A Calculus with Partially Dynamic Records for Typeful Manipulation of JSON Objects

This paper investigates language constructs for high-level and type-safe manipulation of JSON objects in a typed functional language. A major obstacle in representing JSON in a static type system is their heterogeneous nature: in most practical JSON APIs, a JSON array is a heterogeneous list consisting of, for example, objects having common fields and possibly some optional fields. This paper presents a typed calculus that reconciles static typing constraints and heterogeneous JSON arrays based on the idea of partially dynamic records originally proposed and sketched by Buneman and Ohori for complex database object manipulation. Partially dynamic records are dynamically typed records, but some parts of their structures are statically known. This feature enables us to represent JSON objects as typed data structures. The proposed calculus smoothly extends with ML-style pattern matching and record polymorphism. These results yield a typed functional language where the programmer can directly import JSON data as terms having static types, and can manipulate them with the full benefits of static polymorphic type-checking. The proposed calculus has been embodied in SML#, an extension of Standard ML with record polymorphism and other practically useful features. This paper also reports on the details of the implementation and demonstrates its feasibility through examples using actual Web APIs. The SML# version 3.1.0 compiler includes JSON support presented in this paper, and is available from Tohoku University as open-source software under a BSD-style license

コンパクトシティ政策に対する意識とその変容構造：地方自治体の都市計画担当者を対象にして

コンパクトシティ政策の推進には自治体担当者の意識改善が重要である．既にワンショット型レクチャー（OL）の有効性が検証されているが，本研究ではOLの効果をより詳細に把握するため，自治体の都市計画担当者にコンパクトシティ政策に関する意識調査を行い，受容性と担当者の個人属性・政策の実現に向けた障害との関係性を分析した．その結果，1）受容性の低い担当者は予算や専門的知識等を障害と考えており，コンパクトシティ政策に関する基礎的な情報提供で大きな改善が見られたこと，2）元々受容性が高い担当者は改善の変化が小さく，市民の無理解や部署間の調整等の現場の課題を障害と考えていたこと，3）OLを実施しても受容性が改善されない担当者は議員の反対や人手不足等の現場の具体的な課題を障害と考えていたこと等が明らかになった． Improving local government planner attitudes is important to promote compact city policy. Existing studies have demonstrated the effectiveness of one-shot lecture (OL) for awareness improvement. For this study, we administered an attitude survey related to compact city policy and analyzed relations between acceptability and individual attributes or difficulties in realizing compact city policy. Thereby, we ascer-tained OL effects more specifically. Results show the following. 1) Planners with low acceptability consider budgets and expert knowledge as difficulties that can be improved by providing fundamental information related to compact city policy. 2) Planners with originally high acceptability show a slight change in im-provement and consider problems at sites such as a lack of understanding of citizens, and coordination among departments as difficulties. 3) Planners who are not improved acceptably even if OL is implemented consider physical problems such as opposition of lawmakers and lack of human resources as important difficulties

Characterization of tumor-infiltrating immune cells in relation to microbiota in colorectal cancers

博士（医学）福島県立医科大

Synthetic data generation method for hybrid image-tabular data using two generative adversarial networks

The generation of synthetic medical records using generative adversarial networks (GANs) has become increasingly important for addressing privacy concerns and promoting data sharing in the medical field. In this paper, we propose a novel method for generating synthetic hybrid medical records consisting of chest X-ray images (CXRs) and structured tabular data (including anthropometric data and laboratory tests) using an auto-encoding GAN ({\alpha}GAN) and a conditional tabular GAN (CTGAN). Our approach involves training a {\alpha}GAN model on a large public database (pDB) to reduce the dimensionality of CXRs. We then applied the trained encoder of the GAN model to the images in original database (oDB) to obtain the latent vectors. These latent vectors were combined with tabular data in oDB, and these joint data were used to train the CTGAN model. We successfully generated diverse synthetic records of hybrid CXR and tabular data, maintaining correspondence between them. We evaluated this synthetic database (sDB) through visual assessment, distribution of interrecord distances, and classification tasks. Our evaluation results showed that the sDB captured the features of the oDB while maintaining the correspondence between the images and tabular data. Although our approach relies on the availability of a large-scale pDB containing a substantial number of images with the same modality and imaging region as those in the oDB, this method has the potential for the public release of synthetic datasets without compromising the secondary use of data.Comment: 14 page

A novel superior factor widely controlling the rice grain quality

Synthesis of storage starch and protein accumulation is the main action of endosperm organogenesis in term of the economic importance of rice. This event is strongly disturbed by abiotic stresses such as high temperature; thus, the upcoming global warming will cause a crisis with a great impact on food production^1,2^. The enzymes for the protein storage and starch synthesis pathway should work in concert to carry out the organogenesis of rice endosperm^3-5^, but the regulatory mechanism is largely unknown. Here we show that a novel regulatory factor, named OsCEO1, acts as the conductor of endosperm organogenesis during the rice grain filling stage. The physiological properties of _floury-endosperm-2_ (_flo2_) mutants showed many similarities to symptoms of grains developed under high-temperature conditions, suggesting important roles of the responsible gene in sensitivity to high-temperature stress. Our map-based cloning identified the responsible gene for the _flo2_ mutant, _OsCEO1_, which has no homology to any genes of known function. The _OsCEO1_ belongs to a novel conserved gene family and encodes a protein composed of 1,720 amino acid residues containing a TPR (tetratricopeptide repeat) motif, which is considered to mediate a protein-protein interaction. The yeast two-hybrid analysis raised an unknown protein showing homology to a late embryogenesis abundant protein and a putative basic helix-loop-helix protein as candidates for the direct interactor for _OsCEO1_, whereas no enzyme genes for the synthesis of storage substances were detected. The _flo2_ mutant exhibited reduced expression of several genes for putative regulatory proteins as well as many enzymes involved in storage starch and proteins. These results suggest that _OsCEO1_ is a superior conductor of the novel regulatory cascade of endosperm organogenesis and may have important roles in the response to high-temperature stress

Increased Susceptibility to LPS-induced Endotoxin Shock in Secretory Leukoprotease Inhibitor (SLPI)-deficient Mice

Secretory leukoprotease inhibitor (SLPI) protects tissue against the destructive action of neutrophil elastase at the site of inflammation. Recent studies on new functions of SLPI have demonstrated that SLPI may play a larger role in innate immunity than merely as a protease inhibitor. To clarify the functions of SLPI in bacterial infections, we generated SLPI-deficient mice (SLPI−/− mice) and analyzed their response to experimental endotoxin shock induced by lipopolysaccharide (LPS). SLPI−/− mice showed a higher mortality from endotoxin shock than did wild type mice. This may be explained in part by our observation that SLPI−/− macro-phages show higher interleukin 6 and high-mobility group (HMG)-1 production and nuclear factor κB activities after LPS treatment than do SLPI+/+ macrophages. SLPI also affects B cell function. SLPI−/− B cells show more proliferation and IgM production after LPS treatment than SLPI+/+ B cells. Our results suggest that SLPI attenuates excessive inflammatory responses and thus assures balanced functioning of innate immunity

Survey on the Family of the Recursive-Rule Extraction Algorithm

In this paper, we first review the theoretical and historical backgrounds on rule extraction from neural network ensembles. Because the structures of previous neural network ensembles were quite complicated, research on an efficient rule extraction algorithm from neural network ensembles has been sparse, even though a practical need exists for rule extraction in Big Data datasets. We describe the Recursive-Rule extraction (Re-RX) algorithm, which is an important step toward handling large datasets. Then we survey the family of the Recursive-Rule extraction algorithm, i.e. the Multiple-MLP Ensemble Re-RX algorithm, and present concrete applications in financial and medical domains that require extremely high accuracy for classification rules. Finally, we mention two promising ideas to considerably enhance the accuracy of the Multiple-MLP Ensemble Re-RX algorithm. We also discuss developments in the near future that will make the Multiple-MLP Ensemble Re-RX algorithm much more accurate, concise, and comprehensible rule extraction from mixed datasets