Design of Self-Oscillating Gels and Application to Biomimetic Actuators

As a novel biomimetic polymer, we have developed polymer gels with an autonomous self-oscillating function. This was achieved by utilizing oscillating chemical reactions, called the Belousov-Zhabotinsky (BZ) reaction, which is recognized as a chemical model for understanding several autonomous phenomena in biological systems. Under the coexistence of the reactants, the polymer gel undergoes spontaneous swelling-deswelling changes without any on-off switching by external stimuli. In this review, our recent studies on the self-oscillating polymer gels and application to biomimetic actuators are summarized

Wall-crossing for vortex partition function and handsaw quiver varierty

We investigate vortex partition functions defined from integrals over the handsaw quiver varieties of type $A_{1}$ via wall-crossing phenomena. We consider vortex partition functions defined by two types of cohomology classes, and get functional equations for each of them. We also give explicit formula for these partition functions. This gives proofs to formula suggested by physicsts. In particular, we obtain geometric interpretation of formulas for multiple hypergeometric functions including rational limit of the Kajihara transformation formula

Learning Argument Structures with Recurrent Neural Network Grammars

In targeted syntactic evaluations, the syntactic competence of LMs has been investigated through various syntactic phenomena, among which one of the important domains has been argument structure. Argument structures in head-initial languages have been exclusively tested in the previous literature, but may be readily predicted from lexical information of verbs, potentially overestimating the syntactic competence of LMs. In this paper, we explore whether argument structures can be learned by LMs in head-final languages, which could be more challenging given that argument structures must be predicted before encountering verbs during incremental sentence processing, so that the relative weight of syntactic information should be heavier than lexical information. Specifically, we examined double accusative constraint and double dative constraint in Japanese with the sequential and hierarchical LMs: n-gram model, LSTM, GPT-2, and RNNG. Our results demonstrated that the double accusative constraint is captured by all LMs, whereas the double dative constraint is successfully explained only by the hierarchical model. In addition, we probed incremental sentence processing by LMs through the lens of surprisal, and suggested that the hierarchical model may capture deep semantic roles that verbs assign to arguments, while the sequential models seem to be influenced by surface case alignments

Composition, Attention, or Both?

In this paper, we propose a novel architecture called Composition Attention Grammars (CAGs) that recursively compose subtrees into a single vector representation with a composition function, and selectively attend to previous structural information with a self-attention mechanism. We investigate whether these components -- the composition function and the self-attention mechanism -- can both induce human-like syntactic generalization. Specifically, we train language models (LMs) with and without these two components with the model sizes carefully controlled, and evaluate their syntactic generalization performance against six test circuits on the SyntaxGym benchmark. The results demonstrated that the composition function and the self-attention mechanism both play an important role to make LMs more human-like, and closer inspection of linguistic phenomenon implied that the composition function allowed syntactic features, but not semantic features, to percolate into subtree representations.Comment: Accepted by Findings of EMNLP 202

システム生物学・創薬インフォマティクスにおける統計科学のモデリング技術

Open House, ISM in Tachikawa, 2012.6.15統計数理研究所オープンハウス（立川）、H24.6.15ポスター発

データ同化を使って全ゲノム転写動態シミュレーションを実現する

Open House, ISM in Tachikawa, 2011.7.14統計数理研究所オープンハウス（立川）、H23.7.14ポスター発

階層ベイズモデリングと生化学反応シミュレーション －癌細胞の薬剤応答経路予測へ向けて－

Open House, ISM in Tachikawa, 2010.7.9統計数理研究所オープンハウス（立川）、H22.7.9ポスター発

Electron acceleration with improved Stochastic Differential Equation method: cutoff shape of electron distribution in test-particle limit

We develop a method of stochastic differential equation to simulate electron acceleration at astrophysical shocks. Our method is based on It\^{o}'s stochastic differential equations coupled with a particle splitting, employing a skew Brownian motion where an asymmetric shock crossing probability is considered. Using this code, we perform simulations of electron acceleration at stationary plane parallel shock with various parameter sets, and studied how the cutoff shape, which is characterized by cutoff shape parameter $a$, changes with the momentum dependence of the diffusion coefficient $\beta$. In the age-limited cases, we reproduce previous results of other authors, $a\approx2\beta$. In the cooling-limited cases, the analytical expectation $a\approx\beta+1$ is roughly reproduced although we recognize deviations to some extent. In the case of escape-limited acceleration, numerical result fits analytical stationary solution well, but deviates from the previous asymptotic analytical formula $a\approx\beta$.Comment: corrected typos, 10 pages, 4 figures, 2 tables, JHEAp in pres