1,096 research outputs found
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 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
Recommended from our members
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 , changes
with the momentum dependence of the diffusion coefficient . In the
age-limited cases, we reproduce previous results of other authors,
. In the cooling-limited cases, the analytical expectation
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 .Comment: corrected typos, 10 pages, 4 figures, 2 tables, JHEAp in pres
- …