4,399 research outputs found
Dark energy imprints on the kinematic Sunyaev-Zel'dovich signal
We investigate the imprint of dark energy on the kinetic Sunyaev-Zel'dovich
(kSZ) angular power spectrum on scales of to , and find that
the kSZ signal is sensitive to the dark energy parameter. For example, varying
the constant by 20\% around results in a change on the
kSZ spectrum; changing the dark energy dynamics parametrized by by
, a 30\% change on the kSZ spectrum is expected. We discuss the
observational aspects and develop a fitting formula for the kSZ power spectrum.
Finally, we discuss how the precise modeling of the post-reionization signal
would help the constraints on patchy reionization signal, which is crucial for
measuring the duration of reionization.Comment: 12 pages, 9 figures, 2 table
The Empirical Study of Size Effect, Book-to-Market Effect in US Security Market
Banz (1981) found size effect using data over the period 1926–1975. This paper uses data from last 33 years from NYSE, Amex, and Nasdaq to test the existence of size effect and book-to-market effect. In this paper data is sorted by size and book-to-market ratio across quintiles. I runs the time-series regression taking advantage of CAPM model, Fama-French 3-factor model and Carhart 4-factor model to get three different alpha. With all next-month returns, this paper compares those low size/book-to-market next-month returns with those high size/book-to-market next-month returns and uses t-test to verify the existence of these two effects. This paper indicates that B/M (book-to-market) effect still exists. However, size effect does not exist anymore without the tiny firms (with their stock price under $5). In 1980-1990 period, the big-size firm outperform small-size firm by 0.26 percent
Guide the flood : Miami vulnerable neighborhoods flood adaptation design
When it comes to the future of Miami, what else could be instead of waiting to be swallowed by the sea?
This thesis starts with a group pre-research on different aspects related to the rising up sea-level in Miami. By analyzing the existed urban fabric and typologies, the author tries to use the current segregated open space in every block to create a new open space system to reduce the sea-level-rise influence. Therefore, the goal for the thesis is to provide a powerful and gynamic open space system to the rising sea-level.
The thesis is structured in three phases. In Phase one, by analyzing the flood-related issues including where the water come from, the failed urban infrastructure system and the influence to the ecology and people, the author get a holistic understanding of how terrible the climate is in Miami, where and how the water comes to Miami, what people have done to reduce the impacts and who has already been attacked by the water. In Phase two, half of the effort is dedicated to knowing a detail information about the vulnerable neighborhoods through the field trip to Miami. And the other half is dedicated to understanding the site by analyzing the neighborhood typologies to get to know the potential of the existing vulnerable neighborhoods condition. In Phase three, time is spent on identifying and establishing typical blocks design under a series of resilient strategies in neighborhood scale. Through the analysis in phase two, three typical blocks are identified to cover all the blocks in Miami, “mid-tree”, unpermeable and the affordable apartments. With every typical block redesign, a water collect area is created as a contemporary collection basin, with the other area are designed to drain and slow the draining process.
The thesis ends with a design stage zooming out from single block scale to the whole neighborhood scale. It will create several green corridors which not only serves as a flood resilient areas but also offers recreational places for the vulnerable neighborhoods which only have some concentrating spots around some street corners now
3D Object Detection Using Scale Invariant and Feature Reweighting Networks
3D object detection plays an important role in a large number of real-world
applications. It requires us to estimate the localizations and the orientations
of 3D objects in real scenes. In this paper, we present a new network
architecture which focuses on utilizing the front view images and frustum point
clouds to generate 3D detection results. On the one hand, a PointSIFT module is
utilized to improve the performance of 3D segmentation. It can capture the
information from different orientations in space and the robustness to
different scale shapes. On the other hand, our network obtains the useful
features and suppresses the features with less information by a SENet module.
This module reweights channel features and estimates the 3D bounding boxes more
effectively. Our method is evaluated on both KITTI dataset for outdoor scenes
and SUN-RGBD dataset for indoor scenes. The experimental results illustrate
that our method achieves better performance than the state-of-the-art methods
especially when point clouds are highly sparse.Comment: The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19
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