title:
Qualitative Models for Decision Under Uncertainty without the
  Commensurability Assumption

abstract:
  This paper investigates a purely qualitative version of Savage's theory for
decision making under uncertainty. Until now, most representation theorems for
preference over acts rely on a numerical representation of utility and
uncertainty where utility and uncertainty are commensurate. Disrupting the
tradition, we relax this assumption and introduce a purely ordinal axiom
requiring that the Decision Maker (DM) preference between two acts only depends
on the relative position of their consequences for each state. Within this
qualitative framework, we determine the only possible form of the decision rule
and investigate some instances compatible with the transitivity of the strict
preference. Finally we propose a mild relaxation of our ordinality axiom,
leaving room for a new family of qualitative decision rules compatible with
transitivity.


title: Data Analysis with Bayesian Networks: A Bootstrap Approach

abstract:
  In recent years there has been significant progress in algorithms and methods
for inducing Bayesian networks from data. However, in complex data analysis
problems, we need to go beyond being satisfied with inducing networks with high
scores. We need to provide confidence measures on features of these networks:
Is the existence of an edge between two nodes warranted? Is the Markov blanket
of a given node robust? Can we say something about the ordering of the
variables? We should be able to address these questions, even when the amount
of data is not enough to induce a high scoring network. In this paper we
propose Efron's Bootstrap as a computationally efficient approach for answering
these questions. In addition, we propose to use these confidence measures to
induce better structures from the data, and to detect the presence of latent
variables.


title:
Learning Bayesian Network Structure from Massive Datasets: The "Sparse
  Candidate" Algorithm

abstract:
  Learning Bayesian networks is often cast as an optimization problem, where
the computational task is to find a structure that maximizes a statistically
motivated score. By and large, existing learning tools address this
optimization problem using standard heuristic search techniques. Since the
search space is extremely large, such search procedures can spend most of the
time examining candidates that are extremely unreasonable. This problem becomes
critical when we deal with data sets that are large either in the number of
instances, or the number of attributes. In this paper, we introduce an
algorithm that achieves faster learning by restricting the search space. This
iterative algorithm restricts the parents of each variable to belong to a small
subset of candidates. We then search for a network that satisfies these
constraints. The learned network is then used for selecting better candidates
for the next iteration. We evaluate this algorithm both on synthetic and
real-life data. Our results show that it is significantly faster than
alternative search procedures without loss of quality in the learned
structures.


title:
Parameter Priors for Directed Acyclic Graphical Models and the
  Characterization of Several Probability Distributions

abstract:
  We show that the only parameter prior for complete Gaussian DAG models that
satisfies global parameter independence, complete model equivalence, and some
weak regularity assumptions, is the normal-Wishart distribution. Our analysis
is based on the following new characterization of the Wishart distribution: let
W be an n x n, n >= 3, positive-definite symmetric matrix of random variables
and f(W) be a pdf of W. Then, f(W) is a Wishart distribution if and only if
W_{11}-W_{12}W_{22}^{-1}W_{12}' is independent of {W_{12}, W_{22}} for every
block partitioning W_{11}, W_{12}, W_{12}', W_{22} of W. Similar
characterizations of the normal and normal-Wishart distributions are provided
as well. We also show how to construct a prior for every DAG model over X from
the prior of a single regression model.


title: Quantifier Elimination for Statistical Problems

abstract:
  Recent improvement on Tarski's procedure for quantifier elimination in the
first order theory of real numbers makes it feasible to solve small instances
of the following problems completely automatically: 1. listing all equality and
inequality constraints implied by a graphical model with hidden variables. 2.
Comparing graphyical models with hidden variables (i.e., model equivalence,
inclusion, and overlap). 3. Answering questions about the identification of a
model or portion of a model, and about bounds on quantities derived from a
model. 4. Determing whether a given set of independence assertions. We discuss
the foundation of quantifier elimination and demonstrate its application to
these problems.


title: On Transformations between Probability and Spohnian Disbelief Functions

abstract:
  In this paper, we analyze the relationship between probability and Spohn's
theory for representation of uncertain beliefs. Using the intuitive idea that
the more probable a proposition is, the more believable it is, we study
transformations from probability to Sphonian disbelief and vice-versa. The
transformations described in this paper are different from those described in
the literature. In particular, the former satisfies the principles of ordinal
congruence while the latter does not. Such transformations between probability
and Spohn's calculi can contribute to (1) a clarification of the semantics of
nonprobabilistic degree of uncertain belief, and (2) to a construction of a
decision theory for such calculi. In practice, the transformations will allow a
meaningful combination of more than one calculus in different stages of using
an expert system such as knowledge acquisition, inference, and interpretation
of results.


title: A New Model of Plan Recognition

abstract:
  We present a new abductive, probabilistic theory of plan recognition. This
model differs from previous plan recognition theories in being centered around
a model of plan execution: most previous methods have been based on plans as
formal objects or on rules describing the recognition process. We show that our
new model accounts for phenomena omitted from most previous plan recognition
theories: notably the cumulative effect of a sequence of observations of
partially-ordered, interleaved plans and the effect of context on plan
adoption. The model also supports inferences about the evolution of plan
execution in situations where another agent intervenes in plan execution. This
facility provides support for using plan recognition to build systems that will
intelligently assist a user.


title: Multi-objects association in perception of dynamical situation

abstract:
  In current perception systems applied to the rebuilding of the environment
for intelligent vehicles, the part reserved to object association for the
tracking is increasingly significant. This allows firstly to follow the objects
temporal evolution and secondly to increase the reliability of environment
perception. We propose in this communication the development of a multi-objects
association algorithm with ambiguity removal entering into the design of such a
dynamic perception system for intelligent vehicles. This algorithm uses the
belief theory and data modelling with fuzzy mathematics in order to be able to
handle inaccurate as well as uncertain information due to imperfect sensors.
These theories also allow the fusion of numerical as well as symbolic data. We
develop in this article the problem of matching between known and perceived
objects. This makes it possible to update a dynamic environment map for a
vehicle. The belief theory will enable us to quantify the belief in the
association of each perceived object with each known object. Conflicts can
appear in the case of object appearance or disappearance, or in the case of a
confused situation or bad perception. These conflicts are removed or solved
using an assignment algorithm, giving a solution called the " best " and so
ensuring the tracking of some objects present in our environment.


title: A Hybrid Approach to Reasoning with Partially Elicited Preference Models

abstract:
  Classical Decision Theory provides a normative framework for representing and
reasoning about complex preferences. Straightforward application of this theory
to automate decision making is difficult due to high elicitation cost. In
response to this problem, researchers have recently developed a number of
qualitative, logic-oriented approaches for representing and reasoning about
references. While effectively addressing some expressiveness issues, these
logics have not proven powerful enough for building practical automated
decision making systems. In this paper we present a hybrid approach to
preference elicitation and decision making that is grounded in classical
multi-attribute utility theory, but can make effective use of the expressive
power of qualitative approaches. Specifically, assuming a partially specified
multilinear utility function, we show how comparative statements about classes
of decision alternatives can be used to further constrain the utility function
and thus identify sup-optimal alternatives. This work demonstrates that
quantitative and qualitative approaches can be synergistically integrated to
provide effective and flexible decision support.


title: Faithful Approximations of Belief Functions

abstract:
  A conceptual foundation for approximation of belief functions is proposed and
investigated. It is based on the requirements of consistency and closeness. An
optimal approximation is studied. Unfortunately, the computation of the optimal
approximation turns out to be intractable. Hence, various heuristic methods are
proposed and experimantally evaluated both in terms of their accuracy and in
terms of the speed of computation. These methods are compared to the earlier
proposed approximations of belief functions.


title: SPUDD: Stochastic Planning using Decision Diagrams

abstract:
  Markov decisions processes (MDPs) are becoming increasing popular as models
of decision theoretic planning. While traditional dynamic programming methods
perform well for problems with small state spaces, structured methods are
needed for large problems. We propose and examine a value iteration algorithm
for MDPs that uses algebraic decision diagrams(ADDs) to represent value
functions and policies. An MDP is represented using Bayesian networks and ADDs
and dynamic programming is applied directly to these ADDs. We demonstrate our
method on large MDPs (up to 63 million states) and show that significant gains
can be had when compared to tree-structured representations (with up to a
thirty-fold reduction in the number of nodes required to represent optimal
value functions).


title: Probabilistic Latent Semantic Analysis

abstract:
  Probabilistic Latent Semantic Analysis is a novel statistical technique for
the analysis of two-mode and co-occurrence data, which has applications in
information retrieval and filtering, natural language processing, machine
learning from text, and in related areas. Compared to standard Latent Semantic
Analysis which stems from linear algebra and performs a Singular Value
Decomposition of co-occurrence tables, the proposed method is based on a
mixture decomposition derived from a latent class model. This results in a more
principled approach which has a solid foundation in statistics. In order to
avoid overfitting, we propose a widely applicable generalization of maximum
likelihood model fitting by tempered EM. Our approach yields substantial and
consistent improvements over Latent Semantic Analysis in a number of
experiments.


title: Estimating the Value of Computation in Flexible Information Refinement

abstract:
  We outline a method to estimate the value of computation for a flexible
algorithm using empirical data. To determine a reasonable trade-off between
cost and value, we build an empirical model of the value obtained through
computation, and apply this model to estimate the value of computation for
quite different problems. In particular, we investigate this trade-off for the
problem of constructing policies for decision problems represented as influence
diagrams. We show how two features of our anytime algorithm provide reasonable
estimates of the value of computation in this domain.


title: Attention-Sensitive Alerting

abstract:
  We introduce utility-directed procedures for mediating the flow of
potentially distracting alerts and communications to computer users. We present
models and inference procedures that balance the context-sensitive costs of
deferring alerts with the cost of interruption. We describe the challenge of
reasoning about such costs under uncertainty via an analysis of user activity
and the content of notifications. After introducing principles of
attention-sensitive alerting, we focus on the problem of guiding alerts about
email messages. We dwell on the problem of inferring the expected criticality
of email and discuss work on the Priorities system, centering on prioritizing
email by criticality and modulating the communication of notifications to users
about the presence and nature of incoming email.


title: Mini-Bucket Heuristics for Improved Search

abstract:
  The paper is a second in a series of two papers evaluating the power of a new
scheme that generates search heuristics mechanically. The heuristics are
extracted from an approximation scheme called mini-bucket elimination that was
recently introduced. The first paper introduced the idea and evaluated it
within Branch-and-Bound search. In the current paper the idea is further
extended and evaluated within Best-First search. The resulting algorithms are
compared on coding and medical diagnosis problems, using varying strength of
the mini-bucket heuristics.
  Our results demonstrate an effective search scheme that permits controlled
tradeoff between preprocessing (for heuristic generation) and search.
Best-first search is shown to outperform Branch-and-Bound, when supplied with
good heuristics, and sufficient memory space.


title:
A General Algorithm for Approximate Inference and its Application to
  Hybrid Bayes Nets

abstract:
  The clique tree algorithm is the standard method for doing inference in
Bayesian networks. It works by manipulating clique potentials - distributions
over the variables in a clique. While this approach works well for many
networks, it is limited by the need to maintain an exact representation of the
clique potentials. This paper presents a new unified approach that combines
approximate inference and the clique tree algorithm, thereby circumventing this
limitation. Many known approximate inference algorithms can be viewed as
instances of this approach. The algorithm essentially does clique tree
propagation, using approximate inference to estimate the densities in each
clique. In many settings, the computation of the approximate clique potential
can be done easily using statistical importance sampling. Iterations are used
to gradually improve the quality of the estimation.


title: On Supervised Selection of Bayesian Networks

abstract:
  Given a set of possible models (e.g., Bayesian network structures) and a data
sample, in the unsupervised model selection problem the task is to choose the
most accurate model with respect to the domain joint probability distribution.
In contrast to this, in supervised model selection it is a priori known that
the chosen model will be used in the future for prediction tasks involving more
``focused' predictive distributions. Although focused predictive distributions
can be produced from the joint probability distribution by marginalization, in
practice the best model in the unsupervised sense does not necessarily perform
well in supervised domains. In particular, the standard marginal likelihood
score is a criterion for the unsupervised task, and, although frequently used
for supervised model selection also, does not perform well in such tasks. In
this paper we study the performance of the marginal likelihood score
empirically in supervised Bayesian network selection tasks by using a large
number of publicly available classification data sets, and compare the results
to those obtained by alternative model selection criteria, including empirical
crossvalidation methods, an approximation of a supervised marginal likelihood
measure, and a supervised version of Dawids prequential(predictive sequential)
principle.The results demonstrate that the marginal likelihood score does NOT
perform well FOR supervised model selection, WHILE the best results are
obtained BY using Dawids prequential r napproach.


title: Bayesian Poker

abstract:
  Poker is ideal for testing automated reasoning under uncertainty. It
introduces uncertainty both by physical randomization and by incomplete
information about opponents hands.Another source OF uncertainty IS the limited
information available TO construct psychological models OF opponents, their
tendencies TO bluff, play conservatively, reveal weakness, etc. AND the
relation BETWEEN their hand strengths AND betting behaviour. ALL OF these
uncertainties must be assessed accurately AND combined effectively FOR ANY
reasonable LEVEL OF skill IN the game TO be achieved, since good decision
making IS highly sensitive TO those tasks.We describe our Bayesian Poker
Program(BPP), which uses a Bayesian network TO model the programs poker hand,
the opponents hand AND the opponents playing behaviour conditioned upon the
hand, and betting curves which govern play given a probability of winning. The
history of play with opponents is used to improve BPPs understanding OF their
behaviour.We compare BPP experimentally WITH : a simple RULE - based system; a
program which depends exclusively ON hand probabilities(i.e., without opponent
modeling); AND WITH human players.BPP has shown itself TO be an effective
player against ALL these opponents, barring the better humans.We also sketch
out SOME likely ways OF improving play.


title: On Quantified Linguistic Approximation

abstract:
  Most fuzzy systems including fuzzy decision support and fuzzy control systems
provide out-puts in the form of fuzzy sets that represent the inferred
conclusions. Linguistic interpretation of such outputs often involves the use
of linguistic approximation that assigns a linguistic label to a fuzzy set
based on the predefined primary terms, linguistic modifiers and linguistic
connectives. More generally, linguistic approximation can be formalized in the
terms of the re-translation rules that correspond to the translation rules in
ex-plicitation (e.g. simple, modifier, composite, quantification and
qualification rules) in com-puting with words [Zadeh 1996]. However most
existing methods of linguistic approximation use the simple, modifier and
composite re-translation rules only. Although these methods can provide a
sufficient approximation of simple fuzzy sets the approximation of more complex
ones that are typical in many practical applications of fuzzy systems may be
less satisfactory. Therefore the question arises why not use in linguistic
ap-proximation also other re-translation rules corre-sponding to the
translation rules in explicitation to advantage. In particular linguistic
quantifica-tion may be desirable in situations where the conclusions
interpreted as quantified linguistic propositions can be more informative and
natu-ral. This paper presents some aspects of linguis-tic approximation in the
context of the re-translation rules and proposes an approach to linguistic
approximation with the use of quantifi-cation rules, i.e. quantified linguistic
approxima-tion. Two methods of the quantified linguistic approximation are
considered with the use of lin-guistic quantifiers based on the concepts of the
non-fuzzy and fuzzy cardinalities of fuzzy sets. A number of examples are
provided to illustrate the proposed approach.


title: Choosing Among Interpretations of Probability

abstract:
  There is available an ever-increasing variety of procedures for managing
uncertainty. These methods are discussed in the literature of artificial
intelligence, as well as in the literature of philosophy of science. Heretofore
these methods have been evaluated by intuition, discussion, and the general
philosophical method of argument and counterexample. Almost any method of
uncertainty management will have the property that in the long run it will
deliver numbers approaching the relative frequency of the kinds of events at
issue. To find a measure that will provide a meaningful evaluation of these
treatments of uncertainty, we must look, not at the long run, but at the short
or intermediate run. Our project attempts to develop such a measure in terms of
short or intermediate length performance. We represent the effects of practical
choices by the outcomes of bets offered to agents characterized by two
uncertainty management approaches: the subjective Bayesian approach and the
Classical confidence interval approach. Experimental evaluation suggests that
the confidence interval approach can outperform the subjective approach in the
relatively short run.


title: Expected Utility Networks

abstract:
  We introduce a new class of graphical representations, expected utility
networks (EUNs), and discuss some of its properties and potential applications
to artificial intelligence and economic theory. In EUNs not only probabilities,
but also utilities enjoy a modular representation. EUNs are undirected graphs
with two types of arc, representing probability and utility dependencies
respectively. The representation of utilities is based on a novel notion of
conditional utility independence, which we introduce and discuss in the context
of other existing proposals. Just as probabilistic inference involves the
computation of conditional probabilities, strategic inference involves the
computation of conditional expected utilities for alternative plans of action.
We define a new notion of conditional expected utility (EU) independence, and
show that in EUNs node separation with respect to the probability and utility
subgraphs implies conditional EU independence.


title: My Brain is Full: When More Memory Helps

abstract:
  We consider the problem of finding good finite-horizon policies for POMDPs
under the expected reward metric. The policies considered are {em free
finite-memory policies with limited memory}; a policy is a mapping from the
space of observation-memory pairs to the space of action-memeory pairs (the
policy updates the memory as it goes), and the number of possible memory states
is a parameter of the input to the policy-finding algorithms. The algorithms
considered here are preliminary implementations of three search heuristics:
local search, simulated annealing, and genetic algorithms. We compare their
outcomes to each other and to the optimal policies for each instance. We
compare run times of each policy and of a dynamic programming algorithm for
POMDPs developed by Hansen that iteratively improves a finite-state controller
--- the previous state of the art for finite memory policies. The value of the
best policy can only improve as the amount of memory increases, up to the
amount needed for an optimal finite-memory policy. Our most surprising finding
is that more memory helps in another way: given more memory than is needed for
an optimal policy, the algorithms are more likely to converge to optimal-valued
policies.


title: Lazy Evaluation of Symmetric Bayesian Decision Problems

abstract:
  Solving symmetric Bayesian decision problems is a computationally intensive
task to perform regardless of the algorithm used. In this paper we propose a
method for improving the efficiency of algorithms for solving Bayesian decision
problems. The method is based on the principle of lazy evaluation - a principle
recently shown to improve the efficiency of inference in Bayesian networks. The
basic idea is to maintain decompositions of potentials and to postpone
computations for as long as possible. The efficiency improvements obtained with
the lazy evaluation based method is emphasized through examples. Finally, the
lazy evaluation based method is compared with the hugin and valuation-based
systems architectures for solving symmetric Bayesian decision problems.


title: Representing and Combining Partially Specified CPTs

abstract:
  This paper extends previous work with network fragments and
situation-specific network construction. We formally define the asymmetry
network, an alternative representation for a conditional probability table. We
also present an object-oriented representation for partially specified
asymmetry networks. We show that the representation is parsimonious. We define
an algebra for the elements of the representation that allows us to 'factor'
any CPT and to soundly combine the partially specified asymmetry networks.


title: On the Complexity of Policy Iteration

abstract:
  Decision-making problems in uncertain or stochastic domains are often
formulated as Markov decision processes (MDPs). Policy iteration (PI) is a
popular algorithm for searching over policy-space, the size of which is
exponential in the number of states. We are interested in bounds on the
complexity of PI that do not depend on the value of the discount factor. In
this paper we prove the first such non-trivial, worst-case, upper bounds on the
number of iterations required by PI to converge to the optimal policy. Our
analysis also sheds new light on the manner in which PI progresses through the
space of policies.


title:
Approximate Planning for Factored POMDPs using Belief State
  Simplification

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


title: Solving POMDPs by Searching the Space of Finite Policies

abstract:
  Solving partially observable Markov decision processes (POMDPs) is highly
intractable in general, at least in part because the optimal policy may be
infinitely large. In this paper, we explore the problem of finding the optimal
policy from a restricted set of policies, represented as finite state automata
of a given size. This problem is also intractable, but we show that the
complexity can be greatly reduced when the POMDP and/or policy are further
constrained. We demonstrate good empirical results with a branch-and-bound
method for finding globally optimal deterministic policies, and a
gradient-ascent method for finding locally optimal stochastic policies.


title: Learning Finite-State Controllers for Partially Observable Environments

abstract:
  Reactive (memoryless) policies are sufficient in completely observable Markov
decision processes (MDPs), but some kind of memory is usually necessary for
optimal control of a partially observable MDP. Policies with finite memory can
be represented as finite-state automata. In this paper, we extend Baird and
Moore's VAPS algorithm to the problem of learning general finite-state
automata. Because it performs stochastic gradient descent, this algorithm can
be shown to converge to a locally optimal finite-state controller. We provide
the details of the algorithm and then consider the question of under what
conditions stochastic gradient descent will outperform exact gradient descent.
We conclude with empirical results comparing the performance of stochastic and
exact gradient descent, and showing the ability of our algorithm to extract the
useful information contained in the sequence of past observations to compensate
for the lack of observability at each time-step.


title: Bayes Nets in Educational Assessment: Where Do the Numbers Come From?

abstract:
  As observations and student models become complex, educational assessments
that exploit advances in technology and cognitive psychology can outstrip
familiar testing models and analytic methods. Within the Portal conceptual
framework for assessment design, Bayesian inference networks (BINs) record
beliefs about students' knowledge and skills, in light of what they say and do.
Joining evidence model BIN fragments- which contain observable variables and
pointers to student model variables - to the student model allows one to update
belief about knowledge and skills as observations arrive. Markov Chain Monte
Carlo (MCMC) techniques can estimate the required conditional probabilities
from empirical data, supplemented by expert judgment or substantive theory.
Details for the special cases of item response theory (IRT) and multivariate
latent class modeling are given, with a numerical example of the latter.


title:
A Bayesian Network Classifier that Combines a Finite Mixture Model and a
  Naive Bayes Model

abstract:
  In this paper we present a new Bayesian network model for classification that
combines the naive-Bayes (NB) classifier and the finite-mixture (FM)
classifier. The resulting classifier aims at relaxing the strong assumptions on
which the two component models are based, in an attempt to improve on their
classification performance, both in terms of accuracy and in terms of
calibration of the estimated probabilities. The proposed classifier is obtained
by superimposing a finite mixture model on the set of feature variables of a
naive Bayes model. We present experimental results that compare the predictive
performance on real datasets of the new classifier with the predictive
performance of the NB classifier and the FM classifier.


title:
A Variational Approximation for Bayesian Networks with Discrete and
  Continuous Latent Variables

abstract:
  We show how to use a variational approximation to the logistic function to
perform approximate inference in Bayesian networks containing discrete nodes
with continuous parents. Essentially, we convert the logistic function to a
Gaussian, which facilitates exact inference, and then iteratively adjust the
variational parameters to improve the quality of the approximation. We
demonstrate experimentally that this approximation is faster and potentially
more accurate than sampling. We also introduce a simple new technique for
handling evidence, which allows us to handle arbitrary distributions on
observed nodes, as well as achieving a significant speedup in networks with
discrete variables of large cardinality.


title: Loopy Belief Propagation for Approximate Inference: An Empirical Study

abstract:
  Recently, researchers have demonstrated that loopy belief propagation - the
use of Pearls polytree algorithm IN a Bayesian network WITH loops OF error-
correcting codes.The most dramatic instance OF this IS the near Shannon - limit
performance OF Turbo Codes codes whose decoding algorithm IS equivalent TO
loopy belief propagation IN a chain - structured Bayesian network. IN this
paper we ask : IS there something special about the error - correcting code
context, OR does loopy propagation WORK AS an approximate inference schemeIN a
more general setting? We compare the marginals computed using loopy propagation
TO the exact ones IN four Bayesian network architectures, including two real -
world networks : ALARM AND QMR.We find that the loopy beliefs often converge
AND WHEN they do, they give a good approximation TO the correct
marginals.However,ON the QMR network, the loopy beliefs oscillated AND had no
obvious relationship TO the correct posteriors. We present SOME initial
investigations INTO the cause OF these oscillations, AND show that SOME simple
methods OF preventing them lead TO the wrong results.


title:
Learning Bayesian Networks from Incomplete Data with Stochastic Search
  Algorithms

abstract:
  This paper describes stochastic search approaches, including a new stochastic
algorithm and an adaptive mutation operator, for learning Bayesian networks
from incomplete data. This problem is characterized by a huge solution space
with a highly multimodal landscape. State-of-the-art approaches all involve
using deterministic approaches such as the expectation-maximization algorithm.
These approaches are guaranteed to find local maxima, but do not explore the
landscape for other modes. Our approach evolves structure and the missing data.
We compare our stochastic algorithms and show they all produce accurate
results.


title: Learning Bayesian Networks with Restricted Causal Interactions

abstract:
  A major problem for the learning of Bayesian networks (BNs) is the
exponential number of parameters needed for conditional probability tables.
Recent research reduces this complexity by modeling local structure in the
probability tables. We examine the use of log-linear local models. While
log-linear models in this context are not new (Whittaker, 1990; Buntine, 1991;
Neal, 1992; Heckerman and Meek, 1997), for structure learning they are
generally subsumed under a naive Bayes model. We describe an alternative
interpretation, and use a Minimum Message Length (MML) (Wallace, 1987) metric
for structure learning of networks exhibiting causal independence, which we
term first-order networks (FONs). We also investigate local model selection on
a node-by-node basis.


title: The Decision-Theoretic Interactive Video Advisor

abstract:
  The need to help people choose among large numbers of items and to filter
through large amounts of information has led to a flood of research in
construction of personal recommendation agents. One of the central issues in
constructing such agents is the representation and elicitation of user
preferences or interests. This topic has long been studied in Decision Theory,
but surprisingly little work in the area of recommender systems has made use of
formal decision-theoretic techniques. This paper describes DIVA, a
decision-theoretic agent for recommending movies that contains a number of
novel features. DIVA represents user preferences using pairwise comparisons
among items, rather than numeric ratings. It uses a novel similarity measure
based on the concept of the probability of conflict between two orderings of
items. The system has a rich representation of preference, distinguishing
between a user's general taste in movies and his immediate interests. It takes
an incremental approach to preference elicitation in which the user can provide
feedback if not satisfied with the recommendation list. We empirically evaluate
the performance of the system using the EachMovie collaborative filtering
database.


title: Welldefined Decision Scenarios

abstract:
  Influence diagrams serve as a powerful tool for modelling symmetric decision
problems. When solving an influence diagram we determine a set of strategies
for the decisions involved. A strategy for a decision variable is in principle
a function over its past. However, some of the past may be irrelevant for the
decision, and for computational reasons it is important not to deal with
redundant variables in the strategies. We show that current methods (e.g. the
"Decision Bayes-ball" algorithm by Shachter UAI98) do not determine the
relevant past, and we present a complete algorithm.
  Actually, this paper takes a more general outset: When formulating a decision
scenario as an influence diagram, a linear temporal ordering of the decisions
variables is required. This constraint ensures that the decision scenario is
welldefined. However, the structure of a decision scenario often yields certain
decisions conditionally independent, and it is therefore unnecessary to impose
a linear temporal ordering on the decisions. In this paper we deal with partial
influence diagrams i.e. influence diagrams with only a partial temporal
ordering specified. We present a set of conditions which are necessary and
sufficient to ensure that a partial influence diagram is welldefined. These
conditions are used as a basis for the construction of an algorithm for
determining whether or not a partial influence diagram is welldefined.


title: Accelerating EM: An Empirical Study

abstract:
  Many applications require that we learn the parameters of a model from data.
EM is a method used to learn the parameters of probabilistic models for which
the data for some of the variables in the models is either missing or hidden.
There are instances in which this method is slow to converge. Therefore,
several accelerations have been proposed to improve the method. None of the
proposed acceleration methods are theoretically dominant and experimental
comparisons are lacking. In this paper, we present the different proposed
accelerations and try to compare them experimentally. From the results of the
experiments, we argue that some acceleration of EM is always possible, but that
which acceleration is superior depends on properties of the problem.


title: Variational Learning in Mixed-State Dynamic Graphical Models

abstract:
  Many real-valued stochastic time-series are locally linear (Gassian), but
globally non-linear. For example, the trajectory of a human hand gesture can be
viewed as a linear dynamic system driven by a nonlinear dynamic system that
represents muscle actions. We present a mixed-state dynamic graphical model in
which a hidden Markov model drives a linear dynamic system. This combination
allows us to model both the discrete and continuous causes of trajectories such
as human gestures. The number of computations needed for exact inference is
exponential in the sequence length, so we derive an approximate variational
inference technique that can also be used to learn the parameters of the
discrete and continuous models. We show how the mixed-state model and the
variational technique can be used to classify human hand gestures made with a
computer mouse.


title: Graphical Representations of Consensus Belief

abstract:
  Graphical models based on conditional independence support concise encodings
of the subjective belief of a single agent. A natural question is whether the
consensus belief of a group of agents can be represented with equal parsimony.
We prove, under relatively mild assumptions, that even if everyone agrees on a
common graph topology, no method of combining beliefs can maintain that
structure. Even weaker conditions rule out local aggregation within conditional
probability tables. On a more positive note, we show that if probabilities are
combined with the logarithmic opinion pool (LogOP), then commonly held Markov
independencies are maintained. This suggests a straightforward procedure for
constructing a consensus Markov network. We describe an algorithm for computing
the LogOP with time complexity comparable to that of exact Bayesian inference.


title:
SPOOK: A System for Probabilistic Object-Oriented Knowledge
  Representation

abstract:
  In previous work, we pointed out the limitations of standard Bayesian
networks as a modeling framework for large, complex domains. We proposed a new,
richly structured modeling language, {em Object-oriented Bayesian Netorks},
that we argued would be able to deal with such domains. However, it turns out
that OOBNs are not expressive enough to model many interesting aspects of
complex domains: the existence of specific named objects, arbitrary relations
between objects, and uncertainty over domain structure. These aspects are
crucial in real-world domains such as battlefield awareness. In this paper, we
present SPOOK, an implemented system that addresses these limitations. SPOOK
implements a more expressive language that allows it to represent the
battlespace domain naturally and compactly. We present a new inference
algorithm that utilizes the model structure in a fundamental way, and show
empirically that it achieves orders of magnitude speedup over existing
approaches.


title:
Bayesian Networks for Dependability Analysis: an Application to Digital
  Control Reliability

abstract:
  Bayesian Networks (BN) provide robust probabilistic methods of reasoning
under uncertainty, but despite their formal grounds are strictly based on the
notion of conditional dependence, not much attention has been paid so far to
their use in dependability analysis. The aim of this paper is to propose BN as
a suitable tool for dependability analysis, by challenging the formalism with
basic issues arising in dependability tasks. We will discuss how both modeling
and analysis issues can be naturally dealt with by BN. Moreover, we will show
how some limitations intrinsic to combinatorial dependability methods such as
Fault Trees can be overcome using BN. This will be pursued through the study of
a real-world example concerning the reliability analysis of a redundant digital
Programmable Logic Controller (PLC) with majority voting 2:3


title: Enhancing QPNs for Trade-off Resolution

abstract:
  Qualitative probabilistic networks have been introduced as qualitative
abstractions of Bayesian belief networks. One of the major drawbacks of these
qualitative networks is their coarse level of detail, which may lead to
unresolved trade-offs during inference. We present an enhanced formalism for
qualitative networks with a finer level of detail. An enhanced qualitative
probabilistic network differs from a regular qualitative network in that it
distinguishes between strong and weak influences. Enhanced qualitative
probabilistic networks are purely qualitative in nature, as regular qualitative
networks are, yet allow for efficiently resolving trade-offs during inference.


title:
A Possibilistic Model for Qualitative Sequential Decision Problems under
  Uncertainty in Partially Observable Environments

abstract:
  In this article we propose a qualitative (ordinal) counterpart for the
Partially Observable Markov Decision Processes model (POMDP) in which the
uncertainty, as well as the preferences of the agent, are modeled by
possibility distributions. This qualitative counterpart of the POMDP model
relies on a possibilistic theory of decision under uncertainty, recently
developed. One advantage of such a qualitative framework is its ability to
escape from the classical obstacle of stochastic POMDPs, in which even with a
finite state space, the obtained belief state space of the POMDP is infinite.
Instead, in the possibilistic framework even if exponentially larger than the
state space, the belief state space remains finite.


title: Inference Networks and the Evaluation of Evidence: Alternative Analyses

abstract:
  Inference networks have a variety of important uses and are constructed by
persons having quite different standpoints. Discussed in this paper are three
different but complementary methods for generating and analyzing probabilistic
inference networks. The first method, though over eighty years old, is very
useful for knowledge representation in the task of constructing probabilistic
arguments. It is also useful as a heuristic device in generating new forms of
evidence. The other two methods are formally equivalent ways for combining
probabilities in the analysis of inference networks. The use of these three
methods is illustrated in an analysis of a mass of evidence in a celebrated
American law case.


title: Approximate Learning in Complex Dynamic Bayesian Networks

abstract:
  In this paper we extend the work of Smith and Papamichail (1999) and present
fast approximate Bayesian algorithms for learning in complex scenarios where at
any time frame, the relationships between explanatory state space variables can
be described by a Bayesian network that evolve dynamically over time and the
observations taken are not necessarily Gaussian. It uses recent developments in
approximate Bayesian forecasting methods in combination with more familiar
Gaussian propagation algorithms on junction trees. The procedure for learning
state parameters from data is given explicitly for common sampling
distributions and the methodology is illustrated through a real application.
The efficiency of the dynamic approximation is explored by using the Hellinger
divergence measure and theoretical bounds for the efficacy of such a procedure
are discussed.


title: Efficient Value of Information Computation

abstract:
  One of the most useful sensitivity analysis techniques of decision analysis
is the computation of value of information (or clairvoyance), the difference in
value obtained by changing the decisions by which some of the uncertainties are
observed. In this paper, some simple but powerful extensions to previous
algorithms are introduced which allow an efficient value of information
calculation on the rooted cluster tree (or strong junction tree) used to solve
the original decision problem.


title: Learning Hidden Markov Models with Geometrical Constraints

abstract:
  Hidden Markov models (HMMs) and partially observable Markov decision
processes (POMDPs) form a useful tool for modeling dynamical systems. They are
particularly useful for representing environments such as road networks and
office buildings, which are typical for robot navigation and planning. The work
presented here is concerned with acquiring such models. We demonstrate how
domain-specific information and constraints can be incorporated into the
statistical estimation process, greatly improving the learned models in terms
of the model quality, the number of iterations required for convergence and
robustness to reduction in the amount of available data. We present new
initialization heuristics which can be used even when the data suffers from
cumulative rotational error, new update rules for the model parameters, as an
instance of generalized EM, and a strategy for enforcing complete geometrical
consistency in the model. Experimental results demonstrate the effectiveness of
our approach for both simulated and real robot data, in traditionally
hard-to-learn environments.


title: Practical Uses of Belief Functions

abstract:
  We present examples where the use of belief functions provided sound and
elegant solutions to real life problems. These are essentially characterized by
?missing' information. The examples deal with 1) discriminant analysis using a
learning set where classes are only partially known; 2) an information
retrieval systems handling inter-documents relationships; 3) the combination of
data from sensors competent on partially overlapping frames; 4) the
determination of the number of sources in a multi-sensor environment by
studying the inter-sensors contradiction. The purpose of the paper is to report
on such applications where the use of belief functions provides a convenient
tool to handle ?messy' data problems.


title: Multiplicative Factorization of Noisy-Max

abstract:
  The noisy-or and its generalization noisy-max have been utilized to reduce
the complexity of knowledge acquisition. In this paper, we present a new
representation of noisy-max that allows for efficient inference in general
Bayesian networks. Empirical studies show that our method is capable of
computing queries in well-known large medical networks, QMR-DT and CPCS, for
which no previous exact inference method has been shown to perform well.


title: An Update Semantics for Defeasible Obligations

abstract:
  The deontic logic DUS is a Deontic Update Semantics for prescriptive
obligations based on the update semantics of Veltman. In DUS the definition of
logical validity of obligations is not based on static truth values but on
dynamic action transitions. In this paper prescriptive defeasible obligations
are formalized in update semantics and the diagnostic problem of defeasible
deontic logic is discussed. Assume a defeasible obligation `normally A ought to
be (done)' together withthe fact `A is not (done).' Is this an exception of the
normality claim, or is it a violation of the obligation? In this paper we
formalize the heuristic principle that it is a violation, unless there is a
more specific overriding obligation. The underlying motivation from legal
reasoning is that criminals should have as little opportunities as possible to
excuse themselves by claiming that their behavior was exceptional rather than
criminal.


title: Mixture Approximations to Bayesian Networks

abstract:
  Structure and parameters in a Bayesian network uniquely specify the
probability distribution of the modeled domain. The locality of both structure
and probabilistic information are the great benefits of Bayesian networks and
require the modeler to only specify local information. On the other hand this
locality of information might prevent the modeler - and even more any other
person - from obtaining a general overview of the important relationships
within the domain. The goal of the work presented in this paper is to provide
an "alternative" view on the knowledge encoded in a Bayesian network which
might sometimes be very helpful for providing insights into the underlying
domain. The basic idea is to calculate a mixture approximation to the
probability distribution represented by the Bayesian network. The mixture
component densities can be thought of as representing typical scenarios implied
by the Bayesian model, providing intuition about the basic relationships. As an
additional benefit, performing inference in the approximate model is very
simple and intuitive and can provide additional insights. The computational
complexity for the calculation of the mixture approximations criticaly depends
on the measure which defines the distance between the probability distribution
represented by the Bayesian network and the approximate distribution. Both the
KL-divergence and the backward KL-divergence lead to inefficient algorithms.
Incidentally, the latter is used in recent work on mixtures of mean field
solutions to which the work presented here is closely related. We show,
however, that using a mean squared error cost function leads to update
equations which can be solved using the junction tree algorithm. We conclude
that the mean squared error cost function can be used for Bayesian networks in
which inference based on the junction tree is tractable. For large networks,
however, one may have to rely on mean field approximations.


title: How to Elicit Many Probabilities

abstract:
  In building Bayesian belief networks, the elicitation of all probabilities
required can be a major obstacle. We learned the extent of this often-cited
observation in the construction of the probabilistic part of a complex
influence diagram in the field of cancer treatment. Based upon our negative
experiences with existing methods, we designed a new method for probability
elicitation from domain experts. The method combines various ideas, among which
are the ideas of transcribing probabilities and of using a scale with both
numerical and verbal anchors for marking assessments. In the construction of
the probabilistic part of our influence diagram, the method proved to allow for
the elicitation of many probabilities in little time.


title: Probabilistic Belief Change: Expansion, Conditioning and Constraining

abstract:
  The AGM theory of belief revision has become an important paradigm for
investigating rational belief changes. Unfortunately, researchers working in
this paradigm have restricted much of their attention to rather simple
representations of belief states, namely logically closed sets of propositional
sentences. In our opinion, this has resulted in a too abstract categorisation
of belief change operations: expansion, revision, or contraction. Occasionally,
in the AGM paradigm, also probabilistic belief changes have been considered,
and it is widely accepted that the probabilistic version of expansion is
conditioning. However, we argue that it may be more correct to view
conditioning and expansion as two essentially different kinds of belief change,
and that what we call constraining is a better candidate for being considered
probabilistic expansion.


title: Bayesian Control for Concentrating Mixed Nuclear Waste

abstract:
  A control algorithm for batch processing of mixed waste is proposed based on
conditional Gaussian Bayesian networks. The network is compiled during batch
staging for real-time response to sensor input.


title: Contextual Weak Independence in Bayesian Networks

abstract:
  It is well-known that the notion of (strong) conditional independence (CI) is
too restrictive to capture independencies that only hold in certain contexts.
This kind of contextual independency, called context-strong independence (CSI),
can be used to facilitate the acquisition, representation, and inference of
probabilistic knowledge. In this paper, we suggest the use of contextual weak
independence (CWI) in Bayesian networks. It should be emphasized that the
notion of CWI is a more general form of contextual independence than CSI.
Furthermore, if the contextual strong independence holds for all contexts, then
the notion of CSI becomes strong CI. On the other hand, if the weak contextual
independence holds for all contexts, then the notion of CWI becomes weak
independence (WI) nwhich is a more general noncontextual independency than
strong CI. More importantly, complete axiomatizations are studied for both the
class of WI and the class of CI and WI together. Finally, the interesting
property of WI being a necessary and sufficient condition for ensuring
consistency in granular probabilistic networks is shown.


title:
Inference in Multiply Sectioned Bayesian Networks with Extended
  Shafer-Shenoy and Lazy Propagation

abstract:
  As Bayesian networks are applied to larger and more complex problem domains,
search for flexible modeling and more efficient inference methods is an ongoing
effort. Multiply sectioned Bayesian networks (MSBNs) extend the HUGIN inference
for Bayesian networks into a coherent framework for flexible modeling and
distributed inference.Lazy propagation extends the Shafer-Shenoy and HUGIN
inference methods with reduced space complexity. We apply the Shafer-Shenoy and
lazy propagation to inference in MSBNs. The combination of the MSBN framework
and lazy propagation provides a better framework for modeling and inference in
very large domains. It retains the modeling flexibility of MSBNs and reduces
the runtime space complexity, allowing exact inference in much larger domains
given the same computational resources.


title: Time-Critical Dynamic Decision Making

abstract:
  Recent interests in dynamic decision modeling have led to the development of
several representation and inference methods. These methods however, have
limited application under time critical conditions where a trade-off between
model quality and computational tractability is essential. This paper presents
an approach to time-critical dynamic decision modeling. A knowledge
representation and modeling method called the time-critical dynamic influence
diagram is proposed. The formalism has two forms. The condensed form is used
for modeling and model abstraction, while the deployed form which can be
converted from the condensed form is used for inference purposes. The proposed
approach has the ability to represent space-temporal abstraction within the
model. A knowledge-based meta-reasoning approach is proposed for the purpose of
selecting the best abstracted model that provide the optimal trade-off between
model quality and model tractability. An outline of the knowledge-based model
construction algorithm is also provided.


title:
A Method for Speeding Up Value Iteration in Partially Observable Markov
  Decision Processes

abstract:
  We present a technique for speeding up the convergence of value iteration for
partially observable Markov decisions processes (POMDPs). The underlying idea
is similar to that behind modified policy iteration for fully observable Markov
decision processes (MDPs). The technique can be easily incorporated into any
existing POMDP value iteration algorithms. Experiments have been conducted on
several test problems with one POMDP value iteration algorithm called
incremental pruning. We find that the technique can make incremental pruning
run several orders of magnitude faster.


title:
How to solve three fundamental linear matrix inequalities in the
  L\"owner partial ordering

abstract:
  This paper shows how to solve analytically the three fundamental linear
matrix inequalities
  $$
  AXB \succcurlyeq C \, (\succ C), \ \ AXA^*\succcurlyeq B \, (\succ B), \ \ AX
+(AX)^{*} \succcurlyeq B \, (\succ B)
  $$ in the L\"owner partial ordering by using ranks, inertias and generalized
inverses of matrices.


title: Moving mesh cosmology: tracing cosmological gas accretion

abstract:
  We investigate the nature of gas accretion onto haloes and galaxies at z=2
using cosmological hydrodynamic simulations run with the moving mesh code
AREPO. Implementing a Monte Carlo tracer particle scheme to determine the
origin and thermodynamic history of accreting gas, we make quantitative
comparisons to an otherwise identical simulation run with the smoothed particle
hydrodynamics (SPH) code GADGET-3. Contrasting these two numerical approaches,
we find significant physical differences in the thermodynamic history of
accreted gas in haloes above 10^10.5 solar masses. In agreement with previous
work, GADGET simulations show a cold fraction near unity for galaxies forming
in massive haloes, implying that only a small percentage of accreted gas heats
to an appreciable fraction of the virial temperature during accretion. The same
galaxies in AREPO show a much lower cold fraction, <20% in haloes above 10^11
solar masses. This results from a hot gas accretion rate which, at this same
halo mass, is an order of magnitude larger than with GADGET, while the cold
accretion rate is also lower. These discrepancies increase for more massive
systems, and we explain both as due to numerical inaccuracies in the standard
formulation of SPH. We also observe that the relatively sharp transition from
cold to hot mode dominated accretion, at a halo mass of ~10^11, is a
consequence of comparing past gas temperatures to a constant threshold value
independent of virial temperature. Examining the spatial distribution of
accreting gas, we find that gas filaments in GADGET tend to remain collimated
and flow coherently to small radii, or artificially fragment and form a large
number of purely numerical "blobs". Similar gas streams in AREPO show increased
heating and disruption at 0.25-0.5 virial radii and contribute to the hot gas
accretion rate in a manner distinct from classical cooling flows.


title: Progress in the Determination of the Partonic Structure of the Proton

abstract:
  We review the current state of the art in the determination of the parton
substructure of the nucleon, as expressed in terms of parton distribution
functions (PDFs), and probed in high-energy lepton-hadron and hadron-hadron
collisions, and we assess their implications for current precision collider
phenomenology, in particular at the Large Hadron Collider (LHC). We review the
theoretical foundations of PDF determination: the way cross sections are
expressed in terms of PDFs using perturbative QCD factorization and evolution,
the methodology used to extract PDFs from experimental data, and the way in
which different physical processes can be used to constrain different PDFs. We
summarize current knowledge of PDFs and the limitations in accuracy currently
entailed for the computation of hadron collider processes, in particular at the
LHC. We discuss the current main sources of theoretical and phenomenological
uncertainties, and the direction of progress towards their reduction in the
future.


title: Non-Abelian T-duality and the AdS/CFT correspondence:new N=1 backgrounds

abstract:
  We consider non-Abelian T-duality on N=1 supergravity backgrounds possessing
well understood field theory duals. For the case of D3-branes at the tip of the
conifold, we dualise along an SU(2) isometry. The result is a type-IIA geometry
whose lift to M-theory is of the type recently proposed by Bah et. al. as the
dual to certain N=1 SCFT quivers produced by M5-branes wrapping a Riemann
surface. In the non-conformal cases we find smooth duals in massive IIA
supergravity with a Romans mass naturally quantised. We initiate the
interpretation of these geometries in the context of the AdS/CFT
correspondence. We show that the central charge and the entanglement entropy
are left invariant by this dualisation. The backgrounds suggest a form of
Seiberg duality in the dual field theories which also exhibit domain walls and
confinement in the infrared.


title: Chameleonic inflation

abstract:
  We attempt to incorporate inflation into a string theory realization of the
chameleon mechanism. Previously, it was found that the volume modulus,
stabilized by the supersymmetric potential used by Kachru, Kallosh, Linde and
Trivedi (KKLT) and with the right choice of parameters, can generically work as
a chameleon. In this paper, we ask whether inflation can be realized in the
same model. We find that we need a large extra dimensions set-up, as well as a
semi-phenomenological deformation of the Kahler potential in the quantum
region. We also find that an additional KKLT term is required so that there are
now two pieces to the potential, one which drives inflation in the early
universe, and one which is responsible for chameleon screening at late times.
These two pieces of the potential are separated by a large flat desert in field
space. The scalar field must dynamically traverse this desert between the end
of inflation and today, and we find that this can indeed occur under the right
conditions.


title:
Disentangling satellite galaxy populations using orbit tracking in
  simulations

abstract:
  Physical processes regulating star formation in satellite galaxies represent
an area of ongoing research, but the projected nature of observed coordinates
makes separating different populations of satellites (with different processes
at work) difficult. The orbital history of a satellite galaxy leads to its
present-day phase space coordinates; we can also work backwards and use these
coordinates to statistically infer information about the orbital history. We
use merger trees from the MultiDark Run 1 N-body simulation to compile a
catalog of the orbits of satellite haloes in cluster environments. We
parameterize the orbital history by the time since crossing within 2.5 rvir of
the cluster centre and use our catalog to estimate the probability density over
a range of this parameter given a set of present-day projected (i.e.
observable) phase space coordinates. We show that different populations of
satellite haloes, e.g. infalling, backsplash and virialized, occupy distinct
regions of phase space, and semi-distinct regions of projected phase space.
This will allow us to probabilistically determine the time since infall of a
large sample of observed satellite galaxies, and ultimately to study the effect
of orbital history on star formation history (the topic of a future paper). We
test the accuracy of our method and find that we can reliably recover this time
within +/-2.58 Gyr in 68 per cent of cases by using all available phase space
coordinate information, compared to +/-2.64 Gyr using only position coordinates
and +/-3.10 Gyr guessing 'blindly', i.e. using no coordinate information, but
with knowledge of the overall distribution of infall times. In some regions of
phase space, the accuracy of the infall time estimate improves to +/-1.85 Gyr.
Although we focus on time since infall, our method is easily generalizable to
other orbital parameters (e.g. pericentric distance and time).


title: Modular invariance in the gapped XYZ spin 1/2 chain

abstract:
  We show that the elliptic parametrization of the coupling constants of the
quantum XYZ spin chain can be analytically extended outside of their natural
domain, to cover the whole phase diagram of the model, which is composed of 12
adjacent regions, related to one another by a spin rotation. This extension is
based on the modular properties of the elliptic functions and we show how
rotations in parameter space correspond to the double covering PGL(2,Z)of the
modular group, implying that the partition function of the XYZ chain is
invariant under this group in parameter space, in the same way as a Conformal
Field Theory partition function is invariant under the modular group acting in
real space. The encoding of the symmetries of the model into the modular
properties of the partition function could shed light on the general structure
of integrable models.


title:
Radius constraints and minimal equipartition energy of relativistically
  moving synchrotron sources

abstract:
  A measurement of the synchrotron self-absorption flux and frequency provides
tight constraints on the physical size of the source and a robust lower limit
on its energy. This lower limit is also a good estimate of the magnetic field
and electrons' energy, if the two components are at equipartition. This
well-known method was used for decades to study numerous astrophysical sources
moving at non-relativistic (Newtonian) speeds. Here we generalize the Newtonian
equipartition theory to sources moving at relativistic speeds including the
effect of deviation from spherical symmetry expected in such sources. Like in
the Newtonian case, minimization of the energy provides an excellent estimate
of the emission radius and yields a useful lower limit on the energy. We find
that the application of the Newtonian formalism to a relativistic source would
yield a smaller emission radius, and would generally yield a larger lower limit
on the energy (within the observed region). For sources where the
Synchrotron-self-Compton component can be identified, the minimization of the
total energy is not necessary and we present an unambiguous solution for the
parameters of the system.


title: Statistical ensembles of virialized halo matter density profiles

abstract:
  We define and study statistical ensembles of matter density profiles
describing spherically symmetric, virialized dark matter haloes of finite
extent with a given mass and total gravitational potential energy. Our
ensembles include spatial degrees of freedom only, a microstate being a
spherically symmetric matter density function. We provide an exact solution for
the grand canonical partition functional, and show its equivalence to that of
the microcanonical ensemble. We obtain analytically the mean profiles that
correspond to an overwhelming majority of microstates. All such profiles have
an infinitely deep potential well, with the singular isothermal sphere arising
in the infinite temperature limit. Systems with virial radius larger than
gravitational radius exhibit a localization of a finite fraction of the energy
in the very center. The universal logarithmic inner slope of unity of the NFW
haloes is predicted at any mass and energy if an upper bound is set to the
maximal depth of the potential well. In this case, the statistically favored
mean profiles compare well to the NFW profiles. For very massive haloes the
agreement becomes exact.


title: The helium-rich cataclysmic variable SBSS 1108+574

abstract:
  We present time-resolved spectroscopy and photometry of the dwarf nova SBSS
1108+574, obtained during the 2012 outburst. Its quiescent spectrum is
unusually rich in helium, showing broad, double-peaked emission lines from the
accretion disc. We measure a line flux ratio HeI 5875/Halpha = 0.81 +/- 0.04, a
much higher ratio than typically observed in cataclysmic variables (CVs). The
outburst spectrum shows hydrogen and helium in absorption, with weak emission
of Halpha and HeI 6678, as well as strong HeII emission.
  From our photometry, we find the superhump period to be 56.34 +/- 0.18
minutes, in agreement with the previously published result. The spectroscopic
period, derived from the radial velocities of the emission lines, is found to
be 55.3 +/- 0.8 minutes, consistent with a previously identified photometric
orbital period, and significantly below the normal CV period minimum. This
indicates that the donor in SBSS 1108+574 is highly evolved. The superhump
excess derived from our photometry implies a mass ratio of q = 0.086 +/- 0.014.
Our spectroscopy reveals a grazing eclipse of the large outbursting disc. As
the disc is significantly larger during outburst, it is unlikely that an
eclipse will be detectable in quiescence. The relatively high accretion rate
implied by the detection of outbursts, together with the large mass ratio,
suggests that SBSS 1108+574 is still evolving towards its period minimum.


title: Baryon and meson screening masses

abstract:
  In a strongly-coupled quark-gluon plasma, collective excitations of gluons
and quarks should dominate over the excitation of individual quasi-free gluon
and quark modes. To explore this possibility, we computed screening masses for
ground-state light-quark mesons and baryons at leading-order in a
symmetry-preserving truncation scheme for the Dyson-Schwinger equations using a
confining formulation of a contact-interaction at nonzero temperature. Meson
screening masses are obtained from Bethe-Salpeter equations; and baryon
analogues from a novel construction of the Faddeev equation, which employs an
improved quark-exchange approximation in the kernel. Our treatment implements a
deconfinement transition that is coincident with chiral symmetry restoration in
the chiral limit, when both transitions are second order. Despite
deconfinement, in all T=0 bound-state channels, strong correlations persist
above the critical temperature, T>T_c; and, in the spectrum defined by the
associated screening masses, degeneracy between parity-partner correlations is
apparent for T >1.3T_c. Notwithstanding these results, there are reasons
(including Golberger-Treiman relations) to suppose that the inertial masses of
light-quark bound-states, when they may be defined, vanish at the deconfinement
temperature; and that this is a signal of bound-state dissolution. Where a
sensible comparison is possible, our predictions are consistent with results
from contemporary numerical simulations of lattice-regularised QCD.


title:
Spitzer Transits of the Super-Earth GJ1214b and Implications for Its
  Atmosphere

abstract:
  We observed the transiting super-Earth exoplanet GJ1214b using Warm Spitzer
at 4.5 microns wavelength during a 20-day quasi-continuous sequence in May
2011. The goals of our long observation were to accurately define the infrared
transit radius of this nearby super-Earth, to search for the secondary eclipse,
and to search for other transiting planets in the habitable zone of GJ1214. We
here report results from the transit monitoring of GJ1214b, including a
re-analysis of previous transit observations by Desert et al. (2011). In total,
we analyse 14 transits of GJ1214b at 4.5 microns, 3 transits at 3.6 microns,
and 7 new ground-based transits in the I+z band. Our new Spitzer data by
themselves eliminate cloudless solar composition atmospheres for GJ1214b, and
methane-rich models from Howe & Burrows (2012). Using our new Spitzer
measurements to anchor the observed transit radii of GJ1214b at long
wavelengths, and adding new measurements in I+z, we evaluate models from
Benneke & Seager (2012) and Howe & Burrows (2012) using a chi-squared analysis.
We find that the best-fit model exhibits an increase in transit radius at short
wavelengths due to Rayleigh scattering. Pure water atmospheres are also
possible. However, a flat line (no atmosphere detected) remains among the best
of the statistically acceptable models, and better than pure water atmospheres.
We explore the effect of systematic differences among results from different
observational groups, and we find that the Howe & Burrows (2012) tholin-haze
model remains the best fit, even when systematic differences among observers
are considered.


title:
Spontaneous CP violation in A4xSU(5) with Constrained Sequential
  Dominance 2

abstract:
  We revisit a two right-handed neutrino model with two texture zeros, namely
an indirect model based on A4 with the recently proposed new type of
constrained sequential dominance (CSD2), involving vacuum alignments along the
(0,1,-1)^T and (1,0,2)^T directions in flavour space, which are proportional to
the neutrino Dirac mass matrix columns. In this paper we construct a
renormalizable and unified indirect A4xSU(5) model along these lines and show
that, with spontaneous CP violation and a suitable vacuum alignment of the
phases, the charged lepton corrections lead to a reactor angle in good
agreement with results from Daya Bay and RENO. The model predicts a
right-angled unitarity triangle in the quark sector and a Dirac CP violating
oscillation phase in the lepton sector of delta approximately 130 degrees,
while providing a good fit to all quark and lepton masses and mixing angles.


title: Emittance and Phase Space Tomography for the Fermilab Linac

abstract:
  The Fermilab Linac delivers a variable intensity, 400-MeV beam to the The
MuCool Test Area experimental hall via a beam line specifically designed to
facilitate measurements of the Linac beam emittance and properties. A 10 m,
dispersion-free and magnet-free straight utilizes an upstream quadrupole
focusing triplet in combination with the necessary in-straight beam diagnostics
to fully characterize the transverse beam properties. Since the Linac does not
produce a strictly elliptical phase space, tomography must be performed on the
profile data to retrieve the actual particle distribution in phase space. This
is achieved by rotating the phase space distribution using different waist
focusing conditions of the upstream triplet and performing a de-convolution of
the profile data. Preliminary measurements using this diagnostic section are
reported here.


title:
An exact integral relation between the Ni56 mass and the bolometric
  light curve of a type Ia supernova

abstract:
  An exact relation between the Ni56 mass and the bolometric light curve of a
type Ia supernova can be derived as follows, using the following excellent
approximations: 1. the emission is powered solely by Ni56-> Co56 ->Fe56; 2.
each mass element propagates at a non-relativistic velocity which is constant
in time (free coasting); and 3. the internal energy is dominated by radiation.
Under these approximations, the energy E(t) carried by radiation in the ejecta
satisfies: dE/dt=-E(t)/t-L(t)+Q(t), where Q(t) is the deposition of energy by
the decay which is precisely known and L(t) is the bolometric luminosity. By
multiplying this equation by time and integrating over time we find:
E(t)*t=\int_0^t Q(t')t'dt' -\int_0^t L(t')t'dt'. At late time, t>> t_peak, the
energy inside the ejecta decreases rapidly due to its escape, and thus we have
\int_0^t Q(t')t'dt'=\int_0^t L(t')t'dt'. This relation is correct regardless of
the opacities, density distribution or Ni56 deposition distribution in the
ejecta and is very different from "Arnett's rule", L_peak ~ Q(t_peak). By
comparing \int_0^t Q(t')t'dt' with \int_0^t L(t')t'dt' at t~40 day after the
explosion, the mass of Ni56 can be found directly from UV, optical and infrared
observations with modest corrections due to the unobserved gamma-rays and due
to the small residual energy in the ejecta, E(t)*t>0.


title: Supersymmetric Hidden Sectors for Heterotic Standard Models

abstract:
  Within the context of the weakly coupled E8 x E8 heterotic string, we study
the hidden sector of heterotic standard model compactifications to
four-dimensions. Specifically, we present a class of hidden sector vector
bundles - composed of the direct sum of line bundles only - that, together with
an effective bulk five-brane, renders the heterotic standard model entirely N=1
supersymmetric. Two explicit hidden sectors are constructed and analyzed in
this context; one with the gauge group E7 x U(1) arising from a single line
bundle and a second with an SO(12) x U(1) x U(1) gauge group constructed from
the direct sum of two line bundles. Each hidden sector bundle is shown to
satisfy all requisite physical constraints within a finite region of the Kahler
cone. We also clarify that the first Chern class of the line bundles need not
be even in our context, as has often been imposed in the model building
literature.


title:
Multilevel Preconditioning of Discontinuous-Galerkin Spectral Element
  Methods, Part I: Geometrically Conforming Meshes

abstract:
  This paper is concerned with the design, analysis and implementation of
preconditioning concepts for spectral Discontinuous Galerkin discretizations of
elliptic boundary value problems. While presently known techniques realize a
growth of the condition numbers that is logarithmic in the polynomial degrees
when all degrees are equal and quadratic otherwise, our main objective is to
realize full robustness with respect to arbitrarily large locally varying
polynomial degrees degrees, i.e., under mild grading constraints condition
numbers stay uniformly bounded with respect to the mesh size and variable
degrees. The conceptual foundation of the envisaged preconditioners is the
auxiliary space method. The main conceptual ingredients that will be shown in
this framework to yield "optimal" preconditioners in the above sense are
Legendre-Gauss-Lobatto grids in connection with certain associated anisotropic
nested dyadic grids as well as specially adapted wavelet preconditioners for
the resulting low order auxiliary problems. Moreover, the preconditioners have
a modular form that facilitates somewhat simplified partial realizations. One
of the components can, for instance, be conveniently combined with domain
decomposition, at the expense though of a logarithmic growth of condition
numbers. Our analysis is complemented by quantitative experimental studies of
the main components.


title:
Evidence for Dimer Crystal Melting in the Frustrated Spin-Ladder
  BiCu2PO6

abstract:
  In the spin ladder compound BiCu$_2$PO$_6$ there exists a decisive dynamics
of spin excitations that we classify and characterize using inelastic light
scattering. We observe low-energy singlets and a broad triplon continuum
extending from 36 cm$^{-1}$ to 700 cm$^{-1}$ in ($aa$), ($bb$), and ($cc$)
light scattering polarizations. Though isolated spin ladder physics can roughly
account for the observed excitations at high energies, frustration and
interladder interactions need to be considered to fully describe the spectral
distribution and scattering selection rules at low and intermediate energies.
More significantly, an interladder singlet bound mode at 24 cm$^{-1}$, lying
below the continuum, shows its largest scattering intensity in interladder
($ab$) polarization. In contrast, two intraladder bound states at 62 cm$^{-1}$
and 108 cm$^{-1}$ with energies comparable to the continuum are observed with
light polarization along the leg ($bb$) and the rung ($cc$). We attribute the
rich spectrum of singlet bound modes to a melting of a dimer crystal. Our study
provides evidence for a Z$_2$ quantum phase transition from a dimer to a
resonating valence bond state driven by singlet fluctuations.


title: An alternative text representation to TF-IDF and Bag-of-Words

abstract:
  In text mining, information retrieval, and machine learning, text documents
are commonly represented through variants of sparse Bag of Words (sBoW) vectors
(e.g. TF-IDF). Although simple and intuitive, sBoW style representations suffer
from their inherent over-sparsity and fail to capture word-level synonymy and
polysemy. Especially when labeled data is limited (e.g. in document
classification), or the text documents are short (e.g. emails or abstracts),
many features are rarely observed within the training corpus. This leads to
overfitting and reduced generalization accuracy. In this paper we propose Dense
Cohort of Terms (dCoT), an unsupervised algorithm to learn improved sBoW
document features. dCoT explicitly models absent words by removing and
reconstructing random sub-sets of words in the unlabeled corpus. With this
approach, dCoT learns to reconstruct frequent words from co-occurring
infrequent words and maps the high dimensional sparse sBoW vectors into a
low-dimensional dense representation. We show that the feature removal can be
marginalized out and that the reconstruction can be solved for in closed-form.
We demonstrate empirically, on several benchmark datasets, that dCoT features
significantly improve the classification accuracy across several document
classification tasks.


title:
Observational constraints on the powering mechanism of transient
  relativistic jets

abstract:
  We revisit the paradigm of the dependency of jet power on black hole spin in
accreting black hole systems. In a previous paper we showed that the luminosity
of compact jets continuously launched due to accretion onto black holes in
X-ray binaries (analogous to those that dominate the kinetic feedback from AGN)
do not appear to correlate with reported black hole spin measurements. It is
therefore unclear whether extraction of the black hole spin energy is the main
driver powering compact jets from accreting black holes. Occasionally, black
hole X-ray binaries produce discrete, transient (ballistic) jets for a brief
time over accretion state changes. Here, we quantify the dependence of the
power of these transient jets (adopting two methods to infer the jet power) on
black hole spin, making use of all the available data in the current
literature, which includes 12 BHs with both measured spin parameters and radio
flares over the state transition. In several sources, regular, well-sampled
radio monitoring has shown that the peak radio flux differs dramatically
depending on the outburst (up to a factor of 1000) whereas the total power
required to energise the flare may only differ by a factor ~< 4 between
outbursts. The peak flux is determined by the total energy in the flare and the
time over which it is radiated (which can vary considerably between outbursts).
Using a Bayesian fitting routine we rule out a statistically significant
positive correlation between transient jet power measured using these methods,
and current estimates of black hole spin. Even when selecting subsamples of the
data that disregard some methods of black hole spin measurement or jet power
measurement, no correlation is found in all cases.


title: On the representation of quadratic forms by quadratic forms

abstract:
  Using the circle method, we show that for a fixed positive definite integral
quadratic form $A$, the expected asymptotic formula for the number of
representations of a positive definite integral quadratic form $B$ by $A$ holds
true, providing that the dimension of $A$ is large enough in terms of the
dimension of $B$ and the maximum ratio of the successive minima of $B$, and
providing that $B$ is sufficiently large in terms of $A$.


title: The fate of flat directions in higher derivative gravity

abstract:
  We discuss the fate of flat directions in higher derivative gravity by
studying two explicit examples, namely higher derivative gauged supergravity in
five dimensions and higher derivative type IIB string theory in ten dimensions.
In the first case, the supersymmetric spinning black hole solution in
asymptotically AdS spacetime, found by Gutowski and Reall, is analyzed. In this
case we find that the flat direction at the two derivative level is not lifted
after addition of higher derivative terms, and as it turns out, this result
holds even for non-supersymmetric deformations of the higher derivative action.
For the rotating D3-brane solutions in type IIB theory, the dilaton
parametrizes a flat direction at leading order, but its fate changes upon
including order (\alpha^\prime)^3 supersymmetric higher derivative corrections
to the type IIB action, i.e. its leading value gets fixed.


title:
Fermilab PXIE Beam Diagnostics Development and Testing at the HINS Beam
  Facility

abstract:
  Fermilab is planning the construction of a prototype front end of the Project
X linac. The Project X Injector Experiment (PXIE) is expected to accelerate 1
mA cw H- beam up to 30 MeV. Some of the major goals of the project are to test
a cw RFQ and H- source, a broadband bunch-by-bunch beam chopper and a
low-energy superconducting linac. The successful characterization and operation
of such an accelerator places stringent requirements on beam line diagnostics.
These crucial beam measurements include bunch currents, beam orbit, beam phase,
bunch length, transverse profile and emittance, beam halo and tails, as well as
the extinction performance of the broadband chopper. This paper presents PXIE
beam measurement requirements and instrumentation development plans. Also
presented are plans to test many of these instruments at the Fermilab High
Intensity Neutrino Source (HINS) beam facility. Since HINS is already an
operational accelerator, utilizing HINS for instrumentation testing allows for
quicker development of the required PXIE diagnostics.


title:
Can local single-pass methods solve any stationary
  Hamilton-Jacobi-Bellman equation?

abstract:
  The use of local single-pass methods (like, e.g., the Fast Marching method)
has become popular in the solution of some Hamilton-Jacobi equations. The
prototype of these equations is the eikonal equation, for which the methods can
be applied saving CPU time and possibly memory allocation. Then, some natural
questions arise: can local single-pass methods solve any Hamilton-Jacobi
equation? If not, where the limit should be set? This paper tries to answer
these questions. In order to give a complete picture, we present an overview of
some fast methods available in literature and we briefly analyze their main
features. We also introduce some numerical tools and provide several numerical
tests which are intended to exhibit the limitations of the methods. We show
that the construction of a local single-pass method for general Hamilton-Jacobi
equations is very hard, if not impossible. Nevertheless, some special classes
of problems can be actually solved, making local single-pass methods very
useful from the practical point of view.


title:
The depletion of gas in high-redshift dwarf galaxies from an
  inhomogeneous reionization

abstract:
  The reionization of the intergalactic medium (IGM) was likely inhomogeneous
and extended. By heating the IGM and photo-evaporating gas from the outskirts
of galaxies, this process can have a dramatic impact on the growth of
structures. Using a suite of spherically-symmetric collapse simulations
spanning a large parameter space, we study the impact of an ionizing
ultraviolet background (UVB) on the condensation of baryons onto dark matter
halos. We present an expression for the halo baryon fraction, which is an
explicit function of: (i) halo mass; (ii) UVB intensity; (iii) redshift; (iv)
redshift at which the halo was exposed to a UVB. We also present a
corresponding expression for the characteristic or critical mass, defined as
the halo mass which retains half of its baryons compared to the global value.
Since our results are general and physically-motivated, they can be broadly
applied to inhomogeneous reionization models.


title:
Fractionalization of minimal excitations in integer quantum Hall edge
  channels

abstract:
  A theoretical study of the single electron coherence properties of Lorentzian
and rectangular pulses is presented. By combining bosonization and the Floquet
scattering approach, the effect of interactions on a periodic source of voltage
pulses is computed exactly. When such excitations are injected into one of the
channels of a system of two copropagating quantum Hall edge channels, they
fractionalize into pulses whose charge and shape reflects the properties of
interactions. We show that the dependence of fractionalization induced
electron/hole pair production in the pulses amplitude contains clear signatures
of the fractionalization of the individual excitations. We propose an
experimental setup combining a source of Lorentzian pulses and an Hanbury Brown
and Twiss interferometer to measure interaction induced electron/hole pair
production and more generally to reconstruct single electron coherence of these
excitations before and after their fractionalization.


title:
A Superconvergent discontinuous Galerkin method for Volterra
  integro-differential equations, smooth and non-smooth kernels

abstract:
  We study the numerical solution for Volerra integro-differential equations
with smooth and non-smooth kernels. We use a $h$-version discontinuous Galerkin
(DG) method and derive nodal error bounds that are explicit in the parameters
of interest. In the case of non-smooth kernel, it is justified that the
start-up singularities can be resolved at superconvergence rates by using
non-uniformly graded meshes. Our theoretical results are numerically validated
in a sample of test problems.


title: Results on the regularity of square-free monomial ideals

abstract:
  In a 2008 paper, the first author and Van Tuyl proved that the regularity of
the edge ideal of a graph G is at most one greater than the matching number of
G. In this note, we provide a generalization of this result to any square-free
monomial ideal. We define a 2-collage in a simple hypergraph to be a collection
of edges with the property that for any edge E of the hypergraph, there exists
an edge F in the collage such that |E \ F| < 2. The Castelnuovo-Mumford
regularity of the edge ideal of a simple hypergraph is bounded above by a
multiple of the minimum size of a 2-collage. We also give a recursive formula
to compute the regularity of a vertex-decomposable hypergraph. Finally, we show
that regularity in the graph case is bounded by a certain statistic based on
maximal packings of nondegenerate star subgraphs.


title: On Graph Stream Clustering with Side Information

abstract:
  Graph clustering becomes an important problem due to emerging applications
involving the web, social networks and bio-informatics. Recently, many such
applications generate data in the form of streams. Clustering massive, dynamic
graph streams is significantly challenging because of the complex structures of
graphs and computational difficulties of continuous data. Meanwhile, a large
volume of side information is associated with graphs, which can be of various
types. The examples include the properties of users in social network
activities, the meta attributes associated with web click graph streams and the
location information in mobile communication networks. Such attributes contain
extremely useful information and has the potential to improve the clustering
process, but are neglected by most recent graph stream mining techniques. In
this paper, we define a unified distance measure on both link structures and
side attributes for clustering. In addition, we propose a novel optimization
framework DMO, which can dynamically optimize the distance metric and make it
adapt to the newly received stream data. We further introduce a carefully
designed statistics SGS(C) which consume constant storage spaces with the
progression of streams. We demonstrate that the statistics maintained are
sufficient for the clustering process as well as the distance optimization and
can be scalable to massive graphs with side attributes. We will present
experiment results to show the advantages of the approach in graph stream
clustering with both links and side information over the baselines.


title: How does radiative feedback from a UV background impact reionization?

abstract:
  An ionizing UV background (UVB) inhibits gas accretion and photo-evaporates
gas from the shallow potential wells of small, dwarf galaxies. During
cosmological reionization, this effect can result in negative feedback:
suppressing star-formation inside HII regions, thus impeding their continued
growth. It is difficult to model this process, given the enormous range of
scales involved. We tackle this problem using a tiered approach: combining
parameterized results from single-halo collapse simulations with large-scale
models of reionization. In the resulting reionization models, the ionizing
emissivity of galaxies depends on the local values of the reionization redshift
and the UVB intensity. We present a physically-motivated analytic expression
for the average minimum mass of star-forming galaxies, which can be readily
used in modeling galaxy formation. We find that UVB feedback: (i) delays the
end stages of reionization by less than 0.5 in redshift; (ii) results in a more
uniform distribution of HII regions, peaked on smaller-scales (with large-scale
ionization power suppressed by tens of percent); and (iii) suppresses the
global photoionization rate per baryon by a factor of < 2 towards the end of
reionization. However, the impact is modest, since the hydrodynamic response of
the gas to the UVB occurs on a time-scale comparable to reionization. In
particular, the popular approach of modeling UVB feedback with an instantaneous
transition in the minimum mass of star-forming galaxies, dramatically
overestimates its importance. UVB feedback does not significantly affect
reionization unless: (i) molecularly-cooled galaxies contribute significantly
to reionization; or (ii) internal feedback processes strongly couple with UVB
feedback in the early Universe. Since both are considered unlikely, we conclude
that there is no significant self-regulation of reionization by UVB feedback.


title: Structural and magnetic properties of Pr-alloyed MnBi nanostructures

abstract:
  The structural and magnetic properties of Pr-alloyed MnBi (short MnBi-Pr)
nanostructures with a range of Pr concentrations have been investigated. The
nanostructures include thin films having Pr concentrations 0, 2, 3, 5 and 9
atomic percent and melt-spun ribbons having Pr concentrations 0, 2, 4 and 6
percent respectively. Addition of Pr into the MnBi lattice has produced a
significant change in the magnetic properties of these nanostructures including
an increase in coercivity and structural phase transition temperature, and a
decrease in saturation magnetization and anisotropy energy. The highest value
of coercivity measured in the films is 23 kOe and in the ribbons is 5.6 kOe.
The observed magnetic properties are explained as the consequences of competing
ferromagnetic and antiferromagnetic interactions.


title:
The semiclassical theory of discontinuous systems and ray-splitting
  billiards

abstract:
  We analyze the semiclassical limit of spectral theory on manifolds whose
metrics have jump-like discontinuities. Such systems are quite different from
manifolds with smooth Riemannian metrics because the semiclassical limit does
not relate to a classical flow but rather to branching (ray-splitting) billiard
dynamics. In order to describe this system we introduce a dynamical system on
the space of functions on phase space. To identify the quantum dynamics in the
semiclassical limit we compute the principal symbols of the Fourier integral
operators associated to reflected and refracted geodesic rays and identify the
relation between classical and quantum dynamics. In particular we prove a
quantum ergodicity theorem for discontinuous systems. In order to do this we
introduce a new notion of ergodicity for the ray-splitting dynamics. The paper
contains an Appendix written by Yves Colin de Verdiere in which a non-trivial
class of examples is constructed.


title:
The Effect of Many Minor Mergers on the Size Growth of Compact Quiescent
  Galaxies

abstract:
  Massive galaxies with a half-mass radius <~ 1kpc are observed in the early
universe (z~>2), but not in the local universe. In the local universe
similar-mass (within a factor of two) galaxies tend to be a factor of 4 to 5
larger. Dry minor mergers are known to drive the evolution of the size of a
galaxy without much increasing the mass, but it is unclear if the growth in
size is sufficient to explain the observations. We test the hypothesis that
galaxies grow through dry minor mergers by simulating merging galaxies with
mass ratios of q=1:1 (equal mass) to q=1:160. In our N-body simulations the
total mass of the parent galaxy doubles. We confirm that major mergers do not
cause a sufficient growth in size. The observation can be explained with
mergers with a mass ratio of q=1:5--1:10. Smaller mass ratios cause a more
dramatic growth in size, up to a factor of ~17 for mergers with a mass ratio of
1:80. For relatively massive minor mergers q ~> 1:20 the mass of the incoming
child galaxies tend to settle in the halo of the parent galaxy. This is caused
by the tidal stripping of the child galaxies by the time they enter the central
portion of the parent. When the accretion of minor galaxies becomes more
continuous, when q <~ 1:40, the foreign mass tends to concentrate more in the
central region of the parent galaxy. We speculate that this is caused by
dynamic interactions between the child galaxies inside the merger remnant and
the longer merging times when the difference in mass is larger. These
interactions cause dynamical heating which results in accretion of mass inside
the galaxy core and a reduction of the parent's circular velocity and density.


title: Dark matter equation of state from rotational curves of galaxies

abstract:
  In this work we model galactic halos describing the dark matter as a non zero
pressure fluid and derive, not impose, a dark matter equation of state by using
observational data of the rotation curves of galaxies. In order to reach
hydrostatic equilibrium, as expected for the halo, it is mandatory that dark
fluid's pressure should not be zero. The equation of state is obtained by
solving the matter-geometry system of equations assuming different dark matter
density or rotational velocity profiles. The resulting equations of state are,
in general, different to a barotropic equation of state. The free parameters of
the equation of state are fixed by fitting the observed rotational velocities
of a set of galaxies.


title: Lifetime of the Highly Efficient H- Ion Sources

abstract:
  Factors limiting the operating lifetime of Compact Surface Plasma Sources
(CSPS) are analyzed and possible treatments for lifetime enhancement are
considered. Noiseless discharges with lower gas and cesium densities are
produced in experiments with modified discharge cells. With these discharge
cells it is possible to increase the emission aperture and extract the same
beam with a lower discharge current and with correspondingly increased source
lifetime. A design of an advanced CSPS is presented.


title:
Energy-Efficient Spectrum Sharing in Relay-Assisted Cognitive Radio
  Systems

abstract:
  This work characterizes an important solution concept of a relevant spectrum
game. Two energy-efficient sources communicating with their respective
destination compete for an extra channel brought by a relay charging the used
bandwidth through a pricing mechanism. This game is shown to possess a unique
Nash bargaining solution, exploiting a time-sharing argument. This
Pareto-efficient solution can be implemented by using a distributed
optimization algorithm for which each transmitter uses a simple gradient-type
algorithm and alternately updates its spectrum sharing policy. Typical
numerical results show to what extent spectral efficiency can be improved in a
system involving selfish energy-efficient sources.


title: Dedekind's Transposition Principle for lattices of equivalence relations

abstract:
  We prove a version of Dedekind's Transposition Principle that holds in
lattices of equivalence relations.