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Lei You

Lei You contributes to research discovery and scholarly infrastructure.

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gr-qc Information Theory math.IT Computer Vision Machine Learning

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preprint2026arXiv

MedCore: Boundary-Preserving Medical Core Pruning for MedSAM

Medical segmentation foundation models such as SAM and MedSAM provide strong prompt-driven segmentation, but their image encoders are still too large for many clinical settings. Compression is also risky in medicine because a model can keep high Dice while losing boundary fidelity. We propose MedCore, a structured pruning framework for MedSAM. The main idea is to preserve two kinds of structures: structures that became important during SAM-to-MedSAM adaptation, and structures that have high boundary leverage. We identify the first type by a dual-intervention score that compares zeroing a group with resetting it to its original SAM weight. We identify the second type by boundary-aware Fisher estimation. We also introduce a boundary leverage principle, which shows that compression-induced boundary displacement is controlled by logit perturbation on the boundary divided by the logit spatial gradient. This principle explains why boundary metrics can degrade even when Dice remains high. On polyp segmentation benchmarks, MedCore reduces parameters by 60.0% and FLOPs by 58.4% while achieving Dice 0.9549, Boundary F1 0.6388, and HD95 5.14 after recovery fine-tuning. It also reaches 86.6% parameter reduction and 90.4G FLOPs with strong boundary quality. Our analysis further shows that MedSAM lies in a head-fragile boundary regime: head-pruning steps have 2.887 times larger 95th-percentile boundary leverage than MLP-pruning steps, and this logit-level effect is consistent with BF1 and HD95 degradation. Our code is available at https://github.com/cenweizhang/MedCore.

preprint2026arXiv

Revisiting particle circular orbits as probes of black hole thermodynamics

Recent studies propose that black hole phase transitions can be encoded in the circular orbit radius of particles. In this paper, we systematically investigate the reliability of this encoding mechanism. We find that this mechanism is highly reliable in the isobaric ensemble, whereas it may break down in the isothermal ensemble. It turns out that the reliability of this mechanism is directly controlled by the first law of black hole thermodynamics. Interestingly, even if this encoding mechanism fails, we prove that, for any black hole exhibiting criticality, the first law can ensure that, near the critical point, the coexistence gap of the circular orbit radius remains a reliable order parameter and yields exactly the same critical exponents as the standard thermodynamic order parameter. Our results provide a potential way to identify the thermodynamic ensemble of a black hole, and reveal a deeper connection between gravitational geometry and thermodynamics.

preprint2025arXiv

Black hole images as probes of thermodynamic evolution

We investigate how black hole images (shadows and accretion-disk images) encode thermodynamic evolution information across different ensembles, using the Reissner-Nordström-AdS black hole as an illustrative example. Through analytic treatment and numerical verification, we demonstrate that these images encode not only phase transition information but also ensemble information, including additional temperature information in the isothermal ensemble. Phase transition information appears as a sudden increase in image size, which we prove occurs in both isobaric and isothermal ensembles. The ensemble and temperature information originates from a fundamental difference between isobaric and isothermal evolution: image size varies monotonically with the horizon radius along isobars, whereas it exhibits nonmonotonic behavior along isotherms. This contrast serves as a diagnostic tool to distinguish isobaric from isothermal evolution. In the isothermal ensemble, the nonmonotonic behavior introduces an extremal radius whose relative ordering with the small- and large-black hole radii at the phase transition admits three logical possibilities. Our analysis reveals that only two of these possibilities are physically realized, separated by a critical reduced temperature. Furthermore, image evolution in the two resulting temperature intervals exhibits qualitatively differences, demonstrating that black hole images indeed encode temperature information. These results not only enrich the set of observational avenues for probing black hole thermodynamic properties, but also introduce a new paradigm. This paradigm studies phase transitions in conjunction with nonmonotonic evolution, providing a useful framework for exploring thermodynamic imprints in other black hole systems.

preprint2021arXiv

User-centric Performance Optimization with Remote Radio Head Cooperation in C-RAN

In a cloud radio access network (C-RAN), distributed remote radio heads (RRHs) are coordinated by baseband units (BBUs) in the cloud. The centralization of signal processing provides flexibility for coordinated multi-point transmission (CoMP) of RRHs to cooperatively serve user equipments (UEs). We target enhancing UEs' capacity performance, by jointly optimizing the selection of RRHs for serving UEs, i.e., resource allocation (and CoMP selection). We analyze the computational complexity of the problem. Next, we prove that under fixed CoMP selection, the optimal resource allocation amounts to solving a so-called iterated function. Towards user-centric network optimization, we propose an algorithm for the joint optimization problem, aiming at maximumly scaling up the capacity for any target UE group of interest. The proposed algorithm enables network-level performance evaluation for quality of experience.

preprint2020arXiv

A Note on Decoding Order in User Grouping and Power Optimization for Multi-Cell NOMA with Load Coupling

In this technical note, we present a new theoretical result for resource optimization with non-orthogonal multiple access (NOMA). For multi-cell scenarios, a so-called load-coupling model has been proposed to characterize the presence of mutual interference for NOMA, and resource optimization relies on the use of fixed-point iterations [1], [2] across cells. One difficulty here is that the order of decoding for successive interference cancellation (SIC) in NOMA is generally not known a priori. This is because the decoding order in one cell depends on interference, which, in turn, is governed by resource allocation in other cells, and vice versa. To achieve convergence, previous works have used workarounds that pose restrictions to NOMA, such that the SIC decoding order remains in optimization. As a comment to [1], [2], we derive and prove the following result: The convergence is guaranteed, even if the order changes over the iterations. The result not only waives the need of previous workarounds, but also implies that a wide class of resource optimization problems for multi-cell NOMA is tractable, as long as that for single cell is.

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