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Institution

Lehigh University

EducationBethlehem, Pennsylvania, United States
About: Lehigh University is a education organization based out in Bethlehem, Pennsylvania, United States. It is known for research contribution in the topics: Catalysis & Fracture mechanics. The organization has 12684 authors who have published 26550 publications receiving 770061 citations.


Papers
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Proceedings ArticleDOI
01 Oct 2017
TL;DR: This paper proposes Stacked Generative Adversarial Networks (StackGAN) to generate 256 photo-realistic images conditioned on text descriptions and introduces a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold.
Abstract: Synthesizing high-quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing textto- image approaches can roughly reflect the meaning of the given descriptions, but they fail to contain necessary details and vivid object parts. In this paper, we propose Stacked Generative Adversarial Networks (StackGAN) to generate 256.256 photo-realistic images conditioned on text descriptions. We decompose the hard problem into more manageable sub-problems through a sketch-refinement process. The Stage-I GAN sketches the primitive shape and colors of the object based on the given text description, yielding Stage-I low-resolution images. The Stage-II GAN takes Stage-I results and text descriptions as inputs, and generates high-resolution images with photo-realistic details. It is able to rectify defects in Stage-I results and add compelling details with the refinement process. To improve the diversity of the synthesized images and stabilize the training of the conditional-GAN, we introduce a novel Conditioning Augmentation technique that encourages smoothness in the latent conditioning manifold. Extensive experiments and comparisons with state-of-the-arts on benchmark datasets demonstrate that the proposed method achieves significant improvements on generating photo-realistic images conditioned on text descriptions.

2,486 citations

Journal ArticleDOI
TL;DR: The third generation of the Sloan Digital Sky Survey (SDSS-III) took data from 2008 to 2014 using the original SDSS wide-field imager, the original and an upgraded multi-object fiber-fed optical spectrograph, a new near-infrared high-resolution spectrogram, and a novel optical interferometer.
Abstract: The third generation of the Sloan Digital Sky Survey (SDSS-III) took data from 2008 to 2014 using the original SDSS wide-field imager, the original and an upgraded multi-object fiber-fed optical spectrograph, a new near-infrared high-resolution spectrograph, and a novel optical interferometer. All the data from SDSS-III are now made public. In particular, this paper describes Data Release 11 (DR11) including all data acquired through 2013 July, and Data Release 12 (DR12) adding data acquired through 2014 July (including all data included in previous data releases), marking the end of SDSS-III observing. Relative to our previous public release (DR10), DR12 adds one million new spectra of galaxies and quasars from the Baryon Oscillation Spectroscopic Survey (BOSS) over an additional 3000 sq. deg of sky, more than triples the number of H-band spectra of stars as part of the Apache Point Observatory (APO) Galactic Evolution Experiment (APOGEE), and includes repeated accurate radial velocity measurements of 5500 stars from the Multi-Object APO Radial Velocity Exoplanet Large-area Survey (MARVELS). The APOGEE outputs now include measured abundances of 15 different elements for each star. In total, SDSS-III added 2350 sq. deg of ugriz imaging; 155,520 spectra of 138,099 stars as part of the Sloan Exploration of Galactic Understanding and Evolution 2 (SEGUE-2) survey; 2,497,484 BOSS spectra of 1,372,737 galaxies, 294,512 quasars, and 247,216 stars over 9376 sq. deg; 618,080 APOGEE spectra of 156,593 stars; and 197,040 MARVELS spectra of 5,513 stars. Since its first light in 1998, SDSS has imaged over 1/3 of the Celestial sphere in five bands and obtained over five million astronomical spectra.

2,471 citations

Journal ArticleDOI
TL;DR: The authors concluded that CQR is a viable qualitative method and suggest several ideas for research on the method itself and made recommendations for modifications of the method.
Abstract: The authors reviewed the application of consensual qualitative research (CQR) in 27 studies published since the method's introduction to the field in 1997 by C. E. Hill, B. J. Thompson, and E. N. Williams (1997). After first describing the core components and the philosophical underpinnings of CQR, the authors examined how it has been applied in terms of the consensus process, biases, research teams, data collection, data analysis, and writing up the results and discussion sections of articles. On the basis of problems that have arisen in each of these areas, the authors made recommendations for modifications of the method. The authors concluded that CQR is a viable qualitative method and suggest several ideas for research on the method itself.

2,320 citations

Journal ArticleDOI
TL;DR: The authors provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications and discusses how optimization problems arise in machine learning and what makes them challenging.
Abstract: This paper provides a review and commentary on the past, present, and future of numerical optimization algorithms in the context of machine learning applications. Through case studies on text classification and the training of deep neural networks, we discuss how optimization problems arise in machine learning and what makes them challenging. A major theme of our study is that large-scale machine learning represents a distinctive setting in which the stochastic gradient (SG) method has traditionally played a central role while conventional gradient-based nonlinear optimization techniques typically falter. Based on this viewpoint, we present a comprehensive theory of a straightforward, yet versatile SG algorithm, discuss its practical behavior, and highlight opportunities for designing algorithms with improved performance. This leads to a discussion about the next generation of optimization methods for large-scale machine learning, including an investigation of two main streams of research on techniques th...

2,238 citations

Journal ArticleDOI
TL;DR: In this article, a theory of non-local elasticity is presented via the vehicles of global balance laws and the second law of thermodynamics via the use of a localized Clausius-Duhem inequality and a variational statement of Gibbsian global thermodynamics.

2,201 citations


Authors

Showing all 12785 results

NameH-indexPapersCitations
Yang Yang1712644153049
Gang Chen1673372149819
Yi Yang143245692268
Mark D. Griffiths124123861335
Michael Gill12181086338
Masaki Mori110220066676
Kai Nan An10995351638
James R. Rice10827868943
Vinayak P. Dravid10381743612
Andrew M. Jones10376437253
Israel E. Wachs10342732029
Demetrios N. Christodoulides10070451093
Bert M. Weckhuysen10076740945
José Luis García Fierro100102747228
Mordechai Segev9972940073
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202338
2022140
20211,040
20201,054
2019933
2018935