Direct Effect Jurisdiction under the Foreign Sovereign Immunities Act: Searching for an Integrated Approach

Recent decisions by the United States Supreme Court as to the international reach of American antitrust and securities statutes have engendered significant debate about the appropriate extraterritorial application of federal law. Such debates have also slowly come to include some mention of the right application of state law beyond U.S. boundaries through long-arm statutes. The arguments of different commentators and jurists universally support careful consideration of the implications of prescribing a rule of U.S. law to foreign conduct, absent an appropriate basis in international law and practice. The time is now right, therefore, to consider how these debates affect a statute that combines federal and state law and potentially prescribes both of those sources of law abroad in the same action: The Foreign Sovereign Immunities Act (FSIA). This Article discusses the direct effect provision under FSIA\u27s commercial activities exception. It argues that the jurisprudence interpreting the appropriate reach of that provision has become confusing and unworkable, and advocates a reinterpretation in light of the ongoing larger discussion about extraterritoriality in the federal and state law contexts

On GROUSE and Incremental SVD

GROUSE (Grassmannian Rank-One Update Subspace Estimation) is an incremental algorithm for identifying a subspace of Rn from a sequence of vectors in this subspace, where only a subset of components of each vector is revealed at each iteration. Recent analysis has shown that GROUSE converges locally at an expected linear rate, under certain assumptions. GROUSE has a similar flavor to the incremental singular value decomposition algorithm, which updates the SVD of a matrix following addition of a single column. In this paper, we modify the incremental SVD approach to handle missing data, and demonstrate that this modified approach is equivalent to GROUSE, for a certain choice of an algorithmic parameter

Educating for Transition in Work Contexts

Today’s scenarios of constant transformation of society call for a necessary reflection on work from an exquisitely pedagogical point of view, in consideration of the multiple transitions that we are witnessing because of phenomena such as progress, forced digitization, the Covid-19 pandemic, conflicts and economic-financial crises. It’s essential to move, for an education to the transition to the Recommendation of 18 December 2006 of the European Parliament on key competences for lifelong learning, from learning to learn. It is one of the eight competences, but essentially the most characterizing so that “learners take the starting point from what they have previously learned and from their life experiences to use and apply knowledge and skills in a whole range of contexts: at home, at work, in education and training”

High-Dimensional Matched Subspace Detection When Data are Missing

We consider the problem of deciding whether a highly incomplete signal lies within a given subspace. This problem, Matched Subspace Detection, is a classical, well-studied problem when the signal is completely observed. High- dimensional testing problems in which it may be prohibitive or impossible to obtain a complete observation motivate this work. The signal is represented as a vector in R^n, but we only observe m << n of its elements. We show that reliable detection is possible, under mild incoherence conditions, as long as m is slightly greater than the dimension of the subspace in question

PRAKSIS BUDAYA LONTO LEOK SEBAGAI WUJUD PEMERSATU ORANG MANGGARAI

Fokus tulisan ini adalah &nbsp;menilik praksis budaya lonto leok sebagai wujud pemersatu&nbsp; orang Manggarai. Sebagai kearifan lokal (local wisdom) lonto leok menjadi salah satu wadah&nbsp; bagi masyarakat Manggarai dalam mengatur tata kehidupan seperti pemeliharaan perdamaian dan keamanan, penegakan hukum dan adat, juga merupakan prinsip hidup orang Manggarai yang mengungkapkan dan mewujudkan rasa persatuan dan kesatuan sebagai warga masyarakat. Karena itu tulisan ini memiliki tujuan menilik dan menyibak nilai kebersamaan dan kesatuan yang terkandung dalam budaya lonto leok orang Manggarai. Metodologi yang digunakan dalam tulisan ini adalah studi kepustakaan dan&nbsp; internet yang mendukung penulisan karya ilmiah ini. Adapun temuan baru dalam karya ilmia ini yakni lonto leok sebagai kearifan lokal orang manggarai dapat menyatukan pandangan yang berbeda-beda dan menghasilkan suatu keputusan yang membangun demi kebaikan bersama (bonum commune)

Optimally Weighted PCA for High-Dimensional Heteroscedastic Data

Modern applications increasingly involve high-dimensional and heterogeneous data, e.g., datasets formed by combining numerous measurements from myriad sources. Principal Component Analysis (PCA) is a classical method for reducing dimensionality by projecting such data onto a low-dimensional subspace capturing most of their variation, but PCA does not robustly recover underlying subspaces in the presence of heteroscedastic noise. Specifically, PCA suffers from treating all data samples as if they are equally informative. This paper analyzes a weighted variant of PCA that accounts for heteroscedasticity by giving samples with larger noise variance less influence. The analysis provides expressions for the asymptotic recovery of underlying low-dimensional components from samples with heteroscedastic noise in the high-dimensional regime, i.e., for sample dimension on the order of the number of samples. Surprisingly, it turns out that whitening the noise by using inverse noise variance weights is suboptimal. We derive optimal weights, characterize the performance of weighted PCA, and consider the problem of optimally collecting samples under budget constraints.Comment: 52 pages, 13 figure

Towards a Theoretical Analysis of PCA for Heteroscedastic Data

Principal Component Analysis (PCA) is a method for estimating a subspace given noisy samples. It is useful in a variety of problems ranging from dimensionality reduction to anomaly detection and the visualization of high dimensional data. PCA performs well in the presence of moderate noise and even with missing data, but is also sensitive to outliers. PCA is also known to have a phase transition when noise is independent and identically distributed; recovery of the subspace sharply declines at a threshold noise variance. Effective use of PCA requires a rigorous understanding of these behaviors. This paper provides a step towards an analysis of PCA for samples with heteroscedastic noise, that is, samples that have non-uniform noise variances and so are no longer identically distributed. In particular, we provide a simple asymptotic prediction of the recovery of a one-dimensional subspace from noisy heteroscedastic samples. The prediction enables: a) easy and efficient calculation of the asymptotic performance, and b) qualitative reasoning to understand how PCA is impacted by heteroscedasticity (such as outliers).Comment: Presented at 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton