Institution
Shandong University
Education•Jinan, Shandong, China•
About: Shandong University is a education organization based out in Jinan, Shandong, China. It is known for research contribution in the topics: Laser & Cancer. The organization has 99070 authors who have published 99160 publications receiving 1625094 citations. The organization is also known as: Shāndōng Dàxué.
Topics: Laser, Cancer, Cell growth, Population, Apoptosis
Papers published on a yearly basis
Papers
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14 Aug 2005TL;DR: This is the first attack on the full 80-step SHA-1 with complexity less than the 280 theoretical bound, and it is shown that collisions ofSHA-1 can be found with complexityLess than 269 hash operations.
Abstract: In this paper, we present new collision search attacks on the hash function SHA-1. We show that collisions of SHA-1 can be found with complexity less than 269 hash operations. This is the first attack on the full 80-step SHA-1 with complexity less than the 280 theoretical bound.
1,600 citations
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TL;DR: The prevalence of chronic kidney disease in China was high in north and southwest and southwest regions compared with other regions, and economic development was independently associated with the presence of albuminuria.
1,588 citations
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22 May 2005TL;DR: A new powerful attack on MD5 is presented, which unlike most differential attacks, does not use the exclusive-or as a measure of difference, but instead uses modular integer subtraction as the measure.
Abstract: MD5 is one of the most widely used cryptographic hash functions nowadays. It was designed in 1992 as an improvement of MD4, and its security was widely studied since then by several authors. The best known result so far was a semi free-start collision, in which the initial value of the hash function is replaced by a non-standard value, which is the result of the attack. In this paper we present a new powerful attack on MD5 which allows us to find collisions efficiently. We used this attack to find collisions of MD5 in about 15 minutes up to an hour computation time. The attack is a differential attack, which unlike most differential attacks, does not use the exclusive-or as a measure of difference, but instead uses modular integer subtraction as the measure. We call this kind of differential a modular differential. An application of this attack to MD4 can find a collision in less than a fraction of a second. This attack is also applicable to other hash functions, such as RIPEMD and HAVAL.
1,583 citations
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TL;DR: A measurement of the Higgs boson mass is presented based on the combined data samples of the ATLAS and CMS experiments at the CERN LHC in the H→γγ and H→ZZ→4ℓ decay channels.
Abstract: A measurement of the Higgs boson mass is presented based on the combined data samples of the ATLAS and CMS experiments at the CERN LHC in the H→γγ and H→ZZ→4l decay channels. The results are obtained from a simultaneous fit to the reconstructed invariant mass peaks in the two channels and for the two experiments. The measured masses from the individual channels and the two experiments are found to be consistent among themselves. The combined measured mass of the Higgs boson is mH=125.09±0.21 (stat)±0.11 (syst) GeV.
1,567 citations
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03 Dec 2018TL;DR: This work proposes to learn an Χ-transformation from the input points to simultaneously promote two causes: the first is the weighting of the input features associated with the points, and the second is the permutation of the points into a latent and potentially canonical order.
Abstract: We present a simple and general framework for feature learning from point clouds. The key to the success of CNNs is the convolution operator that is capable of leveraging spatially-local correlation in data represented densely in grids (e.g. images). However, point clouds are irregular and unordered, thus directly convolving kernels against features associated with the points will result in desertion of shape information and variance to point ordering. To address these problems, we propose to learn an Χ-transformation from the input points to simultaneously promote two causes: the first is the weighting of the input features associated with the points, and the second is the permutation of the points into a latent and potentially canonical order. Element-wise product and sum operations of the typical convolution operator are subsequently applied on the Χ-transformed features. The proposed method is a generalization of typical CNNs to feature learning from point clouds, thus we call it PointCNN. Experiments show that PointCNN achieves on par or better performance than state-of-the-art methods on multiple challenging benchmark datasets and tasks.
1,535 citations
Authors
Showing all 99666 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jing Wang | 184 | 4046 | 202769 |
Yang Gao | 168 | 2047 | 146301 |
Gang Chen | 167 | 3372 | 149819 |
Yang Yang | 164 | 2704 | 144071 |
Andrew D. Hamilton | 151 | 1334 | 105439 |
Ben Zhong Tang | 149 | 2007 | 116294 |
Yoshio Bando | 147 | 1234 | 80883 |
Guanrong Chen | 141 | 1652 | 92218 |
Karl Jakobs | 138 | 1379 | 97670 |
Jun Chen | 136 | 1856 | 77368 |
Shu Li | 136 | 1001 | 78390 |
Hui Li | 135 | 2982 | 105903 |
Lei Zhang | 135 | 2240 | 99365 |
Elizaveta Shabalina | 133 | 1421 | 92273 |
George A. Calin | 133 | 654 | 106942 |