Browsing by Author "Yuan, X"

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    Adaptive temporal compressive sensing for video
    (2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings, 2013-12-01) Yuan, X; Yang, J; Llull, P; Liao, X; Sapiro, G; Brady, DJ; Carin, L
    This paper introduces the concept of adaptive temporal compressive sensing (CS) for video. We propose a CS algorithm to adapt the compression ratio based on the scene's temporal complexity, computed from the compressed data, without compromising the quality of the reconstructed video. The temporal adaptivity is manifested by manipulating the integration time of the camera, opening the possibility to realtime implementation. The proposed algorithm is a generalized temporal CS approach that can be incorporated with a diverse set of existing hardware systems. © 2013 IEEE.
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    Correction to: AI is a viable alternative to high throughput screening: a 318-target study (Scientific Reports, (2024), 14, 1, (7526), 10.1038/s41598-024-54655-z)
    (Scientific Reports, 2024-12-01) Giles, E; Heifets, A; Artía, Z; Inde, Z; Liu, Z; Zhang, Z; Wang, Z; Su, Z; Chung, Z; Frangos, ZJ; Li, Y; Yen, Y; Sidorova, YA; Tse-Dinh, YC; He, Y; Tang, Y; Li, Y; Pérez-Pertejo, Y; Gupta, YK; Zhu, Y; Sun, Y; Li, Y; Chen, Y; Aldhamen, YA; Hu, Y; Zhang, YJ; Zhang, X; Yuan, X; Wang, X; Qin, X; Yu, X; Xu, X; Qi, X; Lu, X; Wu, X; Blanchet, X; Foong, WE; Bradshaw, WJ; Gerwick, WH; Kerr, WG; Hahn, WC; Donaldson, WA; Van Voorhis, WC; Zhang, W; Tang, W; Li, W; Houry, WA; Lowther, WT; Clayton, WB; Van Hung Le, V; Ronchi, VP; Woods, VA; Scoffone, VC; Maltarollo, VG; Dolce, V; Maranda, V; Segers, VFM; Namasivayam, V; Gunasekharan, V; Robinson, VL; Banerji, V; Tandon, V; Thai, VC; Pai, VP; Desai, UR; Baumann, U; Chou, TF; Chou, T; O’Mara, TA; Banjo, T; Su, T; Lan, T; Ogunwa, TH; Hermle, T; Corson, TW; O’Meara, TR; Kotzé, TJ; Herdendorf, TJ; Richardson, TI; Kampourakis, T; Gillingwater, TH; Jayasinghe, TD; Teixeira, TR; Ikegami, T; Moreda, TL; Haikarainen, T; Akopian, T; Abaffy, T; Swart, T; Mehlman, T; Teramoto, T; Azeem, SM; Dallman, S; Brady-Kalnay, SM; Sarilla, S; Van Doren, SR; Marx, SO; Olson, SH; Poirier, S; Waggoner, SN
    Correction to: Scientific Reportshttps://doi.org/10.1038/s41598-024-54655-z, published online 02 April 2024 The original version of this Article contained errors. In the original version of this article, Ellie Giles was omitted from the Author list. Additionally, the following Affiliation information has been updated: 1. Affiliation 25 was incorrect. Affiliation 25 ‘Queensland University of Technology, Brisbane, USA.’ now reads, ‘Queensland University of Technology, Brisbane, Australia.’ 2. Marta Giorgis was incorrectly affiliated with the ‘University of Aberdeen, Aberdeen, UK.’ The correct Affiliation is listed below: ‘University of Turin, Turin, Italy.’ 3. Affiliations 52, 125 and 261 were duplicated. As a result, the correct Affiliation for Andrew B. Herr, Benjamin Liou, David A. Hildeman, Joseph J. Maciag, Ying Sun, Durga Krishnamurthy, and Stephen N. Waggoner is: ‘Cincinnati Children’s Hospital Medical Center, Cincinnati, USA.’ Furthermore, an outdated version of Figure 1 was typeset. The original Figure 1 and accompanying legend appear below. (Figure presented.) Pairs of representative compounds extracted from AI patents (right) and corresponding prior patents (left) for clinical-stage programs (CDK792,93, A2Ar-antagonist94,95, MALT196,97, QPCTL98,99, USP1100,101, and 3CLpro102,103). The identical atoms between the chemical structures are highlighted in red. Lastly, The Acknowledgements section contained an error. “See Supplementary section S1.” now reads, “See Supplementary section S2.” The original Article has been corrected.
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    Non-Gaussian discriminative factor models via the max-margin rank-likelihood
    (32nd International Conference on Machine Learning, ICML 2015, 2015-01-01) Yuan, X; Henao, R; Tsalik, EL; Langley, RJ; Carin, L
    Copyright © 2015 by the author(s).We consider the problem of discriminative factor analysis for data that are in general non-Gaussian. A Bayesian model based on the ranks of the data is proposed. We first introduce a new max-margin version of the rank-likelihood. A discriminative factor model is then developed, integrating the max-margin rank-likelihood and (linear) Bayesian support vector machines, which are also built on the max-margin principle. The discriminative factor model is further extended to the nonlinear case through mixtures of local linear classifiers, via Dirichlet processes. Fully local conjugacy of the model yields efficient inference with both Markov Chain Monte Carlo and variational Bayes approaches. Extensive experiments on benchmark and real data demonstrate superior performance of the proposed model and its potential for applications in computational biology.