シャープレイ値の類種に関する論文集:公理化とコアとの関係

早大学位記番号:新7690早稲田大

Potential Use of Stem Cells for Kidney Regeneration

Significant advances have been made in stem cell research over the past decade. A number of nonhematopoietic sources of stem cells (or progenitor cells) have been identified, including endothelial stem cells and neural stem cells. These discoveries have been a major step toward the use of stem cells for potential clinical applications of organ regeneration. Accordingly, kidney regeneration is currently gaining considerable attention to replace kidney dialysis as the ultimate therapeutic strategy for renal failure. However, due to anatomic complications, the kidney is believed to be the hardest organ to regenerate; it is virtually impossible to imagine such a complicated organ being completely rebuilt from pluripotent stem cells by gene or chemical manipulation. Nevertheless, several groups are taking on this big challenge. In this manuscript, current advances in renal stem cell research are reviewed and their usefulness for kidney regeneration discussed. We also reviewed the current knowledge of the emerging field of renal stem cell biology

Text Retrieval with Multi-Stage Re-Ranking Models

The text retrieval is the task of retrieving similar documents to a search query, and it is important to improve retrieval accuracy while maintaining a certain level of retrieval speed. Existing studies have reported accuracy improvements using language models, but many of these do not take into account the reduction in search speed that comes with increased performance. In this study, we propose three-stage re-ranking model using model ensembles or larger language models to improve search accuracy while minimizing the search delay. We ranked the documents by BM25 and language models, and then re-ranks by a model ensemble or a larger language model for documents with high similarity to the query. In our experiments, we train the MiniLM language model on the MS-MARCO dataset and evaluate it in a zero-shot setting. Our proposed method achieves higher retrieval accuracy while reducing the retrieval speed decay

The discrete Kuhn-Tucker theorem and its application to auctions

Using a notion of convexity in discrete convex analysis, we introduce a discrete analogue of the Kuhn-Tucker theorem. We apply it to an auction model and show that existing iterative auctions can be viewed as the process of finding a saddle point of the Lagrange function

An On-ramp to Financial Inclusion: Measuring the Impact of Mercy Corps\u27 Community Investment Trust Program on Minority Investors

Mercy Corps\u27 Community Investment Trust (CIT) was launched in 2017 as a low-risk real estate investment and education initiative. The program is now working to replicate this in cities across the United States. However, it has not been able to collect much feedback from existing investors about their experiences and needs. This paper will use primary and secondary research in the form of a literature review and original surveys to collect information about investor demographics, attitudes toward the CIT and investing, risk perceptions, and future investment plans. The results show that U.S.-born versus foreign-born status, ethnicity, and gender affect the types of investments that investors want to pursue and how they feel about having the required knowledge to pursue those investments. Some investors do not think they have the knowledge needed to pursue additional investments and identified a lack of additional funds and a lack of knowledge and experience as barriers to further investing activity. The results also indicate that investors feel comfortable and trust the CIT with their investment. This research will help Mercy Corps\u27 CIT better understand investors\u27 takeaways from the program, what barriers are still affecting investors\u27 ability to invest, and how the CIT can make changes to accommodate unmet needs and help investors on their journey to increased investment literacy and participation

The discrete Kuhn-Tucker theorem and its application to auctions

Using a notion of convexity in discrete convex analysis, we introduce a discrete analogue of the Kuhn-Tucker theorem. We apply it to an auction model and show that existing iterative auctions can be viewed as the process of finding a saddle point of the Lagrange function

Application of the discrete separation theorem to auctions

The separation theorem in discrete convex analysis states that two disjoint discrete convex sets can be separated by a hyperplane with a 0-1 normal vector. We apply this theorem to an auction model and provide a unified approach to existing results. When p is not an equilibrium price vector, i.e., aggregate demand and aggregate supply are disjoint, the separation theorem indicates the existence of excess demand/supply. This observation yields a refined analysis of a characterization of competitive price vectors by Gul and Stacchetti (2000). Adjusting the prices of items in excess demand/supply corresponds to Ausubel's (2006) auction