arXiv subjects

Jinhoon Jeong

Publications and source records attributed to Jinhoon Jeong.

6 recordsLinked to original sources

arXiv2026

Quantum-enabled measurement of gravitational forces at the milligram scale

Gravity is by far the weakest among all known interactions. While it dominates the large-scale structure of the universe, gravitational interactions between small masses in the microscopic range remain underexplored because they are too weak to be experimentally accessible. The interface between gravity, described by general relativity, and quantum mechanics, which describes the microscopic world, is of particular interest. From a theory standpoint, these two major descriptions of nature have not been combined in a satisfactory manner. Technology is gradually becoming mature enough such that probing gravity at the microscopic scale, where quantum mechanics and gravity potentially meet, is becoming accessible. Explorations towards genuine quantum gravity have to be carried out under conditions where the gravitating masses possess non-classical properties, which necessitates ultimate protection against disturbances, such as ultralow temperatures and a small range of motion. Here, we present an experiment at millikelvin temperatures, where the gravity due to a micron-range coherent displacement of an 87 mg source mass imprints on the motion of an oscillating membrane loaded with a 1.3 mg test mass. Vibrations of the membrane are measured in the microwave optomechanical scheme, which is a key setup for investigating non-classical motional states of mechanical oscillators. When considering the combination of total source and test masses, our setup presents two orders of magnitude progress in sensing gravity at the small scale, as well as a 35 dB improvement of gravitational sensitivity at kHz frequencies or above. Our setup offers a plausible approach for studying gravitational interactions between masses prepared in non-classical motional states.

quant-ph↗

arXiv2026

Deterministic Integrity Gates for LLM-Assisted Clinical Manuscript Preparation: An Auditable Biomedical Informatics Architecture

As autonomous research agents and AI co-scientist systems push large language models (LLMs) from drafting toward end-to-end manuscript production, the bottleneck shifts from generation to verification. Fluent LLM output can hide fabricated citations, numbers that drift from source tables, and unmet reporting-guideline items; existing tools generate without verifying, and self-critique inherits the blind spots that produce confident fabrication. We describe an architecture pairing generation with verification, resting on three principles: decompose the workflow into self-contained skills, gate every stage transition with halt-on-failure, and resolve each integrity question with the cheapest sufficient mechanism, a deterministic, re-executable check where one suffices and a prose-level probe only where interpretation is unavoidable. This determinism-where-possible split, organized as an integrity-gate taxonomy, is the core contribution. It is realized as MedSci Skills, an open-source toolkit of 43 skills with a 21-detector deterministic tier, evaluated on three public-dataset pipelines (STARD, PRISMA, STROBE) and a seeded-defect ablation. Across the three pipelines every content-hash manifest verified clean and the gates surfaced real defects; on 27 identical injected defects the deterministic gates detected all 27 with no false positives on the matched clean fixtures, whereas a single-prompt LLM reviewer detected 11, its misses in code, bibliography, and style defects the prose hides. Determinism-where-possible verification yields an auditable, re-executable trail that exposes the evidence a human needs to check an LLM-assisted manuscript: feasibility and reproducibility evidence, not a claim of human-competitive quality, which a separate blinded study addresses. MedSci Skills is MIT-licensed and archived (v3.8.0).

cs.AI↗

arXiv2025

Non-Exponential Relaxation in the Rotating Frame of a Driven Nanomechanical Mode

We present direct observation of the ring-down dynamics in the rotating frame of a resonantly driven single-mode nonlinear nanomechanical resonator. An additional close to resonance harmonic force excites nonlinear oscillations about the fixed point in the rotating frame. When the secondary drive is removed, we measure decay of the in-phase and quadrature components toward this fixed point. We show that the decay of the in-phase signal is non-exponential, even though the vibration amplitude decays exponentially if both forces are switched off. A minimalistic model captures these dynamics as well as the spectrum of the vibrations excited by the additional force, relating them to the dissipation-induced symmetry breaking of the dynamics in the rotating frame.

cond-mat.mes-hall↗

arXiv2024

FedCAR: Cross-client Adaptive Re-weighting for Generative Models in Federated Learning

Generative models trained on multi-institutional datasets can provide an enriched understanding through diverse data distributions. However, training the models on medical images is often challenging due to hospitals' reluctance to share data for privacy reasons. Federated learning(FL) has emerged as a privacy-preserving solution for training distributed datasets across data centers by aggregating model weights from multiple clients instead of sharing raw data. Previous research has explored the adaptation of FL to generative models, yet effective aggregation algorithms specifically tailored for generative models remain unexplored. We hereby propose a novel algorithm aimed at improving the performance of generative models within FL. Our approach adaptively re-weights the contribution of each client, resulting in well-trained shared parameters. In each round, the server side measures the distribution distance between fake images generated by clients instead of directly comparing the Fréchet Inception Distance per client, thereby enhancing efficiency of the learning. Experimental results on three public chest X-ray datasets show superior performance in medical image generation, outperforming both centralized learning and conventional FL algorithms. Our code is available at https://github.com/danny0628/FedCAR.

cs.CV↗

arXiv2020

Automatic Tip Detection of Surgical Instruments in Biportal Endoscopic Spine Surgery

Some endoscopic surgeries require a surgeon to hold the endoscope with one hand and the surgical instruments with the other hand to perform the actual surgery with correct vision. Recent technical advances in deep learning as well as in robotics can introduce robotics to these endoscopic surgeries. This can have numerous advantages by freeing one hand of the surgeon, which will allow the surgeon to use both hands and to use more intricate and sophisticated techniques. Recently, deep learning with convolutional neural network achieves state-of-the-art results in computer vision. Therefore, the aim of this study is to automatically detect the tip of the instrument, localize a point, and evaluate detection accuracy in biportal endoscopic spine surgery. The localized point could be used for the controller's inputs of robotic endoscopy in these types of endoscopic surgeries.

cs.CV↗

arXiv2016

Directly Detecting the Edge Current in a $p_x + ip_y$ Topological Superfluid

Topological superconductors are one of the most actively studied materials these days. They are a promising candidate for hosting Majorana fermions either on their boundaries or in vortex cores. Detecting 1D edge current around the periphery of a 2D $p_x + ip_y$ superconductor would be a hallmark signature of topological superconductivity, but Majorana fermions are not amenable to electronic current measurements due to their charge neutral nature. Thermal conductivity measurements, such as thermal Hall effect, are alternatively proposed, but material synthesis must come first. Superfluid $^3$He-$A$, on the other hand, is a known $p_x + ip_y$ superfluid whose edge current can be measured with a gyroscopic technique. Here, we propose a microelectromechanical system based gyroscope that will not only have enough signal sensitivity to measure the edge current but also be used to observe dimensionality induced phase transitions between different topological superfluids.

cond-mat.supr-con↗