Time-Dependent Spintronic Transport and Current-Induced Spin Transfer Torque in Magnetic Tunnel Junctions

The responses of the electrical current and the current-induced spin transfer torque (CISTT) to an ac bias in addition to a dc bias in a magnetic tunnel junction are investigated by means of the time-dependent nonquilibrium Green function technique. The time-averaged current (time-averaged CISTT) is formulated in the form of a summation of dc current (dc CISTT) multiplied by products of Bessel functions with the energy levels shifted by $m\hbar \omega _{0}$. The tunneling current can be viewed as to happen between the photonic sidebands of the two ferromagnets. The electrons can pass through the barrier easily under high frequencies but difficultly under low frequencies. The tunnel magnetoresistance almost does not vary with an ac field. It is found that the spin transfer torque, still being proportional to the electrical current under an ac bias, can be changed by varying frequency. Low frequencies could yield a rapid decrease of the spin transfer torque, while a large ac signal leads to both decrease of the electrical current and the spin torque. If only an ac bias is present, the spin transfer torque is sharply enhanced at the particular amplitude and frequency of the ac bias. A nearly linear relation between such an amplitude and frequency is observed.Comment: 13 pages,8 figure

Programming with models: modularity and abstraction provide powerful capabilities for systems biology

Mathematical models are increasingly used to understand how phenotypes emerge from systems of molecular interactions. However, their current construction as monolithic sets of equations presents a fundamental barrier to progress. Overcoming this requires modularity, enabling sub-systems to be specified independently and combined incrementally, and abstraction, enabling generic properties of biological processes to be specified independently of specific instances. These, in turn, require models to be represented as programs rather than as datatypes. Programmable modularity and abstraction enables libraries of modules to be created, which can be instantiated and reused repeatedly in different contexts with different components. We have developed a computational infrastructure that accomplishes this. We show here why such capabilities are needed, what is required to implement them and what can be accomplished with them that could not be done previously

Predicting Personality Traits from Spontaneous Modern Greek Text: Overcoming the Barriers

Part 7: First Mining Humanistic Data Workshop (MHDW 2012)International audienceThe present work aims at identifying relations between the morphosyntactic and semantic properties of an author’s writings and his/her personality traits. Machine learning schemata are used to classify an author according to the values of the Big Five traits, or predict their numerical value. Unlike related work, the current approach focuses on Modern Greek text, and makes use of limited data and resources, available at its disposal. Meta-learning and synthetic oversampling help overcome the small dataset and its imbalanced class distribution