BRIEF 发表于 2025-3-25 03:43:49
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Do Hedge Funds Add Value to a Passive Portfolio? Correcting for Non-Normal Returns and Disappearingvestors such as pension funds and endowment funds. Not surprisingly hedge funds have also received increasing attention from the academic community. Non-linear factor models and option strategies have been used in the literature to explain hedge fund returns (see Fung and Hsieh, 2001; Agarwal and Na记成蚂蚁 发表于 2025-3-25 13:47:17
Learners’ Preferred Mathematical Task Types: The Values Perspectivecal tasks, this chapter presents an alternative way in which data might be interpreted. While it was found that across the areas of number and geometry, the three types of tasks were most preferred across different geographical locations, the values perspective demonstrated a remarkably consistent vCrepitus 发表于 2025-3-25 16:15:40
On Very Large Scale Assignment Problems,th 2. nodes. Some conjectured or derived properties regarding fully dense assignment problems including the convergence of the optimal objective function value and the portion of nodes assigned with their kth best arc have been verified for networks up to . = 100,000 in size. Also we demonstrate the我吃花盘旋 发表于 2025-3-25 23:14:22
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The Phenomenon of Competition: Social Anthropological Perspectiveso provide the anthropological factors that accompany such occurrences. The presentation draws on knowledge from social anthropology. First, collaboration and competition are considered. In most countries and cultures, competition is dominant over collaboration and is gaining new ground, mainly due t轻率看法 发表于 2025-3-26 05:43:13
Yang He,Margret Keuper,Bernt Schiele,Mario Fritzrrors and independent groups. In Section 3.1, we develop the asymptotics of the basic statistics and modified classical tests for high-dimensional time-dependent errors. A sufficient condition for the modified classical tests to be asymptotically normal is also presented. This chapter is based mostly on Nagahata and Taniguchi (2018).Minutes 发表于 2025-3-26 10:50:22
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https://doi.org/10.1007/978-3-658-19379-9vex, since this is a requirement for Murota’s algorithm. We introduce . algorithm which minimizes energies with truncated priors by alternating both moves. This algorithm is a fast solver for labeling problems with a high number of labels and convex data terms. We illustrate its performance on image