title: Three techniques for obtaining algebraic circle packings

abstract:
  The main purpose of this article is to demonstrate three techniques for
proving algebraicity statements about circle packings. We give proofs of three
related theorems: (1) that every finite simple planar graph is the contact
graph of a circle packing on the Riemann sphere, equivalently in the complex
plane, all of whose tangency points, centers, and radii are algebraic, (2) that
every flat conformal torus which admits a circle packing whose contact graph
triangulates the torus has algebraic modulus, and (3) that if R is a compact
Riemann surface of genus at least 2, having constant curvature -1, which admits
a circle packing whose contact graph triangulates R, then R is isomorphic to
the quotient of the hyperbolic plane by a subgroup of PSL_2(real algebraic
numbers). The statement (1) is original, while (2) and (3) have been previously
proved in the Ph.D. thesis of McCaughan.
  Our first proof technique is to apply Tarski's Theorem, a result from model
theory, which says that if an elementary statement in the theory of real-closed
fields is true over one real-closed field, then it is true over any real closed
field. This technique works to prove (1) and (2). Our second proof technique is
via an algebraicity result of Thurston on finite co-volume discrete subgroups
of the orientation-preserving-isometry group of hyperbolic 3-space. This
technique works to prove (1). Our first and second techniques had not
previously been applied in this area. Our third and final technique is via a
lemma in real algebraic geometry, and was previously used by McCaughan to prove
(2) and (3). We show that in fact it may be used to prove (1) as well.


title:
Transverse instability of plane wave soliton solutions of the
  Novikov-Veselov equation

abstract:
  The Novikov-Veselov (NV) equation is a dispersive (2+1)-dimensional nonlinear
evolution equation that generalizes the (1+1)-dimensional Korteweg-deVries
(KdV) equation. This paper considers the stability of plane wave soliton
solutions of the NV equation to transverse perturbations. To investigate the
behavior of the perturbations, a hybrid semi-implicit/spectral numerical scheme
was developed, applicable to other nonlinear PDE systems. Numerical simulations
of the evolution of transversely perturbed plane wave solutions are presented.
In particular, it is established that plane wave soliton solutions are not
stable for transverse perturbations.


title: On-chip quantum interference between silicon photon-pair sources

abstract:
  Large-scale integrated quantum photonic technologies will require the on-chip
integration of identical photon sources with reconfigurable waveguide circuits.
Relatively complex quantum circuits have already been demonstrated, but few
studies acknowledge the pressing need to integrate photon sources and waveguide
circuits together on-chip. A key step towards such large-scale quantum
technologies is the integration of just two individual photon sources within a
waveguide circuit, and the demonstration of high-visibility quantum
interference between them. Here, we report a silicon-on-insulator device
combining two four-wave mixing sources, in an interferometer with a
reconfigurable phase shifter. We configure the device to create and manipulate
two-colour (non-degenerate) or same-colour (degenerate), path-entangled or
path-unentangled photon pairs. We observe up to 100.0+/-0.4% visibility quantum
interference on-chip, and up to 95+/-4% off-chip. Our device removes the need
for external photon sources, provides a path to increasing the complexity of
quantum photonic circuits, and is a first step towards fully-integrated quantum
technologies.


title: Lp : A Logic for Statistical Information

abstract:
  This extended abstract presents a logic, called Lp, that is capable of
representing and reasoning with a wide variety of both qualitative and
quantitative statistical information. The advantage of this logical formalism
is that it offers a declarative representation of statistical knowledge;
knowledge represented in this manner can be used for a variety of reasoning
tasks. The logic differs from previous work in probability logics in that it
uses a probability distribution over the domain of discourse, whereas most
previous work (e.g., Nilsson [2], Scott et al. [3], Gaifinan [4], Fagin et al.
[5]) has investigated the attachment of probabilities to the sentences of the
logic (also, see Halpern [6] and Bacchus [7] for further discussion of the
differences). The logic Lp possesses some further important features. First, Lp
is a superset of first order logic, hence it can represent ordinary logical
assertions. This means that Lp provides a mechanism for integrating statistical
information and reasoning about uncertainty into systems based solely on logic.
Second, Lp possesses transparent semantics, based on sets and probabilities of
those sets. Hence, knowledge represented in Lp can be understood in terms of
the simple primative concepts of sets and probabilities. And finally, the there
is a sound proof theory that has wide coverage (the proof theory is complete
for certain classes of models). The proof theory captures a sufficient range of
valid inferences to subsume most previous probabilistic uncertainty reasoning
systems. For example, the linear constraints like those generated by Nilsson's
probabilistic entailment [2] can be generated by the proof theory, and the
Bayesian inference underlying belief nets [8] can be performed. In addition,
the proof theory integrates quantitative and qualitative reasoning as well as
statistical and logical reasoning. In the next section we briefly examine
previous work in probability logics, comparing it to Lp. Then we present some
of the varieties of statistical information that Lp is capable of expressing.
After this we present, briefly, the syntax, semantics, and proof theory of the
logic. We conclude with a few examples of knowledge representation and
reasoning in Lp, pointing out the advantages of the declarative representation
offered by Lp. We close with a brief discussion of probabilities as degrees of
belief, indicating how such probabilities can be generated from statistical
knowledge encoded in Lp. The reader who is interested in a more complete
treatment should consult Bacchus [7].


title: Map Learning with Indistinguishable Locations

abstract:
  Nearly all spatial reasoning problems involve uncertainty of one sort or
another. Uncertainty arises due to the inaccuracies of sensors used in
measuring distances and angles. We refer to this as directional uncertainty.
Uncertainty also arises in combining spatial information when one location is
mistakenly identified with another. We refer to this as recognition
uncertainty. Most problems in constructing spatial representations (maps) for
the purpose of navigation involve both directional and recognition uncertainty.
In this paper, we show that a particular class of spatial reasoning problems
involving the construction of representations of large-scale space can be
solved efficiently even in the presence of directional and recognition
uncertainty. We pay particular attention to the problems that arise due to
recognition uncertainty.


title: Temporal Reasoning with Probabilities

abstract:
  In this paper we explore representations of temporal knowledge based upon the
formalism of Causal Probabilistic Networks (CPNs). Two different
?continuous-time? representations are proposed. In the first, the CPN includes
variables representing ?event-occurrence times?, possibly on different time
scales, and variables representing the ?state? of the system at these times. In
the second, the CPN describes the influences between random variables with
values in () representing dates, i.e. time-points associated with the
occurrence of relevant events. However, structuring a system of inter-related
dates as a network where all links commit to a single specific notion of cause
and effect is in general far from trivial and leads to severe difficulties. We
claim that we should recognize explicitly different kinds of relation between
dates, such as ?cause?, ?inhibition?, ?competition?, etc., and propose a method
whereby these relations are coherently embedded in a CPN using additional
auxiliary nodes corresponding to "instrumental" variables. Also discussed,
though not covered in detail, is the topic concerning how the quantitative
specifications to be inserted in a temporal CPN can be learned from specific
data.


title: Now that I Have a Good Theory of Uncertainty, What Else Do I Need?

abstract:
  Rather than discussing the isolated merits of a nominative theory of
uncertainty, this paper focuses on a class of problems, referred to as Dynamic
Classification Problem (DCP), which requires the integration of many theories,
including a prescriptive theory of uncertainty. We start by analyzing the
Dynamic Classification Problem and by defining its induced requirements on a
supporting (plausible) reasoning system. We provide a summary of the underlying
theory (based on the semantics of many-valed logics) and illustrate the
constraints imposed upon it to ensure the modularity and computational
performance required by the applications. We describe the technologies used for
knowledge engineering (such as object-based simulator to exercise requirements,
and development tools to build the Knowledge Base and functionally validate
it). We emphasize the difference between development environment and run-time
system, describe the rule cross-compiler, and the real-time inference engine
with meta-reasoning capabilities. Finally, we illustrate how our proposed
technology satisfies the pop's requirements and analyze some of the lessons
reamed from its applications to situation assessment problems for Pilot's
Associate and Submarine Commander Associate.


title: Uncertainty and Incompleteness

abstract:
  Two major difficulties in using default logics are their intractability and
the problem of selecting among multiple extensions. We propose an approach to
these problems based on integrating nommonotonic reasoning with plausible
reasoning based on triangular norms. A previously proposed system for reasoning
with uncertainty (RUM) performs uncertain monotonic inferences on an acyclic
graph. We have extended RUM to allow nommonotonic inferences and cycles within
nonmonotonic rules. By restricting the size and complexity of the nommonotonic
cycles we can still perform efficient inferences. Uncertainty measures provide
a basis for deciding among multiple defaults. Different algorithms and
heuristics for finding the optimal defaults are discussed.


title: BaRT: A Bayesian Reasoning Tool for Knowledge Based Systems

abstract:
  As the technology for building knowledge based systems has matured, important
lessons have been learned about the relationship between the architecture of a
system and the nature of the problems it is intended to solve. We are
implementing a knowledge engineering tool called BART that is designed with
these lessons in mind. BART is a Bayesian reasoning tool that makes belief
networks and other probabilistic techniques available to knowledge engineers
building classificatory problem solvers. BART has already been used to develop
a decision aid for classifying ship images, and it is currently being used to
manage uncertainty in systems concerned with analyzing intelligence reports.
This paper discusses how state-of-the-art probabilistic methods fit naturally
into a knowledge based approach to classificatory problem solving, and
describes the current capabilities of BART.


title: Plan Recognition in Stories and in Life

abstract:
  Plan recognition does not work the same way in stories and in "real life"
(people tend to jump to conclusions more in stories). We present a theory of
this, for the particular case of how objects in stories (or in life) influence
plan recognition decisions. We provide a Bayesian network formalization of a
simple first-order theory of plans, and show how a particular network parameter
seems to govern the difference between "life-like" and "story-like" response.
We then show why this parameter would be influenced (in the desired way) by a
model of speaker (or author) topic selection which assumes that facts in
stories are typically "relevant".


title:
An Empirical Evaluation of a Randomized Algorithm for Probabilistic
  Inference

abstract:
  In recent years, researchers in decision analysis and artificial intelligence
(Al) have used Bayesian belief networks to build models of expert opinion.
Using standard methods drawn from the theory of computational complexity,
workers in the field have shown that the problem of probabilistic inference in
belief networks is difficult and almost certainly intractable. K N ET, a
software environment for constructing knowledge-based systems within the
axiomatic framework of decision theory, contains a randomized approximation
scheme for probabilistic inference. The algorithm can, in many circumstances,
perform efficient approximate inference in large and richly interconnected
models of medical diagnosis. Unlike previously described stochastic algorithms
for probabilistic inference, the randomized approximation scheme computes a
priori bounds on running time by analyzing the structure and contents of the
belief network. In this article, we describe a randomized algorithm for
probabilistic inference and analyze its performance mathematically. Then, we
devote the major portion of the paper to a discussion of the algorithm's
empirical behavior. The results indicate that the generation of good trials
(that is, trials whose distribution closely matches the true distribution),
rather than the computation of numerous mediocre trials, dominates the
performance of stochastic simulation. Key words: probabilistic inference,
belief networks, stochastic simulation, computational complexity theory,
randomized algorithms.


title:
Decision Making "Biases" and Support for Assumption-Based Higher-Order
  Reasoning

abstract:
  Unaided human decision making appears to systematically violate consistency
constraints imposed by normative theories; these biases in turn appear to
justify the application of formal decision-analytic models. It is argued that
both claims are wrong. In particular, we will argue that the "confirmation
bias" is premised on an overly narrow view of how conflicting evidence is and
ought to be handled. Effective decision aiding should focus on supporting the
contral processes by means of which knowledge is extended into novel situations
and in which assumptions are adopted, utilized, and revised. The Non- Monotonic
Probabilist represents initial work toward such an aid.


title:
Automated Reasoning Using Possibilistic Logic: Semantics, Belief
  Revision and Variable Certainty Weights

abstract:
  In this paper an approach to automated deduction under uncertainty,based on
possibilistic logic, is proposed ; for that purpose we deal with clauses
weighted by a degree which is a lower bound of a necessity or a possibility
measure, according to the nature of the uncertainty. Two resolution rules are
used for coping with the different situations, and the refutation method can be
generalized. Besides the lower bounds are allowed to be functions of variables
involved in the clause, which gives hypothetical reasoning capabilities. The
relation between our approach and the idea of minimizing abnormality is briefly
discussed. In case where only lower bounds of necessity measures are involved,
a semantics is proposed, in which the completeness of the extended resolution
principle is proved. Moreover deduction from a partially inconsistent knowledge
base can be managed in this approach and displays some form of
non-monotonicity.


title:
How Much More Probable is "Much More Probable"? Verbal Expressions for
  Probability Updates

abstract:
  Bayesian inference systems should be able to explain their reasoning to
users, translating from numerical to natural language. Previous empirical work
has investigated the correspondence between absolute probabilities and
linguistic phrases. This study extends that work to the correspondence between
changes in probabilities (updates) and relative probability phrases, such as
"much more likely" or "a little less likely." Subjects selected such phrases to
best describe numerical probability updates. We examined three hypotheses about
the correspondence, and found the most descriptively accurate of these three to
be that each such phrase corresponds to a fixed difference in probability
(rather than fixed ratio of probabilities or of odds). The empirically derived
phrase selection function uses eight phrases and achieved a 72% accuracy in
correspondence with the subjects' actual usage.


title:
Positive and Negative Explanations of Uncertain Reasoning in the
  Framework of Possibility Theory

abstract:
  This paper presents an approach for developing the explanation capabilities
of rule-based expert systems managing imprecise and uncertain knowledge. The
treatment of uncertainty takes place in the framework of possibility theory
where the available information concerning the value of a logical or numerical
variable is represented by a possibility distribution which restricts its more
or less possible values. We first discuss different kinds of queries asking for
explanations before focusing on the two following types : i) how, a particular
possibility distribution is obtained (emphasizing the main reasons only) ; ii)
why in a computed possibility distribution, a particular value has received a
possibility degree which is so high, so low or so contrary to the expectation.
The approach is based on the exploitation of equations in max-min algebra. This
formalism includes the limit case of certain and precise information.


title: Interval Influence Diagrams

abstract:
  We describe a mechanism for performing probabilistic reasoning in influence
diagrams using interval rather than point valued probabilities. We derive the
procedures for node removal (corresponding to conditional expectation) and arc
reversal (corresponding to Bayesian conditioning) in influence diagrams where
lower bounds on probabilities are stored at each node. The resulting bounds for
the transformed diagram are shown to be optimal within the class of constraints
on probability distributions that can be expressed exclusively as lower bounds
on the component probabilities of the diagram. Sequences of these operations
can be performed to answer probabilistic queries with indeterminacies in the
input and for performing sensitivity analysis on an influence diagram. The
storage requirements and computational complexity of this approach are
comparable to those for point-valued probabilistic inference mechanisms, making
the approach attractive for performing sensitivity analysis and where
probability information is not available. Limited empirical data on an
implementation of the methodology are provided.


title:
Weighing and Integrating Evidence for Stochastic Simulation in Bayesian
  Networks

abstract:
  Stochastic simulation approaches perform probabilistic inference in Bayesian
networks by estimating the probability of an event based on the frequency that
the event occurs in a set of simulation trials. This paper describes the
evidence weighting mechanism, for augmenting the logic sampling stochastic
simulation algorithm [Henrion, 1986]. Evidence weighting modifies the logic
sampling algorithm by weighting each simulation trial by the likelihood of a
network's evidence given the sampled state node values for that trial. We also
describe an enhancement to the basic algorithm which uses the evidential
integration technique [Chin and Cooper, 1987]. A comparison of the basic
evidence weighting mechanism with the Markov blanket algorithm [Pearl, 1987],
the logic sampling algorithm, and the evidence integration algorithm is
presented. The comparison is aided by analyzing the performance of the
algorithms in a simple example network.


title: d-Separation: From Theorems to Algorithms

abstract:
  An efficient algorithm is developed that identifies all independencies
implied by the topology of a Bayesian network. Its correctness and maximality
stems from the soundness and completeness of d-separation with respect to
probability theory. The algorithm runs in time O (l E l) where E is the number
of edges in the network.


title:
The Effects of Perfect and Sample Information on Fuzzy Utilities in
  Decision-Making

abstract:
  In this paper, we first consider a Bayesian framework and model the "utility
function" in terms of fuzzy random variables. On the basis of this model, we
define the "prior (fuzzy) expected utility" associated with each action, and
the corresponding "posterior (fuzzy) expected utility given sample information
from a random experiment". The aim of this paper is to analyze how sample
information can affect the expected utility. In this way, by using some fuzzy
preference relations, we conclude that sample information allows a decision
maker to increase the expected utility on the average. The upper bound on the
value of the expected utility is when the decision maker has perfect
information. Applications of this work to the field of artificial intelligence
are presented through two examples.


title:
Deciding Consistency of Databases Containing Defeasible and Strict
  Information

abstract:
  We propose a norm of consistency for a mixed set of defeasible and strict
sentences, based on a probabilistic semantics. This norm establishes a clear
distinction between knowledge bases depicting exceptions and those containing
outright contradictions. We then define a notion of entailment based also on
probabilistic considerations and provide a characterization of the relation
between consistency and entailment. We derive necessary and sufficient
conditions for consistency, and provide a simple decision procedure for testing
consistency and deciding whether a sentence is entailed by a database. Finally,
it is shown that if al1 sentences are Horn clauses, consistency and entailment
can be tested in polynomial time.


title: The Relationship between Knowledge, Belief and Certainty

abstract:
  We consider the relation between knowledge and certainty, where a fact is
known if it is true at all worlds an agent considers possible and is certain if
it holds with probability 1. We identify certainty with probabilistic belief.
We show that if we assume one fixed probability assignment, then the logic
KD45, which has been identified as perhaps the most appropriate for belief,
provides a complete axiomatization for reasoning about certainty. Just as an
agent may believe a fact although phi is false, he may be certain that a fact
phi, is true although phi is false. However, it is easy to see that an agent
can have such false (probabilistic) beliefs only at a set of worlds of
probability 0. If we restrict attention to structures where all worlds have
positive probability, then S5 provides a complete axiomatization. If we
consider a more general setting, where there might be a different probability
assignment at each world, then by placing appropriate conditions on the support
of the probability function (the set of worlds which have non-zero
probability), we can capture many other well-known modal logics, such as T and
S4. Finally, we consider which axioms characterize structures satisfying
Miller's principle.


title: Heuristic Search as Evidential Reasoning

abstract:
  BPS, the Bayesian Problem Solver, applies probabilistic inference and
decision-theoretic control to flexible, resource-constrained problem-solving.
This paper focuses on the Bayesian inference mechanism in BPS, and contrasts it
with those of traditional heuristic search techniques. By performing sound
inference, BPS can outperform traditional techniques with significantly less
computational effort. Empirical tests on the Eight Puzzle show that after only
a few hundred node expansions, BPS makes better decisions than does the best
existing algorithm after several million node expansions


title: The Compilation of Decision Models

abstract:
  We introduce and analyze the problem of the compilation of decision models
from a decision-theoretic perspective. The techniques described allow us to
evaluate various configurations of compiled knowledge given the nature of
evidential relationships in a domain, the utilities associated with alternative
actions, the costs of run-time delays, and the costs of memory. We describe
procedures for selecting a subset of the total observations available to be
incorporated into a compiled situation-action mapping, in the context of a
binary decision with conditional independence of evidence. The methods allow us
to incrementally select the best pieces of evidence to add to the set of
compiled knowledge in an engineering setting. After presenting several
approaches to compilation, we exercise one of the methods to provide insight
into the relationship between the distribution over weights of evidence and the
preferred degree of compilation.


title: A Tractable Inference Algorithm for Diagnosing Multiple Diseases

abstract:
  We examine a probabilistic model for the diagnosis of multiple diseases. In
the model, diseases and findings are represented as binary variables. Also,
diseases are marginally independent, features are conditionally independent
given disease instances, and diseases interact to produce findings via a noisy
OR-gate. An algorithm for computing the posterior probability of each disease,
given a set of observed findings, called quickscore, is presented. The time
complexity of the algorithm is O(nm-2m+), where n is the number of diseases, m+
is the number of positive findings and m- is the number of negative findings.
Although the time complexity of quickscore i5 exponential in the number of
positive findings, the algorithm is useful in practice because the number of
observed positive findings is usually far less than the number of diseases
under consideration. Performance results for quickscore applied to a
probabilistic version of Quick Medical Reference (QMR) are provided.


title:
Bounded Conditioning: Flexible Inference for Decisions under Scarce
  Resources

abstract:
  We introduce a graceful approach to probabilistic inference called bounded
conditioning. Bounded conditioning monotonically refines the bounds on
posterior probabilities in a belief network with computation, and converges on
final probabilities of interest with the allocation of a complete resource
fraction. The approach allows a reasoner to exchange arbitrary quantities of
computational resource for incremental gains in inference quality. As such,
bounded conditioning holds promise as a useful inference technique for
reasoning under the general conditions of uncertain and varying reasoning
resources. The algorithm solves a probabilistic bounding problem in complex
belief networks by breaking the problem into a set of mutually exclusive,
tractable subproblems and ordering their solution by the expected effect that
each subproblem will have on the final answer. We introduce the algorithm,
discuss its characterization, and present its performance on several belief
networks, including a complex model for reasoning about problems in
intensive-care medicine.


title:
Hierarchical Evidence Accumulation in the Pseiki System and Experiments
  in Model-Driven Mobile Robot Navigation

abstract:
  In this paper, we will review the process of evidence accumulation in the
PSEIKI system for expectation-driven interpretation of images of 3-D scenes.
Expectations are presented to PSEIKI as a geometrical hierarchy of
abstractions. PSEIKI's job is then to construct abstraction hierarchies in the
perceived image taking cues from the abstraction hierarchies in the
expectations. The Dempster-Shafer formalism is used for associating belief
values with the different possible labels for the constructed abstractions in
the perceived image. This system has been used successfully for autonomous
navigation of a mobile robot in indoor environments.


title: A Decision-Theoretic Model for Using Scientific Data

abstract:
  Many Artificial Intelligence systems depend on the agent's updating its
beliefs about the world on the basis of experience. Experiments constitute one
type of experience, so scientific methodology offers a natural environment for
examining the issues attendant to using this class of evidence. This paper
presents a framework which structures the process of using scientific data from
research reports for the purpose of making decisions, using decision analysis
as the basis for the structure and using medical research as the general
scientific domain. The structure extends the basic influence diagram for
updating belief in an object domain parameter of interest by expanding the
parameter into four parts: those of the patient, the population, the study
sample, and the effective study sample. The structure uses biases to perform
the transformation of one parameter into another, so that, for instance,
selection biases, in concert with the population parameter, yield the study
sample parameter. The influence diagram structure provides decision theoretic
justification for practices of good clinical research such as randomized
assignment and blindfolding of care providers. The model covers most research
designs used in medicine: case-control studies, cohort studies, and controlled
clinical trials, and provides an architecture to separate clearly between
statistical knowledge and domain knowledge. The proposed general model can be
the basis for clinical epidemiological advisory systems, when coupled with
heuristic pruning of irrelevant biases; of statistical workstations, when the
computational machinery for calculation of posterior distributions is added;
and of meta-analytic reviews, when multiple studies may impact on a single
population parameter.


title: When Should a Decision Maker Ignore the Advice of a Decision Aid?

abstract:
  This paper argues that the principal difference between decision aids and
most other types of information systems is the greater reliance of decision
aids on fallible algorithms--algorithms that sometimes generate incorrect
advice. It is shown that interactive problem solving with a decision aid that
is based on a fallible algorithm can easily result in aided performance which
is poorer than unaided performance, even if the algorithm, by itself, performs
significantly better than the unaided decision maker. This suggests that unless
certain conditions are satisfied, using a decision aid as an aid is
counterproductive. Some conditions under which a decision aid is best used as
an aid are derived.


title: Inference Policies

abstract:
  It is suggested that an AI inference system should reflect an inference
policy that is tailored to the domain of problems to which it is applied -- and
furthermore that an inference policy need not conform to any general theory of
rational inference or induction. We note, for instance, that Bayesian reasoning
about the probabilistic characteristics of an inference domain may result in
the specification of an nonBayesian procedure for reasoning within the
inference domain. In this paper, the idea of an inference policy is explored in
some detail. To support this exploration, the characteristics of some standard
and nonstandard inference policies are examined.


title: Model-based Influence Diagrams for Machine Vision

abstract:
  We show an approach to automated control of machine vision systems based on
incremental creation and evaluation of a particular family of influence
diagrams that represent hypotheses of imagery interpretation and possible
subsequent processing decisions. In our approach, model-based machine vision
techniques are integrated with hierarchical Bayesian inference to provide a
framework for representing and matching instances of objects and relationships
in imagery and for accruing probabilities to rank order conflicting scene
interpretations. We extend a result of Tatman and Shachter to show that the
sequence of processing decisions derived from evaluating the diagrams at each
stage is the same as the sequence that would have been derived by evaluating
the final influence diagram that contains all random variables created during
the run of the vision system.


title: Defeasible Decisions: What the Proposal is and isn't

abstract:
  In two recent papers, I have proposed a description of decision analysis that
differs from the Bayesian picture painted by Savage, Jeffrey and other classic
authors. Response to this view has been either overly enthusiastic or unduly
pessimistic. In this paper I try to place the idea in its proper place, which
must be somewhere in between. Looking at decision analysis as defeasible
reasoning produces a framework in which planning and decision theory can be
integrated, but work on the details has barely begun. It also produces a
framework in which the meta-decision regress can be stopped in a reasonable
way, but it does not allow us to ignore meta-level decisions. The heuristics
for producing arguments that I have presented are only supposed to be
suggestive; but they are not open to the egregious errors about which some have
worried. And though the idea is familiar to those who have studied heuristic
search, it is somewhat richer because the control of dialectic is more
interesting than the deepening of search.


title:
Experiments Using Belief Functions and Weights of Evidence incorporating
  Statistical Data and Expert Opinions

abstract:
  This paper presents some ideas and results of using uncertainty management
methods in the presence of data in preference to other statistical and machine
learning methods. A medical domain is used as a test-bed with data available
from a large hospital database system which collects symptom and outcome
information about patients. Data is often missing, of many variable types and
sample sizes for particular outcomes is not large. Uncertainty management
methods are useful for such domains and have the added advantage of allowing
for expert modification of belief values originally obtained from data.
Methodological considerations for using belief functions on statistical data
are dealt with in some detail. Expert opinions are Incorporated at various
levels of the project development and results are reported on an application to
liver disease diagnosis. Recent results contrasting the use of weights of
evidence and logistic regression on another medical domain are also presented.


title:
Shootout-89: A Comparative Evaluation of Knowledge-based Systems that
  Forecast Severe Weather

abstract:
  During the summer of 1989, the Forecast Systems Laboratory of the National
Oceanic and Atmospheric Administration sponsored an evaluation of artificial
intelligence-based systems that forecast severe convective storms. The
evaluation experiment, called Shootout-89, took place in Boulder, and focussed
on storms over the northeastern Colorado foothills and plains (Moninger, et
al., 1990). Six systems participated in Shootout-89. These included traditional
expert systems, an analogy-based system, and a system developed using methods
from the cognitive science/judgment analysis tradition. Each day of the
exercise, the systems generated 2 to 9 hour forecasts of the probabilities of
occurrence of: non significant weather, significant weather, and severe
weather, in each of four regions in northeastern Colorado. A verification
coordinator working at the Denver Weather Service Forecast Office gathered
ground-truth data from a network of observers. Systems were evaluated on the
basis of several measures of forecast skill, and on other metrics such as
timeliness, ease of learning, and ease of use. Systems were generally easy to
operate, however the various systems required substantially different levels of
meteorological expertise on the part of their users--reflecting the various
operational environments for which the systems had been designed. Systems
varied in their statistical behavior, but on this difficult forecast problem,
the systems generally showed a skill approximately equal to that of persistence
forecasts and climatological (historical frequency) forecasts. The two systems
that appeared best able to discriminate significant from non significant
weather events were traditional expert systems. Both of these systems required
the operator to make relatively sophisticated meteorological judgments. We are
unable, based on only one summer's worth of data, to determine the extent to
which the greater skill of the two systems was due to the content of their
knowledge bases, or to the subjective judgments of the operator. A follow-on
experiment, Shootout-91, is currently being planned. Interested potential
participants are encouraged to contact the author at the address above.


title: Conditioning on Disjunctive Knowledge: Defaults and Probabilities

abstract:
  Many writers have observed that default logics appear to contain the "lottery
paradox" of probability theory. This arises when a default "proof by
contradiction" lets us conclude that a typical X is not a Y where Y is an
unusual subclass of X. We show that there is a similar problem with default
"proof by cases" and construct a setting where we might draw a different
conclusion knowing a disjunction than we would knowing any particular disjunct.
Though Reiter's original formalism is capable of representing this distinction,
other approaches are not. To represent and reason about this case, default
logicians must specify how a "typical" individual is selected. The problem is
closely related to Simpson's paradox of probability theory. If we accept a
simple probabilistic account of defaults based on the notion that one
proposition may favour or increase belief in another, the "multiple extension
problem" for both conjunctive and disjunctive knowledge vanishes.


title:
Maximum Uncertainty Procedures for Interval-Valued Probability
  Distributions

abstract:
  Measures of uncertainty and divergence are introduced for interval-valued
probability distributions and are shown to have desirable mathematical
properties. A maximum uncertainty inference procedure for marginal interval
distributions is presented. A technique for reconstruction of interval
distributions from projections is developed based on this inference procedure


title:
A Logical Interpretation of Dempster-Shafer Theory, with Application to
  Visual Recognition

abstract:
  We formulate Dempster Shafer Belief functions in terms of Propositional Logic
using the implicit notion of provability underlying Dempster Shafer Theory.
Given a set of propositional clauses, assigning weights to certain
propositional literals enables the Belief functions to be explicitly computed
using Network Reliability techniques. Also, the logical procedure corresponding
to updating Belief functions using Dempster's Rule of Combination is shown.
This analysis formalizes the implementation of Belief functions within an
Assumption-based Truth Maintenance System (ATMS). We describe the extension of
an ATMS-based visual recognition system, VICTORS, with this logical formulation
of Dempster Shafer theory. Without Dempster Shafer theory, VICTORS computes all
possible visual interpretations (i.e. all logical models) without determining
the best interpretation(s). Incorporating Dempster Shafer theory enables
optimal visual interpretations to be computed and a logical semantics to be
maintained.


title:
Strategies for Generating Micro Explanations for Bayesian Belief
  Networks

abstract:
  Bayesian Belief Networks have been largely overlooked by Expert Systems
practitioners on the grounds that they do not correspond to the human inference
mechanism. In this paper, we introduce an explanation mechanism designed to
generate intuitive yet probabilistically sound explanations of inferences drawn
by a Bayesian Belief Network. In particular, our mechanism accounts for the
results obtained due to changes in the causal and the evidential support of a
node.


title: Evidence Absorption and Propagation through Evidence Reversals

abstract:
  The arc reversal/node reduction approach to probabilistic inference is
extended to include the case of instantiated evidence by an operation called
"evidence reversal." This not only provides a technique for computing posterior
joint distributions on general belief networks, but also provides insight into
the methods of Pearl [1986b] and Lauritzen and Spiegelhalter [1988]. Although
it is well understood that the latter two algorithms are closely related, in
fact all three algorithms are identical whenever the belief network is a
forest.


title:
Simulation Approaches to General Probabilistic Inference on Belief
  Networks

abstract:
  A number of algorithms have been developed to solve probabilistic inference
problems on belief networks. These algorithms can be divided into two main
groups: exact techniques which exploit the conditional independence revealed
when the graph structure is relatively sparse, and probabilistic sampling
techniques which exploit the "conductance" of an embedded Markov chain when the
conditional probabilities have non-extreme values. In this paper, we
investigate a family of "forward" Monte Carlo sampling techniques similar to
Logic Sampling [Henrion, 1988] which appear to perform well even in some
multiply connected networks with extreme conditional probabilities, and thus
would be generally applicable. We consider several enhancements which reduce
the posterior variance using this approach and propose a framework and criteria
for choosing when to use those enhancements.


title: Decision under Uncertainty

abstract:
  We derive axiomatically the probability function that should be used to make
decisions given any form of underlying uncertainty.


title:
Freedom: A Measure of Second-order Uncertainty for Intervalic
  Probability Schemes

abstract:
  This paper discusses a new measure that is adaptable to certain intervalic
probability frameworks, possibility theory, and belief theory. As such, it has
the potential for wide use in knowledge engineering, expert systems, and
related problems in the human sciences. This measure (denoted here by F) has
been introduced in Smithson (1988) and is more formally discussed in Smithson
(1989a)o Here, I propose to outline the conceptual basis for F and compare its
properties with other measures of second-order uncertainty. I will argue that F
is an indicator of nonspecificity or alternatively, of freedom, as
distinguished from either ambiguity or vagueness.


title:
Assessment, Criticism and Improvement of Imprecise Subjective
  Probabilities for a Medical Expert System

abstract:
  Three paediatric cardiologists assessed nearly 1000 imprecise subjective
conditional probabilities for a simple belief network representing congenital
heart disease, and the quality of the assessments has been measured using
prospective data on 200 babies. Quality has been assessed by a Brier scoring
rule, which decomposes into terms measuring lack of discrimination and
reliability. The results are displayed for each of 27 diseases and 24
questions, and generally the assessments are reliable although there was a
tendency for the probabilities to be too extreme. The imprecision allows the
judgements to be converted to implicit samples, and by combining with the
observed data the probabilities naturally adapt with experience. This appears
to be a practical procedure even for reasonably large expert systems.


title:
Automated Construction of Sparse Bayesian Networks from Unstructured
  Probabilistic Models and Domain Information

abstract:
  An algorithm for automated construction of a sparse Bayesian network given an
unstructured probabilistic model and causal domain information from an expert
has been developed and implemented. The goal is to obtain a network that
explicitly reveals as much information regarding conditional independence as
possible. The network is built incrementally adding one node at a time. The
expert's information and a greedy heuristic that tries to keep the number of
arcs added at each step to a minimum are used to guide the search for the next
node to add. The probabilistic model is a predicate that can answer queries
about independencies in the domain. In practice the model can be implemented in
various ways. For example, the model could be a statistical independence test
operating on empirical data or a deductive prover operating on a set of
independence statements about the domain.


title: Making Decisions with Belief Functions

abstract:
  A primary motivation for reasoning under uncertainty is to derive decisions
in the face of inconclusive evidence. However, Shafer's theory of belief
functions, which explicitly represents the underconstrained nature of many
reasoning problems, lacks a formal procedure for making decisions. Clearly,
when sufficient information is not available, no theory can prescribe actions
without making additional assumptions. Faced with this situation, some
assumption must be made if a clearly superior choice is to emerge. In this
paper we offer a probabilistic interpretation of a simple assumption that
disambiguates decision problems represented with belief functions. We prove
that it yields expected values identical to those obtained by a probabilistic
analysis that makes the same assumption. In addition, we show how the decision
analysis methodology frequently employed in probabilistic reasoning can be
extended for use with belief functions.


title: Efficient Parallel Estimation for Markov Random Fields

abstract:
  We present a new, deterministic, distributed MAP estimation algorithm for
Markov Random Fields called Local Highest Confidence First (Local HCF). The
algorithm has been applied to segmentation problems in computer vision and its
performance compared with stochastic algorithms. The experiments show that
Local HCF finds better estimates than stochastic algorithms with much less
computation.


title:
Comparing Expert Systems Built Using Different Uncertain Inference
  Systems

abstract:
  This study compares the inherent intuitiveness or usability of the most
prominent methods for managing uncertainty in expert systems, including those
of EMYCIN, PROSPECTOR, Dempster-Shafer theory, fuzzy set theory, simplified
probability theory (assuming marginal independence), and linear regression
using probability estimates. Participants in the study gained experience in a
simple, hypothetical problem domain through a series of learning trials. They
were then randomly assigned to develop an expert system using one of the six
Uncertain Inference Systems (UISs) listed above. Performance of the resulting
systems was then compared. The results indicate that the systems based on the
PROSPECTOR and EMYCIN models were significantly less accurate for certain types
of problems compared to systems based on the other UISs. Possible reasons for
these differences are discussed.


title: Directed Cycles in Belief Networks

abstract:
  The most difficult task in probabilistic reasoning may be handling directed
cycles in belief networks. To the best knowledge of this author, there is no
serious discussion of this problem at all in the literature of probabilistic
reasoning so far.


title:
Can Uncertainty Management be Realized in a Finite Totally Ordered
  Probability Algebra?

abstract:
  In this paper, the feasibility of using finite totally ordered probability
models under Alelinnas's Theory of Probabilistic Logic [Aleliunas, 1988] is
investigated. The general form of the probability algebra of these models is
derived and the number of possible algebras with given size is deduced. Based
on this analysis, we discuss problems of denominator-indifference and
ambiguity-generation that arise in reasoning by cases and abductive reasoning.
An example is given that illustrates how these problems arise. The
investigation shows that a finite probability model may be of very limited
usage.


title:
Normalization and the Representation of Nonmonotonic Knowledge in the
  Theory of Evidence

abstract:
  We discuss the Dempster-Shafer theory of evidence. We introduce a concept of
monotonicity which is related to the diminution of the range between belief and
plausibility. We show that the accumulation of knowledge in this framework
exhibits a nonmonotonic property. We show how the belief structure can be used
to represent typical or commonsense knowledge.


title: Amalgmation in Boolean algebras with operators

abstract:
  We study various forms of amalgamation for Boolean algebras with operations.
We will also have the occasion to weaken the Boolean structure dealing with MV
and BL algebras with operators.


title:
Completeness and interpolation for intuitionistic infinitary predicate
  logic, in connection to finitizing the class of representable Heyting
  polyadic algebras

abstract:
  We study different representation theorems for various reducts of Heyting
polyadic algebras. Superamalgamation is proved for several (natural reducts)
and our results are compared to the finitizability problem in classical
algebraic logic dealing with cylindric and polyadic (Boolean algebras). We also
prove several new neat embedding theorems, and obtain that the class of
representable algebras based on (a generalized) Kripke semantics coincide with
the class of algebras having the neat embedding property, that is those
algebras that are subneat reducts of algebras having $\omega$ extra dimensions.


title: Taking the "Un" out of "Unnovae"

abstract:
  It has long been expected that some massive stars produce stellar mass black
holes (BHs) upon death. Unfortunately, the observational signature of such
events has been unclear. It has even been suggested that the result may be an
"unnova," in which the formation of a BH is marked by the disappearance of a
star rather than an electromagnetic outburst. I argue that when the progenitor
is a red supergiant, evidence for BH creation may instead be a ~3-10 day
optical transient with a peak luminosity of ~10^{40}-10^{41} erg s^{-1}, a
temperature of ~10^4 K, slow ejection speeds of ~200 km s^{-1}, and a spectrum
devoid of the nucleosynthetic products associated with explosive burning. This
signal is the breakout of a shock generated by the hydrodynamic response of a
massive stellar envelope when the protoneutron star loses ~few*0.1Msun to
neutrino emission prior to collapse to a BH. Current and future wide-field,
high-cadence optical surveys make this an ideal time to discover and study
these events. Motivated by the unique parameter space probed by this scenario,
I discuss more broadly the range of properties expected for shock breakout
flashes, with emphasis on progenitors with large radii and/or small shock
energies. This may have application in a wider diversity of explosive events,
from pair instability supernovae to newly discovered but yet to be understood
transients.


title:
The spectrum of IIB supergravity on AdS_5 x S^5/Z_3 and a 1/N^2 test of
  AdS/CFT

abstract:
  We present the complete Kaluza-Klein spectrum resulting from the
compactification of IIB supergravity on S^5/Z_3. Knowledge of this spectrum
allows us to perform a holographic computation of the difference of central
charges c-a of the dual SU(N)^3 quiver gauge theory. We find the numerical
value c-a=3/16, in exact agreement with the field theory result.


title:
Star Formation Rates in Resolved Galaxies: Calibrations with Near and
  Far Infrared Data for NGC5055 and NGC6946

abstract:
  We use the near--infrared Br\gamma hydrogen recombination line as a reference
star formation rate (SFR) indicator to test the validity and establish the
calibration of the {\it Herschel} PACS 70 \mu m emission as a SFR tracer for
sub--galactic regions in external galaxies. Br\gamma offers the double
advantage of directly tracing ionizing photons and of being relatively
insensitive to the effects of dust attenuation. For our first experiment, we
use archival CFHT Br\gamma and Ks images of two nearby galaxies: NGC\,5055 and
NGC\,6946, which are also part of the {\it Herschel} program KINGFISH (Key
Insights on Nearby Galaxies: a Far-Infrared Survey with Herschel). We use the
extinction corrected Br\gamma emission to derive the SFR(70) calibration for
H{\sc ii} regions in these two galaxies. A comparison of the SFR(70)
calibrations at different spatial scales, from 200 pc to the size of the whole
galaxy, reveals that about 50% of the total 70\mu m emission is due to dust
heated by stellar populations that are unrelated to the current star formation.
We use a simple model to qualitatively relate the increase of the SFR(70)
calibration coefficient with decreasing region size to the star formation
timescale. We provide a calibration for an unbiased SFR indicator that combines
the observed H\alpha with the 70 \mu m emission, also for use in H{\sc ii}
regions. We briefly analyze the PACS 100 and 160 \mu m maps and find that
longer wavelengths are not as good SFR indicators as 70\mu m, in agreement with
previous results. We find that the calibrations show about 50% difference
between the two galaxies, possibly due to effects of inclination.


title: On the origin of the radio emission of Sw 1644+57

abstract:
  We apply relativistic equipartition synchrotron arguments to the radio data
of the tidal disruption event candidate Sw 1644+57. We find that, regardless of
the details of the equipartition scenario considered, the energy required to
produce the observed radio (i.e., energy in magnetic field and radio emitting
electrons) must increase by a factor of ~20 during the first 200 days. It then
saturates. This energy increase cannot be alleviated by a varying geometry of
the system. The radio data can be explained by: (i) An afterglow like emission
of the X-ray emitting narrow relativistic jet. The additional energy can arise
here from a slower moving material ejected in the first few days that gradually
catches up with the slowing down blast wave (Berger et al. 2012). However, this
requires at least ~4x10^{53} erg in the slower moving outflow. This is much
more than the energy of the fast moving outflow that produced the early X-rays
and it severely constrains the overall energy budget. (ii) Alternatively, the
radio may arise from a mildly relativistic quasi-spherical outflow. Here, the
energy for the radio emission increases with time to at least ~10^{51} erg
after 200 days. This scenario requires, however, a second X-ray emitting
collimated relativistic component. Given these results, it is worthwhile to
consider models in which the energy of the magnetic field and/or of the radio
emitting electrons increases with time without a continuous energy supply to
the blast wave. This can happen, for example, if the energy is injected
initially mostly in one form (Poynting flux or baryonic) and it is gradually
converted to the other form, leading to a deviation from equipartition. Another
intriguing possibility is that a gradually decreasing Inverse Compton cooling
modifies the synchrotron emission and leads to an increase of the available
energy in the radio emitting electrons (Kumar et al. 2013).


title:
Asteroseismology of KIC 11754974: a high-amplitude SX Phe pulsator in a
  343-day binary system

abstract:
  The candidate SX Phe star KIC 11754974 shows a remarkably high number of
combination frequencies in the Fourier amplitude spectrum: 123 of the 166
frequencies in our multi-frequency fit are linear combinations of independent
modes. Predictable patterns in frequency spacings are seen in the Fourier
transform of the light curve. We present an analysis of 180 d of short-cadence
Kepler photometry and of new spectroscopic data for this evolved, late A-type
star. We infer from the 1150-d, long-cadence light curve, and in two different
ways, that our target is the primary of a 343-d, non-eclipsing binary system.
According to both methods, the mass function is similar, f(M)=0.0207 +/- 0.0003
Msun. The observed pulsations are modelled extensively, using separate,
state-of-the-art, time-dependent convection (TDC) and rotating models. The
models match the observed temperature and low metallicity, finding a mass of
1.50-1.56 Msun. The models suggest the whole star is metal-poor, and that the
low metallicity is not just a surface abundance peculiarity. This is the best
frequency analysis of an SX Phe star, and the only Kepler \delta\ Sct star to
be modelled with both TDC and rotating models.


title:
EUV and X-Ray Observations of Comet Lovejoy (C/2011 W3) in the Lower
  Corona

abstract:
  We present an analysis of EUV and soft X-ray emission detected toward Comet
Lovejoy (C/2011 W3) during its post-perihelion traverse of the solar corona on
December 16, 2011. Observations were recorded by the Atmospheric Imaging
Assembly (AIA) aboard the Solar Dynamics Observatory and the X-Ray Telescope
(XRT) aboard Hinode. A single set of contemporaneous images is explored in
detail, along with prefatory consideration for time evolution using only the
171 A data. For each of the eight passbands, we characterize the emission and
derive outgassing rates where applicable. As material sublimates from the
nucleus and is immersed in coronal plasma, it rapidly ionizes through charge
states seldom seen in this environment. The AIA data show four stages of oxygen
ionization (O III - O VI) along with C IV, while XRT likely captured emission
from O VII, a line typical of the corona. With a nucleus of at least several
hundred meters upon approach to a perihelion that brought the comet to within
0.2 solar radii of the photosphere, Lovejoy was the most significant sungrazer
in recent history. Correspondingly high outgassing rates on the order of
10^32.5 oxygen atoms per second are estimated. Assuming that the neutral oxygen
comes from water, this translates to a mass-loss rate of about 9.5E9 g/s, and
based only on the 171 A observations, we find a total mass loss of
approximately 10^13 g over the AIA egress. Additional and supporting analyses
include a differential emission measure to characterize the coronal
environment, consideration for the opening angle, and a comparison of the
emission's leading edge with the expected position of the nucleus.


title:
A model for the multiwavelength radiation from tidal disruption event
  Swift J1644+57

abstract:
  Gamma-ray observations of a stellar tidal disruption event (TDE) detected by
the Swift satellite and follow up observations in radio, mm, infrared and x-ray
bands have provided a rich data set to study accretion onto massive blackholes,
production of relativistic jets and their interaction with the surrounding
medium. The radio and x-ray data for TDE Swift J1644+57 provide a conflicting
picture regarding the energy in relativistic jet produced in this event: x-ray
data suggest jet energy declining with time as t^{-5/3} whereas the nearly flat
lightcurves in radio and mm bands lasting for about 100 days have been
interpreted as evidence for the total energy output increasing with time. We
show in this work that flat lightcurves do not require addition of energy to
decelerating external shock (which produced radio and mm emission via
synchrotron process), instead the flat behavior is due to inverse-Compton
cooling of electrons by x-ray photons streaming through the external shock; the
higher x-ray flux at earlier times cools electrons more rapidly thereby
reducing the emergent synchrotron flux, and this effect weakens as the x-ray
flux declines with time.


title: On the non-linear scale of cosmological perturbation theory

abstract:
  We discuss the convergence of cosmological perturbation theory. We prove that
the polynomial enhancement of the non-linear corrections expected from the
effects of soft modes is absent in equal-time correlators like the power or
bispectrum. We first show this at leading order by resumming the most important
corrections of soft modes to an arbitrary skeleton of hard fluctuations. We
derive the same result in the eikonal approximation, which also allows us to
show the absence of enhancement at any order. We complement the proof by an
explicit calculation of the power spectrum at two-loop order, and by further
numerical checks at higher orders. Using these insights, we argue that the
modification of the power spectrum from soft modes corresponds at most to
logarithmic corrections. Finally, we discuss the asymptotic behavior in the
large and small momentum regimes and identify the expansion parameter pertinent
to non-linear corrections.


title: Majorana fermions in vortex lattices

abstract:
  We consider Majorana fermions tunneling among an array of vortices in a 2D
chiral p-wave superconductor or equivalent material. The amplitude for Majorana
fermions to tunnel between a pair of vortices is found to necessarily depend on
the background superconducting phase profile; it is found to be proportional to
the sine of half the difference between the phases at the two vortices. Using
this result we study tight-binding models of Majorana fermions in vortices
arranged in triangular or square lattices. In both cases we find that the
aforementioned phase-tunneling relationship leads to the creation of
superlattices where the Majorana fermions form macroscopically degenerate
localizable flat bands at zero energy, in addition to other dispersive bands.
This finding suggests that tunneling processes in these vortex arrays do not
change the energies of a finite fraction of Majorana fermions, contrary to
previous expectation. The presence of flat Majorana bands, and hence
less-than-expected decoherence in these vortex arrays, bodes well for the
prospects of topological quantum computation with large numbers of Majorana
states.


title:
Subgraph Frequencies: Mapping the Empirical and Extremal Geography of
  Large Graph Collections

abstract:
  A growing set of on-line applications are generating data that can be viewed
as very large collections of small, dense social graphs -- these range from
sets of social groups, events, or collaboration projects to the vast collection
of graph neighborhoods in large social networks. A natural question is how to
usefully define a domain-independent coordinate system for such a collection of
graphs, so that the set of possible structures can be compactly represented and
understood within a common space. In this work, we draw on the theory of graph
homomorphisms to formulate and analyze such a representation, based on
computing the frequencies of small induced subgraphs within each graph. We find
that the space of subgraph frequencies is governed both by its combinatorial
properties, based on extremal results that constrain all graphs, as well as by
its empirical properties, manifested in the way that real social graphs appear
to lie near a simple one-dimensional curve through this space.
  We develop flexible frameworks for studying each of these aspects. For
capturing empirical properties, we characterize a simple stochastic generative
model, a single-parameter extension of Erdos-Renyi random graphs, whose
stationary distribution over subgraphs closely tracks the concentration of the
real social graph families. For the extremal properties, we develop a tractable
linear program for bounding the feasible space of subgraph frequencies by
harnessing a toolkit of known extremal graph theory. Together, these two
complementary frameworks shed light on a fundamental question pertaining to
social graphs: what properties of social graphs are 'social' properties and
what properties are 'graph' properties?
  We conclude with a brief demonstration of how the coordinate system we
examine can also be used to perform classification tasks, distinguishing
between social graphs of different origins.


title: Quantum Monte Carlo with Coupled-Cluster wave functions

abstract:
  We introduce a novel many body method which combines two powerful many body
techniques, viz., quantum Monte Carlo and coupled cluster theory. Coupled
cluster wave functions are introduced as importance functions in a Monte Carlo
method designed for the configuration interaction framework to provide rigorous
upper bounds to the ground state energy. We benchmark our method on the
homogeneous electron gas in momentum space. The importance function used is the
coupled cluster doubles wave function. We show that the computational resources
required in our method scale polynomially with system size. Our energy upper
bounds are in very good agreement with previous calculations of similar
accuracy, and they can be systematically improved by including higher order
excitations in the coupled cluster wave function.


title: The moment of core collapse in star clusters with a mass function

abstract:
  Star clusters with multi-mass components dynamically evolve faster than those
modeled with equal-mass components. Using a series of direct $N$-body
simulations, we investigate the dynamical evolution of star clusters with mass
functions, especially their core collapse time. Multi-mass clusters tend to
behave like systems with a smaller number of particles, which we call the
effective number of particles ($N_{eff}$) and for which $N_{eff} = M/m_{max}$
(here $M$ and $m_{max}$ are the total cluster mass and the mass of the most
massive star in the cluster, respectively). We find that the time of core
collapse is inversely proportional to the mass of the most massive star in the
cluster and analytically confirm that this is because the core collapse of
clusters with a mass function proceeds on the dynamical friction timescale of
the most massive stars. As the mass of the most massive star increases,
however, the core-collapse time, which is observed as a core bounce of the
cluster core from the evolution of the core density or core radius, becomes
ambiguous. We find that in that case the total binding energy of the hard
binaries gives a good diagnosis for determining the moment of core collapses.
Based on the results of our simulations, we argue that the core bounce becomes
ambiguous when the mass of the most massive star exceeds 0.1\% of the total
mass of the cluster.


title: A New String in Ten Dimensions?

abstract:
  I suggest the possibility of a new string in ten dimensions. Evidence for
this string is presented both from orientifold physics and from K-theory, along
with a mystery concerning the M-theory description. Motivated by this
possibility, some novel aspects of decoupling limits in heterotic/type I
theories are described; specifically, the decoupled theory on type I
$D$-strings is argued to be three-dimensional rather than two-dimensional.
These decoupled theories provide the matrix model definitions of the
heterotic/type I strings.


title:
Breaking the energy-bandwidth limit of electro-optic modulators: theory
  and a device proposal

abstract:
  In this paper, we quantitatively analyzed the trade-off between energy per
bit for switching and modulation bandwidth of classical electro-optic
modulators. A formally simple energy-bandwidth limit (Eq. 10) is derived for
electro-optic modulators based on intra-cavity index modulation. To overcome
this limit, we propose a dual cavity modulator device which uses a coupling
modulation scheme operating at high bandwidth (> 200 GHz) not limited by cavity
photon lifetime and simultaneously features an ultra-low switching energy of
0.26 aJ, representing over three orders of magnitude energy consumption
reduction compared to state-of-the-art electro-optic modulators.


title: Transformation Inverse Design

abstract:
  We present a new technique for the design of transformation-optics devices
based on large-scale optimization to achieve the optimal effective isotropic
dielectric materials within prescribed index bounds, which is computationally
cheap because transformation optics circumvents the need to solve Maxwell's
equations at each step. We apply this technique to the design of multimode
waveguide bends (realized experimentally in a previous paper) and mode
squeezers, in which all modes are transported equally without scattering. In
addition to the optimization, a key point is the identification of the correct
boundary conditions to ensure reflectionless coupling to untransformed regions
while allowing maximum flexibility in the optimization. Many previous authors
in transformation optics used a certain kind of quasiconformal map which
overconstrained the problem by requiring that the entire boundary shape be
specified a priori while at the same time underconstraining the problem by
employing "slipping" boundary conditions that permit unwanted interface
reflections.


title:
The role of gas dynamical friction in the evolution of embedded stellar
  clusters

abstract:
  Two puzzles associated with open clusters have attracted a lot of attention
-- their formation, with densities and velocity dispersions that are not too
different from those of the star forming regions in the Galaxy, given that the
observed Star Formation Efficiencies (SFE) are low and, the mass segregation
observed / inferred in some of them, at ages significantly less than the
dynamical relaxation times in them. Gas dynamical friction has been considered
before as a mechanism for contracting embedded stellar clusters, by dissipating
their energy. This would locally raise the SFE which might then allow bound
clusters to form. Noticing that dynamical friction is inherently capable of
producing mass segregation, since here, the dissipation rate is proportional to
the mass of the body experiencing the force, we explore further, some of the
details and implications of such a scenario, vis-a-vis observations. Making
analytical approximations, we obtain a boundary value for the density of a star
forming clump of given mass, such that, stellar clusters born in clumps which
have densities higher than this, could emerge bound after gas loss, and for a
clump of given mass and density, we find a critical mass such that,
subcondensations with larger masses than this could suffer significant
segregation within the clump.


title: A Return to the Optimal Detection of Quantum Information

abstract:
  In 1991, Asher Peres and William Wootters wrote a seminal paper on the
nonlocal processing of quantum information [\textit{Phys. Rev. Lett.}
\textbf{66} 1119 (1991)]. We return to their classic problem and solve it in
various contexts. Specifically, for discriminating the "double trine" ensemble
with minimum error, we prove that global operations are more powerful than
local operations with classical communication (LOCC). Even stronger, there
exists a finite gap between the optimal LOCC probability and that obtainable by
separable operations (SEP). Additionally we prove that a two-way, adaptive LOCC
strategy can always beat a one-way protocol. Our results provide the first
known instance of "nonlocality without entanglement" in two qubit pure states.


title:
Primordial Non-Gaussianity of Gravitational Waves in Ho\v{r}ava-Lifshitz
  Gravity

abstract:
  In this paper, we study 3-point correlation function of primordial
gravitational waves generated in the de Sitter background in the framework of
the general covariant Ho\v{r}ava-Lifshitz gravity with an arbitrary coupling
constant $\lambda$. We find that, at cubic order, the interaction Hamiltonian
receives contributions from four terms built of the 3-dimensional Ricci tensor
$R_{ij}$ of the leaves $t = $ constant. In particular, the 3D Ricci scalar $R$
yields the same $k$-dependence as that in general relativity, but with
different magnitude due to coupling with the $U(1)$ field $A$ and a UV history.
Interestingly, the two terms $R_{ij}R^{ij}$ and
$\left(\nabla^{i}R^{jk}\right)\left(\nabla_{i}R_{jk}\right)$ exhibit peaks at
the squeezed limit. We show that this is due to the effects of the polarization
tensors. The signal generated by the fourth term, $R^i_j R^j_k R^k_i$, favors
the equilateral shape when spins of the three tensor fields are the same, but
peaks in between the equilateral and squeezed limits when spins are mixed. The
consistency with the recently-released Planck observations on non-Gaussianity
is also discussed and is found that $\left(H/M_*\right)^2\left(H/M_{pl}\right)
\le 10^{-8}$, where $M_{*}$ denotes the suppression energy of high-order
operators, $M_{pl}$ the Planck mass, and $H$ the energy of inflation.


title: Search for Pressure Induced Quantum Criticality in YbFe2Zn20

abstract:
  Electrical transport measurements of the heavy fermion compound YbFe2Zn20
were carried out under pressures up to 8.23 GPa and down to temperatures of
nearly 0.3 K. The pressure dependence of the low temperature Fermi-liquid state
was assessed by fitting rho(T) = rho_0 + AT^n with n = 2 for T < T_FL. Power
law analysis of the low temperature resistivities indicates n = 2 over a broad
temperature range for P < 5 GPa. However, at higher pressures, the quadratic
temperature dependence is only seen at the very lowest temperatures, and
instead shows a wider range of n < 2 power law behavior in the low temperature
resistivities. As pressure was increased, T_FL diminished from ~11 K at ambient
pressure to ~0.6 K at 8.23 GPa. Over the same pressure range, the A parameter
increased dramatically with a functional form of A proportional to (P-Pc)^-2
with Pc~9.8GPa being the critical pressure for a possible quantum critical
point.


title: Reanalysis of the Radii of the Benchmark Eclipsing Binary V578 Mon

abstract:
  V578 Mon is an eclipsing binary system in which both stars have masses above
10 Msun determined with an accuracy better than 3%. It is one of only five such
massive eclipsing binaries known that also possess eccentric orbits and
measured apsidal motions, thus making it an important benchmark for theoretical
stellar evolution models. However, recently reported determinations of the
radii of V578 Mon differ significantly from previously reported values. We
reanalyze the published data for V578 Mon and trace the discrepancy to the use
of an incorrect formulation for the stellar potentials in the most recent
analysis. Here we report corrected radii for this important benchmark eclipsing
binary.


title:
Towards a more realistic population of bright spiral galaxies in
  cosmological simulations

abstract:
  We present an update to the multiphase SPH galaxy formation code by
Scannapieco et al. We include a more elaborate treatment of the production of
metals, cooling rates based on individual element abundances, and a scheme for
the turbulent diffusion of metals. Our SN feedback model now transfers energy
to the ISM in kinetic and thermal form, and we include a prescription for the
effects of radiation pressure from massive young stars on the ISM. We calibrate
our new code on the well studied Aquarius haloes and then use it to simulate a
sample of 16 galaxies with halo masses between 1x10^11 and 3x10^12 M_sun. In
general, the stellar masses of the sample agree well with the stellar mass to
halo mass relation inferred from abundance matching techniques for redshifts
z=0-4. There is however a tendency to overproduce stars at z>4 and to
underproduce them at z<0.5 in the least massive haloes. Overly high SFRs at z<1
for the most massive haloes are likely connected to the lack of AGN feedback in
our model. The simulated sample also shows reasonable agreement with observed
star formation rates, sizes, gas fractions and gas-phase metallicities at
z=0-3. Remaining discrepancies can be connected to deviations from predictions
for star formation histories from abundance matching. At z=0, the model
galaxies show realistic morphologies, stellar surface density profiles,
circular velocity curves and stellar metallicities, but overly flat metallicity
gradients. 15 out of 16 of our galaxies contain disk components with kinematic
disk fraction ranging between 15 and 65 %. The disk fraction depends on the
time of the last destructive merger or misaligned infall event. Considering the
remaining shortcomings of our simulations we conclude that even higher
kinematic disk fractions may be possible for LambdaCDM haloes with quiet merger
histories, such as the Aquarius haloes.


title:
The H-band Emitting Region of the Luminous Blue Variable P Cygni:
  Spectrophotometry and Interferometry of the Wind

abstract:
  We present the first high angular resolution observations in the nearinfrared
H-band (1.6 microns) of the Luminous Blue Variable star P Cygni. We obtained
six-telescope interferometric observations with the CHARA Array and the MIRC
beam combiner. These show that the spatial flux distribution is larger than
expected for the stellar photosphere. A two component model for the star
(uniform disk) plus a halo (two-dimensional Gaussian) yields an excellent fit
of the observations, and we suggest that the halo corresponds to flux emitted
from the base of the stellar wind. This wind component contributes about 45% of
the H-band flux and has an angular FWHM = 0.96 mas, compared to the predicted
stellar diameter of 0.41 mas. We show several images reconstructed from the
interferometric visibilities and closure phases, and they indicate a generally
spherical geometry for the wind. We also obtained near-infrared
spectrophotometry of P Cygni from which we derive the flux excess compared to a
purely photospheric spectral energy distribution. The H-band flux excess
matches that from the wind flux fraction derived from the two component fits to
the interferometry. We find evidence of significant near-infrared flux
variability over the period from 2006 to 2010 that appears similar to the
variations in the H-alpha emission flux from the wind. Future interferometric
observations may be capable of recording the spatial variations associated with
temporal changes in the wind structure.


title: A study of transit times in Dirac tunneling

abstract:
  Based on the Dirac equation, the behavior of relativistic electrons which
tunnel a potential barrier of height V0 for incoming energies between V0 and
V0+m is studied by using the wave packet formalism. The choice of this incoming
energy zone guarantees that only electrons participate to the tunneling
process. In the opaque limit, as shown in a previous analysis, the transit
velocity is proportional to the barrier width and for relativistic potential
tends to a constant value greater than $c$. In this paper, a new numerical
study shows a very surprising result: superluminal transmissions are also
evident for thin barriers.


title:
Thresholds for shock formation in traffic flow models with Arrhenius
  look-ahead dynamics

abstract:
  We investigate a class of nonlocal conservation laws with the nonlinear
advection coupling both local and nonlocal mechanism, which arises in several
applications such as the collective motion of cells and traffic flows. It is
proved that the $C^1$ solution regularity of this class of conservation laws
will persist at least for a short time. This persistency may continue as long
as the solution gradient remains bounded. Based on this result, we further
identify sub-thresholds for finite time shock formation in traffic flow models
with Arrhenius look-ahead dynamics.


title:
Stability Analysis of Possible Terrestrial Planets in the HD 60532
  System

abstract:
  Investigations were carried out on the stability of the two gas giant system
HD 60532 itself including additional, smaller bodies. The methods used were
numerical simulations with the Lie Series integration method, and secular
perturbation theory. The aims were to find stable regions for potential
Earth-like planets within the system, and confirming the stability of the
detected planets. As results, the given configuration can be considered stable
for a period of at least 65 million years. The two planets create a large
unstable section between 0.25 and 3.6 AU, where no additional body is able to
remain stable. Variations in the mutual inclination between test bodies and the
rest of the system of up to 20{\deg} have little effects on the size of the
zone of instability.


title: Submodules of the Hardy module over polydisc

abstract:
  We say that a submodule $\cls$ of $H^2(\mathbb{D}^n)$ ($n >1$) is co-doubly
commuting if the quotient module $H^2(\mathbb{D}^n)/\cls$ is doubly commuting.
We show that a co-doubly commuting submodule of $H^2(\mathbb{D}^n)$ is
essentially doubly commuting if and only if the corresponding one variable
inner functions are finite Blaschke products or that $n = 2$. In particular, a
co-doubly commuting submodule $\cls$ of $H^2(\mathbb{D}^n)$ is essentially
doubly commuting if and only if $n = 2$ or that $\cls$ is of finite
co-dimension. We obtain an explicit representation of the Beurling-Lax-Halmos
inner functions for those submodules of
$H^2_{H^2(\mathbb{D}^{n-1})}(\mathbb{D})$ which are co-doubly commuting
submodules of $H^2(\mathbb{D}^n)$. Finally, we prove that a pair of co-doubly
commuting submodules of $H^2(\mathbb{D}^n)$ are unitarily equivalent if and
only if they are equal.


title:
Bayesian Odds-Ratio Filters: A Template-Based Method for Online
  Detection of P300 Evoked Responses

abstract:
  Template-based signal detection most often relies on computing a correlation,
or a dot product, between an incoming data stream and a signal template. While
such a correlation results in an ongoing estimate of the magnitude of the
signal in the data stream, it does not directly indicate the presence or
absence of a signal. Instead, the problem of signal detection is one of
model-selection. Here we explore the use of the Bayesian odds-ratio (OR), which
is the ratio of posterior probabilities of a signal-plus-noise model over a
noise-only model. We demonstrate this method by applying it to simulated
electroencephalographic (EEG) signals based on the P300 response, which is
widely used in both Brain Computer Interface (BCI) and Brain Machine Interface
(BMI) systems. The efficacy of this algorithm is demonstrated by comparing the
receiver operating characteristic (ROC) curves of the OR-based (logOR) filter
to the usual correlation method where we find a significant improvement in P300
detection. The logOR filter promises to improve the accuracy and speed of the
detection of evoked brain responses in BCI/BMI applications as well the
detection of template signals in general.


title: On negative mass

abstract:
  The Schwarzschild solution to the matter free, spherically symmetric Einstein
equations has one free parameter, the mass. But the mass can be of any sign.
What is the meaning of the negative mass solutions? The answer to this question
for the case of a pure Schwarzschild negative mass black solution is still
elusive, however, in this essay, we will consider negative mass solutions
within a Schwarzschild-de Sitter geometry. We show that there exist reasonable
configurations of matter, bubbles of distributions of matter, that satisfy the
dominant energy condition everywhere, that are non-singular and well behaved
everywhere, but correspond to the negative mass Schwarzschild-de Sitter
geometry outside the matter distribution. These negative mass bubbles could
occur as the end state of a quantum tunnelling transition.


title: Blind Men and the Elephant: Detecting Evolving Groups In Social News

abstract:
  We propose an automated and unsupervised methodology for a novel
summarization of group behavior based on content preference. We show that graph
theoretical community evolution (based on similarity of user preference for
content) is effective in indexing these dynamics. Combined with text analysis
that targets automatically-identified representative content for each
community, our method produces a novel multi-layered representation of evolving
group behavior. We demonstrate this methodology in the context of political
discourse on a social news site with data that spans more than four years and
find coexisting political leanings over extended periods and a disruptive
external event that lead to a significant reorganization of existing patterns.
Finally, where there exists no ground truth, we propose a new evaluation
approach by using entropy measures as evidence of coherence along the evolution
path of these groups. This methodology is valuable to designers and managers of
online forums in need of granular analytics of user activity, as well as to
researchers in social and political sciences who wish to extend their inquiries
to large-scale data available on the web.


title: Multiscale Fractal Descriptors Applied to Texture Classification

abstract:
  This work proposes the combination of multiscale transform with fractal
descriptors employed in the classification of gray-level texture images. We
apply the space-scale transform (derivative + Gaussian filter) over the
Bouligand-Minkowski fractal descriptors, followed by a threshold over the
filter response, aiming at attenuating noise effects caused by the final part
of this response. The method is tested in the classification of a well-known
data set (Brodatz) and compared with other classical texture descriptor
techniques. The results demonstrate the advantage of the proposed approach,
achieving a higher success rate with a reduced amount of descriptors.


title:
A family of cometary globules at the periphery of Cyg OB1: the star HBHA
  3703-01 and the reflection nebula GM 2-39

abstract:
  The interstellar medium in the region of a family of cometary globules
including the reflection nebula GM 2-39 has been analyzed basing on our
observations with the slit spectrograph, the results of our previous
observations with a Fabry-Perot interferometer in the H-alpha line, Spitzer
archival data together with CO microwave data. The structure of globules' IR
emission, velocity field of ionized gas in the H-alpha line and of molecular
gas in the CO line have been considered. We have detected a CO cavern around
the eastern globules and faint high-velocity H-alpha features of surrounding
gas. The most probable sources of ionizing radiation and wind of the Cyg OB1
association responsible for the globules' formation are proposed. Based on our
multicolour photometry, it has been found that the brightest compact source in
the southern 'head' of the cometary globule - the star HBHA 3703-01 (IRAS
20153+3850) - is a B(5-6)V star with E(B-V)=1.18 mag. The strong and broad
H-alpha emission line was revealed in its spectrum. The spectral energy
distribution of HBHA 3703-01 in the 0.44-24 micron range has been modeled. It
is shown that the star has a hot dust envelope with T(dust)=1400 K and
tau(V)=1.1. Besides, the star HBHA 3703-01 illuminates the encompassing diffuse
nebula GM 2-39 with a diameter of about 30 arcsec. With regard to all obtained
observational data for HBHA 3703-01, this object may be classified as the
Herbig Ae/Be star.


title: Molecular Cloud Evolution V. Cloud Destruction by Stellar Feedback

abstract:
  We present a numerical study of the evolution of molecular clouds, from their
formation by converging flows in the warm ISM, to their destruction by the
ionizing feedback of the massive stars they form. We improve with respect to
our previous simulations by including a different stellar-particle formation
algorithm, which allows them to have masses corresponding to single stars
rather than to small clusters, and with a mass distribution following a
near-Salpeter stellar IMF. We also employ a simplified radiative-transfer
algorithm that allows the stellar particles to feed back on the medium at a
rate that depends on their mass and the local density. Our results are as
follows: a) Contrary to the results from our previous study, where all stellar
particles injected energy at a rate corresponding to a star of ~ 10 Msun, the
dense gas is now completely evacuated from 10-pc regions around the stars
within 10-20 Myr, suggesting that this feat is accomplished essentially by the
most massive stars. b) At the scale of the whole numerical simulations, the
dense gas mass is reduced by up to an order of magnitude, although star
formation (SF) never shuts off completely, indicating that the feedback
terminates SF locally, but new SF events continue to occur elesewhere in the
clouds. c) The SF efficiency (SFE) is maintained globally at the ~ 10% level,
although locally, the cloud with largest degree of focusing of its accretion
flow reaches SFE ~ 30%. d) The virial parameter of the clouds approaches unity
before the stellar feedback begins to dominate the dynamics, becoming much
larger once feedback dominates, suggesting that clouds become unbound as a
consequence of the stellar feedback. e) The erosion of the filaments that feed
the star-forming clumps produces chains of isolated dense blobs reminiscent of
those observed in the vicinity of the dark globule B68.


title: Hiding Image in Image by Five Modulus Method for Image Steganography

abstract:
  This paper is to create a practical steganographic implementation to hide
color image (stego) inside another color image (cover). The proposed technique
uses Five Modulus Method to convert the whole pixels within both the cover and
the stego images into multiples of five. Since each pixels inside the stego
image is divisible by five then the whole stego image could be divided by five
to get new range of pixels 0..51. Basically, the reminder of each number that
is not divisible by five is either 1,2,3 or 4 when divided by 5. Subsequently,
then a 4-by-4 window size has been implemented to accommodate the proposed
technique. For each 4-by-4 window inside the cover image, a number from 1 to 4
could be embedded secretly from the stego image. The previous discussion must
be applied separately for each of the R, G, and B arrays. Moreover, a stego-key
could be combined with the proposed algorithm to make it difficult for any
adversary to extract the secret image from the cover image. Based on the PSNR
value, the extracted stego image has high PSNR value. Hence this new
steganography algorithm is very efficient to hide color images.


title: Stable and Informative Spectral Signatures for Graph Matching

abstract:
  In this paper, we consider the approximate weighted graph matching problem
and introduce stable and informative first and second order compatibility terms
suitable for inclusion into the popular integer quadratic program formulation.
Our approach relies on a rigorous analysis of stability of spectral signatures
based on the graph Laplacian. In the case of the first order term, we derive an
objective function that measures both the stability and informativeness of a
given spectral signature. By optimizing this objective, we design new spectral
node signatures tuned to a specific graph to be matched. We also introduce the
pairwise heat kernel distance as a stable second order compatibility term; we
justify its plausibility by showing that in a certain limiting case it
converges to the classical adjacency matrix-based second order compatibility
function. We have tested our approach on a set of synthetic graphs, the
widely-used CMU house sequence, and a set of real images. These experiments
show the superior performance of our first and second order compatibility terms
as compared with the commonly used ones.


title:
Entanglement entropy and macroscopic quantum states with dipolar bosons
  in a triple-well potential

abstract:
  We study interacting dipolar atomic bosons in a triple-well potential within
a ring geometry. This system is shown to be equivalent to a three-site
Bose-Hubbard model. We analyze the ground state of dipolar bosons by varying
the effective on-site interaction. This analysis is performed both numerically
and analytically by using suitable coherent-state representations of the ground
state. The latter exhibits a variety of forms ranging from the su(3) coherent
state in the delocalization regime to a macroscopic cat-like state with fully
localized populations, passing for a coexistence regime where the ground state
displays a mixed character. We characterize the quantum correlations of the
ground state from the bi-partition perspective. We calculate both numerically
and analytically (within the previous coherent-state representation) the
single-site entanglement entropy which, among various interesting properties,
exhibits a maximum value in correspondence to the transition from the cat-like
to the coexistence regime. In the latter case, we show that the ground-state
mixed form corresponds, semiclassically, to an energy exhibiting two
almost-degenerate minima.


title: Generalization Bounds for Domain Adaptation

abstract:
  In this paper, we provide a new framework to obtain the generalization bounds
of the learning process for domain adaptation, and then apply the derived
bounds to analyze the asymptotical convergence of the learning process. Without
loss of generality, we consider two kinds of representative domain adaptation:
one is with multiple sources and the other is combining source and target data.
  In particular, we use the integral probability metric to measure the
difference between two domains. For either kind of domain adaptation, we
develop a related Hoeffding-type deviation inequality and a symmetrization
inequality to achieve the corresponding generalization bound based on the
uniform entropy number. We also generalized the classical McDiarmid's
inequality to a more general setting where independent random variables can
take values from different domains. By using this inequality, we then obtain
generalization bounds based on the Rademacher complexity. Afterwards, we
analyze the asymptotic convergence and the rate of convergence of the learning
process for such kind of domain adaptation. Meanwhile, we discuss the factors
that affect the asymptotic behavior of the learning process and the numerical
experiments support our theoretical findings as well.


title: A general theory of equilibrium behavior

abstract:
  Economists were content with the concept of the Nash equilibrium as game
theory's solution concept until Daskalakis, Goldberg, and Papadimitriou showed
that finding a Nash equilibrium is most likely a computationally hard problem,
a result that set off a deep scientific crisis. Motivated, in part, by their
result, in this paper, we propose a general theory of equilibrium behavior in
vector fields (and, therefore, also noncooperative games). Our line of
discourse is to show that these universal in nature mathematical objects are
endowed with significant structure, which we probe to unearth atypical,
previously unidentified, equilibrium behavior.


title: Some metalogical properties for infinitary predicate topological logic

abstract:
  We prove completeness, interpolation and omitting types for certain predicate
topological logics that properly extend the first order case. We aslo count the
non isomorphic topological models of a countable theory


title: Large-Treewidth Graph Decompositions and Applications

abstract:
  Treewidth is a graph parameter that plays a fundamental role in several
structural and algorithmic results. We study the problem of decomposing a given
graph $G$ into node-disjoint subgraphs, where each subgraph has sufficiently
large treewidth. We prove two theorems on the tradeoff between the number of
the desired subgraphs $h$, and the desired lower bound $r$ on the treewidth of
each subgraph. The theorems assert that, given a graph $G$ with treewidth $k$,
a decomposition with parameters $h,r$ is feasible whenever $hr^2 \le
k/\polylog(k)$, or $h^3r \le k/\polylog(k)$ holds. We then show a framework for
using these theorems to bypass the well-known Grid-Minor Theorem of Robertson
and Seymour in some applications. In particular, this leads to substantially
improved parameters in some Erdos-Posa-type results, and faster algorithms for
a class of fixed-parameter tractable problems.


title: NMR study of small molecule adsorption in MOF-74-Mg

abstract:
  We calculate the carbon nuclear magnetic resonance (NMR) shielding for CO_2
and the hydrogen shieldings for both H_2 and H_2O inside the metal organic
framework MOF-74-Mg. Our ab initio calculations are at the density functional
theory level using the van der Waals including density functional vdW-DF. The
shieldings are obtained while placing the small molecules throughout the
structure, including the calculated adsorption site for various loading
scenarios. We then explore relationships between loading, rotational and
positional characteristics, and the NMR shieldings for each adsorbate. Our NMR
calculations show a change in the shielding depending on adsorbate, position,
and loading in a range that is experimentally observable. We further provide a
simple model for the energy and the NMR shieldings throughout the cavity of the
MOF. By providing this mapping of shielding to position and loading for these
adsorbates, we argue that NMR probes could be used to provide additional
information about the position at which these small molecules bind within the
MOF, as well as the loading of the adsorbed molecule.


title: Hom-Alternative, Hom-Malcev and Hom-Jordan superalgebras

abstract:
  Hom-alternative, Hom-Malcev and Hom-Jordan superalgebras are
$\mathbb{Z}_{2}$-graded generalizations of Hom-alternative, Hom-Malcev and
Hom-Jordan algebras, which are Hom-type generalizations of alternative, Malcev
and Jordan algebras. In this paper we prove that Hom-alternative superalgebras
are Hom-Malcev-admissible and are also Hom-Jordan-admissible. Home-type
generalizations of some well known identities in alternative superalgebras,
including the $\mathbb{Z}_{2}$-graded Bruck-Kleinfled function are obtained.


title:
Stochastic integral and series representations for strictly stable
  distributions

abstract:
  In this paper we find and develop a stochastic integral representation for
the class of strictly stable distributions. We establish an explicit
relationship between stochastic integral and shot-noise series representations
of strictly stable distributions, which shows that the class of distributions
representable by stochastic integral is larger than the class representable by
a shot-noise series. This inclusion is proper when the stability index is
greater than 1. We also give an explicit description of distributions
possessing both representations.