Competitive Equilibrium from Equal Incomes for Two-Sided Matching

Competitive Equilibrium from Equal Incomes for Two-Sided Matching Using the assignment of students to schools as our leading example, we study many-to-one two-sided matching markets without transfers. Students are endowed with cardinal preferences and schools with ordinal ones, while preferences of both sides need not be strict. Using the idea of a competitive equilibrium from equal incomes (CEEI, Hylland and Zeckhauser (1979)), we propose a new mechanism, the Generalized CEEI, in which students face different prices depending on how schools rank them. It always produces fair (justified-envy-free) and ex ante e¢ cient random assignments and stable deterministic assignments if both students and schools are truth-telling. We show that each student's incentive to misreport vanishes when the market becomes large, given all others are truthful. The mechanism is particularly relevant to school choice as schools' priority orderings over students are usually known and can be considered as their ordinal preferences. More importantly, in settings like school choice where agents have similar ordinal preferences, the mechanismis explicit use of cardinal preferences may significantly improve eficiency. We also discuss its application in school choice with group-specific quotas and in one-sided matching

A Pseudo-Market Approach to Allocation with Priorities

We propose a pseudo-market mechanism for no-monetary-transfer allocation of indivisible objects based on priorities such as those in school choice. Agents are given token money, face priority-specific prices, and buy utility-maximizing random assignments. The mechanism is asymptotically incentive compatible, and the resulting assignments are fair and constrained Pareto efficient. Hylland and Zeckhauser's (1979) position-allocation problem is a special case of our framework, and our results on incentives and fairness are also new in their classical setting

A Pseudo-Market Approach to Allocation with Priorities

We propose a pseudo-market mechanism for no-monetary-transfer allocation of indivisible objects based on priorities such as those in school choice. Agents are given token money, face priority-specific prices, and buy utility-maximizing random assignments. The mechanism is asymptotically incentive compatible, and the resulting assignments are fair and constrained Pareto efficient. Hylland and Zeckhauser's (1979) position-allocation problem is a special case of our framework, and our results on incentives and fairness are also new in their classical setting

Multi-Granularity Attention Model for Group Recommendation

Group recommendation provides personalized recommendations to a group of users based on their shared interests, preferences, and characteristics. Current studies have explored different methods for integrating individual preferences and making collective decisions that benefit the group as a whole. However, most of them heavily rely on users with rich behavior and ignore latent preferences of users with relatively sparse behavior, leading to insufficient learning of individual interests. To address this challenge, we present the Multi-Granularity Attention Model (MGAM), a novel approach that utilizes multiple levels of granularity (i.e., subsets, groups, and supersets) to uncover group members' latent preferences and mitigate recommendation noise. Specially, we propose a Subset Preference Extraction module that enhances the representation of users' latent subset-level preferences by incorporating their previous interactions with items and utilizing a hierarchical mechanism. Additionally, our method introduces a Group Preference Extraction module and a Superset Preference Extraction module, which explore users' latent preferences on two levels: the group-level, which maintains users' original preferences, and the superset-level, which includes group-group exterior information. By incorporating the subset-level embedding, group-level embedding, and superset-level embedding, our proposed method effectively reduces group recommendation noise across multiple granularities and comprehensively learns individual interests. Extensive offline and online experiments have demonstrated the superiority of our method in terms of performance

Proteomics and Metabolomics Unveil Codonopsis pilosula (Franch.) Nannf. Ameliorates Gastric Precancerous Lesions via Regulating Energy Metabolism

Objective: This study aimed to systematically evaluate the efficacy of Codonopsis pilosula (Franch.) Nannf. (Codonopsis Radix, CR) and reveal the mechanism of its effects on suppressing Gastric Precancerous Lesions.Methods: First, we established the GPL rat model which was induced by N-methyl-N′-nitro-N-nitrosoguanidine, a disordered diet, and 40% ethanol. The CR’s anti-Gastric Precancerous Lesions effect was comprehensively evaluated by body weight, pathological section, and serum biochemical indexes. Then, quantitative proteomics and metabolomics were conducted to unveil the disturbed protein-network and pharmacodynamic mechanism. Furthermore, serum pharmacology was employed to confirm that CR’s anti-gastritis and anti-cancer phenotype in cell models.Results: In animal models, CR had been shown to control inflammation and ameliorate Gastric Precancerous Lesions. Considering the combination of proteomics and metabolomics, we found that CR could significantly reverse the biological pathways related to energy metabolism which were disturbed by the Gastric Precancerous Lesions model. Furthermore, the results of serum pharmacology indicated that the Codonopsis Radix containing serum could ameliorate gastritis injury and selectively inhibit the proliferation of gastric cancer cells rather than normal cells, which was closely related to ATP production in the above mentioned cells.Conclusion: In summary, CR exerted anti-Gastric Precancerous Lesions effects by ameliorating gastritis injury and selectively inhibiting the proliferation of gastric cancer cells rather than normal cells. Proteomics and metabolomics unveiled that its efficacy was closely related to its regulation of the energy-metabolism pathway. This research not only provided new ideas for exploring the mechanism of complex systems such as Chinese herbals but also benefited the treatment strategy of Gastric Precancerous Lesions via regulating energy metabolism

Flipped Instruction for Information Literacy: Five Instructional Cases of Academic Librarians

University of California, Berkeley librarians have incorporated the flipped instruction model into information literacy training by focusing on two primary elements: assigning pre-class assignments and increasing active learning techniques. We explore these two elements across five diverse instructional cases, which include one-shot and semester-long classes that were conducted through online or in-person delivery for both graduate and undergraduate students across a range of subject areas (sciences, social sciences, and humanities). We examine the enabling factors and the perceived outcomes of this instructional paradigm. Because students came to class with enhanced library understanding and experience from the pre-class assignment, they were better prepared to engage with the material and articulate additional learning needs. We note students' increased engagement during class and more time available for higher-order learning exercises and discussions. As a result, flipped instruction appears to enable more learning opportunities without increasing classroom time. The challenges of this model are the requisite commitment of time and effort, the need to foster class participation, and the facilitation of active communication within the class. We propose a framework of catalysts, building blocks, and instructional outcomes to help library instructors incorporate flipped instruction elements into their instructional design

Evaluating Assignment without Transfers: A Market Perspective

We show that every (random) assignment/allocation without transfers can be considered as a market outcome with personalized prices and an equal income. One can thus evaluate an assignment by investigating the prices and the induced opportunity sets. When prices are proportional across agents, the assignment is efficient; when prices are common, the assignment is both efficient and envy-free. Moreover, this market perspective reveals a weakness of envy-freeness

幻化之龍：兩千年中國歷史變遷的孔子 (Lives of Confucius)

Huan hua zhi long : liang qian nian Zhongguo li shi bian qian zhong de Kongzi. Chinese translation of Michael Nylan and Thomas A. Wilson, Lives of Confucius: Civilization\u27s Greatest Sage Through the Ages (New York: Doubleday, 2010).https://digitalcommons.hamilton.edu/books/1003/thumbnail.jp

Competitive equilibrium from equal incomes for two-sided matching

Using the assignment of students to schools as our leading example, we study many-to-one two-sided matching markets without transfers. Students are endowed with cardinal preferences and schools with ordinal ones, while preferences of both sides need not be strict. Using the idea of a competitive equilibrium from equal incomes (CEEI, Hylland and Zeckhauser (1979)), we propose a new mechanism, the Generalized CEEI, in which students face different prices depending on how schools rank them. It always produces fair (justified-envy-free) and ex ante efficient random assignments and stable deterministic ones with respect to stated preferences. Moreover, if a group of students are top ranked by all schools, the G-CEEI random assignment is ex ante weakly efficient with respect to students’ welfare. We show that each student’s incentive to misreport vanishes when the market becomes large, given all others are truthful. The mechanism is particularly relevant to school choice since schools’ priority orderings can be considered as their ordinal preferences. More importantly, in settings where agents have similar ordinal preferences, the mechanism’s explicit use of cardinal preferences may significantly improve efficiency. We also discuss its application in school choice with affirmative action suc

A pseudo-market approach to allocation with priorities

We propose a pseudo-market mechanism for no-monetary-transfer allocation of indivisible objects based on priorities such as those in school choice. Agents are given token money, face priority-specific prices, and buy utility-maximizing random assignments. The mechanism is asymptotically incentive compatible, and the resulting assignments are fair and constrained Pareto efficient. Aanund Hylland & Richard Zeckhauser (1979)'s position-allocation problem is a special case of our framework, and our results on incentives and fairness are also new in their classical setting