Search Results - "Shin, Seungyoun"

Conditional motion in-betweening by Kim, Jihoon, Byun, Taehyun, Shin, Seungyoun, Won, Jungdam, Choi, Sungjoon

“…•We propose a conditional motion in-betweening (CMIB) method, providing additional controllability in motion in-betweening (MIB). The proposed method can…”
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Elucidating robust learning with uncertainty-aware corruption pattern estimation by Park, Jeongeun, Shin, Seungyoun, Hwang, Sangheum, Choi, Sungjoon

“…•We propose a simple yet effective robust learning method leveraging a mixture-of-experts model on various noise settings.•The proposed method can not only…”
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Computer–Aided Drug Design (CADD), Chemical Synthesis and Biological Evaluation of Novel Antifungal, Anticancer, and Anti–iNOS Agents by Shin, SeungYoun

“…Each year, more than 300 million people suffer from severe fungal infections, and over 1.7 million people globally die from invasive fungal infections (IFIs)…”
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SAR study of N′-(Salicylidene)heteroarenecarbohydrazides as promising antifungal agents by Sun, Yi, Kim, Saerom, Shin, SeungYoun, Takemura, Kathryn, Matos, Gabriel S., Lazzarini, Cristina, Haranahalli, Krupanandan, Zambito, Julia, Garg, Ashna, Del Poeta, Maurizio, Ojima, Iwao

“…[Display omitted] •Novel class of broad-spectrum fungicidal agents with high potency.•Blocking the fungal sphingolipid glucosylceramide (GlcCer) synthesis.•71…”
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Towards Embedding Dynamic Personas in Interactive Robots: Masquerading Animated Social Kinematic (MASK) by Park, Jeongeun, Jeong, Taemoon, Kim, Hyeonseong, Byun, Taehyun, Shin, Seungyoun, Choi, Keunjun, Kwon, Jaewoon, Lee, Taeyoon, Pan, Matthew, Choi, Sungjoon

“…This letter presents the design and development of an innovative interactive robotic system to enhance audience engagement using character-like personas. Built…”
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Exploring Protein Folding and Light Activation Using Molecular Dynamics Simulations by Shin, SeungYoun

“…Replica Exchange Molecular Dynamics (REMD) simulation is applied to run on two mini-proteins, Trp-cage and chignolin, in the Generalized Born (GB-Neck2) model…”
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Unraveling the Mechanism of a LOV Domain Optogenetic Sensor: A Glutamine Lever Induces Unfolding of the Jα Helix by Iuliano, James N., Collado, Jinnette Tolentino, Gil, Agnieszka A., Ravindran, Pavithran T., Lukacs, Andras, Shin, SeungYoun, Woroniecka, Helena A., Adamczyk, Katrin, Aramini, James M., Edupuganti, Uthama R., Hall, Christopher R., Greetham, Gregory M., Sazanovich, Igor V., Clark, Ian P., Daryaee, Taraneh, Toettcher, Jared E., French, Jarrod B., Gardner, Kevin H., Simmerling, Carlos L., Meech, Stephen R., Tonge, Peter J.

“…Light-activated protein domains provide a convenient, modular, and genetically encodable sensor for optogenetics and optobiology. Although these domains have…”
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Learning Joint Representation of Human Motion and Language by Kim, Jihoon, Yu, Youngjae, Shin, Seungyoun, Byun, Taehyun, Choi, Sungjoon

“…In this work, we present MoLang (a Motion-Language connecting model) for learning joint representation of human motion and language, leveraging both unpaired…”
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Elucidating Robust Learning with Uncertainty-Aware Corruption Pattern Estimation by Park, Jeongeun, Shin, Seungyoun, Hwang, Sangheum, Choi, Sungjoon

“…Robust learning methods aim to learn a clean target distribution from noisy and corrupted training data where a specific corruption pattern is often assumed a…”
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Semi-Supervised Imitation Learning with Mixed Qualities of Demonstrations for Autonomous Driving by Lee, Gunmin, Oh, Wooseok, Oh, Jeongwoo, Shin, Seungyoun, Kim, Dohyeong, Jeong, Jaeyeon, Choi, Sungjoon, Oh, Songhwai

“…In this paper, we consider the problem of autonomous driving using imitation learning in a semi-supervised manner. In particular, both labeled and unlabeled…”
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Conditional Motion In-betweening by Kim, Jihoon, Byun, Taehyun, Shin, Seungyoun, Won, Jungdam, Choi, Sungjoon

“…Pattern Recognition, Volume 132, December 2022, 108894 Motion in-betweening (MIB) is a process of generating intermediate skeletal movement between the given…”
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Semi-Supervised Imitation Learning with Mixed Qualities of Demonstrations for Autonomous Driving by Lee, Gunmin, Oh, Wooseok, Shin, Seungyoun, Kim, Dohyeong, Oh, Jeongwoo, Jeong, Jaeyeon, Choi, Sungjoon, Oh, Songhwai

“…In this paper, we consider the problem of autonomous driving using imitation learning in a semi-supervised manner. In particular, both labeled and unlabeled…”
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