Connecting public schools to community development

In neighborhoods across the country, public schools and community groups are beginning to work together in new and innovative ways. Connie Chung explores the wide variety of roles that public schools can play in community development efforts.Community development ; Public schools

Value and selfhood: pragmatism, Confucianism, and phenomenology

This article articulates a dialogue between Edward Casey, Cheng Chung‐ying, and me that began at the Eastern Division annual meeting in Philadelphia of the American Philosophical Association, in a session sponsored by the International Society for Chinese Philosophy. There, we read brief versions of the papers presented in this issue and commented on one another. Casey represented Continental phenomenology, Cheng the Chinese tradition as he has developed it into onto‐generative hermeneutics, and I the melding of American pragmatic and Confucian traditions that I have been developing

Letter from Connie Chung to Geraldine Ferraro

Handwritten letter from Connie Chung, NBC News, to Geraldine Ferraro, thanking Ferraro for her interview.https://ir.lawnet.fordham.edu/vice_presidential_campaign_correspondence_1984_personal/1008/thumbnail.jp

Minari

This is a film review of Minari (2020), directed by Lee Isaac Chung

On ``A Note on the Economic Lot Size of the Integrated Vendor-Buyer Inventory System Derived without Derivatives'' by Wee and Chung

[[abstract]]Wee and Chung [3] incorporated the integrated single-vendor single-buyer inventory model with backorder, JIT delivery and inspection cost. They used a simple algebraic approach and proved that the model has an optimal solution for the condition of ˜ C = Hb + Hv“2d p − 1” − “ H2 b b + Hb ” > 0. However, they did not provide the optimal solution to the problem when the restriction is not satisfied. In this note, the authors provide some patch works to enhance the volubility of Wee and Chung’s paper.[[notice]]補正完畢[[journaltype]]國內[[incitationindex]]EI[[incitationindex]]TSSC

Manitest: Are classifiers really invariant?

Invariance to geometric transformations is a highly desirable property of automatic classifiers in many image recognition tasks. Nevertheless, it is unclear to which extent state-of-the-art classifiers are invariant to basic transformations such as rotations and translations. This is mainly due to the lack of general methods that properly measure such an invariance. In this paper, we propose a rigorous and systematic approach for quantifying the invariance to geometric transformations of any classifier. Our key idea is to cast the problem of assessing a classifier's invariance as the computation of geodesics along the manifold of transformed images. We propose the Manitest method, built on the efficient Fast Marching algorithm to compute the invariance of classifiers. Our new method quantifies in particular the importance of data augmentation for learning invariance from data, and the increased invariance of convolutional neural networks with depth. We foresee that the proposed generic tool for measuring invariance to a large class of geometric transformations and arbitrary classifiers will have many applications for evaluating and comparing classifiers based on their invariance, and help improving the invariance of existing classifiers.Comment: BMVC 201