A massive disk galaxy at z>3 along the sightline of QSO 1508+5714

We have obtained deep images in the BVRIJHKs bands of the field centered on QSO 1508+5714 (z_{em} =4.28) with the Suprime camera, FOCAS and MOIRCS cameras on Subaru telescope. We report here the detection of a B-dropout galaxy, which is 3\secpoint 5 north-west of the QSO sightline. A photometric redshift analysis is presented to complement the color selection. Given the photometric properties of this object ($M = -22.2$, making $L\approx 3 L^{\ast}$, if placed at its photometric redshift $z\sim 3.5$), as well as the S$\acute{e}$rsic index ($n \sim 1$) derived from a 2-D imaging decomposition of the HST WFPC2 image taken in the $I_{F814}$ filter, the identified system is consistent with a massive disk galaxy at z>3. If confirmed, it would be one of the most distant massive disk galaxies known so far.Comment: 12 pages, 6 figures, accepted by PASJ, Vol.61/No.5, 200

One-Shot Machine Unlearning with Mnemonic Code

Deep learning has achieved significant improvements in accuracy and has been applied to various fields. With the spread of deep learning, a new problem has also emerged; deep learning models can sometimes have undesirable information from an ethical standpoint. This problem must be resolved if deep learning is to make sensitive decisions such as hiring and prison sentencing. Machine unlearning (MU) is the research area that responds to such demands. MU aims at forgetting about undesirable training data from a trained deep learning model. A naive MU approach is to re-train the whole model with the training data from which the undesirable data has been removed. However, re-training the whole model can take a huge amount of time and consumes significant computer resources. To make MU even more practical, a simple-yet-effective MU method is required. In this paper, we propose a one-shot MU method, which does not need additional training. To design one-shot MU, we add noise to the model parameters that are sensitive to undesirable information. In our proposed method, we use the Fisher information matrix (FIM) to estimate the sensitive model parameters. Training data were usually used to evaluate the FIM in existing methods. In contrast, we avoid the need to retain the training data for calculating the FIM by using class-specific synthetic signals called mnemonic code. Extensive experiments using artificial and natural datasets demonstrate that our method outperforms the existing methods.Comment: 14 pages, welcome coment