arXiv subjects

Eunsang Lee

Publications and source records attributed to Eunsang Lee.

14 recordsLinked to original sources

arXiv2026

Hybrid dynamical decoupling and coherent driving for high-fidelity nuclear-spin control in diamond

Nitrogen-vacancy (NV) centers in diamond provide room-temperature electron-nuclear spin registers for quantum sensing and quantum information processing, with surrounding 13C nuclear spins serving as long-lived quantum memories. However, coherent control of large nuclear-spin registers is limited by finite electron-spin coherence and spectral addressability. Existing approaches follow two complementary strategies: dynamical-decoupling (DD) gates exploit filter-function resonances to realize selective conditional evolution but permit only discrete rotation angles, whereas dynamical-decoupling radio-frequency (DDrf) control restores continuous tunability at the cost of stringent hyperfine-geometry and RF-power requirements. Here, we introduce hybrid dynamical-decoupling and radio-frequency (H-DDrf) control, which preserves the DD-induced conditional evolution and employs a geometrically phase-matched RF drive to complete the target operation. This approach reduces both RF power and gate duration while maintaining high-fidelity control, thereby expanding the accessible 13C nuclear-spin register for room-temperature NV-based quantum memories and quantum processors.

quant-ph↗

arXiv2026

Referee: Reference-aware Audiovisual Deepfake Detection

Deepfakes generated by advanced generative models have rapidly posed serious threats, yet existing audiovisual deepfake detection approaches struggle to generalize to unseen manipulation methods. To address this, we propose a novel reference-aware audiovisual deepfake detection method, called Referee to capture fine-grained identity discrepancies. Unlike existing methods that overfit to transient spatiotemporal artifacts, Referee employs identity bottleneck and matching modules to model the relational consistency of speaker-specific cues captured by a single one-shot example as a biometric anchor. Extensive experiments on FakeAVCeleb, FaceForensics++, and KoDF demonstrate that Referee achieves state-of-the-art results on cross-dataset and cross-language evaluation protocols, including a 99.4% AUC on KoDF. These results highlight that explicitly correlating reference-based biometric priors is a key frontier for achieving generalized and reliable audiovisual forensics. The code is available at https://github.com/ewha-mmai/referee.

cs.CV↗

arXiv2025

Optimized Layerwise Approximation for Efficient Private Inference on Fully Homomorphic Encryption

Recent studies have explored the deployment of privacy-preserving deep neural networks utilizing homomorphic encryption (HE), especially for private inference (PI). Many works have attempted the approximation-aware training (AAT) approach in PI, changing the activation functions of a model to low-degree polynomials that are easier to compute on HE by allowing model retraining. However, due to constraints in the training environment, it is often necessary to consider post-training approximation (PTA), using the pre-trained parameters of the existing plaintext model without retraining. Existing PTA studies have uniformly approximated the activation function in all layers to a high degree to mitigate accuracy loss from approximation, leading to significant time consumption. This study proposes an optimized layerwise approximation (OLA), a systematic framework that optimizes both accuracy loss and time consumption by using different approximation polynomials for each layer in the PTA scenario. For efficient approximation, we reflect the layerwise impact on the classification accuracy by considering the actual input distribution of each activation function while constructing the optimization problem. Additionally, we provide a dynamic programming technique to solve the optimization problem and achieve the optimized layerwise degrees in polynomial time. As a result, the OLA method reduces inference times for the ResNet-20 model and the ResNet-32 model by 3.02 times and 2.82 times, respectively, compared to prior state-of-the-art implementations employing uniform degree polynomials. Furthermore, we successfully classified CIFAR-10 by replacing the GELU function in the ConvNeXt model with only 3-degree polynomials using the proposed method, without modifying the backbone model.

cs.CR↗

arXiv2025

Coherent control of solid-state defect spins via patterned boron-doped diamond circuit

Monolithic integration, which refers to the incorporation of all device functionalities within a single material, shows significant potential for creating scalable solid-state quantum devices. This study demonstrated the coherent control of nitrogen-vacancy (NV) spins using an electronic circuit monolithically integrated within diamond: a patterned, conductive boron-doped diamond (BDD) microwave waveguide. First, we validated the high-frequency performance of the circuit by characterizing its impedance up to the microwave range, confirming its capability for efficient microwave transmission. Then, using this monolithically integrated BDD--NV hybrid system, we performed optically detected magnetic resonance and observed noticeable Rabi oscillations driven by the metallic BDD circuit. Importantly, we verified that the BDD antenna has a minimal detrimental impact on the NV spins; microwave-induced heating is negligible under both pulsed and continuous driving, and the spin relaxation time ($T_1$) remains unperturbed. This approach paves the way for a new class of compact, robust, and versatile quantum platforms suitable for sensing and information processing in various environments.

physics.app-ph↗

arXiv2025

Quantum decoherence of nitrogen-vacancy spin ensembles in a nitrogen spin bath in diamond under dynamical decoupling

The negatively charged nitrogen-vacancy (NV) center in diamond has emerged as a leading qubit platform for quantum technology applications. One of the key challenges for NV-based quantum applications is building an accurate model to predict its decoherence properties and their quantum nature. In this study, we combine theory and experiment to investigate NV decoherence dynamics in the presence of nitrogen donor (P1 center) baths, which is one of the dominant decoherence sources in diamond. We employ a cluster-correlation expansion (CCE) method to compute the NV decoherence under the Hahn-echo (HE) and Carr-Purcell-Meiboom-Gill (CPMG) pulse sequences at various P1 concentrations from 1ppm to 300 ppm. We show that the coherence time (T2) increases with the number of pi pulses applied, indicating that the NV spin is decoupled from the P1 bath. Notably, we find that T2 scales quadratically as a function of the pulse number, on a logarithmic scale, as opposed to the linear scaling predicted by widely accepted semi-classical theories in the literature. In our experiment, we measure the CPMG signal for two diamond samples with high P1 concentrations of 0.8ppm and 13ppm. We demonstrate that the T2 scaling is indeed quadratic, thus confirming our theoretical predictions. Our results show that the quantum bath model combined with the CCE method can accurately capture the quantum nature of the P1-driven NV decoherence. Our study opens a new avenue for developing a complete noise model that could be used to optimize the performance of NV-based quantum devices.

quant-ph↗

arXiv2024

Hyperphosphorylation-Induced Phase Transition in Vesicle Delivery Dynamics of Motor Proteins in Neuronal Cells

Synaptic vesicle transport by motor proteins along microtubules is a crucial active process underlying neuronal communication. It is known that microtubules are destabilized by tau-hyperphosphorylation, which causes tau proteins to detach from microtubules and form neurofibril tangles. However, how tau-phosphorylation affects transport dynamics of motor proteins on the microtubule remains unknown. Here, we discover that long-distance unidirectional motion of vesicle-motor protein multiplexes (VMPMs) in living cells is suppressed under tau-hyperphosphorylation, with the consequent loss of fast vesicle-transport along the microtubule. The VMPMs in hyperphosphorylated cells exhibit seemingly bidirectional random motion, with dynamic properties far different from VMPM motion in normal cells. We establish a parsimonious physicochemical model of VMPM's active motion that provides a unified, quantitative explanation and predictions for our experimental results. Our analysis reveals that, under hyperphosphorylation conditions, motor-protein-multiplexes have both static and dynamic motility fluctuations. The loss of the fast vesicle-transport along the microtubule can be a mechanism of neurodegenerative disorders associated with tau-hyperphosphorylation.

physics.bio-ph↗

arXiv2021

Precise Approximation of Convolutional Neural Networks for Homomorphically Encrypted Data

Homomorphic encryption is one of the representative solutions to privacy-preserving machine learning (PPML) classification enabling the server to classify private data of clients while guaranteeing privacy. This work focuses on PPML using word-wise fully homomorphic encryption (FHE). In order to implement deep learning on word-wise homomorphic encryption (HE), the ReLU and max-pooling functions should be approximated by some polynomials for homomorphic operations. Most of the previous studies focus on HE-friendly networks, where the ReLU and max-pooling functions are approximated using low-degree polynomials. However, for the classification of the CIFAR-10 dataset, using a low-degree polynomial requires designing a new deep learning model and training. In addition, this approximation by low-degree polynomials cannot support deeper neural networks due to large approximation errors. Thus, we propose a precise polynomial approximation technique for the ReLU and max-pooling functions. Precise approximation using a single polynomial requires an exponentially high-degree polynomial, which results in a significant number of non-scalar multiplications. Thus, we propose a method to approximate the ReLU and max-pooling functions accurately using a composition of minimax approximate polynomials of small degrees. If we replace the ReLU and max-pooling functions with the proposed approximate polynomials, well-studied deep learning models such as ResNet and VGGNet can still be used without further modification for PPML on FHE. Even pre-trained parameters can be used without retraining. We approximate the ReLU and max-pooling functions in the ResNet-152 using the composition of minimax approximate polynomials of degrees 15, 27, and 29. Then, we succeed in classifying the plaintext ImageNet dataset with 77.52% accuracy, which is very close to the original model accuracy of 78.31%.

cs.CR↗

arXiv2021

Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network

Fully homomorphic encryption (FHE) is one of the prospective tools for privacypreserving machine learning (PPML), and several PPML models have been proposed based on various FHE schemes and approaches. Although the FHE schemes are known as suitable tools to implement PPML models, previous PPML models on FHE encrypted data are limited to only simple and non-standard types of machine learning models. These non-standard machine learning models are not proven efficient and accurate with more practical and advanced datasets. Previous PPML schemes replace non-arithmetic activation functions with simple arithmetic functions instead of adopting approximation methods and do not use bootstrapping, which enables continuous homomorphic evaluations. Thus, they could not use standard activation functions and could not employ a large number of layers. The maximum classification accuracy of the existing PPML model with the FHE for the CIFAR-10 dataset was only 77% until now. In this work, we firstly implement the standard ResNet-20 model with the RNS-CKKS FHE with bootstrapping and verify the implemented model with the CIFAR-10 dataset and the plaintext model parameters. Instead of replacing the non-arithmetic functions with the simple arithmetic function, we use state-of-the-art approximation methods to evaluate these non-arithmetic functions, such as the ReLU, with sufficient precision [1]. Further, for the first time, we use the bootstrapping technique of the RNS-CKKS scheme in the proposed model, which enables us to evaluate a deep learning model on the encrypted data. We numerically verify that the proposed model with the CIFAR-10 dataset shows 98.67% identical results to the original ResNet-20 model with non-encrypted data. The classification accuracy of the proposed model is 90.67%, which is pretty close to that of the original ResNet-20 CNN model...

cs.LG↗

arXiv2021

Suppressing the Rebound of Impacting Droplets from Solvophobic Surfaces by Polymer Additives: Polymer Adsorption and Molecular Mechanisms

A liquid droplet impacting on a solvophobic surface normally rebounds. The rebound is suppressed by a small amount of dissolved polymer. In this work, using multi-body dissipative particle dynamics simulations, two anti-rebound mechanisms, the slow-retraction and the slow-hopping mechanisms, are identified. Which of them dominates depends on the polymer-surface attraction strength. However, these two mechanisms are not excluding each other but may coexist. During the droplet rebound, the surface-adsorbed polymer acts in two ways: the adsorbed beads mediate solvent-surface interactions, and highly stretching unadsorbed polymer segment exerts a retraction force on the liquid. Both actions increase the friction against retraction and the resistance against hopping. We also investigate the effects of the molecular weight and the concentration of the polymer additive, the droplet size, and the impact velocity on the rebound tendency. As the first work to provide a microscopic explanation of the anti-rebound mechanism by polymer additives, this study allows better understanding of wetting behavior by polymer-solution droplets.

cond-mat.soft↗

arXiv2020

Rebound suppression of a droplet impact on a supersolvophobic surface by a small amount of polymer additives

A small amount of polymer dissolved in a droplet suppresses droplet rebound when it impinges on a supersolvophobic surface. This work investigates impacting dynamics of a droplet of dilute polymer solution depending on the molecular weight and the concentration of the polymer by using multi-body dissipative particle dynamics simulations. Either the longer polymer or the high polymer concentration suppresses rebound of a droplet although its shear viscosity and the liquid-vapor surface tension are not different from those of a pure solvent droplet. We found a new mechanism of the anti-rebound in which the resistance is applied against the hopping motion, while behavior of the non-rebounding droplet at the earlier spreading and retraction stages is same as for the rebounding droplets. Two polymer contributions to reducing the rebound tendency are quantitatively analyzed: the alteration of the substrate wettability by the polymer adsorption and the polymer elongation force.

cond-mat.soft↗

arXiv2020

Analysis of error dependencies on NewHope

Among many submissions to the NIST post-quantum cryptography (PQC) project, NewHope is a promising key encapsulation mechanism (KEM) based on the Ring-Learning with errors (Ring-LWE) problem. Since NewHope is an indistinguishability (IND)-chosen ciphertext attack secure KEM by applying the Fujisaki-Okamoto transform to an IND-chosen plaintext attack secure public key encryption, accurate calculation of decryption failure rate (DFR) is required to guarantee resilience against attacks that exploit decryption failures. However, the current upper bound of DFR on NewHope is rather loose because the compression noise, the effect of encoding/decoding of NewHope, and the approximation effect of centered binomial distribution are not fully considered. Furthermore, since NewHope is a Ring-LWE based cryptosystem, there is a problem of error dependency among error coefficients, which makes accurate DFR calculation difficult. In this paper, we derive much tighter upper bound on DFR than the current upper bound using constraint relaxation and union bound. Especially, the above-mentioned factors are all considered in derivation of new upper bound and the centered binomial distribution is not approximated to subgaussian distribution. In addition, since the error dependency is considered, the new upper bound is much closer to the real DFR than the previous upper bound. Furthermore, the new upper bound is parameterized by using Chernoff-Cramer bound in order to facilitate calculation of new upper bound for the parameters of NewHope. Since the new upper bound is much lower than the DFR requirement of PQC, this DFR margin is used to improve the security and bandwidth efficiency of NewHope. As a result, the security level of NewHope is improved by 7.2 % or bandwidth efficiency is improved by 5.9 %.

cs.CR↗

arXiv2019

Improving security and bandwidth efficiency of NewHope using error-correction schemes

Among many submissions to the NIST post-quantum cryptography (PQC) project, NewHope is a promising key encapsulation mechanism (KEM) based on the Ring-Learning with errors (Ring-LWE) problem. Since the most important factors to be considered for PQC are security and cost including bandwidth and time/space complexity, in this paper, by doing exact noise analysis and using Bose Chaudhuri Hocquenghem (BCH) codes, it is shown that the security and bandwidth efficiency of NewHope can be substantially improved. In detail, the decryption failure rate (DFR) of NewHope is recalculated by performing exact noise analysis, and it is shown that the DFR of NewHope has been too conservatively calculated. Since the recalculated DFR is much lower than the required $2^{-128}$, this DFR margin is exploited to improve the security up to 8.5 \% or the bandwidth efficiency up to 5.9 \% without changing the procedure of NewHope. The additive threshold encoding (ATE) used in NewHope is a simple error correcting code (ECC) robust to side channel attack, but its error-correction capability is relatively weak compared with other ECCs. Therefore, if a proper error-correction scheme is applied to NewHope, either security or bandwidth efficiency or both can be improved. Among various ECCs, BCH code has been widely studied for its application to cryptosystems due to its advantages such as no error floor problem. In this paper, the ATE and total noise channel are regarded as a super channel from an information-theoretic viewpoint. Based on this super channel analysis, various concatenated coding schemes of ATE and BCH code for NewHope have been investigated. Through numerical analysis, it is revealed that the security and bandwidth efficiency of NewHope are substantially improved by using the proposed error-correction schemes.

cs.CR↗

arXiv2015

Slowing down of ring polymer diffusion caused by inter-ring threading

Diffusion of long ring polymers in a melt is much slower than the reorganization of their internal structures. While direct evidences for entanglements have not been observed in the long ring polymers unlike linear polymer melts, threading between the rings is suspected to be the main reason for slowing down of ring polymer diffusion. It is, however, difficult to define the threading configuration between two rings because the rings have no chain end. In this work, evidences for threading dynamics of ring polymers are presented by using molecular dynamics simulation and applying a novel analysis method. The simulation results are analyzed in terms of the statistics of persistence and exchange times that have proved useful in studying heterogeneous dynamics of glassy systems. We find that the threading time of ring polymer melts increases more rapidly with the degree of polymerization than that of linear polymer melts. This indicates that threaded ring polymers cannot diffuse until unthreading event occurs, which results in the slowing down of ring polymer diffusion.

cond-mat.soft↗

arXiv2015

Segregated structure of ring polymer melts near the surface: Molecular dynamics simulation study

We study structural properties of a ring polymeric melt confined in a film in comparison to a linear counterpart using molecular dynamics simulations. Local structure orderings of ring and linear polymers in the vicinity of the surface are similar to each other because the length scale of surface-monomer excluded volume interaction is smaller than the size of an ideal blob of the ring. In a long length scale, while the Silberberg hypothesis can be used to provide a physical origin of confined linear polymer results, it no longer holds for a ring polymer case. We also present different structural properties of ring and linear polymers in a melt, including the size of polymers, an adsorbed amount, and the coordination number of a polymer. Our observation reveals that a confined ring in a melt adopts highly segregated conformation due to a topological excluded volume repulsion, which may provide a new perspective to understand the nature of biological processes, such as territorial segregation of chromosomes in eukaryotic nuclei.

cond-mat.soft↗