Results 71 to 80 of about 15,770,233 (254)
Quantum Mutual Information, Fragile Systems and Emergence
Entropy, 2022 In this paper, we present an analytical description of emergence from the density matrix framework as a state of knowledge of the system, and its generalized probability formulation.
Yasmín Navarrete, Sergio Davis
doaj +1 more source
Estimation of Entropy and Mutual Information [PDF]
Neural Computation, 2003 We present some new results on the nonparametric estimation of entropy and mutual information. First, we use an exact local expansion of the entropy function to prove almost sure consistency and central limit theorems for three of the most commonly used discretized information estimators. The setup is related to Grenander's method of sieves and places
openaire +2 more sources
Regulating for mutual gains? Non-union employee representation and the information and consultation directive [PDF]
, 2014 Interest in ‘mutual gains’ has principally been confined to studies of the unionised sector. Yet there is no reason why this conceptual dynamic cannot be extended to the non-unionised realm, specifically in relation to non-union employee representation ...
Dobbins, Tony +10 more
core +1 more source
Ligand‐dependent transcriptional heterogeneity in cell cycle gene expression delays G1/S entry
FEBS Letters, EarlyView.EGF and HRG induce distinct G1/S progression programs in ErbB2‐amplified BT474 breast cancer cells. Despite activating the potent ErbB2–ErbB3 heterodimer, HRG does not accelerate cell‐cycle entry. Instead, EGF promotes earlier restriction‐point passage via ERK–FOS signaling, whereas HRG activates the AKT–MYC axis, driving transcriptional heterogeneity ...
Ririn Rahmala Febri +5 more
wiley +1 more source
Weighted Mutual Information for Aggregated Kernel Clustering
Entropy, 2020 Background: A common task in machine learning is clustering data into different groups based on similarities. Clustering methods can be divided in two groups: linear and nonlinear. A commonly used linear clustering method is K-means.
Nezamoddin N. Kachouie, Meshal Shutaywi
doaj +1 more source
Artificial molecular machines and motors—Design and control of nanoscale motion
FEBS Letters, EarlyView.Molecules are constantly moving because of thermal fluctuations, but random motion alone cannot be exploited to perform directional tasks. Artificial molecular machines use chemical, electrical, or light energy to bias this motion. Molecular shuttles, rotary motors, and supramolecular pumps illustrate how nanoscale movement can be controlled and ...
Leonardo Andreoni, Alberto Credi
wiley +1 more source
Space-time generalization of mutual information
Journal of High Energy PhysicsThe mutual information characterizes correlations between spatially separated regions of a system. Yet, in experiments we often measure dynamical correlations, which involve probing operators that are also separated in time.
Paolo Glorioso +2 more
doaj +1 more source
A context‐dependent modulatory role for eIF6 in acquired resistance to vemurafenib in melanoma
FEBS Letters, EarlyView.Acquired resistance to vemurafenib upregulates the translation factor eIF6 in melanoma cells. Silencing eIF6 in resistant cells reduces proliferation and partially restores drug sensitivity, whereas its overexpression increases sensitivity across melanoma lines regardless of BRAF status, via modulation of mTOR, S6K, and MAPK signaling.
George Kyriakopoulos +9 more
wiley +1 more source
B-spline mutual information independent component analysis [PDF]
, 2010 Mutual Information is one of the most natural criteria when developing independent component analysis (ICA). Although utilized to some level it has always been difficult to calculate.
Li, Yan, Walters-Williams, Janett
core +1 more source
Machine Learning with Squared-Loss Mutual Information
Entropy, 2012 Mutual information (MI) is useful for detecting statistical independence between random variables, and it has been successfully applied to solving various machine learning problems.
Masashi Sugiyama
doaj +1 more source