Information fusion between knowledge and data in Bayesian network structure learning

Bayesian Networks (BNs) have become a powerful technology for reasoning under uncertainty, particularly in areas that require causal assumptions that enable us to simulate the effect of intervention. The graphical structure of these models can be determined by causal knowledge, learnt from data, or a combination of both. While it seems plausible that the best approach in constructing a causal graph involves combining knowledge with machine learning, this approach remains underused in practice. We implement and evaluate 10 knowledge approaches with application to different case studies and BN structure learning algorithms available in the open-source Bayesys structure learning system. The approaches enable us to specify pre-existing knowledge that can be obtained from heterogeneous sources, to constrain or guide structure learning. Each approach is assessed in terms of structure learning effectiveness and efficiency, including graphical accuracy, model fitting, complexity, and runtime; making this the first paper that provides a comparative evaluation of a wide range of knowledge approaches for BN structure learning. Because the value of knowledge depends on what data are available, we illustrate the results both with limited and big data. While the overall results show that knowledge becomes less important with big data due to higher learning accuracy rendering knowledge less important, some of the knowledge approaches are actually found to be more important with big data. Amongst the main conclusions is the observation that reduced search space obtained from knowledge does not always imply reduced computational complexity, perhaps because the relationships implied by the data and knowledge are in tension

Novel magnetic topological insulator FeBi2_2Te4_4 with controllable topological quantum phase

Here, we report a new intrinsic magnetic topological insulator FeBi$_2$Te$_4$ based on first-principles calculations and it can achieve a rich topological phase under pressure modulation. Without pressure, we predict that both FeBi$_2$Te$_4$ ferromagnetic and antiferromagnetic orders are non-trivial topological insulators. Furthermore, FeBi$_2$Te$_4$ of FM-z order will undergo a series of phase transitions from topological insulator to semimetals and then to trivial insulator under pressure. Finally, we further clarify and verify topological phase transitions with low-energy effective model calculations. This topological phase transition process is attributed to the synergy of the magnetic moment and the spin-orbit coupling. The unique topological properties of FeBi$_2$Te$_4$ will be of great interest in driving the development of quantum effects

Effective and efficient structure learning with pruning and model averaging strategies

Learning the structure of a Bayesian Network (BN) with score-based solutions involves exploring the search space of possible graphs and moving towards the graph that maximises a given objective function. Some algorithms offer exact solutions that guarantee to return the graph with the highest objective score, while others offer approximate solutions in exchange for reduced computational complexity. This paper describes an approximate BN structure learning algorithm, which we call Model Averaging Hill-Climbing (MAHC), that combines two novel strategies with hill-climbing search. The algorithm starts by pruning the search space of graphs, where the pruning strategy can be viewed as an aggressive version of the pruning strategies that are typically applied to combinatorial optimisation structure learning problems. It then performs model averaging in the hill-climbing search process and moves to the neighbouring graph that maximises the objective function, on average, for that neighbouring graph and over all its valid neighbouring graphs. Comparisons with other algorithms spanning different classes of learning suggest that the combination of aggressive pruning with model averaging is both effective and efficient, particularly in the presence of data noise

Topological Phases, Local Magnetic Moments, and Spin Polarization Triggered by C558-Line Defects in Graphene

We study the electronic properties of a novel topological defect structure for graphene interspersed with C558-line defects along the Armchair boundary. This system has the topological property of being topologically three-periodic and the type-II Dirac-fermionic character of the embedded topological phase. At the same time, we show computationally that the topological properties of the system are overly dependent on the coupling of this line defect. Using strain engineering to regulate the magnitude of hopping at the defect, the position of the energy level can be easily changed to achieve a topological phase transition. We also discuss the local magnetic moment and the ferromagnetic ground state in the context of line defects, which is the conclusion after considering additional Coulomb interactions. This leads to spin polarization of the whole system. Finally, by modulating the local magnetic moment at the position of the line defect, we achieve a tunable spin quantum conductance in a one-dimensional nanoribbon. Near the Fermi energy level, it also has the property of complete spin polarization. Consequently, spin filtering can be achieved by varying the incident energy of the electrons.Comment: 8 pages, 6 figure

香料企業におけるERPシステムの基本モデル及びその応用

本論文では,ERP (Enterprise Resource Planning)システムの研究開発と実施の観点から香料企業の特徴を分析し,香料企業におけるERPシステムの基本モデルを示した。また,筆者らが開発したERPシステムの実例を通して,この基本モデルの具体的な応用と得られた効果について述べた。最後に,運用組織,管理,技術の三つの要素について,成功のための枠組みを分析し,成功のキーとなる要因の分析を行った。香料企業においては,原料および製品の種類の多さ,顧客ニーズの多様さ,生産プロセスの単純さ,在庫品が占める資金量の多さ,頻繁に新製品の研究開発が行われるという産業の特徴を持っている。香料企業のERPシステム基本モデルの特徴は,新製品研究開発,注文書処理,原料購入の3つのプロセスが交差する点にある。顕著な効果を上げた実例の成功要因の分析により,3種類12個の主な成功要因が導き出された。実例とする企業の背景と情報化の段階の特徴により,これらの要因で最も重要なことは,知識転化を中心とし,プロジェクト組織を横断した緊密な協力を行い,ユーザ企業が最低限の基本的な管理水準を持ち,成熟した基本モデルと何処でも通用可能なシステムを採用するということが指摘できる

A multicentre single arm phase 2 trial of neoadjuvant pyrotinib and letrozole plus dalpiciclib for triple-positive breast cancer.

peer reviewedCurrent therapies for HER2-positive breast cancer have limited efficacy in patients with triple-positive breast cancer (TPBC). We conduct a multi-center single-arm phase 2 trial to test the efficacy and safety of an oral neoadjuvant therapy with pyrotinib, letrozole and dalpiciclib (a CDK4/6 inhibitor) in patients with treatment-naïve, stage II-III TPBC with a Karnofsky score of ≥70 (NCT04486911). The primary endpoint is the proportion of patients with pathological complete response (pCR) in the breast and axilla. The secondary endpoints include residual cancer burden (RCB)-0 or RCB-I, objective response rate (ORR), breast pCR (bpCR), safety and changes in molecular targets (Ki67) from baseline to surgery. Following 5 cycles of 4-week treatment, the results meet the primary endpoint with a pCR rate of 30.4% (24 of 79; 95% confidence interval (CI), 21.3-41.3). RCB-0/I is 55.7% (95% CI, 44.7-66.1). ORR is 87.4%, (95% CI, 78.1-93.2) and bpCR is 35.4% (95% CI, 25.8-46.5). The mean Ki67 expression reduces from 40.4% at baseline to 17.9% (P < 0.001) at time of surgery. The most frequent grade 3 or 4 adverse events are neutropenia, leukopenia, and diarrhoea. There is no serious adverse event- or treatment-related death. This fully oral, chemotherapy-free, triplet combined therapy has the potential to be an alternative neoadjuvant regimen for patients with TPBC