Model of Jovian F region ionosphere (Saturnian ionosphere in offset dipole approximation)

Researchers investigated the offset effect of Saturn's dipole on its ionosphere. The magnetic field of Saturn is primarily that of a dipole closely aligned to the rotational axis, but displaced northward from the center by a distance approximately equal to 0.05 R sub S, R sub S being the reference radius of Saturn. This offset effect would manifest itself most prominently between the ionospheric profiles in the Northern and Southern Hemispheres of Saturn

Model of Jovian F region ionosphere

To date, seven electron density profiles of the Jovian ionosphere have been furnished by the radio occultation experiments aboard the Pioneer and Voyager space probes. The data correspond to various localities (latitudes and longitudes) and times (dawn and dusk) and phases of sunspot cycle (high and low). This renders comparative studies difficult. Nevertheless, the possibility of existence of diurnal variation, equatorial anomaly, and auroral particle precipitation in the Jovian ionosphere have been put forth. The grand magnitude and depth of the equatorial anomaly, in particular, is a matter of great interest and speculation. Correct interpretations of the data and the physical processes in the complex Jovian atmospheric environment will remain a major task for the Aeronomer for decades to come. Model studies of a Jovian ionosphere created by solar EUV radiation and subjected to model ExB drifts showed that equatorial anomaly similar to that in the terrestrial ionosphere can indeed be produced in the Jovian ionosphere. However, owing to the difference in size and rotation period of the two planets and the ionic compositions, much larger drift velocities are required to produce a comparable anomaly in the Jovian atmosphere

An empirical comparison of supervised machine learning techniques in bioinformatics

Research in bioinformatics is driven by the experimental data. Current biological databases are populated by vast amounts of experimental data. Machine learning has been widely applied to bioinformatics and has gained a lot of success in this research area. At present, with various learning algorithms available in the literature, researchers are facing difficulties in choosing the best method that can apply to their data. We performed an empirical study on 7 individual learning systems and 9 different combined methods on 4 different biological data sets, and provide some suggested issues to be considered when answering the following questions: (i) How does one choose which algorithm is best suitable for their data set? (ii) Are combined methods better than a single approach? (iii) How does one compare the effectiveness of a particular algorithm to the others

Bucket shaking stops bunch dancing in Tevatron

Bunches in Tevatron are known to be longitudinally unstable: their collective oscillations, also called "dancing bunches," persist without any signs of decay. Typically, a damper is used to stop these oscillations, but recently, it was theoretically predicted that the oscillations can be stabilized by means of small bucket shaking. Dedicated measurements in Tevatron have shown that this method does stop the dancing.Comment: 3 pp. Particle Accelerator, 24th Conference (PAC'11) 2011. 28 Mar - 1 Apr 2011. New York, US

Characterisation of FAD-family folds using a machine learning approach

Flavin adenine dinucleotide (FAD) and its derivatives play a crucial role in biological processes. They are major organic cofactors and electron carriers in both enzymatic activities and biochemical pathways. We have analysed the relationships between sequence and structure of FAD-containing proteins using a machine learning approach. Decision trees were generated using the C4.5 algorithm as a means of automatically generating rules from biological databases (TOPS, CATH and PDB). These rules were then used as background knowledge for an ILP system to characterise the four different classes of FAD-family folds classified in Dym and Eisenberg (2001). These FAD-family folds are: glutathione reductase (GR), ferredoxin reductase (FR), p-cresol methylhydroxylase (PCMH) and pyruvate oxidase (PO). Each FADfamily was characterised by a set of rules. The “knowledge patterns” generated from this approach are a set of rules containing conserved sequence motifs, secondary structure sequence elements and folding information. Every rule was then verified using statistical evaluation on the measured significance of each rule. We show that this machine learning approach is capable of learning and discovering interesting patterns from large biological databases and can generate “knowledge patterns” that characterise the FADcontaining proteins, and at the same time classify these proteins into four different families

An Evaluation of the Cooperative System in the Philippines

The Philippine cooperative system has been characterized by success and failure. This paper explores the reasons for the circumstances, the problems plaguing the system and its implications on the movement in general. A review of various studies is conducted.cooperative system

Improved Pseudorandom Generators from Pseudorandom Multi-Switching Lemmas

We give the best known pseudorandom generators for two touchstone classes in unconditional derandomization: an $\varepsilon$-PRG for the class of size-$M$ depth-$d$ $\mathsf{AC}^0$ circuits with seed length $\log(M)^{d+O(1)}\cdot \log(1/\varepsilon)$, and an $\varepsilon$-PRG for the class of $S$-sparse $\mathbb{F}_2$ polynomials with seed length $2^{O(\sqrt{\log S})}\cdot \log(1/\varepsilon)$. These results bring the state of the art for unconditional derandomization of these classes into sharp alignment with the state of the art for computational hardness for all parameter settings: improving on the seed lengths of either PRG would require breakthrough progress on longstanding and notorious circuit lower bounds. The key enabling ingredient in our approach is a new \emph{pseudorandom multi-switching lemma}. We derandomize recently-developed \emph{multi}-switching lemmas, which are powerful generalizations of H{\aa}stad's switching lemma that deal with \emph{families} of depth-two circuits. Our pseudorandom multi-switching lemma---a randomness-efficient algorithm for sampling restrictions that simultaneously simplify all circuits in a family---achieves the parameters obtained by the (full randomness) multi-switching lemmas of Impagliazzo, Matthews, and Paturi [IMP12] and H{\aa}stad [H{\aa}s14]. This optimality of our derandomization translates into the optimality (given current circuit lower bounds) of our PRGs for $\mathsf{AC}^0$ and sparse $\mathbb{F}_2$ polynomials

Rule Extraction, Fuzzy ARTMAP, and Medical Databases

This paper shows how knowledge, in the form of fuzzy rules, can be derived from a self-organizing supervised learning neural network called fuzzy ARTMAP. Rule extraction proceeds in two stages: pruning removes those recognition nodes whose confidence index falls below a selected threshold; and quantization of continuous learned weights allows the final system state to be translated into a usable set of rules. Simulations on a medical prediction problem, the Pima Indian Diabetes (PID) database, illustrate the method. In the simulations, pruned networks about 1/3 the size of the original actually show improved performance. Quantization yields comprehensible rules with only slight degradation in test set prediction performance.British Petroleum (89-A-1204); Defense Advanced Research Projects Agency (AFOSR-90-0083, ONR-N00014-92-J-4015); National Science Foundation (IRI-90-00530); Office of Naval Research (N00014-91-J-4100); Air Force Office of Scientific Research (90-0083); Institute of Systems Science (National University of Singapore