March 2013 arXiv papers — page 67

Sławomir Michalik

We study the Cauchy problem for a general homogeneous linear partial differential equation in two complex variables with constant coefficients and with divergent initial data. We state necessary and sufficient conditions for the summability of formal power series solutions in terms of properties of divergent Cauchy data. We consider both the summability in o

Matthew Russo, Christopher Thompson

The theory of cold, relativistic, magnetohydrodynamic outflows is generalized by the inclusion of an intense radiation source. In some contexts, such the breakout of a gamma-ray burst jet from a star, the outflow is heated to a high temperature at a large optical depth. Eventually it becomes transparent and is pushed to a higher Lorentz factor by a combinati

Guillermo A. Blanc, Tim Weinzirl, Mimi Song, Amanda Heiderman

We present the survey design, data reduction, and spectral fitting pipeline for the VIRUS-P Exploration of Nearby Galaxies (VENGA). VENGA is an integral field spectroscopic survey, which maps the disks of 30 nearby spiral galaxies. Targets span a wide range in Hubble type, star formation activity, morphology, and inclination. The VENGA data-cubes have 5.6&#3

Ilhee Kim, Ringi Kim, Paul Seymour

A finite tree $T$ with $|V(T)| \geq 2$ is called {\it automorphism-free} if there is no non-trivial automorphism of $T$. Let $\mathcal{AFT}$ be the poset with the element set of all finite automorphism-free trees (up to graph isomorphism) ordered by $T_1 \preceq T_2$ if $T_1$ can be obtained from $T_2$ by successively deleting one leaf at a time in such a wa

Peter Kalmus, Michele Zanolin, Sergey Klimenko

We describe a general method to observationally exclude a theoretical model for gravitational wave (GW) emission from a transient astrophysical source (event) by using a null detection from a network of GW detectors. In the case of multiple astrophysical events with no GW detection, statements about individual events can be combined to increase the exclusion

Oleg Antipin, Marc Gillioz, Francesco Sannino

We propose a novel constraint on the gauge dynamics of strongly interacting gauge theories stemming from the a theorem. The inequality we suggest is used to provide a lower bound on the conformal window of four dimensional gauge theories.

Manuel Kraemer

In order to find the correct theory of quantum gravity, one has to look for observational effects in any candidate theory. Here, we focus on canonical quantum gravity and calculate the quantum-gravitational contributions to the anisotropy spectrum of the cosmic microwave background that arise from a semiclassical approximation to the Wheeler-DeWitt equation.

Paul Johnson

Double Hurwitz numbers have at least four equivalent definitions. Most naturally, they count covers of the Riemann sphere by genus g curves with certain specified ramification data. This is classically equivalent to counting certain collections of permutations. More recently, double Hurwitz numbers have been expressed as a count of certain ribbon graphs, or

Lucien Hardy

We discuss how to reconstruct quantum theory from operational postulates. In particular, the following postulates are consistent only with for classical probability theory and quantum theory. Logical Sharpness: There is a one-to-one map between pure states and maximal effects such that we get unit probability. This maximal effect does not give probability eq

A. K. Dupree

For the first time in a classical T Tauri star, we are able to trace an accretion event signaled by an hour-long enhancement of X-rays from the accretion shock and revealed through substantial sequential changes in optical emission line profiles. Downflowing turbulent material appears in H-alpha and H-beta emission. He D3 (5876 Angstrom) broadens, coupled wi

Chervin F. P. Laporte, Matthew G. Walker, Jorge Peñarrubia

We generate stellar distribution functions (DFs) in triaxial haloes in order to examine the reliability of slopes $Γ\equiv Δ{\rm log} M / Δ{\rm log} r$ inferred by applying mass estimators of the form $M\propto R_eσ^2$ (i.e. assuming spherical symmetry, where $R_e$ and $σ$ are luminous effective radius and global velocity dispersion, respectively) to two ste

G. A. Ogrean, M. Brüggen, R. J. van Weeren, H. Röttgering

The cluster 1RXS J0603.3+4214 is a merging galaxy cluster that hosts three radio relics and a giant radio halo. The northern relic, the Toothbrush, is 1.9-Mpc long and has an unusual linear morphology. According to simple diffusive shock acceleration theory, its radio spectral index indicates a Mach number of 3.3-4.6. Here, we present results from a deep XMM

Martin A. Guerrero, Orsola De Marco

The occurrence of stellar wind in central stars planetary nebulae (CSPNe) can be revealed by the presence of P Cygni profiles of high-excitation lines overimposed on the stellar continuum. We have examined the entire FUSE archive and merged all useful observations of CSPNe to produce the highest quality spectra that can be used to assess the occurrence of st

S. I. Sadavoy, J. Di Francesco, D. Johnstone, M. J. Currie

We present Herschel observations from the Herschel Gould Belt Survey and SCUBA-2 science verification observations from the JCMT Gould Belt Survey of the B1 clump in the Perseus molecular cloud. We determined the dust emissivity index using four different techniques to combine the Herschel PACS+SPIRE data at 160 - 500 microns with the SCUBA-2 data at 450 mic

Christoph Englert, Matthew McCullough

Many beyond the Standard Model (BSM) scenarios involve Higgs couplings to additional electroweak fields. It is well established that these new fields may modify Higgs gamma-gamma and gamma-Z decays at one-loop. However, one unexplored aspect of such scenarios is that by electroweak symmetry one should also expect modifications to the Higgs Z-Z coupling at on

Oleg Antipin, Marc Gillioz, Esben Mølgaard, Francesco Sannino

We investigate the a theorem for nonsupersymmetric gauge-Yukawa theories beyond the leading order in perturbation theory. The exploration is first performed in a model-independent manner and then applied to a specific relevant example. Here, a rich fixed point structure appears including the presence of a merging phenomenon between non-trivial fixed points f

Andrew C. Potter, Liang Fu

We propose a Josephson junction setup based on a topological insulator (TI) thin film to detect Majorana states, which exploits the unique helical and extended nature of the TI surface state. When the magnetic flux through the junction is close to an integer number of flux quanta, Majorana states, present on both surfaces of the film, give rise to a narrow p

Tatsuma Nishioka, Kazuya Yonekura

The coefficient tau_{RR} of the two-point function of the superconformal U(1)_R currents of N=2 SCFTs in three-dimensions is recently shown to be obtained by differentiating the partition function on a squashed three-sphere with respect to the squashing parameter. With this method, we compute the tau_{RR} for N=2 Wess-Zumino models and SQCD numerically for s

Dhagash Mehta, Daniel A. Stariolo, Michael Kastner

We study the 3-spin spherical model with mean-field interactions and Gaussian random couplings. For moderate system sizes of up to 20 spins, we obtain all stationary points of the energy landscape by means of the numerical polynomial homotopy continuation method. On the basis of these stationary points, we analyze the complexity and other quantities related

Hong Xu, Yen-Teh Hsia, Philippe Smets

In this paper, we present a decision support system based on belief functions and the pignistic transformation. The system is an integration of an evidential system for belief function propagation and a valuation-based system for Bayesian decision analysis. The two subsystems are connected through the pignistic transformation. The system takes as inputs the

Nic Wilson

This paper examines the concept of a combination rule for belief functions. It is shown that two fairly simple and apparently reasonable assumptions determine Dempster's rule, giving a new justification for it.

Pei Wang

In a probability-based reasoning system, Bayes' theorem and its variations are often used to revise the system's beliefs. However, if the explicit conditions and the implicit conditions of probability assignments `me properly distinguished, it follows that Bayes' theorem is not a generally applicable revision rule. Upon properly distinguishing be

Carl G. Wagner, Bruce Tonn

An elaboration of Dempster's method of constructing belief functions suggests a broadly applicable strategy for constructing lower probabilities under a variety of evidentiary constraints.

Fengming Song, Ping Liang

In this paper, the concept of possibilistic evidence which is a possibility distribution as well as a body of evidence is proposed over an infinite universe of discourse. The inference with possibilistic evidence is investigated based on a unified inference framework maintaining both the compatibility of concepts and the consistency of the probability logic.

Philippe Smets

Jeffrey's rule of conditioning has been proposed in order to revise a probability measure by another probability function. We generalize it within the framework of the models based on belief functions. We show that several forms of Jeffrey's conditionings can be defined that correspond to the geometrical rule of conditioning and to Dempster's rul

Serafin Moral, Luis M. de Campos

This paper presents a procedure to determine a complete belief function from the known values of belief for some of the subsets of the frame of discerment. The method is based on the principle of minimum commitment and a new principle called the focusing principle. This additional principle is based on the idea that belief is specified for the most relevant

Jurg Kohlas, Paul-Andre Monney

The classical propositional assumption-based model is extended to incorporate probabilities for the assumptions. Then it is placed into the framework of evidence theory. Several authors like Laskey, Lehner (1989) and Provan (1990) already proposed a similar point of view, but the first paper is not as much concerned with mathematical foundations, and Provan&

Jiwen W. Guan, David A. Bell

Evidential reasoning is now a leading topic in Artificial Intelligence. Evidence is represented by a variety of evidential functions. Evidential reasoning is carried out by certain kinds of fundamental operation on these functions. This paper discusses two of the basic operations on evidential functions, the discount operation and the well-known orthogonal s

Dimiter Driankov, Jerome Lang

A key issue in the handling of temporal data is the treatment of persistence; in most approaches it consists in inferring defeasible confusions by extrapolating from the actual knowledge of the history of the world; we propose here a gradual modelling of persistence, following the idea that persistence is decreasing (the further we are from the last time poi

Craig Boutilier

We present a semantics for adding uncertainty to conditional logics for default reasoning and belief revision. We are able to treat conditional sentences as statements of conditional probability, and express rules for revision such as "If A were believed, then B would be believed to degree p." This method of revision extends conditionalization by all

Robert F. Bordley

Shafer's theory of belief and the Bayesian theory of probability are two alternative and mutually inconsistent approaches toward modelling uncertainty in artificial intelligence. To help reduce the conflict between these two approaches, this paper reexamines expected utility theory-from which Bayesian probability theory is derived. Expected utility theor

Michael S. K. M. Wong, Z. W. Wang

This paper introduces a qualitative measure of ambiguity and analyses its relationship with other measures of uncertainty. Probability measures relative likelihoods, while ambiguity measures vagueness surrounding those judgments. Ambiguity is an important representation of uncertain knowledge. It deals with a different, type of uncertainty modeled by subject

Simon Parsons, E. H. Mamdani

In this paper some initial work towards a new approach to qualitative reasoning under uncertainty is presented. This method is not only applicable to qualitative probabilistic reasoning, as is the case with other methods, but also allows the qualitative propagation within networks of values based upon possibility theory and Dempster-Shafer evidence theory. T

John Fox, Paul J. Krause, Morten Elvang-Gøransson

Argumentation is the process of constructing arguments about propositions, and the assignment of statements of confidence to those propositions based on the nature and relative strength of their supporting arguments. The process is modelled as a labelled deductive system, in which propositions are doubly labelled with the grounds on which they are based and

Adnan Darwiche

A major reason behind the success of probability calculus is that it possesses a number of valuable tools, which are based on the notion of probabilistic independence. In this paper, I identify a notion of logical independence that makes some of these tools available to a class of propositional databases, called argument databases. Specifically, I suggest a

Salem Benferhat, Didier Dubois, Henri Prade

This paper presents and discusses several methods for reasoning from inconsistent knowledge bases. A so-called argumentative-consequence relation taking into account the existence of consistent arguments in favor of a conclusion and the absence of consistent arguments in favor of its contrary, is particularly investigated. Flat knowledge bases, i.e. without

Nevin Lianwen Zhang, Runping Qi, David L. Poole

To determine the value of perfect information in an influence diagram, one needs first to modify the diagram to reflect the change in information availability, and then to compute the optimal expected values of both the original diagram and the modified diagram. The value of perfect information is the difference between the two optimal expected values. This

Tom S. Verma, Judea Pearl

In order to find a causal explanation for data presented in the form of covariance and concentration matrices it is necessary to decide if the graph formed by such associations is a projection of a directed acyclic graph (dag). We show that the general problem of deciding whether such a dag exists is NP-complete.

Ross D. Shachter, Pierre Ndilikilikesha

The potential influence diagram is a generalization of the standard "conditional" influence diagram, a directed network representation for probabilistic inference and decision analysis [Ndilikilikesha, 1991]. It allows efficient inference calculations corresponding exactly to those on undirected graphs. In this paper, we explore the relationship betw

Sumit Sarkar

Tree structures have been shown to provide an efficient framework for propagating beliefs [Pearl,1986]. This paper studies the problem of finding an optimal approximating tree. The star decomposition scheme for sets of three binary variables [Lazarsfeld,1966; Pearl,1986] is shown to enhance the class of probability distributions that can support tree structu

Carlos Rojas-Guzman, Mark A. Kramer

Bayesian belief networks can be used to represent and to reason about complex systems with uncertain, incomplete and conflicting information. Belief networks are graphs encoding and quantifying probabilistic dependence and conditional independence among variables. One type of reasoning of interest in diagnosis is called abductive inference (determination of

David L. Poole

This paper discusses how conflicts (as used by the consistency-based diagnosis community) can be adapted to be used in a search-based algorithm for computing prior and posterior probabilities in discrete Bayesian Networks. This is an "anytime" algorithm, that at any stage can estimate the probabilities and give an error bound. Whereas the most popula

Todd Michael Mansell

This paper describes ongoing research into planning in an uncertain environment. In particular, it introduces U-Plan, a planning system that constructs quantitatively ranked plans given an incomplete description of the state of the world. U-Plan uses a DempsterShafer interval to characterise uncertain and incomplete information about the state of the world.

Zhaoyu Li, Bruce D'Ambrosio

Given a belief network with evidence, the task of finding the I most probable explanations (MPE) in the belief network is that of identifying and ordering the I most probable instantiations of the non-evidence nodes of the belief network. Although many approaches have been proposed for solving this problem, most work only for restricted topologies (i.e., sin

Paul E. Lehner, Azar Sadigh

Two algorithms are presented for "compiling" influence diagrams into a set of simple decision rules. These decision rules define simple-to-execute, complete, consistent, and near-optimal decision procedures. These compilation algorithms can be used to derive decision procedures for human teams solving time constrained decision problems.

Marek J. Druzdzel, Max Henrion

Intercausal reasoning is a common inference pattern involving probabilistic dependence of causes of an observed common effect. The sign of this dependence is captured by a qualitative property called product synergy. The current definition of product synergy is insufficient for intercausal reasoning where there are additional uninstantiated causes of the com

Thomas L. Dean, Leslie Pack Kaelbling, Jak Kirman, Ann Nicholson

We describe a method for time-critical decision making involving sequential tasks and stochastic processes. The method employs several iterative refinement routines for solving different aspects of the decision making problem. This paper concentrates on the meta-level control problem of deliberation scheduling, allocating computational resources to these rou

Bruce D'Ambrosio

Propositional representation services such as truth maintenance systems offer powerful support for incremental, interleaved, problem-model construction and evaluation. Probabilistic inference systems, in contrast, have lagged behind in supporting this incrementality typically demanded by problem solvers. The problem, we argue, is that the basic task of proba

Peter Che, Richard E. Neapolitan, James Kenevan, Martha Evens

In recent years the belief network has been used increasingly to model systems in Al that must perform uncertain inference. The development of efficient algorithms for probabilistic inference in belief networks has been a focus of much research in AI. Efficient algorithms for certain classes of belief networks have been developed, but the problem of reportin

Lisa J. Burnell, Eric J. Horvitz

We describe the integration of logical and uncertain reasoning methods to identify the likely source and location of software problems. To date, software engineers have had few tools for identifying the sources of error in complex software packages. We describe a method for diagnosing software problems through combining logical and uncertain reasoning analys

Soe-Tsyr Yuan

Numerous methods for probabilistic reasoning in large, complex belief or decision networks are currently being developed. There has been little research on automating the dynamic, incremental construction of decision models. A uniform value-driven method of decision model construction is proposed for the hierarchical complete diagnosis. Hierarchical complete

Joe Suzuki

This paper addresses learning stochastic rules especially on an inter-attribute relation based on a Minimum Description Length (MDL) principle with a finite number of examples, assuming an application to the design of intelligent relational database systems. The stochastic rule in this paper consists of a model giving the structure like the dependencies of a

Moninder Singh, Marco Valtorta

Previous algorithms for the construction of Bayesian belief network structures from data have been either highly dependent on conditional independence (CI) tests, or have required an ordering on the nodes to be supplied by the user. We present an algorithm that integrates these two approaches - CI tests are used to generate an ordering on the nodes from the

Ron Musick

As belief networks are used to model increasingly complex situations, the need to automatically construct them from large databases will become paramount. This paper concentrates on solving a part of the belief network induction problem: that of learning the quantitative structure (the conditional probabilities), given the qualitative structure. In particula

Wai Lam, Fahiem Bacchus

In previous work we developed a method of learning Bayesian Network models from raw data. This method relies on the well known minimal description length (MDL) principle. The MDL principle is particularly well suited to this task as it allows us to tradeoff, in a principled way, the accuracy of the learned network against its practical usefulness. In this pa

John W. Egar, Mark A. Musen

One of the most difficult aspects of modeling complex dilemmas in decision-analytic terms is composing a diagram of relevance relations from a set of domain concepts. Decision models in domains such as medicine, however, exhibit certain prototypical patterns that can guide the modeling process. Medical concepts can be classified according to semantic types t

Marie desJardins

PAGODA (Probabilistic Autonomous Goal-Directed Agent) is a model for autonomous learning in probabilistic domains [desJardins, 1992] that incorporates innovative techniques for using the agent's existing knowledge to guide and constrain the learning process and for representing, reasoning with, and learning probabilistic knowledge. This paper describes t

Fahiem Bacchus

We present a mechanism for constructing graphical models, specifically Bayesian networks, from a knowledge base of general probabilistic information. The unique feature of our approach is that it uses a powerful first-order probabilistic logic for expressing the general knowledge base. This logic allows for the representation of a wide range of logical and p

Sampath Srinivas

The Noisy-Or model is convenient for describing a class of uncertain relationships in Bayesian networks [Pearl 1988]. Pearl describes the Noisy-Or model for Boolean variables. Here we generalize the model to nary input and output variables and to arbitrary functions other than the Boolean OR function. This generalization is a useful modeling aid for construc

Solomon Eyal Shimony

Relevance-based explanation is a scheme in which partial assignments to Bayesian belief network variables are explanations (abductive conclusions). We allow variables to remain unassigned in explanations as long as they are irrelevant to the explanation, where irrelevance is defined in terms of statistical independence. When multiple-valued variables exist i

Prakash P. Shenoy

Valuation networks have been proposed as graphical representations of valuation-based systems (VBSs). The VBS framework is able to capture many uncertainty calculi including probability theory, Dempster-Shafer's belief-function theory, Spohn's epistemic belief theory, and Zadeh's possibility theory. In this paper, we show how valuation networks e

William B. Poland, Ross D. Shachter

Problems of probabilistic inference and decision making under uncertainty commonly involve continuous random variables. Often these are discretized to a few points, to simplify assessments and computations. An alternative approximation is to fit analytically tractable continuous probability distributions. This approach has potential simplicity and accuracy a

Kim-Leng Poh, Eric J. Horvitz

We investigate the value of extending the completeness of a decision model along different dimensions of refinement. Specifically, we analyze the expected value of quantitative, conceptual, and structural refinement of decision models. We illustrate the key dimensions of refinement with examples. The analyses of value of model refinement can be used to focus

Kim-Leng Poh, Michael R. Fehling

Probabilistic conceptual network is a knowledge representation scheme designed for reasoning about concepts and categorical abstractions in utility-based categorization. The scheme combines the formalisms of abstraction and inheritance hierarchies from artificial intelligence, and probabilistic networks from decision analysis. It provides a common framework

Izhar Matzkevich, Bruce Abramson

This paper identifies and solves a new optimization problem: Given a belief network (BN) and a target ordering on its variables, how can we efficiently derive its minimal I-map whose arcs are consistent with the target ordering? We present three solutions to this problem, all of which lead to directed acyclic graphs based on the original BN's recursive b

Izhar Matzkevich, Bruce Abramson

One topic that is likely to attract an increasing amount of attention within the Knowledge-base systems research community is the coordination of information provided by multiple experts. We envision a situation in which several experts independently encode information as belief networks. A potential user must then coordinate the conclusions and recommendati

John F. Lemmer

Causal Models are like Dependency Graphs and Belief Nets in that they provide a structure and a set of assumptions from which a joint distribution can, in principle, be computed. Unlike Dependency Graphs, Causal Models are models of hierarchical and/or parallel processes, rather than models of distributions (partially) known to a model builder through some s

Kathryn Blackmond Laskey

When eliciting probability models from experts, knowledge engineers may compare the results of the model with expert judgment on test scenarios, then adjust model parameters to bring the behavior of the model more in line with the expert's intuition. This paper presents a methodology for analytic computation of sensitivity values to measure the impact of

Eric J. Horvitz, Adrian Klein

We take a utility-based approach to categorization. We construct generalizations about events and actions by considering losses associated with failing to distinguish among detailed distinctions in a decision model. The utility-based methods transform detailed states of the world into more abstract categories comprised of disjunctions of the states. We show

Morten Elvang-Gøransson, Paul J. Krause, John Fox

From an inconsistent database non-trivial arguments may be constructed both for a proposition, and for the contrary of that proposition. Therefore, inconsistency in a logical database causes uncertainty about which conclusions to accept. This kind of uncertainty is called logical uncertainty. We define a concept of "acceptability", which induces a me

Didier Dubois, Henri Prade

Relational models for diagnosis are based on a direct description of the association between disorders and manifestations. This type of model has been specially used and developed by Reggia and his co-workers in the late eighties as a basic starting point for approaching diagnosis problems. The paper proposes a new relational model which includes Reggia'

Francisco Javier Diez

Spiegelhalter and Lauritzen [15] studied sequential learning in Bayesian networks and proposed three models for the representation of conditional probabilities. A forth model, shown here, assumes that the parameter distribution is given by a product of Gaussian functions and updates them from the _ and _r messages of evidence propagation. We also generalize

Paul Dagum, Adam Galper

The inherent intractability of probabilistic inference has hindered the application of belief networks to large domains. Noisy OR-gates [30] and probabilistic similarity networks [18, 17] escape the complexity of inference by restricting model expressiveness. Recent work in the application of belief-network models to time-series analysis and forecasting [9,

Peter J. Regan

This paper describes a normative system design that incorporates diagnosis, dynamic evolution, decision making, and information gathering. A single influence diagram demonstrates the design's coherence, yet each activity is more effectively modeled and evaluated separately. Application to offshore oil platforms illustrates the design. For this applicatio

Paul Dagum, Adam Galper

Dynamic network models (DNMs) are belief networks for temporal reasoning. The DNM methodology combines techniques from time series analysis and probabilistic reasoning to provide (1) a knowledge representation that integrates noncontemporaneous and contemporaneous dependencies and (2) methods for iteratively refining these dependencies in response to the eff

Steven M. LaValle, Seth A. Hutchinson

In this paper we address the uncertainty issues involved in the low-level vision task of image segmentation. Researchers in computer vision have worked extensively on this problem, in which the goal is to partition (or segment) an image into regions that are homogeneous or uniform in some sense. This segmentation is often utilized by some higher level proces

Harold P. Lehmann, Ross D. Shachter

Influence diagrams are ideal knowledge representations for Bayesian statistical models. However, these diagrams are difficult for end users to interpret and to manipulate. We present a user-based architecture that enables end users to create and to manipulate the knowledge representation. We use the problem of physicians' interpretation of two-arm parall

Gregory M. Provan

This paper addresses the tradeoffs which need to be considered in reasoning using probabilistic network representations, such as Influence Diagrams (IDs). In particular, we examine the tradeoffs entailed in using Temporal Influence Diagrams (TIDs) which adequately capture the temporal evolution of a dynamic system without prohibitive data and computational r

Scott A. Musman, L. W. Chang

The problems associated with scaling involve active and challenging research topics in the area of artificial intelligence. The purpose is to solve real world problems by means of AI technologies, in cases where the complexity of representation of the real world problem is potentially combinatorial. In this paper, we present a novel approach to cope with the

Russ B. Altman

We have developed a general Bayesian algorithm for determining the coordinates of points in a three-dimensional space. The algorithm takes as input a set of probabilistic constraints on the coordinates of the points, and an a priori distribution for each point location. The output is a maximum-likelihood estimate of the location of each point. We use the ext

Judea Pearl

The primary theme of this investigation is a decision theoretic account of conditional ought statements (e.g., "You ought to do A, if C") that rectifies glaring deficiencies in classical deontic logic. The resulting account forms a sound basis for qualitative decision theory, thus providing a framework for qualitative planning under uncertainty. In p

Marek J. Druzdzel, Herbert A. Simon

We address the problem of causal interpretation of the graphical structure of Bayesian belief networks (BBNs). We review the concept of causality explicated in the domain of structural equations models and show that it is applicable to BBNs. In this view, which we call mechanism-based, causality is defined within models and causal asymmetries arise when mech

O. V. Ogloblya, G. M. Kuznetsova

We analyzed the effects of a spin voltage as well as a conventionally applied voltage in a QD system with a different number of quantum states in the dot region in presence of Coulombic interaction between the quantum dot and two leads. We extended the NEGF treatment developed for noninteracting leads onto the case of four quantum states $m =\{σ,λ\}=\{\pm,\p

Jonathan Zrake, Andrew I. MacFadyen

The simultaneous detection of electromagnetic and gravitational wave emission from merging neutron star binaries would aid greatly in their discovery and interpretation. By studying turbulent amplification of magnetic fields in local high-resolution simulations of neutron star merger conditions, we demonstrate that magnetar-level (~10^16) G fields are presen

M. Shifman, A. Yung

We derive an exact unified formula for all condensates (quark and monopole) in the hybrid r vacua in N=2 supersymmetric QCD slightly deformed by a μA^2 term. The gauge group is assumed to be U(N) and the number of the quark flavors N_f subject to the condition N< N_f < 2N. In the r vacua r quarks and N-r-1 monopoles from non-overlapping subgroups of U(N) dev

Dan Petersen

We give a new proof of formality of the operad of little disks. The proof makes use of an operadic version of a simple formality criterion for commutative differential graded algebras due to Sullivan. We see that formality is a direct consequence of the fact that the Grothendieck-Teichmüller group operates on the chain operad of little disks.

Yuan-Yen Tai, Jian-Xin Zhu, Matthias J. Graf, C. S. Ting

We develop a minimal multiorbital tight-binding model with realistic hopping parameters. The model breaks the symmetry of the tetragonal point group by lowering it from $C_4$ to $D_{2d}$, which accurately describes the Fermi surface evolution of the electron-doped BaFe$_{2-x}$Co$_x$As$_2$ and hole-doped Ba$_{1-y}$K$_y$Fe$_2$As$_2$ compounds. An investigation

Ralph Brinks

Secular trends of the incidence of NCDs are especially important as they indicate changes of the risk profile of a population. The article describes a method for detecting secular trends in the incidence from a series of prevalence data - without requiring costly follow-up studies or running a register. After describing the theory, the method is applied to t

C. Brenner Mariotto, M. V. T. Machado

In this work we investigate the inclusive and exclusive photoproduction of dileptons, which is relevant for the physics programme to be studied in the proposed electron-proton collider, the LHeC. In the inclusive case, the process is sensitive to the parton distribution functions in the photon whereas the exclusive channel is connected to the small-$x$ QCD d

Riccardo Comin, Andrea Damascelli

Angle-resolved photoemission spectroscopy (ARPES) is one of the most direct methods of studying the electronic structure of solids. By measuring the kinetic energy and angular distribution of the electrons photoemitted from a sample illuminated with sufficiently high-energy radiation, one can gain information on both the energy and momentum of the electrons

J. -F. Hochedez, C. Timmermans, A. Hauchecorne, M. Meftah

When Charge Coupled Devices are used for scientific observations, their dark signal is a hindrance. In their pristine state, most CCD pixels are `cool&#39;; they exhibit low, quasi uniform dark current, which can be estimated and corrected for. In space, after having been hit by an energetic particle, pixels can turn `hot&#39;. They start delivering excessiv

Nanny Wermuth, D. R. Cox

With graphical Markov models, one can investigate complex dependences, summarize some results of statistical analyses with graphs and use these graphs to understand implications of well-fitting models. The models have a rich history and form an area that has been intensively studied and developed in recent years. We give a brief review of the main concepts a

Victoria Zinde-Walsh

The paper considers probability distribution, density, conditional distribution and density and conditional moments as well as their kernel estimators in spaces of generalized functions. This approach does not require restrictions on classes of distributions common in nonparametric estimation. Density in usual function spaces is not well-posed; this paper es

K. D. Leka, G. Barnes, A. C. Birch, I. Gonzalez-Hernandez

This first paper in a series describes the design of a study testing whether pre-appearance signatures of solar magnetic active regions were detectable using various tools of local helioseismology. The ultimate goal is to understand flux-emergence mechanisms by setting observational constraints on pre-appearance subsurface changes, for comparison with result

Thomas K. Gaisser

The flux of high-energy (>GeV) neutrinos consists primarily of those produced by cosmic-ray interactions in the atmosphere. The contribution from extraterrestrial sources is still unknown. Current limits suggest that the observed spectrum is dominated by atmospheric neutrinos up to at least 100 TeV. The contribution of charmed hadrons to the flux of atmosphe

Raphael Flauger, Daniel Green, Rafael A. Porto

The n-point correlation functions in single-field inflation obey a set of consistency conditions in the exact squeezed limit which are not present in multi-field models, and thus are powerful tools to distinguish between the two. However, these consistency conditions may be violated for a finite range of scales in single-field models, for example by departur

L. Fang, M. Hoener, O. Gessner, F. Tarantelli

We investigate the creation of double K-shell holes in N2 molecules via sequential absorption of two photons on a timescale shorter than the core-hole lifetime by using intense x-ray pulses from the Linac Coherent Light Source free electron laser. The production and decay of these states is characterized by photoelectron spectroscopy and Auger electron spect

H. Quevedo, F. Nettel, C. S. Lopez-Monsalvo, A. Bravetti

In this work we employ a recently devised metric within the Geometrothermodynamics program to study ordinary thermodynamic systems. The new feature of this metric is that, in addition to Legendre symmetry, it exhibits invariance under a change of representation. This metric was derived in a previous work by the authors while addressing the problem of the con

Péter Kálmán, Dániel P. Kis, Tamás Keszthelyi

The S-matrix element and the cross section of the laser-assisted nuclear photoeffect are recalculated in a gauge invariant manner taking into account the effect of the Coulomb field of the remainder nucleus. The γ-photon energy dependence of the laser free cross section obtained in the plane wave and long-wavelength Coulomb-Volkov approximations are compared

German Urbina-Villalba, Kareem Rahn-Chique

The stability of alkane-in-water nanoemulsions during the sub-stationary regime is studied by means of Emulsion Stability Simulations (ESS). The effects of Ostwald ripening, flocculation, coalescence, gravity, and hydration forc- es are considered. According to these calculations flocculation and coalescence are predominant during the first few seconds after