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Institution

University of California

EducationOakland, California, United States
About: University of California is a education organization based out in Oakland, California, United States. It is known for research contribution in the topics: Population & Layer (electronics). The organization has 55175 authors who have published 52933 publications receiving 1491169 citations. The organization is also known as: UC & University of California System.


Papers
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Journal ArticleDOI
TL;DR: The authors found that negative attitudes were associated with endorsement of a binary conception of gender; higher levels of psychological authoritarianism, political conservatism, and anti-egalitarianism, and (for women) religiosity; and lack of personal contact with sexual minorities.
Abstract: Using data from a national probability sample of heterosexual U.S. adults (N = 2,281), the present study describes the distribution and correlates of men’s and women’s attitudes toward transgender people. Feeling thermometer ratings of transgender people were strongly correlated with attitudes toward gay men, lesbians, and bisexuals, but were significantly less favorable. Attitudes toward transgender people were more negative among heterosexual men than women. Negative attitudes were associated with endorsement of a binary conception of gender; higher levels of psychological authoritarianism, political conservatism, and anti-egalitarianism, and (for women) religiosity; and lack of personal contact with sexual minorities. In regression analysis, sexual prejudice accounted for much of the variance in transgender attitudes, but respondent gender, educational level, authoritarianism, anti-egalitarianism, and (for women) religiosity remained significant predictors with sexual prejudice statistically controlled. Implications and directions for future research on attitudes toward transgender people are discussed.

412 citations

Book ChapterDOI
08 Oct 2016
TL;DR: In this paper, the task of video summarization is cast as a structured prediction problem, and LSTM is used to model the variable-range temporal dependency among video frames to derive both representative and compact video summaries.
Abstract: We propose a novel supervised learning technique for summarizing videos by automatically selecting keyframes or key subshots. Casting the task as a structured prediction problem, our main idea is to use Long Short-Term Memory (LSTM) to model the variable-range temporal dependency among video frames, so as to derive both representative and compact video summaries. The proposed model successfully accounts for the sequential structure crucial to generating meaningful video summaries, leading to state-of-the-art results on two benchmark datasets. In addition to advances in modeling techniques, we introduce a strategy to address the need for a large amount of annotated data for training complex learning approaches to summarization. There, our main idea is to exploit auxiliary annotated video summarization datasets, in spite of their heterogeneity in visual styles and contents. Specifically, we show that domain adaptation techniques can improve learning by reducing the discrepancies in the original datasets’ statistical properties.

411 citations

Journal ArticleDOI
TL;DR: This paper explores an intriguing scenario for view synthesis: extrapolating views from imagery captured by narrow-baseline stereo cameras, including VR cameras and now-widespread dual-lens camera phones, and proposes a learning framework that leverages a new layered representation that is called multiplane images (MPIs).
Abstract: The view synthesis problem---generating novel views of a scene from known imagery---has garnered recent attention due in part to compelling applications in virtual and augmented reality. In this paper, we explore an intriguing scenario for view synthesis: extrapolating views from imagery captured by narrow-baseline stereo cameras, including VR cameras and now-widespread dual-lens camera phones. We call this problem stereo magnification, and propose a learning framework that leverages a new layered representation that we call multiplane images (MPIs). Our method also uses a massive new data source for learning view extrapolation: online videos on YouTube. Using data mined from such videos, we train a deep network that predicts an MPI from an input stereo image pair. This inferred MPI can then be used to synthesize a range of novel views of the scene, including views that extrapolate significantly beyond the input baseline. We show that our method compares favorably with several recent view synthesis methods, and demonstrate applications in magnifying narrow-baseline stereo images.

410 citations

Journal ArticleDOI
TL;DR: Findings suggest that modulation of AQP4 expression or function may be beneficial in several cerebral disorders, including hyponatremic brain edema, hydrocephalus, stroke, tumor, infection, epilepsy, and traumatic brain injury.
Abstract: Aquaporin-4 (AQP4) is a water-channel protein expressed strongly in the brain, predominantly in astrocyte foot processes at the borders between the brain parenchyma and major fluid compartments, including cerebrospinal fluid (CSF) and blood. This distribution suggests that AQP4 controls water fluxes into and out of the brain parenchyma. Experiments using AQP4-null mice provide strong evidence for AQP4 involvement in cerebral water balance. AQP4-null mice are protected from cellular (cytotoxic) brain edema produced by water intoxication, brain ischemia, or meningitis. However, AQP4 deletion aggravates vasogenic (fluid leak) brain edema produced by tumor, cortical freeze, intraparenchymal fluid infusion, or brain abscess. In cytotoxic edema, AQP4 deletion slows the rate of water entry into brain, whereas in vasogenic edema, AQP4 deletion reduces the rate of water outflow from brain parenchyma. AQP4 deletion also worsens obstructive hydrocephalus. Recently, AQP4 was also found to play a major role in processes unrelated to brain edema, including astrocyte migration and neuronal excitability. These findings suggest that modulation of AQP4 expression or function may be beneficial in several cerebral disorders, including hyponatremic brain edema, hydrocephalus, stroke, tumor, infection, epilepsy, and traumatic brain injury.

409 citations

Patent
12 Dec 1991
TL;DR: Chimeric proteins and DNA encoding chimeric proteins are provided, where the chimeric protein is characterized by an extracellular domain capable of binding to a ligand in a non-MHC restricted manner, a transmembrane domain and a cytoplasmic domain capable activating a signaling pathway as mentioned in this paper.
Abstract: Chimeric proteins and DNA encoding chimeric proteins are provided, where the chimeric proteins are characterized by an extracellular domain capable of binding to a ligand in a non-MHC restricted manner, a transmembrane domain and a cytoplasmic domain capable of activating a signaling pathway. The extracellular domain and cytoplasmic domain are not naturally found together. Binding of ligand to the extracellular domain results in transduction of a signal and activation of a signaling pathway in the cell, whereby the cell may be induced to carry out various functions relating to the signalling pathway. A wide variety of extracellular domains may be employed as receptors, where such domains may be naturally occurring or synthetic. The chimeric DNA may be used to modify lymphocytes as well as hematopoietic stem cells as precursors to a number of important cell types.

408 citations


Authors

Showing all 55232 results

NameH-indexPapersCitations
Meir J. Stampfer2771414283776
George M. Whitesides2401739269833
Michael Karin236704226485
Fred H. Gage216967185732
Rob Knight2011061253207
Martin White1962038232387
Simon D. M. White189795231645
Scott M. Grundy187841231821
Peidong Yang183562144351
Patrick O. Brown183755200985
Michael G. Rosenfeld178504107707
George M. Church172900120514
David Haussler172488224960
Yang Yang1712644153049
Alan J. Heeger171913147492
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202322
2022105
2021775
20201,069
20191,225
20181,684