arXiv subjects

Takaaki Yoshino

Publications and source records attributed to Takaaki Yoshino.

4 recordsLinked to original sources

arXiv2021

Optically induced sieve effect for nanoparticles near a nanofiber taper

We demonstrate size selective optical trapping and transport for nanoparticles near an optical nanofiber taper. Using a two-wavelength, counter-propagating mode configuration, we show that 100 nm diameter and 150 nm diameter gold nanospheres (GNSs) are trapped by the evanescent field in the taper region at different optical powers. Conversely, when one nanoparticle species is trapped the other may be transported, leading to a sieve-like effect. Our results show that sophisticated optical manipulation can be achieved in a passive configuration by taking advantage of mode behavior in nanophotonics devices.

quant-ph↗

arXiv2020

Detecting and explaining changes in various assets' relationships in financial markets

We study the method for detecting relationship changes in financial markets and providing human-interpretable network visualization to support the decision-making of fund managers dealing with multi-assets. First, we construct co-occurrence networks with each asset as a node and a pair with a strong relationship in price change as an edge at each time step. Second, we calculate Graph-Based Entropy to represent the variety of price changes based on the network. Third, we apply the Differential Network to finance, which is traditionally used in the field of bioinformatics. By the method described above, we can visualize when and what kind of changes are occurring in the financial market, and which assets play a central role in changes in financial markets. Experiments with multi-asset time-series data showed results that were well fit with actual events while maintaining high interpretability. It is suggested that this approach is useful for fund managers to use as a new option for decision making.

q-fin.GN↗

arXiv2020

Optical transport of sub-micron lipid vesicles along an optical nanofibre

Enhanced manipulation and analysis of bio-particles using light confined in nano-scale dielectric structures has proceeded apace in the last several years. Small mode volumes, along with the lack of a need for bulky optical elements give advantages in sensitivity and scalability relative to conventional optical manipulation. However, manipulation of lipid vesicles (liposomes) remains difficult, particularly in the sub-micron diameter regime. Here we demonstrate the optical trapping and transport of sub-micron diameter liposomes along an optical nanofiber using the nanofiber mode's evanescent field. We find that nanofiber diameters below a nominal diffraction limit give optimal results. Our results pave the way for integrated optical transport and analysis of liposome-like bio-particles, as well as their coupling to nano-optical resonators.

physics.bio-ph↗

arXiv2019

Tangled String for Multi-Scale Explanation of Contextual Shifts in Stock Market

The original research question here is given by marketers in general, i.e., how to explain the changes in the desired timescale of the market. Tangled String, a sequence visualization tool based on the metaphor where contexts in a sequence are compared to tangled pills in a string, is here extended and diverted to detecting stocks that trigger changes in the market and to explaining the scenario of contextual shifts in the market. Here, the sequential data on the stocks of top 10 weekly increase rates in the First Section of the Tokyo Stock Exchange for 12 years are visualized by Tangled String. The changing in the prices of stocks is a mixture of various timescales and can be explained in the time-scale set as desired by using TS. Also, it is found that the change points found by TS coincided by high precision with the real changes in each stock price. As TS has been created from the data-driven innovation platform called Innovators Marketplace on Data Jackets and is extended to satisfy data users, this paper is as evidence of the contribution of the market of data to data-driven innovations.

cs.CE↗