Typical dynamics of plane rational maps with equal degrees

Let $f:\mathbb{CP}^2\dashrightarrow\mathbb{CP^2}$ be a rational map with algebraic and topological degrees both equal to $d\geq 2$. Little is known in general about the ergodic properties of such maps. We show here, however, that for an open set of automorphisms $T:\mathbb{CP}^2\to\mathbb{CP}^2$, the perturbed map $T\circ f$ admits exactly two ergodic measures of maximal entropy $\log d$, one of saddle and one of repelling type. Neither measure is supported in an algebraic curve, and $T\circ f$ is `fully two dimensional' in the sense that it does not preserve any singular holomorphic foliation. Absence of an invariant foliation extends to all $T$ outside a countable union of algebraic subsets. Finally, we illustrate all of our results in a more concrete particular instance connected with a two dimensional version of the well-known quadratic Chebyshev map.Comment: Many small changes in accord with referee comments and suggestion

The structure and energetics of Cr(CO)6 and Cr(CO)5

The geometric structure of Cr(CO)6 is optimized at the modified coupled pair functional (MCPF), single and double excitation coupled-cluster (CCSD) and CCSD(T) levels of theory (including a perturbational estimate for connected triple excitations), and the force constants for the totally symmetric representation are determined. The geometry of Cr(CO)5 is partially optimized at the MCPF, CCSD, and CCSD(T) levels of theory. Comparison with experimental data shows that the CCSD(T) method gives the best results for the structures and force constants, and that remaining errors are probably due to deficiencies in the one-particle basis sets used for CO. The total binding energies of Cr(CO)6 and Cr(CO)5 are also determined at the MCPF, CCSD, and CCSD(T) levels of theory. The CCSD(T) method gives a much larger total binding energy than either the MCPF or CCSD methods. An analysis of the basis set superposition error (BSSE) at the MCPF level of treatment points out limitations in the one-particle basis used. Calculations using larger basis sets reduce the BSSE, but the total binding energy of Cr(CO)6 is still significantly smaller than the experimental value, although the first CO bond dissociation energy of Cr(CO)6 is well described. An investigation of 3s3p correlation reveals only a small effect. In the largest basis set, the total CO binding energy of Cr(CO)6 is estimated to be 140 kcal/mol at the CCSD(T) level of theory, or about 86 percent of the experimental value. The remaining discrepancy between the experimental and theoretical value is probably due to limitations in the one-particle basis, rather than limitations in the correlation treatment. In particular an additional d function and an f function on each C and O are needed to obtain quantitative results. This is underscored by the fact that even using a very large primitive set (1042 primitive functions contracted to 300 basis functions), the superposition error for the total binding energy of Cr(CO)6 is 22 kcal/mol at the MCPF level of treatment

Extracting Event Dynamics from Event-by-Event Analysis

The problem of eliminating the statistical fluctuations and extracting the event dynamics from event-by-event analysis is discussed. New moments $G_p$ (for continuous distribution), and $G_{q,p}$ (for anomalous distribution) are proposed, which are experimentally measurable and can eliminate the Poissonian type statistical fluctuations to recover the dynamical moments $C_p$ and $C_{q,p}$. In this way, the dynamical distribution of the event-averaged transverse momentum \bar{\pt} can be extracted, and the anomalous scaling of dynamical distribution, if exists, can be recovered, through event-by-event analysis of experimental data.Comment: 15 pages, 2 eps figures, Phys. Rev. C accepte

Near-wall velocity of suspended particles in microchannel flow

This contribution investigates the characteristic reduction of the particle velocity with respect to the velocity profile of a pure liquid (water) in a pressure driven flow (PDF). It is shown by simulations and experiments that particles are slowed down once their local perturbation "cloud" of the velocity field hits the wall. We show that this effect scales with the ratio of the distance of sphere's surface from the wall, a, and the radius, a, of the sphere, i.e. delta/a

Diffuse PeV neutrinos from EeV cosmic ray sources: semi-relativistic hypernova remnants in star-forming galaxies

We argue that the excess of sub-PeV/PeV neutrinos recently reported by IceCube could plausibly originate through pion-production processes in the same sources responsible for cosmic rays (CRs) with energy above the second knee around $10^{18}\,$eV. The pion production efficiency for escaping CRs that produce PeV neutrinos is required to be $\gtrsim 0.1$ in such sources. On the basis of current data, we identify semi-relativistic hypernova remants as possible sources that satisfy the requirements. By virtue of their fast ejecta, such objects can accelerate protons to EeV energies, which in turn can interact with the dense surrounding medium during propagation in their host galaxies to produce sufficient high-energy neutrinos via proton--proton ($pp$) collisions. Their accompanying gamma ray flux can remain below the diffuse isotropic gamma ray background observed by the {\it Fermi} Large Area Telescope (LAT). In order to test this scenario and discriminate from alternatives, the density of target protons/nuclei and the residence time of CRs in the interacting region are crucial uncertainties that need to be clarified. As long as the neutrinos and EeV CRs originate from the same source class, detection of $\gtrsim 10\,$PeV neutrinos may be expected within 5-10 years' operation of IceCube. Together with further observations in the PeV range, the neutrinos can help in revealing the currently unknown sources of EeV CRs.Comment: 9 pages, 2 figures; large amount of content added; PRD accepte

Towards two-stage service representation and reasoning: from lightweight annotations to comprehensive semantics

Semantics are used to mark up a wide variety of data-centric Web resources but are not used to annotate online functionality in significant numbers. That is despite considerable research dedicated to Semantic Web Services (SWS). This has led to the emergence of a new Linked Services approach with simplified and less costly to produce service models, which targets a wider audience and allows even non-SWS developers to annotate services. However, such models merely aim at enabling semantic search by humans or automated service clustering rather than automation of service tasks such as discovery or orchestration. Thus, more expressive solutions are still required to achieve automated discovery and orchestration of services. In this paper, we describe our investigation into combining the strengths of two distinct approaches to modeling semantic Web services – 'lightweight' Linked Services and 'heavyweight' SWS automation - into a coherent SWS framework. In our vision, such integration is achieved by means of model cross-referencing and model transformation and augmentation

A Look at Online Targeted Advertising in Information Systems Research

Although online targeted advertising, as a maturing research area in the discipline of information systems (IS), has great influence in practice, there have been few if any literature reviews on research in the area of online targeted advertising. In this paper, 68 articles are systematically analyzed, to assess the state of research on online targeted advertising. This paper summarizes the methodologies employed in prior research studies and uses a concept matrix to categorize the literature into three main dimensions ¬– focus on people (web users), focus on organizations (advertisers and ads brokers), and focus on technology (data mining etc.). Furthermore, this paper proposes a framework, through which important research themes and concepts are synthesized, to provide IS researchers with an overview of this research area and to identify those topics where much research has already been done and those topics where more research is needed

Caffeine-Induced Global Reductions in Resting-State BOLD Connectivity Reflect Widespread Decreases in MEG Connectivity.

In resting-state functional magnetic resonance imaging (fMRI), the temporal correlation between spontaneous fluctuations of the blood oxygenation level dependent (BOLD) signal from different brain regions is used to assess functional connectivity. However, because the BOLD signal is an indirect measure of neuronal activity, its complex hemodynamic nature can complicate the interpretation of differences in connectivity that are observed across conditions or subjects. For example, prior studies have shown that caffeine leads to widespread reductions in BOLD connectivity but were not able to determine if neural or vascular factors were primarily responsible for the observed decrease. In this study, we used source-localized magnetoencephalography (MEG) in conjunction with fMRI to further examine the origins of the caffeine-induced changes in BOLD connectivity. We observed widespread and significant (p < 0.01) reductions in both MEG and fMRI connectivity measures, suggesting that decreases in the connectivity of resting-state neuro-electric power fluctuations were primarily responsible for the observed BOLD connectivity changes. The MEG connectivity decreases were most pronounced in the beta band. By demonstrating the similarity in MEG and fMRI based connectivity changes, these results provide evidence for the neural basis of resting-state fMRI networks and further support the potential of MEG as a tool to characterize resting-state connectivity

Learning For Predictive Control: A Dual Gaussian Process Approach

An important issue in model-based control design is that an accurate dynamic model of the system is generally nonlinear, complex, and costly to obtain. This limits achievable control performance in practice. Gaussian process (GP) based estimation of system models is an effective tool to learn unknown dynamics directly from input/output data. However, conventional GP-based control methods often ignore the computational cost associated with accumulating data during the operation of the system and how to handle forgetting in continuous adaption. In this paper, we present a novel Dual Gaussian Process (DGP) based model predictive control (MPC) strategy that enables efficient use of online learning based predictive control without the danger of catastrophic forgetting. The bio-inspired DGP structure is a combination of a long-term GP and a short-term GP, where the long-term GP is used to keep the learned knowledge in memory and the short-term GP is employed to rapidly compensate unknown dynamics during online operation. Furthermore, a novel recursive online update strategy for the short-term GP is proposed to successively improve the learnt model during online operation. Effectiveness of the proposed strategy is demonstrated via numerical simulations.Comment: arXiv admin note: substantial text overlap with arXiv:2112.1166