Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting

This paper proposes a weakly- and self-supervised deep convolutional neural network (WSSDCNN) for content-aware image retargeting. Our network takes a source image and a target aspect ratio, and then directly outputs a retargeted image. Retargeting is performed through a shift map, which is a pixel-wise mapping from the source to the target grid. Our method implicitly learns an attention map, which leads to a content-aware shift map for image retargeting. As a result, discriminative parts in an image are preserved, while background regions are adjusted seamlessly. In the training phase, pairs of an image and its image-level annotation are used to compute content and structure losses. We demonstrate the effectiveness of our proposed method for a retargeting application with insightful analyses.Comment: 10 pages, 11 figures. To appear in ICCV 2017, Spotlight Presentatio

Noise Map Guidance: Inversion with Spatial Context for Real Image Editing

Text-guided diffusion models have become a popular tool in image synthesis, known for producing high-quality and diverse images. However, their application to editing real images often encounters hurdles primarily due to the text condition deteriorating the reconstruction quality and subsequently affecting editing fidelity. Null-text Inversion (NTI) has made strides in this area, but it fails to capture spatial context and requires computationally intensive per-timestep optimization. Addressing these challenges, we present Noise Map Guidance (NMG), an inversion method rich in a spatial context, tailored for real-image editing. Significantly, NMG achieves this without necessitating optimization, yet preserves the editing quality. Our empirical investigations highlight NMG's adaptability across various editing techniques and its robustness to variants of DDIM inversions.Comment: ICLR 202

Elevated cellular cholesterol in Familial Alzheimer’s presenilin 1 mutation is associated with lipid raft localization of β-amyloid precursor protein

Familial Alzheimer’s disease (FAD)-associated presenilin 1 (PS1) serves as a catalytic subunit of γ-secretase complex, which mediates the proteolytic liberation of β-amyloid (Aβ) from β-amyloid precursor protein (APP). In addition to its proteolytic role, PS1 is involved in non-proteolytic functions such as protein trafficking and ion channel regulation. Furthermore, postmortem AD brains as well as AD patients showed dysregulation of cholesterol metabolism. Since cholesterol has been implicated in regulating Aβ production, we investigated whether the FAD PS1-associated cholesterol elevation could influence APP processing. We found that in CHO cells stably expressing FAD-associated PS1 ΔE9, total cholesterol levels are elevated compared to cells expressing wild-type PS1. We also found that localization of APP in cholesterol-enriched lipid rafts is substantially increased in the mutant cells. Reducing the cholesterol levels by either methyl-β-cyclodextrin or an inhibitor of CYP51, an enzyme mediating the elevated cholesterol in PS1 ΔE9-expressing cells, significantly reduced lipid raft-associated APP. In contrast, exogenous cholesterol increased lipid raft-associated APP. These data suggest that in the FAD PS1 ΔE9 cells, the elevated cellular cholesterol level contributes to the altered APP processing by increasing APP localized in lipid rafts

Unraveling hidden rules behind the wet-to-dry transition of bubble array by glass-box physics rule learner

A liquid–gas foam, here called bubble array, is a ubiquitous phenomenon widely observed in daily lives, food, pharmaceutical and cosmetic products, and even bio- and nano-technologies. This intriguing phenomenon has been often studied in a well-controlled environment in laboratories, computations, or analytical models. Still, real-world bubble undergoes complex nonlinear transitions from wet to dry conditions, which are hard to describe by unified rules as a whole. Here, we show that a few early-phase snapshots of bubble array can be learned by a glass-box physics rule learner (GPRL) leading to prediction rules of future bubble array. Unlike the black-box machine learning approach, the glass-box approach seeks to unravel expressive rules of the phenomenon that can evolve. Without known principles, GPRL identifies plausible rules of bubble prediction with an elongated bubble array data that transitions from wet to dry states. Then, the best-so-far GPRL-identified rule is applied to an independent circular bubble array, demonstrating the potential generality of the rule. We explain how GPRL uses the spatio-temporal convolved information of early bubbles to mimic the scientist’s perception of bubble sides, shapes, and inter-bubble influences. This research will help combine foam physics and machine learning to better understand and control bubbles.This article is published as Cho, In Ho, Sinchul Yeom, Tanmoy Sarkar, and Tae-Sik Oh. "Unraveling hidden rules behind the wet-to-dry transition of bubble array by glass-box physics rule learner." Scientific Reports 12, no. 1 (2022): 3191. doi: https://doi.org/10.1038/s41598-022-07170-y. Copyright 2022 The Authors. This Open Access article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/)

Towards standardizing Korean Grammatical Error Correction: Datasets and Annotation

Research on Korean grammatical error correction (GEC) is limited compared to other major languages such as English and Chinese. We attribute this problematic circumstance to the lack of a carefully designed evaluation benchmark for Korean. Thus, in this work, we first collect three datasets from different sources (Kor-Lang8, Kor-Native, and Kor-Learner) to cover a wide range of error types and annotate them using our newly proposed tool called Korean Automatic Grammatical error Annotation System (KAGAS). KAGAS is a carefully designed edit alignment & classification tool that considers the nature of Korean on generating an alignment between a source sentence and a target sentence, and identifies error types on each aligned edit. We also present baseline models fine-tuned over our datasets. We show that the model trained with our datasets significantly outperforms the public statistical GEC system (Hanspell) on a wider range of error types, demonstrating the diversity and usefulness of the datasets.Comment: Add affiliation and email addres

Linear Wave Reflection by Trench with Various Shapes

author's final versionTwo types of analytical solutions for waves propagating over an asymmetric trench are derived. One is a shallow water wave model and the other is an extended model applicable to deeper water. The water depth inside the trench varies in proportion to a power of distance from the center of the trench (where the center means the deepest water depth point and the origin of -coordinate in this study). The mild-slope equation is transformed into a second order ordinary differential equation with variable coefficients based on the longwave assumption or Hunts (1979) approximate solution for wave dispersion. The analytical solutions are then obtained by using the power series technique. The analytical solutions are compared with the numerical solution of the hyperbolic mild-slope equations. After obtaining the analytical solutions under various conditions, the results are analyzed