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X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
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TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20230101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20241101T140000
DTEND;TZID=Asia/Seoul:20241101T150000
DTSTAMP:20260423T202701
CREATED:20241024T085401Z
LAST-MODIFIED:20241029T034102Z
UID:10201-1730469600-1730473200@www.ibs.re.kr
SUMMARY:Derivation and simulation of a computational model of active cell populations: How overlap avoidance\, deformability\, cell-cell junctions and cytoskeletal forces affect alignment - Kevin SPINICCI
DESCRIPTION:In this talk\, we discuss the paper : “Derivation and simulation of a computational model of active cell populations: How overlap avoidance\, deformability\, cell-cell junctions and cytoskeletal forces affect alignment” by Leech et al\, nature biotechnology\, https://doi.org/10.1371/journal.pcbi.1011879. \nZoom: https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09 \nAbstract \nCollective alignment of cell populations is a commonly observed phenomena in biology. An important example are aligning fibroblasts in healthy or scar tissue. In this work we derive and simulate a mechanistic agent-based model of the collective behaviour of actively moving and interacting cells\, with a focus on understanding collective alignment. The derivation strategy is based on energy minimisation. The model ingredients are motivated by data on the behaviour of different populations of aligning fibroblasts and include: Self-propulsion\, overlap avoidance\, deformability\, cell-cell junctions and cytoskeletal forces. We find that there is an optimal ratio of self-propulsion speed and overlap avoidance that maximises collective alignment. Further we find that deformability aids alignment\, and that cell-cell junctions by themselves hinder alignment. However\, if cytoskeletal forces are transmitted via cell-cell junctions we observe strong collective alignment over large spatial scales.
URL:https://www.ibs.re.kr/bimag/event/batch-effects-in-single-cell-rna-sequencing-data-are-corrected-by-matching-mutual-nearest-neighbors-kevin-spinicci/
LOCATION:Daejeon
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20241108T140000
DTEND;TZID=Asia/Seoul:20241108T160000
DTSTAMP:20260423T202701
CREATED:20241104T150449Z
LAST-MODIFIED:20241104T150551Z
UID:10220-1731074400-1731081600@www.ibs.re.kr
SUMMARY:Cluster-based network modeling—From snapshots to complex dynamical systems - Olive R. Cawiding
DESCRIPTION:Abstract: We propose a universal method for data-driven modeling of complex nonlinear dynamics from time-resolved snapshot data without prior knowledge. Complex nonlinear dynamics govern many fields of science and engineering. Data-driven dynamic modeling often assumes a low-dimensional subspace or manifold for the state. We liberate ourselves from this assumption by proposing cluster-based network modeling (CNM) bridging machine learning\, network science\, and statistical physics. CNM describes short- and long-term behavior and is fully automatable\, as it does not rely on application-specific knowledge. CNM is demonstrated for the Lorenz attractor\, ECG heartbeat signals\, Kolmogorov flow\, and a high-dimensional actuated turbulent boundary layer. Even the notoriously difficult modeling benchmark of rare events in the Kolmogorov flow is solved. This automatable universal data-driven representation of complex nonlinear dynamics complements and expands network connectivity science and promises new fast-track avenues to understand\, estimate\, predict\, and control complex systems in all scientific fields.
URL:https://www.ibs.re.kr/bimag/event/cluster-based-network-modeling-from-snapshots-to-complex-dynamical-systems/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20241115T090000
DTEND;TZID=Asia/Seoul:20241115T110000
DTSTAMP:20260423T202701
CREATED:20241112T000249Z
LAST-MODIFIED:20241112T041049Z
UID:10232-1731661200-1731668400@www.ibs.re.kr
SUMMARY:Next generation reservoir computing - Kang Min Lee
DESCRIPTION:In this talk\, we discuss the paper “Next generation reservoir computing”\, by Gauthier\, et.al\, Nat. Comm.\, 2021. \nAbstract : Reservoir computing is a best-in-class machine learning algorithm for processing information generated by dynamical systems using observed time-series data. Importantly\, it requires very small training data sets\, uses linear optimization\, and thus requires minimal computing resources. However\, the algorithm uses randomly sampled matrices to define the underlying recurrent neural network and has a multitude of metaparameters that must be optimized. Recent results demonstrate the equivalence of reservoir computing to nonlinear vector autoregression\, which requires no random matrices\, fewer metaparameters\, and provides interpretable results. Here\, we demonstrate that nonlinear vector autoregression excels at reservoir computing benchmark tasks and requires even shorter training data sets and training time\, heralding the next generation of reservoir computing.
URL:https://www.ibs.re.kr/bimag/event/next-generation-reservoir-computing-kang-min-lee/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20241122T100000
DTEND;TZID=Asia/Seoul:20241122T113000
DTSTAMP:20260423T202701
CREATED:20241119T001534Z
LAST-MODIFIED:20241119T001534Z
UID:10259-1732269600-1732275000@www.ibs.re.kr
SUMMARY:SVD-AE: An asymmetric autoencoder with SVD regularization for multivariate time series anomaly detection - Myna Lim
DESCRIPTION:In this talk\, we discuss the paper “SVD-AE: An asymmetric autoencoder with SVD regularization for multivariate time series anomaly detection” by Yueyue Yao\, et.al.\, Neural Networks\, 2024.  \nAbstract  \n\n\n\nAnomaly detection in multivariate time series is of critical importance in many real-world applications\, such as system maintenance and Internet monitoring. In this article\, we propose a novel unsupervised framework called SVD-AE to conduct anomaly detection in multivariate time series. The core idea is to fuse the strengths of both SVD and autoencoder to fully capture complex normal patterns in multivariate time series. An asymmetric autoencoder architecture is proposed\, where two encoders are used to capture features in time and variable dimensions and a shared decoder is used to generate reconstructions based on latent representations from both dimensions. A new regularization based on singular value decomposition theory is designed to force each encoder to learn features in the corresponding axis with mathematical supports delivered. A specific loss component is further proposed to align Fourier coefficients of inputs and reconstructions. It can preserve details of original inputs\, leading to enhanced feature learning capability of the model. Extensive experiments on three real world datasets demonstrate the proposed algorithm can achieve better performance on multivariate time series anomaly detection tasks under highly unbalanced scenarios compared with baseline algorithms.
URL:https://www.ibs.re.kr/bimag/event/svd-ae-an-asymmetric-autoencoder-with-svd-regularization-for-multivariate-time-series-anomaly-detection-myna-lim/
LOCATION:B232 Seminar Room\, IBS\, 55 Expo-ro Yuseong-gu\, Daejeon\, Daejeon\, 34126\, Korea\, Republic of
CATEGORIES:Journal Club
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
END:VEVENT
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