Narrow Results

We compare some recent experimental results obtained at DESY, SLAC and Jefferson Lab., with the predictions of the statistical model, we have previously proposed. The result of this comparison is very satisfactory.

By extending the statistical distributions to the transverse degree of freedom, we account for a multiplicative factor in the Fermi–Dirac functions of the light quarks, we were led to introduce in a previous work to comply with experiment. We can also get light antiquark distributions, similar to those we proposed earlier.

As shown recently, one can obtain additional information from the measured charged particle multiplicity distributions, $P(N)$, by extracting information from the modified combinants, $C_j$. This information is encoded in their specific oscillatory behavior, which can be described only by some combinations of compound distributions such as the Binomial Distribution. This idea has been applied to $pp$ and $p\bar{p}$ processes thus far. In this note, we show that an even stronger effect is observed in the $C_j$ deduced from $e^{+}{e}^{-}$ collisions. We present its possible explanation in terms of the Generalized Multiplicity Distribution (GMD) proposed some time ago.

Multiparticle production processes provide valuable information about the mechanism of the conversion of the initial energy of projectiles into a number of secondaries by measuring their multiplicity distributions and their distributions in phase space. They therefore serve as a reference point for more involved measurements. Distributions in phase space are usually investigated using the statistical approach, very successful in general but failing in cases of small colliding systems, small multiplicities, and at the edges of the allowed phase space, in which cases the underlying dynamical effects competing with the statistical distributions take over. We discuss an alternative approach, which applies to the whole phase space without detailed knowledge of dynamics. It is based on a modification of the usual statistics by generalizing it to a superstatistical form. We stress particularly the scaling and self-similar properties of such an approach manifesting themselves as the phenomena of the log-periodic oscillations and oscillations of temperature caused by sound waves in hadronic matter. Concerning the multiplicity distributions we discuss in detail the phenomenon of the oscillatory behavior of the modified combinants apparently observed in experimental data.

We present a comprehensive insight into counting distributions from the perspective of the combinants extracted from them. In particular, we focus on cases where these combinants exhibit oscillatory behavior that can provide an invaluable new source of information about the dynamics of the process under study. We show that such behavior can be described only by specific combinations of compound distributions based on the Binomial Distribution and provide their analytical forms which can be used in further investigations and which can be helpful in the analysis of all other types of counting distributions.

Statistics of N quantum density soliton waves [B. M. Mirza, Mod. Phys. Lett. B28 (2014) 1450148] is extended here to the case of systems with symmetric wave function. Since many such systems exhibit condensation phenomena, application is made of the soliton wave statistics to investigate condensation and phase transition in quantum gases such as ⁴He and also dense systems such as the alkali atoms. Specific heat discontinuities are used to determine the condensation temperature for dense quantum gases and liquids. For the model case of helium the statistical theory is shown to predict not only the observed superfluid condensation temperature (2.17 ± 0.01 K) correctly but also the normal condensation temperature (4.21 ± 0.02 K), as well as the exact specific heat λ-profile.

Expressions of temperature-dependent perpendicular (ℑ_⊥) and parallel (ℑ_‖) moments of inertia, including isovector pairing effects, have been established using the cranking method. They are derived from recently proposed temperature-dependent gap equations. The obtained expressions generalize the conventional finite-temperature BCS (FTBCS) ones. Numerical calculations have been carried out within the framework of the schematic Richardson model as well as for nuclei such as N = Z, using the single-particle energies and eigenstates of a deformed Woods–Saxon mean-field. ℑ_⊥ and ℑ_‖ have been studied as a function of the temperature. It has been shown that the isovector pairing effect on both the perpendicular and parallel moments of inertia is non-negligible at finite temperature. These correlations must thus be taking into account in studies of warm rotating nuclei in the N ≃ Z region.

A simple model of particle creation and annihilation in an isolated assembly of particles with conserved energy and fixed volume, the Cell Model (CM), is formulated. With increasing time, particle number distribution, obtained by averaging over many systems, approaches a time-independent, steady state (SS) distribution. Dependence of the SS distribution on creation and annihilation conditional reaction probabilities is studied. The results obtained for the SS are compared with predictions of statistical mechanics within the microcanonical ensemble (MCE). In general, the predictions of both models are different. They agree only if the creation and annihilation conditional reaction probabilities are equal. This condition also results in the detailed balance in the SS.

The vehicle body-in-prime static stiffness can significantly affect vehicle NVH, ride and handling, and durability performance. Stiffness target setting is a crucial step in a vehicle design and development process. Traditionally, benchmarking data and CAE models are used to help identify and set the target, yet they have proven to be time consuming and very subjective. On the other hand, statistical models obtained through data mining or meta-modeling have been tested and applied to various problems, including design optimization. In the present paper, we attempt to utilize this technology for providing more objective vehicle static stiffness targets. Stiffness data characteristics of various vehicle classes are benchmarked. Different statistical modeling techniques are examined using the data to set stiffness targets. The validity and limitations of these techniques are also discussed.

We investigate parameter identifiability of spectral distributions of random matrices. In particular, we treat compound Wishart type and signal-plus-noise type. We show that each model is identifiable up to some kind of rotation of parameter space. Our method is based on free probability theory.

Finding elements of proteins that influence ligand binding specificity is an essential aspect of research in many fields. To assist in this effort, this paper presents two statistical models, based on the same theoretical foundation, for evaluating structural similarity among binding cavities. The first model specializes in the "unified" comparison of whole cavities, enabling the selection of cavities that are too dissimilar to have similar binding specificity. The second model enables a "regionalized" comparison of cavities within a user-defined region, enabling the selection of cavities that are too dissimilar to bind the same molecular fragments in the given region. We applied these models to analyze the ligand binding cavities of the serine protease and enolase superfamilies. Next, we observed that our unified model correctly separated sets of cavities with identical binding preferences from other sets with varying binding preferences, and that our regionalized model correctly distinguished cavity regions that are too dissimilar to bind similar molecular fragments in the user-defined region. These observations point to applications of statistical modeling that can be used to examine and, more importantly, identify influential structural similarities within binding site structure in order to better detect influences on protein–ligand binding specificity.

DNA-binding proteins are a main component of gene regulation as they activate or repress gene expression by binding to specific binding sites in target regions of genomic DNA. However, de-novo discovery of these binding sites in target regions obtained by wet-lab experiments is a challenging problem in computational biology, which has not yet been solved satisfactorily.

Here, we present a detailed description and analysis of the de-novo motif discovery tool Dispom, which has been developed for finding binding sites of DNA-binding proteins that are differentially abundant in a set of target regions compared to a set of control regions. Two additional features of Dispom are its capability of modeling positional preferences of binding sites and adjusting the length of the motif in the learning process.

Dispom yields an increased prediction accuracy compared to existing tools for de-novo motif discovery, suggesting that the combination of searching for differentially abundant motifs, inferring their positional distributions, and adjusting the motif lengths is beneficial for de-novo motif discovery. When applying Dispom to promoters of auxin-responsive genes and those of ABI3 target genes from Arabidopsis thaliana, we identify relevant binding motifs with pronounced positional distributions. These results suggest that learning motifs, their positional distributions, and their lengths by a discriminative learning principle may aid motif discovery from ChIP-chip and gene expression data.

We make Dispom freely available as part of Jstacs, an open-source Java library that is tailored to statistical sequence analysis. To facilitate extensions of Dispom, we describe its implementation using Jstacs in this manuscript. In addition, we provide a stand-alone application of Dispom at http://www.jstacs.de/index.php/Dispom for instant use.

The purpose of this paper is to exploit the geometric structure of quantum mechanics and of statistical manifolds to study the qualitative effect that the quantum properties have in the statistical description of a system. We show that the end points of geodesics in the classical setting coincide with the probability distributions that minimise Shannon’s entropy, i.e. with distributions of zero dispersion. In the quantum setting this happens only for particular initial conditions, which in turn correspond to classical submanifolds. This result can be interpreted as a geometric manifestation of the uncertainty principle.

In this note we prove certain necessary and sufficient conditions for the existence of an embedding of statistical manifolds. In particular, we prove that any smooth (C¹ resp.) statistical manifold can be embedded into the space of probability measures on a finite set. As a result, we get positive answers to the Lauritzen question on a realization of smooth (C¹ resp.) statistical manifolds as statistical models.

No abstract received.