Author: "T. Chang" / Publication Year Range: Last 10 years - Searchworks@Jio Institute Digital Library Search Results

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2,865 results on '"T. Chang"'

1. Rituximab induced cerebral venous sinus thrombosis in a patient with anti-N-methyl-d-aspartate receptor-antibody encephalitis: a case report and review of literature

Author: S. Maathury, R. Thevarajah, and T. Chang
Subjects: Cerebral venous sinus thrombosis, NMDARE, Rituximab, Sri Lanka, Medicine
Abstract: Abstract Background Cerebral venous sinus thrombosis has not been reported in anti-N-methyl-d-aspartate receptor-antibody encephalitis in the absence of an underlying thrombotic state while rituximab induced cerebral venous sinus thrombosis is rarely reported. We report a patient with anti-N-methyl-d-aspartate receptor-antibody encephalitis without a prothrombotic state who developed cerebral venous sinus thrombosis following rituximab treatment. Case presentation A 15-year-old Sri Lankan girl who had been in remission following an episode of anti-N-methyl-d-aspartate receptor-antibody encephalitis 2 years ago, presented with a relapse of anti-N-methyl-d-aspartate receptor-antibody encephalitis characterized by recurrent seizures, mutism, and cognitive abnormalities. Since response was inadequate to first-line immunotherapy, she was administered four doses of rituximab at weekly intervals. Two days after the fourth dose, she developed increasing headaches, and her cranial magnetic resonance venogram confirmed the development of cerebral venous sinus thrombosis. Screening for prothrombotic states were negative. She made an unremarkable recovery following anticoagulation. Conclusion This case highlights the occurrence of the rare but serious complication of cerebral venous sinus thrombosis following rituximab in the context of anti-N-methyl-d-aspartate receptor-antibody encephalitis and informs the clinician to be wary of new onset headache in patients with anti-N-methyl-d-aspartate receptor-antibody encephalitis treated with immunotherapy.
Published: 2024
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2. ANALYSIS OF OVERESTIMATION IN HISTORICAL ICE FLOW VELOCITY MAPS IN WESTERN PACIFIC OCEAN SECTOR, ANTARCTICA

Author: S. Ge, Y. Cheng, R. Li, H. Cui, Z. Yu, T. Chang, S. Luo, Z. Li, G. Li, A. Zhao, X. Yuan, Y. Li, M. Xia, X. Wang, and G. Qiao
Subjects: Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Abstract: High accuracy reconstruction of historical ice flow velocity fields is crucial in mass balance research of the Antarctic Ice Sheet by utilizing the input-output approach. A historical flow velocity of the Western Pacific Ocean sector region of East Antarctica from 1963 to 1989 was mapped and then corrected for its velocity overestimation. In this study, we analyzed the spatial distribution of the corrected values, and further assessed the relationship between the corrected values and related factors including timespan of image pairs, ice flow velocity, the spatial acceleration of ice flow velocity, and surface slope. The results indicate that the corrected ice flow velocity points are mostly dispersed between a buffer 25 km upstream and a buffer 25 km downstream the grounding line, with the largest mean value emerging in the region between the grounding line and its 25 km downstream. The corrected values exhibit linear correlation with three conditions: 1) when the ice flow velocity range is 0 – 1586 m/y, 2) spatial acceleration is 0 – 69 (m/y)/km, and 3) the slope is 0 – 1.95 degrees and the R2 is higher than 0.7. However, the correlation between timespan and corrected values is not obvious. The corrected values for the floating region have a greater linear correlation with all three factors than the inland region. Ice flow velocity, spatial acceleration, and surface slope all have an influence on the distribution of the corrected values of the reconstructed historical ice flow velocity maps, and may further affect the assessment of the mass balance of the Antarctic Ice Sheet.
Published: 2022
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3. ACTIVE LAYER THICKNESS RETRIEVAL OVER THE QINGHAI-TIBET PLATEAU FROM 2000 TO 2020 BASED ON INSAR-MEASURED SUBSIDENCE AND MULTI-LAYER SOIL MOISTURE

Author: T. Chang, J. Han, Z. Li, Y. Wen, T. Hao, P. Lu, and R. Li
Subjects: Technology, Engineering (General). Civil engineering (General), TA1-2040, Applied optics. Photonics, TA1501-1820
Abstract: Active layer thickness (ALT) is an important index to reflect the stability of permafrost. The retrieval of ALT based on Interferometric Synthetic Aperture Radar (InSAR) technology has been investigated recently in permafrost research. However, most of such studies are carried out in a limited extend and relatively short temporal coverage. The combination of temporal-spatial multi-layer soil moisture data and multi-temporal InSAR is a promising approach for the large-scale characterization of ALT. In this study, we employed Small Baseline Subset Interferometry (SBAS-InSAR) technology to obtain the seasonal surface deformation from radar images of Envisat and Sentinel-1 in a permafrost region of Qinghai-Tibet Plateau (QTP). We attempt to verify and calibrate the temporal-spatial multi-layer soil moisture product in combination with the in-situ data. Based on the land subsidence data and the temporal-spatial multi-layer soil moisture data, we further improve method to retrieve the ALT information. This paper describes the progress so far and point out the future work.
Published: 2021
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4. Research Note: Orange corn altered the cecal microbiome in laying hens

Author: T. Chang, J. Ngo, J.I. Vargas, E. Rocheford, T. Rocheford, D. Ortiz, D.M. Karcher, and T.A. Johnson
Subjects: carotenoid, microbiome, hen, feed additive, Animal culture, SF1-1100
Abstract: ABSTRACT: Carotenoids, which are pigments known to have many health benefits, such as their antioxidant properties, are being researched for their potential as a feed additive for production animals. These pigments are found in varying quantities in different breeds of corn, and their impact on the chicken microbiome requires further investigation. This 35 d laying hen (Novagen White) feeding trial involved varying the levels and composition of carotenoids by changing the corn source: white (0.9 µg total carotinoids/g total diet), yellow (5.7 µg/g), and orange (24.9 µg/g). For each of the three corn diet treatments, 6 replicate cages were randomly assigned. The cecal microbial community composition of the hens was then studied by 16S rRNA gene amplicon sequencing. The composition of the cecal bacterial community, as determined by Bray-Curtis dissimilarity, was different (P < 0.05) in chickens fed the orange corn diet, compared to chickens on the white corn diet, but there was no statistical difference between animals fed yellow corn compared to the white or orange corn groups. There was no change in the alpha diversity between any of the groups. Within Lactobacillus, which is one of the most abundant genera, 2 amplicon sequence variants (ASVs) were decreased and one ASV was increased in the orange corn group compared to both the white and yellow corn groups. While previous studies showed that orange corn did not alter the community composition in broilers, it appears that orange corn based feed may alter the community composition of laying hens.
Published: 2022
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5. Observation of the semileptonic decays D 0 → K S 0 π − π 0 e + ν e $$ {\textrm{D}}^0\to {\textrm{K}}_{\textrm{S}}^0{\pi}^{-}{\pi}^0{\textrm{e}}^{+}{\nu}_{\textrm{e}} $$ and D + → K S 0 π + π − e + ν e $$ {\textrm{D}}^{+}\to {\textrm{K}}_{\textrm{S}}^0{\pi}^{+}{\pi}^{-}{\textrm{e}}^{+}{\nu}_{\textrm{e}} $$

Author: The BESIII collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, M. R. An, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, J. F. Chang, T. T. Chang, W. L. Chang, G. R. Che, G. Chelkov, C. Chen, Chao Chen, G. Chen, H. S. Chen, M. L. Chen, S. J. Chen, S. M. Chen, T. Chen, X. R. Chen, X. T. Chen, Y. B. Chen, Y. Q. Chen, Z. J. Chen, W. S. Cheng, S. K. Choi, X. Chu, G. Cibinetto, S. C. Coen, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Z. H. Duan, P. Egorov, Y. L. Fan, J. Fang, S. S. Fang, W. X. Fang, Y. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, K. Fischer, M. Fritsch, C. Fritzsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, Z. L. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, T. T. Han, W. Y. Han, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, T. Hussain, N. Hüsken, W. Imoehl, J. Jackson, S. Jaeger, S. Janchiv, J. H. Jeong, Q. Ji, Q. P. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, X. Q. Jia, Z. K. Jia, H. J. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, T. Johansson, X. Kui, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, R. Kappert, M. Kavatsyuk, B. C. Ke, A. Khoukaz, R. Kiuchi, R. Kliemt, O. B. Kolcu, B. Kopf, M. Kuessner, A. Kupsc, W. Kühn, J. J. Lane, P. Larin, A. Lavania, L. Lavezzi, T. T. Lei, Z. H. Lei, H. Leithoff, M. Lellmann, T. Lenz, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. Li, H. B. Li, H. J. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, J. W. Li, K. L. Li, Ke Li, L. J. Li, L. K. Li, Lei Li, M. H. Li, P. R. Li, Q. X. Li, S. X. Li, T. Li, W. D. Li, W. G. Li, X. H. Li, X. L. Li, Xiaoyu Li, Y. G. Li, Z. J. Li, Z. X. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. Z. Liao, Y. P. Liao, J. Libby, A. Limphirat, D. X. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. H. Liu, Fang Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. M. Liu, Huanhuan Liu, Huihui Liu, J. B. Liu, J. L. Liu, J. Y. Liu, K. Liu, K. Y. Liu, Ke Liu, L. Liu, L. C. Liu, Lu Liu, M. H. Liu, P. L. Liu, Q. Liu, S. B. Liu, T. Liu, W. K. Liu, W. M. Liu, X. Liu, Y. Liu, Y. B. Liu, Z. A. Liu, Z. Q. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, F. C. Ma, H. L. Ma, J. L. Ma, L. L. Ma, M. M. Ma, Q. M. Ma, R. Q. Ma, R. T. Ma, X. Y. Ma, Y. Ma, Y. M. Ma, F. E. Maas, M. Maggiora, S. Malde, Q. A. Malik, A. Mangoni, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, I. B. Nikolaev, Z. Ning, S. Nisar, Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, Y. P. Pei, M. Pelizaeus, H. P. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, S. Pogodin, V. Prasad, F. Z. Qi, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, C. F. Qiao, J. J. Qin, L. Q. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, S. Q. Qu, C. F. Redmer, K. J. Ren, A. Rivetti, V. Rodin, M. Rolo, G. Rong, Ch. Rosner, S. N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. C. Shi, J. L. Shi, J. Y. Shi, Q. Q. Shi, R. S. Shi, X. Shi, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, Y. J. Su, G. B. Sun, G. X. Sun, H. Sun, H. K. Sun, J. F. Sun, K. Sun, L. Sun, S. S. Sun, T. Sun, W. Y. Sun, Y. Sun, Y. J. Sun, Y. Z. Sun, Z. T. Sun, Y. X. Tan, C. J. Tang, G. Y. Tang, J. Tang, Y. A. Tang, L. Y. Tao, Q. T. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, S. J. Wang, B. Wang, B. L. Wang, Bo Wang, C. W. Wang, D. Y. Wang, F. Wang, H. J. Wang, H. P. Wang, J. P. Wang, K. Wang, L. L. Wang, M. Wang, Meng Wang, S. Wang, T. Wang, T. J. Wang, W. Wang, W. P. Wang, X. Wang, X. F. Wang, X. J. Wang, X. L. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. N. Wang, Y. Q. Wang, Yaqian Wang, Yi Wang, Z. Wang, Z. L. Wang, Z. Y. Wang, Ziyi Wang, D. Wei, D. H. Wei, F. Weidner, S. P. Wen, C. W. Wenzel, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, C. Wu, J. F. Wu, L. H. Wu, L. J. Wu, X. Wu, X. H. Wu, Y. Wu, Y. J. Wu, Z. Wu, L. Xia, X. M. Xian, T. Xiang, D. Xiao, G. Y. Xiao, S. Y. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. F. Xu, C. J. Xu, G. F. Xu, H. Y. Xu, Q. J. Xu, Q. N. Xu, W. Xu, W. L. Xu, X. P. Xu, Y. C. Xu, Z. P. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, Tao Yang, Y. Yang, Y. F. Yang, Y. X. Yang, Yifan Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, J. H. Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, T. Yu, X. D. Yu, C. Z. Yuan, L. Yuan, S. C. Yuan, X. Q. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, A. A. Zafar, F. R. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, A. Q. Zhang, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, H. Zhang, H. H. Zhang, H. Q. Zhang, H. Y. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, Jiawei Zhang, L. M. Zhang, L. Q. Zhang, Lei Zhang, P. Zhang, Q. Y. Zhang, Shuihan Zhang, Shulei Zhang, X. D. Zhang, X. M. Zhang, X. Y. Zhang, Xuyan Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Yan Zhang, Yao Zhang, Z. H. Zhang, Z. L. Zhang, Z. Y. Zhang, G. Zhao, J. Zhao, J. Y. Zhao, J. Z. Zhao, Lei Zhao, Ling Zhao, M. G. Zhao, S. J. Zhao, Y. B. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, L. P. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, J. Zhu, K. Zhu, K. J. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, S. Q. Zhu, T. J. Zhu, W. J. Zhu, Y. C. Zhu, Z. A. Zhu, J. H. Zou, and J. Zu
Subjects: Branching fraction, Charm Physics, e +-e − Experiments, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Abstract By analyzing e + e − annihilation data corresponding to an integrated luminosity of 2.93 fb −1 collected at a center-of-mass energy of 3.773 GeV with the BESIII detector, the first observation of the semileptonic decays D 0 → K S 0 π − π 0 e + ν e $$ {D}^0\to {K}_S^0{\pi}^{-}{\pi}^0{e}^{+}{\nu}_e $$ and D + → K S 0 π + π − e + ν e $$ {D}^{+}\to {K}_S^0{\pi}^{+}{\pi}^{-}{e}^{+}{\nu}_e $$ is reported. In the hypothesis that all events correspond to K 1(1270) decays, the branching fractions are measured to be B D 0 → K 1 1270 − → K S 0 π − π 0 e + ν e = 1.69 − 0.46 + 0.53 ± 0.15 × 10 − 4 $$ \mathcal{B}\left({D}^0\to {K}_1{(1270)}^{-}\left(\to {K}_S^0{\pi}^{-}{\pi}^0\right){e}^{+}{\nu}_e\right)=\left({1.69}_{-0.46}^{+0.53}\pm 0.15\right)\times {10}^{-4} $$ and B D + → K ¯ 1 1270 0 → K S 0 π + π − e + ν e = 1.47 − 0.40 + 0.45 ± 0.14 × 10 − 4 $$ \mathcal{B}\left({D}^{+}\to {\overline{K}}_1{(1270)}^0\left(\to {K}_S^0{\pi}^{+}{\pi}^{-}\right){e}^{+}{\nu}^e\right)=\left({1.47}_{-0.40}^{+0.45}\pm 0.14\right)\times {10}^{-4} $$ with statistical significance of 5.4σ and 5.6σ, respectively. When combined with measurements of the K 1(1270) → K + π − π decays, the absolute branching fractions are determined to be B D 0 → K 1 1270 − e + ν e = 1.08 − 0.13 − 0.10 + 0.14 + 0.08 ± 0.21 × 10 − 3 $$ \mathcal{B}\left({D}^0\to {K}_1{(1270)}^{-}{e}^{+}{\nu}_e\right)=\left({1.08}_{-0.13-0.10}^{+0.14+0.08}\pm 0.21\right)\times {10}^{-3} $$ and B D + → K ¯ 1 1270 0 e + ν e = 1.70 − 0.23 + 0.26 ± 0.13 ± 0.35 × 10 − 3 $$ \mathcal{B}\left({D}^{+}\to {\overline{K}}_1{(1270)}^0{e}^{+}{\nu}_e\right)=\left({1.70}_{-0.23}^{+0.26}\pm 0.13\pm 0.35\right)\times {10}^{-3} $$ . The first and second uncertainties are statistical and systematic, respectively, and the third uncertainties originate from the assumed branching fractions of the K 1(1270) → Kππ decays.
Published: 2024
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6. An Investigation of Multi-feature Extraction and Super-resolution with Fast Microphone Arrays.

Author: Eric T. Chang, Runsheng Wang, Peter Ballentine, Jingxi Xu 0002, Trey Smith, Brian Coltin, Ioannis Kymissis, and Matei T. Ciocarlie
Published: 2024
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7. R×R: Rapid eXploration for Reinforcement Learning via Sampling-based Reset Distributions and Imitation Pre-training.

Author: Gagan Khandate, Tristan Luca Saidi, Siqi Shang, Eric T. Chang, Yang Liu, Seth Dennis, Johnson Adams, and Matei T. Ciocarlie
Published: 2024
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8. Hypergraph: A Unified and Uniform Definition with Application to Chemical Hypergraph.

Author: Daniel T. Chang
Published: 2024
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9. Human Non-Compliance with Robot Spatial Ownership Communicated via Augmented Reality: Implications for Human-Robot Teaming Safety.

Author: Christine T. Chang, Matthew B. Luebbers, Mitchell Hebert, and Bradley Hayes
Published: 2023
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10. The Impact of COVID-19 on Science Education: Early Evidence from California

Author: Public Policy Institute of California (PPIC), Gao, Niu, DiRanna, Kathy, and Fay, Maria T. Chang
Abstract: The California Next Generation Science Standards (CA NGSS)--adopted in 2013--have the potential to improve scientific literacy and strengthen the global competitiveness of California's workforce. However, longstanding underinvestment in science education and the unprecedented disruptions caused by COVID-19 have heightened challenges faced by California's districts, schools, and students. In this report, the authors use data from multiple sources to understand the impact of COVID-19 on science education when schools were mainly virtual. The authors found that: (COVID-19 derailed science education in most; (2) Support for science education was limited; (3) County offices of education received fewer requests for science-related assistance; (4) Most district recovery plans do not prioritize science education; and (5) California can take steps to support equitable investments in science education. [For "The Impact of COVID-19 on Science Education. Policy Brief," see ED623555. For "The Impact of COVID-19 on Science Education. Technical Appendices," see ED623557.]
Published: 2022

11. Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI).

Author: Maciej Wozniak, Christine T. Chang, Matthew B. Luebbers, Bryce Ikeda, Michael E. Walker, Eric Rosen, and Thomas Roy Groechel
Published: 2023
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12. Collaborative Planning and Negotiation in Human-Robot Teams.

Author: Christine T. Chang, Mitchell Hebert, and Bradley Hayes
Published: 2023
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13. Modeling local coronavirus outbreaks.

Author: Joseph T. Chang and Edward H. Kaplan
Published: 2023
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14. Adaptive Region-Specific Loss for Improved Medical Image Segmentation.

Author: Yizheng Chen, Lequan Yu, Jen-Yeu Wang, Neil Panjwani, Jean-Pierre Obeid, Wu Liu, Lianli Liu, Nataliya Kovalchuk, Michael Francis Gensheimer, Lucas Kas Vitzthum, Beth M. Beadle, Daniel T. Chang, Quynh-Thu Le, Bin Han, and Lei Xing 0001
Published: 2023
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15. R ˟ R: Rapid eXploration for Reinforcement learning via sampling-based reset distributions and imitation pre-training.

Author: Gagan Khandate, Tristan Luca Saidi, Siqi Shang, Eric T. Chang, Yang Liu, Seth Dennis, Johnson Adams, and Matei T. Ciocarlie
Published: 2024
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16. Virtual, Augmented, and Mixed Reality for HRI (VAM-HRI).

Author: Christine T. Chang, Eric Rosen, Thomas R. Groechel, Michael E. Walker, and Jessica Zosa Forde
Published: 2022
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17. Sampling-based Exploration for Reinforcement Learning of Dexterous Manipulation.

Author: Gagan Khandate, Siqi Shang, Eric T. Chang, Tristan Luca Saidi, Johnson Adams, and Matei T. Ciocarlie
Published: 2023
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18. Self-Assembled Matrigel-Free iPSC-Derived Liver Organoids Demonstrate Wide-Ranging Highly Differentiated Liver Functions

Author: Yun Weng, Simon Han, Maria T Sekyi, Tao Su, Aras N Mattis, and Tammy T Chang
Published: 2022
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19. A Tool for Organizing Key Characteristics of Virtual, Augmented, and Mixed Reality for Human-Robot Interaction Systems: Synthesizing VAM-HRI Trends and Takeaways.

Author: Thomas R. Groechel, Michael E. Walker, Christine T. Chang, Eric Rosen, and Jessica Zosa Forde
Published: 2022
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20. Creative Software Development with Team Boundary Management.

Author: Jamie Y. T. Chang, Xiaosong (Jason) Wu, Richard Discenza, and Gary Klein 0001
Published: 2022
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21. Concept-Oriented Deep Learning with Large Language Models.

Author: Daniel T. Chang
Published: 2023
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22. Variational Quantum Classifiers for Natural-Language Text.

Author: Daniel T. Chang
Published: 2023
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23. Extraction of Text from Optic Nerve Optical Coherence Tomography Reports.

Author: Iyad Majid, Youchen Victor Zhang, Robert T. Chang, and Sophia Y. Wang
Published: 2023
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24. An Investigation of Multi-feature Extraction and Super-resolution with Fast Microphone Arrays.

Author: Eric T. Chang, Runsheng Wang, Peter Ballentine, Jingxi Xu 0002, Trey Smith, Brian Coltin, Ioannis Kymissis, and Matei T. Ciocarlie
Published: 2023
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25. The Stanford Acuity Test: A Precise Vision Test Using Bayesian Techniques and a Discovery in Human Visual Response.

Author: Chris Piech, Ali Malik, Laura M. Scott, Robert T. Chang, and Charles Lin
Published: 2020
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26. Transformation of womens status in Taiwan, 1920-2020

Author: T. Chang, Doris, primary
Published: 2022
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27. Virtual, Augmented, and Mixed Reality for Human-Robot Interaction (VAM-HRI).

Author: Eric Rosen, Thomas R. Groechel, Michael E. Walker, Christine T. Chang, and Jessica Zosa Forde
Published: 2021
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28. Spectral Neighbor Joining for Reconstruction of Latent Tree Models.

Author: Ariel Jaffe, Noah Amsel, Yariv Aizenbud, Boaz Nadler, Joseph T. Chang, and Yuval Kluger
Published: 2021
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29. Automated model versus treating physician for predicting survival time of patients with metastatic cancer.

Author: Michael Francis Gensheimer, Sonya Aggarwal, Kathryn R. K. Benson, Justin N. Carter, Solomon Henry, Douglas J. Wood, Scott G. Soltys, Steven Hancock, Erqi Pollom, Nigam H. Shah, and Daniel T. Chang
Published: 2021
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30. High speed (1ns) and low voltage (1.5V) demonstration of 8Kb SOT-MRAM array.

Author: M. Y. Song, C. M. Lee, S. Y. Yang, G. L. Chen, K. M. Chen, I J. Wang, Y. C. Hsin, K. T. Chang, C. F. Hsu, S. H. Li, J. H. Wei, T. Y. Lee, M. F. Chang, X. Y. Bao, C. H. Diaz, and S. J. Lin
Published: 2022
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31. Understanding Patient Attitudes Toward Multifocal Intraocular Lenses in Online Medical Forums Through Sentiment Analysis.

Author: Sophia Y. Wang, Tina Hernandez-Boussard, Robert T. Chang, and Suzann Pershing
Published: 2019
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32. Automated hepatobiliary toxicity prediction after liver stereotactic body radiation therapy with deep learning-based portal vein segmentation.

Author: Bulat Ibragimov, Diego A. S. Toesca, Daniel T. Chang, Yixuan Yuan, Albert C. Koong, and Lei Xing 0001
Published: 2020
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33. Embodied-Symbolic Contrastive Graph Self-Supervised Learning for Molecular Graphs.

Author: Daniel T. Chang
Published: 2022
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34. Dual Embodied-Symbolic Concept Representations for Deep Learning.

Author: Daniel T. Chang
Published: 2022
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35. Parameterized Quantum Circuits with Quantum Kernels for Machine Learning: A Hybrid Quantum-Classical Approach.

Author: Daniel T. Chang
Published: 2022
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36. Distance-Geometric Graph Attention Network (DG-GAT) for 3D Molecular Geometry.

Author: Daniel T. Chang
Published: 2022
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37. Variational Quantum Kernels with Task-Specific Quantum Metric Learning.

Author: Daniel T. Chang
Published: 2022
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38. Connecticut Redistricting Analysis.

Author: Kyle Evans and Katherine T. Chang
Published: 2022
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39. Exemplar-Based Contrastive Self-Supervised Learning with Few-Shot Class Incremental Learning.

Author: Daniel T. Chang
Published: 2022

40. 'Our Dog Probably Thinks Christmas is Really Boring': Re-mediating Science Education for Feminist-Inspired Inquiry.

Author: Annie Kelly, Christine T. Chang, Christian Hill, Mary Etta West, Mary Yoder, Joseph L. Polman, Shaun K. Kane, Michael Eisenberg, and R. Benjamin Shapiro
Published: 2020

41. The Impacts of Reward Systems on the Inter-Team Cooperation in Information Technology NPD.

Author: Zih-An Shen, Chwan-Ming Jimmy Wang, Jacob C. Tsai, Jason Wu, Jamie Y. T. Chang, and James J. Jiang
Published: 2022

42. Deep 3D Dose Analysis for Prediction of Outcomes After Liver Stereotactic Body Radiation Therapy.

Author: Bulat Ibragimov, Diego A. S. Toesca, Yixuan Yuan, Albert C. Koong, Daniel T. Chang, and Lei Xing 0001
Published: 2018
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43. Neural Networks for Deep Radiotherapy Dose Analysis and Prediction of Liver SBRT Outcomes.

Author: Bulat Ibragimov, Diego A. S. Toesca, Yixuan Yuan, Albert C. Koong, Daniel T. Chang, and Lei Xing 0001
Published: 2019
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