title:
Analytical Approximation of the Neutrino Oscillation Matter Effects at
  large $\theta_{13}$

abstract:
  We argue that the neutrino oscillation probabilities in matter are best
understood by allowing the mixing angles and mass-squared differences in the
standard parametrization to `run' with the matter effect parameter
$a=2\sqrt{2}G_F N_e E$, where $N_e$ is the electron density in matter and $E$
is the neutrino energy. We present simple analytical approximations to these
`running' parameters. We show that for the moderately large value of
$\theta_{13}$, as discovered by the reactor experiments, the running of the
mixing angle $\theta_{23}$ and the CP violating phase $\delta$ can be
neglected. It simplifies the analysis of the resulting expressions for the
oscillation probabilities considerably. Approaches which attempt to directly
provide approximate analytical expressions for the oscillation probabilities in
matter suffer in accuracy due to their reliance on expansion in $\theta_{13}$,
or in simplicity when higher order terms in $\theta_{13}$ are included. We
demonstrate the accuracy of our method by comparing it to the exact numerical
result, as well as the direct approximations of Cervera et al., Akhmedov et
al., Asano and Minakata, and Freund. We also discuss the utility of our
approach in figuring out the required baseline lengths and neutrino energies
for the oscillation probabilities to exhibit certain desirable features.


title:
Characterization of Infrared Dark Clouds -- NH$_3$ Observations of an
  Absorption-contrast Selected IRDC Sample

abstract:
  Despite increasing research in massive star formation, little is known about
its earliest stages. Infrared Dark Clouds (IRDCs) are cold, dense and massive
enough to harbour the sites of future high-mass star formation. But up to now,
mainly small samples have been observed and analysed. To understand the
physical conditions during the early stages of high-mass star formation, it is
necessary to learn more about the physical conditions and stability in
relatively unevolved IRDCs. Thus, for characterising IRDCs studies of large
samples are needed. We investigate a complete sample of 218 northern hemisphere
high-contrast IRDCs using the ammonia (1,1)- and (2,2)-inversion transitions.
We detected ammonia (1,1)-inversion transition lines in 109 of our IRDC
candidates. Using the data we were able to study the physical conditions within
the star-forming regions statistically. We compared them with the conditions in
more evolved regions which have been observed in the same fashion as our sample
sources. Our results show that IRDCs have, on average, rotation temperatures of
15 K, are turbulent (with line width FWHMs around 2 km s$^{-1}$), have ammonia
column densities on the order of $10^{14}$ cm$^{-2}$ and molecular hydrogen
column densities on the order of $10^{22}$ cm$^{-2}$. Their virial masses are
between 100 and a few 1000 M$_\odot$. The comparison of bulk kinetic and
potential energies indicate that the sources are close to virial equilibrium.
IRDCs are on average cooler and less turbulent than a comparison sample of
high-mass protostellar objects, and have lower ammonia column densities. Virial
parameters indicate that the majority of IRDCs are currently stable, but are
expected to collapse in the future.


title: Quantum interference hybrid spin-current injector

abstract:
  We propose a quantum interference spin-injector nanodevice consisting of a
superconductor-normal metal hybrid loop connected to a
superconductor-ferromagnet bilayer via a tunneling junction. We show that for
certain values of the applied voltage bias across the tunnel barrier and the
magnetic flux through the loop the spin-current can be fully polarized.
Moreover, by tuning the magnetic flux one can switch the sign of the spin
polarization. This operation can be performed at frequencies within the tens of
GHz range. We explore the nanodevice in a wide range of parameters, establish
the optimum conditions for its experimental realization and discuss its
possible applications.


title:
A Summary: Quantum Singularities in Static and Conformally Static
  Spacetimes

abstract:
  This is a summary of how the definition of quantum singularity is extended
from static space-times to conformally static space-times. Examples are given.


title: Ending-based Strategies for Part-of-speech Tagging

abstract:
  Probabilistic approaches to part-of-speech tagging rely primarily on
whole-word statistics about word/tag combinations as well as contextual
information. But experience shows about 4 per cent of tokens encountered in
test sets are unknown even when the training set is as large as a million
words. Unseen words are tagged using secondary strategies that exploit word
features such as endings, capitalizations and punctuation marks. In this work,
word-ending statistics are primary and whole-word statistics are secondary.
First, a tagger was trained and tested on word endings only. Subsequent
experiments added back whole-word statistics for the words occurring most
frequently in the training set. As grew larger, performance was expected to
improve, in the limit performing the same as word-based taggers. Surprisingly,
the ending-based tagger initially performed nearly as well as the word-based
tagger; in the best case, its performance significantly exceeded that of the
word-based tagger. Lastly, and unexpectedly, an effect of negative returns was
observed - as grew larger, performance generally improved and then declined. By
varying factors such as ending length and tag-list strategy, we achieved a
success rate of 97.5 percent.


title: Properties of a Two-Sphere Singularity

abstract:
  Recently B\"ohmer and Lobo have shown that a metric due to Florides can be
extended to reveal a classical singularity that has the form of a two-sphere.
Here we discuss and expand on the classical singularity properties and then
show the classical singularity is not healed by a quantum analysis.


title:
An Evaluation of an Algorithm for Inductive Learning of Bayesian Belief
  Networks Usin

abstract:
  Bayesian learning of belief networks (BLN) is a method for automatically
constructing belief networks (BNs) from data using search and Bayesian scoring
techniques. K2 is a particular instantiation of the method that implements a
greedy search strategy. To evaluate the accuracy of K2, we randomly generated a
number of BNs and for each of those we simulated data sets. K2 was then used to
induce the generating BNs from the simulated data. We examine the performance
of the program, and the factors that influence it. We also present a simple BN
model, developed from our results, which predicts the accuracy of K2, when
given various characteristics of the data set.


title: Probabilistic Constraint Satisfaction with Non-Gaussian Noise

abstract:
  We have previously reported a Bayesian algorithm for determining the
coordinates of points in three-dimensional space from uncertain constraints.
This method is useful in the determination of biological molecular structure.
It is limited, however, by the requirement that the uncertainty in the
constraints be normally distributed. In this paper, we present an extension of
the original algorithm that allows constraint uncertainty to be represented as
a mixture of Gaussians, and thereby allows arbitrary constraint distributions.
We illustrate the performance of this algorithm on a problem drawn from the
domain of molecular structure determination, in which a multicomponent
constraint representation produces a much more accurate solution than the old
single component mechanism. The new mechanism uses mixture distributions to
decompose the problem into a set of independent problems with unimodal
constraint uncertainty. The results of the unimodal subproblems are
periodically recombined using Bayes' law, to avoid combinatorial explosion. The
new algorithm is particularly suited for parallel implementation.


title: A Bayesian Method Reexamined

abstract:
  This paper examines the "K2" network scoring metric of Cooper and Herskovits.
It shows counterintuitive results from applying this metric to simple networks.
One family of noninformative priors is suggested for assigning equal scores to
equivalent networks.


title:
Laplace's Method Approximations for Probabilistic Inference in Belief
  Networks with Continuous Variables

abstract:
  Laplace's method, a family of asymptotic methods used to approximate
integrals, is presented as a potential candidate for the tool box of techniques
used for knowledge acquisition and probabilistic inference in belief networks
with continuous variables. This technique approximates posterior moments and
marginal posterior distributions with reasonable accuracy [errors are O(n^-2)
for posterior means] in many interesting cases. The method also seems promising
for computing approximations for Bayes factors for use in the context of model
selection, model uncertainty and mixtures of pdfs. The limitations, regularity
conditions and computational difficulties for the implementation of Laplace's
method are comparable to those associated with the methods of maximum
likelihood and posterior mode analysis.


title: Generating New Beliefs From Old

abstract:
  In previous work [BGHK92, BGHK93], we have studied the random-worlds approach
-- a particular (and quite powerful) method for generating degrees of belief
(i.e., subjective probabilities) from a knowledge base consisting of objective
(first-order, statistical, and default) information. But allowing a knowledge
base to contain only objective information is sometimes limiting. We
occasionally wish to include information about degrees of belief in the
knowledge base as well, because there are contexts in which old beliefs
represent important information that should influence new beliefs. In this
paper, we describe three quite general techniques for extending a method that
generates degrees of belief from objective information to one that can make use
of degrees of belief as well. All of our techniques are bloused on well-known
approaches, such as cross-entropy. We discuss general connections between the
techniques and in particular show that, although conceptually and technically
quite different, all of the techniques give the same answer when applied to the
random-worlds method.


title:
Counterfactual Probabilities: Computational Methods, Bounds and
  Applications

abstract:
  Evaluation of counterfactual queries (e.g., "If A were true, would C have
been true?") is important to fault diagnosis, planning, and determination of
liability. In this paper we present methods for computing the probabilities of
such queries using the formulation proposed in [Balke and Pearl, 1994], where
the antecedent of the query is interpreted as an external action that forces
the proposition A to be true. When a prior probability is available on the
causal mechanisms governing the domain, counterfactual probabilities can be
evaluated precisely. However, when causal knowledge is specified as conditional
probabilities on the observables, only bounds can computed. This paper develops
techniques for evaluating these bounds, and demonstrates their use in two
applications: (1) the determination of treatment efficacy from studies in which
subjects may choose their own treatment, and (2) the determination of liability
in product-safety litigation.


title: Novikov homology, jump loci and Massey products

abstract:
  Let X be a finite CW-complex, denote its fundamental group by G. Let R be an
n-dimensional complex repesentation of G. Any element A of the first cohomology
group of X with complex coefficients gives rise to the exponential deformation
of the representation R, which can be considered as a curve in the space of
representations. We show that the cohomology of X with local coefficients
corresponding to the generic point of this curve is computable from a spectral
sequence starting from the cohomology of X with R-twisted coefficients. We
compute the differentials of the spectral sequence in terms of Massey products.
We show that the spectral sequence degenerates in case when X is a Kaehler
manifold, and the representation R is semi-simple.
  If A is a real cohomology class, one associates to the triple (X,R,A) the
twisted Novikov homology (a module over the Novikov ring). We show that the
twisted Novikov Betti numbers equal the Betti numbers of X with coefficients in
the above local system. We investigate the dependance of these numbers on A and
prove that they are constant in the complement to a finite number of integral
hyperplanes in the first cohomology group.


title:
Modus Ponens Generating Function in the Class of ^-valuations of
  Plausibility

abstract:
  We discuss the problem of construction of inference procedures which can
manipulate with uncertainties measured in ordinal scales and fulfill to the
property of strict monotonicity of conclusion. The class of A-valuations of
plausibility is considered where operations based only on information about
linear ordering of plausibility values are used. In this class the modus ponens
generating function fulfiling to the property of strict monotonicity of
conclusions is introduced.


title: Approximation Algorithms for the Loop Cutset Problem

abstract:
  We show how to find a small loop curser in a Bayesian network. Finding such a
loop cutset is the first step in the method of conditioning for inference. Our
algorithm for finding a loop cutset, called MGA, finds a loop cutset which is
guaranteed in the worst case to contain less than twice the number of variables
contained in a minimum loop cutset. We test MGA on randomly generated graphs
and find that the average ratio between the number of instances associated with
the algorithms' output and the number of instances associated with a minimum
solution is 1.22.


title: Possibility and Necessity Functions over Non-classical Logics

abstract:
  We propose an integration of possibility theory into non-classical logics. We
obtain many formal results that generalize the case where possibility and
necessity functions are based on classical logic. We show how useful such an
approach is by applying it to reasoning under uncertain and inconsistent
information.


title: Exploratory Model Building

abstract:
  Some instances of creative thinking require an agent to build and test
hypothetical theories. Such a reasoner needs to explore the space of not only
those situations that have occurred in the past, but also those that are
rationally conceivable. In this paper we present a formalism for exploring the
space of conceivable situation-models for those domains in which the knowledge
is primarily probabilistic in nature. The formalism seeks to construct
consistent, minimal, and desirable situation-descriptions by selecting suitable
domain-attributes and dependency relationships from the available domain
knowledge.


title: Learning in Multi-level Stochastic games with Delayed Information

abstract:
  Distributed decision-makers are modeled as players in a game with two levels.
High level decisions concern the game environment and determine the willingness
of the players to form a coalition (or group). Low level decisions involve the
actions to be implemented within the chosen environment. Coalition and action
strategies are determined by probability distributions, which are updated using
learning automata schemes. The payoffs are also probabilistic and there is
uncertainty in the state vector since information is delayed. The goal is to
reach equilibrium in both levels of decision making; the results show the
conditions for instability, based on the age of information.


title: Planning with External Events

abstract:
  I describe a planning methodology for domains with uncertainty in the form of
external events that are not completely predictable. The events are represented
by enabling conditions and probabilities of occurrence. The planner is
goal-directed and backward chaining, but the subgoals are suggested by
analyzing the probability of success of the partial plan rather than being
simply the open conditions of the operators in the plan. The partial plan is
represented as a Bayesian belief net to compute its probability of success.
Since calculating the probability of success of a plan can be very expensive I
introduce two other techniques for computing it, one that uses Monte Carlo
simulation to estimate it and one based on a Markov chain representation that
uses knowledge about the dependencies between the predicates describing the
domain.


title: Properties of Bayesian Belief Network Learning Algorithms

abstract:
  Bayesian belief network learning algorithms have three basic components: a
measure of a network structure and a database, a search heuristic that chooses
network structures to be considered, and a method of estimating the probability
tables from the database. This paper contributes to all these three topics. The
behavior of the Bayesian measure of Cooper and Herskovits and a minimum
description length (MDL) measure are compared with respect to their properties
for both limiting size and finite size databases. It is shown that the MDL
measure has more desirable properties than the Bayesian measure when a
distribution is to be learned. It is shown that selecting belief networks with
certain minimallity properties is NP-hard. This result justifies the use of
search heuristics instead of exact algorithms for choosing network structures
to be considered. In some cases, a collection of belief networks can be
represented by a single belief network which leads to a new kind of probability
table estimation called smoothing. We argue that smoothing can be efficiently
implemented by incorporating it in the search heuristic. Experimental results
suggest that for learning probabilities of belief networks smoothing is
helpful.


title: A Stratified Simulation Scheme for Inference in Bayesian Belief Networks

abstract:
  Simulation schemes for probabilistic inference in Bayesian belief networks
offer many advantages over exact algorithms; for example, these schemes have a
linear and thus predictable runtime while exact algorithms have exponential
runtime. Experiments have shown that likelihood weighting is one of the most
promising simulation schemes. In this paper, we present a new simulation scheme
that generates samples more evenly spread in the sample space than the
likelihood weighting scheme. We show both theoretically and experimentally that
the stratified scheme outperforms likelihood weighting in average runtime and
error in estimates of beliefs.


title: Efficient Estimation of the Value of Information in Monte Carlo Models

abstract:
  The expected value of information (EVI) is the most powerful measure of
sensitivity to uncertainty in a decision model: it measures the potential of
information to improve the decision, and hence measures the expected value of
outcome. Standard methods for computing EVI use discrete variables and are
computationally intractable for models that contain more than a few variables.
Monte Carlo simulation provides the basis for more tractable evaluation of
large predictive models with continuous and discrete variables, but so far
computation of EVI in a Monte Carlo setting also has appeared impractical. We
introduce an approximate approach based on pre-posterior analysis for
estimating EVI in Monte Carlo models. Our method uses a linear approximation to
the value function and multiple linear regression to estimate the linear model
from the samples. The approach is efficient and practical for extremely large
models. It allows easy estimation of EVI for perfect or partial information on
individual variables or on combinations of variables. We illustrate its
implementation within Demos (a decision modeling system), and its application
to a large model for crisis transportation planning.


title: Symbolic Probabilitistic Inference in Large BN2O Networks

abstract:
  A BN2O network is a two level belief net in which the parent interactions are
modeled using the noisy-or interaction model. In this paper we discuss
application of the SPI local expression language to efficient inference in
large BN2O networks. In particular, we show that there is significant
structure, which can be exploited to improve over the Quickscore result. We
further describe how symbolic techniques can provide information which can
significantly reduce the computation required for computing all cause posterior
marginals. Finally, we present a novel approximation technique with preliminary
experimental results.


title:
Action Networks: A Framework for Reasoning about Actions and Change
  under Uncertainty

abstract:
  This work proposes action networks as a semantically well-founded framework
for reasoning about actions and change under uncertainty. Action networks add
two primitives to probabilistic causal networks: controllable variables and
persistent variables. Controllable variables allow the representation of
actions as directly setting the value of specific events in the domain, subject
to preconditions. Persistent variables provide a canonical model of persistence
according to which both the state of a variable and the causal mechanism
dictating its value persist over time unless intervened upon by an action (or
its consequences). Action networks also allow different methods for quantifying
the uncertainty in causal relationships, which go beyond traditional
probabilistic quantification. This paper describes both recent results and work
in progress.


title: On the Relation between Kappa Calculus and Probabilistic Reasoning

abstract:
  We study the connection between kappa calculus and probabilistic reasoning in
diagnosis applications. Specifically, we abstract a probabilistic belief
network for diagnosing faults into a kappa network and compare the ordering of
faults computed using both methods. We show that, at least for the example
examined, the ordering of faults coincide as long as all the causal relations
in the original probabilistic network are taken into account. We also provide a
formal analysis of some network structures where the two methods will differ.
Both kappa rankings and infinitesimal probabilities have been used extensively
to study default reasoning and belief revision. But little has been done on
utilizing their connection as outlined above. This is partly because the
relation between kappa and probability calculi assumes that probabilities are
arbitrarily close to one (or zero). The experiments in this paper investigate
this relation when this assumption is not satisfied. The reported results have
important implications on the use of kappa rankings to enhance the knowledge
engineering of uncertainty models.


title: A Structured, Probabilistic Representation of Action

abstract:
  When agents devise plans for execution in the real world, they face two
important forms of uncertainty: they can never have complete knowledge about
the state of the world, and they do not have complete control, as the effects
of their actions are uncertain. While most classical planning methods avoid
explicit uncertainty reasoning, we believe that uncertainty should be
explicitly represented and reasoned about. We develop a probabilistic
representation for states and actions, based on belief networks. We define
conditional belief nets (CBNs) to capture the probabilistic dependency of the
effects of an action upon the state of the world. We also use a CBN to
represent the intrinsic relationships among entities in the environment, which
persist from state to state. We present a simple projection algorithm to
construct the belief network of the state succeeding an action, using the
environment CBN model to infer indirect effects. We discuss how the qualitative
aspects of belief networks and CBNs make them appropriate for the various
stages of the problem solving process, from model construction to the design of
planning algorithms.


title: Integrating Planning and Execution in Stochastic Domains

abstract:
  We investigate planning in time-critical domains represented as Markov
Decision Processes, showing that search based techniques can be a very powerful
method for finding close to optimal plans. To reduce the computational cost of
planning in these domains, we execute actions as we construct the plan, and
sacrifice optimality by searching to a fixed depth and using a heuristic
function to estimate the value of states. Although this paper concentrates on
the search algorithm, we also discuss ways of constructing heuristic functions
suitable for this approach. Our results show that by interleaving search and
execution, close to optimal policies can be found without the computational
requirements of other approaches.


title: Localized Partial Evaluation of Belief Networks

abstract:
  Most algorithms for propagating evidence through belief networks have been
exact and exhaustive: they produce an exact (point-valued) marginal probability
for every node in the network. Often, however, an application will not need
information about every n ode in the network nor will it need exact
probabilities. We present the localized partial evaluation (LPE) propagation
algorithm, which computes interval bounds on the marginal probability of a
specified query node by examining a subset of the nodes in the entire network.
Conceptually, LPE ignores parts of the network that are "too far away" from the
queried node to have much impact on its value. LPE has the "anytime" property
of being able to produce better solutions (tighter intervals) given more time
to consider more of the network.


title:
A Probabilistic Model of Action for Least-Commitment Planning with
  Information Gather

abstract:
  AI planning algorithms have addressed the problem of generating sequences of
operators that achieve some input goal, usually assuming that the planning
agent has perfect control over and information about the world. Relaxing these
assumptions requires an extension to the action representation that allows
reasoning both about the changes an action makes and the information it
provides. This paper presents an action representation that extends the
deterministic STRIPS model, allowing actions to have both causal and
informational effects, both of which can be context dependent and noisy. We
also demonstrate how a standard least-commitment planning algorithm can be
extended to include informational actions and contingent execution.


title: Some Properties of Joint Probability Distributions

abstract:
  Several Artificial Intelligence schemes for reasoning under uncertainty
explore either explicitly or implicitly asymmetries among probabilities of
various states of their uncertain domain models. Even though the correct
working of these schemes is practically contingent upon the existence of a
small number of probable states, no formal justification has been proposed of
why this should be the case. This paper attempts to fill this apparent gap by
studying asymmetries among probabilities of various states of uncertain models.
By rewriting the joint probability distribution over a model's variables into a
product of individual variables' prior and conditional probability
distributions, and applying central limit theorem to this product, we can
demonstrate that the probabilities of individual states of the model can be
expected to be drawn from highly skewed, log-normal distributions. With
sufficient asymmetry in individual prior and conditional probability
distributions, a small fraction of states can be expected to cover a large
portion of the total probability space with the remaining states having
practically negligible probability. Theoretical discussion is supplemented by
simulation results and an illustrative real-world example.


title: An Ordinal View of Independence with Application to Plausible Reasoning

abstract:
  An ordinal view of independence is studied in the framework of possibility
theory. We investigate three possible definitions of dependence, of increasing
strength. One of them is the counterpart to the multiplication law in
probability theory, and the two others are based on the notion of conditional
possibility. These two have enough expressive power to support the whole
possibility theory, and a complete axiomatization is provided for the strongest
one. Moreover we show that weak independence is well-suited to the problems of
belief change and plausible reasoning, especially to address the problem of
blocking of property inheritance in exception-tolerant taxonomic reasoning.


title: Penalty logic and its Link with Dempster-Shafer Theory

abstract:
  Penalty logic, introduced by Pinkas, associates to each formula of a
knowledge base the price to pay if this formula is violated. Penalties may be
used as a criterion for selecting preferred consistent subsets in an
inconsistent knowledge base, thus inducing a non-monotonic inference relation.
A precise formalization and the main properties of penalty logic and of its
associated non-monotonic inference relation are given in the first part. We
also show that penalty logic and Dempster-Shafer theory are related, especially
in the infinitesimal case.


title: Value of Evidence on Influence Diagrams

abstract:
  In this paper, we introduce evidence propagation operations on influence
diagrams and a concept of value of evidence, which measures the value of
experimentation. Evidence propagation operations are critical for the
computation of the value of evidence, general update and inference operations
in normative expert systems which are based on the influence diagram
(generalized Bayesian network) paradigm. The value of evidence allows us to
compute directly an outcome sensitivity, a value of perfect information and a
value of control which are used in decision analysis (the science of decision
making under uncertainty). More specifically, the outcome sensitivity is the
maximum difference among the values of evidence, the value of perfect
information is the expected value of the values of evidence, and the value of
control is the optimal value of the values of evidence. We also discuss an
implementation and a relative computational efficiency issues related to the
value of evidence and the value of perfect information.


title: Conditional Independence in Possibility Theory

abstract:
  Possibilistic conditional independence is investigated: we propose a
definition of this notion similar to the one used in probability theory. The
links between independence and non-interactivity are investigated, and
properties of these relations are given. The influence of the conjunction used
to define a conditional measure of possibility is also highlighted: we examine
three types of conjunctions: Lukasiewicz - like T-norms, product-like T-norms
and the minimum operator.


title: Backward Simulation in Bayesian Networks

abstract:
  Backward simulation is an approximate inference technique for Bayesian belief
networks. It differs from existing simulation methods in that it starts
simulation from the known evidence and works backward (i.e., contrary to the
direction of the arcs). The technique's focus on the evidence leads to improved
convergence in situations where the posterior beliefs are dominated by the
evidence rather than by the prior probabilities. Since this class of situations
is large, the technique may make practical the application of approximate
inference in Bayesian belief networks to many real-world problems.


title: Learning Gaussian Networks

abstract:
  We describe algorithms for learning Bayesian networks from a combination of
user knowledge and statistical data. The algorithms have two components: a
scoring metric and a search procedure. The scoring metric takes a network
structure, statistical data, and a user's prior knowledge, and returns a score
proportional to the posterior probability of the network structure given the
data. The search procedure generates networks for evaluation by the scoring
metric. Previous work has concentrated on metrics for domains containing only
discrete variables, under the assumption that data represents a multinomial
sample. In this paper, we extend this work, developing scoring metrics for
domains containing all continuous variables or a mixture of discrete and
continuous variables, under the assumption that continuous data is sampled from
a multivariate normal distribution. Our work extends traditional statistical
approaches for identifying vanishing regression coefficients in that we
identify two important assumptions, called event equivalence and parameter
modularity, that when combined allow the construction of prior distributions
for multivariate normal parameters from a single prior Bayesian network
specified by a user.


title:
On Testing Whether an Embedded Bayesian Network Represents a Probability
  Model

abstract:
  Testing the validity of probabilistic models containing unmeasured (hidden)
variables is shown to be a hard task. We show that the task of testing whether
models are structurally incompatible with the data at hand, requires an
exponential number of independence evaluations, each of the form: "X is
conditionally independent of Y, given Z." In contrast, a linear number of such
evaluations is required to test a standard Bayesian network (one per vertex).
On the positive side, we show that if a network with hidden variables G has a
tree skeleton, checking whether G represents a given probability model P
requires the polynomial number of such independence evaluations. Moreover, we
provide an algorithm that efficiently constructs a tree-structured Bayesian
network (with hidden variables) that represents P if such a network exists, and
further recognizes when such a network does not exist.


title: Epsilon-Safe Planning

abstract:
  We introduce an approach to high-level conditional planning we call
epsilon-safe planning. This probabilistic approach commits us to planning to
meet some specified goal with a probability of success of at least 1-epsilon
for some user-supplied epsilon. We describe several algorithms for epsilon-safe
planning based on conditional planners. The two conditional planners we discuss
are Peot and Smith's nonlinear conditional planner, CNLP, and our own linear
conditional planner, PLINTH. We present a straightforward extension to
conditional planners for which computing the necessary probabilities is simple,
employing a commonly-made but perhaps overly-strong independence assumption. We
also discuss a second approach to epsilon-safe planning which relaxes this
independence assumption, involving the incremental construction of a
probability dependence model in conjunction with the construction of the plan
graph.


title: Generating Bayesian Networks from Probability Logic Knowledge Bases

abstract:
  We present a method for dynamically generating Bayesian networks from
knowledge bases consisting of first-order probability logic sentences. We
present a subset of probability logic sufficient for representing the class of
Bayesian networks with discrete-valued nodes. We impose constraints on the form
of the sentences that guarantee that the knowledge base contains all the
probabilistic information necessary to generate a network. We define the
concept of d-separation for knowledge bases and prove that a knowledge base
with independence conditions defined by d-separation is a complete
specification of a probability distribution. We present a network generation
algorithm that, given an inference problem in the form of a query Q and a set
of evidence E, generates a network to compute P(Q|E). We prove the algorithm to
be correct.


title: Abstracting Probabilistic Actions

abstract:
  This paper discusses the problem of abstracting conditional probabilistic
actions. We identify two distinct types of abstraction: intra-action
abstraction and inter-action abstraction. We define what it means for the
abstraction of an action to be correct and then derive two methods of
intra-action abstraction and two methods of inter-action abstraction which are
correct according to this criterion. We illustrate the developed techniques by
applying them to actions described with the temporal action representation used
in the DRIPS decision-theoretic planner and we describe how the planner uses
abstraction to reduce the complexity of planning.


title: On Modal Logics for Qualitative Possibility in a Fuzzy Setting

abstract:
  Within the possibilistic approach to uncertainty modeling, the paper presents
a modal logical system to reason about qualitative (comparative) statements of
the possibility (and necessity) of fuzzy propositions. We relate this
qualitative modal logic to the many--valued analogues MVS5 and MVKD45 of the
well known modal logics of knowledge and belief S5 and KD45 respectively.
Completeness results are obtained for such logics and therefore, they extend
previous existing results for qualitative possibilistic logics in the classical
non-fuzzy setting.


title: A New Look at Causal Independence

abstract:
  Heckerman (1993) defined causal independence in terms of a set of temporal
conditional independence statements. These statements formalized certain types
of causal interaction where (1) the effect is independent of the order that
causes are introduced and (2) the impact of a single cause on the effect does
not depend on what other causes have previously been applied. In this paper, we
introduce an equivalent a temporal characterization of causal independence
based on a functional representation of the relationship between causes and the
effect. In this representation, the interaction between causes and effect can
be written as a nested decomposition of functions. Causal independence can be
exploited by representing this decomposition in the belief network, resulting
in representations that are more efficient for inference than general causal
models. We present empirical results showing the benefits of a
causal-independence representation for belief-network inference.


title:
Learning Bayesian Networks: The Combination of Knowledge and Statistical
  Data

abstract:
  We describe algorithms for learning Bayesian networks from a combination of
user knowledge and statistical data. The algorithms have two components: a
scoring metric and a search procedure. The scoring metric takes a network
structure, statistical data, and a user's prior knowledge, and returns a score
proportional to the posterior probability of the network structure given the
data. The search procedure generates networks for evaluation by the scoring
metric. Our contributions are threefold. First, we identify two important
properties of metrics, which we call event equivalence and parameter
modularity. These properties have been mostly ignored, but when combined,
greatly simplify the encoding of a user's prior knowledge. In particular, a
user can express her knowledge-for the most part-as a single prior Bayesian
network for the domain. Second, we describe local search and annealing
algorithms to be used in conjunction with scoring metrics. In the special case
where each node has at most one parent, we show that heuristic search can be
replaced with a polynomial algorithm to identify the networks with the highest
score. Third, we describe a methodology for evaluating Bayesian-network
learning algorithms. We apply this approach to a comparison of metrics and
search procedures.


title: A Decision-Based View of Causality

abstract:
  Most traditional models of uncertainty have focused on the associational
relationship among variables as captured by conditional dependence. In order to
successfully manage intelligent systems for decision making, however, we must
be able to predict the effects of actions. In this paper, we attempt to unite
two branches of research that address such predictions: causal modeling and
decision analysis. First, we provide a definition of causal dependence in
decision-analytic terms, which we derive from consequences of causal dependence
cited in the literature. Using this definition, we show how causal dependence
can be represented within an influence diagram. In particular, we identify two
inadequacies of an ordinary influence diagram as a representation for cause. We
introduce a special class of influence diagrams, called causal influence
diagrams, which corrects one of these problems, and identify situations where
the other inadequacy can be eliminated. In addition, we describe the
relationships between Howard Canonical Form and existing graphical
representations of cause.


title: Probabilistic Description Logics

abstract:
  On the one hand, classical terminological knowledge representation excludes
the possibility of handling uncertain concept descriptions involving, e.g.,
"usually true" concept properties, generalized quantifiers, or exceptions. On
the other hand, purely numerical approaches for handling uncertainty in general
are unable to consider terminological knowledge. This paper presents the
language ACP which is a probabilistic extension of terminological logics and
aims at closing the gap between the two areas of research. We present the
formal semantics underlying the language ALUP and introduce the probabilistic
formalism that is based on classes of probabilities and is realized by means of
probabilistic constraints. Besides inferring implicitly existent probabilistic
relationships, the constraints guarantee terminological and probabilistic
consistency. Altogether, the new language ALUP applies to domains where both
term descriptions and uncertainty have to be handled.


title:
An Experimental Comparison of Numerical and Qualitative Probabilistic
  Reasoning

abstract:
  Qualitative and infinitesimal probability schemes are consistent with the
axioms of probability theory, but avoid the need for precise numerical
probabilities. Using qualitative probabilities could substantially reduce the
effort for knowledge engineering and improve the robustness of results. We
examine experimentally how well infinitesimal probabilities (the kappa-calculus
of Goldszmidt and Pearl) perform a diagnostic task - troubleshooting a car that
will not start by comparison with a conventional numerical belief network. We
found the infinitesimal scheme to be as good as the numerical scheme in
identifying the true fault. The performance of the infinitesimal scheme worsens
significantly for prior fault probabilities greater than 0.03. These results
suggest that infinitesimal probability methods may be of substantial practical
value for machine diagnosis with small prior fault probabilities.


title:
An Alternative Proof Method for Possibilistic Logic and its Application
  to Terminological Logics

abstract:
  Possibilistic logic, an extension of first-order logic, deals with
uncertainty that can be estimated in terms of possibility and necessity
measures. Syntactically, this means that a first-order formula is equipped with
a possibility degree or a necessity degree that expresses to what extent the
formula is possibly or necessarily true. Possibilistic resolution yields a
calculus for possibilistic logic which respects the semantics developed for
possibilistic logic. A drawback, which possibilistic resolution inherits from
classical resolution, is that it may not terminate if applied to formulas
belonging to decidable fragments of first-order logic. Therefore we propose an
alternative proof method for possibilistic logic. The main feature of this
method is that it completely abstracts from a concrete calculus but uses as
basic operation a test for classical entailment. We then instantiate
possibilistic logic with a terminological logic, which is a decidable subclass
o f first-order logic but nevertheless much more expressive than propositional
logic. This yields an extension of terminological logics towards the
representation of uncertain knowledge which is satisfactory from a semantic as
well as algorithmic point of view.


title: Possibilistic Conditioning and Propagation

abstract:
  We give an axiomatization of confidence transfer - a known conditioning
scheme - from the perspective of expectation-based inference in the sense of
Gardenfors and Makinson. Then, we use the notion of belief independence to
"filter out" different proposal s of possibilistic conditioning rules, all are
variations of confidence transfer. Among the three rules that we consider, only
Dempster's rule of conditioning passes the test of supporting the notion of
belief independence. With the use of this conditioning rule, we then show that
we can use local computation for computing desired conditional marginal
possibilities of the joint possibility satisfying the given constraints. It
turns out that our local computation scheme is already proposed by Shenoy.
However, our intuitions are completely different from that of Shenoy. While
Shenoy just defines a local computation scheme that fits his framework of
valuation-based systems, we derive that local computation scheme from II(,8) =
tI(,8 I a) * II(a) and appropriate independence assumptions, just like how the
Bayesians derive their local computation scheme.


title: The Automated Mapping of Plans for Plan Recognition

abstract:
  To coordinate with other agents in its environment, an agent needs models of
what the other agents are trying to do. When communication is impossible or
expensive, this information must be acquired indirectly via plan recognition.
Typical approaches to plan recognition start with a specification of the
possible plans the other agents may be following, and develop special
techniques for discriminating among the possibilities. Perhaps more desirable
would be a uniform procedure for mapping plans to general structures supporting
inference based on uncertain and incomplete observations. In this paper, we
describe a set of methods for converting plans represented in a flexible
procedural language to observation models represented as probabilistic belief
networks.


title: A Logic for Default Reasoning About Probabilities

abstract:
  A logic is defined that allows to express information about statistical
probabilities and about degrees of belief in specific propositions. By
interpreting the two types of probabilities in one common probability space,
the semantics given are well suited to model the influence of statistical
information on the formation of subjective beliefs. Cross entropy minimization
is a key element in these semantics, the use of which is justified by showing
that the resulting logic exhibits some very reasonable properties.


title: Optimal Junction Trees

abstract:
  The paper deals with optimality issues in connection with updating beliefs in
networks. We address two processes: triangulation and construction of junction
trees. In the first part, we give a simple algorithm for constructing an
optimal junction tree from a triangulated network. In the second part, we argue
that any exact method based on local calculations must either be less efficient
than the junction tree method, or it has an optimality problem equivalent to
that of triangulation.


title: From Influence Diagrams to Junction Trees

abstract:
  We present an approach to the solution of decision problems formulated as
influence diagrams. This approach involves a special triangulation of the
underlying graph, the construction of a junction tree with special properties,
and a message passing algorithm operating on the junction tree for computation
of expected utilities and optimal decision policies.


title:
Reduction of Computational Complexity in Bayesian Networks through
  Removal of Weak Dependencies

abstract:
  The paper presents a method for reducing the computational complexity of
Bayesian networks through identification and removal of weak dependencies
(removal of links from the (moralized) independence graph). The removal of a
small number of links may reduce the computational complexity dramatically,
since several fill-ins and moral links may be rendered superfluous by the
removal. The method is described in terms of impact on the independence graph,
the junction tree, and the potential functions associated with these. An
empirical evaluation of the method using large real-world networks demonstrates
the applicability of the method. Further, the method, which has been
implemented in Hugin, complements the approximation method suggested by Jensen
& Andersen (1990).


title: Using New Data to Refine a Bayesian Network

abstract:
  We explore the issue of refining an existent Bayesian network structure using
new data which might mention only a subset of the variables. Most previous
works have only considered the refinement of the network's conditional
probability parameters, and have not addressed the issue of refining the
network's structure. We develop a new approach for refining the network's
structure. Our approach is based on the Minimal Description Length (MDL)
principle, and it employs an adapted version of a Bayesian network learning
algorithm developed in our previous work. One of the adaptations required is to
modify the previous algorithm to account for the structure of the existent
network. The learning algorithm generates a partial network structure which can
then be used to improve the existent network. We also present experimental
evidence demonstrating the effectiveness of our approach.


title: Syntax-based Default Reasoning as Probabilistic Model-based Diagnosis

abstract:
  We view the syntax-based approaches to default reasoning as a model-based
diagnosis problem, where each source giving a piece of information is
considered as a component. It is formalized in the ATMS framework (each source
corresponds to an assumption). We assume then that all sources are independent
and "fail" with a very small probability. This leads to a probability
assignment on the set of candidates, or equivalently on the set of consistent
environments. This probability assignment induces a Dempster-Shafer belief
function which measures the probability that a proposition can be deduced from
the evidence. This belief function can be used in several different ways to
define a non-monotonic consequence relation. We study and compare these
consequence relations. The -case of prioritized knowledge bases is briefly
considered.


title: Induction of Selective Bayesian Classifiers

abstract:
  In this paper, we examine previous work on the naive Bayesian classifier and
review its limitations, which include a sensitivity to correlated features. We
respond to this problem by embedding the naive Bayesian induction scheme within
an algorithm that c arries out a greedy search through the space of features.
We hypothesize that this approach will improve asymptotic accuracy in domains
that involve correlated features without reducing the rate of learning in ones
that do not. We report experimental results on six natural domains, including
comparisons with decision-tree induction, that support these hypotheses. In
closing, we discuss other approaches to extending naive Bayesian classifiers
and outline some directions for future research.


title: Fuzzy Geometric Relations to Represent Hierarchical Spatial Information

abstract:
  A model to represent spatial information is presented in this paper. It is
based on fuzzy constraints represented as fuzzy geometric relations that can be
hierarchically structured. The concept of spatial template is introduced to
capture the idea of interrelated objects in two-dimensional space. The
representation model is used to specify imprecise or vague information
consisting in relative locations and orientations of template objects. It is
shown in this paper how a template represented by this model can be matched
against a crisp situation to recognize a particular instance of this template.
Furthermore, the proximity measure (fuzzy measure) between the instance and the
template is worked out - this measure can be interpreted as a degree of
similarity. In this context, template recognition can be viewed as a case of
fuzzy pattern recognition. The results of this work have been implemented and
applied to a complex military problem from which this work originated.


title: Constructing Belief Networks to Evaluate Plans

abstract:
  This paper examines the problem of constructing belief networks to evaluate
plans produced by an knowledge-based planner. Techniques are presented for
handling various types of complicating plan features. These include plans with
context-dependent consequences, indirect consequences, actions with
preconditions that must be true during the execution of an action,
contingencies, multiple levels of abstraction multiple execution agents with
partially-ordered and temporally overlapping actions, and plans which reference
specific times and time durations.


title: Operator Selection While Planning Under Uncertainty

abstract:
  This paper describes the best first search strategy used by U-Plan (Mansell
1993a), a planning system that constructs quantitatively ranked plans given an
incomplete description of an uncertain environment. U-Plan uses uncertain and
incomplete evidence de scribing the environment, characterizes it using a
Dempster-Shafer interval, and generates a set of possible world states. Plan
construction takes place in an abstraction hierarchy where strategic decisions
are made before tactical decisions. Search through this abstraction hierarchy
is guided by a quantitative measure (expected fulfillment) based on decision
theory. The search strategy is best first with the provision to update expected
fulfillment and review previous decisions in the light of planning
developments. U-Plan generates multiple plans for multiple possible worlds, and
attempts to use existing plans for new world situations. A super-plan is then
constructed, based on merging the set of plans and appropriately timed
knowledge acquisition operators, which are used to decide between plan
alternatives during plan execution.


title: Model-Based Diagnosis with Qualitative Temporal Uncertainty

abstract:
  In this paper we describe a framework for model-based diagnosis of dynamic
systems, which extends previous work in this field by using and expressing
temporal uncertainty in the form of qualitative interval relations a la Allen.
Based on a logical framework extended by qualitative and quantitative temporal
constraints we show how to describe behavioral models (both consistency- and
abductive-based), discuss how to use abstract observations and show how
abstract temporal diagnoses are computed. This yields an expressive framework,
which allows the representation of complex temporal behavior allowing us to
represent temporal uncertainty. Due to its abstraction capabilities computation
is made independent of the number of observations and time points in a temporal
setting. An example of hepatitis diagnosis is used throughout the paper.


title: Incremental Dynamic Construction of Layered Polytree Networks

abstract:
  Certain classes of problems, including perceptual data understanding,
robotics, discovery, and learning, can be represented as incremental,
dynamically constructed belief networks. These automatically constructed
networks can be dynamically extended and modified as evidence of new
individuals becomes available. The main result of this paper is the incremental
extension of the singly connected polytree network in such a way that the
network retains its singly connected polytree structure after the changes. The
algorithm is deterministic and is guaranteed to have a complexity of single
node addition that is at most of order proportional to the number of nodes (or
size) of the network. Additional speed-up can be achieved by maintaining the
path information. Despite its incremental and dynamic nature, the algorithm can
also be used for probabilistic inference in belief networks in a fashion
similar to other exact inference algorithms.


title: Models of Consensus for Multiple Agent Systems

abstract:
  Models of consensus are used to manage multiple agent systems in order to
choose between different recommendations provided by the system. It is assumed
that there is a central agent that solicits recommendations or plans from other
agents. That agent the n determines the consensus of the other agents, and
chooses the resultant consensus recommendation or plan. Voting schemes such as
this have been used in a variety of domains, including air traffic control.
This paper uses an analytic model to study the use of consensus in multiple
agent systems. The binomial model is used to study the probability that the
consensus judgment is correct or incorrect. That basic model is extended to
account for both different levels of agent competence and unequal prior odds.
The analysis of that model is critical in the investigation of multiple agent
systems, since the model leads us to conclude that in some cases consensus
judgment is not appropriate. In addition, the results allow us to determine how
many agents should be used to develop consensus decisions, which agents should
be used to develop consensus decisions and under which conditions the consensus
model should be used.


title: A Probabilistic Calculus of Actions

abstract:
  We present a symbolic machinery that admits both probabilistic and causal
information about a given domain and produces probabilistic statements about
the effect of actions and the impact of observations. The calculus admits two
types of conditioning operators: ordinary Bayes conditioning, P(y|X = x), which
represents the observation X = x, and causal conditioning, P(y|do(X = x)), read
the probability of Y = y conditioned on holding X constant (at x) by deliberate
action. Given a mixture of such observational and causal sentences, together
with the topology of the causal graph, the calculus derives new conditional
probabilities of both types, thus enabling one to quantify the effects of
actions (and policies) from partially specified knowledge bases, such as
Bayesian networks in which some conditional probabilities may not be available.


title: Robust Planning in Uncertain Environments

abstract:
  This paper describes a novel approach to planning which takes advantage of
decision theory to greatly improve robustness in an uncertain environment. We
present an algorithm which computes conditional plans of maximum expected
utility. This algorithm relies on a representation of the search space as an
AND/OR tree and employs a depth-limit to control computation costs. A numeric
robustness factor, which parameterizes the utility function, allows the user to
modulate the degree of risk-aversion employed by the planner. Via a look-ahead
search, the planning algorithm seeks to find an optimal plan using expected
utility as its optimization criterion. We present experimental results obtained
by applying our algorithm to a non-deterministic extension of the blocks world
domain. Our results demonstrate that the robustness factor governs the degree
of risk embodied in the conditional plans computed by our algorithm.


title: Anytime Decision Making with Imprecise Probabilities

abstract:
  This paper examines methods of decision making that are able to accommodate
limitations on both the form in which uncertainty pertaining to a decision
problem can be realistically represented and the amount of computing time
available before a decision must be made. The methods are anytime algorithms in
the sense of Boddy and Dean 1991. Techniques are presented for use with Frisch
and Haddawy's [1992] anytime deduction system, with an anytime adaptation of
Nilsson's [1986] probabilistic logic, and with a probabilistic database model.


title: Three Approaches to Probability Model Selection

abstract:
  This paper compares three approaches to the problem of selecting among
probability models to fit data (1) use of statistical criteria such as Akaike's
information criterion and Schwarz's "Bayesian information criterion," (2)
maximization of the posterior probability of the model, and (3) maximization of
an effectiveness ratio? trading off accuracy and computational cost. The
unifying characteristic of the approaches is that all can be viewed as
maximizing a penalized likelihood function. The second approach with suitable
prior distributions has been shown to reduce to the first. This paper shows
that the third approach reduces to the second for a particular form of the
effectiveness ratio, and illustrates all three approaches with the problem of
selecting the number of components in a mixture of Gaussian distributions.
Unlike the first two approaches, the third can be used even when the candidate
models are chosen for computational efficiency, without regard to physical
interpretation, so that the likelihood and the prior distribution over models
cannot be interpreted literally. As the most general and computationally
oriented of the approaches, it is especially useful for artificial intelligence
applications.


title: Knowledge Engineering for Large Belief Networks

abstract:
  We present several techniques for knowledge engineering of large belief
networks (BNs) based on the our experiences with a network derived from a large
medical knowledge base. The noisyMAX, a generalization of the noisy-OR gate, is
used to model causal in dependence in a BN with multi-valued variables. We
describe the use of leak probabilities to enforce the closed-world assumption
in our model. We present Netview, a visualization tool based on causal
independence and the use of leak probabilities. The Netview software allows
knowledge engineers to dynamically view sub-networks for knowledge engineering,
and it provides version control for editing a BN. Netview generates
sub-networks in which leak probabilities are dynamically updated to reflect the
missing portions of the network.


title: Solving Asymmetric Decision Problems with Influence Diagrams

abstract:
  While influence diagrams have many advantages as a representation framework
for Bayesian decision problems, they have a serious drawback in handling
asymmetric decision problems. To be represented in an influence diagram, an
asymmetric decision problem must be symmetrized. A considerable amount of
unnecessary computation may be involved when a symmetrized influence diagram is
evaluated by conventional algorithms. In this paper we present an approach for
avoiding such unnecessary computation in influence diagram evaluation.


title: Belief Maintenance in Bayesian Networks

abstract:
  Bayesian Belief Networks (BBNs) are a powerful formalism for reasoning under
uncertainty but bear some severe limitations: they require a large amount of
information before any reasoning process can start, they have limited
contradiction handling capabilities, and their ability to provide explanations
for their conclusion is still controversial. There exists a class of reasoning
systems, called Truth Maintenance Systems (TMSs), which are able to deal with
partially specified knowledge, to provide well-founded explanation for their
conclusions, and to detect and handle contradictions. TMSs incorporating
measure of uncertainty are called Belief Maintenance Systems (BMSs). This paper
describes how a BMS based on probabilistic logic can be applied to BBNs, thus
introducing a new class of BBNs, called Ignorant Belief Networks, able to
incrementally deal with partially specified conditional dependencies, to
provide explanations, and to detect and handle contradictions.


title:
Belief Updating by Enumerating High-Probability Independence-Based
  Assignments

abstract:
  Independence-based (IB) assignments to Bayesian belief networks were
originally proposed as abductive explanations. IB assignments assign fewer
variables in abductive explanations than do schemes assigning values to all
evidentially supported variables. We use IB assignments to approximate marginal
probabilities in Bayesian belief networks. Recent work in belief updating for
Bayes networks attempts to approximate posterior probabilities by finding a
small number of the highest probability complete (or perhaps evidentially
supported) assignments. Under certain assumptions, the probability mass in the
union of these assignments is sufficient to obtain a good approximation. Such
methods are especially useful for highly-connected networks, where the maximum
clique size or the cutset size make the standard algorithms intractable. Since
IB assignments contain fewer assigned variables, the probability mass in each
assignment is greater than in the respective complete assignment. Thus, fewer
IB assignments are sufficient, and a good approximation can be obtained more
efficiently. IB assignments can be used for efficiently approximating posterior
node probabilities even in cases which do not obey the rather strict skewness
assumptions used in previous research. Two algorithms for finding the high
probability IB assignments are suggested: one by doing a best-first heuristic
search, and another by special-purpose integer linear programming. Experimental
results show that this approach is feasible for highly connected belief
networks.


title: Global Conditioning for Probabilistic Inference in Belief Networks

abstract:
  In this paper we propose a new approach to probabilistic inference on belief
networks, global conditioning, which is a simple generalization of Pearl's
(1986b) method of loopcutset conditioning. We show that global conditioning, as
well as loop-cutset conditioning, can be thought of as a special case of the
method of Lauritzen and Spiegelhalter (1988) as refined by Jensen et al (199Oa;
1990b). Nonetheless, this approach provides new opportunities for parallel
processing and, in the case of sequential processing, a tradeoff of time for
memory. We also show how a hybrid method (Suermondt and others 1990) combining
loop-cutset conditioning with Jensen's method can be viewed within our
framework. By exploring the relationships between these methods, we develop a
unifying framework in which the advantages of each approach can be combined
successfully.


title: Belief Induced by the Partial Knowledge of the Probabilities

abstract:
  We construct the belief function that quantifies the agent, beliefs about
which event of Q will occurred when he knows that the event is selected by a
chance set-up and that the probability function associated to the chance set up
is only partially known.


title:
Ignorance and the Expressiveness of Single- and Set-Valued Probability
  Models of Belief

abstract:
  Over time, there have hen refinements in the way that probability
distributions are used for representing beliefs. Models which rely on single
probability distributions depict a complete ordering among the propositions of
interest, yet human beliefs are sometimes not completely ordered. Non-singleton
sets of probability distributions can represent partially ordered beliefs.
Convex sets are particularly convenient and expressive, but it is known that
there are reasonable patterns of belief whose faithful representation require
less restrictive sets. The present paper shows that prior ignorance about three
or more exclusive alternatives and the emergence of partially ordered beliefs
when evidence is obtained defy representation by any single set of
distributions, but yield to a representation baud on several uts. The partial
order is shown to be a partial qualitative probability which shares some
intuitively appealing attributes with probability distributions.


title: A Probabilistic Approach to Hierarchical Model-based Diagnosis

abstract:
  Model-based diagnosis reasons backwards from a functional schematic of a
system to isolate faults given observations of anomalous behavior. We develop a
fully probabilistic approach to model based diagnosis and extend it to support
hierarchical models. Our scheme translates the functional schematic into a
Bayesian network and diagnostic inference takes place in the Bayesian network.
A Bayesian network diagnostic inference algorithm is modified to take advantage
of the hierarchy to give computational gains.


title:
Semigraphoids Are Two-Antecedental Approximations of Stochastic
  Conditional Independence Models

abstract:
  The semigraphoid closure of every couple of CI-statements (GI=conditional
independence) is a stochastic CI-model. As a consequence of this result it is
shown that every probabilistically sound inference rule for CI-model, having at
most two antecedents, is derivable from the semigraphoid inference rules. This
justifies the use of semigraphoids as approximations of stochastic CI-models in
probabilistic reasoning. The list of all 19 potential dominant elements of the
mentioned semigraphoid closure is given as a byproduct.


title: Exceptional Subclasses in Qualitative Probability

abstract:
  System Z+ [Goldszmidt and Pearl, 1991, Goldszmidt, 1992] is a formalism for
reasoning with normality defaults of the form "typically if phi then + (with
strength cf)" where 6 is a positive integer. The system has a critical
shortcoming in that it does not sanction inheritance across exceptional
subclasses. In this paper we propose an extension to System Z+ that rectifies
this shortcoming by extracting additional conditions between worlds from the
defaults database. We show that the additional constraints do not change the
notion of the consistency of a database. We also make comparisons with
competing default reasoning systems.


title: A Defect in Dempster-Shafer Theory

abstract:
  By analyzing the relationships among chance, weight of evidence and degree of
beliefwe show that the assertion "probability functions are special cases of
belief functions" and the assertion "Dempster's rule can be used to combine
belief functions based on distinct bodies of evidence" together lead to an
inconsistency in Dempster-Shafer theory. To solve this problem, we must reject
some fundamental postulates of the theory. We introduce a new approach for
uncertainty management that shares many intuitive ideas with D-S theory, while
avoiding this problem.


title: State-space Abstraction for Anytime Evaluation of Probabilistic Networks

abstract:
  One important factor determining the computational complexity of evaluating a
probabilistic network is the cardinality of the state spaces of the nodes. By
varying the granularity of the state spaces, one can trade off accuracy in the
result for computational efficiency. We present an anytime procedure for
approximate evaluation of probabilistic networks based on this idea. On
application to some simple networks, the procedure exhibits a smooth
improvement in approximation quality as computation time increases. This
suggests that state-space abstraction is one more useful control parameter for
designing real-time probabilistic reasoners.


title: General Belief Measures

abstract:
  Probability measures by themselves, are known to be inappropriate for
modeling the dynamics of plain belief and their excessively strong
measurability constraints make them unsuitable for some representational tasks,
e.g. in the context of firstorder knowledge. In this paper, we are therefore
going to look for possible alternatives and extensions. We begin by delimiting
the general area of interest, proposing a minimal list of assumptions to be
satisfied by any reasonable quasi-probabilistic valuation concept. Within this
framework, we investigate two particularly interesting kinds of quasi-measures
which are not or much less affected by the traditional problems. * Ranking
measures, which generalize Spohn-type and possibility measures. * Cumulative
measures, which combine the probabilistic and the ranking philosophy, allowing
thereby a fine-grained account of static and dynamic belief.


title: Generating Graphoids from Generalised Conditional Probability

abstract:
  We take a general approach to uncertainty on product spaces, and give
sufficient conditions for the independence structures of uncertainty measures
to satisfy graphoid properties. Since these conditions are arguably more
intuitive than some of the graphoid properties, they can be viewed as
explanations why probability and certain other formalisms generate graphoids.
The conditions include a sufficient condition for the Intersection property
which can still apply even if there is a strong logical relations hip between
the variables. We indicate how these results can be used to produce theories of
qualitative conditional probability which are semi-graphoids and graphoids.


title: On Axiomatization of Probabilistic Conditional Independencies

abstract:
  This paper studies the connection between probabilistic conditional
independence in uncertain reasoning and data dependency in relational
databases. As a demonstration of the usefulness of this preliminary
investigation, an alternate proof is presented for refuting the conjecture
suggested by Pearl and Paz that probabilistic conditional independencies have a
complete axiomatization.


title: Evidential Reasoning with Conditional Belief Functions

abstract:
  In the existing evidential networks with belief functions, the relations
among the variables are always represented by joint belief functions on the
product space of the involved variables. In this paper, we use conditional
belief functions to represent such relations in the network and show some
relations of these two kinds of representations. We also present a propagation
algorithm for such networks. By analyzing the properties of some special
evidential networks with conditional belief functions, we show that the
reasoning process can be simplified in such kinds of networks.


title: Inter-causal Independence and Heterogeneous Factorization

abstract:
  It is well known that conditional independence can be used to factorize a
joint probability into a multiplication of conditional probabilities. This
paper proposes a constructive definition of inter-causal independence, which
can be used to further factorize a conditional probability. An inference
algorithm is developed, which makes use of both conditional independence and
inter-causal independence to reduce inference complexity in Bayesian networks.


title: Constructing Complicated Spheres

abstract:
  Fast and efficient homology algorithms are in demand in the applied sciences
for analyzing solid materials and proteins, processing digital imaging data, or
pattern classification among others. Recent advances employ discrete Morse
theory as a preprocessor. Research in this area has lead to the need to find
complicated test examples. We present an infinite series of examples that have
been constructed to test some of the latest algorithms under development. This
family of 4-spheres (known as the Akbulut-Kirby spheres) is based on a
handlebody construction via finitely presented groups.


title:
Interpretation of the X(3872) as a charmonium state plus an extra
  component due to the coupling to the meson-meson continuum

abstract:
  We present a quark model calculation of the charmonium spectrum with self
energy corrections due to the coupling to the meson-meson continuum. The bare
masses used in the calculation are computed within the relativized quark model
by Godfrey and Isgur. The strong decay widths of 3S, 2P, 1D and 2D $c \bar c$
states are also calculated, in order to set the values of the $^3P_0$
pair-creation model's parameters we use to compute the vertex functions of the
loop integrals. Finally, the nature of the X(3872) resonance is analyzed and
the main possibilities ($c \bar c$ state or $D \bar D^*$ molecule) are
discussed. According to our results, the X(3872) is compatible with the meson
$\chi_{c1}(2P)$, with $J^{PC} = 1^{++}$, and is thus interpreted as a $c \bar
c$ core plus higher Fock components due to the coupling to the meson-meson
continuum. These $J^{PC} = 1^{++}$ quantum numbers are in agreement with the
experimental results found by the LHCb collaboration. In our view, the
X(3872)'s mass is lower than the quark model's predictions because of self
energy shifts.


title:
Estimations of the magnetic field strength in the torus of AGN using
  near-infrared polarimetry

abstract:
  An optically and geometrically thick torus obscures the central engine of
Active Galactic Nuclei (AGN) from some lines of sight. From a
magnetohydrodynamical framework, the torus can be considered to be a particular
region of clouds surrounding the central engine where the clouds are dusty and
optically thick. In this framework, the magnetic field plays an important role
in the creation, morphology and evolution of the torus. If the dust grains
within the clouds are assumed to be aligned by paramagnetic alignment, then the
ratio of the intrinsic polarisation and visual extinction, P(%)/Av, is a
function of the magnetic field strength.
  To estimate the visual extinction through the torus and constrain the
polarisation mechanisms in the nucleus of AGN, we developed a polarisation
model to fit both the total and polarised flux in a 1.2" (~263pc) aperture of
the type 2 AGN, IC5063. The polarisation model is consistent with the nuclear
polarisation observed at K being produced by dichroic absorption from aligned
dust grains with a visual extinction through the torus of 48$\pm$2 mag. We
estimated the intrinsic polarisation arising from dichroic absorption to be
P$_{K}^{dic}$=12.5$\pm$2.7%.
  We consider the physical conditions and environment of the gas and dust for
the torus of IC5063. Then, through paramagnetic alignment, we estimate a
magnetic field strength in the range of 12-128mG in the NIR emitting regions of
the torus of IC5063. Alternatively, we estimate the magnetic field strength in
the plane of the sky using the Chandrasekhar-Fermi method. The minimum magnetic
field strength in the plane of the sky is estimated to be 13 and 41 mG
depending of the conditions within the torus of IC5063. These techniques afford
the chance to make a survey of AGN, to investigate the effects of magnetic
field strength on the torus, accretion, and interaction to the host galaxy.


title: Walking signals in Nf=8 QCD on the lattice

abstract:
  We investigate chiral and conformal properties of the lattice QCD with eight
flavors (Nf=8) through meson spectrum using the Highly Improved Staggered Quark
(HISQ) action. We also compare our results with those of Nf=12 and Nf=4 which
we study on the same systematics. We find that the decay constant F_pi of the
pseudoscalar meson "pion" is non-zero, with its mass M_pi consistent with zero,
both in the chiral limit extrapolation of the chiral perturbation theory
(ChPT). We also measure other quantities which we find are in accord with the
pi data results: The rho meson mass is consistent with non-zero in the chiral
limit, and so is the chiral condensate, with its value neatly coinciding with
that from the Gell-Mann-Oakes-Renner relation in the chiral limit. Thus our
data for the Nf=8 QCD are consistent with the spontaneously broken chiral
symmetry. Remarkably enough, while the Nf=8 data near the chiral limit are well
described by the ChPT, those for the relatively large fermion bare mass m_f
away from the chiral limit actually exhibit a finite-size hyperscaling
relation, suggesting a large anomalous dimension gamma_m ~ 1. This implies that
there exists a remnant of the infrared conformality, and suggests that a
typical technicolor ("one-family model") as modeled by the Nf=8 QCD can be a
walking technicolor theory having an approximate scale invariance with large
anomalous dimension gamma_m ~ 1.


title:
Classical and quantum Big Brake cosmology for scalar field and tachyonic
  models

abstract:
  We study a relation between the cosmological singularities in classical and
quantum theory, comparing the classical and quantum dynamics in some models
possessing the Big Brake singularity - the model based on a scalar field and
two models based on a tachyon-pseudo-tachyon field . It is shown that the
effect of quantum avoidance is absent for the soft singularities of the Big
Brake type while it is present for the Big Bang and Big Crunch singularities.
Thus, there is some kind of a classical - quantum correspondence, because soft
singularities are traversable in classical cosmology, while the strong Big Bang
and Big Crunch singularities are not traversable.


title: The effect of quantum fluctuations on the coloring of random graphs

abstract:
  We present a study of the coloring problem (antiferromagnetic Potts model) of
random regular graphs, submitted to quantum fluctuations induced by a
transverse field, using the quantum cavity method and quantum Monte-Carlo
simulations. We determine the order of the quantum phase transition encountered
at low temperature as a function of the transverse field and discuss the
structure of the quantum spin glass phase. In particular, we conclude that the
quantum adiabatic algorithm would fail to solve efficiently typical instances
of these problems because of avoided level crossings within the quantum spin
glass phase, caused by a competition between energetic and entropic effects.

categories: cond-mat.stat-mech cond-mat.dis-nn cs.CC quant-ph


title:
Application of equivalence method to Monge-Amp\`ere equations: Elliptic
  case

abstract:
  The application of equivalence method to classify Monge-Amp\`ere system leads
to three orbits, parabolic case, hyperbolic case and elliptic case wich
correspond to three types of Monge-Amp\`ere systems. In this paper we will
study the elliptic case and give a presentation of the group as a complex
group.


title: Kernelization Using Structural Parameters on Sparse Graph Classes

abstract:
  Meta-theorems for polynomial (linear) kernels have been the subject of
intensive research in parameterized complexity. Heretofore, meta-theorems for
linear kernels exist on graphs of bounded genus, $H$-minor-free graphs, and
$H$-topological-minor-free graphs. To the best of our knowledge, no
meta-theorems for polynomial kernels are known for any larger sparse graph
classes; e.g., for classes of bounded expansion or for nowhere dense ones. In
this paper we prove such meta-theorems for the two latter cases. More
specifically, we show that graph problems that have finite integer index (FII)
have linear kernels on graphs of bounded expansion when parameterized by the
size of a modulator to constant-treedepth graphs. For nowhere dense graph
classes, our result yields almost-linear kernels. While our parameter may seem
rather strong, we argue that a linear kernelization result on graphs of bounded
expansion with a weaker parameter (than treedepth modulator) would fail to
include some of the problems covered by our framework. Moreover, we only
require the problems to have FII on graphs of constant treedepth. This allows
us to prove linear kernels for problems such as Longest Path/Cycle, Exact
$s,t$-Path, Treewidth, and Pathwidth, which do not have FII on general graphs
(and the first two not even on bounded treewidth graphs).


title: Equivariant Jeffrey-Kirwan localization theorem in non-compact setting

abstract:
  We generalize the Jeffrey-Kirwan localization theorem for non-compact
symplectic and hyperKahler quotients. Similarly to the circle compact
integration of Hausel and Proudfoot we define equivariant integrals on
non-compact manifolds using the Atiyah-Bott-Berline-Vergne localization formula
as formal definition. We introduce a so called equivariant Jeffrey-Kirwan
residue and we show that it shares similar properties as the usual one. Our
localization formula has the same structure as the usual Jeffrey-Kirwan
formula, but it uses formal integration and equivariant residue. We also give a
version for hyperKahler quotients. Finally, we apply our formula to compute the
equivariant cohomology ring of Hilbert scheme of points on the plane
constructed as a hyperKahler quotient.


title:
Non-Abelian dual superconductivity and Gluon propagators in the deep IR
  region for SU(3) Yang-Mills theory

abstract:
  We have proposed the non-Abelian dual superconductivity picture for quark
confinement in the SU(3) Yang-Mills (YM) theory, and have given numerical
evidences for the restricted-field dominance and the non-Abelian magnetic
monopole dominance in the string tension by applying a new formulation of the
YM theory on a lattice. To establish the non-Abelian dual superconductivity
picture for quark confinement, we have observed the non-Abelian dual Meissner
effect in the SU(3) Yang-Mills theory by measuring the chromoelectric flux
created by the quark-antiquark source, and the non-Abelian magnetic monopole
currents induced around the flux. We conclude that the dual superconductivity
of the SU(3) Yang-Mills theory is strictly the type I and that this type of
dual superconductivity is reproduced by the restricted field and the
non-Abelian magnetic monopole part, in sharp contrast to the SU(2) case: the
border of type I and type II.


title:
Vandermonde-subspace Frequency Division Multiplexing for Two-Tiered
  Cognitive Radio Networks

abstract:
  Vandermonde-subspace frequency division multiplexing (VFDM) is an overlay
spectrum sharing technique for cognitive radio. VFDM makes use of a precoder
based on a Vandermonde structure to transmit information over a secondary
system, while keeping an orthogonal frequency division multiplexing
(OFDM)-based primary system interference-free. To do so, VFDM exploits
frequency selectivity and the use of cyclic prefixes by the primary system.
Herein, a global view of VFDM is presented, including also practical aspects
such as linear receivers and the impact of channel estimation. We show that
VFDM provides a spectral efficiency increase of up to 1 bps/Hz over cognitive
radio systems based on unused band detection. We also present some key design
parameters for its future implementation and a feasible channel estimation
protocol. Finally we show that, even when some of the theoretical assumptions
are relaxed, VFDM provides non-negligible rates while protecting the primary
system.


title:
Interplay between Kondo suppression and Lifshitz transitions in
  YbRh$_2$Si$_2$ at high magnetic fields

abstract:
  We investigate the magnetic field dependent thermopower, thermal
conductivity, resistivity and Hall effect in the heavy fermion metal YbRh2Si2.
In contrast to reports on thermodynamic measurements, we find in total three
transitions at high fields, rather than a single one at 10 T. Using the Mott
formula together with renormalized band calculations, we identify Lifshitz
transitions as their origin. The predictions of the calculations show that all
experimental results rely on an interplay of a smooth suppression of the Kondo
effect and the spin splitting of the flat hybridized bands.


title:
A study on the conditioning of finite element equations with arbitrary
  anisotropic meshes via a density function approach

abstract:
  The linear finite element approximation of a general linear diffusion problem
with arbitrary anisotropic meshes is considered. The conditioning of the
resultant stiffness matrix and the Jacobi preconditioned stiffness matrix is
investigated using a density function approach proposed by Fried in 1973. It is
shown that the approach can be made mathematically rigorous for general domains
and used to develop bounds on the smallest eigenvalue and the condition number
that are sharper than existing estimates in one and two dimensions and
comparable in three and higher dimensions. The new results reveal that the mesh
concentration near the boundary has less influence on the condition number than
the mesh concentration in the interior of the domain. This is especially true
for the Jacobi preconditioned system where the former has little or almost no
influence on the condition number. Numerical examples are presented.


title: Lie monads and dualities

abstract:
  We study dualities between Lie algebras and Lie coalgebras, and their
respective (co)representations. To allow a study of dualities in an
infinite-dimensional setting, we introduce the notions of Lie monads and Lie
comonads, as special cases of YB-Lie algebras and YB-Lie coalgebras in additive
monoidal categories. We show that (strong) dualities between Lie algebras and
Lie coalgebras are closely related to (iso)morphisms between associated Lie
monads and Lie comonads. In the case of a duality between two Hopf algebras -in
the sense of Takeuchi- we recover a duality between a Lie algebra and a Lie
coalgebra -in the sense defined in this note- by computing the primitive and
the indecomposables elements, respectively.


title: Bernoulli and self-destructive percolation on non-amenable graphs

abstract:
  In this note we study some properties of infinite percolation clusters on
non-amenable graphs. In particular, we study the percolative properties of the
complement of infinite percolation clusters. An approach based on
mass-transport is adapted to show that for a large class of non-amenable
graphs, the graph obtained by removing each site contained in an infinite
percolation cluster has critical percolation threshold which can be arbitrarily
close to the critical threshold for the original graph, almost surely, as p
approaches p_c. Closely related is the self-destructive percolation process,
introduced by J. van den Berg and R. Brouwer, for which we prove that an
infinite cluster emerges for any small reinforcement.


title:
Spectral Study of the HESS J1745-290 Gamma-Ray Source as Dark Matter
  Signal

abstract:
  We study the main spectral features of the gamma-ray fluxes observed by the
High Energy Stereoscopic System (HESS) from the J1745-290 Galactic Center
source during the years 2004, 2005 and 2006. In particular, we show that these
data are well fitted as the secondary gamma-rays photons generated from dark
matter annihilating into Standard Model particles in combination with a simple
power law background. We present explicit analyses for annihilation in a single
standard model particle-antiparticle pair. In this case, the best fits are
obtained for the u and d quark-antiquark channels and for the WW and ZZ gauge
bosons, with background spectral index compatible with the Fermi-Large Area
Telescope (LAT) data from the same region. The fits return a heavy WIMP, with a
mass above 10 TeV approximately, but well below the unitarity limit for thermal
relic annihilation.


title: Seven-dimensional forest fires

abstract:
  We show that in high dimensional Bernoulli percolation, removing from a thin
infinite cluster a much thinner infinite cluster leaves an infinite component.
This observation has implications for the van den Berg-Brouwer forest fire
process, also known as self-destructive percolation, for dimension high enough.


title: Quasipolar Subrings of $3\times 3$ Matrix Rings

abstract:
  An element $a$ of a ring $R$ is called \emph{quasipolar} provided that there
exists an idempotent $p\in R$ such that $p\in comm^2(a)$, $a+p\in U(R)$ and
$ap\in R^{qnil}$. A ring $R$ is \emph{quasipolar} in case every element in $R$
is quasipolar. In this paper, we determine conditions under which subrings of
$3\times 3$ matrix rings over local rings are quasipolar. Namely, if $R$ is a
bleached local ring, then we prove that $\mathcal{T}_3(R)$ is quasipolar if and
only if $R$ is uniquely bleached. Furthermore, it is shown that $T_n(R)$ is
quasipolar if and only if $T_n\big(R[[x]]\big)$ is quasipolar for any positive
integer $n$.


title: Prime injections and quasipolarities

abstract:
  Let $p$ be a prime number. Consider the injection \[
  \iota:\mathbb{Z}/n\mathbb{Z}\to\mathbb{Z}/pn\mathbb{Z}:x\mapsto px,
  \] and the elements $e^{u}.v:=(u,v)\in \mathbb{Z}/n\mathbb{Z}\rtimes
\mathbb{Z}/n\mathbb{Z}^{\times}$ and $e^{w}.r:=(w,r)\in \mathbb{Z}_{p n}\rtimes
\mathbb{Z}_{p n}^{\times}$. Suppose $e^{u}.v\in \mathbb{Z}/n\mathbb{Z}\rtimes
\mathbb{Z}/n\mathbb{Z}^{\times}$ is seen as an automorphism of
$\mathbb{Z}/n\mathbb{Z}$ by $e^{u}.v(x)=vx+u$; then $e^{u}.v$ is a
quasipolarity if it is an involution without fixed points. In this brief note
give an explicit formula for the number of quasipolarites of
$\mathbb{Z}/n\mathbb{Z}$ in terms of the prime decomposition of $n$, and we
prove sufficient conditions such that $(e^{w}.r)\circ \iota =\iota\circ
(e^{u}.v)$, where $e^{w}.r$ and $e^{u}.v$ are quasipolarities.


title: Geometrical pumping in spin coupled double quantum dots

abstract:
  We analytically investigate the non-equilibrium pumping for a double quantum
dots system on the basis of the quantum master equation (QME), where the double
quantum dots are connected to two external leads by the spin coupling. Each of
leads has two tunable parameters, temperature and chemical potential. Using QME
formalism, we obtain an analytical expression of the Berry-like phase for
eigenstate of the QME in the parameter space. We show that the Berry-like
curvature is non zero in the whole region in the parameter space. We also show
that the Berry-like curvature vanishes in the case of single quantum dot and
the case of isolated two dots.


title: Systematics of Evaluated Half-Lives of Double-Beta Decay

abstract:
  A new evaluation of 2beta-decay half lives and their systematics is
presented. These data extend the previous evaluation and include the analysis
of all recent measurements. The nuclear matrix elements for 2beta-decay
transitions in 12 nuclei have been extracted. The recommended values are
compared with the large-scale shell-model, QRPA calculations, and experimental
data. A T2nu 1/2 ~ 1/E8 systematic trend has been observed for recommended
128,130Te values. This trend indicates similarities for nuclear matrix elements
in Te nuclei and was predicted for 2beta(2nu)-decay mode. The complete list of
results is available online at http://www.nndc.bnl.gov/bbdecay/.


title:
Precursor superconducting diamagnetism in YBa2Cu3O7-d with in-plane or
  intercalated magnetic impurities

abstract:
  The magnetization around the superconducting transition was measured in
YBa2Cu3O7-d with magnetic impurities in the CuO2 layers (Cu substituted by Zn
or Ni) or between them (Y substituted by Gd or Pr). While some of these
impurities have an important effect on the superconducting transition
temperature (Tc), the precursor diamagnetism observed above Tc is not
appreciably affected. This result contrasts with recent observations in a
conventional BCS superconductor (La), in which the precursor diamagnetism was
found to increase several orders of magnitude with the addition of a small
amount (a few atomic percent) of Gd or Pr magnetic impurities.


title: Highly Fluorinated Graphene

abstract:
  We give the results of density functional calculations for graphene with a
widely varying fluorine adsorptions. We give a systematic analysis of the
adsorption energies, lattice constants, bulk modulus, bandgap openings, and
magnetic properties. We find that a number of different adsorption geometries
and a range of physical properties can occur for each adsorbate coverage. The
systems are found to range from metallic to semiconducting with widely vary
band gaps, and a number of interesting magnetic phases are found. We expect
that many of these structures may occur in real materials systems. Further that
a listing of the properties found here may help in determining what fluorinated
graphenes are produced experimentally.


title: On singular Bosonic linear channels

abstract:
  Properties of Bosonic linear (quasi-free) channels, in particular, Bosonic
Gaussian channels with two types of degeneracy are considered.
  The first type of degeneracy can be interpreted as existence of noise-free
canonical variables (for Gaussian channels it means that $\det\alpha=0$). It is
shown that this degeneracy implies existence of (infinitely many) "direct sum
decompositions" of Bosonic linear channel, which clarifies reversibility
properties of this channel (described in arXiv:1212.2354) and provides explicit
construction of reversing channels.
  The second type of degeneracy consists in rank deficiency of the operator
describing transformations of canonical variables. It is shown that this
degeneracy implies existence of (infinitely many) decompositions of input space
into direct sum of orthogonal subspaces such that the restriction of Bosonic
linear channel to each of these subspaces is a discrete classical-quantum
channel.


title:
Dynamical phase transitions in supercooled liquids: interpreting
  measurements of dynamical activity

abstract:
  We study dynamical phase transitions in a model supercooled liquid. These
transitions occur in ensembles of trajectories that are biased towards low (or
high) dynamical activity. We compare two different measures of activity that
were introduced in recent papers and we find that they are anti-correlated with
each other. To interpret this result, we show that the two measures couple to
motion on different length and time scales. We find that inactive states with
very slow structural relaxation nevertheless have increased molecular motion on
short scales. We discuss these results in terms of the potential energy
landscape of the system and in terms of the liquid structure in active/inactive
states.


title: B(E2) Evaluation for 0+ to 2+ Transitions in Even-Even Nuclei

abstract:
  A collaborative study by Brookhaven-McMaster-Central Michigan is underway to
evaluate B(E2)$\uparrow$ for 0$^{+}_{1}$ $\rightarrow$ 2$^{+}_{1}$ transitions.
This work is a continuation of a previous USNDP evaluation and has been
motivated by a large number of recent measurements and nuclear theory
developments. It includes an extended compilation, data evaluation procedures
and shell model calculations. The subset of B(E2)$\uparrow$ recommended values
for nuclei of relevance to the double-beta decay problem is presented, and
evaluation policies of experimental data and systematics are discussed. Future
plans for completion of the B(E2;0$^{+}_{1}$ $\rightarrow$ 2$^{+}_{1}$)
evaluation project are also described.


title: Selecting microscopic Equations of State

abstract:
  We compare a set of equations of state derived within microscopic many-body
approaches, and study their predictions as far as phenomenological data on
nuclei from heavy ion collisions, and astrophysical observations on neutron
stars are concerned. We find that all the data, taken together, put strong
constraints not easy to be fulfilled accurately. However, no major
discrepancies are found among the selected equations of state and with respect
to the data. The results provide an estimate of the uncertainty on the
theoretical prediction at a microscopic level of the nuclear equation of state.


title: Nuclear Science References Database

abstract:
  The Nuclear Science References (NSR) database together with its associated
Web interface, is the world's only comprehensive source of easily accessible
low- and intermediate-energy nuclear physics bibliographic information for more
than 210,000 articles since the beginning of nuclear science. The
weekly-updated NSR database provides essential support for nuclear data
evaluation, compilation and research activities. The principles of the database
and Web application development and maintenance are described. Examples of
nuclear structure, reaction and decay applications are specifically included.
The complete NSR database is freely available at the websites of the National
Nuclear Data Center http://www.nndc.bnl.gov/nsr and the International Atomic
Energy Agency http://www-nds.iaea.org/nsr.