Bayes factorを用いたRAIアルゴリズムによる大規模ベイジアンネットワーク学習

漸近一致性をもつベイジアンネットワークの構造学習はNP困難である．これまで動的計画法やA*探索，整数計画法による探索アルゴリズムが開発されてきたが，未だに60ノード程度の構造学習を限界とし，大規模構造学習の実現のためには，全く異なるアプローチの開発が急務である．一方で因果モデルの研究分野では，条件付き独立性テスト（CIテスト）と方向付けによる画期的に計算量を削減した構造学習アプローチが提案されている．このアプローチは制約ベースアプローチと呼ばれ，RAIアルゴリズムが最も高精度な最先端学習法として知られている．しかしRAIアルゴリズムは，CIテストに仮説検定法または条件付き相互情報量を用いている．前者の精度は帰無仮説が正しい確率を表すp値とユーザが設定する有意水準に依存する．p値はデータ数の増加により小さい値を取り，誤って帰無仮説を棄却してしまう問題が知られている．一方で，後者の精度はしきい値の設定に強く影響する．したがって，漸近的に真の構造を学習できる保証がない．本論文では，漸近一致性を有するBayes factorを用いたCIテストをRAIアルゴリズムに組み込む．これにより，数百ノードをもつ大規模構造学習を実現する．数種類のベンチマークネットワークを用いたシミュレーション実験により，本手法の有意性を示す．A score-based learning Bayesian networks is NP-hard. On the other hands, constraint-based approach, that can dynamically relaxes the computational cost, is applicable to learning huge Bayesian network structures. The approach uses conditional independence (CI) tests based on the conditional mutual information and statistical testings. However, those CI tests have no consistency. In this paper, we propose a new constraint-based learning method that uses the CI test based on the Bayes factor, which have consistency. The proposed method combines it to the RAI algorithm, that is a state-of-the-art algorithm of the constraint-based approach. The experimental result shows our proposed method provides empirically best performance

Superior Electrochemical Performance of a Ni-P/Si Negative Electrode for Li-ion Batteries in an Ionic Liquid Electrolyte

To achieve electrode performance with both high capacity and long cycle life, we investigated the effect of the anion structure in an ionic liquid electrolyte on the electrochemical performance of an annealed Ni-P/(etched Si) negative electrode for Li-ion batteries. The electrode maintained a discharge capacity of 1890 mA h g-1 after 250 cycles in bis(fluorosulfonyl)amide-based ionic liquid electrolyte, which was approximately three times higher than that in bis(trifluoromethanesulfonyl)amide-based electrolyte

Elderly patient with 5q spinal muscular atrophy type 4 markedly improved by Nusinersen

Available online 17 May 2020.ArticleJournal of the Neurological Sciences.415:116901(2020)journal articl

Dexmedetomidine and sleep during HFNC

Purpose : High-flow nasal cannula oxygen therapy (HFNC) is a new type of non-invasive respiratory support for acute respiratory failure patients. However, patients receiving HFNC often develop sleep disturbances. We therefore examined whether dexmedetomidine could preserve the sleep characteristics in patients who underwent HFNC. Patients and Methods : This was a pilot, randomized controlled study. We assigned critically ill patients treated with HFNC to receive dexmedetomidine (0.2 to 0.7 μg / kg / h, DEX group) or not (non-DEX group) at night (9:00 p.m. to 6:00 a.m.). Polysomnograms were monitored during the study period. The primary outcomes were total sleep time (TST), sleep efficiency and duration of stage 2 non-rapid eye movement (stage N2) sleep. Results : Of the 28 patients who underwent randomization, 24 were included in the final analysis (12 patients per group). Dexmedetomidine increased the TST (369 min vs. 119 min, p = 0.024) and sleep efficiency (68% vs. 22%, P = 0.024). The duration of stage N2 was increased in the DEX group compared with the non-DEX group, but this finding did not reach statistical significance. The incidences of respiratory depression and hemodynamic instability were similar between the two groups. Conclusions : In critically ill patients who underwent HFNC, dexmedetomidine may optimize the sleep quantity without any adverse events