title:
A survey on subideals of operators and an introduction to
  subideal-traces

abstract:
  Operator ideals in B(H) are well understood and exploited but ideals inside
them have only recently been studied starting with the 1983 seminal work of
Fong and Radjavi and continuing with two recent articles by the authors of this
survey. This article surveys this study embodied in these three articles. A
subideal is a two-sided ideal of J (for specificity also called a J-ideal) for
J an arbitrary ideal of B(H). In this terminology we alternatively call J a
B(H)-ideal.
  This surveys these three articles in which we developed a complete
characterization of all J-ideals generated by sets of cardinality strictly less
than the cardinality of the continuum. So a central theme is the impact of
generating sets for subideals on their algebraic structure. This
characterization includes in particular finitely and countably generated
J-ideals. It was obtained by first generalizing to arbitrary principal J-ideals
the 1983 work of Fong-Radjavi who determined which principal K(H)-ideals are
also B(H)-ideals. A key property in our investigation turned out to be
J-softness of a B(H)-ideal I inside J, that is, IJ = I, a generalization of a
recent notion of K(H)-softness of B(H)-ideals introduced by Kaftal-Weiss and
earlier exploited for Banach spaces by Mityagin and Pietsch. This study of
subideals and the study of elementary operators with coefficient constraints
are closely related. Here we also introduce and study a notion of
subideal-traces where classical traces (unitarily invariant linear functionals)
need not make sense for subideals that are not B(H)-ideals.


title: When Cellular Meets WiFi in Wireless Small Cell Networks

abstract:
  The deployment of small cell base stations(SCBSs) overlaid on existing
macro-cellular systems is seen as a key solution for offloading traffic,
optimizing coverage, and boosting the capacity of future cellular wireless
systems. The next-generation of SCBSs is envisioned to be multi-mode, i.e.,
capable of transmitting simultaneously on both licensed and unlicensed bands.
This constitutes a cost-effective integration of both WiFi and cellular radio
access technologies (RATs) that can efficiently cope with peak wireless data
traffic and heterogeneous quality-of-service requirements. To leverage the
advantage of such multi-mode SCBSs, we discuss the novel proposed paradigm of
cross-system learning by means of which SCBSs self-organize and autonomously
steer their traffic flows across different RATs. Cross-system learning allows
the SCBSs to leverage the advantage of both the WiFi and cellular worlds. For
example, the SCBSs can offload delay-tolerant data traffic to WiFi, while
simultaneously learning the probability distribution function of their
transmission strategy over the licensed cellular band. This article will first
introduce the basic building blocks of cross-system learning and then provide
preliminary performance evaluation in a Long-Term Evolution (LTE) simulator
overlaid with WiFi hotspots. Remarkably, it is shown that the proposed
cross-system learning approach significantly outperforms a number of benchmark
traffic steering policies.


title: The minimal adjoint-SU(5)xZ_4 GUT model

abstract:
  An extension of the adjoint SU(5) model with a flavour symmetry based on the
Z_4 group is investigated. The Z_4 symmetry is introduced with the aim of
leading the up- and down-quark mass matrices to the
Nearest-Neighbour-Interaction form. As a consequence of the discrete symmetry
embedded in the SU(5) gauge group, the charged lepton mass matrix also gets the
same form. Within this model, light neutrinos get their masses through type-I,
type-III and one-loop radiative seesaw mechanisms, implemented, respectively,
via a singlet, a triplet and an octet from the adjoint fermionic 24 fields. It
is demonstrated that the neutrino phenomenology forces the introduction of at
least three 24 fermionic multiplets. The symmetry SU(5)xZ_4 allows only two
viable zero textures for the effective neutrino mass matrix. It is showed that
one texture is only compatible with normal hierarchy and the other with
inverted hierarchy in the light neutrino mass spectrum. Finally, it is also
demonstrated that Z_4 freezes out the possibility of proton decay through
exchange of colour Higgs triplets at tree-level.


title: Probing Scotogenic Effects in Higgs Boson Decays

abstract:
  The recent observation of a Higgs boson at the LHC and experimental
confirmation of nonvanishing neutrino-mixing parameter sin(theta_13) offer
important means to test physics beyond the standard model. We explore this
within the context of the scotogenic model, in which neutrinos acquire mass
radiatively via one-loop interactions with dark matter. Starting with a
two-parameter neutrino-mixing matrix which is consistent with the latest
neutrino-oscillation data at the one-sigma level, we derive different sets of
solutions for the Yukawa couplings of the nonstandard particles in the model
and use the results to consider the Higgs decays into final states involving
the new particles. Assuming that the lightest one of them serves as fermionic
cold dark matter, we show that such decays are allowed by various experimental
and theoretical constraints to have substantial rates that are already
restricted by the current LHC data. We also look at their correlations with the
Higgs decays into gamma gamma and gamma Z. Upcoming LHC measurements of the
Higgs boson can therefore either detect scotogenic signals or place further
constraints on the model.


title: Modified Higgs Physics from Composite Light Flavors

abstract:
  We point out that Higgs rates into gauge bosons can be significantly modified
in composite pseudo Nambu--Goldstone boson (pNGB) Higgs models if quarks
belonging to the first two generation are relatively composite objects as well.
Although the lightness of the latter a priori screen them from the electroweak
symmetry breaking sector, we show, in an effective two-site description, that
their partners can lead to order one shifts in radiative Higgs couplings to
gluons and photons. Moreover, due to the pseudo-Goldstone nature of the Higgs
boson, the size of these corrections is completely controlled by the degree of
compositeness of the individual light quarks. The current measurements of
flavor-blind Higgs decay rates at the LHC thus provide an indirect probe of the
flavor structure of the framework of pNGB Higgs compositeness.


title:
5D Perspective on Higgs Production at the Boundary of a Warped Extra
  Dimension

abstract:
  A comprehensive, five-dimensional calculation of Higgs-boson production in
gluon fusion is performed for both the minimal and the custodially protected
Randall-Sundrum (RS) model, with Standard Model fields propagating in the bulk
and the scalar sector confined on or near the IR brane. For the first time, an
exact expression for the gg->h amplitude in terms of the five-dimensional
fermion propagator is derived, which includes the full dependence on the
Higgs-boson mass. Various results in the literature are reconciled and shown to
correspond to different incarnations of the RS model, in which the Higgs field
is either localized on the IR brane or is described in terms of a narrow bulk
state. The results in the two scenarios differ in a qualitative way: the gg->h
amplitude is suppressed in models where the scalar sector is localized on the
IR brane, while it tends to be enhanced in bulk Higgs models. In both cases,
effects of higher-dimensional operators contributing to the gg->h amplitude at
tree level are shown to be numerically suppressed under reasonable assumptions.
There is no smooth cross-over between the two scenarios, since the effective
field-theory description breaks down in the transition region. A detailed
phenomenological analysis of Higgs production in various RS scenarios is
presented, and for each scenario the regions of parameter space already
excluded by LHC data are derived.


title: ARCO1: An Application of Belief Networks to the Oil Market

abstract:
  Belief networks are a new, potentially important, class of knowledge-based
models. ARCO1, currently under development at the Atlantic Richfield Company
(ARCO) and the University of Southern California (USC), is the most advanced
reported implementation of these models in a financial forecasting setting.
ARCO1's underlying belief network models the variables believed to have an
impact on the crude oil market. A pictorial market model-developed on a MAC II-
facilitates consensus among the members of the forecasting team. The system
forecasts crude oil prices via Monte Carlo analyses of the network. Several
different models of the oil market have been developed; the system's ability to
be updated quickly highlights its flexibility.


title:
"Conditional Inter-Causally Independent" Node Distributions, a Property
  of "Noisy-Or" Models

abstract:
  This paper examines the interdependence generated between two parent nodes
with a common instantiated child node, such as two hypotheses sharing common
evidence. The relation so generated has been termed "intercausal." It is shown
by construction that inter-causal independence is possible for binary
distributions at one state of evidence. For such "CICI" distributions, the two
measures of inter-causal effect, "multiplicative synergy" and "additive
synergy" are equal. The well known "noisy-or" model is an example of such a
distribution. This introduces novel semantics for the noisy-or, as a model of
the degree of conflict among competing hypotheses of a common observation.


title: Combining Multiple-Valued Logics in Modular Expert Systems

abstract:
  The way experts manage uncertainty usually changes depending on the task they
are performing. This fact has lead us to consider the problem of communicating
modules (task implementations) in a large and structured knowledge based system
when modules have different uncertainty calculi. In this paper, the analysis of
the communication problem is made assuming that (i) each uncertainty calculus
is an inference mechanism defining an entailment relation, and therefore the
communication is considered to be inference-preserving, and (ii) we restrict
ourselves to the case which the different uncertainty calculi are given by a
class of truth functional Multiple-valued Logics.


title: Constraint Propagation with Imprecise Conditional Probabilities

abstract:
  An approach to reasoning with default rules where the proportion of
exceptions, or more generally the probability of encountering an exception, can
be at least roughly assessed is presented. It is based on local uncertainty
propagation rules which provide the best bracketing of a conditional
probability of interest from the knowledge of the bracketing of some other
conditional probabilities. A procedure that uses two such propagation rules
repeatedly is proposed in order to estimate any simple conditional probability
of interest from the available knowledge. The iterative procedure, that does
not require independence assumptions, looks promising with respect to the
linear programming method. Improved bounds for conditional probabilities are
given when independence assumptions hold.


title: Bayesian Networks Aplied to Therapy Monitoring

abstract:
  We propose a general Bayesian network model for application in a wide class
of problems of therapy monitoring. We discuss the use of stochastic simulation
as a computational approach to inference on the proposed class of models. As an
illustration we present an application to the monitoring of cytotoxic
chemotherapy in breast cancer.


title: Some Properties of Plausible Reasoning

abstract:
  This paper presents a plausible reasoning system to illustrate some broad
issues in knowledge representation: dualities between different reasoning
forms, the difficulty of unifying complementary reasoning styles, and the
approximate nature of plausible reasoning. These issues have a common
underlying theme: there should be an underlying belief calculus of which the
many different reasoning forms are special cases, sometimes approximate. The
system presented allows reasoning about defaults, likelihood, necessity and
possibility in a manner similar to the earlier work of Adams. The system is
based on the belief calculus of subjective Bayesian probability which itself is
based on a few simple assumptions about how belief should be manipulated.
Approximations, semantics, consistency and consequence results are presented
for the system. While this puts these often discussed plausible reasoning forms
on a probabilistic footing, useful application to practical problems remains an
issue.


title: Theory Refinement on Bayesian Networks

abstract:
  Theory refinement is the task of updating a domain theory in the light of new
cases, to be done automatically or with some expert assistance. The problem of
theory refinement under uncertainty is reviewed here in the context of Bayesian
statistics, a theory of belief revision. The problem is reduced to an
incremental learning task as follows: the learning system is initially primed
with a partial theory supplied by a domain expert, and thereafter maintains its
own internal representation of alternative theories which is able to be
interrogated by the domain expert and able to be incrementally refined from
data. Algorithms for refinement of Bayesian networks are presented to
illustrate what is meant by "partial theory", "alternative theory
representation", etc. The algorithms are an incremental variant of batch
learning algorithms from the literature so can work well in batch and
incremental mode.


title: Combination of Upper and Lower Probabilities

abstract:
  In this paper, we consider several types of information and methods of
combination associated with incomplete probabilistic systems. We discriminate
between 'a priori' and evidential information. The former one is a description
of the whole population, the latest is a restriction based on observations for
a particular case. Then, we propose different combination methods for each one
of them. We also consider conditioning as the heterogeneous combination of 'a
priori' and evidential information. The evidential information is represented
as a convex set of likelihood functions. These will have an associated
possibility distribution with behavior according to classical Possibility
Theory.


title:
A Probabilistic Analysis of Marker-Passing Techniques for
  Plan-Recognition

abstract:
  Useless paths are a chronic problem for marker-passing techniques. We use a
probabilistic analysis to justify a method for quickly identifying and
rejecting useless paths. Using the same analysis, we identify key conditions
and assumptions necessary for marker-passing to perform well.


title: Symbolic Probabilistic Inference with Continuous Variables

abstract:
  Research on Symbolic Probabilistic Inference (SPI) [2, 3] has provided an
algorithm for resolving general queries in Bayesian networks. SPI applies the
concept of dependency directed backward search to probabilistic inference, and
is incremental with respect to both queries and observations. Unlike
traditional Bayesian network inferencing algorithms, SPI algorithm is goal
directed, performing only those calculations that are required to respond to
queries. Research to date on SPI applies to Bayesian networks with
discrete-valued variables and does not address variables with continuous
values. In this papers, we extend the SPI algorithm to handle Bayesian networks
made up of continuous variables where the relationships between the variables
are restricted to be ?linear gaussian?. We call this variation of the SPI
algorithm, SPI Continuous (SPIC). SPIC modifies the three basic SPI operations:
multiplication, summation, and substitution. However, SPIC retains the
framework of the SPI algorithm, namely building the search tree and recursive
query mechanism and therefore retains the goal-directed and incrementality
features of SPI.


title: Symbolic Probabilistic Inference with Evidence Potential

abstract:
  Recent research on the Symbolic Probabilistic Inference (SPI) algorithm[2]
has focused attention on the importance of resolving general queries in
Bayesian networks. SPI applies the concept of dependency-directed backward
search to probabilistic inference, and is incremental with respect to both
queries and observations. In response to this research we have extended the
evidence potential algorithm [3] with the same features. We call the extension
symbolic evidence potential inference (SEPI). SEPI like SPI can handle generic
queries and is incremental with respect to queries and observations. While in
SPI, operations are done on a search tree constructed from the nodes of the
original network, in SEPI, a clique-tree structure obtained from the evidence
potential algorithm [3] is the basic framework for recursive query processing.
In this paper, we describe the systematic query and caching procedure of SEPI.
SEPI begins with finding a clique tree from a Bayesian network-the standard
procedure of the evidence potential algorithm. With the clique tree, various
probability distributions are computed and stored in each clique. This is the
?pre-processing? step of SEPI. Once this step is done, the query can then be
computed. To process a query, a recursive process similar to the SPI algorithm
is used. The queries are directed to the root clique and decomposed into
queries for the clique's subtrees until a particular query can be answered at
the clique at which it is directed. The algorithm and the computation are
simple. The SEPI algorithm will be presented in this paper along with several
examples.


title:
A Bayesian Method for Constructing Bayesian Belief Networks from
  Databases

abstract:
  This paper presents a Bayesian method for constructing Bayesian belief
networks from a database of cases. Potential applications include
computer-assisted hypothesis testing, automated scientific discovery, and
automated construction of probabilistic expert systems. Results are presented
of a preliminary evaluation of an algorithm for constructing a belief network
from a database of cases. We relate the methods in this paper to previous work,
and we discuss open problems.


title:
Local Expression Languages for Probabilistic Dependence: a Preliminary
  Report

abstract:
  We present a generalization of the local expression language used in the
Symbolic Probabilistic Inference (SPI) approach to inference in belief nets
[1l, [8]. The local expression language in SPI is the language in which the
dependence of a node on its antecedents is described. The original language
represented the dependence as a single monolithic conditional probability
distribution. The extended language provides a set of operators (*, +, and -)
which can be used to specify methods for combining partial conditional
distributions. As one instance of the utility of this extension, we show how
this extended language can be used to capture the semantics, representational
advantages, and inferential complexity advantages of the "noisy or"
relationship.


title: Symbolic Decision Theory and Autonomous Systems

abstract:
  The ability to reason under uncertainty and with incomplete information is a
fundamental requirement of decision support technology. In this paper we argue
that the concentration on theoretical techniques for the evaluation and
selection of decision options has distracted attention from many of the wider
issues in decision making. Although numerical methods of reasoning under
uncertainty have strong theoretical foundations, they are representationally
weak and only deal with a small part of the decision process. Knowledge based
systems, on the other hand, offer greater flexibility but have not been
accompanied by a clear decision theory. We describe here work which is under
way towards providing a theoretical framework for symbolic decision procedures.
A central proposal is an extended form of inference which we call
argumentation; reasoning for and against decision options from generalised
domain theories. The approach has been successfully used in several decision
support applications, but it is argued that a comprehensive decision theory
must cover autonomous decision making, where the agent can formulate questions
as well as take decisions. A major theoretical challenge for this theory is to
capture the idea of reflection to permit decision agents to reason about their
goals, what they believe and why, and what they need to know or do in order to
achieve their goals.


title: A Reason Maintenace System Dealing with Vague Data

abstract:
  A reason maintenance system which extends an ATMS through Mukaidono's fuzzy
logic is described. It supports a problem solver in situations affected by
incomplete information and vague data, by allowing nonmonotonic inferences and
the revision of previous conclusions when contradictions are detected.


title: Advances in Probabilistic Reasoning

abstract:
  This paper discuses multiple Bayesian networks representation paradigms for
encoding asymmetric independence assertions. We offer three contributions: (1)
an inference mechanism that makes explicit use of asymmetric independence to
speed up computations, (2) a simplified definition of similarity networks and
extensions of their theory, and (3) a generalized representation scheme that
encodes more types of asymmetric independence assertions than do similarity
networks.


title: Probability Estimation in Face of Irrelevant Information

abstract:
  In this paper, we consider one aspect of the problem of applying decision
theory to the design of agents that learn how to make decisions under
uncertainty. This aspect concerns how an agent can estimate probabilities for
the possible states of the world, given that it only makes limited observations
before committing to a decision. We show that the naive application of
statistical tools can be improved upon if the agent can determine which of his
observations are truly relevant to the estimation problem at hand. We give a
framework in which such determinations can be made, and define an estimation
procedure to use them. Our framework also suggests several extensions, which
show how additional knowledge can be used to improve tile estimation procedure
still further.


title: An Approximate Nonmyopic Computation for Value of Information

abstract:
  Value-of-information analyses provide a straightforward means for selecting
the best next observation to make, and for determining whether it is better to
gather additional information or to act immediately. Determining the next best
test to perform, given a state of uncertainty about the world, requires a
consideration of the value of making all possible sequences of observations. In
practice, decision analysts and expert-system designers have avoided the
intractability of exact computation of the value of information by relying on a
myopic approximation. Myopic analyses are based on the assumption that only one
additional test will be performed, even when there is an opportunity to make a
large number of observations. We present a nonmyopic approximation for value of
information that bypasses the traditional myopic analyses by exploiting the
statistical properties of large samples.


title:
Search-based Methods to Bound Diagnostic Probabilities in Very Large
  Belief Nets

abstract:
  Since exact probabilistic inference is intractable in general for large
multiply connected belief nets, approximate methods are required. A promising
approach is to use heuristic search among hypotheses (instantiations of the
network) to find the most probable ones, as in the TopN algorithm. Search is
based on the relative probabilities of hypotheses which are efficient to
compute. Given upper and lower bounds on the relative probability of partial
hypotheses, it is possible to obtain bounds on the absolute probabilities of
hypotheses. Best-first search aimed at reducing the maximum error progressively
narrows the bounds as more hypotheses are examined. Here, qualitative
probabilistic analysis is employed to obtain bounds on the relative probability
of partial hypotheses for the BN20 class of networks networks and a
generalization replacing the noisy OR assumption by negative synergy. The
approach is illustrated by application to a very large belief network, QMR-BN,
which is a reformulation of the Internist-1 system for diagnosis in internal
medicine.


title: Time-Dependent Utility and Action Under Uncertainty

abstract:
  We discuss representing and reasoning with knowledge about the time-dependent
utility of an agent's actions. Time-dependent utility plays a crucial role in
the interaction between computation and action under bounded resources. We
present a semantics for time-dependent utility and describe the use of
time-dependent information in decision contexts. We illustrate our discussion
with examples of time-pressured reasoning in Protos, a system constructed to
explore the ideal control of inference by reasoners with limit abilities.


title: Non-monotonic Reasoning and the Reversibility of Belief Change

abstract:
  Traditional approaches to non-monotonic reasoning fail to satisfy a number of
plausible axioms for belief revision and suffer from conceptual difficulties as
well. Recent work on ranked preferential models (RPMs) promises to overcome
some of these difficulties. Here we show that RPMs are not adequate to handle
iterated belief change. Specifically, we show that RPMs do not always allow for
the reversibility of belief change. This result indicates the need for
numerical strengths of belief.


title: Belief and Surprise - A Belief-Function Formulation

abstract:
  We motivate and describe a theory of belief in this paper. This theory is
developed with the following view of human belief in mind. Consider the belief
that an event E will occur (or has occurred or is occurring). An agent either
entertains this belief or does not entertain this belief (i.e., there is no
"grade" in entertaining the belief). If the agent chooses to exercise "the will
to believe" and entertain this belief, he/she/it is entitled to a degree of
confidence c (1 > c > 0) in doing so. Adopting this view of human belief, we
conjecture that whenever an agent entertains the belief that E will occur with
c degree of confidence, the agent will be surprised (to the extent c) upon
realizing that E did not occur.


title:
Evidential Reasoning in a Categorial Perspective: Conjunction and
  Disjunction of Belief Functions

abstract:
  The categorial approach to evidential reasoning can be seen as a combination
of the probability kinematics approach of Richard Jeffrey (1965) and the
maximum (cross-) entropy inference approach of E. T. Jaynes (1957). As a
consequence of that viewpoint, it is well known that category theory provides
natural definitions for logical connectives. In particular, disjunction and
conjunction are modelled by general categorial constructions known as products
and coproducts. In this paper, I focus mainly on Dempster-Shafer theory of
belief functions for which I introduce a category I call Dempster?s category. I
prove the existence of and give explicit formulas for conjunction and
disjunction in the subcategory of separable belief functions. In Dempster?s
category, the new defined conjunction can be seen as the most cautious
conjunction of beliefs, and thus no assumption about distinctness (of the
sources) of beliefs is needed as opposed to Dempster?s rule of combination,
which calls for distinctness (of the sources) of beliefs.


title: Reasoning with Mass Distributions

abstract:
  The concept of movable evidence masses that flow from supersets to subsets as
specified by experts represents a suitable framework for reasoning under
uncertainty. The mass flow is controlled by specialization matrices. New
evidence is integrated into the frame of discernment by conditioning or
revision (Dempster's rule of conditioning), for which special specialization
matrices exist. Even some aspects of non-monotonic reasoning can be represented
by certain specialization matrices.


title:
A Logic of Graded Possibility and Certainty Coping with Partial
  Inconsistency

abstract:
  A semantics is given to possibilistic logic, a logic that handles weighted
classical logic formulae, and where weights are interpreted as lower bounds on
degrees of certainty or possibility, in the sense of Zadeh's possibility
theory. The proposed semantics is based on fuzzy sets of interpretations. It is
tolerant to partial inconsistency. Satisfiability is extended from
interpretations to fuzzy sets of interpretations, each fuzzy set representing a
possibility distribution describing what is known about the state of the world.
A possibilistic knowledge base is then viewed as a set of possibility
distributions that satisfy it. The refutation method of automated deduction in
possibilistic logic, based on previously introduced generalized resolution
principle is proved to be sound and complete with respect to the proposed
semantics, including the case of partial inconsistency.


title: Conflict and Surprise: Heuristics for Model Revision

abstract:
  Any probabilistic model of a problem is based on assumptions which, if
violated, invalidate the model. Users of probability based decision aids need
to be alerted when cases arise that are not covered by the aid's model.
Diagnosis of model failure is also necessary to control dynamic model
construction and revision. This paper presents a set of decision theoretically
motivated heuristics for diagnosing situations in which a model is likely to
provide an inadequate representation of the process being modeled.


title: Reasoning under Uncertainty: Some Monte Carlo Results

abstract:
  A series of monte carlo studies were performed to compare the behavior of
some alternative procedures for reasoning under uncertainty. The behavior of
several Bayesian, linear model and default reasoning procedures were examined
in the context of increasing levels of calibration error. The most interesting
result is that Bayesian procedures tended to output more extreme posterior
belief values (posterior beliefs near 0.0 or 1.0) than other techniques, but
the linear models were relatively less likely to output strong support for an
erroneous conclusion. Also, accounting for the probabilistic dependencies
between evidence items was important for both Bayesian and linear updating
procedures.


title: Representation Requirements for Supporting Decision Model Formulation

abstract:
  This paper outlines a methodology for analyzing the representational support
for knowledge-based decision-modeling in a broad domain. A relevant set of
inference patterns and knowledge types are identified. By comparing the
analysis results to existing representations, some insights are gained into a
design approach for integrating categorical and uncertain knowledge in a
context sensitive manner.


title: A Language for Planning with Statistics

abstract:
  When a planner must decide whether it has enough evidence to make a decision
based on probability, it faces the sample size problem. Current planners using
probabilities need not deal with this problem because they do not generate
their probabilities from observations. This paper presents an event based
language in which the planner's probabilities are calculated from the binomial
random variable generated by the observed ratio of one type of event to
another. Such probabilities are subject to error, so the planner must
introspect about their validity. Inferences about the probability of these
events can be made using statistics. Inferences about the validity of the
approximations can be made using interval estimation. Interval estimation
allows the planner to avoid making choices that are only weakly supported by
the planner's evidence.


title: A Modification to Evidential Probability

abstract:
  Selecting the right reference class and the right interval when faced with
conflicting candidates and no possibility of establishing subset style
dominance has been a problem for Kyburg's Evidential Probability system.
Various methods have been proposed by Loui and Kyburg to solve this problem in
a way that is both intuitively appealing and justifiable within Kyburg's
framework. The scheme proposed in this paper leads to stronger statistical
assertions without sacrificing too much of the intuitive appeal of Kyburg's
latest proposal.


title: Investigation of Variances in Belief Networks

abstract:
  The belief network is a well-known graphical structure for representing
independences in a joint probability distribution. The methods, which perform
probabilistic inference in belief networks, often treat the conditional
probabilities which are stored in the network as certain values. However, if
one takes either a subjectivistic or a limiting frequency approach to
probability, one can never be certain of probability values. An algorithm
should not only be capable of reporting the probabilities of the alternatives
of remaining nodes when other nodes are instantiated; it should also be capable
of reporting the uncertainty in these probabilities relative to the uncertainty
in the probabilities which are stored in the network. In this paper a method
for determining the variances in inferred probabilities is obtained under the
assumption that a posterior distribution on the uncertainty variables can be
approximated by the prior distribution. It is shown that this assumption is
plausible if their is a reasonable amount of confidence in the probabilities
which are stored in the network. Furthermore in this paper, a surprising upper
bound for the prior variances in the probabilities of the alternatives of all
nodes is obtained in the case where the probability distributions of the
probabilities of the alternatives are beta distributions. It is shown that the
prior variance in the probability at an alternative of a node is bounded above
by the largest variance in an element of the conditional probability
distribution for that node.


title: A Sensitivity Analysis of Pathfinder: A Follow-up Study

abstract:
  At last year?s Uncertainty in AI Conference, we reported the results of a
sensitivity analysis study of Pathfinder. Our findings were quite
unexpected-slight variations to Pathfinder?s parameters appeared to lead to
substantial degradations in system performance. A careful look at our first
analysis, together with the valuable feedback provided by the participants of
last year?s conference, led us to conduct a follow-up study. Our follow-up
differs from our initial study in two ways: (i) the probabilities 0.0 and 1.0
remained unchanged, and (ii) the variations to the probabilities that are close
to both ends (0.0 or 1.0) were less than the ones close to the middle (0.5).
The results of the follow-up study look more reasonable-slight variations to
Pathfinder?s parameters now have little effect on its performance. Taken
together, these two sets of results suggest a viable extension of a common
decision analytic sensitivity analysis to the larger, more complex settings
generally encountered in artificial intelligence.


title: Non-monotonic Negation in Probabilistic Deductive Databases

abstract:
  In this paper we study the uses and the semantics of non-monotonic negation
in probabilistic deductive data bases. Based on the stable semantics for
classical logic programming, we introduce the notion of stable formula,
functions. We show that stable formula, functions are minimal fixpoints of
operators associated with probabilistic deductive databases with negation.
Furthermore, since a. probabilistic deductive database may not necessarily have
a stable formula function, we provide a stable class semantics for such
databases. Finally, we demonstrate that the proposed semantics can handle
default reasoning naturally in the context of probabilistic deduction.


title:
Management of Uncertainty in the Multi-Level Monitoring and Diagnosis of
  the Time of Flight Scintillation Array

abstract:
  We present a general architecture for the monitoring and diagnosis of large
scale sensor-based systems with real time diagnostic constraints. This
architecture is multileveled, combining a single monitoring level based on
statistical methods with two model based diagnostic levels. At each level,
sources of uncertainty are identified, and integrated methodologies for
uncertainty management are developed. The general architecture was applied to
the monitoring and diagnosis of a specific nuclear physics detector at Lawrence
Berkeley National Laboratory that contained approximately 5000 components and
produced over 500 channels of output data. The general architecture is
scalable, and work is ongoing to apply it to detector systems one and two
orders of magnitude more complex.


title:
Integrating Probabilistic Rules into Neural Networks: A Stochastic EM
  Learning Algorithm

abstract:
  The EM-algorithm is a general procedure to get maximum likelihood estimates
if part of the observations on the variables of a network are missing. In this
paper a stochastic version of the algorithm is adapted to probabilistic neural
networks describing the associative dependency of variables. These networks
have a probability distribution, which is a special case of the distribution
generated by probabilistic inference networks. Hence both types of networks can
be combined allowing to integrate probabilistic rules as well as unspecified
associations in a sound way. The resulting network may have a number of
interesting features including cycles of probabilistic rules, hidden
'unobservable' variables, and uncertain and contradictory evidence.


title: Representing Bayesian Networks within Probabilistic Horn Abduction

abstract:
  This paper presents a simple framework for Horn clause abduction, with
probabilities associated with hypotheses. It is shown how this representation
can represent any probabilistic knowledge representable in a Bayesian belief
network. The main contributions are in finding a relationship between logical
and probabilistic notions of evidential reasoning. This can be used as a basis
for a new way to implement Bayesian Networks that allows for approximations to
the value of the posterior probabilities, and also points to a way that
Bayesian networks can be extended beyond a propositional language.


title: Dynamic Network Updating Techniques For Diagnostic Reasoning

abstract:
  A new probabilistic network construction system, DYNASTY, is proposed for
diagnostic reasoning given variables whose probabilities change over time.
Diagnostic reasoning is formulated as a sequential stochastic process, and is
modeled using influence diagrams. Given a set O of observations, DYNASTY
creates an influence diagram in order to devise the best action given O.
Sensitivity analyses are conducted to determine if the best network has been
created, given the uncertainty in network parameters and topology. DYNASTY uses
an equivalence class approach to provide decision thresholds for the
sensitivity analysis. This equivalence-class approach to diagnostic reasoning
differentiates diagnoses only if the required actions are different. A set of
network-topology updating algorithms are proposed for dynamically updating the
network when necessary.


title: High Level Path Planning with Uncertainty

abstract:
  For high level path planning, environments are usually modeled as distance
graphs, and path planning problems are reduced to computing the shortest path
in distance graphs. One major drawback of this modeling is the inability to
model uncertainties, which are often encountered in practice. In this paper, a
new tool, called U-yraph, is proposed for environment modeling. A U-graph is an
extension of distance graphs with the ability to handle a kind of uncertainty.
By modeling an uncertain environment as a U-graph, and a navigation problem as
a Markovian decision process, we can precisely define a new optimality
criterion for navigation plans, and more importantly, we can come up with a
general algorithm for computing optimal plans for navigation tasks.


title: Formal Model of Uncertainty for Possibilistic Rules

abstract:
  Given a universe of discourse X-a domain of possible outcomes-an experiment
may consist of selecting one of its elements, subject to the operation of
chance, or of observing the elements, subject to imprecision. A priori
uncertainty about the actual result of the experiment may be quantified,
representing either the likelihood of the choice of :r_X or the degree to which
any such X would be suitable as a description of the outcome. The former case
corresponds to a probability distribution, while the latter gives a possibility
assignment on X. The study of such assignments and their properties falls
within the purview of possibility theory [DP88, Y80, Z783. It, like probability
theory, assigns values between 0 and 1 to express likelihoods of outcomes.
Here, however, the similarity ends. Possibility theory uses the maximum and
minimum functions to combine uncertainties, whereas probability theory uses the
plus and times operations. This leads to very dissimilar theories in terms of
analytical framework, even though they share several semantic concepts. One of
the shared concepts consists of expressing quantitatively the uncertainty
associated with a given distribution. In probability theory its value
corresponds to the gain of information that would result from conducting an
experiment and ascertaining an actual result. This gain of information can
equally well be viewed as a decrease in uncertainty about the outcome of an
experiment. In this case the standard measure of information, and thus
uncertainty, is Shannon entropy [AD75, G77]. It enjoys several advantages-it is
characterized uniquely by a few, very natural properties, and it can be
conveniently used in decision processes. This application is based on the
principle of maximum entropy; it has become a popular method of relating
decisions to uncertainty. This paper demonstrates that an equally integrated
theory can be built on the foundation of possibility theory. We first show how
to define measures of in formation and uncertainty for possibility assignments.
Next we construct an information-based metric on the space of all possibility
distributions defined on a given domain. It allows us to capture the notion of
proximity in information content among the distributions. Lastly, we show that
all the above constructions can be carried out for continuous
distributions-possibility assignments on arbitrary measurable domains. We
consider this step very significant-finite domains of discourse are but
approximations of the real-life infinite domains. If possibility theory is to
represent real world situations, it must handle continuous distributions both
directly and through finite approximations. In the last section we discuss a
principle of maximum uncertainty for possibility distributions. We show how
such a principle could be formalized as an inference rule. We also suggest it
could be derived as a consequence of simple assumptions about combining
information. We would like to mention that possibility assignments can be
viewed as fuzzy sets and that every fuzzy set gives rise to an assignment of
possibilities. This correspondence has far reaching consequences in logic and
in control theory. Our treatment here is independent of any special
interpretation; in particular we speak of possibility distributions and
possibility measures, defining them as measurable mappings into the interval
[0, 1]. Our presentation is intended as a self-contained, albeit terse summary.
Topics discussed were selected with care, to demonstrate both the completeness
and a certain elegance of the theory. Proofs are not included; we only offer
illustrative examples.


title: Deliberation and its Role in the Formation of Intentions

abstract:
  Deliberation plays an important role in the design of rational agents
embedded in the real-world. In particular, deliberation leads to the formation
of intentions, i.e., plans of action that the agent is committed to achieving.
In this paper, we present a branching time possible-worlds model for
representing and reasoning about, beliefs, goals, intentions, time, actions,
probabilities, and payoffs. We compare this possible-worlds approach with the
more traditional decision tree representation and provide a transformation from
decision trees to possible worlds. Finally, we illustrate how an agent can
perform deliberation using a decision-tree representation and then use a
possible-worlds model to form and reason about his intentions.


title:
Handling Uncertainty during Plan Recognition in Task-Oriented
  Consultation Systems

abstract:
  During interactions with human consultants, people are used to providing
partial and/or inaccurate information, and still be understood and assisted. We
attempt to emulate this capability of human consultants; in computer
consultation systems. In this paper, we present a mechanism for handling
uncertainty in plan recognition during task-oriented consultations. The
uncertainty arises while choosing an appropriate interpretation of a user?s
statements among many possible interpretations. Our mechanism handles this
uncertainty by using probability theory to assess the probabilities of the
interpretations, and complements this assessment by taking into account the
information content of the interpretations. The information content of an
interpretation is a measure of how well defined an interpretation is in terms
of the actions to be performed on the basis of the interpretation. This measure
is used to guide the inference process towards interpretations with a higher
information content. The information content for an interpretation depends on
the specificity and the strength of the inferences in it, where the strength of
an inference depends on the reliability of the information on which the
inference is based. Our mechanism has been developed for use in task-oriented
consultation systems. The domain that we have chosen for exploration is that of
a travel agency.


title: Truth as Utility: A Conceptual Synthesis

abstract:
  This paper introduces conceptual relations that synthesize utilitarian and
logical concepts, extending the logics of preference of Rescher. We define
first, in the context of a possible worlds model, constraint-dependent measures
that quantify the relative quality of alternative solutions of reasoning
problems or the relative desirability of various policies in control, decision,
and planning problems. We show that these measures may be interpreted as truth
values in a multi valued logic and propose mechanisms for the representation of
complex constraints as combinations of simpler restrictions. These extended
logical operations permit also the combination and aggregation of goal-specific
quality measures into global measures of utility. We identify also relations
that represent differential preferences between alternative solutions and
relate them to the previously defined desirability measures. Extending
conventional modal logic formulations, we introduce structures for the
representation of ignorance about the utility of alternative solutions.
Finally, we examine relations between these concepts and similarity based
semantic models of fuzzy logic.


title:
Pulcinella: A General Tool for Propagating Uncertainty in Valuation
  Networks

abstract:
  We present PULCinella and its use in comparing uncertainty theories.
PULCinella is a general tool for Propagating Uncertainty based on the Local
Computation technique of Shafer and Shenoy. It may be specialized to different
uncertainty theories: at the moment, Pulcinella can propagate probabilities,
belief functions, Boolean values, and possibilities. Moreover, Pulcinella
allows the user to easily define his own specializations. To illustrate
Pulcinella, we analyze two examples by using each of the four theories above.
In the first one, we mainly focus on intrinsic differences between theories. In
the second one, we take a knowledge engineer viewpoint, and check the adequacy
of each theory to a given problem.


title: Structuring Bodies of Evidence

abstract:
  In this article we present two ways of structuring bodies of evidence, which
allow us to reduce the complexity of the operations usually performed in the
framework of evidence theory. The first structure just partitions the focal
elements in a body of evidence by their cardinality. With this structure we are
able to reduce the complexity on the calculation of the belief functions Bel,
Pl, and Q. The other structure proposed here, the Hierarchical Trees, permits
us to reduce the complexity of the calculation of Bel, Pl, and Q, as well as of
the Dempster's rule of combination in relation to the brute-force algorithm.
Both these structures do not require the generation of all the subsets of the
reference domain.


title:
On the Generation of Alternative Explanations with Implications for
  Belief Revision

abstract:
  In general, the best explanation for a given observation makes no promises on
how good it is with respect to other alternative explanations. A major
deficiency of message-passing schemes for belief revision in Bayesian networks
is their inability to generate alternatives beyond the second best. In this
paper, we present a general approach based on linear constraint systems that
naturally generates alternative explanations in an orderly and highly efficient
manner. This approach is then applied to cost-based abduction problems as well
as belief revision in Bayesian net works.


title: Completing Knowledge by Competing Hierarchies

abstract:
  A control strategy for expert systems is presented which is based on Shafer's
Belief theory and the combination rule of Dempster. In contrast to well known
strategies it is not sequentially and hypotheses-driven, but parallel and self
organizing, determined by the concept of information gain. The information
gain, calculated as the maximal difference between the actual evidence
distribution in the knowledge base and the potential evidence determines each
consultation step. Hierarchically structured knowledge is an important
representation form and experts even use several hierarchies in parallel for
constituting their knowledge. Hence the control strategy is applied to a
layered set of distinct hierarchies. Depending on the actual data one of these
hierarchies is chosen by the control strategy for the next step in the
reasoning process. Provided the actual data are well matched to the structure
of one hierarchy, this hierarchy remains selected for a longer consultation
time. If no good match can be achieved, a switch from the actual hierarchy to a
competing one will result, very similar to the phenomenon of restructuring in
problem solving tasks. Up to now the control strategy is restricted to multi
hierarchical knowledge bases with disjunct hierarchies. It is implemented in
the expert system IBIG (inference by information gain), being presently applied
to acquired speech disorders (aphasia).


title: A Graph-Based Inference Method for Conditional Independence

abstract:
  The graphoid axioms for conditional independence, originally described by
Dawid [1979], are fundamental to probabilistic reasoning [Pearl, 19881. Such
axioms provide a mechanism for manipulating conditional independence assertions
without resorting to their numerical definition. This paper explores a
representation for independence statements using multiple undirected graphs and
some simple graphical transformations. The independence statements derivable in
this system are equivalent to those obtainable by the graphoid axioms.
Therefore, this is a purely graphical proof technique for conditional
independence.


title: A Fusion Algorithm for Solving Bayesian Decision Problems

abstract:
  This paper proposes a new method for solving Bayesian decision problems. The
method consists of representing a Bayesian decision problem as a
valuation-based system and applying a fusion algorithm for solving it. The
fusion algorithm is a hybrid of local computational methods for computation of
marginals of joint probability distributions and the local computational
methods for discrete optimization problems.


title: Algorithms for Irrelevance-Based Partial MAPs

abstract:
  Irrelevance-based partial MAPs are useful constructs for domain-independent
explanation using belief networks. We look at two definitions for such partial
MAPs, and prove important properties that are useful in designing algorithms
for computing them effectively. We make use of these properties in modifying
our standard MAP best-first algorithm, so as to handle irrelevance-based
partial MAPs.


title: About Updating

abstract:
  Survey of several forms of updating, with a practical illustrative example.
We study several updating (conditioning) schemes that emerge naturally from a
common scenarion to provide some insights into their meaning. Updating is a
subtle operation and there is no single method, no single 'good' rule. The
choice of the appropriate rule must always be given due consideration. Planchet
(1989) presents a mathematical survey of many rules. We focus on the practical
meaning of these rules. After summarizing the several rules for conditioning,
we present an illustrative example in which the various forms of conditioning
can be explained.


title: Compressed Constraints in Probabilistic Logic and Their Revision

abstract:
  In probabilistic logic entailments, even moderate size problems can yield
linear constraint systems with so many variables that exact methods are
impractical. This difficulty can be remedied in many cases of interest by
introducing a three valued logic (true, false, and "don't care"). The
three-valued approach allows the construction of "compressed" constraint
systems which have the same solution sets as their two-valued counterparts, but
which may involve dramatically fewer variables. Techniques to calculate point
estimates for the posterior probabilities of entailed sentences are discussed.


title: Detecting Causal Relations in the Presence of Unmeasured Variables

abstract:
  The presence of latent variables can greatly complicate inferences about
causal relations between measured variables from statistical data. In many
cases, the presence of latent variables makes it impossible to determine for
two measured variables A and B, whether A causes B, B causes A, or there is
some common cause. In this paper I present several theorems that state
conditions under which it is possible to reliably infer the causal relation
between two measured variables, regardless of whether latent variables are
acting or not.


title:
A Method for Integrating Utility Analysis into an Expert System for
  Design Evaluation

abstract:
  In mechanical design, there is often unavoidable uncertainty in estimates of
design performance. Evaluation of design alternatives requires consideration of
the impact of this uncertainty. Expert heuristics embody assumptions regarding
the designer's attitude towards risk and uncertainty that might be reasonable
in most cases but inaccurate in others. We present a technique to allow
designers to incorporate their own unique attitude towards uncertainty as
opposed to those assumed by the domain expert's rules. The general approach is
to eliminate aspects of heuristic rules which directly or indirectly include
assumptions regarding the user's attitude towards risk, and replace them with
explicit, user-specified probabilistic multi attribute utility and probability
distribution functions. We illustrate the method in a system for material
selection for automobile bumpers.


title: From Relational Databases to Belief Networks

abstract:
  The relationship between belief networks and relational databases is
examined. Based on this analysis, a method to construct belief networks
automatically from statistical relational data is proposed. A comparison
between our method and other methods shows that our method has several
advantages when generalization or prediction is deeded.


title: A Monte-Carlo Algorithm for Dempster-Shafer Belief

abstract:
  A very computationally-efficient Monte-Carlo algorithm for the calculation of
Dempster-Shafer belief is described. If Bel is the combination using Dempster's
Rule of belief functions Bel, ..., Bel,7, then, for subset b of the frame C),
Bel(b) can be calculated in time linear in 1(31 and m (given that the weight of
conflict is bounded). The algorithm can also be used to improve the complexity
of the Shenoy-Shafer algorithms on Markov trees, and be generalised to
calculate Dempster-Shafer Belief over other logics.


title: Compatibility of Quantitative and Qualitative Representations of Belief

abstract:
  The compatibility of quantitative and qualitative representations of beliefs
was studied extensively in probability theory. It is only recently that this
important topic is considered in the context of belief functions. In this
paper, the compatibility of various quantitative belief measures and
qualitative belief structures is investigated. Four classes of belief measures
considered are: the probability function, the monotonic belief function,
Shafer's belief function, and Smets' generalized belief function. The analysis
of their individual compatibility with different belief structures not only
provides a sound b<msis for these quantitative measures, but also alleviates
some of the difficulties in the acquisition and interpretation of numeric
belief numbers. It is shown that the structure of qualitative probability is
compatible with monotonic belief functions. Moreover, a belief structure
slightly weaker than that of qualitative belief is compatible with Smets'
generalized belief functions.


title: An Efficient Implementation of Belief Function Propagation

abstract:
  The local computation technique (Shafer et al. 1987, Shafer and Shenoy 1988,
Shenoy and Shafer 1986) is used for propagating belief functions in so called a
Markov Tree. In this paper, we describe an efficient implementation of belief
function propagation on the basis of the local computation technique. The
presented method avoids all the redundant computations in the propagation
process, and so makes the computational complexity decrease with respect to
other existing implementations (Hsia and Shenoy 1989, Zarley et al. 1988). We
also give a combined algorithm for both propagation and re-propagation which
makes the re-propagation process more efficient when one or more of the prior
belief functions is changed.


title:
A Non-Numeric Approach to Multi-Criteria/Multi-Expert Aggregation Based
  on Approximate Reasoning

abstract:
  We describe a technique that can be used for the fusion of multiple sources
of information as well as for the evaluation and selection of alternatives
under multi-criteria. Three important properties contribute to the uniqueness
of the technique introduced. The first is the ability to do all necessary
operations and aggregations with information that is of a nonnumeric linguistic
nature. This facility greatly reduces the burden on the providers of
information, the experts. A second characterizing feature is the ability
assign, again linguistically, differing importance to the criteria or in the
case of information fusion to the individual sources of information. A third
significant feature of the approach is its ability to be used as method to find
a consensus of the opinion of multiple experts on the issue of concern. The
techniques used in this approach are base on ideas developed from the theory of
approximate reasoning. We illustrate the approach with a problem of project
selection.


title: Why Do We Need Foundations for Modelling Uncertainties?

abstract:
  Surely we want solid foundations. What kind of castle can we build on sand?
What is the point of devoting effort to balconies and minarets, if the
foundation may be so weak as to allow the structure to collapse of its own
weight? We want our foundations set on bedrock, designed to last for
generations. Who would want an architect who cannot certify the soundness of
the foundations of his buildings?


title: Object-oriented approach to Rapid Custom Instruction design

abstract:
  Due to continuous evolution of Systems-on-Chip (SoC), the complexity of their
design and development has augmented exponentially. To deal with the
ever-growing complexity of such embedded systems, we introduce, in this paper,
an object-oriented approach to rapid SoC design using auto-generation of
hardware custom instructions to simplify and accelerate the SoC design process.
In our approach, a Data Flow Graph (DFG) is adopted as a representation of the
arithmetic operation to convert it to a custom instruction. Then VHDL code will
be automatically generated. The input C code is automatically updated for
calling the new hardware components. To prove the effectiveness of the proposed
approach, a Java source code framework named Automatic Custom Architecture
generator (ACAgen) is developed. Experimental results on 3D sample application
validate our approach and demonstrate how the proposed framework facilitates
and accelerates the SoC design process at low costs.


title:
Probabilistic Solution of the General Robin Boundary Value Problem on
  Arbitrary Domains

abstract:
  Using a capacity approach, and the theory of measure's perturbation of
Dirichlet forms, we give the probabilistic representation of the General Robin
boundary value problems on an arbitrary domain $\Omega$, involving smooth
measures, which give arise to a new process obtained by killing the general
reflecting Brownian motion at a random time. We obtain some properties of the
semigroup directly from its probabilistic representation, and some convergence
theorems, and also a probabilistic interpretation of the phenomena occurring on
the boundary.


title: An outline of polar spaces: basics and advances

abstract:
  This paper is an extended version of a series of lectures on polar spaces
given during the workshop and conference 'Groups and Geometries', held at the
Indian Statistical Institute in Bangalore in December 2012. The aim of this
paper is to give an overview of the theory of polar spaces focusing on some
research topics related to polar spaces. We survey the fundamental results
about polar spaces starting from classical polar spaces. Then we introduce and
report on the state of the art on the following research topics: polar spaces
of infinite rank, embedding polar spaces in groups and projective embeddings of
dual polar spaces.


title: Scheme dependence of instanton counting in ALE spaces

abstract:
  There have been two distinct schemes studied in the literature for instanton
counting in A_{p-1} asymptotically locally Euclidean (ALE) spaces. We point out
that the two schemes---namely the counting of orbifolded instantons and
instanton counting in the resolved space---lead in general to different results
for partition functions. We illustrate this observation in the case of N=2 U(N)
gauge theory with 2N flavors on the A_{p-1} ALE space. We propose simple
relations between the instanton partition functions given by the two schemes
and test them by explicit calculations.


title:
X-ray Eclipse Diagnosis of the Evolving Mass Loss in the Recurrent Nova
  U Scorpii 2010

abstract:
  We report the Suzaku detection of the earliest X-ray eclipse seen in the
recurrent nova U Scorpii 2010. A target-of-opportunity observation 15 days
after the outburst found a 27+/-5% dimming in the 0.2-1.0 keV energy band at
the predicted center of an eclipse. In comparison with the X-ray eclipse depths
seen at two later epochs by XMM-Newton, the source region shrank by about
10-20% between days 15 and 35 after the outburst. The X-ray eclipses appear to
be deeper than or similar to contemporaneous optical eclipses, suggesting the
X-ray and optical source region extents are comparable on day 15. We raise the
possibility of the energy dependency in the photon escape regions, and that
this would be a result of the supersoft X-ray opacity being higher than the
Thomson scattering optical opacity at the photosphere due to bound-free
transitions in abundant metals that are not fully ionized. Assuming a
spherically symmetric explosion model, we constrain the mass-loss rate as a
function of time. For a ratio of actual to Thomson opacity of 10-100 in
supersoft X-rays, we find a total ejecta mass of about 10^{-7}-10^{-6}
M_{\odot}.


title: About Landau-Hopf scenario in a system of coupled self-oscillators

abstract:
  The conditions are discussed for which an ensemble of interacting oscillators
may demonstrate the Landau-Hopf scenario of successive birth of multi-frequency
quasi-periodic motions. A model is proposed that is a network of five globally
coupled oscillators characterized by controlled degree of activation of
individual oscillators. Illustrations are given for successive birth of tori of
increasing dimension via quasi-periodic Hopf bifurcations.


title: Live music programming in Haskell

abstract:
  We aim for composing algorithmic music in an interactive way with multiple
participants. To this end we have developed an interpreter for a sub-language
of the non-strict functional programming language Haskell that allows the
modification of a program during its execution. Our system can be used both for
musical live-coding and for demonstration and education of functional
programming.


title:
Determining Fundamental Properties of Matter Created in
  Ultrarelativistic Heavy-Ion Collisions

abstract:
  Posterior distributions for physical parameters describing relativistic
heavy-ion collisions, such as the viscosity of the quark-gluon plasma, are
extracted through a comparison of hydrodynamic-based transport models to
experimental results from 100$A$ GeV + 100$A$ GeV Au+Au collisions at the
Relativistic Heavy Ion Collider (RHIC). By simultaneously varying six
parameters and by evaluating several classes of observables, we are able to
explore the complex intertwined dependencies of observables to model
parameters. We obtain a full multi-dimensional posterior distribution for the
model output given a large set of experimental observations, the methods
developed here provide a range of acceptable values for each parameter, and
reveal correlations between them. The breadth of observables and the number of
parameters considered here go far beyond previous studies in this field. The
statistical tools, which are based upon Gaussian Process emulators, are tested
in detail and should be extendable to larger data sets and a higher number of
parameters.


title: Driven Geometric Phase Gates with Trapped Ions

abstract:
  We describe a hybrid laser-microwave scheme to implement two-qubit geometric
phase gates in crystals of trapped ions. The proposed gates can attain errors
below the fault-tolerance threshold in the presence of thermal, dephasing,
laser-phase, and microwave-intensity noise. Moreover, our proposal is
technically less demanding than previous schemes, since it does not require a
laser arrangement with interferometric stability. The laser beams are tuned
close to a single vibrational sideband to entangle the qubits, while strong
microwave drivings provide the geometric character to the gate, and thus
protect the qubits from these different sources of noise. A thorough analytic
and numerical study of the performance of these gates in realistic noisy
regimes is presented.


title:
Multiwavelength observations of V479 Andromedae: a close compact binary
  with an identity crisis

abstract:
  We conducted a multi-wavelength study to unveil the properties of the
extremely long-period cataclysmic variable V479 And. We performed series of
observations, including moderate to high spectral resolution optical
spectrophotometry, X-ray observations with Swift, linear polarimetry and
near-IR photometry. This binary system is a low-inclination ~ 17^o system with
a 0.594093(4) day orbital period. The absorption line complex in the spectra
indicate a G8--K0 spectral type for the donor star, which has departed from the
zero-age main sequence. This implies a distance to the object of about 4 kpc.
The primary is probably a massive 1.1-1.4 Msun magnetic white dwarf, accreting
matter at a rate M(dot) > 10^-10 Msun/ yr. This rate can be achieved if the
donor star fills its corresponding Roche lobe, but there is little
observational evidence for a mass-transfer stream in this system. An
alternative explanation is a stellar wind from the donor star, although such a
high rate mass loss is not anticipated from a subgiant. If the strongly
magnetic white dwarf in V479 And. is confirmed by future observations, the
system the polar with the longest observed orbital period. We also discuss the
evolutionary state of V479 And.


title:
Bose-Einstein Condensation of Photons in a Microscopic Optical
  Resonator: Towards Photonic Lattices and Coupled Cavities

abstract:
  Bose-Einstein condensation has in the last two decades been observed in cold
atomic gases and in solid-state physics quasiparticles, exciton-polaritons and
magnons, respectively. The perhaps most widely known example of a bosonic gas,
photons in blackbody radiation, however exhibits no Bose-Einstein condensation,
because the particle number is not conserved and at low temperatures the
photons disappear in the system's walls instead of massively occupying the
cavity ground mode. This is not the case in a small optical cavity, with a
low-frequency cutoff imprinting a spectrum of photon energies restricted to
values well above the thermal energy. The here reported experiments are based
on a microscopic optical cavity filled with dye solution at room temperature.
Recent experiments of our group observing Bose-Einstein condensation of photons
in such a setup are described. Moreover, we discuss some possible applications
of photon condensates to realize quantum manybody states in periodic photonic
lattices and photonic Josephson devices.


title:
Defects in quasicrystals, revisited II- perfect and imperfect
  dislocations

abstract:
  In this paper, the second part of a survey of the geometric properties of
defects in quasicrystals studied from the Volterra viewpoint (see ref. [1]), we
show that: 1$- $ a {\sf disvection line} L$_{||} \subset \mathrm E_{||}$ of
Burgers vector $\textbf b =\textbf b_{||}+\textbf b_\bot $ splits naturally
along L$_{||}$ into a {\sf perfect dislocation} of Burgers vector $\textbf
b_{||}$ and an {\sf imperfect dislocation} of Burgers vector related to
$\textbf b_{\bot}$, akin to a stacking fault, (a 'phason' defect), 2$- $ the
'phason' defects are classified according to the relative position of
$\Sigma_{\bot}$ with respect to a partition of the acceptance window AW which
depends on the direction of $\textbf b_\bot $. The perpendicular cut surface
$\Sigma_{\bot}\subset \mathrm {AW}$ here introduced is a mapping of the usual
cut surface $\Sigma_{||}\subset\mathrm E_{||}$. Imperfect dislocations in QCs
are somewhat similar to Kronberg's synchroshear dislocations. It is also shown
that climb must generically be easier than glide.


title: A Uranian Trojan and the frequency of temporary giant-planet co-orbitals

abstract:
  Trojan objects share a planet's orbit, never straying far from the triangular
Lagrangian points, 60 degrees ahead of (L4) or behind (L5) the planet. We
report the detection of a Uranian Trojan; in our numerical integrations, 2011
QF99 oscillates around the Uranian L4 Lagrange point for >70 kyr and remains
co-orbital for ~1 Myr before becoming a Centaur. We constructed a Centaur
model, supplied from the transneptunian region, to estimate temporary
co-orbital capture frequency and duration (to factor of two accuracy), finding
that at any time 0.4% and 2.8% of the population will be Uranian and Neptunian
co-orbitals, respectively. The co-orbital fraction (~2.4%) among Centaurs in
the IAU Minor Planet Centre database is thus as expected under transneptunian
supply.


title:
The 111 and 129 GeV gamma-ray lines from annihilations in the Milky Way
  dark matter halo, dark disk and subhalos

abstract:
  Recently a series of indications have been put forward suggesting the
presence of two gamma-ray lines at 110-130 GeV (centered at 111 and 129 GeV).
Signals of these lines have been observed toward the Galactic center, at some
galaxy clusters and among some of the unassociated point sources of the 2 years
Fermi catalogue. Such a combination of signals could be generated by dark
matter annihilations in the main dark matter halo, its substructures and nearby
galaxy clusters. We discuss in this work the consistency between the number of
events observed at the line energies in the sky and the predictions using
results from the Via Lactea II numerical simulation and extrapolations below
its mass resolution, taking into account that the annihilation cross-section to
the lines can be estimated from the Galactic center signal. We find that some
extrapolations to small substructures can naturally account for the point
sources signal, although the hypothesis of background only cannot be rejected.
We also study the morphology of the gamma-ray sky at the 2 lines energies,
testing different Galactic diffuse background models to account for
interstellar medium uncertainties, and different assumptions on the DM diffuse
component profile. We find from template fits that within reasonable diffuse
background uncertainties the presence of a spherical halo component is
preferred with cuspier dark matter halo profiles being preferable even from the
full sky fit. We finally check the impact of a dark disk component, suggested
by cosmological simulations that include baryons and find that thin dark disks
can not be disfavored, thus possibly accounting for the preferentially closer
to the Galactic disk distribution of the point sources lines signal.


title:
Stop and sbottom search using dileptonic $M_{T2}$ variable and boosted
  top technique at the LHC

abstract:
  The ATLAS and CMS experiments at the CERN LHC have collected about 20
$fb^{-1}$ of data each at the end of their 8 TeV run, and ruled out a huge
swath of parameter space in the context of Minimally Supersymmetric Standard
Model(MSSM). Limits on masses of the gluino ($\tilde{g}$) and the squarks of
the first two generations($\tilde{q}$) have been pushed to the TeV range. Light
third generation squarks namely stop and sbottom of sub-TeV masses, on the
other hand, are still allowed by their direct search limits. Interestingly, the
discovery of a Standard Model(SM) higgs boson like particle with a mass of
$\sim$ 125 GeV favours a light third generation which is also motivated by
naturalness arguments. Decays of stop and sbottom quarks can in general produce
a number of distinct final states which necessitate different search strategies
in the collider experiments. In this paper we, on the other hand, propose a
general search strategy to look for third generation squarks in the final state
which contains a top quark in the sample along with two additional hard leptons
and substantial missing transverse momentum. We illustrate that a search
strategy using the dileptonic $M_{T2}$, the effective mass $m_{eff}$ and jet
substructure to reconstruct the hadronic top quark can be very effective to
reduce the SM backgrounds. With the proposed search strategy, we estimate that
the third generation squarks with masses up to about 800 GeV can be probed at
the 14 TeV LHC with a 100 $fb^{-1}$ data set. We also interpret our results in
two simplified scenarios where we consider the stop (sbottom) pair production
followed by their subsequent decay to a top quark and the second lightest
neutralino (lightest chargino). In this case also we find that stop (sbottom)
mass up to 1 TeV (0.9 TeV) can be discovered at the 14 TeV LHC with 100
$fb^{-1}$ integrated luminosity.


title: Exponential prefixed polynomial equations

abstract:
  A prefixed polynomial equation is an equation of the form $P(t_1,\ldots,t_n)
= 0$, where $P$ is a polynomial whose variables $t_1,\ldots,t_n$ range over the
natural numbers, preceded by quantifiers over some, or all, of its variables.
Here, we consider exponential prefixed polynomial equations (EPPEs), where
variables can also occur as exponents. We obtain a relatively concise EPPE
equivalent to the combinatorial principle of the Paris-Harrington theorem for
pairs (which is independent of primitive recursive arithmetic), as well as an
EPPE equivalent to Goodstein's theorem (which is independent of Peano
arithmetic). Some new devices are used in addition to known methods for the
elimination of bounded universal quantifiers for Diophantine predicates.


title: Speech Recognition with Deep Recurrent Neural Networks

abstract:
  Recurrent neural networks (RNNs) are a powerful model for sequential data.
End-to-end training methods such as Connectionist Temporal Classification make
it possible to train RNNs for sequence labelling problems where the
input-output alignment is unknown. The combination of these methods with the
Long Short-term Memory RNN architecture has proved particularly fruitful,
delivering state-of-the-art results in cursive handwriting recognition. However
RNN performance in speech recognition has so far been disappointing, with
better results returned by deep feedforward networks. This paper investigates
\emph{deep recurrent neural networks}, which combine the multiple levels of
representation that have proved so effective in deep networks with the flexible
use of long range context that empowers RNNs. When trained end-to-end with
suitable regularisation, we find that deep Long Short-term Memory RNNs achieve
a test set error of 17.7% on the TIMIT phoneme recognition benchmark, which to
our knowledge is the best recorded score.


title: Thawing models in the presence of a generalized Chaplygin gas

abstract:
  In this paper we consider a cosmological model whose main components are a
scalar field and a generalized Chaplygin gas. We obtain an exact solution for a
flat arbitrary potential. This solution have the right dust limit when the
Chaplygin parameter $A\rightarrow 0$. We use the dynamical systems approach in
order to describe the cosmological evolution of the mixture for an exponential
self-interacting scalar field potential. We study the scalar field with an
arbitrary self-interacting potential using the "Method of $f$-devisers." Our
results are illustrated for the special case of a coshlike potential. We find
that usual scalar-field-dominated and scaling solutions cannot be late-time
attractors in the presence of the Chaplygin gas (with $\alpha>0$). We recover
the standard results at the dust limit ($A\rightarrow 0$). In particular, for
the exponential potential, the late-time attractor is a pure generalized
Chaplygin solution mimicking an effective cosmological constant. In the case of
arbitrary potentials, the late-time attractors are de Sitter solutions in the
form of a cosmological constant, a pure generalized Chaplygin solution or a
continuum of solutions, when the scalar field and the Chaplygin gas densities
are of the same orders of magnitude. The different situations depend on the
parameter choices.


title: The many polarizations of powers of maximal ideals

abstract:
  In this paper, we study different polarizations of powers of the maximal
ideal m^d, and polarizations of its related square-free version I_d. For n = 3,
we show that every minimal free cellular resolution of m^d comes from a certain
polarization of the ideal m^d. When I is a square-free ideal, we show that the
Alexander dual of any polarization of I is a polarization of the Alexander dual
ideal of I. We apply this theorem and study different polarizations of I_d and
its Alexander dual I_{n-d+1} simultaneously. We do this by giving a
combinatorial description of the polarizations, which has a natural duality. We
study the case of d = 2 and d = n-1 in more detail. Here, we show that there is
a one- to-one correspondence between spanning trees of the complete graph K_n
and the maximal polarizations of these ideals.


title: Not Conformally-Einstein Metrics in Conformal Gravity

abstract:
  The equations of motion of four-dimensional conformal gravity, whose
Lagrangian is the square of the Weyl tensor, require that the Bach tensor
$E_{\mu\nu}= (\nabla^\rho\nabla^\sigma + \ft12
R^{\rho\sigma})C_{\mu\rho\nu\sigma}$ vanishes. Since $E_{\mu\nu}$ is zero for
any Einstein metric, and any conformal scaling of such a metric, it follows
that large classes of solutions in four-dimensional conformal gravity are
simply given by metrics that are conformal to Einstein metrics (including
Ricci-flat). In fact it becomes more intriguing to find solutions that are {\it
not} conformally Einstein. We obtain five new such vacua, which are homogeneous
and have asymptotic generalized Lifshitz anisotropic scaling symmetry. Four of
these solutions can be further generalized to metrics that are conformal to
classes of pp-waves, with a covariantly-constant null vector. We also obtain
large classes of generalized Lifshitz vacua in Einstein-Weyl gravity.


title:
An uncountable family of 3-generated groups with isomorphic profinite
  completions

abstract:
  We construct an uncountable family of 3-generated residually finite
just-infinite groups with isomorphic profinite completions. We also show that
word growth rate is not a profinite property.


title: Global minimal models for endomorphisms of projective space

abstract:
  We prove the existence of global minimal models for rational morphisms
$\phi:{\mathbb P}^N\rightarrow{\mathbb P}^N$ of projective space defined over
the field of fractions of a principal ideal domain.


title: Electromagnetic Response of Weyl Semimetals

abstract:
  It has been suggested recently, based on subtle field-theoretical
considerations, that the electromagnetic response of Weyl semimetals and the
closely related Weyl insulators can be characterized by an axion term E.B with
space and time dependent axion angle. Here we construct a minimal lattice model
of the Weyl medium and study its electromagnetic response by a combination of
analytical and numerical techniques. We confirm the existence of the anomalous
Hall effect expected on the basis of the field theory treatment. We find,
contrary to the latter, that chiral magnetic effect (that is, ground-state
charge current induced by the applied magnetic field) is absent in both the
semimetal and the insulator phase. We elucidate the reasons for this
discrepancy.


title:
SICLE: A high-throughput tool for extracting evolutionary relationships
  from phylogenetic trees

abstract:
  We present the phylogeny analysis software SICLE (Sister Clade Extractor), an
easy-to-use, high- throughput tool to describe the nearest neighbors to a node
of interest in a phylogenetic tree as well as the support value for the
relationship. The application is a command line utility that can be embedded
into a phylogenetic analysis pipeline or can be used as a subroutine within
another C++ program. As a test case, we applied this new tool to the published
phylome of Salinibacter ruber, a species of halophilic Bacteriodetes,
identifying 13 unique sister relationships to S. ruber across the 4589 gene
phylogenies. S. ruber grouped with bacteria, most often other Bacteriodetes, in
the majority of phylogenies, but 91 phylogenies showed a branch-supported
sister association between S. ruber and Archaea, an evolutionarily intriguing
relationship indicative of horizontal gene transfer. This test case
demonstrates how SICLE makes it possible to summarize the phylogenetic
information produced by automated phylogenetic pipelines to rapidly identify
and quantify the possible evolutionary relationships that merit further
investigation. SICLE is available for free for noncommercial use at
http://eebweb.arizona.edu/sicle/.


title:
Mid-infrared femtosecond laser pulse filamentation in hollow waveguides:
  a comparison of simulation methods

abstract:
  This work compares computational methods for laser pulse propagation in
hollow waveguides filled with rare gases at high pressures, with applications
in extreme nonlinear optics in the mid-infrared wavelength region. As the
wavelength of light \lambda=2\pi/k increases with respect to the transverse
size R of a leaky waveguide, the loss of light out of the waveguide upon
propagation, in general, increases. The now standard numerical approach for
studying such structures is based on expansion of the propagating field into
approximate leaky waveguide modes. We compare this approach to a new method
that resolves the electric field in real space and correctly captures the
energy loss through the waveguide wall. The comparison reveals that the
expansion-based approach overestimates losses that occur in nonlinearly
reshaped pulsed waveforms. For a modest increase in computational effort, the
new method offers a physically more accurate model to describe phenomena (e.g.,
extreme pulse-selfcompression) in waveguides with smaller values of kR.


title:
Effect of a High Opacity on the Light Curves of Radioactively Powered
  Transients from Compact Object Mergers

abstract:
  The coalescence of compact objects are a promising astrophysical sources of
gravitational wave (GW) signals. The ejection of r-process material from such
mergers may lead to a radioactively-powered electromagnetic counterpart which,
if discovered, would enhance the science return of a GW detection. As very
little is known about the optical properties of heavy r-process elements,
previous light curve models have adopted opacities similar to those of iron
group elements. Here we report that the presence of heavier elements,
particularly the lanthanides, increase the ejecta opacity by several orders of
magnitude. We include these higher opacities in time dependent,
multi-wavelength radiative transport calculations to predict the broadband
light curves of one-dimensional models over a range of parameters (ejecta
masses from 0.001 to 0.1 solar masses and velocities from 0.1 to 0.3c). We find
that the higher opacities lead to much longer duration light curves which can
last a week or more. The emission is shifted toward the infrared bands due to
strong optical line blanketing, and the colors at later times are
representative of a blackbody near the recombination temperature of the
lanthanides (T ~ 2500K). We further consider the case in which a second mass
outflow, composed of 56Ni, is ejected from a disk wind, and show that the net
result is a distinctive two component spectral energy distribution, with a
bright optical peak due to 56Ni and an infrared peak due to r-process ejecta.
We briefly consider the prospects for detection and identification of these
transients.


title: Opacities and Spectra of the r-process Ejecta from Neutron Star Mergers

abstract:
  Material ejected during (or immediately following) the merger of two neutron
stars may assemble into heavy elements by the r-process. The subsequent
radioactive decay of the nuclei can power electromagnetic emission similar to,
but significantly dimmer than, an ordinary supernova. Identifying such events
is an important goal of future transient surveys, offering new perspectives on
the origin of r-process nuclei and the astrophysical sources of gravitational
waves. Predictions of the transient light curves and spectra, however, have
suffered from the uncertain optical properties of heavy ions. Here we consider
the opacity of expanding r-process material and argue that it is dominated by
line transitions from those ions with the most complex valence electron
structure, namely the lanthanides. For a few representative ions, we run atomic
structure models to calculate radiative data for tens of millions of lines. We
find that the resulting r-process opacities are orders of magnitude larger than
that of ordinary (e.g., iron-rich) supernova ejecta. Radiative transport
calculations using these new opacities indicate that the transient emission
should be dimmer and redder than previously thought. The spectra appear
pseudo-blackbody, with broad absorption features, and peak in the infrared (~1
micron). We discuss uncertainties in the opacities and attempt to quantify
their impact on the spectral predictions. The results have important
implications for observational strategies to find and study the radioactively
powered electromagnetic counterparts to compact object mergers.


title: Lecture notes on embedded contact homology

abstract:
  These notes give an introduction to embedded contact homology (ECH) of
contact three-manifolds, gathering together many basic notions which are
scattered across a number of papers. We also discuss the origins of ECH,
including various remarks and examples which have not been previously
published. Finally, we review the recent application to four-dimensional
symplectic embedding problems. This article is based on lectures given in
Budapest and Munich in the summer of 2012, a series of accompanying blog
postings at floerhomology.wordpress.com, and related lectures at UC Berkeley in
Fall 2012. There is already a brief introduction to ECH in the 2010 ICM
proceedings, but the present notes give much more background and detail.


title:
Micromegas-TPC operation at high pressure in Xenon-trimethylamine
  mixtures

abstract:
  We present in this work measurements performed with a small Micromegas-TPC
using a xenon-trimethylamine (Xe-TMA) Penning-mixture as filling gas.
Measurements of gas gain and energy resolutions for 22.1 keV X-rays are
presented, spanning several TMA concentrations and pressures between 1 and 10
bar. Across this pressure range, the best energy resolution and largest
increase in gain at constant field (a standard figure for characterizing
Penning-like energy transfers) is observed to be in the 1.5%-2.5% TMA region. A
gain increase (at constant field) up to a factor 100 and a best energy
resolution improved by up to a factor 3 with respect to the one previously
reported in pure Xe -operated Micromegas, can be obtained. In virtue of the
VUV-quenching properties of the mixture, the overall maximum gain achievable is
also notably increased (up to 400 at 10bar), a factor x 3 higher than in pure
Xe. In addition, preliminary measurements of the electron drift velocity in a
modified setup have been performed and show good agreement with the one
obtained from Magboltz. These results are of great interest for calorimetric
applications in gas Xe TPCs, in particular for the search of the neutrino-less
double beta decay of Xe-136.


title: In-plane and out-of-plane correlations in Ho(1-x)YxMnO3

abstract:
  Strong magnetic-ferroelectric coupling in hexagonal HoMnO3 has been observed
previously at spin reorientation temperature between P6'3cm' and the P6'3cm'
magnetic phases. In contrast, YMnO3 has only a single magnetic phase (P6'3cm')
and no sign of strong magnetic-ferroelectric coupling. In order to investigate
the P6'3cm' to P6'3c'm spin reorientation transition, single crystals of
Ho(1-x)YxMnO3 at varying compositions were grown. Neutron scattering
measurements reveal quasielastic scattering in the YMnO3-like phase (P6'3c'm)
centered on the Bragg peak corresponding to HoMnO3-like phase (P6'3cm') and
vice versa. This scattering may be due to short-lived fluctuations into the
"wrong" magnetic phase at domain boundaries. These results suggest that there
is strong phase competition between the P6'3cm' and P6'3c'm phases even in pure
YMnO3, and this competition may play an important role in the
magnetic-ferroelectric coupling observed in HoMnO3.


title: Fiber scrambling for high-resolution spectrographs. I. Lick Observatory

abstract:
  In this paper, we report all results obtained with a fiber scrambler on the
Hamilton spectrograph at Lick Observatory. We demonstrate an improvement in the
stability of the instrumental profile using this fiber scrambler. Additionally,
we present data obtained with a double scrambler that further improves the
stability of the instrument by a factor 2. These results show that errors
related to the coupling between the telescope and the spectrograph are the
dominant source of instrumental profile variability at Lick Observatory. In
particular, we show a strong correlation between instrumental profile
variations and hour angle, most likely due to pointing-dependent illumination
of the spectrograph optics.


title:
Some Remarks on the Spectral Problem Underlying the Camassa-Holm
  Hierarchy

abstract:
  We consider left-definite eigenvalue problems $A \psi = \lambda B \psi$, with
$A \geq \varepsilon I$ for some $\varepsilon > 0$ and $B$ self-adjoint, but $B$
not necessarily positive or negative definite, applicable, in particular, to
the eigenvalue problem underlying the Camassa-Holm hierarchy. In fact, we will
treat a more general version where $A$ represents a positive definite
Schr\"odinger or Sturm-Liouville operator $T$ in $L^2(\bbR; dx)$ associated
with a differential expression of the form $\tau = - (d/dx) p(x) (d/dx) +
q(x)$, $x \in \bbR$, and $B$ represents an operator of multiplication by $r(x)$
in $L^2(\bbR; dx)$, which, in general, is not a weight, that is, it is not
nonnegative a.e.\ on $\bbR$. Our methods naturally permit us to treat certain
classes of distributions (resp., measures) for the coefficients $q$ and $r$ and
hence considerably extend the scope of this (generalized) eigenvalue problem,
without having to change the underlying Hilbert space $L^2(\bbR; dx)$. Our
approach relies on rewriting the eigenvalue problem $A \psi = \lambda B \psi$
in the form $A^{-1/2} B A^{-1/2} \chi = \lambda^{-1} \chi$, $\chi = A^{1/2}
\psi$, and a careful study of (appropriate realizations of) the operator
$A^{-1/2} B A^{-1/2}$ in $L^2(\bbR; dx)$.
  In the course of our treatment we employ a supersymmetric formalism which
permits us to factor the second-order operator $T$ into a product of two
first-order operators familiar from (and inspired by) Miura's transformation
linking the KdV and mKdV hierarchy of nonlinear evolution equations. We also
treat the case of periodic coefficients $q$ and $r$, where $q$ may be a
distribution and $r$ generates a measure and hence no smoothness is assumed for
$q$ and $r$.


title: Optimizing the basis of B->K*ll observables in the full kinematic range

abstract:
  We discuss the observables for the B->K*(->K pi)ll decay, focusing on both
CP-averaged and CP-violating observables at large and low hadronic recoil with
special emphasis on their low sensitivity to form-factor uncertainties. We
identify an optimal basis of observables that balances theoretical and
experimental advantages, which will guide the New Physics searches in the short
term. We discuss some advantages of the observables in the basis, and in
particular their improved sensitivity to New Physics compared to other
observables. We present predictions within the Standard Model for the
observables of interest, integrated over the appropriate bins including lepton
mass corrections. Finally, we present bounds on the S-wave contribution to the
distribution coming from the B->K_0*ll decay, which will help to establish the
systematic error associated to this pollution.


title:
Geometric realization of special cases of local Langlands and
  Jacquet-Langlands correspondences

abstract:
  Let F be a non-Archimedean local field and let E be an unramified extension
of F of degree n>1. To each sufficiently generic multiplicative character of E
(the details are explained in the body of the paper) one can associate an
irreducible n-dimensional representation of the Weil group W_F of F, which
corresponds to an irreducible supercuspidal representation \pi\ of GL_n(F) via
the local Langlands correspondence. In turn, via the Jacquet-Langlands
correspondence, \pi\ corresponds to an irreducible representation \rho\ of the
multiplicative group of the central division algebra over F with invariant 1/n.
In this note we give a new geometric construction of the representations \pi\
and \rho, which is simpler than the existing algebraic approaches (in
particular, the use of the Weil representation over finite fields is
eliminated).


title: Regularization of chattering phenomena via bounded variation control

abstract:
  In control theory, the term chattering is used to refer to strong
oscillations of controls, such as an infinite number of switchings over a
compact interval of times. In this paper we focus on three typical occurences
of chattering: the Fuller phenomenon, referring to situations where an optimal
control switches an infinite number of times over a compact set; the Robbins
phenomenon, concerning optimal control problems with state constraints, meaning
that the optimal trajectory touches the boundary of the constraint set an
infinite number of times over a compact time interval; the Zeno phenomenon,
referring as well to an infinite number of switchings over a compact set, for
hybrid optimal control problems. From the practical point of view, when trying
to compute an optimal trajectory, for instance by means of a shooting method,
chattering may be a serious obstacle to convergence.
  In this paper we propose a general regularization procedure, by adding an
appropriate penalization of the total variation. This produces a quasi-optimal
control, and we prove that the family of quasi-optimal solutions converges to
the optimal solution of the initial problem as the penalization tends to zero.
Under additional assumptions, we also quantify the quasi-optimality property by
determining a speed of convergence of the costs.


title:
Planar undulator motion excited by a fixed traveling wave: Quasiperiodic
  Averaging, normal forms and the FEL Pendulum

abstract:
  We present a mathematical analysis of planar motion of energetic electrons
moving through a planar dipole undulator, excited by a fixed planar polarized
plane wave Maxwell field in the X-Ray Free Electron Laser (FEL) regime. Our
starting point is the 6D Lorentz system, which allows planar motions, and we
examine this dynamical system as the wavelength of the traveling wave varies.
By scalings and transformations the 6D system is reduced, without
approximation, to a 2D system in a form for a rigorous asymptotic analysis
using the Method of Averaging (MoA), a long time perturbation theory. The two
dependent variables are a scaled energy deviation and a generalization of the
so-called ponderomotive phase. As the wavelength varies the system passes
through resonant and nonresonant (NR) zones and we develop NR and
near-to-resonant (NtoR) normal form approximations. For a special initial
condition and on resonance, the NtoR normal form reduces to the well-known FEL
pendulum system. We then state and prove NR and NtoR first-order averaging
theorems which give near optimal error bounds for the MoA approximations. The
proofs are novel in that they do not use a near identity transformation and
they use a system of differential inequalities. The NR case is an example of
quasiperiodic averaging where the small divisor problem enters in the simplest
possible way. To our knowledge the planar problem has not been analyzed with
the generality we aspire to here nor has the standard FEL pendulum system been
derived with associated error bounds as we do here.