Progress report on the Heavy Ions in Space (HIIS) experiment

One of the objectives of the Heavy Ions In Space (HIIS) experiment is to investigate heavy ions which appear at Long Duration Exposure Facility (LDEF) below the geomagnetic cutoff for fully-ionized galactic cosmic rays. Possible sources of such 'below-cutoff' particles are partially-ionized solar energetic particles, the anomalous component of cosmic rays, and magnetospherically-trapped particles. In recent years, there have also been reports of below-cutoff ions which do not appear to be from any known source. Although most of these observations are based on only a handful of ions, they have led to speculation about 'partially-ionized galactic cosmic rays' and 'near-by cosmic ray sources'. The collecting power of HIIS is order of magnitude larger than that of the instruments which reported these results, so HIIS should be able to confirm these observations and perhaps discover the source of these particles. Preliminary results on below-cutoff heavy-ions are reported. Observations to possible known sources of such ions are compared. A second objective of the HIIS experiment is to measure the elemental composition of ultraheavy galactic cosmic rays, beginning in the tin-barium region of the periodic table. A report on the status of this analysis is presented

Turbulence transition and the edge of chaos in pipe flow

The linear stability of pipe flow implies that only perturbations of sufficient strength will trigger the transition to turbulence. In order to determine this threshold in perturbation amplitude we study the \emph{edge of chaos} which separates perturbations that decay towards the laminar profile and perturbations that trigger turbulence. Using the lifetime as an indicator and methods developed in (Skufca et al, Phys. Rev. Lett. {\bf 96}, 174101 (2006)) we show that superimposed on an overall $1/\Re$-scaling predicted and studied previously there are small, non-monotonic variations reflecting folds in the edge of chaos. By tracing the motion in the edge we find that it is formed by the stable manifold of a unique flow field that is dominated by a pair of downstream vortices, asymmetrically placed towards the wall. The flow field that generates the edge of chaos shows intrinsic chaotic dynamics.Comment: 4 pages, 5 figure

An efficient algorithm for learning to rank from preference graphs

In this paper, we introduce a framework for regularized least-squares (RLS) type of ranking cost functions and we propose three such cost functions. Further, we propose a kernel-based preference learning algorithm, which we call RankRLS, for minimizing these functions. It is shown that RankRLS has many computational advantages compared to the ranking algorithms that are based on minimizing other types of costs, such as the hinge cost. In particular, we present efficient algorithms for training, parameter selection, multiple output learning, cross-validation, and large-scale learning. Circumstances under which these computational benefits make RankRLS preferable to RankSVM are considered. We evaluate RankRLS on four different types of ranking tasks using RankSVM and the standard RLS regression as the baselines. RankRLS outperforms the standard RLS regression and its performance is very similar to that of RankSVM, while RankRLS has several computational benefits over RankSVM

Matrix representations, linear transformations, and kernels for disambiguation in natural language

In the application of machine learning methods with natural language inputs, the words and their positions in the input text are some of the most important features. In this article, we introduce a framework based on a word-position matrix representation of text, linear feature transformations of the word-position matrices, and kernel functions constructed from the transformations. We consider two categories of transformations, one based on word similarities and the second on their positions, which can be applied simultaneously in the framework in an elegant way. We show how word and positional similarities obtained by applying previously proposed techniques, such as latent semantic analysis, can be incorporated as transformations in the framework. We also introduce novel ways to determine word and positional similarities. We further present efficient algorithms for computing kernel functions incorporating the transformations on the word-position matrices, and, more importantly, introduce a highly efficient method for prediction. The framework is particularly suitable to natural language disambiguation tasks where the aim is to select for a single word a particular property from a set of candidates based on the context of the word. We demonstrate the applicability of the framework to this type of tasks using context-sensitive spelling error correction on the Reuters News corpus as a model problem

Information extraction and transmission techniques for spaceborne synthetic aperture radar images

Information extraction and transmission techniques for synthetic aperture radar (SAR) imagery were investigated. Four interrelated problems were addressed. An optimal tonal SAR image classification algorithm was developed and evaluated. A data compression technique was developed for SAR imagery which is simple and provides a 5:1 compression with acceptable image quality. An optimal textural edge detector was developed. Several SAR image enhancement algorithms have been proposed. The effectiveness of each algorithm was compared quantitatively

Association of the tumour necrosis factor alpha -308 but not the interleukin 10 -627 promoter polymorphism with genetic susceptibility to primary sclerosing cholangitis

BACKGROUND AND AIMS Primary sclerosing cholangitis (PSC) is a chronic cholestatic liver disease of unknown aetiology. Abnormalities in immune regulation and genetic associations suggest that PSC is an immune mediated disease. Several polymorphisms within the tumour necrosis factor α (TNF-α) and interleukin 10 (IL-10) promoter genes have been described which influence expression of these cytokines. This study examines the possible association between polymorphisms at the −308 and −627 positions in the TNF-α and IL-10 promoter genes, respectively, and susceptibility to PSC. METHODS TNF-α −308 genotypes were studied by polymerase chain reaction (PCR) in 160 PSC patients from Norway and the UK compared with 145 ethnically matched controls. IL-10 −627 genotypes were studied by PCR in 90 PSC patients compared with 84 ethnically matched controls. RESULTS A total of 16% of Norwegian PSC patients and 12% of British PSC patients were homozygous for the TNF2 allele compared with 3% and 6% of respective controls. The TNF2 allele was present in 60% of PSC patients versus 30% of controls (ORcombined data=3.2 (95% confidence intervals (CI) 1.8–4.5); pcorr=10−5). The association between the TNF2 allele and susceptibility to PSC was independent of the presence of concurrent inflammatory bowel disease (IBD) in the PSC patients; 61% of PSC patients without IBD had TNF2 compared with 30% of controls (ORcombined data=3.2 (95% CI 1.2–9.0); pcorr=0.006 ). There was no difference in the −627 IL-10 polymorphism distributions between patients and controls in either population. The increase in TNF2 allele in PSC patients only occurs in the presence of DRB1*0301 (DR3) and B8. In the combined population data, DRB1*0301 showed a stronger association with susceptibility to PSC than both the TNF2 and B8 alleles (ORcombined data=3.8, pcorr=10−6 v ORcombined data=3.2, pcorr=10−5 vORcombined data =3.41, pcorr=10−4, respectively). CONCLUSIONS This study identified a significant association between possession of the TNF2 allele, a G→A substitution at position −308 in the TNF-α promoter, and susceptibility to PSC. This association was secondary to the association of PSC with the A1-B8-DRB1*0301-DQA1*0501-DQB1*0201 haplotype. No association was found between the IL-10 −627 promoter polymorphism and PSC

Solar mean magnetic field variability: A wavelet approach to Wilcox Solar Observatory and SOHO/Michelson Doppler Imager observations

Solar mean magnetic field (SMMF) measurements from the Wilcox Solar Observatory and with the SOHO/MDI instrument are described and analyzed. Even though two completely different methods of observation are used, the two data sets obtained show a strong similarity. Using continuous wavelet transforms, SMMF variability is found at a number of temporal scales. Detected SMMF signals with a 1–2 year period are considered to be linked to variations in the internal rotation of the Sun. Intermediate SMMF oscillations with a period of 80–200 days are probably connected to the evolution of large active regions. We also find evidence for 90 min variations with coronal mass ejections as a suggested origin

Efficient cross-validation for kernelized least-squares regression with sparse basis expansions

We propose an efficient algorithm for calculating hold-out and cross-validation (CV) type of estimates for sparse regularized least-squares predictors. Holding out H data points with our method requires O(min(H^2n,Hn^2)) time provided that a predictor with n basis vectors is already trained. In addition to holding out training examples, also some of the basis vectors used to train the sparse regularized least-squares predictor with the whole training set can be removed from the basis vector set used in the hold-out computation. In our experiments, we demonstrate the speed improvements provided by our algorithm in practice, and we empirically show the benefits of removing some of the basis vectors during the CV rounds