March 2013 arXiv papers — page 12

Kenneth Basye, Thomas L. Dean

Nearly all spatial reasoning problems involve uncertainty of one sort or another. Uncertainty arises due to the inaccuracies of sensors used in measuring distances and angles. We refer to this as directional uncertainty. Uncertainty also arises in combining spatial information when one location is mistakenly identified with another. We refer to this as recog

Fahiem Bacchus

This extended abstract presents a logic, called Lp, that is capable of representing and reasoning with a wide variety of both qualitative and quantitative statistical information. The advantage of this logical formalism is that it offers a declarative representation of statistical knowledge; knowledge represented in this manner can be used for a variety of r

Bo Chamberlain, Finn Verner Jensen, Frank Jensen, Torsten Nordahl

After a brief introduction to causal probabilistic networks and the HUGIN approach, the problem of conflicting data is discussed. A measure of conflict is defined, and it is used in the medical diagnostic system MUNIN. Finally, it is discussed how to distinguish between conflicting data and a rare case.

Philippe Smets, Yen-Teh Hsia

Inappropriate use of Dempster's rule of combination has led some authors to reject the Dempster-Shafer model, arguing that it leads to supposedly unacceptable conclusions when defaults are involved. A most classic example is about the penguin Tweety. This paper will successively present: the origin of the miss-management of the Tweety example; two types

Mary McLeish

Considerable attention has been given to the problem of non-monotonic reasoning in a belief function framework. Earlier work (M. Ginsberg) proposed solutions introducing meta-rules which recognized conditional independencies in a probabilistic sense. More recently an e-calculus formulation of default reasoning (J. Pearl) shows that the application of Dempste

David Sher

A scientific reasoning system makes decisions using objective evidence in the form of independent experimental trials, propositional axioms, and constraints on the probabilities of events. As a first step towards this goal, we propose a system that derives probability intervals from objective evidence in those forms. Our reasoning system can manage uncertain

Michael Shwe, Gregory F. Cooper

We analyzed the convergence properties of likelihood- weighting algorithms on a two-level, multiply connected, belief-network representation of the QMR knowledge base of internal medicine. Specifically, on two difficult diagnostic cases, we examined the effects of Markov blanket scoring, importance sampling, demonstrating that the Markov blanket scoring and

Linda C. van der Gaag

Many AI researchers argue that probability theory is only capable of dealing with uncertainty in situations where a full specification of a joint probability distribution is available, and conclude that it is not suitable for application in knowledge-based systems. Probability intervals, however, constitute a means for expressing incompleteness of informatio

Gerhard Paaß

In this paper the elicitation of probabilities from human experts is considered as a measurement process, which may be disturbed by random 'measurement noise'. Using Bayesian concepts a second order probability distribution is derived reflecting the uncertainty of the input probabilities. The algorithm is based on an approximate sample representation

Kuo-Chu Chang, Robert Fung

In almost all situation assessment problems, it is useful to dynamically contract and expand the states under consideration as assessment proceeds. Contraction is most often used to combine similar events or low probability events together in order to reduce computation. Expansion is most often used to make distinctions of interest which have significant pro

John Yen, Piero P. Bonissone

A major difficulty in developing and maintaining very large knowledge bases originates from the variety of forms in which knowledge is made available to the KB builder. The objective of this research is to bring together two complementary knowledge representation schemes: term subsumption languages, which represent and reason about defining characteristics o

Thomas D. Wu

This paper derives a formula for computing the conditional probability of a set of candidates, where a candidate is a set of disorders that explain a given set of positive findings. Such candidate sets are produced by a recent method for multidisorder diagnosis called symptom clustering. A symptom clustering represents a set of candidates compactly as a cart

Michael S. K. M. Wong, P. Lingras

One of the most important aspects in any treatment of uncertain information is the rule of combination for updating the degrees of uncertainty. The theory of belief functions uses the Dempster rule to combine two belief functions defined by independent bodies of evidence. However, with limited dependency information about the accumulated belief the Dempster

Nic Wilson

This paper describes a natural framework for rules, based on belief functions, which includes a repre- sentation of numerical rules, default rules and rules allowing and rules not allowing contraposition. In particular it justifies the use of the Dempster-Shafer Theory for representing a particular class of rules, Belief calculated being a lower probability

Stuart Russell

Decision-theoretic control of search has previously used as its basic unit. of computation the generation and evaluation of a complete set of successors. Although this simplifies analysis, it results in some lost opportunities for pruning and satisficing. This paper therefore extends the analysis of the value of computation to cover individual successor eval

Michael Pittarelli

Probability estimation by maximum entropy reconstruction of an initial relative frequency estimate from its projection onto a hypergraph model of the approximate conditional independence relations exhibited by it is investigated. The results of this study suggest that use of this estimation technique may improve the quality of decisions that must be made on

Hung-Trung Nguyen

This note is concerned with a formal analysis of the problem of non-monotonic reasoning in intelligent systems, especially when the uncertainty is taken into account in a quantitative way. A firm connection between logic and probability is established by introducing conditioning notions by means of formal structures that do not rely on quantitative measures.

Kathryn Blackmond Laskey

A framework is presented for a computational theory of probabilistic argument. The Probabilistic Reasoning Environment encodes knowledge at three levels. At the deepest level are a set of schemata encoding the system's domain knowledge. This knowledge is used to build a set of second-level arguments, which are structured for efficient recapture of the kn

A. J. Hanson

We point out the need to use probability amplitudes rather than probabilities to model evidence accumulation in decision processes involving real physical sensors. Optical information processing systems are given as typical examples of systems that naturally gather evidence in this manner. We derive a new, amplitude-based generalization of the Hough transfor

Robert Fung, S. L. Crawford, Lee A. Appelbaum, Richard M. Tong

While concept-based methods for information retrieval can provide improved performance over more conventional techniques, they require large amounts of effort to acquire the concepts and their qualitative and quantitative relationships. This paper discusses an architecture for probabilistic concept-based information retrieval which addresses the knowledge ac

Mary McLeish, P. Yao, T. Stirtzinger

This paper describes recent work on an ongoing project in medical diagnosis at the University of Guelph. A domain on which experts are not very good at pinpointing a single disease outcome is explored. On-line medical data is available over a relatively short period of time. Belief Functions (Dempster-Shafer theory) are first extracted from data and then mod

F. Correa da Silva, Alan Bundy

Incidence Calculus and Dempster-Shafer Theory of Evidence are both theories to describe agents' degrees of belief in propositions, thus being appropriate to represent uncertainty in reasoning systems. This paper presents a straightforward equivalence proof between some special cases of these theories.

L. W. Chang, Rangasami L. Kashyap

In this paper a new mathematical procedure is presented for combining different pieces of evidence which are represented in the interval form to reflect our knowledge about the truth of a hypothesis. Evidences may be correlated to each other (dependent evidences) or conflicting in supports (conflicting evidences). First, assuming independent evidences, we pr

Hamid R. Berenji, Yung-Yaw Chen, Chuen-Chien Lee, Jyh-Shing Jang

This paper presents a new technique for the design of approximate reasoning based controllers for dynamic physical systems with interacting goals. In this approach, goals are achieved based on a hierarchy defined by a control knowledge base and remain highly interactive during the execution of the control task. The approach has been implemented in a rule-bas

Alessandro Saffiotti

In this paper, we suggest marrying Dempster-Shafer (DS) theory with Knowledge Representation (KR). Born out of this marriage is the definition of "Dempster-Shafer Belief Bases", abstract data types representing uncertain knowledge that use DS theory for representing strength of belief about our knowledge, and the linguistic structures of an arbitrary

Robert Kennes, Philippe Smets

In this paper we associate with every (directed) graph G a transformation called the Mobius transformation of the graph G. The Mobius transformation of the graph (O) is of major significance for Dempster-Shafer theory of evidence. However, because it is computationally very heavy, the Mobius transformation together with Dempster's rule of combination is

Prakash P. Shenoy, Glenn Shafer

This paper describes valuation-based systems for representing and solving discrete optimization problems. In valuation-based systems, we represent information in an optimization problem using variables, sample spaces of variables, a set of values, and functions that map sample spaces of sets of variables to the set of values. The functions, called valuations

Philippe Smets

Dempster-Shafer's model aims at quantifying degrees of belief But there are so many interpretations of Dempster-Shafer's theory in the literature that it seems useful to present the various contenders in order to clarify their respective positions. We shall successively consider the classical probability model, the upper and lower probabilities model

Ronald Fagin, Joseph Y. Halpern

We define a new notion of conditional belief, which plays the same role for Dempster-Shafer belief functions as conditional probability does for probability functions. Our definition is different from the standard definition given by Dempster, and avoids many of the well-known problems of that definition. Just as the conditional probability Pr (lB) is a prob

Didier Dubois, Henri Prade

This paper discusses how a measure of uncertainty representing a state of knowledge can be updated when a new information, which may be pervaded with uncertainty, becomes available. This problem is considered in various framework, namely: Shafer's evidence theory, Zadeh's possibility theory, Spohn's theory of epistemic states. In the two first ca

Ronald R. Yager

We are concerned with the problem of introducing credibility type information into reasoning systems. The concept of credibility allows us to discount information provided by agents. An important characteristic of this kind of procedure is that a complete lack of credibility rather than resulting in the negation of the information provided results in the nul

Soumitra Dutta, Piero P. Bonissone

Rule based reasoning (RBR) and case based reasoning (CBR) have emerged as two important and complementary reasoning methodologies in artificial intelligence (Al). For problem solving in complex, real world situations, it is useful to integrate RBR and CBR. This paper presents an approach to achieve a compact and seamless integration of RBR and CBR within the

Enrique H. Ruspini

This paper addresses fundamental issues on the nature of the concepts and structures of fuzzy logic, focusing, in particular, on the conceptual and functional differences that exist between probabilistic and possibilistic approaches. A semantic model provides the basic framework to define possibilistic structures and concepts by means of a function that quan

Jaap Suermondt, Gregory F. Cooper, David Heckerman

Cutset conditioning and clique-tree propagation are two popular methods for performing exact probabilistic inference in Bayesian belief networks. Cutset conditioning is based on decomposition of a subset of network nodes, whereas clique-tree propagation depends on aggregation of nodes. We describe a means to combine cutset conditioning and clique- tree propa

Jonathan Stillman

We introduce a new heuristic algorithm for the problem of finding minimum size loop cutsets in multiply connected belief networks. We compare this algorithm to that proposed in [Suemmondt and Cooper, 1988]. We provide lower bounds on the performance of these algorithms with respect to one another and with respect to optimal. We demonstrate that no heuristic

Michelle Baker, Terrance E. Boult

This paper analyzes the circumstances under which Bayesian networks can be pruned in order to reduce computational complexity without altering the computation for variables of interest. Given a problem instance which consists of a query and evidence for a set of nodes in the network, it is possible to delete portions of the network which do not participate i

Wilson X. Wen

In this paper, optimum decomposition of belief networks is discussed. Some methods of decomposition are examined and a new method - the method of Minimum Total Number of States (MTNS) - is proposed. The problem of optimum belief network decomposition under our framework, as under all the other frameworks, is shown to be NP-hard. According to the computationa

Ross D. Shachter, Stig K. Andersen, Kim-Leng Poh

In recent years, there have been intense research efforts to develop efficient methods for probabilistic inference in probabilistic influence diagrams or belief networks. Many people have concluded that the best methods are those based on undirected graph structures, and that those methods are inherently superior to those based on node reduction operations o

Lambert E. Wixson

Objects in the world usually appear in context, participating in spatial relationships and interactions that are predictable and expected. Knowledge of these contexts can be used in the task of using a mobile camera to search for a specified object in a room. We call this the object search task. This paper is concerned with representing this knowledge in a m

Tom S. Verma, Judea Pearl

Scientists often use directed acyclic graphs (days) to model the qualitative structure of causal theories, allowing the parameters to be estimated from observational data. Two causal models are equivalent if there is no experiment which could distinguish one from the other. A canonical representation for causal models is presented which yields an efficient g

Sampath Srinivas, John S. Breese

IDEAL (Influence Diagram Evaluation and Analysis in Lisp) is a software environment for creation and evaluation of belief networks and influence diagrams. IDEAL is primarily a research tool and provides an implementation of many of the latest developments in belief network and influence diagram evaluation in a unified framework. This paper describes IDEAL an

Keung-Chi Ng, Bruce Abramson

Knowledge elicitation is one of the major bottlenecks in expert system design. Systems based on Bayes nets require two types of information--network structure and parameters (or probabilities). Both must be elicited from the domain expert. In general, parameters have greater opacity than structure, and more time is spent in their refinement than in any other

Richard E. Neapolitan, James Kenevan

The causal (belief) network is a well-known graphical structure for representing independencies in a joint probability distribution. The exact methods and the approximation methods, which perform probabilistic inference in causal networks, often treat the conditional probabilities which are stored in the network as certain values. However, if one takes eithe

J. W. Miller, R. M. Goodman

A method of calculating probability values from a system of marginal constraints is presented. Previous systems for finding the probability of a single attribute have either made an independence assumption concerning the evidence or have required, in the worst case, time exponential in the number of attributes of the system. In this paper a closed form solut

L. Liu, Y. Ma, D. Wilkins, Z. Bian

This paper describes a generalization of previous methods for constructing tree-structured belief network with hidden variables. The major new feature of the described method is the ability to produce a tree decomposition even when there are errors in the correlation data among the input variables. This is an important extension of existing methods since the

Paul E. Lehner

A series of monte carlo studies were performed to assess the extent to which different inference procedures robustly output reasonable belief values in the context of increasing levels of judgmental imprecision. It was found that, when compared to an equal-weights linear model, the Bayesian procedures are more likely to deduce strong support for a hypothesis

Frank Jensen, S. K. Anderson

When expert systems based on causal probabilistic networks (CPNs) reach a certain size and complexity, the "combinatorial explosion monster" tends to be present. We propose an approximation scheme that identifies rarely occurring cases and excludes these from being processed as ordinary cases in a CPN-based expert system. Depending on the topology an

Michael C. Horsch, David L. Poole

In this paper we present a framework for dynamically constructing Bayesian networks. We introduce the notion of a background knowledge base of schemata, which is a collection of parameterized conditional probability statements. These schemata explicitly separate the general knowledge of properties an individual may have from the specific knowledge of particu

Peter Haddawy

To operate intelligently in the world, an agent must reason about its actions. The consequences of an action are a function of both the state of the world and the action itself. Many aspects of the world are inherently stochastic, so a representation for reasoning about actions must be able to express chances of world states as well as indeterminacy in the e

A. Elfes

In this paper we provide an overview of a new framework for robot perception, real-world modelling, and navigation that uses a stochastic tesselated representation of spatial information called the Occupancy Grid. The Occupancy Grid is a multi-dimensional random field model that maintains probabilistic estimates of the occupancy state of each cell in a spati

R. Martin Chavez, Gregory F. Cooper

In recent years, researchers in decision analysis and artificial intelligence (AI) have used Bayesian belief networks to build models of expert opinion. Using standard methods drawn from the theory of computational complexity, workers in the field have shown that the problem of exact probabilistic inference on belief networks almost certainly requires expone

John S. Breese, Kenneth W. Fertig

In previous work (Fertig and Breese, 1989; Fertig and Breese, 1990) we defined a mechanism for performing probabilistic reasoning in influence diagrams using interval rather than point-valued probabilities. In this paper we extend these procedures to incorporate decision nodes and interval-valued value functions in the diagram. We derive the procedures for c

Ingo Beinlich, Edward H. Herskovits

We describe an environment that considerably simplifies the process of generating Bayesian belief networks. The system has been implemented on readily available, inexpensive hardware, and provides clarity and high performance. We present an introduction to Bayesian belief networks, discuss algorithms for inference with these networks, and delineate the class

K. Bayse, M. Lejter, Keiji Kanazawa

A significant problem in designing mobile robot control systems involves coping with the uncertainty that arises in moving about in an unknown or partially unknown environment and relying on noisy or ambiguous sensor data to acquire knowledge about that environment. We describe a control system that chooses what activity to engage in next on the basis of exp

Solomon Eyal Shimony, Eugene Charniak

We present a new algorithm for finding maximum a-posterior) (MAP) assignments of values to belief networks. The belief network is compiled into a network consisting only of nodes with boolean (i.e. only 0 or 1) conditional probabilities. The MAP assignment is then found using a best-first search on the resulting network. We argue that, as one would anticipat

Robert P. Goldman, Eugene Charniak

We describe a method for incrementally constructing belief networks. We have developed a network-construction language similar to a forward-chaining language using data dependencies, but with additional features for specifying distributions. Using this language, we can define parameterized classes of probabilistic models. These parameterized models make it p

David Einav, Michael R. Fehling

This paper focuses on managing the cost of deliberation before action. In many problems, the overall quality of the solution reflects costs incurred and resources consumed in deliberation as well as the cost and benefit of execution, when both the resource consumption in deliberation phase, and the costs in deliberation and execution are uncertain and may be

John S. Breese, Eric J. Horvitz

The intelligent reformulation or restructuring of a belief network can greatly increase the efficiency of inference. However, time expended for reformulation is not available for performing inference. Thus, under time pressure, there is a tradeoff between the time dedicated to reformulating the network and the time applied to the implementation of a solution

Edward H. Herskovits, Gregory F. Cooper

Kutato is a system that takes as input a database of cases and produces a belief network that captures many of the dependence relations represented by those data. This system incorporates a module for determining the entropy of a belief network and a module for constructing belief networks based on entropy calculations. Kutato constructs an initial belief ne

David L. Poole, Gregory M. Provan

Within diagnostic reasoning there have been a number of proposed definitions of a diagnosis, and thus of the most likely diagnosis, including most probable posterior hypothesis, most probable interpretation, most probable covering hypothesis, etc. Most of these approaches assume that the most likely diagnosis must be computed, and that a definition of what s

Dekang Lin, Randy Goebel

We propose an abductive diagnosis theory that integrates probabilistic, causal and taxonomic knowledge. Probabilistic knowledge allows us to select the most likely explanation; causal knowledge allows us to make reasonable independence assumptions; taxonomic knowledge allows causation to be modeled at different levels of detail, and allows observations be de

Yizong Cheng

Attribute weighting and differential weighting, two major mechanisms for computing context-dependent similarity or dissimilarity measures are studied and compared. A dissimilarity measure based on subset size in the context is proposed and its metrization and application are given. It is also shown that while all attribute weighting dissimilarity measures ar

Max Henrion, Marek J. Druzdzel

Comprehensible explanations of probabilistic reasoning are a prerequisite for wider acceptance of Bayesian methods in expert systems and decision support systems. A study of human reasoning under uncertainty suggests two different strategies for explaining probabilistic reasoning: The first, qualitative belief propagation, traces the qualitative effect of ev

Michael P. Wellman

Functional dependencies restrict the potential interactions among variables connected in a probabilistic network. This restriction can be exploited in qualitative probabilistic reasoning by introducing deterministic variables and modifying the inference rules to produce stronger conclusions in the presence of functional relations. I describe how to accomplis

Monica Patriche

We obtain new equilibrium theorems for fuzzy abstract economies with correspondences being w-upper semicontinuous or having e-USS-property.

Benjamin Nachman, Christopher G. Lester

Many particle physics analyses which need to discriminate some background process from a signal ignore event-by-event resolutions of kinematic variables. Adding this information, as is done for missing momentum significance, can only improve the power of existing techniques. We therefore propose the use of significance variables which combine kinematic infor

Hidenori S. Fukano, Kimmo Tuominen

We discuss a model which involves the top quark condensation and the walking technicolor. We focus on the scalar boson in such a model from the viewpoint of the observed scalar boson at the LHC.

G. E. Astrakharchik, I. Brouzos

Properties of a single impurity in a one-dimensional Fermi gas are investigated in homogeneous and trapped geometries. In a homogeneous system we use McGuire's expression [J. B. McGuire, J. Math. Phys. 6, 432 (1965)] to obtain interaction and kinetic energies, as well as the local pair correlation function. The energy of a trapped system is obtained (i)

Ricardo H. Nochetto, Abner J. Salgado, Ignacio Tomas

The unsteady Micropolar Navier-Stokes Equations (MNSE) are a system of parabolic partial differential equations coupling linear velocity and pressure with angular velocity: material particles have both translational and rotational degrees of freedom. We propose and analyze a first order semi-implicit fully-discrete scheme for the MNSE, which decouples the co

Christopher Minas, Edward Curry, Giovanni Montana

Due to rapid technological advances, a wide range of different measurements can be obtained from a given biological sample including single nucleotide polymorphisms, copy number variation, gene expression levels, DNA methylation and proteomic profiles. Each of these distinct measurements provides the means to characterize a certain aspect of biological diver

Alfio Bonanno, Vadim Urpin

The study of the magnetic field in stellar radiation zones is an important topic in modern astrophysics because the magnetic field can play an important role in several transport phenomena such as mixing and angular momentum transport. We consider the influence of rotation on stability of a predominantly toroidal magnetic field in the radiation zone. We find

Hua Sun, Syed A. Jafar

An interference alignment perspective is used to identify the simplest instances (minimum possible number of edges in the alignment graph, no more than 2 interfering messages at any destination) of index coding problems where non-Shannon information inequalities are necessary for capacity characterization. In particular, this includes the first known example

Yuancheng Fan, Jin Han, Hongqiang Li

Conventional method for Gaussian beam collimation is to expand the beam waist to achieve a smaller divergence angle. Recent transformation optics offers an unconventional path to control the electromagnetic field. In this paper we show that a Gaussian beam can be converted into a plane wave-like beam by transformation media within the scale of several wavele

Masahiro Ibe, Tsutomu T. Yanagida, Norimi Yokozaki

We discuss the minimal supersymmetric standard model with "split-family" spectrum where the sfermions in the first two generations are in the hundreds GeV to a TeV range while the sfermions in the third generation are in the range of tens TeV. With the split-family spectrum, the deviation of the muon g-2 and the observed Higgs boson mass are explaine

Alfred Bennun

The Red cell-Hb-CSF functions as a sensor adapting response to Hb heterotropic equilibriums. At the lungs O2 and Mg2+, each one increasing affinity for the other stabilize the relax (R) form [(O2)4Hb(Mg)2].(H2O)R. At tissue level, the inclusion of H+ and 2,3-DPG excludes O2 and Mg2+ to stabilize the tense (T) form 2,3-DPG-deoxyHb-(H2O)T. Both senses are inte

Monica Patriche

We study the conditions under which the iterated elimination of strictly dominated strategies is order independent and we identify a class of discontinuous games for which order does not matter. In this way, we answer the open problem raised by M. Dufwenberg and M. Stegeman (2002) and generalize their main results. We also establish new theorems concerning t

Dongwook Lee

In this paper, we extend the unsplit staggered mesh scheme (USM) for 2D magnetohydrodynamics (MHD) (Lee and Deane, 2009) to a full 3D MHD scheme. The scheme is a finite-volume Godunov method consisting of a constrained transport (CT) method and an efficient and accurate single-step, directionally unsplit multidimensional data reconstruction-evolution algorit

John P. Sheckelton, James R. Neilson, Daniel G. Soltan, Tyrel M. McQueen

The emergence of complex electronic behaviour from simple ingredients has resulted in the discovery of numerous states of matter. Many examples are found in systems exhibiting geometric magnetic frustration, which prevents simultaneous satisfaction of all magnetic interactions. This frustration gives rise to complex magnetic properties such as chiral spin st

Irfan Siap, Ismail Aydogdu

In this paper, we count the number of matrices whose rows generate different $\mathbb{Z}_2\mathbb{Z}_8$ additive codes. This is a natural generalization of the well known Gaussian numbers that count the number of matrices whose rows generate vector spaces with particular dimension over finite fields. Due to this similarity we name this numbers as Mixed Gener

Rahul Biswas, Lindy Blackburn, Junwei Cao, Reed Essick

The sensitivity of searches for astrophysical transients in data from the LIGO is generally limited by the presence of transient, non-Gaussian noise artifacts, which occur at a high-enough rate such that accidental coincidence across multiple detectors is non-negligible. Furthermore, non-Gaussian noise artifacts typically dominate over the background contrib

Monica Patriche

We introduce the notions of weakly *-concave and weakly naturally quasi-concave correspondence and prove fixed point theorems and continuous selection theorems for these kind of correspondences. As applications in the game theory, by using a tehnique based on a continuous selection, we establish new existence results for the equilibrium of the abstract econo

Rafael Bassi Stern, Joseph Born Kadane

De Finetti's betting argument is used to justify finitely additive probabilities when only finitely many bets are considered. Under what circumstances can countably many bets be used to justify countable additivity? In this framework, one faces issues such as the convergence of the returns of the bet. Generalizations of de Finetti's argument depend o

Alexander A. Philippov, Roman R. Rafikov

Eclipsing binary DI Herculis (DI Her) is known to exhibit anomalously slow apsidal precession, below the rate predicted by the general relativity. Recent measurements of the Rossiter-McLauglin effect indicate that stellar spins in DI Her are almost orthogonal to the orbital angular momentum, which explains the anomalous precession in agreement with the earli

Monica Patriche

We introduce the notions of w-lower semicontinuous and almost w-lower semicontinuous correspondence with respect to a given set and prove a new fixed-point theorem. We also introduce the notion of correspondence with e-LSCS-property. As applications we obtain some new equilibrium theorems for abstract economies and for generalized multiobjective games.

V. R. C. Mourão Roque, G. Lugones

We study the evolution of turbulence in the early universe at the QCD epoch using a state-of-the-art equation of state derived from lattice QCD simulations. Since the transition is a crossover we assume that temperature and velocity fluctuations were generated by some event in the previous history of the Universe and survive until the QCD epoch due to the ex

Monica Patriche

We extend the study of the iterated elimination of strictly dominated strategies (IESDS) from Nash strategic games to a class of qualitative games. Also in this case, the IESDS process leads us to a kind of 'rationalizable' result. We define several types of dominance relation and game reduction and establish conditions under which a unique and nonem

Cody Jones

We introduce a fault-tolerant construction to implement a composite quantum operation of four overlapping Toffoli gates. The same construction can produce two independent Toffoli gates. This result lowers resource overheads in designs for quantum computers by more than an order of magnitude. The procedure uses Clifford operations and 64 copies of the non-Cli

M. Adeel Ajaib, Ilia Gogoladze, Qaisar Shafi, Cem Salih Un

We revisit a class of supersymmetric SO(10) models with t-b-tau Yukawa coupling unification condition, with emphasis on the prediction of the Higgs mass. We discuss qualitative features in this model that lead to a Higgs mass prediction close to 125 GeV. We show this with two distinct computing packages, Isajet and SuSpect, and also show that they yield simi

Tony Pan, Abraham Loeb

Current surveys are underway to utilize gravitational lensing by galaxy clusters with Einstein radii >35" in the search for the highest redshift galaxies. Associated supernova from the epoch of reionization would have their fluxes boosted above the detection threshold, extending their duration of visibility. We predict that the James Webb Space Telescope

Tony Pan, Daniel J. Patnaude, Abraham Loeb

For supernova powered by the conversion of kinetic energy into radiation due to the interactions of the ejecta with a dense circumstellar shell, we show that there could be X-ray analogues of optically super-luminous SNe with comparable luminosities and energetics. We consider X-ray emission from the forward shock of SNe ejecta colliding into an optically-th

Andrew C. Potter, Patrick A. Lee

In mean-field descriptions of nodal d-wave superconductors, generic edges exhibit dispersionless Majorana fermion bands at zero-energy. These states give rise to an extensive ground-state degeneracy, and are protected by time-reversal (TR) symmetry. We argue that the infinite density of states of these flat-bands make them inherently unstable to interactions

Thomas Hartman

Two-dimensional conformal field theories with a large central charge and a small number of low-dimension operators are studied using the conformal block expansion. A universal formula is derived for the Renyi entropies of N disjoint intervals in the ground state, valid to all orders in a series expansion. This is possible because the full perturbative answer

Vincenzo Cirigliano, Susan Gardner, Barry Holstein

We consider the role of precision measurements of beta decays and light meson semi-leptonic decays in probing physics beyond the Standard Model in the LHC era. We describe all low-energy charged-current processes within and beyond the Standard Model using an effective field theory framework. We first discuss the theoretical hadronic input which in these prec

Liang Dai, Donghui Jeong, Marc Kamionkowski, Jens Chluba

Physical models for the hemispherical power asymmetry in the cosmic microwave background (CMB) reported by the Planck Collaboration must satisfy CMB constraints to the homogeneity of the Universe and quasar constraints to power asymmetries. We survey a variety of models for the power asymmetry and show that consistent models include a modulated scale-depende

O. Sh. Mukhtarov, K. Aydemir

In this paper we are concerned with a new class of BVP' s consisting of eigendependent boundary conditions and two supplementary transmission conditions at one interior point. By modifying some techniques of classical Sturm-Liouville theory and suggesting own approaches we find asymptotic formulas for the eigenvalues and eigenfunction.

K. Aydemir, O. Sh. Mukhtarov

One important innovation here is that for the Sturm-Liouville considered equation together with eigenparameter dependent boundary conditions and two supplementary transmission conditions at one interior point. We develop Green's function method for spectral analysis of the considered problem in modified Hilbert space.

O. Sh. Mukhtarov, K. Aydemir

In this study by applying an own technique we investigate some asymptotic approximation properties of new type discontinuous boundary-value problems, which consists of a Sturm-Liouville equation together with eigenparameter-dependent boundary and transmission conditions.

G. Abbas

In this Letter, we examine the phantom energy accretion onto a Kehagias-Sfetsos black hole in Ho$\check{r}$ava Lifshitz gravity. To discuss the accretion process onto the black hole, the equations of phantom flow near the black hole have been derived. It is found that mass of the black hole decreases because of phantom accretion. We discuss the conditions fo

Valentin Keyantuo, Pedro J. Miana, Luis Sánchez-Lajusticia

In this paper we give sharp extension results for convoluted solutions of abstract Cauchy problems in Banach spaces. The main technique is the use of algebraic structure (for usual convolution product $\ast$) of these solutions which are defined by a version of the Duhamel formula. We define algebra homomorphisms from a new class of test-functions and apply

B. Hunt, J. D. Sanchez-Yamagishi, A. F. Young, K. Watanabe

Van der Waals heterostructures comprise a new class of artificial materials formed by stacking atomically-thin planar crystals. Here, we demonstrate band structure engineering of a van der Waals heterostructure composed of a monolayer graphene flake coupled to a rotationally-aligned hexagonal boron nitride substrate. The spatially-varying interlayer atomic r