Between Restitution and International Morality

This Essay explores a rush of restitution cases throughout the globe. The author sees in the pattern formed by these cases a central component of a new international morality. The Essay claims that these cases testify to a new globalism that pays greater attention to human rights. The author underscores the increasing way in which our histories shape our identities. Both realism and tentativeness of the historical identity have become part of the growing liberal political space that includes no longer merely Western countries, but has become attractive to numerous diverse groups and nations globally

Adversarial Removal of Demographic Attributes from Text Data

Recent advances in Representation Learning and Adversarial Training seem to succeed in removing unwanted features from the learned representation. We show that demographic information of authors is encoded in -- and can be recovered from -- the intermediate representations learned by text-based neural classifiers. The implication is that decisions of classifiers trained on textual data are not agnostic to -- and likely condition on -- demographic attributes. When attempting to remove such demographic information using adversarial training, we find that while the adversarial component achieves chance-level development-set accuracy during training, a post-hoc classifier, trained on the encoded sentences from the first part, still manages to reach substantially higher classification accuracies on the same data. This behavior is consistent across several tasks, demographic properties and datasets. We explore several techniques to improve the effectiveness of the adversarial component. Our main conclusion is a cautionary one: do not rely on the adversarial training to achieve invariant representation to sensitive features

Hardness Amplification of Optimization Problems

In this paper, we prove a general hardness amplification scheme for optimization problems based on the technique of direct products. We say that an optimization problem ? is direct product feasible if it is possible to efficiently aggregate any k instances of ? and form one large instance of ? such that given an optimal feasible solution to the larger instance, we can efficiently find optimal feasible solutions to all the k smaller instances. Given a direct product feasible optimization problem ?, our hardness amplification theorem may be informally stated as follows: If there is a distribution D over instances of ? of size n such that every randomized algorithm running in time t(n) fails to solve ? on 1/?(n) fraction of inputs sampled from D, then, assuming some relationships on ?(n) and t(n), there is a distribution D\u27 over instances of ? of size O(n??(n)) such that every randomized algorithm running in time t(n)/poly(?(n)) fails to solve ? on 99/100 fraction of inputs sampled from D\u27. As a consequence of the above theorem, we show hardness amplification of problems in various classes such as NP-hard problems like Max-Clique, Knapsack, and Max-SAT, problems in P such as Longest Common Subsequence, Edit Distance, Matrix Multiplication, and even problems in TFNP such as Factoring and computing Nash equilibrium

The Development of the Natural Method (Ivrit Be-Ivrit) in the Teaching of Hebrew in Jewish Schools in Modern Times

This dissertation traces the course of the Natural Method known as Ivrit Be-Ivrit to the renewed interest in Hebrew on the part of the Haskalah. It likewise points to the process of adaptation of ideas from the field of general progressive education to the Jewish school system with special reference to the methodology of teaching Hebrew. It also seeks to establish the organic relationship between the Zionist vision and the dream of transforming Hebrew once again into the Jewish verancular with the classroom as the most important medium. Special attention is given to the arguments put forth by the exponents of the Natural Method as well as to the views expounded by the opponents of Ivrit Be-Ivrit