Self-reproduction in k-inflation

We study cosmological self-reproduction in models of inflation driven by a scalar field $\phi$ with a noncanonical kinetic term ($k$-inflation). We develop a general criterion for the existence of attractors and establish conditions selecting a class of $k$-inflation models that admit a unique attractor solution. We then consider quantum fluctuations on the attractor background. We show that the correlation length of the fluctuations is of order $c_{s}H^{-1}$, where $c_{s}$ is the speed of sound. By computing the magnitude of field fluctuations, we determine the coefficients of Fokker-Planck equations describing the probability distribution of the spatially averaged field $\phi$. The field fluctuations are generally large in the inflationary attractor regime; hence, eternal self-reproduction is a generic feature of $k$-inflation. This is established more formally by demonstrating the existence of stationary solutions of the relevant FP equations. We also show that there exists a (model-dependent) range $\phi_{R}<\phi<\phi_{\max}$ within which large fluctuations are likely to drive the field towards the upper boundary $\phi=\phi_{\max}$, where the semiclassical consideration breaks down. An exit from inflation into reheating without reaching $\phi_{\max}$ will occur almost surely (with probability 1) only if the initial value of $\phi$ is below $\phi_{R}$. In this way, strong self-reproduction effects constrain models of $k$-inflation.Comment: RevTeX 4, 17 pages, 1 figur

Consequences of wall stiffness for a beta-soft potential

Modifications of the infinite square well E(5) and X(5) descriptions of transitional nuclear structure are considered. The eigenproblem for a potential with linear sloped walls is solved. The consequences of the introduction of sloped walls and of a quadratic transition operator are investigated.Comment: RevTeX 4, 8 pages, as published in Phys. Rev.

Argentina spectral-agronomic multitemporal data set

A multitemporal LANDSAT spectral data set was created. The data set is over five 5 nm-by-6 nm areas over Argentina and contains by field, the spectral data, vegetation type and cloud cover information

Neutron Capture Cross Sections for the Weak s Process

In past decades a lot of progress has been made towards understanding the main s-process component that takes place in thermally pulsing Asymptotic Giant Branch (AGB) stars. During this process about half of the heavy elements, mainly between 90<=A<=209 are synthesized. Improvements were made in stellar modeling as well as in measuring relevant nuclear data for a better description of the main s process. The weak s process, which contributes to the production of lighter nuclei in the mass range 56<=A<=90 operates in massive stars (M>=8Msolar) and is much less understood. A better characterization of the weak s component would help disentangle the various contributions to element production in this region. For this purpose, a series of measurements of neutron-capture cross sections have been performed on medium-mass nuclei at the 3.7-MV Van de Graaff accelerator at FZK using the activation method. Also, neutron captures on abundant light elements with A<56 play an important role for s-process nucleosynthesis, since they act as neutron poisons and affect the stellar neutron balance. New results are presented for the (n,g) cross sections of 41K and 45Sc, and revisions are reported for a number of cross sections based on improved spectroscopic information

Revealing protein-lncRNA interaction

Long non-coding RNAs (lncRNAs) are associated to a plethora of cellular functions, most of which require the interaction with one or more RNA-binding proteins (RBPs); similarly, RBPs are often able to bind a large number of different RNAs. The currently available knowledge is already drawing an intricate network of interactions, whose deregulation is frequently associated to pathological states. Several different techniques were developed in the past years to obtain protein-RNA binding data in a high-throughput fashion. In parallel, in silico inference methods were developed for the accurate computational prediction of the interaction of RBP-lncRNA pairs. The field is growing rapidly, and it is foreseeable that in the near future, the protein-lncRNA interaction network will rise, offering essential clues for a better understanding of lncRNA cellular mechanisms and their disease-associated perturbations

Computer Simulation of A New Method to Dry Lumber Using Solar Energy and Absorption Refrigeration

A mathematical model for a Solar-absorption dehumidification lumber dryer has been developed. Performance of a commercial size lumber kiln of 60 m3 (25,000 board-feet) was simulated for a southern Illinois climate for four different seasons of the year. The solar-absorption system dried yellow-poplar lumber as fast as a conventional vapor-compression dehumidification system while reducing the electrical energy costs by 85%. Capital costs and yearly fuel costs were compared to an oil-fired boiler system, a wood-fired boiler system, and a vapor-compression lumber-drying system

A novel structure-based encoding for machine-learning applied to the inference of SH3 domain specificity

MOTIVATION: Unravelling the rules underlying protein-protein and protein-ligand interactions is a crucial step in understanding cell machinery. Peptide recognition modules (PRMs) are globular protein domains which focus their binding targets on short protein sequences and play a key role in the frame of protein-protein interactions. High-throughput techniques permit the whole proteome scanning of each domain, but they are characterized by a high incidence of false positives. In this context, there is a pressing need for the development of in silico experiments to validate experimental results and of computational tools for the inference of domain-peptide interactions. RESULTS: We focused on the SH3 domain family and developed a machine-learning approach for inferring interaction specificity. SH3 domains are well-studied PRMs which typically bind proline-rich short sequences characterized by the PxxP consensus. The binding information is known to be held in the conformation of the domain surface and in the short sequence of the peptide. Our method relies on interaction data from high-throughput techniques and benefits from the integration of sequence and structure data of the interacting partners. Here, we propose a novel encoding technique aimed at representing binding information on the basis of the domain-peptide contact residues in complexes of known structure. Remarkably, the new encoding requires few variables to represent an interaction, thus avoiding the 'curse of dimension'. Our results display an accuracy >90% in detecting new binders of known SH3 domains, thus outperforming neural models on standard binary encodings, profile methods and recent statistical predictors. The method, moreover, shows a generalization capability, inferring specificity of unknown SH3 domains displaying some degree of similarity with the known data

Optimal control of circuit quantum electrodynamics in one and two dimensions

Optimal control can be used to significantly improve multi-qubit gates in quantum information processing hardware architectures based on superconducting circuit quantum electrodynamics. We apply this approach not only to dispersive gates of two qubits inside a cavity, but, more generally, to architectures based on two-dimensional arrays of cavities and qubits. For high-fidelity gate operations, simultaneous evolutions of controls and couplings in the two coupling dimensions of cavity grids are shown to be significantly faster than conventional sequential implementations. Even under experimentally realistic conditions speedups by a factor of three can be gained. The methods immediately scale to large grids and indirect gates between arbitrary pairs of qubits on the grid. They are anticipated to be paradigmatic for 2D arrays and lattices of controllable qubits.Comment: Published version

A neural strategy for the inference of SH3 domain-peptide interaction specificity

The SH3 domain family is one of the most representative and widely studied cases of so-called Peptide Recognition Modules (PRM). The polyproline II motif PxxP that generally characterizes its ligands does not reflect the complex interaction spectrum of the over 1500 different SH3 domains, and the requirement of a more refined knowledge of their specificity implies the setting up of appropriate experimental and theoretical strategies. Due to the limitations of the current technology for peptide synthesis, several experimental high-throughput approaches have been devised to elucidate protein-protein interaction mechanisms. Such approaches can rely on and take advantage of computational techniques, such as regular expressions or position specific scoring matrices (PSSMs) to pre-process entire proteomes in the search for putative SH3 targets. In this regard, a reliable inference methodology to be used for reducing the sequence space of putative binding peptides represents a valuable support for molecular and cellular biologists