戦後ドイツの経済再建と女性労働

Articledepartmental bulletin pape

Study and Surveys on changes of the Sagami River basin, Kanagawa Prefecture

活動報告departmental bulletin pape

SBML Level 3: an extensible format for the exchange and reuse of biological models

Abstract Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction‐based models and packages that extend the core with features suited to other model types including constraint‐based models, reaction‐diffusion models, logical network models, and rule‐based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single‐cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution

SBML Level 3: an extensible format for the exchange and reuse of biological models

Systems biology has experienced dramatic growth in the number, size, and complexity of computational models. To reproduce simulation results and reuse models, researchers must exchange unambiguous model descriptions. We review the latest edition of the Systems Biology Markup Language (SBML), a format designed for this purpose. A community of modelers and software authors developed SBML Level 3 over the past decade. Its modular form consists of a core suited to representing reaction-based models and packages that extend the core with features suited to other model types including constraint-based models, reaction-diffusion models, logical network models, and rule-based models. The format leverages two decades of SBML and a rich software ecosystem that transformed how systems biologists build and interact with models. More recently, the rise of multiscale models of whole cells and organs, and new data sources such as single-cell measurements and live imaging, has precipitated new ways of integrating data with models. We provide our perspectives on the challenges presented by these developments and how SBML Level 3 provides the foundation needed to support this evolution.journal articl

<論説>イタリア式資本・利益会計から社会会計へ : 複式簿記の本質をめぐって

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契約関係における情報提供義務(一) : 非対等当事者間における契約を中心に

2000-12-25departmental bulletin pape

ビスコスフィンガリング計測システムの改良の試み

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Experiments for the Evaluation of Personal-Computer-Based LAN Systems Constructed on the Network Systems for General Purpose Host Computer

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Consciousness of Teachers at Elementaly and Junior High Schools toward Special Support Education

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事例ベース推論を行うニューラルモデルの説明性とハブ現象の関係

ニューラルネットワークを用いたモデル（ニューラルモデル）によって，画像処理や自然言語処理の諸タスクにおける予測性能は飛躍的に向上した．一方で，「なぜモデルがそのような予測をしたのか」を理解することは，人間にとって極めて困難であることが指摘されている．予測の「説明性」に関する問題点に対して，k 近傍法のように訓練事例との類似度にもとづいて予測を行うモデルが近年注目を集めている．この種のモデルは事例ベースモデルと呼ばれ，予測への貢献度の高い訓練事例を予測根拠として提示することが容易であるという利点を持つ．しかし，k 近傍法においては，同じ訓練事例が複数のテスト事例の近傍事例として過度に重複して出現する「ハブ」と呼ばれる現象が度々観測される．これまでの研究で，ハブ現象が事例ベースニューラルモデルの説明性に与える影響は明らかになっていない．本研究では，画像と言語データを用いた分類問題において，ニューラルモデルの枠組みで k 近傍法を使用する場面を想定し，ハブ現象が予測の説明性に悪影響を与えることを定量的に示し，かつその問題の緩和策について明らかにする．journal articl