Many real world data, which we deal with today, often have very high dimensions. These high-dimensional data can be seen as collections of data points from a union of low-dimensional subspaces.

Recent advances in computing power have enabled the generation of large datasets for materials, enabling data-driven approaches to problem-solving in materials science, including materials discovery. Machine learning is a primary tool for manipulating such large datasets, predicting unknown material properties and uncovering relationships between structure and property. Among state-of-the-art machine learning algorithms, gradient-boosted regression trees (GBRT) are known to provide highly accurate predictions, as well as interpretable analysis based on the importance of features. Here, in a search for lead-free perovskites for use in solar cells, we applied the GBRT algorithm to a dataset of electronic structures for candidate halide double perovskites to predict heat of formation and bandgap. Statistical analysis of the selected features identifies design guidelines for the discovery of new lead-free perovskites.

We study oscillatory and oscillation suppressed phases in coupled counter-rotating nonlinear oscillators. We demonstrate the existence of limit cycle, amplitude death, and oscillation death, and also clarify the Hopf, pitchfork, and infinite period bifurcations between them. Especially, the oscillation death is a new type of oscillation suppressions of which the inhomogeneous steady states are neutrally stable. We discuss the robust neutral stability of the oscillation death in non-conservative systems via the anti-parity-time-symmetric phase transitions at exceptional points in terms of non-Hermitian systems.

A quantitative survey of water scavenger beetles Sternolophus rufipes and Hydrochara affinis in paddy fields is essential not only for evaluating the impact of climate change on ecosystems but also for quantifying the stability of paddy fields. Many researchers classify insects in insect traps visually and manually count the number of individuals in each species. This manual survey method is time-consuming, fatiguing, and tedious. In this paper, we present a simple method to classify and count beetles in noisy trap images. The proposed method uses the beetles' body size and spots made by the light reflecting off the backs of beetles.

Subterranean termites live in large colonies under the ground where they build an elaborate network of tunnels for foraging. In this study, we explored how the termite population size can be estimated using partial information on tunnel patterns. To achieve this, we used an agent-based model to create tunnel patterns that were characterized by three variables:

Automatic annotation for three-dimensional (3D) cephalometric analysis has been limited by computational complexity and computing performance.

One unanswered question about the binary neutron star coalescence GW170817 is the nature of its post-merger remnant. A previous search for post-merger gravitational waves targeted high-frequency signals from a possible neutron star remnant with a maximum signal duration of 500 s.

We describe directed searches for continuous gravitational waves (GWs) from 16 well-localized candidate neutron stars, assuming none of the stars has a binary companion.

We present a multi-messenger measurement of the Hubble constant H 0 using the binary？black-hole merger GW170814 as a standard siren, combined with a photometric redshift catalog from the Dark Energy Survey (DES).

Advanced LIGO's second observing run (O2), conducted from 2016 November 30 to 2017 August 25, combined with Advanced Virgo's first observations in 2017 August, witnessed the birth of gravitational-wave multimessenger astronomy.