Можливості ефективної організації та стимулювання бажаних трансформацій

У статті розглядається поняття інформаційного впливу як багаторівневого феномена. Вводиться розмежування між інформаційними впливами під час взаємодії різних соціальних систем і змальовано ефект інформаційного впливу на соціально-економічний розвиток суспільства. У статті автор продовжує аналіз власної концепції розвитку соціальних технологій постмодерного м’якого управління з точки зору стану національної безпеки. Він розробляє модель постмодерного інформаційного впливу – стимулювання бажаних змін.The article examines the notion of information influence as a multilevel phenomenon. The division is applied between information influence of different social systems interaction and the impact of information influence upon society’s social and economic development is shown. In the article the author continues analysis of his conception of postmodern soft management social technologies development from the point of view of national security situation. He elaborates the model of postmodern information influence – desirable changes stimulation

Spin Resolution of the Electron-Gas Correlation Energy: Positive same-spin contribution

The negative correlation energy per particle of a uniform electron gas of density parameter $r_s$ and spin polarization $\zeta$ is well known, but its spin resolution into up-down, up-up, and down-down contributions is not. Widely-used estimates are incorrect, and hamper the development of reliable density functionals and pair distribution functions. For the spin resolution, we present interpolations between high- and low-density limits that agree with available Quantum Monte Carlo data. In the low-density limit for $\zeta = 0$, we find that the same-spin correlation energy is unexpectedly positive, and we explain why. We also estimate the up and down contributions to the kinetic energy of correlation.Comment: new version, to appear in PRB Rapid Communicatio

Digital pathology: Multiple instance learning can detect Barrett's cancer.

We study diagnosis of Barrett's cancer from hematoxylin & eosin (H & E) stained histopathological biopsy images using multiple instance learning (MIL). We partition tissue cores into rectangular patches, and construct a feature vector consisting of a large set of cell-level and patch-level features for each patch. In MIL terms, we treat each tissue core as a bag (group of instances with a single group-level ground-truth label) and each patch an instance. After a benchmarking study on several MIL approaches, we find that a graph-based MIL algorithm, mi-Graph [1], gives the best performance (87% accuracy, 0.93 AUC), due to its inherent suitability to bags with spatially-correlated instances. In patch-level diagnosis, we reach 82% accuracy and 0.89 AUC using Bayesian logistic regression. We also pursue a study on feature importance, which shows that patch-level color and texture features and cell-level features all have significant contribution to prediction