On Fast and Robust Information Spreading in the Vertex-Congest Model

This paper initiates the study of the impact of failures on the fundamental problem of \emph{information spreading} in the Vertex-Congest model, in which in every round, each of the $n$ nodes sends the same $O(\log{n})$-bit message to all of its neighbors. Our contribution to coping with failures is twofold. First, we prove that the randomized algorithm which chooses uniformly at random the next message to forward is slow, requiring $\Omega(n/\sqrt{k})$ rounds on some graphs, which we denote by $G_{n,k}$, where $k$ is the vertex-connectivity. Second, we design a randomized algorithm that makes dynamic message choices, with probabilities that change over the execution. We prove that for $G_{n,k}$ it requires only a near-optimal number of $O(n\log^3{n}/k)$ rounds, despite a rate of $q=O(k/n\log^3{n})$ failures per round. Our technique of choosing probabilities that change according to the execution is of independent interest.Comment: Appears in SIROCCO 2015 conferenc

2-Vertex Connectivity in Directed Graphs

We complement our study of 2-connectivity in directed graphs, by considering the computation of the following 2-vertex-connectivity relations: We say that two vertices v and w are 2-vertex-connected if there are two internally vertex-disjoint paths from v to w and two internally vertex-disjoint paths from w to v. We also say that v and w are vertex-resilient if the removal of any vertex different from v and w leaves v and w in the same strongly connected component. We show how to compute the above relations in linear time so that we can report in constant time if two vertices are 2-vertex-connected or if they are vertex-resilient. We also show how to compute in linear time a sparse certificate for these relations, i.e., a subgraph of the input graph that has O(n) edges and maintains the same 2-vertex-connectivity and vertex-resilience relations as the input graph, where n is the number of vertices.Comment: arXiv admin note: substantial text overlap with arXiv:1407.304

Introduction to “The social theories of classical political economy and modern economic policy”

This is the first-ever English translation of an 1891 essay by Carl Menger published in the most important newspaper of the Habsburg Empire, the Neue Freie Presse. Menger writes the piece as a defense of classical political economy in general and of Adam Smith in particular, focusing on misinterpretations of Smith’s work by the Younger Historical School in Germany. The essay reveals that Menger saw himself as working in a liberal tradition going back to Smith and classical political economy, rather than as a marginalist revolutionary who broke with classical political economy. It is a rare instance where Menger, holding the chair of economic theory at the University of Vienna, publicly expresses recommendations on economic policy. The essay represents Smith and the other classical political economists as socially motivated scholars concerned with just reforms to benefit ordinary people. Menger argues that the classical political economists were inclined toward liberal reforms but were by no means rigid exponents of laissez-faire. The essay is preceded here by an introduction authored by the translators Erwin Dekker and Stefan Kolev

Gravity with torsion as deformed BF theory

We study a family of (possibly non topological) deformations of BF theory for the Lie algebra obtained by quadratic extension of so(3, 1) by an orthogonal module. The resulting theory, called quadratically extended General Relativity (qeGR), is shown to be classically equivalent to certain models of gravity with dynamical torsion. The classical equivalence is shown to promote to a stronger notion of equivalence within the Batalin–Vilkovisky formalism. In particular, both Palatini–Cartan gravity and a deformation thereof by a dynamical torsion term, called (quadratic) generalised Holst theory, are recovered from the standard Batalin–Vilkovisky formulation of qeGR by elimination of generalised auxiliary fields.&#xD

PySurf:A Framework for Database Accelerated Direct Dynamics

The greatest restriction to the theoretical study of the dynamics of photoinduced processes is computationally expensive electronic structure calculations. Machine learning algorithms have the potential to reduce the number of these computations significantly. Here, PySurf is introduced as an innovative code framework, which is specifically designed for rapid prototyping and development tasks for data science applications in computational chemistry. It comes with powerful Plugin and Workflow engines, which allows intuitive customization for individual tasks. Data is automatically stored through the database framework, which enables additional interpolation of properties in previously evaluated regions of the conformational space. To illustrate the potential of the framework, a code for nonadiabatic surface hopping simulations based on the Landau-Zener algorithm is presented here. Deriving gradients from the interpolated potential energy surfaces allows for full-dimensional nonadiabatic surface hopping simulations using only adiabatic energies (energy only). Simulations of a pyrazine model and ab initio-based calculations of the SO2 molecule show that energy-only calculations with PySurf are able to correctly predict the nonadiabatic dynamics of these prototype systems. The results reveal the degree of sophistication, which can be achieved by the database accelerated energy-only surface hopping simulations being competitive to commonly used semiclassical approaches

HOAX: A Hyperparameter Optimization Algorithm Explorer for Neural Networks

Computational chemistry has become an important tool to predict and understand molecular properties and reactions. Even though recent years have seen a significant growth in new algorithms and computational methods that speed up quantum chemical calculations, the bottleneck for trajectory-based methods to study photoinduced processes is still the huge number of electronic structure calculations. In this work, we present an innovative solution, in which the amount of electronic structure calculations is drastically reduced, by employing machine learning algorithms and methods borrowed from the realm of artificial intelligence. However, applying these algorithms effectively requires finding optimal hyperparameters, which remains a challenge itself. Here we present an automated user-friendly framework, HOAX, to perform the hyperparameter optimization for neural networks, which bypasses the need for a lengthy manual process. The neural network generated potential energy surfaces (PESs) reduces the computational costs compared to the ab initio-based PESs. We perform a comparative investigation on the performance of different hyperparameter optimiziation algorithms, namely grid search, simulated annealing, genetic algorithm, and bayesian optimizer in finding the optimal hyperparameters necessary for constructing the well-performing neural network in order to fit the PESs of small organic molecules. Our results show that this automated toolkit not only facilitate a straightforward way to perform the hyperparameter optimization but also the resulting neural networks-based generated PESs are in reasonable agreement with the ab initio-based PESs.Comment: 18 page

Influence of the Environment on Shaping the Absorption of Monomeric Infrared Fluorescent Proteins

Infrared fluorescent proteins (iRFPs) are potential candidates for deep-tissue in vivo imaging. Here, we provide molecular-level insights into the role of the protein environment in the structural stability of the chromophore within the protein binding pocket through the flexible hydrogen-bonding network using molecular dynamics simulation. Furthermore, we present systematic excited-state analysis to characterize the nature of the first two excited states and the role of the environment in shaping the nature of the chromophore's excited states within the hybrid quantum mechanics/molecular mechanics framework. Our results reveal that the environment red-shifts the absorption of the chromophore by about 0.32 eV compared to the isolated counterpart, and besides the structural stability, the protein environment does not alter the nature of the excited state of the chromophore significantly. Our study contributes to the fundamental understanding of the excited-state processes of iRFPs in a complex environment and provides a design principle for developing iRFPs with desired spectral properties

Singlet fission in tetracene:An excited state analysis

Singlet fission is a potential mechanism to enhance the performance of current solar cells. However, the actual mechanism is still a matter of debate, with charge transfer states believed to play an essential role. The probability of the overall process can be related to the electronic coupling between the electronic states. Here, we explore the excited states of three pairs of tetracene with different relative orientation in the crystal structure showing different electronic couplings and identify the role of charge transfer states. First, a suitable theoretical method for the study of the tetracene pairs is determined by comparing time-dependent density functional theory with wave function-based methods in terms of excitation energies, so-called exciton descriptors, and graphical tools such as electron-hole correlation plots and natural transition orbitals. The results show the presence of low-lying charge transfer states in those tetracene pairs with non-zero electronic coupling, suggesting a superexchange-mediated mechanism, and high-lying charge resonance states for the pair with zero electronic coupling. Finally, the lower electron-hole correlation coefficients for pairs with non-zero coupling speak in favour of the superexchange-mediated mechanism, as a weaker Coulombic attraction due to the mixing with charge transfer states further facilitates the formation of the (Formula presented.) state from the photoexcited molecule

High spatial resolution and temporally resolved t(2) (*) mapping of normal human myocardium at 7.0 tesla: an ultrahigh field magnetic resonance feasibility study

Myocardial tissue characterization using T(2) (*) relaxation mapping techniques is an emerging application of (pre)clinical cardiovascular magnetic resonance imaging. The increase in microscopic susceptibility at higher magnetic field strengths renders myocardial T(2) (*) mapping at ultrahigh magnetic fields conceptually appealing. This work demonstrates the feasibility of myocardial T(2) (*) imaging at 7.0 T and examines the applicability of temporally-resolved and high spatial resolution myocardial T(2) (*) mapping. In phantom experiments single cardiac phase and dynamic (CINE) gradient echo imaging techniques provided similar T(2) (*) maps. In vivo studies showed that the peak-to-peak B(0) difference following volume selective shimming was reduced to approximately 80 Hz for the four chamber view and mid-ventricular short axis view of the heart and to 65 Hz for the left ventricle. No severe susceptibility artifacts were detected in the septum and in the lateral wall for T(2) (*) weighting ranging from TE = 2.04 ms to TE = 10.2 ms. For TE >7 ms, a susceptibility weighting induced signal void was observed within the anterior and inferior myocardial segments. The longest T(2) (*) values were found for anterior (T(2) (*) = 14.0 ms), anteroseptal (T(2) (*) = 17.2 ms) and inferoseptal (T(2) (*) = 16.5 ms) myocardial segments. Shorter T(2) (*) values were observed for inferior (T(2) (*) = 10.6 ms) and inferolateral (T(2) (*) = 11.4 ms) segments. A significant difference (p = 0.002) in T(2) (*) values was observed between end-diastole and end-systole with T(2) (*) changes of up to approximately 27% over the cardiac cycle which were pronounced in the septum. To conclude, these results underscore the challenges of myocardial T(2) (*) mapping at 7.0 T but demonstrate that these issues can be offset by using tailored shimming techniques and dedicated acquisition schemes