arXiv subjects

Hoyong Kim

Publications and source records attributed to Hoyong Kim.

7 recordsLinked to original sources

arXiv2025

Multi-Functional Metasurfaces with M-Type Ferrites: Shaping the Future of mmWave Absorption and Beam Steering

This paper presents a comprehensive review and tutorial on multi-functional metasurfaces integrated with M-type ferrite materials for millimeter-wave (mmWave) absorption and beam control. As wireless communication systems transition toward beyond-5G architectures, including non-terrestrial networks (NTNs), the demand for adaptive, low-profile electromagnetic surfaces that can manage interference while enabling beam reconfiguration becomes increasingly critical. Conventional metasurfaces often struggle to simultaneously achieve high absorption and beamforming over wide frequency ranges due to intrinsic material and structural limitations. This paper reviews the state-of-the-art in metasurface design for dual-functionality, particularly those combining frequency-selective magnetic materials with periodic surface lattices, to enable passive, compact, and reconfigurable reflectors and absorbers. Special emphasis is placed on the role of M-type ferrites in enhancing absorption via ferromagnetic resonance, and on the use of surface-wave trapping mechanisms to achieve narrowband and broadband functionality. A case study of a ferrite-based hybrid "reflectsorber" (reflectorarray + absorber) is presented to demonstrate key design concepts, analytical models, and application scenarios relevant to satellite, UAV, and NTN ground station deployments. Future directions for low-loss, tunable, and scalable metasurfaces in next-generation wireless infrastructures are also discussed.

physics.app-ph↗

arXiv2024

Asymptotic Midpoint Mixup for Margin Balancing and Moderate Broadening

In the feature space, the collapse between features invokes critical problems in representation learning by remaining the features undistinguished. Interpolation-based augmentation methods such as mixup have shown their effectiveness in relieving the collapse problem between different classes, called inter-class collapse. However, intra-class collapse raised in coarse-to-fine transfer learning has not been discussed in the augmentation approach. To address them, we propose a better feature augmentation method, asymptotic midpoint mixup. The method generates augmented features by interpolation but gradually moves them toward the midpoint of inter-class feature pairs. As a result, the method induces two effects: 1) balancing the margin for all classes and 2) only moderately broadening the margin until it holds maximal confidence. We empirically analyze the collapse effects by measuring alignment and uniformity with visualizing representations. Then, we validate the intra-class collapse effects in coarse-to-fine transfer learning and the inter-class collapse effects in imbalanced learning on long-tailed datasets. In both tasks, our method shows better performance than other augmentation methods.

cs.LG↗

arXiv2024

Revisiting Softmax Masking: Stop Gradient for Enhancing Stability in Replay-based Continual Learning

In replay-based methods for continual learning, replaying input samples in episodic memory has shown its effectiveness in alleviating catastrophic forgetting. However, the potential key factor of cross-entropy loss with softmax in causing catastrophic forgetting has been underexplored. In this paper, we analyze the effect of softmax and revisit softmax masking with negative infinity to shed light on its ability to mitigate catastrophic forgetting. Based on the analyses, it is found that negative infinity masked softmax is not always compatible with dark knowledge. To improve the compatibility, we propose a general masked softmax that controls the stability by adjusting the gradient scale to old and new classes. We demonstrate that utilizing our method on other replay-based methods results in better performance, primarily by enhancing model stability in continual learning benchmarks, even when the buffer size is set to an extremely small value.

cs.LG↗

arXiv2021

Learning from Matured Dumb Teacher for Fine Generalization

The flexibility of decision boundaries in neural networks that are unguided by training data is a well-known problem typically resolved with generalization methods. A surprising result from recent knowledge distillation (KD) literature is that random, untrained, and equally structured teacher networks can also vastly improve generalization performance. It raises the possibility of existence of undiscovered assumptions useful for generalization on an uncertain region. In this paper, we shed light on the assumptions by analyzing decision boundaries and confidence distributions of both simple and KD-based generalization methods. Assuming that a decision boundary exists to represent the most general tendency of distinction on an input sample space (i.e., the simplest hypothesis), we show the various limitations of methods when using the hypothesis. To resolve these limitations, we propose matured dumb teacher based KD, conservatively transferring the hypothesis for generalization of the student without massive destruction of trained information. In practical experiments on feed-forward and convolution neural networks for image classification tasks on MNIST, CIFAR-10, and CIFAR-100 datasets, the proposed method shows stable improvement to the best test performance in the grid search of hyperparameters. The analysis and results imply that the proposed method can provide finer generalization than existing methods.

cs.LG↗

arXiv2017

Minimal control power of controlled dense coding and genuine tripartite entanglement

We investigate minimal control power (MCP) for controlled dense coding defined by the channel capacity. We obtain MCPs for extended three-qubit Greenberger-Horne-Zeilinger (GHZ) states and generalized three-qubit $W$ states. Among those GHZ states, the standard GHZ state is found to maximize the MCP and so does the standard $W$ state among the $W$-type states. We find the lower and upper bounds of the MCP and show for pure states that the lower bound, zero, is achieved if and only if the three-qubit state is biseparable or fully separable. The upper bound is achieved only for the standard GHZ state. Since the MCP is nonzero only when a three-qubit entanglement exists, this quantity may be a good candidate to measure the degree of genuine tripartite entanglement.

quant-ph↗

arXiv2015

Two different types of optical hybrid qubits for teleportation in a lossy environment

We investigate the performance of quantum teleportation under a lossy environment using two different types of optical hybrid qubits. One is the hybrid of the vacuum and single-photon states with coherent states and the other is the hybrid of polarized single-photon states with coherent states. We have shown that the hybrid qubit using vacuum and single-photon states is generally more robust to photon loss effects compared to the one using the photon polarization with respect to fidelities and success probabilities of quantum teleportation.

quant-ph↗

arXiv2014

Transfer of different types of optical qubits over a lossy environment

We compare three different types of optical qubits for information transfer via quantum teleportation and direction transmission under photon losses. The three types of qubits are (1) qubits using the vacuum and the single-photon (VSP) states, (2) single-photon qubits using polarization degrees of freedom, i.e., polarized single-photon (PSP) qubits, and (3) coherent-state qubits that use two coherent states with opposite phases as the qubit basis. Our analysis shows that the teleportation scheme outperforms the direct transmission for most of cases as far as fidelities are concerned. Overall, VSP qubits are found to be the most efficient for both the direct transmission and teleportation under photon loss effects. The coherent-state qubits are more robust than PSP qubits either when their amplitudes are small as $|α| \lesssim 1.22$ or when photon loss effects are strong. Our results would provide useful and timely information for the development of practical optical quantum information processing particularly in the context of hybrid architectures.

quant-ph↗