Spatial-and-Frequency-aware Restoration method for Images based on Diffusion Models

Diffusion models have recently emerged as a promising framework for Image Restoration (IR), owing to their ability to produce high-quality reconstructions and their compatibility with established methods. Existing methods for solving noisy inverse problems in IR, considers the pixel-wise data-fidelity. In this paper, we propose SaFaRI, a spatial-and-frequency-aware diffusion model for IR with Gaussian noise. Our model encourages images to preserve data-fidelity in both the spatial and frequency domains, resulting in enhanced reconstruction quality. We comprehensively evaluate the performance of our model on a variety of noisy inverse problems, including inpainting, denoising, and super-resolution. Our thorough evaluation demonstrates that SaFaRI achieves state-of-the-art performance on both the ImageNet datasets and FFHQ datasets, outperforming existing zero-shot IR methods in terms of LPIPS and FID metrics

?-Graphic Delta-Matroids and Their Applications

For an abelian group ?, a ?-labelled graph is a graph whose vertices are labelled by elements of ?. We prove that a certain collection of edge sets of a ?-labelled graph forms a delta-matroid, which we call a ?-graphic delta-matroid, and provide a polynomial-time algorithm to solve the separation problem, which allows us to apply the symmetric greedy algorithm of Bouchet to find a maximum weight feasible set in such a delta-matroid. We present two algorithmic applications on graphs; Maximum Weight Packing of Trees of Order Not Divisible by k and Maximum Weight S-Tree Packing. We also discuss various properties of ?-graphic delta-matroids

Γ\Gamma-graphic delta-matroids and their applications

For an abelian group $\Gamma$, a $\Gamma$-labelled graph is a graph whose vertices are labelled by elements of $\Gamma$. We prove that a certain collection of edge sets of a $\Gamma$-labelled graph forms a delta-matroid, which we call a $\Gamma$-graphic delta-matroid, and provide a polynomial-time algorithm to solve the separation problem, which allows us to apply the symmetric greedy algorithm of Bouchet to find a maximum weight feasible set in such a delta-matroid. We present two algorithmic applications on graphs; Maximum Weight Packing of Trees of Order Not Divisible by $k$ and Maximum Weight $S$-Tree Packing. We also discuss various properties of $\Gamma$-graphic delta-matroids.Comment: 16 pages, 2 figure

Analysis of Plie Raft Interaction in Sand With Centrifuge Tests

In the conventional design for piled rafts, the load capacity of the raft is not in general taken into account and the load capacity of piles is only considered for the estimation of the total load carrying capacity of the piled rafts. As a consequence, piled rafts are often designed with excessively conservative safety margin, raising a need of further investigation of the load capacity mechanism of piled rafts. In this study, a series of centrifuge load tests using model group piles and piled rafts are conducted and used to compare the axial load carrying behaviors of group piles and piled rafts for different soil conditions. Instrumented model piles and rafts are manufactured and introduced into the centrifuge tests. Different density conditions of test sands were considered in the tests. From the test results, it is revealed that the load carrying capacity increase for piled rafts differ for different soil conditions. The load capacity of piled rafts is greater than those of the group piles by 13% for dense sand cases and by 22% for loose sand cases

Trimmed Mean Group Estimation

This paper develops robust panel estimation in the form of trimmed mean group estimation for potentially heterogenous panel regression models. It trims outlying individuals of which the sample variances of regressors are either extremely small or large. The limiting distribution of the trimmed estimator can be obtained in a similar way to the standard mean group estimator, provided the random coefficients are conditionally homoskedastic. We consider two trimming methods. The first one is based on the order statistic of the sample variance of each regressor. The second one is based on the Mahalanobis depth of the sample variances of regressors. We apply them to the mean group estimation of the two-way fixed effects model with potentially heterogeneous slope parameters and to the common correlated effects regression, and we derive limiting distribution of each estimator. As an empirical illustration, we consider the effect of police on property crime rates using the U.S. state-level panel data

The signaling effect of group-type profile pictures in the sharing economy: The case of Airbnb

While Airbnb hosts may publish various details of their property on the online platform to persuade travelers to make bookings, they choose to post limited information about themselves, except their profile pictures. Based on the signaling and uncertainty reduction theories, we focus on the impact of host profile pictures on bookings and hypothesize that (1) the presence of a profile picture induces the travelers to trust the host more, (2) the number of people in a picture, a proxy for sociality in trustworthiness, increases bookings, and (3) these two impacts are intensified for properties in risky neighborhoods. Collecting profile pictures of 14,799 hosts on Airbnb, we utilized a deep learning-based face detection technique to extract the number of different faces in a profile and ran random effects models to test our hypotheses. This study is unique in its using archival data to show the impact of profile pictures on bookings