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1. Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey

Author

Conde, Marcos V., Lei, Zhijun, Li, Wen, Stejerean, Cosmin, Katsavounidis, Ioannis, Timofte, Radu, Yoon, Kihwan, Gankhuyag, Ganzorig, Lv, Jiangtao, Sun, Long, Pan, Jinshan, Dong, Jiangxin, Tang, Jinhui, Li, Zhiyuan, Wei, Hao, Ge, Chenyang, Zhang, Dongyang, Liu, Tianle, Chen, Huaian, Jin, Yi, Zhou, Menghan, Yan, Yiqiang, Gao, Si, Wu, Biao, Liu, Shaoli, Zheng, Chengjian, Zhang, Diankai, Wang, Ning, Qiu, Xintao, Zhou, Yuanbo, Wu, Kongxian, Dai, Xinwei, Tang, Hui, Deng, Wei, Gao, Qingquan, Tong, Tong, Lee, Jae-Hyeon, Choi, Ui-Jin, Yan, Min, Liu, Xin, Wang, Qian, Ye, Xiaoqian, Du, Zhan, Zhang, Tiansen, Peng, Long, Guo, Jiaming, Di, Xin, Liao, Bohao, Du, Zhibo, Xia, Peize, Pei, Renjing, Wang, Yang, Cao, Yang, Zha, Zhengjun, Han, Bingnan, Yu, Hongyuan, Wu, Zhuoyuan, Wan, Cheng, Liu, Yuqing, Yu, Haodong, Li, Jizhe, Huang, Zhijuan, Huang, Yuan, Zou, Yajun, Guan, Xianyu, Jia, Qi, Zhang, Heng, Yin, Xuanwu, Zuo, Kunlong, Moon, Hyeon-Cheol, Jeong, Tae-hyun, Yang, Yoonmo, Kim, Jae-Gon, Jeong, Jinwoo, and Kim, Sunjei

Subjects
Computer Science - Computer Vision and Pattern Recognition, Electrical Engineering and Systems Science - Image and Video Processing
Abstract

This paper introduces a novel benchmark as part of the AIS 2024 Real-Time Image Super-Resolution (RTSR) Challenge, which aims to upscale compressed images from 540p to 4K resolution (4x factor) in real-time on commercial GPUs. For this, we use a diverse test set containing a variety of 4K images ranging from digital art to gaming and photography. The images are compressed using the modern AVIF codec, instead of JPEG. All the proposed methods improve PSNR fidelity over Lanczos interpolation, and process images under 10ms. Out of the 160 participants, 25 teams submitted their code and models. The solutions present novel designs tailored for memory-efficiency and runtime on edge devices. This survey describes the best solutions for real-time SR of compressed high-resolution images., Comment: CVPR 2024, AI for Streaming (AIS) Workshop

Published
2024

2. Blind Biological Sequence Denoising with Self-Supervised Set Learning

Author

Ng, Nathan, Park, Ji Won, Lee, Jae Hyeon, Kelly, Ryan Lewis, Ra, Stephen, and Cho, Kyunghyun

Subjects
Quantitative Biology - Genomics, Computer Science - Machine Learning
Abstract

Biological sequence analysis relies on the ability to denoise the imprecise output of sequencing platforms. We consider a common setting where a short sequence is read out repeatedly using a high-throughput long-read platform to generate multiple subreads, or noisy observations of the same sequence. Denoising these subreads with alignment-based approaches often fails when too few subreads are available or error rates are too high. In this paper, we propose a novel method for blindly denoising sets of sequences without directly observing clean source sequence labels. Our method, Self-Supervised Set Learning (SSSL), gathers subreads together in an embedding space and estimates a single set embedding as the midpoint of the subreads in both the latent and sequence spaces. This set embedding represents the "average" of the subreads and can be decoded into a prediction of the clean sequence. In experiments on simulated long-read DNA data, SSSL methods denoise small reads of $\leq 6$ subreads with 17% fewer errors and large reads of $>6$ subreads with 8% fewer errors compared to the best baseline. On a real dataset of antibody sequences, SSSL improves over baselines on two self-supervised metrics, with a significant improvement on difficult small reads that comprise over 60% of the test set. By accurately denoising these reads, SSSL promises to better realize the potential of high-throughput DNA sequencing data for downstream scientific applications.

Published
2023

3. MoleCLUEs: Molecular Conformers Maximally In-Distribution for Predictive Models

Author

Maser, Michael, Tagasovska, Natasa, Lee, Jae Hyeon, and Watkins, Andrew

Subjects
Computer Science - Machine Learning, Quantitative Biology - Quantitative Methods
Abstract

Structure-based molecular ML (SBML) models can be highly sensitive to input geometries and give predictions with large variance. We present an approach to mitigate the challenge of selecting conformations for such models by generating conformers that explicitly minimize predictive uncertainty. To achieve this, we compute estimates of aleatoric and epistemic uncertainties that are differentiable w.r.t. latent posteriors. We then iteratively sample new latents in the direction of lower uncertainty by gradient descent. As we train our predictive models jointly with a conformer decoder, the new latent embeddings can be mapped to their corresponding inputs, which we call \textit{MoleCLUEs}, or (molecular) counterfactual latent uncertainty explanations \citep{antoran2020getting}. We assess our algorithm for the task of predicting drug properties from 3D structure with maximum confidence. We additionally analyze the structure trajectories obtained from conformer optimizations, which provide insight into the sources of uncertainty in SBML., Comment: NeurIPS 2023 AI for Science Workshop

Published
2023

5. Trap-Based Pest Counting: Multiscale and Deformable Attention CenterNet Integrating Internal LR and HR Joint Feature Learning

Author

Lee, Jae-Hyeon and Son, Chang-Hwan

Subjects
Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence
Abstract

Pest counting, which predicts the number of pests in the early stage, is very important because it enables rapid pest control, reduces damage to crops, and improves productivity. In recent years, light traps have been increasingly used to lure and photograph pests for pest counting. However, pest images have a wide range of variability in pest appearance owing to severe occlusion, wide pose variation, and even scale variation. This makes pest counting more challenging. To address these issues, this study proposes a new pest counting model referred to as multiscale and deformable attention CenterNet (Mada-CenterNet) for internal low-resolution (LR) and high-resolution (HR) joint feature learning. Compared with the conventional CenterNet, the proposed Mada-CenterNet adopts a multiscale heatmap generation approach in a two-step fashion to predict LR and HR heatmaps adaptively learned to scale variations, that is, changes in the number of pests. In addition, to overcome the pose and occlusion problems, a new between-hourglass skip connection based on deformable and multiscale attention is designed to ensure internal LR and HR joint feature learning and incorporate geometric deformation, thereby resulting in an improved pest counting accuracy. Through experiments, the proposed Mada-CenterNet is verified to generate the HR heatmap more accurately and improve pest counting accuracy owing to multiscale heatmap generation, joint internal feature learning, and deformable and multiscale attention. In addition, the proposed model is confirmed to be effective in overcoming severe occlusions and variations in pose and scale. The experimental results show that the proposed model outperforms state-of-the-art crowd counting and object detection models.

Published
2023

6. SupSiam: Non-contrastive Auxiliary Loss for Learning from Molecular Conformers

Author

Maser, Michael, Park, Ji Won, Lin, Joshua Yao-Yu, Lee, Jae Hyeon, Frey, Nathan C., and Watkins, Andrew

Subjects
Computer Science - Machine Learning
Abstract

We investigate Siamese networks for learning related embeddings for augmented samples of molecular conformers. We find that a non-contrastive (positive-pair only) auxiliary task aids in supervised training of Euclidean neural networks (E3NNs) and increases manifold smoothness (MS) around point-cloud geometries. We demonstrate this property for multiple drug-activity prediction tasks while maintaining relevant performance metrics, and propose an extension of MS to probabilistic and regression settings. We provide an analysis of representation collapse, finding substantial effects of task-weighting, latent dimension, and regularization. We expect the presented protocol to aid in the development of reliable E3NNs from molecular conformers, even for small-data drug discovery programs., Comment: Submitted to the MLDD workshop, ICLR 2023

Published
2023

7. Multi-segment preserving sampling for deep manifold sampler

Author

Berenberg, Daniel, Lee, Jae Hyeon, Kelow, Simon, Park, Ji Won, Watkins, Andrew, Gligorijević, Vladimir, Bonneau, Richard, Ra, Stephen, and Cho, Kyunghyun

Subjects
Computer Science - Machine Learning, Quantitative Biology - Biomolecules
Abstract

Deep generative modeling for biological sequences presents a unique challenge in reconciling the bias-variance trade-off between explicit biological insight and model flexibility. The deep manifold sampler was recently proposed as a means to iteratively sample variable-length protein sequences by exploiting the gradients from a function predictor. We introduce an alternative approach to this guided sampling procedure, multi-segment preserving sampling, that enables the direct inclusion of domain-specific knowledge by designating preserved and non-preserved segments along the input sequence, thereby restricting variation to only select regions. We present its effectiveness in the context of antibody design by training two models: a deep manifold sampler and a GPT-2 language model on nearly six million heavy chain sequences annotated with the IGHV1-18 gene. During sampling, we restrict variation to only the complementarity-determining region 3 (CDR3) of the input. We obtain log probability scores from a GPT-2 model for each sampled CDR3 and demonstrate that multi-segment preserving sampling generates reasonable designs while maintaining the desired, preserved regions.

Published
2022

11. Overview of the DESI Legacy Imaging Surveys

Author

Dey, Arjun, Schlegel, David J., Lang, Dustin, Blum, Robert, Burleigh, Kaylan, Fan, Xiaohui, Findlay, Joseph R., Finkbeiner, Doug, Herrera, David, Juneau, Stephanie, Landriau, Martin, Levi, Michael, McGreer, Ian, Meisner, Aaron, Myers, Adam D., Moustakas, John, Nugent, Peter, Patej, Anna, Schlafly, Edward F., Walker, Alistair R., Valdes, Francisco, Weaver, Benjamin A., Zou, Christophe Yeche Hu, Zhou, Xu, Abareshi, Behzad, Abbott, T. M. C., Abolfathi, Bela, Aguilera, C., Alam, Shadab, Allen, Lori, Alvarez, A., Annis, James, Ansarinejad, Behzad, Aubert, Marie, Beechert, Jacqueline, Bell, Eric F., BenZvi, Segev Y., Beutler, Florian, Bielby, Richard M., Bolton, Adam S., Buckley-Geer, Cesar Briceno Elizabeth J., Butler, Karen, Calamida, Annalisa, Carlberg, Raymond G., Carter, Paul, Casas, Ricard, Castander, Francisco J., Choi, Yumi, Comparat, Johan, Cukanovaite, Elena, Delubac, Timothee, DeVries, Kaitlin, Dey, Sharmila, Dhungana, Govinda, Dickinson, Mark, Ding, Zhejie, Donaldson, John B., Duan, Yutong, Duckworth, Christopher J., Eftekharzadeh, Sarah, Eisenstein, Daniel J., Etourneau, Thomas, Fagrelius, Parker A., Farihi, Jay, Fitzpatrick, Mike, Font-Ribera, Andreu, Fulmer, Leah, Gansicke, Boris T., Gaztanaga, Enrique, George, Koshy, Gerdes, David W., Gontcho, Satya Gontcho A, Gorgoni, Claudio, Green, Gregory, Guy, Julien, Harmer, Diane, Hernandez, M., Honscheid, Klaus, Lijuan, Huang, James, David, Jannuzi, Buell T., Jiang, Linhua, Joyce, Richard, Karcher, Armin, Karkar, Sonia, Kehoe, Robert, Kneib, Jean-Paul, Kueter-Young, Andrea, Lan, Ting-Wen, Lauer, Tod, Guillou, Laurent Le, Van Suu, Auguste Le, Lee, Jae Hyeon, Lesser, Michael, Levasseur, Laurence Perreault, Li, Ting S., Mann, Justin L., Marshall, Bob, Martinez-Vazquez, C. E., Martini, Paul, Bourboux, Helion du Mas des, McManus, Sean, Meier, Tobias Gabriel, Menard, Brice, Metcalfe, Nigel, Munoz-Gutierrez, Andrea, Najita, Joan, Napier, Kevin, Narayan, Gautham, Newman, Jeffrey A., Nie, Jundan, Nord, Brian, Norman, Dara J., Olsen, Knut A. G., Paat, Anthony, Palanque-Delabrouille, Nathalie, Peng, Xiyan, Poppett, Claire L., Poremba, Megan R., Prakash, Abhishek, Rabinowitz, David, Raichoor, Anand, Rezaie, Mehdi, Robertson, A. N., Roe, Natalie A., Ross, Ashley J., Ross, Nicholas P., Rudnick, Gregory, Safonova, Sasha, Saha, Abhijit, Sanchez, F. Javier, Savary, Elodie, Schweiker, Heidi, Scott, Adam, Seo, Hee-Jong, Shan, Huanyuan, Silva, David R., Slepian, Zachary, Soto, Christian, Sprayberry, David, Staten, Ryan, Stillman, Coley M., Stupak, Robert J., Summers, David L., Tie, Suk Sien, Tirado, H., Vargas-Magana, Mariana, Vivas, A. Katherina, Wechsler, Risa H., Williams, Doug, Yang, Jinyi, Yang, Qian, Yapici, Tolga, Zaritsky, Dennis, Zenteno, A., Zhang, Kai, Zhang, Tianmeng, Zhou, Rongpu, and Zhou, Zhimin

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics
Abstract

The DESI Legacy Imaging Surveys are a combination of three public projects (the Dark Energy Camera Legacy Survey, the Beijing-Arizona Sky Survey, and the Mayall z-band Legacy Survey) that will jointly image approximately 14,000 deg^2 of the extragalactic sky visible from the northern hemisphere in three optical bands (g, r, and z) using telescopes at the Kitt Peak National Observatory and the Cerro Tololo Inter-American Observatory. The combined survey footprint is split into two contiguous areas by the Galactic plane. The optical imaging is conducted using a unique strategy of dynamically adjusting the exposure times and pointing selection during observing that results in a survey of nearly uniform depth. In addition to calibrated images, the project is delivering a catalog, constructed by using a probabilistic inference-based approach to estimate source shapes and brightnesses. The catalog includes photometry from the grz optical bands and from four mid-infrared bands (at 3.4, 4.6, 12 and 22 micorons) observed by the Wide-field Infrared Survey Explorer (WISE) satellite during its full operational lifetime. The project plans two public data releases each year. All the software used to generate the catalogs is also released with the data. This paper provides an overview of the Legacy Surveys project., Comment: 47 pages, 18 figures; accepted for publication in the Astronomical Journal

Published
2018
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12. Overview of the DESI Legacy Imaging Surveys

Author

Dey, Arjun, Schlegel, David J, Lang, Dustin, Blum, Robert, Burleigh, Kaylan, Fan, Xiaohui, Findlay, Joseph R, Finkbeiner, Doug, Herrera, David, Juneau, Stéphanie, Landriau, Martin, Levi, Michael, McGreer, Ian, Meisner, Aaron, Myers, Adam D, Moustakas, John, Nugent, Peter, Patej, Anna, Schlafly, Edward F, Walker, Alistair R, Valdes, Francisco, Weaver, Benjamin A, Yèche, Christophe, Zou, Hu, Zhou, Xu, Abareshi, Behzad, Abbott, TMC, Abolfathi, Bela, Aguilera, C, Alam, Shadab, Allen, Lori, Alvarez, A, Annis, James, Ansarinejad, Behzad, Aubert, Marie, Beechert, Jacqueline, Bell, Eric F, BenZvi, Segev Y, Beutler, Florian, Bielby, Richard M, Bolton, Adam S, Briceño, César, Buckley-Geer, Elizabeth J, Butler, Karen, Calamida, Annalisa, Carlberg, Raymond G, Carter, Paul, Casas, Ricard, Castander, Francisco J, Choi, Yumi, Comparat, Johan, Cukanovaite, Elena, Delubac, Timothée, DeVries, Kaitlin, Dey, Sharmila, Dhungana, Govinda, Dickinson, Mark, Ding, Zhejie, Donaldson, John B, Duan, Yutong, Duckworth, Christopher J, Eftekharzadeh, Sarah, Eisenstein, Daniel J, Etourneau, Thomas, Fagrelius, Parker A, Farihi, Jay, Fitzpatrick, Mike, Font-Ribera, Andreu, Fulmer, Leah, Gänsicke, Boris T, Gaztanaga, Enrique, George, Koshy, Gerdes, David W, Gontcho, Satya Gontcho A, Gorgoni, Claudio, Green, Gregory, Guy, Julien, Harmer, Diane, Hernandez, M, Honscheid, Klaus, Huang, Lijuan, James, David J, Jannuzi, Buell T, Jiang, Linhua, Joyce, Richard, Karcher, Armin, Karkar, Sonia, Kehoe, Robert, Jean-Paul, Kneib, Kueter-Young, Andrea, Lan, Ting-Wen, Lauer, Tod R, Le Guillou, Laurent, Le Van Suu, Auguste, Lee, Jae Hyeon, Lesser, Michael, Levasseur, Laurence Perreault, Li, Ting S, Mann, Justin L, and Marshall, Robert

Subjects
Space Sciences, Physical Sciences, catalogs, surveys, astro-ph.IM, Astronomical and Space Sciences, Astronomy & Astrophysics, Astronomical sciences, Particle and high energy physics
Abstract

The DESI Legacy Imaging Surveys (http://legacysurvey.org/) are a combination of three public projects (the Dark Energy Camera Legacy Survey, the Beijing-Arizona Sky Survey, and the Mayall z-band Legacy Survey) that will jointly image ≈14,000 deg2 of the extragalactic sky visible from the northern hemisphere in three optical bands (g, r, and z) using telescopes at the Kitt Peak National Observatory and the Cerro Tololo Inter-American Observatory. The combined survey footprint is split into two contiguous areas by the Galactic plane. The optical imaging is conducted using a unique strategy of dynamically adjusting the exposure times and pointing selection during observing that results in a survey of nearly uniform depth. In addition to calibrated images, the project is delivering a catalog, constructed by using a probabilistic inference-based approach to estimate source shapes and brightnesses. The catalog includes photometry from the grz optical bands and from four mid-infrared bands (at 3.4, 4.6, 12, and 22 μm) observed by the Wide-field Infrared Survey Explorer satellite during its full operational lifetime. The project plans two public data releases each year. All the software used to generate the catalogs is also released with the data. This paper provides an overview of the Legacy Surveys project.

Published
2019

14. Enhancing Penetration Performance and Drug Delivery of Polymeric Microneedles Using Silica Nanoparticle Coatings.

Author

Kim, Sohyun, Choi, Hyewon, Jeong, Hyejoong, Ortiz, Wilfredo Méndez, Cheon, Hwayeong, Jeon, Jae Yong, Lee, Jae Hyeon, Han, Jeong Hwan, Stebe, Kathleen J., Lee, Daeyeon, and Yoon, Hyunsik

Subjects
PERFORMANCE-enhancing drugs, SILICA films, DRUG coatings, THIN films, SURFACE potential
Abstract

Microneedle (MN) technology offers a powerful approach for transdermal delivery enabling painless injection and facilitating self‐administration without the need for professional assistance. However, the weak mechanical strength of MNs can lead to inefficient drug delivery and serious skin irritation if the MNs fracture during administration and leave fragments under the skin. Thus, the MNs need to be mechanically robust to avoid fracture during penetration through the skin while maintaining efficient drug delivery. Herein, the polymer‐based MNs with layer‐by‐layer (LbL) films of silica (SiO2) nanoparticles (NPs) and a polycation (poly(diallyldimethylammonium chloride) (PDADMAC)) followed by hydrothermal calcination are reinforced. The mechanical strength of the MNs is significantly improved after LbL assembly and shows lower threshold pressure to penetrate skins. Moreover, their drug loading and releasing properties are significantly enhanced due to an increase in the surface area and interfacial interaction. These SiO2 nanoparticle‐containing LbL thin films have great potential for the surface modification of 3D microstructured devices such as MNs, as evidenced by their enhanced mechanical strength and drug coating efficiency that result in a promising MN drug delivery model. [ABSTRACT FROM AUTHOR]

Published
2024
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17. The DESI Experiment Part I: Science,Targeting, and Survey Design

Author

DESI Collaboration, Aghamousa, Amir, Aguilar, Jessica, Ahlen, Steve, Alam, Shadab, Allen, Lori E., Prieto, Carlos Allende, Annis, James, Bailey, Stephen, Balland, Christophe, Ballester, Otger, Baltay, Charles, Beaufore, Lucas, Bebek, Chris, Beers, Timothy C., Bell, Eric F., Bernal, José Luis, Besuner, Robert, Beutler, Florian, Blake, Chris, Bleuler, Hannes, Blomqvist, Michael, Blum, Robert, Bolton, Adam S., Briceno, Cesar, Brooks, David, Brownstein, Joel R., Buckley-Geer, Elizabeth, Burden, Angela, Burtin, Etienne, Busca, Nicolas G., Cahn, Robert N., Cai, Yan-Chuan, Cardiel-Sas, Laia, Carlberg, Raymond G., Carton, Pierre-Henri, Casas, Ricard, Castander, Francisco J., Cervantes-Cota, Jorge L., Claybaugh, Todd M., Close, Madeline, Coker, Carl T., Cole, Shaun, Comparat, Johan, Cooper, Andrew P., Cousinou, M. -C., Crocce, Martin, Cuby, Jean-Gabriel, Cunningham, Daniel P., Davis, Tamara M., Dawson, Kyle S., de la Macorra, Axel, De Vicente, Juan, Delubac, Timothée, Derwent, Mark, Dey, Arjun, Dhungana, Govinda, Ding, Zhejie, Doel, Peter, Duan, Yutong T., Ealet, Anne, Edelstein, Jerry, Eftekharzadeh, Sarah, Eisenstein, Daniel J., Elliott, Ann, Escoffier, Stéphanie, Evatt, Matthew, Fagrelius, Parker, Fan, Xiaohui, Fanning, Kevin, Farahi, Arya, Farihi, Jay, Favole, Ginevra, Feng, Yu, Fernandez, Enrique, Findlay, Joseph R., Finkbeiner, Douglas P., Fitzpatrick, Michael J., Flaugher, Brenna, Flender, Samuel, Font-Ribera, Andreu, Forero-Romero, Jaime E., Fosalba, Pablo, Frenk, Carlos S., Fumagalli, Michele, Gaensicke, Boris T., Gallo, Giuseppe, Garcia-Bellido, Juan, Gaztanaga, Enrique, Fusillo, Nicola Pietro Gentile, Gerard, Terry, Gershkovich, Irena, Giannantonio, Tommaso, Gillet, Denis, Gonzalez-de-Rivera, Guillermo, Gonzalez-Perez, Violeta, Gott, Shelby, Graur, Or, Gutierrez, Gaston, Guy, Julien, Habib, Salman, Heetderks, Henry, Heetderks, Ian, Heitmann, Katrin, Hellwing, Wojciech A., Herrera, David A., Ho, Shirley, Holland, Stephen, Honscheid, Klaus, Huff, Eric, Hutchinson, Timothy A., Huterer, Dragan, Hwang, Ho Seong, Laguna, Joseph Maria Illa, Ishikawa, Yuzo, Jacobs, Dianna, Jeffrey, Niall, Jelinsky, Patrick, Jennings, Elise, Jiang, Linhua, Jimenez, Jorge, Johnson, Jennifer, Joyce, Richard, Jullo, Eric, Juneau, Stéphanie, Kama, Sami, Karcher, Armin, Karkar, Sonia, Kehoe, Robert, Kennamer, Noble, Kent, Stephen, Kilbinger, Martin, Kim, Alex G., Kirkby, David, Kisner, Theodore, Kitanidis, Ellie, Kneib, Jean-Paul, Koposov, Sergey, Kovacs, Eve, Koyama, Kazuya, Kremin, Anthony, Kron, Richard, Kronig, Luzius, Kueter-Young, Andrea, Lacey, Cedric G., Lafever, Robin, Lahav, Ofer, Lambert, Andrew, Lampton, Michael, Landriau, Martin, Lang, Dustin, Lauer, Tod R., Goff, Jean-Marc Le, Guillou, Laurent Le, Van Suu, Auguste Le, Lee, Jae Hyeon, Lee, Su-Jeong, Leitner, Daniela, Lesser, Michael, Levi, Michael E., L'Huillier, Benjamin, Li, Baojiu, Liang, Ming, Lin, Huan, Linder, Eric, Loebman, Sarah R., Lukić, Zarija, Ma, Jun, MacCrann, Niall, Magneville, Christophe, Makarem, Laleh, Manera, Marc, Manser, Christopher J., Marshall, Robert, Martini, Paul, Massey, Richard, Matheson, Thomas, McCauley, Jeremy, McDonald, Patrick, McGreer, Ian D., Meisner, Aaron, Metcalfe, Nigel, Miller, Timothy N., Miquel, Ramon, Moustakas, John, Myers, Adam, Naik, Milind, Newman, Jeffrey A., Nichol, Robert C., Nicola, Andrina, da Costa, Luiz Nicolati, Nie, Jundan, Niz, Gustavo, Norberg, Peder, Nord, Brian, Norman, Dara, Nugent, Peter, O'Brien, Thomas, Oh, Minji, Olsen, Knut A. G., Padilla, Cristobal, Padmanabhan, Hamsa, Padmanabhan, Nikhil, Palanque-Delabrouille, Nathalie, Palmese, Antonella, Pappalardo, Daniel, Pâris, Isabelle, Park, Changbom, Patej, Anna, Peacock, John A., Peiris, Hiranya V., Peng, Xiyan, Percival, Will J., Perruchot, Sandrine, Pieri, Matthew M., Pogge, Richard, Pollack, Jennifer E., Poppett, Claire, Prada, Francisco, Prakash, Abhishek, Probst, Ronald G., Rabinowitz, David, Raichoor, Anand, Ree, Chang Hee, Refregier, Alexandre, Regal, Xavier, Reid, Beth, Reil, Kevin, Rezaie, Mehdi, Rockosi, Constance M., Roe, Natalie, Ronayette, Samuel, Roodman, Aaron, Ross, Ashley J., Ross, Nicholas P., Rossi, Graziano, Rozo, Eduardo, Ruhlmann-Kleider, Vanina, Rykoff, Eli S., Sabiu, Cristiano, Samushia, Lado, Sanchez, Eusebio, Sanchez, Javier, Schlegel, David J., Schneider, Michael, Schubnell, Michael, Secroun, Aurélia, Seljak, Uros, Seo, Hee-Jong, Serrano, Santiago, Shafieloo, Arman, Shan, Huanyuan, Sharples, Ray, Sholl, Michael J., Shourt, William V., Silber, Joseph H., Silva, David R., Sirk, Martin M., Slosar, Anze, Smith, Alex, Smoot, George F., Som, Debopam, Song, Yong-Seon, Sprayberry, David, Staten, Ryan, Stefanik, Andy, Tarle, Gregory, Tie, Suk Sien, Tinker, Jeremy L., Tojeiro, Rita, Valdes, Francisco, Valenzuela, Octavio, Valluri, Monica, Vargas-Magana, Mariana, Verde, Licia, Walker, Alistair R., Wang, Jiali, Wang, Yuting, Weaver, Benjamin A., Weaverdyck, Curtis, Wechsler, Risa H., Weinberg, David H., White, Martin, Yang, Qian, Yeche, Christophe, Zhang, Tianmeng, Zhao, Gong-Bo, Zheng, Yi, Zhou, Xu, Zhou, Zhimin, Zhu, Yaling, Zou, Hu, and Zu, Ying

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

DESI (Dark Energy Spectroscopic Instrument) is a Stage IV ground-based dark energy experiment that will study baryon acoustic oscillations (BAO) and the growth of structure through redshift-space distortions with a wide-area galaxy and quasar redshift survey. To trace the underlying dark matter distribution, spectroscopic targets will be selected in four classes from imaging data. We will measure luminous red galaxies up to $z=1.0$. To probe the Universe out to even higher redshift, DESI will target bright [O II] emission line galaxies up to $z=1.7$. Quasars will be targeted both as direct tracers of the underlying dark matter distribution and, at higher redshifts ($ 2.1 < z < 3.5$), for the Ly-$\alpha$ forest absorption features in their spectra, which will be used to trace the distribution of neutral hydrogen. When moonlight prevents efficient observations of the faint targets of the baseline survey, DESI will conduct a magnitude-limited Bright Galaxy Survey comprising approximately 10 million galaxies with a median $z\approx 0.2$. In total, more than 30 million galaxy and quasar redshifts will be obtained to measure the BAO feature and determine the matter power spectrum, including redshift space distortions.

Published
2016

18. The DESI Experiment Part II: Instrument Design

Author

DESI Collaboration, Aghamousa, Amir, Aguilar, Jessica, Ahlen, Steve, Alam, Shadab, Allen, Lori E., Prieto, Carlos Allende, Annis, James, Bailey, Stephen, Balland, Christophe, Ballester, Otger, Baltay, Charles, Beaufore, Lucas, Bebek, Chris, Beers, Timothy C., Bell, Eric F., Bernal, José Luis, Besuner, Robert, Beutler, Florian, Blake, Chris, Bleuler, Hannes, Blomqvist, Michael, Blum, Robert, Bolton, Adam S., Briceno, Cesar, Brooks, David, Brownstein, Joel R., Buckley-Geer, Elizabeth, Burden, Angela, Burtin, Etienne, Busca, Nicolas G., Cahn, Robert N., Cai, Yan-Chuan, Cardiel-Sas, Laia, Carlberg, Raymond G., Carton, Pierre-Henri, Casas, Ricard, Castander, Francisco J., Cervantes-Cota, Jorge L., Claybaugh, Todd M., Close, Madeline, Coker, Carl T., Cole, Shaun, Comparat, Johan, Cooper, Andrew P., Cousinou, M. -C., Crocce, Martin, Cuby, Jean-Gabriel, Cunningham, Daniel P., Davis, Tamara M., Dawson, Kyle S., de la Macorra, Axel, De Vicente, Juan, Delubac, Timothée, Derwent, Mark, Dey, Arjun, Dhungana, Govinda, Ding, Zhejie, Doel, Peter, Duan, Yutong T., Ealet, Anne, Edelstein, Jerry, Eftekharzadeh, Sarah, Eisenstein, Daniel J., Elliott, Ann, Escoffier, Stéphanie, Evatt, Matthew, Fagrelius, Parker, Fan, Xiaohui, Fanning, Kevin, Farahi, Arya, Farihi, Jay, Favole, Ginevra, Feng, Yu, Fernandez, Enrique, Findlay, Joseph R., Finkbeiner, Douglas P., Fitzpatrick, Michael J., Flaugher, Brenna, Flender, Samuel, Font-Ribera, Andreu, Forero-Romero, Jaime E., Fosalba, Pablo, Frenk, Carlos S., Fumagalli, Michele, Gaensicke, Boris T., Gallo, Giuseppe, Garcia-Bellido, Juan, Gaztanaga, Enrique, Fusillo, Nicola Pietro Gentile, Gerard, Terry, Gershkovich, Irena, Giannantonio, Tommaso, Gillet, Denis, Gonzalez-de-Rivera, Guillermo, Gonzalez-Perez, Violeta, Gott, Shelby, Graur, Or, Gutierrez, Gaston, Guy, Julien, Habib, Salman, Heetderks, Henry, Heetderks, Ian, Heitmann, Katrin, Hellwing, Wojciech A., Herrera, David A., Ho, Shirley, Holland, Stephen, Honscheid, Klaus, Huff, Eric, Hutchinson, Timothy A., Huterer, Dragan, Hwang, Ho Seong, Laguna, Joseph Maria Illa, Ishikawa, Yuzo, Jacobs, Dianna, Jeffrey, Niall, Jelinsky, Patrick, Jennings, Elise, Jiang, Linhua, Jimenez, Jorge, Johnson, Jennifer, Joyce, Richard, Jullo, Eric, Juneau, Stéphanie, Kama, Sami, Karcher, Armin, Karkar, Sonia, Kehoe, Robert, Kennamer, Noble, Kent, Stephen, Kilbinger, Martin, Kim, Alex G., Kirkby, David, Kisner, Theodore, Kitanidis, Ellie, Kneib, Jean-Paul, Koposov, Sergey, Kovacs, Eve, Koyama, Kazuya, Kremin, Anthony, Kron, Richard, Kronig, Luzius, Kueter-Young, Andrea, Lacey, Cedric G., Lafever, Robin, Lahav, Ofer, Lambert, Andrew, Lampton, Michael, Landriau, Martin, Lang, Dustin, Lauer, Tod R., Goff, Jean-Marc Le, Guillou, Laurent Le, Van Suu, Auguste Le, Lee, Jae Hyeon, Lee, Su-Jeong, Leitner, Daniela, Lesser, Michael, Levi, Michael E., L'Huillier, Benjamin, Li, Baojiu, Liang, Ming, Lin, Huan, Linder, Eric, Loebman, Sarah R., Lukić, Zarija, Ma, Jun, MacCrann, Niall, Magneville, Christophe, Makarem, Laleh, Manera, Marc, Manser, Christopher J., Marshall, Robert, Martini, Paul, Massey, Richard, Matheson, Thomas, McCauley, Jeremy, McDonald, Patrick, McGreer, Ian D., Meisner, Aaron, Metcalfe, Nigel, Miller, Timothy N., Miquel, Ramon, Moustakas, John, Myers, Adam, Naik, Milind, Newman, Jeffrey A., Nichol, Robert C., Nicola, Andrina, da Costa, Luiz Nicolati, Nie, Jundan, Niz, Gustavo, Norberg, Peder, Nord, Brian, Norman, Dara, Nugent, Peter, O'Brien, Thomas, Oh, Minji, Olsen, Knut A. G., Padilla, Cristobal, Padmanabhan, Hamsa, Padmanabhan, Nikhil, Palanque-Delabrouille, Nathalie, Palmese, Antonella, Pappalardo, Daniel, Pâris, Isabelle, Park, Changbom, Patej, Anna, Peacock, John A., Peiris, Hiranya V., Peng, Xiyan, Percival, Will J., Perruchot, Sandrine, Pieri, Matthew M., Pogge, Richard, Pollack, Jennifer E., Poppett, Claire, Prada, Francisco, Prakash, Abhishek, Probst, Ronald G., Rabinowitz, David, Raichoor, Anand, Ree, Chang Hee, Refregier, Alexandre, Regal, Xavier, Reid, Beth, Reil, Kevin, Rezaie, Mehdi, Rockosi, Constance M., Roe, Natalie, Ronayette, Samuel, Roodman, Aaron, Ross, Ashley J., Ross, Nicholas P., Rossi, Graziano, Rozo, Eduardo, Ruhlmann-Kleider, Vanina, Rykoff, Eli S., Sabiu, Cristiano, Samushia, Lado, Sanchez, Eusebio, Sanchez, Javier, Schlegel, David J., Schneider, Michael, Schubnell, Michael, Secroun, Aurélia, Seljak, Uros, Seo, Hee-Jong, Serrano, Santiago, Shafieloo, Arman, Shan, Huanyuan, Sharples, Ray, Sholl, Michael J., Shourt, William V., Silber, Joseph H., Silva, David R., Sirk, Martin M., Slosar, Anze, Smith, Alex, Smoot, George F., Som, Debopam, Song, Yong-Seon, Sprayberry, David, Staten, Ryan, Stefanik, Andy, Tarle, Gregory, Tie, Suk Sien, Tinker, Jeremy L., Tojeiro, Rita, Valdes, Francisco, Valenzuela, Octavio, Valluri, Monica, Vargas-Magana, Mariana, Verde, Licia, Walker, Alistair R., Wang, Jiali, Wang, Yuting, Weaver, Benjamin A., Weaverdyck, Curtis, Wechsler, Risa H., Weinberg, David H., White, Martin, Yang, Qian, Yeche, Christophe, Zhang, Tianmeng, Zhao, Gong-Bo, Zheng, Yi, Zhou, Xu, Zhou, Zhimin, Zhu, Yaling, Zou, Hu, and Zu, Ying

Subjects
Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Cosmology and Nongalactic Astrophysics
Abstract

DESI (Dark Energy Spectropic Instrument) is a Stage IV ground-based dark energy experiment that will study baryon acoustic oscillations and the growth of structure through redshift-space distortions with a wide-area galaxy and quasar redshift survey. The DESI instrument is a robotically-actuated, fiber-fed spectrograph capable of taking up to 5,000 simultaneous spectra over a wavelength range from 360 nm to 980 nm. The fibers feed ten three-arm spectrographs with resolution $R= \lambda/\Delta\lambda$ between 2000 and 5500, depending on wavelength. The DESI instrument will be used to conduct a five-year survey designed to cover 14,000 deg$^2$. This powerful instrument will be installed at prime focus on the 4-m Mayall telescope in Kitt Peak, Arizona, along with a new optical corrector, which will provide a three-degree diameter field of view. The DESI collaboration will also deliver a spectroscopic pipeline and data management system to reduce and archive all data for eventual public use.

Published
2016

19. Enhanced Oxidation Resistance and Interface Stability of Atomic-Layer-Deposited MoNxElectrodes via TiN Passivation for DRAM Cell Capacitor Applications

Author

Kang, Wangu, Ahn, Ji Sang, Lee, Jae Hyeon, Choi, Byung Joon, and Han, Jeong Hwan

Abstract

The continuous miniaturization of dynamic random-access memory (DRAM) capacitors has amplified the demand for electrode materials featuring specific characteristics, such as low resistivity, high work function, chemical stability, excellent interface quality with high-k dielectrics, and superior mechanical properties. In this study, molybdenum nitride (MoNx) films were deposited using a plasma-enhanced atomic layer deposition (PEALD) employing bis(isopropylcyclopentadienyl)molybdenum(IV) dihydride and NH3plasma for DRAM capacitor electrode applications. Depending on the deposition temperatures of the PEALD MoNxfilms ranging from 200 to 400 °C, the Mo/N ratio and crystal structure varied, transitioning from the cubic NaCl-B1-type MoN phase with Mo/N ratio of 1.4 to the cubic γ-Mo2N phase with Mo/N ratio of 1.9. Notably, MoNxfilms grown at 400 °C exhibited low resistivity (435 μΩ·cm), a high work function (5.28 eV), and superior mechanical hardness (11.3 GPa) compared to ALD TiN films. Despite these excellent properties, the PEALD MoNxelectrode demonstrated insufficient chemical stability, particularly in terms of oxidation resistance and interface quality with ALD HfxZr1–xO2(HZO) films. This resulted in poor morphology and the formation of significant oxygen-deficient HZO layers (such as HfO2–x), leading to considerable degradation in the electrical performance of metal–insulator–metal (MIM) capacitors. To mitigate this issue, a thin (2.5–14 nm) ALD TiN layer was introduced as a passivation layer between the MoNxbottom electrode and HZO dielectric. The TiN-passivated MoNx(TiN/MoNx) electrode showed substantially enhanced oxidation resistance and reduced interfacial reactions with the HZO dielectric. Consequently, MIM capacitors with TiN/MoNxbottom electrodes demonstrated outstanding electrical performance, including excellent dielectric properties, low leakage current density, and high mechanical strength. Hence, this study proposes a promising candidates for storage nodes in the next-generation DRAM capacitors.

Published
2024
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20. A Comparison Study on the Metabolites in PC-3, RWPE-1, and Chrysin-Treated PC-3 Cells.

Author

Lee, Jae-Hyeon, Kim, Jung-Eun, Lee, Eun-Ok, and Lee, Hyo-Jeong

Subjects
GAS chromatography/Mass spectrometry (GC-MS), PRINCIPAL components analysis, EXTRACELLULAR fluid, PROSTATE-specific antigen, ORGANIC acids, METABOLITES, CARBOXYLIC acids
Abstract

Prostate cancer is frequently diagnosed and the leading cause of death in men worldwide. Prostate-specific antigen (PSA) blood tests and biopsies are the primary methods for diagnosing prostate cancer; however, their accuracy is less than 50%. Therefore, there is a need to develop diagnostic tests that minimize patient discomfort during examination and adequate biomarkers that are more accurate, sensitive, and specific for the detection of prostate cancer. This study investigated the application of metabolomics to identify biomarkers in prostate cancer biofluids. In addition, changes in prostate cancer metabolite levels induced by chrysin, a natural anticancer compound, were evaluated and compared with those in non-treated prostate cancer cells. Gas chromatography-mass spectrometry (GC-MS)-based metabolomic profiling was performed to investigate the differences in metabolic alterations among prostate cancer, normal prostate, and chrysin-treated prostate cancer cells. Pairwise comparisons of the extracellular fluid metabolomes were performed using principal component analysis (PCA), partial least squares–discriminant analysis (PLS-DA), and Student's t-test. The results revealed significantly different patterns among the metabolite groups, including alcohols, amino acids, carboxylic acids, organic acids, sugars, and urea. The RWPE-1- and chrysin-treated PC-3 (PC-3 Chr) cell groups showed similar tendencies for 23 metabolites, while the groups showed significant differences from the PC-3 group. Most amino acids showed higher concentrations in PC-3 cells than in the normal cell line RWPE-1 cells and PC-3 Chr cells. Our results revealed that GC-MS might be an effective diagnostic tool to detect prostate cancer and contribute to finding new tumor markers for prostate cancer as the basis for new ideas. [ABSTRACT FROM AUTHOR]

Published
2024
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25. Preparation of self-healing EPDM/ionomer thermoplastic vulcanizates with ionic network for automotive application: Effects of maleic anhydride grafted EPDM as compatibilizer

Author

Jin, Woo Seok, primary, Sahu, Pranabesh, additional, Paria, Sarbaranjan, additional, Jeong, Seongrok, additional, Jeon, Jun Ha, additional, Yun, Ju-ho, additional, Lee, Jae Hyeon, additional, Park, Sung Min, additional, Kim, Nam Il, additional, and Oh, Jeong Seok, additional

Published
2023
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28. MoleCLUEs: Optimizing Molecular Conformers by Minimization of Differentiable Uncertainty

Author

Maser, Michael, Tagasovska, Natasa, Lee, Jae Hyeon, and Watkins, Andrew

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, FOS: Biological sciences, Quantitative Biology - Quantitative Methods, Quantitative Methods (q-bio.QM), Machine Learning (cs.LG)
Abstract

Structure-based models in the molecular sciences can be highly sensitive to input geometries and give predictions with large variance under subtle coordinate perturbations. We present an approach to mitigate this failure mode by generating conformations that explicitly minimize uncertainty in a predictive model. To achieve this, we compute differentiable estimates of aleatoric \textit{and} epistemic uncertainties directly from learned embeddings. We then train an optimizer that iteratively samples embeddings to reduce these uncertainties according to their gradients. As our predictive model is constructed as a variational autoencoder, the new embeddings can be decoded to their corresponding inputs, which we call \textit{MoleCLUEs}, or (molecular) counterfactual latent uncertainty explanations \citep{antoran2020getting}. We provide results of our algorithm for the task of predicting drug properties with maximum confidence as well as analysis of the differentiable structure simulations., Submitted to the Differentiable Almost Everything Workshop, ICML 2023

Published
2023

32. 3PTZ and 3PXZ small molecular hole-transporting materials in polymer light-emitting diodes.

Author

Shin, Dong Su, Park, Young Jae, Lee, Jae Hyeon, Kim, Ji-Yeon, Lee, Hyunbok, Kim, Kitae, Yi, Yeonjin, Kwon, Ji Eon, Lee, Kyunam, Park, Soo Young, Yim, Sang-Youp, Park, Donghee, and Son, Dong Ick

Abstract

In the field of organic display devices, the solution process of organic materials still requires a lot of research in order to realize advantages such as a simple manufacturing process and flexible characteristics. Here, we propose novel small-molecule hole-transporting materials of 1,3,5-tri(10H-phenothiazin-10-yl)benzene (3PTZ) and 1,3,5-tri(10H-phenoxazin-10-yl)benzene (3PXZ) applicable to spin-coating techniques for solution-processed polymer light-emitting diodes (PLEDs). With optical analysis, electrical analysis, and density functional theory (DFT) modeling, accurate energy band diagrams of 3PTZ and 3PXZ as hole transport layers (HTLs) in the PLED structure were revealed. The current–voltage (I–V) and electroluminescence (EL) results of PLEDs with 3PTZ (3PXZ) HTLs showed enhanced luminance and quantum efficiency. The maximum luminance and quantum efficiencies of PLEDs with the 3PTZ HTL increased 1.4 and 1.76 times compared to those of the reference PLED without a HTL. The 3PTZ (3PXZ) HTLs with the optimal energy band structure exhibited similar performances regardless of the deposition processes like spin-coating and thermal evaporation. Devices based on 3PTZ (3PXZ) materials show 1.2 (1.11) times increased quantum efficiencies compared with that of devices based on poly-TPD, a representative polymer HTL material. Comparing two small molecular materials as a HTL, the PLED with 3PTZ showed 1.08 times increased quantum efficiency compared with the PED with 3PXZ, attributed to the higher electron barrier height of 3PTZ than that of 3PXZ. To investigate parameters for quantum efficiency improvement of PLEDs with small molecular 3PTZ HTL, time-resolved photoluminescence (TRPL) and I–V curve fittings were additionally analyzed. [ABSTRACT FROM AUTHOR]

Published
2023
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43. A Study on Kant's 'Perpetual Peace' -from the Perspective of Moral Philosophy

Author

이재현 ( Lee Jae-hyeon )

Abstract

본 논문은 칸트「영구평화론」을 정치철학의 관점, 그리고 역사철학의 관점에 이은 세 번째 연구이다. 칸트 영구평화를 도덕철학의 관점에서 조망해 보아야 하는 이유는 정치철학이나 역사철학의 관점에서의 영구평화가 결국은 그 두 연구의 기저에 도덕철학이 자리잡고 있기 때문이다. 칸트 도덕철학의 관점에서 영구평화 논의가 도덕교육에서 지니는 궁극적 함의를 필자는 다음과 같이 정리한다. 첫째, 영구평화 논의 기저에는 도덕적 주체인 이성적 자유의지를 지닌 인간은 존엄성을 지닌 존재이고, 그 존엄성은 지켜져야 한다. 둘째, 영구평화는 그러한 인간의 존엄성 실현을 위한 도덕성 실현과 직접적으로 관련되는 문제이다. 셋째, 영구평화는 이러한 도덕성이 실현되기 위한 정치·사회적 환경과 그 조건을 탐구하는 과정에서 영구평화가 하나의 당위로서 제시된다.

Published
2017

46. Schisandra Extract and Ascorbic Acid Synergistically Enhance Cognition in Mice through Modulation of Mitochondrial Respiration

Author

Jang, Yunseon, primary, Lee, Jae Hyeon, additional, Lee, Min Joung, additional, Kim, Soo Jeong, additional, Ju, Xianshu, additional, Cui, Jianchen, additional, Zhu, Jiebo, additional, Lee, Yu Lim, additional, Namgung, Eunji, additional, Sung, Han Wool John, additional, Lee, Hong Won, additional, Ryu, Min Jeong, additional, Oh, Eungseok, additional, Chung, Woosuk, additional, Kweon, Gi Ryang, additional, Choi, Chun Whan, additional, and Heo, Jun Young, additional

Published
2020
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50. 3,4,5-Trihydroxycinnamic acid attenuates lipopolysaccharide (LPS)-induced acute lung injury via downregulating inflammatory molecules and upregulating HO-1/AMPK activation

Author

Lee, Jae-Won, primary, Chun, Wanjoo, additional, Kwon, Ok-Kyoung, additional, Park, Hyun Ah, additional, Lim, Yourim, additional, Lee, Jae-Hyeon, additional, Kim, Doo-Young, additional, Kim, Jung Hee, additional, Lee, Hyeong-Kyu, additional, Ryu, Hyung Won, additional, Oh, Sei-Ryang, additional, and Ahn, Kyung-Seop, additional

Published
2018
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