기초과학연구원 의생명수학그룹
|
S
Sun
|
M
Mon
|
T
Tue
|
W
Wed
|
T
Thu
|
F
Fri
|
S
Sat
|
|---|---|---|---|---|---|---|
|
0 events,
|
0 events,
|
0 events,
|
1 event,
-
Abstravt: Latent space dynamics identification (LaSDI) is an interpretable data-driven framework that follows three distinct steps, i.e., compression, dynamics identification, and prediction. Compression allows high-dimensional data to be reduced so that they can be easily fit into an interpretable model. Dynamics identification lets you derive the interpretable model, usually some form of parameterized differential equations … |
0 events,
|
1 event,
-
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. Zoom: https://us06web.zoom.us/j/99567630778?pwd=N2ZrUWtqZzJ0YURVTzlZT3JJR3FUQT09 Abstract Collective alignment of cell populations is a commonly observed phenomena in biology. An important example … |
0 events,
|
|
0 events,
|
0 events,
|
0 events,
|
0 events,
|
0 events,
|
1 event,
-
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) … |
0 events,
|
|
0 events,
|
0 events,
|
0 events,
|
1 event,
-
Abstract: The effect of malaria on the developing world is devastating. Each year there are more than 200 million cases and over 400,000 deaths, with children under the age of five the most vulnerable. Ambitious malaria elimination targets have been set by the World Health Organization for 2030. These involve the elimination of the disease … |
0 events,
|
1 event,
-
In this talk, we discuss the paper "Next generation reservoir computing", by Gauthier, et.al, Nat. Comm., 2021. Abstract : 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. … |
0 events,
|
|
0 events,
|
0 events,
|
0 events,
|
0 events,
|
0 events,
|
1 event,
-
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. Abstract Anomaly 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 … |
0 events,
|
|
0 events,
|
0 events,
|
0 events,
|
0 events,
|
0 events,
|
1 event,
-
Abstract: Malignant gliomas are highly invasive brain tumors. Recent attention has focused on their capacity for network-driven invasion, whereby mitotic events can be followed by the migration of nuclei along long thin cellular protrusions, termed tumour microtubes (TM). Here I develop a mathematical model that describes this microtube-driven invasion of gliomas. I show that scaling … |
0 events,
|