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Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference
TLDR
This work proposes a new conceptualization of the continual learning problem in terms of a temporally symmetric trade-off between transfer and interference that can be optimized by enforcing gradient alignment across examples, and introduces a new algorithm, Meta-Experience Replay, that directly exploits this view by combining experience replay with optimization based meta-learning.
Gene Expression Profiling of B Cell Chronic Lymphocytic Leukemia Reveals a Homogeneous Phenotype Related to Memory B Cells 〉
TLDR
Comparison of CLL profiles with those of purified normal B cell subpopulations indicates that the common CLL profile is more related to memory B cells than to those derived from naive B cells, CD5+ B Cells, and GC centroblasts and centrocytes.
A modular gradient-sensing network for chemotaxis in Escherichia coli revealed by responses to time-varying stimuli
TLDR
The results show how dynamic input–output measurements, time honored in physiology, can serve as powerful tools in deciphering cell‐signaling mechanisms.
Transcriptional analysis of the B cell germinal center reaction
TLDR
Analysis of transcriptional changes that occur in B cells during GC transit by gene expression profiling provides insights into the dynamics of the GC reaction and represents the basis for the analysis of B cell malignancies.
Modeling the chemotactic response of Escherichia coli to time-varying stimuli
TLDR
A general theoretical model based on receptor adaptation and receptor–receptor cooperativity is developed that provides a quantitative system-level description of the chemotaxis signaling pathway and can be used to predict E. coliChemotaxis responses to arbitrary temporal signals.
Analysis of Gene Expression Microarrays for Phenotype Classification
TLDR
A supervised learning algorithm is proposed that couples a complex, non-linear similarity metric, which maximizes the probability of discovering discriminative gene expression patterns, and a pattern discovery algorithm called SPLASH, which discovers efficiently and deterministically all statistically significant gene Expression patterns in the phenotype set.
Flocks, herds, and schools: A quantitative theory of flocking
We present a quantitative continuum theory of ``flocking'': the collective coherent motion of large numbers of self-propelled organisms. In agreement with everyday experience, our model predicts the
Quantitative Modeling of Escherichia coli Chemotactic Motion in Environments Varying in Space and Time
TLDR
The model can be used to study E. coli chemotaxis behavior in arbitrary spatiotemporally varying environments and agrees quantitatively with the classical capillary assay experiments where the attractant concentration changes both in space and time.
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