Dan Lafferty v. Hank Galetka, Warden, Utah State Prison, Uinta Facility : Appellant\u27s Pro Se Brief

Appeal from the denial and Summary Judgement entered on Mr. Lafferty\u27s Petition for Habeas Corpus entered by the Honorable Judge Frank G. Noel of the Third Judicial District Court, Salt Lake County, State of Utah, on or about the 30th day of March 1995

Tau Physics from B Factories

Some recent $\tau$-physics results are presented from the BaBar and Belle experiments at the SLAC and KEK B factories, which produce copious numbers of $\tau$-lepton pairs. Measurements of the tau mass and lifetime allow to test lepton universality and CPT invariance, while searches for lepton-flavour violation in tau decays are powerful ways to look for physics beyond the Standard Model. In semihadronic, non-strange tau decays, the vector hadronic final state is particularly important in helping determine the hadronic corrections to the anomalous magnetic moment of the muon, while studies of strange final states are the best available ways to measure the CKM matrix element $V_{\rm us}$ and the mass of the strange quark.Comment: Presented at Charm 2006, International Workshop on Tau-Charm Physics, June 05-07 2006, Beijing, Chin

Investigation of gaseous nuclear rocket technology Quarterly progress report, 16 Sep. - 15 Dec. 1967

Fuel retention, flow characteristics, and transparence of materials studied as part of gaseous nuclear rocket investigatio

High-dimensional Ising model selection using ℓ1{\ell_1}-regularized logistic regression

We consider the problem of estimating the graph associated with a binary Ising Markov random field. We describe a method based on $\ell_1$-regularized logistic regression, in which the neighborhood of any given node is estimated by performing logistic regression subject to an $\ell_1$-constraint. The method is analyzed under high-dimensional scaling in which both the number of nodes $p$ and maximum neighborhood size $d$ are allowed to grow as a function of the number of observations $n$. Our main results provide sufficient conditions on the triple $(n,p,d)$ and the model parameters for the method to succeed in consistently estimating the neighborhood of every node in the graph simultaneously. With coherence conditions imposed on the population Fisher information matrix, we prove that consistent neighborhood selection can be obtained for sample sizes $n=\Omega(d^3\log p)$ with exponentially decaying error. When these same conditions are imposed directly on the sample matrices, we show that a reduced sample size of $n=\Omega(d^2\log p)$ suffices for the method to estimate neighborhoods consistently. Although this paper focuses on the binary graphical models, we indicate how a generalization of the method of the paper would apply to general discrete Markov random fields.Comment: Published in at http://dx.doi.org/10.1214/09-AOS691 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Measurement of the spectral function for the τ- →k-KSντ decay

open238siThe decay tau(-) -> K- K(S)v(tau) has been studied using 430 x 10(6) e(+) e(-) -> tau(+) tau(-) events produced at a center-of-mass energy around 10.6 GeV at the PEP-II collider and studied with the BABAR detector. The mass spectrum of the K- K-S system has been measured and the spectral function has been obtained. The measured branching fraction B(tau(-) -> K- K(S)v(tau)) = (0.739 +/- 0.011 (stat) +/- 0.020 (syst)) x 10(-3) is found to be in agreement with earlier measurements.openLees, J.P.; Poireau, V.; Tisserand, V.; Grauges, E.; Palano, A.; Eigen, G.; Brown, D.N.; Kolomensky, Yu.G.; Fritsch, M.; Koch, H.; Schroeder, T.; Hearty, C.; Mattison, T.S.; McKenna, J.A.; So, R.Y.; Blinov, V.E.; Buzykaev, A.R.; Druzhinin, V.P.; Golubev, V.B.; Kozyrev, E.A.; Kravchenko, E.A.; Onuchin, A.P.; Serednyakov, S.I.; Skovpen, Yu.I.; Solodov, E.P.; Todyshev, K.Yu.; Lankford, A.J.; Gary, J.W.; Long, O.; Eisner, A.M.; Lockman, W.S.; Panduro Vazquez, W.; Chao, D.S.; Cheng, C.H.; Echenard, B.; Flood, K.T.; Hitlin, D.G.; Kim, J.; Li, Y.; Miyashita, T.S.; Ongmongkolkul, P.; Porter, F.C.; Röhrken, M.; Huard, Z.; Meadows, B.T.; Pushpawela, B.G.; Sokoloff, M.D.; Sun, L.; Smith, J.G.; Wagner, S.R.; Bernard, D.; Verderi, M.; Bettoni, D.; Bozzi, C.; Calabrese, R.; Cibinetto, G.; Fioravanti, E.; Garzia, I.; Luppi, E.; Santoro, V.; Calcaterra, A.; De Sangro, R.; Finocchiaro, G.; Martellotti, S.; Patteri, P.; Peruzzi, I.M.; Piccolo, M.; Rotondo, M.; Zallo, A.; Passaggio, S.; Patrignani, C.; Lacker, H.M.; Bhuyan, B.; Mallik, U.; Chen, C.; Cochran, J.; Prell, S.; Gritsan, A.V.; Arnaud, N.; Davier, M.; Le Diberder, F.; Lutz, A.M.; Wormser, G.; Lange, D.J.; Wright, D.M.; Coleman, J.P.; Gabathuler, E.; Hutchcroft, D.E.; Payne, D.J.; Touramanis, C.; Bevan, A.J.; Di Lodovico, F.; Sacco, R.; Cowan, G.; Banerjee, Sw.; Brown, D.N.; Davis, C.L.; Denig, A.G.; Gradl, W.; Griessinger, K.; Hafner, A.; Schubert, K.R.; Barlow, R.J.; Lafferty, G.D.; Cenci, R.; Jawahery, A.; Roberts, D.A.; Cowan, R.; Robertson, S.H.; Seddon, R.M.; Dey, B.; Neri, N.; Palombo, F.; Cheaib, R.; Cremaldi, L.; Godang, R.; Summers, D.J.; Taras, P.; De Nardo, G.; Sciacca, C.; Raven, G.; Jessop, C.P.; Losecco, J.M.; Honscheid, K.; Kass, R.; Gaz, A.; Margoni, M.; Posocco, M.; Simi, G.; Simonetto, F.; Stroili, R.; Akar, S.; Ben-Haim, E.; Bomben, M.; Bonneaud, G.R.; Calderini, G.; Chauveau, J.; Marchiori, G.; Ocariz, J.; Biasini, M.; Manoni, E.; Rossi, A.; Batignani, G.; Bettarini, S.; Carpinelli, M.; Casarosa, G.; Chrzaszcz, M.; Forti, F.; Giorgi, M.A.; Lusiani, A.; Oberhof, B.; Paoloni, E.; Rama, M.; Rizzo, G.; Walsh, J.J.; Zani, L.; Smith, A.J.S.; Anulli, F.; Faccini, R.; Ferrarotto, F.; Ferroni, F.; Pilloni, A.; Piredda, G.; Bünger, C.; Dittrich, S.; Grünberg, O.; Heß, M.; Leddig, T.; Voß, C.; Waldi, R.; Adye, T.; Wilson, F.F.; Emery, S.; Vasseur, G.; Aston, D.; Cartaro, C.; Convery, M.R.; Dorfan, J.; Dunwoodie, W.; Ebert, M.; Field, R.C.; Fulsom, B.G.; Graham, M.T.; Hast, C.; Innes, W.R.; Kim, P.; Leith, D.W.G.S.; Luitz, S.; Macfarlane, D.B.; Muller, D.R.; Neal, H.; Ratcliff, B.N.; Roodman, A.; Sullivan, M.K.; Va'Vra, J.; Wisniewski, W.J.; Purohit, M.V.; Wilson, J.R.; Randle-Conde, A.; Sekula, S.J.; Ahmed, H.; Bellis, M.; Burchat, P.R.; Puccio, E.M.T.; Alam, M.S.; Ernst, J.A.; Gorodeisky, R.; Guttman, N.; Peimer, D.R.; Soffer, A.; Spanier, S.M.; Ritchie, J.L.; Schwitters, R.F.; Izen, J.M.; Lou, X.C.; Bianchi, F.; De Mori, F.; Filippi, A.; Gamba, D.; Lanceri, L.; Vitale, L.; Martinez-Vidal, F.; Oyanguren, A.; Albert, J.; Beaulieu, A.; Bernlochner, F.U.; King, G.J.; Kowalewski, R.; Lueck, T.; Nugent, I.M.; Roney, J.M.; Sobie, R.J.; Tasneem, N.; Gershon, T.J.; Harrison, P.F.; Latham, T.E.; Prepost, R.; Wu, S.L.Lees, J. P.; Poireau, V.; Tisserand, V.; Grauges, E.; Palano, A.; Eigen, G.; Brown, D. N.; Kolomensky, Yu. G.; Fritsch, M.; Koch, H.; Schroeder, T.; Hearty, C.; Mattison, T. S.; Mckenna, J. A.; So, R. Y.; Blinov, V. E.; Buzykaev, A. R.; Druzhinin, V. P.; Golubev, V. B.; Kozyrev, E. A.; Kravchenko, E. A.; Onuchin, A. P.; Serednyakov, S. I.; Skovpen, Yu. I.; Solodov, E. P.; Todyshev, K. Yu.; Lankford, A. J.; Gary, J. W.; Long, O.; Eisner, A. M.; Lockman, W. S.; Panduro Vazquez, W.; Chao, D. S.; Cheng, C. H.; Echenard, B.; Flood, K. T.; Hitlin, D. G.; Kim, J.; Li, Y.; Miyashita, T. S.; Ongmongkolkul, P.; Porter, F. C.; Röhrken, M.; Huard, Z.; Meadows, B. T.; Pushpawela, B. G.; Sokoloff, M. D.; Sun, L.; Smith, J. G.; Wagner, S. R.; Bernard, D.; Verderi, M.; Bettoni, D.; Bozzi, C.; Calabrese, R.; Cibinetto, G.; Fioravanti, E.; Garzia, I.; Luppi, E.; Santoro, V.; Calcaterra, A.; De Sangro, R.; Finocchiaro, G.; Martellotti, S.; Patteri, P.; Peruzzi, I. M.; Piccolo, M.; Rotondo, M.; Zallo, A.; Passaggio, S.; Patrignani, C.; Lacker, H. M.; Bhuyan, B.; Mallik, U.; Chen, C.; Cochran, J.; Prell, S.; Gritsan, A. V.; Arnaud, N.; Davier, M.; Le Diberder, F.; Lutz, A. M.; Wormser, G.; Lange, D. J.; Wright, D. M.; Coleman, J. P.; Gabathuler, E.; Hutchcroft, D. E.; Payne, D. J.; Touramanis, C.; Bevan, A. J.; Di Lodovico, F.; Sacco, R.; Cowan, G.; Banerjee, Sw.; Brown, D. N.; Davis, C. L.; Denig, A. G.; Gradl, W.; Griessinger, K.; Hafner, A.; Schubert, K. R.; Barlow, R. J.; Lafferty, G. D.; Cenci, R.; Jawahery, A.; Roberts, D. A.; Cowan, R.; Robertson, S. H.; Seddon, R. M.; Dey, B.; Neri, N.; Palombo, F.; Cheaib, R.; Cremaldi, L.; Godang, R.; Summers, D. J.; Taras, P.; De Nardo, G.; Sciacca, C.; Raven, G.; Jessop, C. P.; Losecco, J. M.; Honscheid, K.; Kass, R.; Gaz, A.; Margoni, M.; Posocco, M.; Simi, G.; Simonetto, F.; Stroili, R.; Akar, S.; Ben-Haim, E.; Bomben, M.; Bonneaud, G. R.; Calderini, G.; Chauveau, J.; Marchiori, G.; Ocariz, J.; Biasini, M.; Manoni, E.; Rossi, A.; Batignani, G.; Bettarini, S.; Carpinelli, M.; Casarosa, G.; Chrzaszcz, M.; Forti, F.; Giorgi, M. A.; Lusiani, A.; Oberhof, B.; Paoloni, E.; Rama, M.; Rizzo, G.; Walsh, J. J.; Zani, L.; Smith, A. J. S.; Anulli, F.; Faccini, R.; Ferrarotto, F.; Ferroni, F.; Pilloni, A.; Piredda, G.; Bünger, C.; Dittrich, S.; Grünberg, O.; Heß, M.; Leddig, T.; Voß, C.; Waldi, R.; Adye, T.; Wilson, F. F.; Emery, S.; Vasseur, G.; Aston, D.; Cartaro, C.; Convery, M. R.; Dorfan, J.; Dunwoodie, W.; Ebert, M.; Field, R. C.; Fulsom, B. G.; Graham, M. T.; Hast, C.; Innes, W. R.; Kim, P.; Leith, D. W. G. S.; Luitz, S.; Macfarlane, D. B.; Muller, D. R.; Neal, H.; Ratcliff, B. N.; Roodman, A.; Sullivan, M. K.; Va'Vra, J.; Wisniewski, W. J.; Purohit, M. V.; Wilson, J. R.; Randle-Conde, A.; Sekula, S. J.; Ahmed, H.; Bellis, M.; Burchat, P. R.; Puccio, E. M. T.; Alam, M. S.; Ernst, J. A.; Gorodeisky, R.; Guttman, N.; Peimer, D. R.; Soffer, A.; Spanier, S. M.; Ritchie, J. L.; Schwitters, R. F.; Izen, J. M.; Lou, X. C.; Bianchi, F.; De Mori, F.; Filippi, A.; Gamba, D.; Lanceri, L.; Vitale, L.; Martinez-Vidal, F.; Oyanguren, A.; Albert, J.; Beaulieu, A.; Bernlochner, F. U.; King, G. J.; Kowalewski, R.; Lueck, T.; Nugent, I. M.; Roney, J. M.; Sobie, R. J.; Tasneem, N.; Gershon, T. J.; Harrison, P. F.; Latham, T. E.; Prepost, R.; Wu, S. L

Probabilistic Constraint Logic Programming

This paper addresses two central problems for probabilistic processing models: parameter estimation from incomplete data and efficient retrieval of most probable analyses. These questions have been answered satisfactorily only for probabilistic regular and context-free models. We address these problems for a more expressive probabilistic constraint logic programming model. We present a log-linear probability model for probabilistic constraint logic programming. On top of this model we define an algorithm to estimate the parameters and to select the properties of log-linear models from incomplete data. This algorithm is an extension of the improved iterative scaling algorithm of Della-Pietra, Della-Pietra, and Lafferty (1995). Our algorithm applies to log-linear models in general and is accompanied with suitable approximation methods when applied to large data spaces. Furthermore, we present an approach for searching for most probable analyses of the probabilistic constraint logic programming model. This method can be applied to the ambiguity resolution problem in natural language processing applications.Comment: 35 pages, uses sfbart.cl

Sparse Additive Models

We present a new class of methods for high-dimensional nonparametric regression and classification called sparse additive models (SpAM). Our methods combine ideas from sparse linear modeling and additive nonparametric regression. We derive an algorithm for fitting the models that is practical and effective even when the number of covariates is larger than the sample size. SpAM is closely related to the COSSO model of Lin and Zhang (2006), but decouples smoothing and sparsity, enabling the use of arbitrary nonparametric smoothers. An analysis of the theoretical properties of SpAM is given. We also study a greedy estimator that is a nonparametric version of forward stepwise regression. Empirical results on synthetic and real data are presented, showing that SpAM can be effective in fitting sparse nonparametric models in high dimensional data

Knowledge-based Query Expansion in Real-Time Microblog Search

Since the length of microblog texts, such as tweets, is strictly limited to 140 characters, traditional Information Retrieval techniques suffer from the vocabulary mismatch problem severely and cannot yield good performance in the context of microblogosphere. To address this critical challenge, in this paper, we propose a new language modeling approach for microblog retrieval by inferring various types of context information. In particular, we expand the query using knowledge terms derived from Freebase so that the expanded one can better reflect users' search intent. Besides, in order to further satisfy users' real-time information need, we incorporate temporal evidences into the expansion method, which can boost recent tweets in the retrieval results with respect to a given topic. Experimental results on two official TREC Twitter corpora demonstrate the significant superiority of our approach over baseline methods.Comment: 9 pages, 9 figure