Institution
Stony Brook University
Education•Stony Brook, New York, United States•
About: Stony Brook University is a education organization based out in Stony Brook, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 32534 authors who have published 68218 publications receiving 3035131 citations. The organization is also known as: State University of New York at Stony Brook & SUNY Stony Brook.
Papers published on a yearly basis
Papers
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TL;DR: The size of the observed net cloud forcing is about four times as large as the expected value of radiative forcing from a doubling of CO2, and small changes in the cloud-radiative forcing fields can play a significant role as a climate feedback mechanism.
Abstract: The spaceborne Earth Radiation Budget Experiment was begun in 1984 to obtain quantitative estimates of the global distributions of cloud-radiative forcing The magnitude of the observed net cloud forcing is about four times greater than the expected value of radiative forcing from a doubling of CO2; the shortwave and longwave components of cloud forcing are about 10 times as large as those for a CO2 doubling Small changes in the cloud-radiative forcing fields can therefore play a significant role as a climate-feedback mechanism
1,631 citations
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TL;DR: DeepWalk is an online learning algorithm which builds useful incremental results, and is trivially parallelizable, which make it suitable for a broad class of real world applications such as network classification, and anomaly detection.
Abstract: We present DeepWalk, a novel approach for learning latent representations of vertices in a network. These latent representations encode social relations in a continuous vector space, which is easily exploited by statistical models. DeepWalk generalizes recent advancements in language modeling and unsupervised feature learning (or deep learning) from sequences of words to graphs. DeepWalk uses local information obtained from truncated random walks to learn latent representations by treating walks as the equivalent of sentences. We demonstrate DeepWalk's latent representations on several multi-label network classification tasks for social networks such as BlogCatalog, Flickr, and YouTube. Our results show that DeepWalk outperforms challenging baselines which are allowed a global view of the network, especially in the presence of missing information. DeepWalk's representations can provide $F_1$ scores up to 10% higher than competing methods when labeled data is sparse. In some experiments, DeepWalk's representations are able to outperform all baseline methods while using 60% less training data. DeepWalk is also scalable. It is an online learning algorithm which builds useful incremental results, and is trivially parallelizable. These qualities make it suitable for a broad class of real world applications such as network classification, and anomaly detection.
1,629 citations
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United States Environmental Protection Agency1, University of Maryland Center for Environmental Science2, North Carolina State University3, Woods Hole Oceanographic Institution4, San Francisco State University5, National Oceanic and Atmospheric Administration6, Stony Brook University7, University of South Florida St. Petersburg8, Delaware Department of Natural Resources and Environmental Control9, University of South Carolina10, South Carolina Department of Natural Resources11, Maryland Department of Natural Resources Police12, Old Dominion University13, Chesapeake Research Consortium14, University of Alaska Fairbanks15
TL;DR: In January 2003, the US Environmental Protection Agency sponsored a "roundtable discussion" to develop a consensus on the relationship between eutrophication and harmful algal blooms, specifically targeting those relationships for which management actions may be appropriate.
1,622 citations
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18 Jun 2018
TL;DR: This work proposes a general attack algorithm, Robust Physical Perturbations (RP2), to generate robust visual adversarial perturbations under different physical conditions and shows that adversarial examples generated using RP2 achieve high targeted misclassification rates against standard-architecture road sign classifiers in the physical world under various environmental conditions, including viewpoints.
Abstract: Recent studies show that the state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples, resulting from small-magnitude perturbations added to the input. Given that that emerging physical systems are using DNNs in safety-critical situations, adversarial examples could mislead these systems and cause dangerous situations. Therefore, understanding adversarial examples in the physical world is an important step towards developing resilient learning algorithms. We propose a general attack algorithm, Robust Physical Perturbations (RP2), to generate robust visual adversarial perturbations under different physical conditions. Using the real-world case of road sign classification, we show that adversarial examples generated using RP2 achieve high targeted misclassification rates against standard-architecture road sign classifiers in the physical world under various environmental conditions, including viewpoints. Due to the current lack of a standardized testing method, we propose a two-stage evaluation methodology for robust physical adversarial examples consisting of lab and field tests. Using this methodology, we evaluate the efficacy of physical adversarial manipulations on real objects. With a perturbation in the form of only black and white stickers, we attack a real stop sign, causing targeted misclassification in 100% of the images obtained in lab settings, and in 84.8% of the captured video frames obtained on a moving vehicle (field test) for the target classifier.
1,617 citations
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Francis Crick Institute1, Fox Chase Cancer Center2, Washington University in St. Louis3, Cold Spring Harbor Laboratory4, Salk Institute for Biological Studies5, Howard Hughes Medical Institute6, Cornell University7, Goethe University Frankfurt8, Fred Hutchinson Cancer Research Center9, Massachusetts Institute of Technology10, Harvard University11, University of Manchester12, New York University13, University of Texas Health Science Center at Houston14, University of Pennsylvania15, Stony Brook University16, Hofstra University17, Weizmann Institute of Science18, Oregon Health & Science University19, University of California, San Francisco20, King's College London21, Johns Hopkins University22
TL;DR: This Consensus Statement issues a call to action for all cancer researchers to standardize assays and report metadata in studies of cancer-associated fibroblasts to advance the understanding of this important cell type in the tumour microenvironment.
Abstract: Cancer-associated fibroblasts (CAFs) are a key component of the tumour microenvironment with diverse functions, including matrix deposition and remodelling, extensive reciprocal signalling interactions with cancer cells and crosstalk with infiltrating leukocytes. As such, they are a potential target for optimizing therapeutic strategies against cancer. However, many challenges are present in ongoing attempts to modulate CAFs for therapeutic benefit. These include limitations in our understanding of the origin of CAFs and heterogeneity in CAF function, with it being desirable to retain some antitumorigenic functions. On the basis of a meeting of experts in the field of CAF biology, we summarize in this Consensus Statement our current knowledge and present a framework for advancing our understanding of this critical cell type within the tumour microenvironment.
1,616 citations
Authors
Showing all 32829 results
Name | H-index | Papers | Citations |
---|---|---|---|
Zhong Lin Wang | 245 | 2529 | 259003 |
Dennis W. Dickson | 191 | 1243 | 148488 |
Hyun-Chul Kim | 176 | 4076 | 183227 |
David Baker | 173 | 1226 | 109377 |
J. N. Butler | 172 | 2525 | 175561 |
Roderick T. Bronson | 169 | 679 | 107702 |
Nora D. Volkow | 165 | 958 | 107463 |
Jovan Milosevic | 152 | 1433 | 106802 |
Thomas E. Starzl | 150 | 1625 | 91704 |
Paolo Boffetta | 148 | 1455 | 93876 |
Jacques Banchereau | 143 | 634 | 99261 |
Larry R. Squire | 143 | 472 | 85306 |
John D. E. Gabrieli | 142 | 480 | 68254 |
Alexander Milov | 142 | 1143 | 93374 |
Meenakshi Narain | 142 | 1805 | 147741 |