arXiv subjects

Qiuyu Yu

Publications and source records attributed to Qiuyu Yu.

8 recordsLinked to original sources

arXiv2026

Action-grounded tissue affordance enables anticipatory auto-framing that lowers surgeon cognitive workload during laparoscopic surgery

In laparoscopy, surgeon gaze tracks where the instruments will act; easing this demand through visual attention modeling requires dense labels of those interaction loci. These encode tacit knowledge: experts converge on consensus loci yet struggle to state the rules. Here we show that such labels can be recovered from completed actions in surgical videos, in which recorded instrument trajectories are converted into dense, continuous supervision. DiffeoAfford grounds tissue affordance by attaching instrument tips to the tissue and transporting them through deformation using diffeomorphism-constrained tracking, matching context-informed annotators' accuracy. Trained on these labels and never on gaze, a real-time model aligns with surgeon gaze more closely in space and time than does camera-assistant gaze. The framework also transfers across procedures: on hysterectomy videos, a separately trained predictor reaches 95.16% directional consistency with subsequent camera motion. In 12 paired cholecystectomies (24 procedures), the auto-framing application AffordView, which proactively centers predicted targets in view, lowered surgeon cognitive workload on converging subjective, physiological, and behavioral measures, including a reduced number of verbal instructions to the camera assistant. Deriving supervision from action rather than manual annotation offers a scalable route to anticipatory assistance.

cs.CV↗

arXiv2026

From One to Two: A Second Binary Millisecond Pulsar in the Globular Cluster M92 (NGC 6341)

We report the discovery and phase connected-timing solution of a second millisecond binary pulsar, PSR J1717+4308B (M92B), in the globular cluster M92 (NGC 6341) using the Five-hundred-meter Aperture Spherical radio Telescope. This new pulsar, with a spin period of 3.51 ms and a dispersion measure (DM) of 35.29 pc cm$^{-3}$, was discovered through frequency-domain acceleration searches. The timing solution shows that M92B is in a binary system with an orbital period of 2.3 days, an eccentricity of $\simeq 4.8 \times 10^{-4}$, and a minimum companion mass of 0.2 $\,M_\odot$. M92B lies within the cluster core radius in projection, and its negative spin period derivative ($\dot{P}$) is consistent with acceleration in the cluster potential. The measured negative $\dot{P}$ of M92B, together with a DM consistent with that of M92A ($< 0.2\, \rm pc\,cm^{-3}$), confirms that both pulsars are members of the cluster. A Bayesian Markov Chain Monte Carlo analysis based on these two pulsars yields broad constraints on the core structural parameters of M92 that are consistent with $N$-body dynamical modeling. This demonstrates that pulsar timing can provide useful dynamical information in sparse pulsar samples.

astro-ph.HE↗

arXiv2026

LoopVLA: Learning Sufficiency in Recurrent Refinement for Vision-Language-Action Models

Current Vision-Language-Action (VLA) models typically treat the deepest representation of a vision-language backbone as universally optimal for action prediction. However, robotic manipulation is composed of many frequent closed-loop spatial adjustments, for which excessive abstraction may waste computation and weaken low-level geometric cues essential for precise control. Existing early-exit strategies attempt to reduce computation by stopping at predefined layers or applying heuristic rules such as action consistency, but they do not directly answer when a representation is actually sufficient for action. In this paper, we present LoopVLA, a recurrent VLA architecture that jointly learns representation refinement, action prediction, and sufficiency estimation. LoopVLA iteratively applies a shared Transformer block to refine multimodal tokens, and at each iteration produces both a candidate action and a sufficiency score that estimates whether further refinement is necessary. By sharing parameters across iterations, LoopVLA decouples refinement from absolute layer indices and grounds sufficiency estimation in the evolving representation itself. Since sufficiency has no direct supervision, we introduce a self-supervised distribution alignment objective, where intermediate confidence scores are trained to match the relative action quality across refinement steps, thereby linking sufficiency learning to policy optimization signals. Experiments on LIBERO, LIBERO-Plus, and VLA-Arena show that LoopVLA pushes the efficiency-performance frontier of VLA policies, reducing parameters by 45% and improving inference throughput by up to 1.7 times while matching or outperforming strong baselines in task success.

cs.AI↗

arXiv2026

The FAST Discovery of a binary millisecond pulsar PSR~J1647-0156B (M12B) with a candidate cross matching algorithm

We propose a pulsar candidate cross matching algorithm to sift radio pulsar search candidates from repeated observations of the same sky location such as globular clusters, high energy sources, or supernova remnants. Our method uses both the candidate spin period ($P$) and dispersion measure (DM) value; if two or more candidates from different observations have similar spin periods to within 1\%, and dispersion measure values within 10\%, they are likely to correspond to the same candidate detection. We have demonstrated the effectiveness of our method through the discovery of the pulsar M12B with the Five-hundred-meter Aperture Spherical radio Telescope (FAST). This pulsar has a spin period of 2.76\,ms and a dispersion measure of $42.70 \pm 0.05\,\mathrm{cm}^{-3}~\mathrm{pc}$. This pulsar has a profile with three peaks, being faint, showing scintillation. It is in an approximately 0.53-day orbit. Our discovery indicates that more pulsars might be effectively discovered if the algorithm is applied to the search results from other archival globular cluster observations.

astro-ph.HE↗

arXiv2022

Model Predictive Spherical Image-Based Visual Servoing On $SO(3)$ for Aggressive Aerial Tracking

This paper presents an image-based visual servo control (IBVS) method for a first-person-view (FPV) quadrotor to conduct aggressive aerial tracking. There are three major challenges to maneuvering an underactuated vehicle using IBVS: (i) finding a visual feature representation that is robust to large rotations and is suited to be an optimization variable; (ii) keeping the target visible without sacrificing the robot's agility; and (iii) compensating for the rotational effects in the detected features. We propose a complete design framework to address these problems. First, we employ a rotation on $SO(3)$ to represent a spherical image feature on $S^{2}$ to gain singularity-free and second-order differentiable properties. To ensure target visibility, we formulate the IBVS as a nonlinear model predictive control (NMPC) problem with three constraints taken into account: the robot's physical limits, target visibility, and time-to-collision (TTC). Furthermore, we propose a novel attitude-compensation scheme to enable formulating the visibility constraint in the actual image plane instead of a virtual fix-orientation image plane. It guarantees that the visibility constraint is valid under large rotations. Extensive experimental results show that our method can track a fast-moving target stably and aggressively without the aid of a localization system.

cs.RO↗

arXiv2020

A Blockchain-based Platform Architecture for Multimedia Data Management

Massive amounts of multimedia data (i.e., text, audio, video, graphics and animation) are being generated everyday. Conventionally, multimedia data are managed by the platforms maintained by multimedia service providers, which are generally designed using centralised architecture. However, such centralised architecture may lead to a single point of failure and disputes over royalties or other rights. It is hard to ensure the data integrity and track fulfilment of obligations listed on the copyright agreement. To tackle these issues, in this paper, we present a blockchain-based platform architecture for multimedia data management. We adopt self-sovereign identity for identity management and design a multi-level capability-based mechanism for access control. We implement a proof-of-concept prototype using the proposed approach and evaluate it using a use case. The results show that the proposed approach is feasible and has scalable performance.

cs.CR↗

arXiv2020

Blockchain-based Federated Learning for Device Failure Detection in Industrial IoT

Device failure detection is one of most essential problems in industrial internet of things (IIoT). However, in conventional IIoT device failure detection, client devices need to upload raw data to the central server for model training, which might lead to disclosure of sensitive business data. Therefore, in this paper, to ensure client data privacy, we propose a blockchain-based federated learning approach for device failure detection in IIoT. First, we present a platform architecture of blockchain-based federated learning systems for failure detection in IIoT, which enables verifiable integrity of client data. In the architecture, each client periodically creates a Merkle tree in which each leaf node represents a client data record, and stores the tree root on a blockchain. Further, to address the data heterogeneity issue in IIoT failure detection, we propose a novel centroid distance weighted federated averaging (CDW\_FedAvg) algorithm taking into account the distance between positive class and negative class of each client dataset. In addition, to motivate clients to participate in federated learning, a smart contact based incentive mechanism is designed depending on the size and the centroid distance of client data used in local model training. A prototype of the proposed architecture is implemented with our industry partner, and evaluated in terms of feasibility, accuracy and performance. The results show that the approach is feasible, and has satisfactory accuracy and performance.

cs.DC↗

arXiv2019

A PRESTO-based Parallel Pulsar Search Pipeline Used for FAST Drift Scan Data

We developed a pulsar search pipeline based on PRESTO (PulsaR Exploration and Search Toolkit). This pipeline simply runs dedispersion, FFT (Fast Fourier Transformation), and acceleration search in process-level parallel to shorten the processing time. With two parallel strategies, the pipeline can highly shorten the processing time in both the normal searches or acceleration searches. This pipeline was first tested with PMPS (Parkes Multibeam Pulsar Survery) data and discovered two new faint pulsars. Then, it was successfully used in processing the FAST (Five-hundred-meter Aperture Spherical radio Telescope) drift scan data with tens of new pulsar discoveries up to now. The pipeline is only CPU-based and can be easily and quickly deployed in computing nodes for testing purposes or data processes.

astro-ph.IM↗