Genome sequencing of cells that live inside glass cages reveals their past history

One-quarter of the total primary production on earth is contributed by diatoms1. These are photosynthetic, unicellular algae with ornamented silica shells found in all aquatic and moist environments. They form the base of energy-efficient food webs that support all aquatic life forms. More than 250 genera of living diatoms, with as many as 100,000 species are known2. Fossil diatoms are known as early as the Cretaceous, 144–65 m.y. ago3. In India, deposits of diatoms occur in Rajasthan and are known as ‘multani mitti’. Multani mitti or Indian Fuller’s earth or diatomaceous earth as it is called in the West, is applied as a paste on the surface of the skin for 15–20 min and then washed-off. This leaves the skin feeling smooth, soft, moist and rejuvenated. Diatomaceous earth is now being used in the formulation of soaps, cleansing products, face powders and skincare preparations. Diatomaceous earth is a mineral material consisting mainly of siliceous fragments of various species of fossilized remains of diatoms.

Belief revision - an axiomatic approach

Belief revision systems aim at keeping a database consistent. They mostly concentrate on how to record and maintain dependencies. We propose an axiomatic system, called MFOT, as a solution to the problem of belief revision. MFOT has a set of proper axioms which selects a set of most plausible and consistent input beliefs. The proposed nonmonotonic inference rule further maintains consistency while generating the consequences of input beliefs. It also permits multiple property inheritance with exceptions. We have also examined some important properties of the proposed axiomatic system. We also propose a belief revision model that is object-centered. The relevance of such a model in maintaining the beliefs of a physician is examined.

Improved large-MIMO detection based on damped belief propagation

In this paper, we consider the application of belief propagation (BP) to achieve near-optimal signal detection in large multiple-input multiple-output (MIMO) systems at low complexities. Large-MIMO architectures based on spatial multiplexing (V-BLAST) as well as non-orthogonal space-time block codes(STBC) from cyclic division algebra (CDA) are considered. We adopt graphical models based on Markov random fields (MRF) and factor graphs (FG). In the MRF based approach, we use pairwise compatibility functions although the graphical models of MIMO systems are fully/densely connected. In the FG approach, we employ a Gaussian approximation (GA) of the multi-antenna interference, which significantly reduces the complexity while achieving very good performance for large dimensions. We show that i) both MRF and FG based BP approaches exhibit large-system behavior, where increasingly closer to optimal performance is achieved with increasing number of dimensions, and ii) damping of messages/beliefs significantly improves the bit error performance.