arXiv subjects

Junhui Gao

Publications and source records attributed to Junhui Gao.

9 recordsLinked to original sources

arXiv2026

Governing Equations of Human Sleep Macro-Structure:Symbolic Discovery from 1,106 Nights

Human sleep is organized into a small number of cycles per night: deep sleep dominates early cycles, REM lengthens toward morning, and light sleep clusters at cycle boundaries. These regularities have long been described qualitatively but never distilled into closed-form laws from large-scale data. A total of 1,106 nights (Sleep-EDF 153 + SHHS 953) are segmented into REM-anchored cycles, and four cycle-indexed statistics follow simple closed-form equations: cycle length declines nearly linearly (R^2=0.996), REM duration follows an inverted U (R^2=0.873), deep-sleep (N3) run length decays exponentially (R^2=0.987) with a time constant of 2.94 cycles, consistent with the homeostatic Process S of the two-process model, and light-sleep (N1) run length follows a U shape (R^2=0.956), confirming a "long-short-long" pattern across the night. The laws are reproduced by per-night differentiable fitting (142/142 and 773/773 nights converge), by unconstrained symbolic regression (PySR), and by a 500-night generative synthesizer built from them. Their robustness is established against the two main methodological threats: bootstrap CIs computed by resampling whole nights (1,000 replications) exclude zero for every shape term, and a five-level scan of the REM-merge segmentation threshold preserves every qualitative form. These appear to be the first closed-form laws governing the macro-structure of sleep, distilled and tested across an age-mixed sample (healthy young adults and community-based older adults).

cs.AI↗

arXiv2026

Duration-Aware World Models for Sleep Staging with Dwell-Time Constraints

Closed-loop neuromodulation requires sleep staging that respects stage-specific minimum dwells: stimulation policies switch by sleep stage, and a policy change is permitted only after the current stage has persisted for its minimum duration. Discriminative classifiers fragment stage trajectories---for example, a 30 s Wake segment inside a 10 min N2 run---and even hidden semi-Markov smoothing leaves 11.3% of segments below the minimum dwell threshold. This paper presents a world model in which a recurrent state-space model (RSSM) learns dwell structure endogenously from EEG, via duration-aware latent sampling, a survival-curve dwell prior, and transition-matrix calibration, and a trajectory-level hard-constraint layer guarantees the minimum dwell at decoding. On SHHS-1 (200 subjects, 160/20/20, three repeated splits), the model reaches a test accuracy of 0.690 +/- 0.053 (95% CI over 3 folds) versus 0.641 +/- 0.042 for an identically split discriminative baseline, with macro-F1 +17.5 pp and better accuracy on all three folds. On Sleep-EDF the constraint layer cuts below-threshold segments from 71.8% to 0.1% with no accuracy loss (0.734 to 0.737), and an online locked scheduler reduces RL policy switches by 87% per night while keeping every policy run at least 90 s. To the best of the author's knowledge, this is the first system to couple learned dwell statistics with a decoding-time hard guarantee. Code and pretrained weights will be released.

q-bio.QM↗

arXiv2026

TriDeliver: Cooperative Air-Ground Instant Delivery with UAVs, Couriers, and Crowdsourced Ground Vehicles

Instant delivery, shipping items before critical deadlines, is essential in daily life. While multiple delivery agents, such as couriers, Unmanned Aerial Vehicles (UAVs), and crowdsourced agents, have been widely employed, each of them faces inherent limitations (e.g., low efficiency/labor shortages, flight control, and dynamic capabilities, respectively), preventing them from meeting the surging demands alone. This paper proposes TriDeliver, the first hierarchical cooperative framework, integrating human couriers, UAVs, and crowdsourced ground vehicles (GVs) for efficient instant delivery. To obtain the initial scheduling knowledge for GVs and UAVs as well as improve the cooperative delivery performance, we design a Transfer Learning (TL)-based algorithm to extract delivery knowledge from couriers' behavioral history and transfer their knowledge to UAVs and GVs with fine-tunings, which is then used to dispatch parcels for efficient delivery. Evaluated on one-month real-world trajectory and delivery datasets, it has been demonstrated that 1) by integrating couriers, UAVs, and crowdsourced GVs, TriDeliver reduces the delivery cost by $65.8\%$ versus state-of-the-art cooperative delivery by UAVs and couriers; 2) TriDeliver achieves further improvements in terms of delivery time ($-17.7\%$), delivery cost ($-9.8\%$), and impacts on original tasks of crowdsourced GVs ($-43.6\%$), even with the representation of the transferred knowledge by simple neural networks, respectively.

cs.RO↗

arXiv2026

CA3D: Computing Accessibility-Aware Cooperative 3D Deployment of Multiple UAVs

This letter investigates computing-accessibility-aware cooperative 3D deployment of multiple UAVs for task completion enhancement, termed CA3D. We first provide a theoretical analysis showing that computing accessibility is the key mechanism linking UAV deployment to delay-constrained task completion, and that UAV inter-spacing creates a fundamental tradeoff between computing-resource accessibility and task completion. We then develop a cooperative 3D deployment design that jointly balances accessible computing capacity, task completion probability, and redundant UAV overlap. Simulation results under heterogeneous computing node capacities show that CA3D consistently outperforms Random, Fixed, and Greedy deployment baselines under both hotspot and random ground user (GU) distributions. Under the hotspot GU distribution, CA3D achieves nearly full task completion, improving the task completion probability by about 3.3x over Random deployment when the number of UAVs is 8. Under a more challenging random GU distribution, CA3D still achieves about 35% higher task completion probability than the best baseline when the number of UAVs is 12. These results demonstrate that computing-accessibility-aware cooperative 3D deployment improves not only task completion but also robustness to GU distribution changes.

cs.NI↗

arXiv2026

Collaborative Air-Ground Sensing, Communication, Computing, Storage, and Intelligence for Low-Altitude Economy

Low-altitude economy (LAE) is transforming low-altitude airspace into a new cyber-physical infrastructure. Although air-ground communications have been widely studied, LAE is fundamentally different in the sense that it is mission-centric with diverse requirements, such as stringent safety and compliance constraints not be effectively addressed with a communication-centric design alone, which makes air-ground collaboration indispensable: Only through effectively coordinating air-ground infrastructure and resources can LAE missions be fulfilled. Consequently, LAE calls for task-driven, closed-loop, multi-resource orchestration of Sensing, Communication, Computing, Storage, and Intelligence (SCCSI), where key decisions must be co-designed under mobility and uncertainty. In this paper, we first present a novel framework that connects (i) LAE scenarios and a requirement--resource coupling matrix, (ii) an air--ground collaborative architecture, and (iii) methodological toolboxes for SCCSI co-optimization and online decision-making. We then systematically review enabling technologies for collaborative SCCSI resources and capabilities, emphasizing their coupling and end-to-end tradeoffs. Finally, we summarize testbeds, datasets, and evaluation metrics, and provide representative use cases to illustrate how the proposed framework translates application requirements into practical task-driven optimization designs, together with open challenges and a roadmap toward scalable and trustworthy LAE deployment.

cs.NI↗

arXiv2023

STGIC: a graph and image convolution-based method for spatial transcriptomic clustering

Spatial transcriptomic (ST) clustering employs spatial and transcription information to group spots spatially coherent and transcriptionally similar together into the same spatial domain. Graph convolution network (GCN) and graph attention network (GAT), fed with spatial coordinates derived adjacency and transcription profile derived feature matrix are often used to solve the problem. Our proposed method STGIC (spatial transcriptomic clustering with graph and image convolution) utilizes an adaptive graph convolution (AGC) to get high quality pseudo-labels and then resorts to dilated convolution framework (DCF) for virtual image converted from gene expression information and spatial coordinates of spots. The dilation rates and kernel sizes are set appropriately and updating of weight values in the kernels is made to be subject to the spatial distance from the position of corresponding elements to kernel centers so that feature extraction of each spot is better guided by spatial distance to neighbor spots. Self-supervision realized by KL-divergence, spatial continuity loss and cross entropy calculated among spots with high confidence pseudo-labels make up the training objective of DCF. STGIC attains state-of-the-art (SOTA) clustering performance on the benchmark dataset of human dorsolateral prefrontal cortex (DLPFC). Besides, it's capable of depicting fine structures of other tissues from other species as well as guiding the identification of marker genes. Also, STGIC is expandable to Stereo-seq data with high spatial resolution.

q-bio.QM↗

arXiv2019

FPScreen: A Rapid Similarity Search Tool for Massive Molecular Library Based on Molecular Fingerprint Comparison

We designed a fast similarity search engine for large molecular libraries: FPScreen. We downloaded 100 million molecules' structure files in PubChem with SDF extension, then applied a computational chemistry tool RDKit to convert each structure file into one line of text in MACCS format and stored them in a text file as our molecule library. The similarity search engine compares the similarity while traversing the 166-bit strings in the library file line by line. FPScreen can complete similarity search through 100 million entries in our molecule library within one hour. That is very fast as a biology computation tool. Additionally, we divided our library into several strides for parallel processing. FPScreen was developed in WEB mode.

cs.OH↗

arXiv2018

Building a language evolution tree based on word vector combination model

In this paper, we try to explore the evolution of language through case calculations. First, we chose the novels of eleven British writers from 1400 to 2005 and found the corresponding works; Then, we use the natural language processing tool to construct the corresponding eleven corpora, and calculate the respective word vectors of 100 high-frequency words in eleven corpora; Next, for each corpus, we concatenate the 100 word vectors from beginning to end into one; Finally, we use the similarity comparison and hierarchical clustering method to generate the relationship tree between the combined eleven word vectors. This tree represents the relationship between eleven corpora. We found that in the tree generated by clustering, the distance between the corpus and the year corresponding to the corpus are basically the same. This means that we have discovered a specific language evolution tree. To verify the stability and versatility of this method, we add three other themes: Dickens's eight works, the 19th century poets' works, and art criticism of recent 60 years. For these four themes, we tested different parameters such as the time span of the corpus, the time interval between the corpora, the dimension of the word vector, and the number of high-frequency public words. The results show that this is fairly stable and versatile.

cs.CL↗

arXiv2013

The drag-adjoint field of a circular cylinder wake at Reynolds numbers 20, 100 and 500

This paper analyzes the adjoint solution of the Navier-Stokes equation. We focus on flow across a circular cylinder at three Reynolds numbers, Re_D=20, 100 and 500. The quantity of interest in the adjoint formulation is the drag on the cylinder. We use classical fluid mechanics approaches to analyze the adjoint solution, which is a vector field similar to a flow field. Production and dissipation of kinetic energy of the adjoint field is discussed. We also derive the evolution of circulation of the adjoint field along a closed material contour. These analytical results are used to explain three numerical solutions of the adjoint equations presented in this paper. The adjoint solution at Re_D=20, a viscous steady state flow, exhibits a downstream suction and an upstream jet, opposite of the expected behavior of a flow field. The adjoint solution at Re_D=100, a periodic 2D unsteady flow, exhibits periodic, bean shaped circulation in the near wake region. The adjoint solution at Re_D=500, a turbulent 3D unsteady flow, has complex dynamics created by the shear layer in the near wake. The magnitude of the adjoint solution increases exponentially at the rate of the first Lyapunov exponent. These numerical results correlate well with the theoretical analysis presented in this paper.

physics.flu-dyn↗