Canceled Kolmogorov-Arnold Networks – U Jin Choi

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper : "KAN: Kolmogorov-Arnold Networks," by Z Liu et al. Abstract: Inspired by the Kolmogorov-Arnold representation theorem, we propose Kolmogorov-Arnold Networks (KANs) as promising alternatives to Multi-Layer Perceptrons (MLPs). While MLPs have fixed activation functions on nodes ("neurons"), KANs have learnable activation functions on edges ("weights"). KANs have no

Laplacian renormalization group for heterogeneous networks – Gyuyoung Hwang

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we study and discuss the paper "Laplacian renormalization group for heterogeneous networks" by Pablo Villegas et.al, Nature Physics, 2023. Abstract  The renormalization group is the cornerstone of the modern theory of universality and phase transitions and it is a powerful tool to scrutinize symmetries and organizational scales in dynamical systems. However, its

Solving Inverse Problems in Medical Imaging with Score-Based Generative Models – U Jin Choi

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper : “Solving Inverse Problems in Medical Imaging with Score-Based Generative Models” by Y Song et al. Reconstructing medical images from partial measurements is an important inverse problem in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Existing solutions based on machine learning typically train a model to directly

cellFlow: a generative flow-based model for single-cell count data – Hyun Kim

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper "cellFlow: a generative flow-based model for single-cell count data" by A. Palma et.al, ICLR, 2024. Abstract Generative modeling for single-cell RNA-seq has proven transformative in crucial fields such as learning single-cell representations and perturbation responses. However, despite their appeal in relevant applications involving data augmentation and unseen cell

Diffusion Posterior Sampling for Linear Inverse Problem Solving- A Filtering Perspective – U Jin Choi

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper : “Diffusion Posterior Sampling for Linear Inverse Problem Solving- A Filtering Perspective” by Z. Dou& Y. Song Diffusion models have achieved tremendous success in generating high-dimensional data like images, videos and audio. These models provide powerful data priors that can solve linear inverse problems in zero shot through

Enhanced Gaussian Process Surrogates for Optimization and Sampling by Pure Exploration – Hwanwoo Kim

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

Abstract: In this talk, we propose novel noise-free Bayesian optimization strategies that rely on a random exploration step to enhance the accuracy of Gaussian process surrogate models. The new algorithms retain the ease of implementation of the classical GP-UCB algorithm, but the additional random exploration step accelerates their convergence, nearly achieving the optimal convergence rate.

Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model – Seokhwan Moon

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper "Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model" by F. W. Townes et.al., Genome Biology, 2019. Abstract  Single-cell RNA-Seq (scRNA-Seq) profiles gene expression of individual cells. Recent scRNA-Seq datasets have incorporated unique molecular identifiers (UMIs). Using negative controls, we show UMI counts follow

CARE as a wearable derived feature linking circadian amplitude to human cognitive functions – Dongju Lim

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper "CARE as a wearable derived feature linking circadian amplitude to human cognitive functions" by Shuya Cui et.al., npj Digital Medicine, 2023. Abstract Circadian rhythms are crucial for regulating physiological and behavioral processes. Pineal hormone melatonin is often used to measure circadian amplitude but its collection is costly and

Biomolecular Condensates: Principles and Models, Jeong-Mo Choi

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

Over the past decade, the phase behavior of biomolecules has garnered significant attention, particularly due to its biological implications, such as the reversible formation and dissociation of biomolecular condensates. These condensates perform diverse and essential functions within cells, including the acceleration of chemical reactions. Recent advances aim to uncover the fundamental principles of these systems

GIST-IBS-AMC Sleep Medicine Symposium

Conference room, (B109) Daejeon, Daejeon, Korea, Republic of

The field of sleep science is rapidly advancing, with increasing attention focused on unraveling the mystery of sleep across various scientific domains. Sleep medicine, which emerged from scientific research on sleep, is a highly important field that can enhance the quality of life and health. Since sleep medicine requires the integration of various specialized knowledge,

Plausible, robust biological oscillations through allelic buffering – Eui Min Jeong

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper "Plausible, robust biological oscillations through allelic buffering" by F-S. Hsieh et.al, Cell Systems, 2024. at the Journal Club.  Abstract Biological oscillators can specify time- and dose-dependent functions via dedicated control of their oscillatory dynamics. However, how biological oscillators, which recurrently activate noisy biochemical processes, achieve robust oscillations remains

Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality – Yun Min Song

B232 Seminar Room, IBS 55 Expo-ro Yuseong-gu, Daejeon, Daejeon, Korea, Republic of

In this talk, we discuss the paper "Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality" by H. Yuan et.al, npj digital medicine, 2024, at the Journal Club. Abstract  Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for

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