{\rm BH} - M_\star$ relation weakly evolving with redshift. The model also naturally generates SMBH accretion rates peaking within 1 Gyr of their host SFHs. We note that all the main predictions on galaxy quenched fractions and SMBH growth histories and scaling relations are degenerate with those expected in a halo quenching model. The comprehensive data-driven model presented in this work represents an invaluable tool to investigate SMBH demography across time and environments in an accurate, physically motivated manner, ideally suited to rapidly explore the implications from large surveys, such as Euclid and Rubin-LSST.

astro-ph.GA↗

arXiv2025

How do cold pools influence the size of tropical cyclone embryos?

The size of tropical cyclone (TC) embryos is an essential predictor of TC genesis. Recent studies have identified cold pools and planetary rotation as factors that increase and decrease TC embryo size. While the planetary rotation effect has been depicted using a quasi-geostrophic (QG) model, the cold pool effect still lacks a theoretical model. This paper presents a cloud chain model to derive the length scale regarding the influence of cold pools on the TC embryo vortex. Within the model, the amount of rain evaporation during a single convective event determines the wind speed and humidity at the cold pool edge, influencing the amount of sub-cloud moisture convergence for the next-generation convection and, therefore, the intensity of the next-generation cold pool. A perturbation analysis shows that cold pools exhibit a nonlocal dependence on air-column humidity, with the influence range determined by the cold pool size and a convective memory weight. The memory weight relies on the sum of the contributions of mechanical lifting and thermodynamic forcing to convective initiation. A crucial parameter is the ratio of rain evaporation to surface evaporation in a cold pool. By coupling the cloud chain model with the QG equation, an analytical expression for the TC embryo size is obtained. The theory captures the trend but overestimates the TC embryo size in cloud-permitting simulations. The deviation might be due to the oversimplification in estimating the fractional contribution of cold pools to convective initiation.

physics.ao-ph↗

arXiv2025

Semi-empirical Framework of Supermassive Black Hole Evolution: Highlighting a possible tension between Demographics and Gravitational Wave Background

The evolution of the supermassive Black Hole (BH) population across cosmic times remains a central unresolved issue in modern astrophysics, due to the many noticeable uncertainties in the involved physical processes that span a huge range of spatial, temporal and energy scales. Here we tackle the problem via a semi-empirical approach with minimal assumptions and data-driven inputs. This is based on a continuity plus Smoluchowski equation framework that allows to unitarily describe the two primary modes of BH growth: gas accretion and binary mergers. Key quantities related to the latter processes are incorporated through educated parameterizations, and then constrained in a Bayesian setup from joint observational estimates of the local BH mass function, of the large-scale BH clustering, and of the nano-Hz stochastic gravitational wave (GW) background measured from Pulsar Timimg Array (PTA) experiments. We find that the BH accretion-related parameters are strongly dependent on the local BH mass function determination: higher normalizations and flatter high-mass slopes in the latter imply lower radiative efficiencies and mean Eddington ratios with a stronger redshift evolution. Additionally, the binary BH merger rate is estimated to be a fraction $\lesssim 10^{-1}$ of the galaxy merger rate derived from galaxy pairs counts by \texttt{JWST}, and constrained not to exceed the latter at $\gtrsim 2σ$. Relatedly, we highlight hints of a possible tension between current constraints on BH demographics and the interpretation of the nano-Hz GW background as predominantly caused by binary BH mergers. Specifically, we bound the latter's contribution to $\lesssim 30-50\%$ at $\sim 3σ$, suggesting that additional astrophysical/cosmological sources are needed to explain the residual part of the signal measured by PTA experiments.

astro-ph.CO↗

arXiv2025

The modulation of vortex growth by periodic convective activity

An important process in tropical cyclone formation is the development of a deep, warm core, which corresponds to the growth of a barotropic cyclone. Persistent convective activity is known to be crucial for the growth of barotropic vorticity. However, it remains unclear whether the fluctuating component of convective activity, such as that caused by the diurnal cycle and inertial-gravity waves, also accelerates the vortex development. To investigate this problem, numerical simulations are performed in an axisymmetric model with the Boussinesq approximation on the f-plane. Convection is parameterized with a bulk-plume mass-flux scheme. To represent a mesoscale convective system modulated by the diurnal cycle, periodic convective mass flux is imposed in a local region. The convection induces periodic diabatic heating and convective momentum transfer in the vertical direction (CMT). The CMT is an irreversible effect that breaks the quadrature phase relation between vertical velocity and vertical vorticity, producing a residual barotropic vorticity in each cycle. The barotropic vorticity consists of a barotropic cyclonic core and an anticyclonic shell. The cyclonic core is produced by the vertical advection and stretching of vertical vorticity. The anticyclonic shell is produced by the radial advection and tilting of radial vorticity. The analytical solution reproduces the formation and growth of the core-shell vorticity structure. This research reveals a potential acceleration effect of periodic convective activity on tropical cyclone genesis.

physics.ao-ph↗

arXiv2025

Integrating Offline Pre-Training with Online Fine-Tuning: A Reinforcement Learning Approach for Robot Social Navigation

Offline reinforcement learning (RL) has emerged as a promising framework for addressing robot social navigation challenges. However, inherent uncertainties in pedestrian behavior and limited environmental interaction during training often lead to suboptimal exploration and distributional shifts between offline training and online deployment. To overcome these limitations, this paper proposes a novel offline-to-online fine-tuning RL algorithm for robot social navigation by integrating Return-to-Go (RTG) prediction into a causal Transformer architecture. Our algorithm features a spatiotem-poral fusion model designed to precisely estimate RTG values in real-time by jointly encoding temporal pedestrian motion patterns and spatial crowd dynamics. This RTG prediction framework mitigates distribution shift by aligning offline policy training with online environmental interactions. Furthermore, a hybrid offline-online experience sampling mechanism is built to stabilize policy updates during fine-tuning, ensuring balanced integration of pre-trained knowledge and real-time adaptation. Extensive experiments in simulated social navigation environments demonstrate that our method achieves a higher success rate and lower collision rate compared to state-of-the-art baselines. These results underscore the efficacy of our algorithm in enhancing navigation policy robustness and adaptability. This work paves the way for more reliable and adaptive robotic navigation systems in real-world applications.

cs.RO↗

arXiv2025

Distributed quantum algorithm for divergence estimation and beyond

With the rapid advancement of quantum information technology, designing efficient distributed quantum algorithms to perform various information processing tasks remains challenging. In this paper, we consider a distributed scenario where two parties, Alice and Bob, given access to matrices $A$ and $B$ respectively, aim to estimate ${\rm Tr}(f(A)g(B))$, where $f$ and $g$ are known functions. In this task, only local quantum operations, classical communication and single-qubit measurements are allowed due to the high cost of quantum communication and entangled measurements. We propose a distributed quantum algorithm framework to compute ${\rm Tr}(f(A)g(B))$ within an additive error $\varepsilon$. The proposed algorithm framework requires $\widetilde{O}\left(d^2 / (δ\varepsilon^2)\right)$ quantum queries and two-qubit gates, assuming that the minimum singular value of each matrix $A, B \in \mathbb{C}^{d \times d}$ is at least $δ$. Additionally, our algorithm framework uses a simple Hadamard test architecture, enabling easier quantum hardware implementation. This algorithm framework allows for various applications including quantum divergence estimation, distributed solving of linear system, and distributed Hamiltonian simulation, while preserving the privacy of both parties. Furthermore, we establish a lower bound of $Ω\left(\max\left\{1 /\varepsilon^2, \sqrt{dr}/\varepsilon\right\}\right)$ on the query complexity for the task, where $r$ denotes the rank of the input matrices. We believe this framework holds broad applicability across a range of distributed quantum computing tasks.

quant-ph↗

arXiv2025

Collab-Overcooked: Benchmarking and Evaluating Large Language Models as Collaborative Agents

Large Language Models (LLMs) based agent systems have made great strides in real-world applications beyond traditional NLP tasks. This paper proposes a new LLM-based Multi-Agent System (LLM-MAS) benchmark, Collab-Overcooked, built on the popular Overcooked-AI game with more applicable and challenging tasks in interactive environments. Collab-Overcooked extends existing benchmarks in two novel ways. First, it provides a multi-agent framework supporting diverse tasks and objectives and encourages collaboration through natural language communication. Second, it introduces a spectrum of process-oriented evaluation metrics to assess the fine-grained collaboration capabilities of different LLM agents, a dimension often overlooked in prior work. We conduct extensive experiments with 13 popular LLMs and show that, while the LLMs exhibit a strong ability in goal interpretation, there are significant shortcomings in active collaboration and continuous adaptation, which are critical for efficiently fulfilling complex tasks. Notably, we highlight the strengths and weaknesses of LLM-MAS and provide insights for improving and evaluating LLM-MAS on a unified and open-source benchmark. The environments, 30 open-ended tasks, and the evaluation package are publicly available at https://github.com/YusaeMeow/Collab-Overcooked.

cs.CL↗

arXiv2025

Characterizing the roles of transitory obscured phases and inner torus in shaping the fractions of obscured AGN at cosmic noon

The origin of obscuration in Active Galactic Nuclei (AGN) is still a matter of contention. It is unclear whether obscured AGN are primarily due to line-of-sight effects, a transitory, dust-enshrouded phase in galaxy evolution, or a combination of both. The role of an inner torus around the central SMBH also remains unclear in pure Evolution models. We use cosmological semi-analytic models and semi-empirical prescriptions to explore obscuration effects in AGN at 1<z<3. We consider a realistic object-by-object modelling of AGN evolution including different light curves (LCs) composed of phases of varying levels of obscuration, mimicking the possible clearing effects of strong AGN feedback. Evolution models characterized by AGN LCs with relatively short pre-peak obscured phases followed by more extended optical/UV visible post-peak phases, struggle to reproduce the high fraction of obscured AGN at z~2-3 inferred from X-ray surveys. Evolution models characterised by LCs with sharp post-peak declines or persistent or multiple obscuration phases are more successful, although they still face challenges in reproducing the steady drop in the fractions of obscured AGN with increasing luminosity measured by some groups. Invoking a fine-tuning in the input LCs, with more luminous AGN defined by longer optical/UV visible windows, can improve the match to the decreasing fractions of obscured AGN with luminosity. Alternatively, a long-lived central torus-like component, with thickness decreasing with increasing AGN power, naturally boosts the luminosity-dependent fractions of obscured AGN, suggesting that small-scale orientation effects may still represent a key component even in Evolution models. We also find that in our models major mergers and starbursts, when considered in isolation, fall short in accounting for the large fractions of highly obscured faint AGN detected at cosmic noon.

astro-ph.GA↗

arXiv2025

A Survey on Efficiency Optimization Techniques for DNN-based Video Analytics: Process Systems, Algorithms, and Applications

The explosive growth of video data in recent years has brought higher demands for video analytics, where accuracy and efficiency remain the two primary concerns. Deep neural networks (DNNs) have been widely adopted to ensure accuracy; however, improving their efficiency in video analytics remains an open challenge. Different from existing surveys that make summaries of DNN-based video mainly from the accuracy optimization aspect, in this survey, we aim to provide a thorough review of optimization techniques focusing on the improvement of the efficiency of DNNs in video analytics. We organize existing methods in a bottom-up manner, covering multiple perspectives such as hardware support, data processing, operational deployment, etc. Finally, based on the optimization framework and existing works, we analyze and discuss the problems and challenges in the performance optimization of DNN-based video analytics.

cs.CV↗

arXiv2025

IAP: Improving Continual Learning of Vision-Language Models via Instance-Aware Prompting

Recent pre-trained vision-language models (PT-VLMs) often face a Multi-Domain Task Incremental Learning (MTIL) scenario in practice, where several classes and domains of multi-modal tasks are incrementally arrived. Without access to previously seen tasks and unseen tasks, memory-constrained MTIL suffers from forward and backward forgetting. To alleviate the above challenges, parameter-efficient fine-tuning techniques (PEFT), such as prompt tuning, are employed to adapt the PT-VLM to the diverse incrementally learned tasks. To achieve effective new task adaptation, existing methods only consider the effect of PEFT strategy selection, but neglect the influence of PEFT parameter setting (e.g., prompting). In this paper, we tackle the challenge of optimizing prompt designs for diverse tasks in MTIL and propose an Instance-Aware Prompting (IAP) framework. Specifically, our Instance-Aware Gated Prompting (IA-GP) strategy enhances adaptation to new tasks while mitigating forgetting by adaptively assigning prompts across transformer layers at the instance level. Our Instance-Aware Class-Distribution-Driven Prompting (IA-CDDP) improves the task adaptation process by determining an accurate task-label-related confidence score for each instance. Experimental evaluations across 11 datasets, using three performance metrics, demonstrate the effectiveness of our proposed method. The source codes are available at https://github.com/FerdinandZJU/IAP.

cs.CV↗

arXiv2025

SAMBLE: Shape-Specific Point Cloud Sampling for an Optimal Trade-Off Between Local Detail and Global Uniformity

Driven by the increasing demand for accurate and efficient representation of 3D data in various domains, point cloud sampling has emerged as a pivotal research topic in 3D computer vision. Recently, learning-to-sample methods have garnered growing interest from the community, particularly for their ability to be jointly trained with downstream tasks. However, previous learning-based sampling methods either lead to unrecognizable sampling patterns by generating a new point cloud or biased sampled results by focusing excessively on sharp edge details. Moreover, they all overlook the natural variations in point distribution across different shapes, applying a similar sampling strategy to all point clouds. In this paper, we propose a Sparse Attention Map and Bin-based Learning method (termed SAMBLE) to learn shape-specific sampling strategies for point cloud shapes. SAMBLE effectively achieves an improved balance between sampling edge points for local details and preserving uniformity in the global shape, resulting in superior performance across multiple common point cloud downstream tasks, even in scenarios with few-point sampling.

cs.CV↗

arXiv2025

6D Pose Estimation on Point Cloud Data through Prior Knowledge Integration: A Case Study in Autonomous Disassembly

The accurate estimation of 6D pose remains a challenging task within the computer vision domain, even when utilizing 3D point cloud data. Conversely, in the manufacturing domain, instances arise where leveraging prior knowledge can yield advancements in this endeavor. This study focuses on the disassembly of starter motors to augment the engineering of product life cycles. A pivotal objective in this context involves the identification and 6D pose estimation of bolts affixed to the motors, facilitating automated disassembly within the manufacturing workflow. Complicating matters, the presence of occlusions and the limitations of single-view data acquisition, notably when motors are placed in a clamping system, obscure certain portions and render some bolts imperceptible. Consequently, the development of a comprehensive pipeline capable of acquiring complete bolt information is imperative to avoid oversight in bolt detection. In this paper, employing the task of bolt detection within the scope of our project as a pertinent use case, we introduce a meticulously devised pipeline. This multi-stage pipeline effectively captures the 6D information with regard to all bolts on the motor, thereby showcasing the effective utilization of prior knowledge in handling this challenging task. The proposed methodology not only contributes to the field of 6D pose estimation but also underscores the viability of integrating domain-specific insights to tackle complex problems in manufacturing and automation.

cs.CV↗