January 2013 arXiv papers — page 23

Luigi Portinale, Andrea Bobbio

Bayesian Networks (BN) provide robust probabilistic methods of reasoning under uncertainty, but despite their formal grounds are strictly based on the notion of conditional dependence, not much attention has been paid so far to their use in dependability analysis. The aim of this paper is to propose BN as a suitable tool for dependability analysis, by challe

Avi Pfeffer, Daphne Koller, Brian Milch, Ken T. Takusagawa

In previous work, we pointed out the limitations of standard Bayesian networks as a modeling framework for large, complex domains. We proposed a new, richly structured modeling language, {em Object-oriented Bayesian Netorks}, that we argued would be able to deal with such domains. However, it turns out that OOBNs are not expressive enough to model many inter

David M. Pennock, Michael P. Wellman

Graphical models based on conditional independence support concise encodings of the subjective belief of a single agent. A natural question is whether the consensus belief of a group of agents can be represented with equal parsimony. We prove, under relatively mild assumptions, that even if everyone agrees on a common graph topology, no method of combining b

Vladimir Pavlovic, Brendan J. Frey, Thomas S. Huang

Many real-valued stochastic time-series are locally linear (Gassian), but globally non-linear. For example, the trajectory of a human hand gesture can be viewed as a linear dynamic system driven by a nonlinear dynamic system that represents muscle actions. We present a mixed-state dynamic graphical model in which a hidden Markov model drives a linear dynamic

Luis E. Ortiz, Leslie Pack Kaelbling

Many applications require that we learn the parameters of a model from data. EM is a method used to learn the parameters of probabilistic models for which the data for some of the variables in the models is either missing or hidden. There are instances in which this method is slow to converge. Therefore, several accelerations have been proposed to improve th

Thomas D. Nielsen, Finn Verner Jensen

Influence diagrams serve as a powerful tool for modelling symmetric decision problems. When solving an influence diagram we determine a set of strategies for the decisions involved. A strategy for a decision variable is in principle a function over its past. However, some of the past may be irrelevant for the decision, and for computational reasons it is imp

Hien Nguyen, Peter Haddawy

The need to help people choose among large numbers of items and to filter through large amounts of information has led to a flood of research in construction of personal recommendation agents. One of the central issues in constructing such agents is the representation and elicitation of user preferences or interests. This topic has long been studied in Decis

Julian R. Neil, Chris S. Wallace, Kevin B. Korb

A major problem for the learning of Bayesian networks (BNs) is the exponential number of parameters needed for conditional probability tables. Recent research reduces this complexity by modeling local structure in the probability tables. We examine the use of log-linear local models. While log-linear models in this context are not new (Whittaker, 1990; Bunti

James W. Myers, Kathryn Blackmond Laskey, Tod S. Levitt

This paper describes stochastic search approaches, including a new stochastic algorithm and an adaptive mutation operator, for learning Bayesian networks from incomplete data. This problem is characterized by a huge solution space with a highly multimodal landscape. State-of-the-art approaches all involve using deterministic approaches such as the expectatio

Kevin Murphy, Yair Weiss, Michael I. Jordan

Recently, researchers have demonstrated that loopy belief propagation - the use of Pearls polytree algorithm IN a Bayesian network WITH loops OF error- correcting codes.The most dramatic instance OF this IS the near Shannon - limit performance OF Turbo Codes codes whose decoding algorithm IS equivalent TO loopy belief propagation IN a chain - structured Baye

Kevin Murphy

We show how to use a variational approximation to the logistic function to perform approximate inference in Bayesian networks containing discrete nodes with continuous parents. Essentially, we convert the logistic function to a Gaussian, which facilitates exact inference, and then iteratively adjust the variational parameters to improve the quality of the ap

Stefano Monti, Gregory F. Cooper

In this paper we present a new Bayesian network model for classification that combines the naive-Bayes (NB) classifier and the finite-mixture (FM) classifier. The resulting classifier aims at relaxing the strong assumptions on which the two component models are based, in an attempt to improve on their classification performance, both in terms of accuracy and

Robert Mislevy, Russell Almond, Duanli Yan, Linda S. Steinberg

As observations and student models become complex, educational assessments that exploit advances in technology and cognitive psychology can outstrip familiar testing models and analytic methods. Within the Portal conceptual framework for assessment design, Bayesian inference networks (BINs) record beliefs about students' knowledge and skills, in light of

Nicolas Meuleau, Leonid Peshkin, Kee-Eung Kim, Leslie Pack Kaelbling

Reactive (memoryless) policies are sufficient in completely observable Markov decision processes (MDPs), but some kind of memory is usually necessary for optimal control of a partially observable MDP. Policies with finite memory can be represented as finite-state automata. In this paper, we extend Baird and Moore's VAPS algorithm to the problem of learni

Nicolas Meuleau, Kee-Eung Kim, Leslie Pack Kaelbling, Anthony R. Cassandra

Solving partially observable Markov decision processes (POMDPs) is highly intractable in general, at least in part because the optimal policy may be infinitely large. In this paper, we explore the problem of finding the optimal policy from a restricted set of policies, represented as finite state automata of a given size. This problem is also intractable, bu

David A. McAllester, Satinder Singh

We are interested in the problem of planning for factored POMDPs. Building on the recent results of Kearns, Mansour and Ng, we provide a planning algorithm for factored POMDPs that exploits the accuracy-efficiency tradeoff in the belief state simplification introduced by Boyen and Koller.

Yishay Mansour, Satinder Singh

Decision-making problems in uncertain or stochastic domains are often formulated as Markov decision processes (MDPs). Policy iteration (PI) is a popular algorithm for searching over policy-space, the size of which is exponential in the number of states. We are interested in bounds on the complexity of PI that do not depend on the value of the discount factor

Suzanne M. Mahoney, Kathryn Blackmond Laskey

This paper extends previous work with network fragments and situation-specific network construction. We formally define the asymmetry network, an alternative representation for a conditional probability table. We also present an object-oriented representation for partially specified asymmetry networks. We show that the representation is parsimonious. We defi

Anders L. Madsen, Finn Verner Jensen

Solving symmetric Bayesian decision problems is a computationally intensive task to perform regardless of the algorithm used. In this paper we propose a method for improving the efficiency of algorithms for solving Bayesian decision problems. The method is based on the principle of lazy evaluation - a principle recently shown to improve the efficiency of inf

Christopher Lusena, Tong Li, Shelia Sittinger, Chris Wells

We consider the problem of finding good finite-horizon policies for POMDPs under the expected reward metric. The policies considered are {em free finite-memory policies with limited memory}; a policy is a mapping from the space of observation-memory pairs to the space of action-memeory pairs (the policy updates the memory as it goes), and the number of possi

Henry E. Kyburg, Choh Man Teng

There is available an ever-increasing variety of procedures for managing uncertainty. These methods are discussed in the literature of artificial intelligence, as well as in the literature of philosophy of science. Heretofore these methods have been evaluated by intuition, discussion, and the general philosophical method of argument and counterexample. Almos

Ryszard Kowalczyk

Most fuzzy systems including fuzzy decision support and fuzzy control systems provide out-puts in the form of fuzzy sets that represent the inferred conclusions. Linguistic interpretation of such outputs often involves the use of linguistic approximation that assigns a linguistic label to a fuzzy set based on the predefined primary terms, linguistic modifier

Kevin B. Korb, Ann Nicholson, Nathalie Jitnah

Poker is ideal for testing automated reasoning under uncertainty. It introduces uncertainty both by physical randomization and by incomplete information about opponents hands.Another source OF uncertainty IS the limited information available TO construct psychological models OF opponents, their tendencies TO bluff, play conservatively, reveal weakness, etc.

Petri Kontkanen, Petri Myllymaki, Tomi Silander, Henry Tirri

Given a set of possible models (e.g., Bayesian network structures) and a data sample, in the unsupervised model selection problem the task is to choose the most accurate model with respect to the domain joint probability distribution. In contrast to this, in supervised model selection it is a priori known that the chosen model will be used in the future for

Daphne Koller, Uri Lerner, Dragomir Anguelov

The clique tree algorithm is the standard method for doing inference in Bayesian networks. It works by manipulating clique potentials - distributions over the variables in a clique. While this approach works well for many networks, it is limited by the need to maintain an exact representation of the clique potentials. This paper presents a new unified approa

Kalev Kask, Rina Dechter

The paper is a second in a series of two papers evaluating the power of a new scheme that generates search heuristics mechanically. The heuristics are extracted from an approximation scheme called mini-bucket elimination that was recently introduced. The first paper introduced the idea and evaluated it within Branch-and-Bound search. In the current paper the

Eric J. Horvitz, Andy Jacobs, David Hovel

We introduce utility-directed procedures for mediating the flow of potentially distracting alerts and communications to computer users. We present models and inference procedures that balance the context-sensitive costs of deferring alerts with the cost of interruption. We describe the challenge of reasoning about such costs under uncertainty via an analysis

Michael C. Horsch, David L. Poole

We outline a method to estimate the value of computation for a flexible algorithm using empirical data. To determine a reasonable trade-off between cost and value, we build an empirical model of the value obtained through computation, and apply this model to estimate the value of computation for quite different problems. In particular, we investigate this tr

Thomas Hofmann

Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent Semantic Analysis which stems from linear algebra and performs a

Jesse Hoey, Robert St-Aubin, Alan Hu, Craig Boutilier

Markov decisions processes (MDPs) are becoming increasing popular as models of decision theoretic planning. While traditional dynamic programming methods perform well for problems with small state spaces, structured methods are needed for large problems. We propose and examine a value iteration algorithm for MDPs that uses algebraic decision diagrams(ADDs) t

David Harmanec

A conceptual foundation for approximation of belief functions is proposed and investigated. It is based on the requirements of consistency and closeness. An optimal approximation is studied. Unfortunately, the computation of the optimal approximation turns out to be intractable. Hence, various heuristic methods are proposed and experimantally evaluated both

Vu A. Ha, Peter Haddawy

Classical Decision Theory provides a normative framework for representing and reasoning about complex preferences. Straightforward application of this theory to automate decision making is difficult due to high elicitation cost. In response to this problem, researchers have recently developed a number of qualitative, logic-oriented approaches for representin

Dominique Gruyer, Veronique Berge-Cherfaoui

In current perception systems applied to the rebuilding of the environment for intelligent vehicles, the part reserved to object association for the tracking is increasingly significant. This allows firstly to follow the objects temporal evolution and secondly to increase the reliability of environment perception. We propose in this communication the develop

Robert P. Goldman, Christopher W. Geib, Christopher A. Miller

We present a new abductive, probabilistic theory of plan recognition. This model differs from previous plan recognition theories in being centered around a model of plan execution: most previous methods have been based on plans as formal objects or on rules describing the recognition process. We show that our new model accounts for phenomena omitted from mos

Phan H. Giang, Prakash P. Shenoy

In this paper, we analyze the relationship between probability and Spohn's theory for representation of uncertain beliefs. Using the intuitive idea that the more probable a proposition is, the more believable it is, we study transformations from probability to Sphonian disbelief and vice-versa. The transformations described in this paper are different fr

Dan Geiger, Christopher Meek

Recent improvement on Tarski's procedure for quantifier elimination in the first order theory of real numbers makes it feasible to solve small instances of the following problems completely automatically: 1. listing all equality and inequality constraints implied by a graphical model with hidden variables. 2. Comparing graphyical models with hidden varia

Nir Friedman, Iftach Nachman, Dana Pe'er

Learning Bayesian networks is often cast as an optimization problem, where the computational task is to find a structure that maximizes a statistically motivated score. By and large, existing learning tools address this optimization problem using standard heuristic search techniques. Since the search space is extremely large, such search procedures can spend

Nir Friedman, Moises Goldszmidt, Abraham Wyner

In recent years there has been significant progress in algorithms and methods for inducing Bayesian networks from data. However, in complex data analysis problems, we need to go beyond being satisfied with inducing networks with high scores. We need to provide confidence measures on features of these networks: Is the existence of an edge between two nodes wa

Helene Fargier, Patrice Perny

This paper investigates a purely qualitative version of Savage's theory for decision making under uncertainty. Until now, most representation theorems for preference over acts rely on a numerical representation of utility and uncertainty where utility and uncertainty are commensurate. Disrupting the tradition, we relax this assumption and introduce a pur

Kazuo J. Ezawa, Greg Napiorkowski, Mariusz Kossarski

We describe challenges in the risk management of smart card based electronic cash industry and describe a method to evaluate the effectiveness of distributed intelligence on the smart card. More specifically, we discuss the evaluation of distributed intelligence function called "on-chip risk management" of the smart card for the global electronic cas

Didier Dubois, Michel Grabisch, Henri Prade, Philippe Smets

The problem of assessing the value of a candidate is viewed here as a multiple combination problem. On the one hand a candidate can be evaluated according to different criteria, and on the other hand several experts are supposed to assess the value of candidates according to each criterion. Criteria are not equally important, experts are not equally competen

Michael I. Dekhtyar, Alex Dekhtyar, V. S. Subrahmanian

Hybrid Probabilistic Programs (HPPs) are logic programs that allow the programmer to explicitly encode his knowledge of the dependencies between events being described in the program. In this paper, we classify HPPs into three classes called HPP_1,HPP_2 and HPP_r,r>= 3. For these classes, we provide three types of results for HPPs. First, we develop algorith

Richard Dearden, Nir Friedman, David Andre

Reinforcement learning systems are often concerned with balancing exploration of untested actions against exploitation of actions that are known to be good. The benefit of exploration can be estimated using the classical notion of Value of Information - the expected improvement in future decision quality arising from the information acquired by exploration.

Denver Dash, Marek J. Druzdzel

We present a hybrid constraint-based/Bayesian algorithm for learning causal networks in the presence of sparse data. The algorithm searches the space of equivalence classes of models (essential graphs) using a heuristic based on conventional constraint-based techniques. Each essential graph is then converted into a directed acyclic graph and scored using a B

Sanjoy Dasgupta

We consider the task of learning the maximum-likelihood polytree from data. Our first result is a performance guarantee establishing that the optimal branching (or Chow-Liu tree), which can be computed very easily, constitutes a good approximation to the best polytree. We then show that it is not possible to do very much better, since the learning problem is

James Cussens

Recent work on loglinear models in probabilistic constraint logic programming is applied to first-order probabilistic reasoning. Probabilities are defined directly on the proofs of atomic formulae, and by marginalisation on the atomic formulae themselves. We use Stochastic Logic Programs (SLPs) composed of labelled and unlabelled definite clauses to define t

Gregory F. Cooper, Changwon Yoo

This paper describes a Bayesian method for combining an arbitrary mixture of observational and experimental data in order to learn causal Bayesian networks. Observational data are passively observed. Experimental data, such as that produced by randomized controlled trials, result from the experimenter manipulating one or more variables (typically randomly) a

Jie Cheng, Russell Greiner

In this paper, we empirically evaluate algorithms for learning four types of Bayesian network (BN) classifiers - Naive-Bayes, tree augmented Naive-Bayes, BN augmented Naive-Bayes and general BNs, where the latter two are learned using two variants of a conditional-independence (CI) based BN-learning algorithm. Experimental results show the obtained classifie

Xavier Boyen, Nir Friedman, Daphne Koller

Dynamic Bayesian networks provide a compact and natural representation for complex dynamic systems. However, in many cases, there is no expert available from whom a model can be elicited. Learning provides an alternative approach for constructing models of dynamic systems. In this paper, we address some of the crucial computational aspects of learning the st

Craig Boutilier, Moises Goldszmidt, Bikash Sabata

Market-based mechanisms such as auctions are being studied as an appropriate means for resource allocation in distributed and mulitagent decision problems. When agents value resources in combination rather than in isolation, they must often deliberate about appropriate bidding strategies for a sequence of auctions offering resources of interest. We briefly d

Craig Boutilier, Ronen I. Brafman, Holger H. Hoos, David L. Poole

In many domains it is desirable to assess the preferences of users in a qualitative rather than quantitative way. Such representations of qualitative preference orderings form an importnat component of automated decision tools. We propose a graphical representation of preferences that reflects conditional dependence and independence of preference statements

Magnus Boman, Paul Davidsson, Hakan L. Younes

Our hypothesis is that by equipping certain agents in a multi-agent system controlling an intelligent building with automated decision support, two important factors will be increased. The first is energy saving in the building. The second is customer value---how the people in the building experience the effects of the actions of the agents. We give evidence

Salem Benferhat, Didier Dubois, Laurent Garcia, Henri Prade

Possibilistic logic bases and possibilistic graphs are two different frameworks of interest for representing knowledge. The former stratifies the pieces of knowledge (expressed by logical formulas) according to their level of certainty, while the latter exhibits relationships between variables. The two types of representations are semantically equivalent whe

Philip S. Barry, Kathryn Blackmond Laskey

This paper examines the use of Bayesian Networks to tackle one of the tougher problems in requirements engineering, translating user requirements into system requirements. The approach taken is to model domain knowledge as Bayesian Network fragments that are glued together to form a complete view of the domain specific system requirements. User requirements

Katy S. Azoury, Manfred K. Warmuth

We consider on-line density estimation with a parameterized density from the exponential family. The on-line algorithm receives one example at a time and maintains a parameter that is essentially an average of the past examples. After receiving an example the algorithm incurs a loss which is the negative log-likelihood of the example w.r.t. the past paramete

Hagai Attias

Current methods for learning graphical models with latent variables and a fixed structure estimate optimal values for the model parameters. Whereas this approach usually produces overfitting and suboptimal generalization performance, carrying out the Bayesian program of computing the full posterior distributions over the parameters remains a difficult proble

Gustavo Arroyo-Figueroa, Luis Enrique Sucar

Diagnosis and prediction in some domains, like medical and industrial diagnosis, require a representation that combines uncertainty management and temporal reasoning. Based on the fact that in many cases there are few state changes in the temporal range of interest, we propose a novel representation called Temporal Nodes Bayesian Networks (TNBN). In a TNBN e

Erdoğan Şen

In this thesis we examined mathematical properties of Fibonacci numbers and applications of this numbers in the nature,geometry and economy.We obtained Golden section and proved some mathematical identities using Golden section. Infinity of the prime numbers proved by using properties of Fibonacci numbers. Encounterings with Fibonacci numbers in the nature a

Miguel C. N. Fiolhais, Hanno Essén

The most general electrodynamic equations of a perfect conducting state are obtained using a variational principle in a classical framework, following an approach by Pierre-Gilles de Gennes. London equations are derived as the time-independent case of these equations, corresponding to the magnetostatic minimal energy state of the perfect conducting system. F

Gavril Farkas

We discuss in this short note how basic facts about divisors on moduli spaces of pointed curves give a solution to the Brill-Noether problem of nonexistence of linear series with prescribed ramification at unassigned points.

David Sundkvist, Forrest Mozer

Seventy measurements of electron heating at the Earth's quasi-perpendicular bow shock are analyzed in terms of Maxwellian-temperatures obtained from fits to the core electrons that separate thermal heating from supra-thermal acceleration. The perpendicular temperatures are both greater and lesser than expected for adiabatic compression. The average paral

N. Ahmadiniaz, A. Huet, A. Raya, C. Schubert

An interesting class of background field configurations in QED are the O(2)xO(3) symmetric fields. Those backgrounds have some instanton-like properties and yield a one-loop effective action that is highly nontrivial but amenable to numerical calculation, for both scalar and spinor QED. Here we use the recently developed "partial-wave-cutoff method"

Yu-Feng Yao, Hao Chang

Let $\mathfrak{g}$ be the $p$-dimensional Witt algebra over an algebraically closed field $k$ of characteristic $p>3$. Let $\mathscr{N}={x\in\ggg\mid x^{[p]}=0}$ be the nilpotent variety of $\mathfrak{g}$, and $\mathscr{C}(\mathscr{N}):=\{(x,y)\in \mathscr{N}\times\mathscr{N}\mid [x,y]=0\}$ the nilpotent commuting variety of $\mathfrak{g}$. As an analogue of

O. Zeki Okuyucu

In [5], Matsuda and Yorozo obtained that Mannheim curves in 4-dimensional Euclidean space. In this study, we define quaternionic Mannheim curves and we give some characterizations of them in Euclidean 3-space and 4-space.

Emmanuel Jacquet, François Robert

The D/H ratios of carbonaceous chondrites, believed to reflect that of water in the inner early solar system, are intermediate between the protosolar value and that of most comets. The isotopic composition of cometary water has been accounted for by several models where the isotopic composition of water vapor evolved by isotopic exchange with hydrogen gas in

Maximilian Kiesel, Christian Platt, Werner Hanke, Ronny Thomale

Since its discovery, the superconducting phase in water-intercalated sodium cobaltates NaxCoO2 yH2O (x~0.3, y~1.3) has posed fundamental challenges in terms of experimental investigation and theoretical understanding. By a combined dynamical mean-field and renormalization group approach, we find an anisotropic chiral d+id wave state as a consequence of multi

Christian Lübbe

This article examines the coincidence of the projective and conformal Weyl tensors associated to a given connection D. The connection may be a general Weyl connection associated to a conformal class of metrics [g]. The main result for n>3 is that the Weyl tensors coincide iff D is the Levi-Civita connection of an Einstein metric.

T. Khabiboulline, C. M. Ginsburg, I. Gonin, R. Madrak

In Fermilab we are build and tested several superconducting Single Spoke Resonators (SSR1, β=0.22) which can be used for acceleration of low beta ions. Fist two cavities performed very well during cold test in Vertical Test Station at FNAL. One dressed cavity was also tested successfully in Horizontal Test Station. Currently we are building 8 cavity cryomodu

Christiane P. Goulart, Mentar Mahmudi, Kristina A. Crona, Stephen D. Jacobs

The evolution of antibiotic resistance among bacteria threatens our continued ability to treat infectious diseases. The need for sustainable strategies to cure bacterial infections has never been greater. So far, all attempts to restore susceptibility after resistance has arisen have been unsuccessful, including restrictions on prescribing [1] and antibiotic

Juan D. Velez, Luis A. Wills, Natalia Agudelo

Let G denote a group and let W be an algebra over a commutative ring R. We will say that W is a G-graded twisted algebra (not necessarily commutative, neither associative) if there exists a G-grading W=\bigoplus_{g \in G}W_{g} where each summand W_{g} is a free rank one R -module, and W has no monomial zero divisors (for each pair of nonzero elements w_{a},w

Nikolay V. Larionov, Mikhail I. Kolobov

Using two equivalent approaches, Heisenberg-Langevin and density operator, we investigate the properties of nanolaser: an incoherently pumped single two-level system interacting with a single-cavity mode of finite finesse. We show that in the case of good-cavity regime the Heisenberg-Langevin approach provides the analytical results for linewidth, amplitude

V. Lebedev, A. Chen, R Pasquinelli, D. Peterson

A capability to provide a large variety of bunch patterns is crucial for the concept of the Project X serving MW-range beam to several experiments simultaneously. This capability will be realized by the Medium Energy Beam Transport's (MEBT) chopping system that will divert 80% of all bunches of the initially 5mA, 2.1 MeV CW 162.5 MHz beam to an absorber

Mark R. Krumholz, Richard M. Crutcher, Charles L. H. Hull

The gas from which stars form is magnetized, and strong magnetic fields can efficiently transport angular momentum. Most theoretical models of this phenomenon find that it should prevent formation of large (>100 AU), rotationally-supported disks around most protostars, even when non-ideal magnetohydrodynamic (MHD) effects that allow the field and gas to deco

O. V. Tarasov, A. A. Vladimirov

A detailed description of the method for analytical evaluation of the three-loop contributions to renormalization group functions is presented. This method is employed to calculate the charge renormalization function and anomalous dimensions for non-Abelian gauge theories with fermions in the three-loop approximation. A three-loop expression for the effectiv

Lucile Savary, Leon Balents

Quantum spin liquids are highly entangled ground states of quantum systems with emergent gauge structure, fractionalized spinon excitations, and other unusual properties. While these features clearly distinguish quantum spin liquids from conventional, mean-field-like states at zero temperature (T), their status at T>0 is less clear. Strictly speaking, it is

Stacy H. Teng, Sylvain Veilleux, Andrew J. Baker

We present the results of a 21-cm HI survey of 27 local massive gas-rich late-stage mergers and merger remnants with the Robert C. Byrd Green Bank Telescope (GBT). These remnants were selected from the Quasar/ULIRG Evolution Study (QUEST) sample of ultraluminous infrared galaxies (ULIRGs; L$_{8-1000 μm} > 10^{12}$ L$_\odot$) and quasars; our targets are all

Marek Sikora, Mitchell C. Begelman

We argue that the magnetic flux threading the black hole, rather than black hole spin or Eddington ratio, is the dominant factor in launching powerful jets and thus determining the radio loudness of active galactic nuclei (AGN). Most AGN are radio quiet because the thin accretion disks that feed them are inefficient in depositing magnetic flux close to the b

Alexey V. Gorshkov, Kaden R. A. Hazzard, Ana Maria Rey

We show that ultracold polar molecules pinned in an optical lattice can be used to access a variety of exotic spin models, including the Kitaev honeycomb model. Treating each molecule as a rigid rotor, we use DC electric and microwave fields to define superpositions of rotational levels as effective spin degrees of freedom, while dipole-dipole interactions g

Ka-Ming Tam, Shan-Wen Tsai, D. K. Campbell

The search for realistic one-dimensional (1D) models that exhibit dominant superconducting (SC) fluctuations effects has a long history. In these 1D systems, the effects of commensurate band fillings--strongest at half-filling--and electronic repulsions typically lead to a finite charge gap and the favoring of insulating density wave ordering over supercondu

Alan R. White

The bound-state S-Matrix of QUD (SU(5) gauge theory with massless left-handed 5 + 15 + 40 + 45* fermions) might underly the success of the Standard Model. The dynamical role of the QUD top quark leads to two Higgs-like scalar resonances, the eta_6 - which reproduces Standard Model top events, and the eta_3 - which could have a mass ~ 125 GeV. The participati

Mark S. Marley, Andrew S. Ackerman, Jeffrey N. Cuzzi, Daniel Kitzmann

Clouds and hazes are commonplace in the atmospheres of solar system planets and are likely ubiquitous in the atmospheres of extrasolar planets as well. Clouds affect every aspect of a planetary atmosphere, from the transport of radiation, to atmospheric chemistry, to dynamics and they influence - if not control - aspects such as surface temperature and habit

M. Catelan, G. I. Torrealba, C. Caceres, H. A. Smith

We present a calibration of the metallicity and physical parameters (temperature, luminosity, gravity, mass, radius) for RR Lyrae stars using the ugriz SDSS photometric system. Our work is based on calculations of synthetic horizontal branches (HBs), fully taking into account evolutionary effects for a wide range in metallicities and HB morphologies. We prov

John MacQuarrie, Peter Symonds

Brauer Theory for a finite group can be viewed as a method for comparing the representations of the group in characteristic 0 with those in prime characteristic. Here we generalize much of the machinery of Brauer theory to the setting of profinite groups. By regarding Grothendieck groups as functors we describe corresponding Grothendieck groups for profinite

J. Murphy, C. P. Strehlow, K. Cho, M. A. Tanatar

Temperature-dependent London penetration depth, $λ(T)$, was measured in optimally - doped, $x=$0.35, as-grown ($T_c \approx$25 K, RRR=$ρ(300K)/ρ(T_c)$=4.5) and annealed ($T_c \approx$35 K, RRR=6.4) single crystals of SrFe$_2$(As$_{1-x}$P$_{x}$)$_2$ iron - based superconductor. Annealing increases the RRR and decreases the absolute value of the London penetra

S. Deser, M. Sandora, A. Waldron

We show that consistent nonlinear Partially Massless models cannot be obtained starting from "f-g" massive gravity, with "f" the embedding de Sitter space. The obstruction, which is also the source of f-g acausality, is the very same fifth constraint that removes the notorious sixth ghost excitation. Here, however, it blocks extension of the

L. Rast, T. J. Sullivan, V. K. Tewary

We propose a composite layered structure for tunable, low-loss plasmon resonances, which con- sists of a noble-metal thin film coated in graphene and supported on a hexagonal boron nitride (hBN) substrate. We calculate electron energy loss spectra (EELS) for these structures, and nu- merically demonstrate that bulk plasmon losses in noble-metal films can be

C. Baffes, M. Church, J. Leibfritz, S. Oplt

A high energy beam absorber has been built for the Advanced Superconducting Test Accelerator (ASTA) at Fermilab. In the facility's initial configuration, an electron beam will be accelerated through 3 TTF-type or ILC-type RF cryomodules to an energy of 750MeV. The electron beam will be directed to one of multiple downstream experimental and diagnostic be

L. R. Prost, A. Shemyakin

Fermilab's Recycler ring was used as a storage ring for accumulation and subsequent manipulations of 8-GeV antiprotons destined for the Tevatron collider. To satisfy these missions, a unique electron cooling system was designed, developed and successfully implemented. The most important features that distinguish the Recycler cooler from other existing el

S. Ferraz-Mello

We expand the equations used in MacDonald's 1964 theory and Fourier analyze the tidal variations of the height at one point on the Earth surface, and also the tidal potential at such point. It is shown that no intrinsic law is relating the lag of the tide components to their frequencies. In other words, no simple rheology is being intrinsically fixed by

Swagata Mallik, Gargi Chaudhuri

The ratio of symmetry energy coefficient to temperature $C_{sym}/T$ is extracted from different prescriptions using the isotopic as well as the isobaric yield distributions obtained in different projectile fragmentation reactions. It is found that the values extracted from our theoretical calculation agree with those extracted from the experimental data but

Philip C. Myers

Mass functions of starless dense cores (CMFs) may arise from contraction and dispersal of core-forming filaments. In an illustrative model, a filament contracts radially by self-gravity, increasing the mass of its cores. During this contraction, FUV photoevaporation and ablation by shocks and winds disperse filament gas and limit core growth. The stopping ti

Clément Dombry

The maximum likelihood method offers a standard way to estimate the three parameters of a generalized extreme value (GEV) distribution. Combined with the block maxima method, it is often used in practice to assess the extreme value index and normalization constants of a distribution satisfying a first order extreme value condition, assuming implicitely that

M. Xiao

In the post-Nova era, the protons are directly transported from the Booster ring to the Recycler ring rather than the Main Injector. For Mu2e and g-2 project, the Debuncher ring will be modified into a Delivery ring to deliver the protons to both Mu2e and g-2 experiemnts. Therefore, It requires the transport of protons from the Recycler Ring to the Delivery

C. Robles

We study Hodge representations of absolutely simple Q-algebraic groups with Hodge numbers h = (1,1,...,1). For those groups that are not of type A, we give a classification of the R-irreducible representations; a similar classification for type A does not seem possible.

Sergey A. Dyachenko, Pavel M. Lushnikov, Natalia Vladimirova

A reduced Keller-Segel equation (RKSE) is a parabolic-elliptic system of partial differential equations which describes bacterial aggregation and the collapse of a self-gravitating gas of brownian particles. We consider RKSE in two dimensions, where solution has a critical collapse (blow-up) if the total number of bacteria exceeds a critical value. We study

Bogdan A. Pastrav, Cristina C. Popescu, Richard J. Tuffs, Anne E. Sansom

We present results of a study to quantify the effects of dust on the derived photometric parameters of disks (old stellar disks and young stellar disks) and bulges: disk scale-lengths, axis-ratios, central surface-brightness, bulge effective radii and Sersic indexes. The changes in the derived photometric parameters from their intrinsic values (as seen in th

Sophie A. Murray

Sunspots are regions of decreased brightness on the visible surface of the Sun (photosphere) that are associated with strong magnetic fields. They have been found to be locations associated with solar flares, which occur when energy stored in sunspot magnetic fields is suddenly released. The processes involved in flaring and the link between sunspot magnetic

Lucia Guaita, Harold Francke, Eric Gawiser, Franz E. Bauer

To investigate the ingredients, which allow star-forming galaxies to present Lyalpha line in emission, we studied the kinematics and gas phase metallicity (Z) of the interstellar medium. We used multi-object NIR spectroscopy with Magellan/MMIRS to study nebular emission from z=2-3 star-forming galaxies discovered in 3 MUSYC fields. We detected emission lines

G. Stasinska, C. Morisset, S. Simon-Diaz, F. Bresolin

While collisionally excited lines in HII regions allow one to easily probe the chemical composition of the interstellar medium in galaxies, the possible presence of important temperature fluctuations casts some doubt on the derived abundances. To provide new insights into this question, we have carried out a detailed study of a giant HII region, H 1013, loca

Christian A. Cruz-Santiago

We investigate the tree level multi-gluon components of the gluon light cone wavefunctions in the light cone gauge keeping the exact kinematics of the gluon emissions. We focus on the components with all helicities identical to the helicity of the incoming gluon. The recurrence relations for the gluon wavefunctions are derived. In the case when the virtualit