Graph theoretical structures in logic programs and default theories

In this paper we present a graph representation of logic programs and default theories. We show that many of the semantics proposed for logic programs can be expressed in terms of notions emerging from graph theory, establishing in this way a link between the fields. Namely the stable models, the partial stable models, and the well-founded semantics correspond respectively to the kernels, semikernels and the initial acyclic part of the associated graph. This link allows us to consider both theoretical problems (existence, uniqueness) and computational problems (tractability, algorithms, approximations) from a more abstract and rather combinatorial point of view. It also provides a clear and intuitive understanding about how conflicts between rules are resolved within the different semantics. Furthermore, we extend the basic framework developed for logic programs to the case of Default Logic by introducing the notions of partial, deterministic and well-founded extensions for default theories. These semantics capture different ways of reasoning with a default theory

Riparian woodland flora in upland rivers of Western Greece

Although natural riparian woodlands are an important feature that affects the quality of aquatic conditionsin streams and rivers, surveying riparian zone flora is rarely implemented in the Mediterraneancountries. We developed a rapid assessment method for gathering standardized plot-based woody flora andvegetation data from riparian woodlands. In 2005 we surveyed 218 streamside vegetation plots at 109 sitesin upland areas of four major rivers in mainland Greece (Alfios, Acheloos, Arachthos, and Aoos). Herewe describe the survey method and provide selected results from its initial implementation. The simplicityand effectiveness of this survey procedure supports the use of rapid site-based biodiversity surveys for riparianzones alongside aquatic status assessments

On kernels, defaults and even graphs

Extensions in prerequisite-free, disjunction-free default theories have been shown to be in direct correspondence with kernels of directed graphs; hence default theories without odd cycles always have a ``standard'' kind of an extension. We show that, although all ``standard'' extensions can be enumerated explicitly, several other problems remain intractable for such theories: Telling whether a non-standard extension exists, enumerating all extensions, and finding the minimal standard extension. We also present a new graph-theoretic algorithm, based on vertex feedback sets, for enumerating all extensions of a general prerequisite-free, disjunction-free default theory (possibly with odd cycles). The algorithm empirically performs well for quite large theories

Role of the σ\sigma-resonance in determining the convergence of chiral perturbation theory

The dimensionless parameter $\xi = M_\pi^2/(16 \pi^2 F_\pi^2)$, where $F_\pi$ is the pion decay constant and $M_\pi$ is the pion mass, is expected to control the convergence of chiral perturbation theory applicable to QCD. Here we demonstrate that a strongly coupled lattice gauge theory model with the same symmetries as two-flavor QCD but with a much lighter $\sigma$-resonance is different. Our model allows us to study efficiently the convergence of chiral perturbation theory as a function of $\xi$. We first confirm that the leading low energy constants appearing in the chiral Lagrangian are the same when calculated from the $p$-regime and the $\epsilon$-regime as expected. However, $\xi \lesssim 0.002$ is necessary before 1-loop chiral perturbation theory predicts the data within 1%. For $\xi > 0.0035$ the data begin to deviate dramatically from 1-loop chiral perturbation theory predictions. We argue that this qualitative change is due to the presence of a light $\sigma$-resonance in our model. Our findings may be useful for lattice QCD studies.Comment: 5 pages, 6 figures, revtex forma

Gauge Unification in Higher Dimensions

A complete 5-dimensional SU(5) unified theory is constructed which, on compactification on the orbifold with two different Z_2's (Z_2 and Z_2'), yields the minimal supersymmetric standard model. The orbifold accomplishes SU(5) gauge symmetry breaking, doublet-triplet splitting, and a vanishing of proton decay from operators of dimension 5. Until 4d supersymmetry is broken, all proton decay from dimension 4 and dimension 5 operators is forced to vanish by an exact U(1)_R symmetry. Quarks and leptons and their Yukawa interactions are located at the Z_2 orbifold fixed points, where SU(5) is unbroken. A new mechanism for introducing SU(5) breaking into the quark and lepton masses is introduced, which originates from the SU(5) violation in the zero-mode structure of bulk multiplets. Even though SU(5) is absent at the Z_2' orbifold fixed point, the brane threshold corrections to gauge coupling unification are argued to be negligibly small, while the logarithmic corrections are small and in a direction which improves the agreement with the experimental measurements of the gauge couplings. Furthermore, the X gauge boson mass is lowered, so that proton decay to e^+ \pi^0 is expected with a rate within about one order of magnitude of the current limit. Supersymmetry breaking occurs on the Z_2' orbifold fixed point, and is felt directly by the gauge and Higgs sectors, while squarks and sleptons acquire mass via gaugino mediation, solving the supersymmetric flavor problem.Comment: 21 pages, Latex, references added, final versio

Modélisation de la relation pluie-débit à l'aide des réseaux de neurones artificiels

Identifier tous les processus physiques élémentaires du cycle hydrologique qui peuvent avoir lieu dans un bassin versant et attribuer à chacun d'eux une description analytique permettant la prévision conduisent à des structures complexes employant un nombre élevé de paramètres difficilement accessibles. En outre, ces processus, même simplifiés, sont généralement non linéaires. Le recours à des modèles à faible nombre de variables, capables de traiter la non-linéarité, s'avère nécessaire.C'est dans cette optique que nous proposons une méthode de modélisation de la relation pluie et débit basée sur l'utilisation de réseaux neuronaux. Les performances de ces derniers dans la modélisation non linéaire ont été déjà prouvées dans plusieurs domaines scientifiques (biologie, géologie, chimie, physique). Dans ce travail, nous utilisons l'algorithme de la rétropropagation des erreurs avec un réseau à 3 couches de neurones. La fonction de transfert appliquée est de type sigmoïde. Pour prédire le débit à un moment donné, on présente à l'entrée du réseau des valeurs de pluies et de débits observés à des instants précédents. La structure du réseau est optimisée pour obtenir une bonne capacité prévisionnelle sur des données n'ayant pas participé au calage.L'application du réseau à des données pluviométriques et débimétriques du bassin de l'oued Beth permet d'obtenir de bonnes prévisions d'un ou plusieurs pas de temps, aussi bien journalières qu'hebdomadaires. Pour les données n'ayant pas participé au calage, les coefficients de corrélation entre les valeurs observées et les valeurs estimées par les différents modèles sont élevés. Ils varient de 0.72 à 0.91 pour les coefficients de corrélation de Pearson et de 0.73 à 0.95 pour les coefficients de Spearman.Identification of the elementary processes of the hydrological cycle in a drainage basin, and the comprehensive description of each of them, lead to hydrological models with a complex structure including a high number of relatively inaccessible parameters. Moreover these processes, even when simplified, are generally non-linear. Using models with a smaller number of parameters, in order to cope with non-linearity, is therefore necessary.In this perspective, we propose an artificial neural network for rainfall-runoff modeling. Performances of this method in non-linear modeling have been already demonstrated in several scientific fields (biology, geology, chemistry, physics). In the present work, we use the error back-propagation algorithm with a three-layer neural network. The transfer functions belong to the sigmoidal type at each layer. To predict the runoff at a given moment, the input variables are the rainfall and the runoff values observed for the previous time period. The structure of the network (number of hidden nodes, learning coefficient and momentum values) is optimized to guarantee a good prediction of the runoff, using a set of test data (validation set) not used in the training phase.Data compiled in our model are a ten year set of rainfall-runoff values collected by the Rabat hydraulic administration (September 1983 to April 1993) in the Beth Wadi catchment. In this study, we develop two types of models according to two different time steps (daily and weekly). The data are subdivided into two sets: a first set to train the model (training set) and a second set to test the model (validation set). For the daily timestep model, we used data of the last two years: April 1991 to April 1993. The initial 365 data (April 1991- April 1992) constitute the training set and the 365 remaining data constitute the validation set. For the weekly data (Monday to Sunday averages), we have 502 pairs of values. We worked by preserving the last 120 values as the validation set and trained the neural network with the remaining data, i.e. 382 pairs of values of weekly rainfall-runoff.Three types of estimation have been carried out:1. at instant prediction: prediction of runoff at time t taking into account rainfall values at time t, as well as runoff and rainfall values at preceding times (until t-1); 2. one step ahead prediction: prediction of runoff at time t from rainfall and runoff values at the preceding times (until t-1); 3. multistep prediction: prediction of runoff values for a period from t-jh until t, given that values of the runoff for the period 1 to t-jh-1 and values of the rainfall at times 1 to t are available (h is the timestep). The step time is daily for the at instant prediction and weekly for one step ahead and multistep predictions. The choice of input variables is determined by autocorrelation function (ACF) and partial autocorrelation function (PACF) analyses on runoff values, and cross-correlation function (CCF) analysis between rainfall and runoff values. For the at instant prediction, the input vector is composed by runoff values of the four days preceding day t, and rainfall values for the three last preceding days as well as its value on day t. For the one step ahead prediction, the input vector is composed of runoff values of the five weeks preceding week t, and rainfall values for the three preceding weeks (without considering the rainfall at time t). Finally, for the multistep prediction, the input vector is the same as for the one step ahead prediction but rainfall values include time t. The runoff values for the week t-jh+1, as well as for the following weeks, are computed by feed backing to the input vector the runoff value predicted for the preceding week.The rainfall-runoff models allow a good estimation for one or several timesteps, daily as well as weekly. In the validation set, correlation coefficients between observed and estimated values are high. In the at instant prediction, we obtain the Pearson correlation coefficient R=0.772 and the Spearman correlation coefficient CR=0.958. The weak value of R as compared to CR is explained by a few extremely high values of error of prediction. In the one step ahead prediction (R=0.887 and CR=0.782) and multistep prediction (R=0.908 and CR=0.727), the R coefficients are higher that CR. This confirms that predicted values are in good agreement with the peaks of observed values (absence of large exceptional errors). In all cases, the results obtained are better than those obtained with linear methods. The neural network models can thus be recommended for time series studies in environmental sciences

Isatuximab plus pomalidomide and dexamethasone in relapsed/refractory multiple myeloma patients with renal impairment: ICARIA-MM subgroup analysis

The randomized, phase 3 ICARIA-MM study investigated isatuximab (Isa) with pomalidomide and dexamethasone (Pd) versus Pd in patients with relapsed/refractory multiple myeloma and ?2 prior lines. This prespecified subgroup analysis examined efficacy in patients with renal impairment (RI; estimated glomerular filtration rate <60 mL/min/1.73 m²). Isa 10 mg/kg was given intravenously once weekly in cycle 1, and every 2 weeks in subsequent 28-day cycles. Patients received standard doses of Pd. Median progression-free survival (PFS) for patients with RI was 9.5 months with Isa-Pd (n = 55) and 3.7 months with Pd (n = 49; hazard ratio [HR] 0.50; 95% confidence interval [CI], 0.30-0.85). Without RI, median PFS was 12.7 months with Isa-Pd (n = 87) and 7.9 months with Pd (n = 96; HR 0.58; 95% CI, 0.38-0.88). The overall response rate (ORR) with and without RI was higher with Isa-Pd (56 and 68%) than Pd (25 and 43%). Complete renal response rates were 71.9% (23/32) with Isa-Pd and 38.1% (8/21) with Pd; these lasted ?60 days in 31.3% (10/32) and 19.0% (4/21) of patients, respectively. Isa pharmacokinetics were comparable between the subgroups, suggesting no need for dose adjustment in patients with RI. In summary, the addition of Isa to Pd improved PFS, ORR and renal response rates

Probing SO(10) symmetry breaking patterns through sfermion mass relations

We consider supersymmetric SO(10) grand unification where the unified gauge group can break to the Standard Model gauge group through different chains. The breaking of SO(10) necessarily involves the reduction of the rank, and consequent generation of non-universal supersymmetry breaking scalar mass terms. We derive squark and slepton mass relations, taking into account these non-universal contributions to the sfermion masses, which can help distinguish between the different chains through which the SO(10) gauge group breaks to the Standard Model gauge group. We then study some implications of these non-universal supersymmetry breaking scalar masses for the low energy phenomenology.Comment: 13 pages, latex using revtex4, contains 2 figures, replaced with version accepted for publicatio

Leptonic Flavor and CP Violation

We discuss how neutrino oscillation experiments can probe new sources of leptonic flavor and CP violation.Comment: 8 pages, latex, no figures. Invited talk given at KAON 2001, Pisa, Italy, June 12 - 17, 200

Composite Quarks and Leptons from Dynamical Supersymmetry Breaking without Messengers

We present new theories of dynamical SUSY breaking in which the strong interactions that break SUSY also give rise to composite quarks and leptons with naturally small Yukawa couplings. In these models, SUSY breaking is communicated directly to the composite fields without ``messenger'' interactions. The compositeness scale can be anywhere between 10 TeV and the Planck scale. These models can naturally solve the supersymmetric flavor problem, and generically predict sfermion mass unification independent from gauge unification.Comment: 27 pages, LaTeX; Clarified flavor symmetry of strong interactions; corrected overestimate of FCNC's; conclusions strengthene