Commodity currency: an alternative route to currency union

Proposals have been made for a common currency for East Asia, but the countries preparing to participate need to be in a state of economic convergence. We show that at least six countries of East Asia already satisfy this condition. There also needs to be a mechanism by which the new currency relates to other reserve currencies. We demonstrate that a numéraire could be defined solely from the actual worldwide consumption of food and energy per capita, linked to fiat currencies via world market prices. We show that real resource prices are stable in real terms, and likely to remain so. Furthermore, the link from energy prices to food commodity prices is permanent, arising from energy inputs in agriculture, food processing and distribu-tion. Calibration of currency value using a yardstick such as our SI numéraire offers an unbiased measure of the con-sistently stable cost of subsistence in the face of volatile currency exchange rates. This has the advantage that the par-ticipating countries need only agree to currency governance based on a common standards institution, a much less on-erous form of agreement than would be required in the creation of a common central bank.

Random prism: an alternative to random forests

Ensemble learning techniques generate multiple classifiers, so called base classifiers, whose combined classification results are used in order to increase the overall classification accuracy. In most ensemble classifiers the base classifiers are based on the Top Down Induction of Decision Trees (TDIDT) approach. However, an alternative approach for the induction of rule based classifiers is the Prism family of algorithms. Prism algorithms produce modular classification rules that do not necessarily fit into a decision tree structure. Prism classification rulesets achieve a comparable and sometimes higher classification accuracy compared with decision tree classifiers, if the data is noisy and large. Yet Prism still suffers from overfitting on noisy and large datasets. In practice ensemble techniques tend to reduce the overfitting, however there exists no ensemble learner for modular classification rule inducers such as the Prism family of algorithms. This article describes the first development of an ensemble learner based on the Prism family of algorithms in order to enhance Prism’s classification accuracy by reducing overfitting.

A novel alternative to cryopreservation for the short-term storage of stem cells for use in cell therapy using alginate encapsulation

Efficient transport of stem/progenitor cells without affecting their survival and function is a key factor in any practical cell-based therapy. However, the current approach using liquid nitrogen for the transfer of stem cells requires a short delivery time window is technically challenging and financially expensive. The present study aims to use semipermeable alginate hydrogels (crosslinked by strontium) to encapsulate, store, and release stem cells, to replace the conventional cryopreservation method for the transport of therapeutic cells within world-wide distribution time frame. Human mesenchymal stem cell (hMSC) and mouse embryonic stem cells (mESCs) were successfully stored inside alginate hydrogels for 5 days under ambient conditions in an air-tight environment (sealed cryovial). Cell viability, of the cells extracted from alginate gel, gave 74% (mESC) and 80% (hMSC) survival rates, which compared favorably to cryopreservation. More importantly, the subsequent proliferation rate and detection of common stem cell markers (both in mRNA and protein level) from hMSCs and mESCs retrieved from alginate hydrogels were also comparable to (if not better than) results gained following cryopreservation. In conclusion, this new and simple application of alginate hydrogel encapsulation may offer a cheap and robust alternative to cryopreservation for the transport and storage of stem cells for both clinical and research purposes.

Random Prism: a noise-tolerant alternative to Random Forests

Ensemble learning can be used to increase the overall classification accuracy of a classifier by generating multiple base classifiers and combining their classification results. A frequently used family of base classifiers for ensemble learning are decision trees. However, alternative approaches can potentially be used, such as the Prism family of algorithms that also induces classification rules. Compared with decision trees, Prism algorithms generate modular classification rules that cannot necessarily be represented in the form of a decision tree. Prism algorithms produce a similar classification accuracy compared with decision trees. However, in some cases, for example, if there is noise in the training and test data, Prism algorithms can outperform decision trees by achieving a higher classification accuracy. However, Prism still tends to overfit on noisy data; hence, ensemble learners have been adopted in this work to reduce the overfitting. This paper describes the development of an ensemble learner using a member of the Prism family as the base classifier to reduce the overfitting of Prism algorithms on noisy datasets. The developed ensemble classifier is compared with a stand-alone Prism classifier in terms of classification accuracy and resistance to noise.

Mechanism for synchronized motion between two humans in mutual tapping experiments: Transition from alternative mode to synchronization mode

We performed mutual tapping experiments between two humans to investigate the conditions required for synchronized motion. A transition from an alternative mode to a synchronization mode was discovered under the same conditions when a subject changed from a reactive mode to an anticipation mode in single tapping experiments. Experimental results suggest that the cycle time for each tapping motion is tuned by a proportional control that is based on synchronization errors and cycle time errors. As the tapping frequency increases, the mathematical model based on the feedback control in the sensory-motor closed loop predicts a discrete mode transition as the gain factors of the proportional control decease. The conditions of the synchronization were shown as a consequence of the coupled dynamics based on the subsequent feedback loop in the sensory-motor system.