Minijet-associated Dilepton Production in Ultrarelativistic Heavy Ion Collisions

Dilepton production associated with minijets is calculated in ultrarelativistic heavy ion collisions using the first order approximation of the dilepton fragmentation functions of quarks and gluons. The full QCD evolution of the fragmentation functions is also studied. We find that the dilepton pairs from the fragmentation of minijets are comparable to direct Drell-Yan at $\sqrt{s}=200$ AGeV for small dilepton invariant mass $M\sim$ 1-2 GeV/c$^2$ while dominant over a large range of mass at $\sqrt{s}=6400$ AGeV.Comment: 23 pages in RevTex, 5 figures in uuencoded files, LBL-3440

Azimuthal and single spin asymmetry in deep-inelastic lepton-nucleon scattering

We derive a general framework for describing semi-inclusive deep-inelastic lepton-nucleon scattering in terms of the unintegrated parton distributions and other higher twist parton correlations. Such a framework provides a consistent approach to the calculation of inclusive and semi-inclusive cross sections including higher twist effects. As an example, we calculate the azimuthal asymmetries to the order of 1/Q in semi-inclusive process with transversely polarized target. A non-vanishing single-spin asymmetry in the ``triggered inclusive process'' is predicted to be 1/Q suppressed with a part of the coefficient related to a moment of the Sivers function.Comment: 9 pages, 1 figur

Decoding the Encoding of Functional Brain Networks: an fMRI Classification Comparison of Non-negative Matrix Factorization (NMF), Independent Component Analysis (ICA), and Sparse Coding Algorithms

Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet mathematical constraints such as sparse coding and positivity both provide alternate biologically-plausible frameworks for generating brain networks. Non-negative Matrix Factorization (NMF) would suppress negative BOLD signal by enforcing positivity. Spatial sparse coding algorithms ($L1$ Regularized Learning and K-SVD) would impose local specialization and a discouragement of multitasking, where the total observed activity in a single voxel originates from a restricted number of possible brain networks. The assumptions of independence, positivity, and sparsity to encode task-related brain networks are compared; the resulting brain networks for different constraints are used as basis functions to encode the observed functional activity at a given time point. These encodings are decoded using machine learning to compare both the algorithms and their assumptions, using the time series weights to predict whether a subject is viewing a video, listening to an audio cue, or at rest, in 304 fMRI scans from 51 subjects. For classifying cognitive activity, the sparse coding algorithm of $L1$ Regularized Learning consistently outperformed 4 variations of ICA across different numbers of networks and noise levels (p$<$0.001). The NMF algorithms, which suppressed negative BOLD signal, had the poorest accuracy. Within each algorithm, encodings using sparser spatial networks (containing more zero-valued voxels) had higher classification accuracy (p$<$0.001). The success of sparse coding algorithms may suggest that algorithms which enforce sparse coding, discourage multitasking, and promote local specialization may capture better the underlying source processes than those which allow inexhaustible local processes such as ICA

Experimental Investigation of Blast-Pressure Attenuation by Cellular Concrete

Results from an experimental investigation of the dynamic response of cellular concrete subjected to blast-pressure loading are presented. The cellular concrete has large entrained porosity in the form of uniformly distributed air cells in a matrix of hardened cement. Under quasi-static loading, once the applied stress exceeds the crushing strength of the cellular concrete, crushing and densification of material results in an upward concave stress-strain response. The shock-tube experimental test setup used for generating blast-pressure loading in a controlled manner is described. Experimental results from the cellular concrete subjected to blast-pressure loading with pressure amplitude greater than its crushing strength indicate that a compression stress wave, which produces compaction of the material due to collapse of the cellular structure, is produced in the material. As the compaction front propagates in the material, there is a continuous decrease in its amplitude. The impulse of the blast pressure wave is conserved. When a sufficient length of the cellular concrete is present, the applied blast pressure wave is completely attenuated to a rectangular stress pulse. The transmitted stress to a substrate from cellular concrete when an applied blast pressure wave is completely attenuated resembles a rectangular stress pulse of amplitude slightly higher than the crushing strength of the material with a duration predicted by the applied blast impulse

Effective photon mass in nuclear matter and finite nuclei

Electromagnetic field in nuclear matter and nuclei are studied. In the nuclear matter, because the expectation value of the electric charge density operator is not zero, different in vacuum, the U(1) local gauge symmetry of electric charge is spontaneously broken, and consequently, the photon gains an effective mass through the Higgs mechanism. An alternative way to study the effective mass of photon is to calculate the self-energy of photon perturbatively. It shows that the effective mass of photon is about $5.42MeV$ in the symmetric nuclear matter at the saturation density $\rho_0 = 0.16fm^{-3}$ and about $2.0MeV$ at the surface of ${}^{238}U$. It seems that the two-body decay of a massive photon causes the sharp lines of electron-positron pairs in the low energy heavy ion collision experiments of ${}^{238}U+{}^{232}Th$ .Comment: 10 pages, 2 figures, 1 table, REVTEX4, submitted to Int. J. Mod. Phys.

Time Series Prediction with Recurrent Neural Networks using a Hybrid PSO-EA Algorithm

To predict the 100 missing values from the time series consisting of 5000 data given for the IJCNN 2004 time series prediction competition, we applied an architecture which automates the design of recurrent neural networks using a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of particle swarm optimization (PSO) and evolutionary algorithm (EA). By combining the searching abilities of these two global optimization methods, the evolution of individuals is no longer restricted to be in the same generation, and better performed individuals may produce offspring to replace those with poor performance. The novel algorithm is then applied to the recurrent neural network for the time series prediction. The experimental results show that our approach gives good performance in predicting the missing values from the time series

Apaf-1 overexpression partially overcomes apoptotic resistance in a cisplatin-selected HeLa cell line

AbstractInhibition of caspase-3-mediated apoptosis has been hypothesized to be associated with chemoresistance. Investigations of apoptosis revealed that cytosolic cytochrome c is associated with a complex of apoptotic protease activating factor-1 (Apaf-1), an adapter molecule, and caspase-9 to activate caspase-3. However, whether these apoptotic molecules are involved in acquired cisplatin resistance is not understood. The present work shows reduced activation of caspase-3 and apoptosis in a cisplatin-selected HeLa cell line. Ac-DEVD-CHO, a caspase-3 inhibitor, inhibited cisplatin-induced apoptosis about 60–70% in both cell lines. Ac-LEHD-CHO, a caspase-9 inhibitor or Ac-IETD-CHO, a caspase-8 inhibitor, inhibited cisplatin-induced caspase-3 activation and apoptosis similarly in both cell lines. In addition, cisplatin induced the activation of caspase-9, the upstream activator of caspase-3, in a dose-dependent manner, and the activation of caspase-9 was less induced in resistant cells. The accumulation of cytosolic cytochrome c, an activator of caspase-9, and the induction of the mitochondrial membrane-associated voltage-dependent anion channel were also reduced in cisplatin-resistant cells. However, the concentration of Bcl-2 family proteins in cisplatin-resistant cells was normal. The concentration of Apaf-1 was unaltered in both cell lines. Increasing the cellular concentration of Apaf-1 through the transient expression of the gene increased the induction of apoptosis in resistant cells, associated with enhanced activation of caspase-9, caspase-3 and DNA fragmentation factor. Regression analysis reveals that the modification factor, the ratio of the slope in the linear range of the dose–response curve with Apaf-1 to the slope without Apaf-1, is 1.5 and 4.75 in the HeLa and cisplatin-resistant HeLa cells, respectively. These results indicate that apoptosis and caspases are less induced in cisplatin-selected HeLa cells. They also suggest that ectopic overexpression of Apaf-1 may partially reverse the acquired cisplatin resistance

Cluster Structure of Disoriented Chiral Condensates in Rapidity Distribution

We study the creation of disoriented chiral condensates with some initial boundary conditions that may be expected in the relativistic heavy ion collisions. The equations of motion in the linear $\sigma$-model are solved numerically with and without a Lorentz-boost invariance. We suggest that a distinct cluster structure of coherent pion production in the rapidity distribution may emerge due to a quench and may be observed in experiments.Comment: 10 pages in LaTex, 2 uuencoded ps figures, LBL-3493