Song-Ju Kim

Random Number Generation by a Two-Dimensional Crystal of Protein Molecules

We discuss 2D and binary self-assemblies of protein molecules using apo-ferritin and holo-ferritin, which have identical outer-shell structures but different inner structures. The assemblies do not show any phase separation but form 2D monomolecular-layer crystals. Statistical analyses showed a random molecular distribution in the crystal where the molar ratio was conserved as it was in the solution. This molecular pattern is readily prepared, but it is neither reproducible nor predictable and hence can be used as a nanometer-scale cryptographic device or an identification tag.

A note on the roles of quantum and mechanical models in social biophysics

Recent advances in the applications of quantum models into various disciplines such as cognitive science, social sciences, economics, and biology witnessed enormous achievements and possible future progress. In this paper, we propose one of the most promising directions in the applications of quantum models: the combination of quantum and mechanical models in social biophysics. The possible resulting discipline may be called as experimental quantum social biophysics and could foster our understandings of the relationships between the society and individuals.

High-Density Physical Random Number Generator Using Spin Signals in Multidomain Ferromagnetic Layer

A high-density random number generator (RNG) based on spin signals in a multidomain ferromagnetic layer in a magnetic tunnel junction (MTJ) is proposed and fabricated. Unlike conventional spin-based RNGs, the proposed method does not require one to control an applied current, leading to a time delay in the system. RNG demonstrations are performed at room temperature. The randomness of the bit sequences generated by the proposed RNG is verified using the FIPS 140-2 statistical test suite provided by the NIST. The test results validate the effectiveness of the proposed RNGs. Our results suggest that we can obtain high-density, ultrafast RNGs if we can achieve high integration on the chip.

Performance in Multi-Armed Bandit Tasks in Relation to Ambiguity-Preference Within a Learning Algorithm

Ellsberg paradox in decision theory posits that people will inevitably choose a known probability of winning over an unknown probability of winning even if the known probability is low. One of prevailing theories which addresses the Ellsberg paradox is known as ’ambiguity-aversion’. In this study, we investigate the properties of ambiguity-aversion in four distinct types of reinforcement learning algorithms: ucb1-tuned, modified ucb1-tuned, softmax, and tug-of-war. We take as our sample a scenario in which there are two slot machines and each machine dispenses a coin according to a probability that is generated by its own probability density function (PDF). We then investigate the choices of a learning algorithm in such multi-armed bandit tasks. There are different reactions in multi-armed bandit tasks, depending on the ambiguity-preference in the learning algorithms. Notably, we discovered clear performance enhancement related to ambiguity-preference in a learning algorithm. Although this study does not directly address the issue of ambiguity-aversion theory highlighted in Ellsberg paradox, the differences between different learning algorithms suggests that there is room for further study regarding the Ellsberg paradox and decision theory.

Physarum-Inspired Electronic and Nanoelectronic Computing Systems

Electronic and nanoelectronic systems implementing a Physarum-inspired computing architecture are presented. The system is designed to solve computationally demanding problems. The core of the electronic system consists of a capacitor network with star topology. Charging and discharging of the capacitors under charge conservation mimics the spatiotemporal dynamics of an amoeboid organism, exhibiting the sophisticated ability of exploring a solution space. Small fluctuations inherently involved in electronic devices are used to explore solution space. We constructed electronic Physarum and successfully demonstrated solution search capability through finding solutions of optimization problems including constraint satisfaction problem and satisfiability problem. Nanoelectronics implementation of the electron Physarum using electron Brownian ratchet devices is proposed toward the ultra-small system operating ultra-low power consumption. A unique feature of the system is that the system acquires spontaneous solution search capability from unavoidable fluctuation in nanostructure and nanodevices. Recent research results of fabrication and characterization of electron Brownian ratchet device using semiconductor nanowire are described.

Nanophotonics for efficient solution searching and decision making

At present, there is great demand for novel computing devices and architectures that can overcome the limitations of conventional technologies based solely on electron transfer, including the need to reduce energy consumption and solve computationally demanding problems. In particular, conventional digital computers have difficulty in dealing with intractable problems in which the number of possible solutions increases exponentially as a function of the problem size (referred to as ‘combinatorial explosion’). It is also challenging for conventional computers to make efficient and adaptive decisions in the uncertain, dynamically changing environments seen in real-world applications. A promising solution is near-field nanophotonics, which has been extensively studied with the aim of unveiling and exploiting light-matter interactions that occur at a scale below the wavelength of light. Recent progress made in experimental technologies—both in nanomaterial fabrication, such as quantum dots (QDs), and in characterization—is driving further advancements in the field. We have shown that the dynamics of optical energy transfer mediated by near-field interactions can be exploited to solve solution-searching and decision-making problems.1–4 This suggests that computing systems based on near-field nanophotonics may one day be able to exhibit intellectual abilities. When QDs share common resonant energy levels mediated by optical near-field interactions, optical energy is transferred from smaller QDs to larger ones. This process has been experimentally demonstrated in various quantum nanostructures, such as those fabricated in indium gallium arsenide, zinc oxide, and cadmium selenide. In addition, we have shown this transfer of optical Figure 1. (a) Quantum-dot-based decision maker (QDM) consisting of five quantum dots (denoted QDLL, QDML, QDS, QDMR, and QDLR) interacting via optical near-fields. The subscripts 1, 2, and 3 denote the (1,1,1), (2,1,1), and (2,2,2) energy levels, respectively, while U represents the various optical near-field interactions. (b) Quick adaptation of the QDM to a dynamically changing environment, which in this case is the change of reward probabilities between two slot machines (PA and PB). Softmax: Best conventional algorithm.

A Nanophotonic Computing Paradigm: Problem-Solving and Decision-Making Systems Using Spatiotemporal Photoexcitation Transfer Dynamics

In contrast to conventional digital computers that operate as instructed by programmers, biological organisms solve problems and make decisions through intrinsic spatiotemporal dynamics in which their dynamic components process environmental information in a self-organized manner. Previously, we formulated two mathematical models of spatiotemporal dynamics by which the single-celled amoeba (a plasmodial slime mold), which exhibits complex spatiotemporal oscillatory dynamics and sophisticated computing capabilities, could solve a problem and make a decision by changing its amorphous shape in dynamic and uncertain environments. These models can also be implemented by various physical systems that exhibit suitable spatiotemporal dynamics resembling the amoeba’s shape-changing capability. Here we demonstrate that the photoexcitation transfer phenomena in certain quantum nanostructures mediated by optical near-field interactions mimic the amoeba-like spatiotemporal dynamics and can be used to solve two highly complex problems; the satisfiability problem, which is one of the most difficult combinatorial optimization problems, to determine whether a given logical proposition is self-consistent, and the multi-armed bandit problem, which is a decision-making problem in finding the most profitable option from among a number of options that provide rewards with different unknown probabilities. Our problem-solving and decision-making models exhibited better performances than conventionally known best algorithms. These demonstrations pave the way for a novel nanophotonic computing paradigm in which both coherent and dissipative processes are exploited for performing powerful solution searching and efficient decision-making with low energy consumption.