The task of out-of-distribution (OOD) detection is vital to realize safe and reliable operation for real-world applications. After the failure of likelihood-based detection in high dimensions had been shown, approaches based on the typical set have been attracting attention; however, they still have not achieved satisfactory performance. Beginning by presenting the failure case of the typicality-based approach, we propose a new reconstruction error-based approach that employs normalizing flow (NF). We further introduce a typicality-based penalty, and by incorporating it into the reconstruction error in NF, we propose a new OOD detection method, penalized reconstruction error (PRE). Because the PRE detects test inputs that lie off the in-distribution manifold, it effectively detects adversarial examples as well as OOD examples. We show the effectiveness of our method through the evaluation using natural image datasets, CIFAR-10, TinyImageNet, and ILSVRC2012.

In this paper, we present a new topic variation detection method that combines a topic extraction method with a change point detection method. It extracts topics from time-series text data as the feature of each time and detects change points from the changing patterns of the extracted topics. We applied the method to analyze effective albeit underutilized text data that contained the Japanese Prime Minister's (PM's) detailed daily activities of over 32 years. Our method and data provide novel insights into the empirical analysis of the political business cycle, a classical issue in economics and political science. For example, because our approach enabled us to directly observe and analyze the PM's actions, it overcame empirical challenges that previous researchers encountered owing to the unobservability of the PM's behavior. Our empirical observations are mostly consistent with recent theoretical developments regarding this topic. Despite limitations, by employing a completely new method and data, our approach enhances our understanding and provides new insights into this classic issue.

Recommender systems support user decision-making, and explanations of recommendations further facilitate their usefulness. Previous explanation styles are based on similar users, similar items, demographics of users, and contents of items. Contexts, such as usage scenarios and accompanying persons, have not been used for explanations, although they influence user decisions. In this paper, we propose a context style explanation method, presenting contexts suitable for consuming recommended items. The expected impacts of context style explanations are 1) persuasiveness: recognition of suitable context for usage motivates users to consume items, and 2) usefulness: envisioning context helps users to make right choices because the values of items depend on contexts. We evaluate context style persuasiveness and usefulness by a crowdsourcing-based user study in a restaurant recommendation setting. The context style explanation is compared to demographic and content style explanations. We also combine context style and other explanation styles, confirming that hybrid styles improve persuasiveness and usefulness of explanation.

UNLABELLED: The advent of high-throughput DNA sequencers has increased the pace of collecting enormous amounts of genomic information, yielding billions of nucleotides on a weekly basis. This advance represents an improvement of two orders of magnitude over traditional Sanger sequencers in terms of the number of nucleotides per unit time, allowing even small groups of researchers to obtain huge volumes of genomic data over fairly short period. Consequently, a pressing need exists for the development of personalized genome browsers for analyzing these immense amounts of locally stored data. The UTGB (University of Tokyo Genome Browser) Toolkit is designed to meet three major requirements for personalization of genome browsers: easy installation of the system with minimum efforts, browsing locally stored data and rapid interactive design of web interfaces tailored to individual needs. The UTGB Toolkit is licensed under an open source license. AVAILABILITY: The software is freely available at http://utgenome.org/.