SMPConv: Self-moving Point Representations for Continuous Convolution

Continuous convolution has recently gained prominence due to its ability to handle irregularly sampled data and model long-term dependency. Also, the promising experimental results of using large convolutional kernels have catalyzed the development of continuous convolution since they can construct large kernels very efficiently. Leveraging neural networks, more specifically multilayer perceptrons (MLPs), is by far the most prevalent approach to implementing continuous convolution. However, there are a few drawbacks, such as high computational costs, complex hyperparameter tuning, and limited descriptive power of filters. This paper suggests an alternative approach to building a continuous convolution without neural networks, resulting in more computationally efficient and improved performance. We present self-moving point representations where weight parameters freely move, and interpolation schemes are used to implement continuous functions. When applied to construct convolutional kernels, the experimental results have shown improved performance with drop-in replacement in the existing frameworks. Due to its lightweight structure, we are first to demonstrate the effectiveness of continuous convolution in a large-scale setting, e.g., ImageNet, presenting the improvements over the prior arts. Our code is available on https://github.com/sangnekim/SMPConvComment: Accepted to CVPR 202

Phase Engineering of Two-dimensional Transition Metal Dichalcogenides for Electrocatalyst Application

Department of Energy Engineering (Energy Engineering)Two Dimensional (2D) materials such as transition metal chalcogenides (TMDs) has been studied, but several problems still remain as disturbance for application to energy materials like electrocatalyst. In this work, we present a phase engineering of TMDs for electrocatalyst via highly reactive molten Potassium (K) metal intercalation. The 2H to 1T phase transition of TMDs has been exploited for application of the 1T phase with metallic property to applications such as electrocatalysts. However, the improving stability of thermodynamically metastable 1T TMDs remains an important challenge to overcome for using 1T phase properties. In addition, scalable synthesis methods of 1T TMDs, which are necessary for a wide range of applications, have to be developed. In this work, we presented a synthesis method of 1T phase MoS2 using molten K metal intercalation suitable for scalable method and confirmed the sucessfully phase transition 2H to 1T phase and improvement of 1T phase stability by K atom doping in the MoS2 basal plane. Furthermore, K atoms are doped in MoS2 basal plane, which can donate electron continuously to MoS2, which achieved long-term stability, thermal stability, and high power laser stability. Furthermore, we applied K doped 1T MoS2 to the hydrogen evolution reaction (HER) electrocatalyst and confirmed the improved HER performance owing to high electrical conductivity and basal plane activation of K-doped 1T MoS2 compared to 2H MoS2 and 1T MoS2 (n-BuLi), and high phase stability of K-doped 1T MoS2 exhibits high HER stability.clos

iColoriT: Towards Propagating Local Hint to the Right Region in Interactive Colorization by Leveraging Vision Transformer

Point-interactive image colorization aims to colorize grayscale images when a user provides the colors for specific locations. It is essential for point-interactive colorization methods to appropriately propagate user-provided colors (i.e., user hints) in the entire image to obtain a reasonably colorized image with minimal user effort. However, existing approaches often produce partially colorized results due to the inefficient design of stacking convolutional layers to propagate hints to distant relevant regions. To address this problem, we present iColoriT, a novel point-interactive colorization Vision Transformer capable of propagating user hints to relevant regions, leveraging the global receptive field of Transformers. The self-attention mechanism of Transformers enables iColoriT to selectively colorize relevant regions with only a few local hints. Our approach colorizes images in real-time by utilizing pixel shuffling, an efficient upsampling technique that replaces the decoder architecture. Also, in order to mitigate the artifacts caused by pixel shuffling with large upsampling ratios, we present the local stabilizing layer. Extensive quantitative and qualitative results demonstrate that our approach highly outperforms existing methods for point-interactive colorization, producing accurately colorized images with a user's minimal effort. Official codes are available at https://pmh9960.github.io/research/iColoriTComment: Accepted to WACV 202

Kaluza-Klein masses of bulk fields with general boundary conditions in AdS5_5

Recently bulk Randall-Sundrum theories with the gauge group $SU(2)_L \times SU(2)_R \times U(1)_{B-L}$ have drawn a lot of interest as an alternative to electroweak symmetry breaking mechanism. These models are in better agreement with electroweak precision data since custodial isospin symmetry on the IR brane is protected by the extended bulk gauge symmetry. We comprehensively study, in the S^1/\ZZ orbifold, the bulk gauge and fermion fields with the general boundary conditions as well as the bulk and localized mass terms. Master equations to determine the Kaluza-Klein (KK) mass spectra are derived without any approximation, which is an important basic step for various phenomenologies at high energy colliders. The correspondence between orbifold boundary conditions and localized mass terms is demonstrated not only in the gauge sector but also in the fermion sector. As the localized mass increases, the first KK fermion mass is shown to decrease while the first KK gauge boson mass to increase. The degree of gauge coupling universality violation is computed to be small in most parameter space, and its correlation with the mass difference between the top quark and light quark KK mode is also studied.Comment: 25 pages with 10 figures, Final version accepted by PR

Two Higgs doublet models for the LHC Higgs boson data at s=\sqrt{s}= 7 and 8 TeV

Updated LHC data on the new 126 GeV boson during the 7 and 8 TeV runnings strengthen the standard model Higgs boson interpretation further. Through the global $\chi^2$ analysis, we investigate whether the new particle could be one of the scalar particles in two Higgs doublet models. Four types (Type I, II, X and Y) are comprehensively studied. Taking the recent analysis on the spin-parity of the new boson, we consider two scenarios: the new boson is either the light CP-even one ($h^0$) or the heavy CP-even one ($H^0$). It is found that both scenarios are consistent with the new data, not only in the parameter regions near the decoupling limit but also in other regions far from the decoupling limit. In addition, the current data are compatible with the possibility that the light Higgs boson $h^0$ is hidden in the mass window of 90-100 GeV. The diphoton or $\tau\tau$ channel can provide a probe of this possibility by the enhanced signal rates.Comment: To appear in JHE

Hydra: Multi-head Low-rank Adaptation for Parameter Efficient Fine-tuning

The recent surge in large-scale foundation models has spurred the development of efficient methods for adapting these models to various downstream tasks. Low-rank adaptation methods, such as LoRA, have gained significant attention due to their outstanding parameter efficiency and no additional inference latency. This paper investigates a more general form of adapter module based on the analysis that parallel and sequential adaptation branches learn novel and general features during fine-tuning, respectively. The proposed method, named Hydra, due to its multi-head computational branches, combines parallel and sequential branch to integrate capabilities, which is more expressive than existing single branch methods and enables the exploration of a broader range of optimal points in the fine-tuning process. In addition, the proposed adaptation method explicitly leverages the pre-trained weights by performing a linear combination of the pre-trained features. It allows the learned features to have better generalization performance across diverse downstream tasks. Furthermore, we perform a comprehensive analysis of the characteristics of each adaptation branch with empirical evidence. Through an extensive range of experiments, encompassing comparisons and ablation studies, we substantiate the efficiency and demonstrate the superior performance of Hydra. This comprehensive evaluation underscores the potential impact and effectiveness of Hydra in a variety of applications. Our code is available on \url{https://github.com/extremebird/Hydra