title:
Elicitation of Probabilities for Belief Networks: Combining Qualitative
  and Quantitative Information

abstract:
  Although the usefulness of belief networks for reasoning under uncertainty is
widely accepted, obtaining numerical probabilities that they require is still
perceived a major obstacle. Often not enough statistical data is available to
allow for reliable probability estimation. Available information may not be
directly amenable for encoding in the network. Finally, domain experts may be
reluctant to provide numerical probabilities. In this paper, we propose a
method for elicitation of probabilities from a domain expert that is
non-invasive and accommodates whatever probabilistic information the expert is
willing to state. We express all available information, whether qualitative or
quantitative in nature, in a canonical form consisting of (in) equalities
expressing constraints on the hyperspace of possible joint probability
distributions. We then use this canonical form to derive second-order
probability distributions over the desired probabilities.


title: Numerical Representations of Acceptance

abstract:
  Accepting a proposition means that our confidence in this proposition is
strictly greater than the confidence in its negation. This paper investigates
the subclass of uncertainty measures, expressing confidence, that capture the
idea of acceptance, what we call acceptance functions. Due to the monotonicity
property of confidence measures, the acceptance of a proposition entails the
acceptance of any of its logical consequences. In agreement with the idea that
a belief set (in the sense of Gardenfors) must be closed under logical
consequence, it is also required that the separate acceptance o two
propositions entail the acceptance of their conjunction. Necessity (and
possibility) measures agree with this view of acceptance while probability and
belief functions generally do not. General properties of acceptance functions
are estabilished. The motivation behind this work is the investigation of a
setting for belief revision more general than the one proposed by Alchourron,
Gardenfors and Makinson, in connection with the notion of conditioning.


title:
Fraud/Uncollectible Debt Detection Using a Bayesian Network Based
  Learning System: A Rare Binary Outcome with Mixed Data Structures

abstract:
  The fraud/uncollectible debt problem in the telecommunications industry
presents two technical challenges: the detection and the treatment of the
account given the detection. In this paper, we focus on the first problem of
detection using Bayesian network models, and we briefly discuss the application
of a normative expert system for the treatment at the end. We apply Bayesian
network models to the problem of fraud/uncollectible debt detection for
telecommunication services. In addition to being quite successful at predicting
rare event outcomes, it is able to handle a mixture of categorical and
continuous data. We present a performance comparison using linear and
non-linear discriminant analysis, classification and regression trees, and
Bayesian network models


title: A Constraint Satisfaction Approach to Decision under Uncertainty

abstract:
  The Constraint Satisfaction Problem (CSP) framework offers a simple and sound
basis for representing and solving simple decision problems, without
uncertainty. This paper is devoted to an extension of the CSP framework
enabling us to deal with some decisions problems under uncertainty. This
extension relies on a differentiation between the agent-controllable decision
variables and the uncontrollable parameters whose values depend on the
occurrence of uncertain events. The uncertainty on the values of the parameters
is assumed to be given under the form of a probability distribution. Two
algorithms are given, for computing respectively decisions solving the problem
with a maximal probability, and conditional decisions mapping the largest
possible amount of possible cases to actual decisions.


title: Plausibility Measures: A User's Guide

abstract:
  We examine a new approach to modeling uncertainty based on plausibility
measures, where a plausibility measure just associates with an event its
plausibility, an element is some partially ordered set. This approach is easily
seen to generalize other approaches to modeling uncertainty, such as
probability measures, belief functions, and possibility measures. The lack of
structure in a plausibility measure makes it easy for us to add structure on an
"as needed" basis, letting us examine what is required to ensure that a
plausibility measure has certain properties of interest. This gives us insight
into the essential features of the properties in question, while allowing us to
prove general results that apply to many approaches to reasoning about
uncertainty. Plausibility measures have already proved useful in analyzing
default reasoning. In this paper, we examine their "algebraic properties,"
analogues to the use of + and * in probability theory. An understanding of such
properties will be essential if plausibility measures are to be used in
practice as a representation tool.


title: Testing Identifiability of Causal Effects

abstract:
  This paper concerns the probabilistic evaluation of the effects of actions in
the presence of unmeasured variables. We show that the identification of causal
effect between a singleton variable X and a set of variables Y can be
accomplished systematically, in time polynomial in the number of variables in
the graph. When the causal effect is identifiable, a closed-form expression can
be obtained for the probability that the action will achieve a specified goal,
or a set of goals.


title:
A Characterization of the Dirichlet Distribution with Application to
  Learning Bayesian Networks

abstract:
  We provide a new characterization of the Dirichlet distribution. This
characterization implies that under assumptions made by several previous
authors for learning belief networks, a Dirichlet prior on the parameters is
inevitable.


title: Fast Belief Update Using Order-of-Magnitude Probabilities

abstract:
  We present an algorithm, called Predict, for updating beliefs in causal
networks quantified with order-of-magnitude probabilities. The algorithm takes
advantage of both the structure and the quantification of the network and
presents a polynomial asymptotic complexity. Predict exhibits a conservative
behavior in that it is always sound but not always complete. We provide
sufficient conditions for completeness and present algorithms for testing these
conditions and for computing a complete set of plausible values. We propose
Predict as an efficient method to estimate probabilistic values and illustrate
its use in conjunction with two known algorithms for probabilistic inference.
Finally, we describe an application of Predict to plan evaluation, present
experimental results, and discuss issues regarding its use with conditional
logics of belief, and in the characterization of irrelevance.


title: Transforming Prioritized Defaults and Specificity into Parallel Defaults

abstract:
  We show how to transform any set of prioritized propositional defaults into
an equivalent set of parallel (i.e., unprioritized) defaults, in
circumscription. We give an algorithm to implement the transform. We show how
to use the transform algorithm as a generator of a whole family of inferencing
algorithms for circumscription. The method is to employ the transform algorithm
as a front end to any inferencing algorithm, e.g., one of the previously
available, that handles the parallel (empty) case of prioritization. Our
algorithms provide not just coverage of a new expressive class, but also
alternatives to previous algorithms for implementing the previously covered
class (?layered?) of prioritization. In particular, we give a new
query-answering algorithm for prioritized cirumscription which is sound and
complete for the full expressive class of unrestricted finite prioritization
partial orders, for propositional defaults (or minimized predicates). By
contrast, previous algorithms required that the prioritization partial order be
layered, i.e., structured similar to the system of rank in the military. Our
algorithm enables, for the first time, the implementation of the most useful
class of prioritization: non-layered prioritization partial orders. Default
inheritance, for example, typically requires non-layered prioritization to
represent specificity adequately. Our algorithm enables not only the
implementation of default inheritance (and specificity) within prioritized
circumscription, but also the extension and combination of default inheritance
with other kinds of prioritized default reasoning, e.g.: with stratified logic
programs with negation-as-failure. Such logic programs are previously known to
be representable equivalently as layered-priority predicate circumscriptions.
Worst-case, the transform increases the number of defaults exponentially. We
discuss how inferencing is practically implementable nevertheless in two kinds
of situations: general expressiveness but small numbers of defaults, or
expressive special cases with larger numbers of defaults. One such expressive
special case is non-?top-heaviness? of the prioritization partial order. In
addition to its direct implementation, the transform can also be exploited
analytically to generate special case algorithms, e.g., a tractable transform
for a class within default inheritance (detailed in another, forthcoming
paper). We discuss other aspects of the significance of the fundamental result.
One can view the transform as reducing n degrees of partially ordered belief
confidence to just 2 degrees of confidence: for-sure and (unprioritized)
default. Ordinary, parallel default reasoning, e.g., in parallel
circumscription or Poole's Theorist, can be viewed in these terms as reducing 2
degrees of confidence to just 1 degree of confidence: that of the non-monotonic
theory's conclusions. The expressive reduction's computational complexity
suggests that prioritization is valuable for its expressive conciseness, just
as defaults are for theirs. For Reiter's Default Logic and Poole's Theorist,
the transform implies how to extend those formalisms so as to equip them with a
concept of prioritization that is exactly equivalent to that in
circumscription. This provides an interesting alternative to Brewka's approach
to equipping them with prioritization-type precedence.


title: Efficient Decision-Theoretic Planning: Techniques and Empirical Analysis

abstract:
  This paper discusses techniques for performing efficient decision-theoretic
planning. We give an overview of the DRIPS decision-theoretic refinement
planning system, which uses abstraction to efficiently identify optimal plans.
We present techniques for automatically generating search control information,
which can significantly improve the planner's performance. We evaluate the
efficiency of DRIPS both with and without the search control rules on a complex
medical planning problem and compare its performance to that of a
branch-and-bound decision tree algorithm.


title: Fuzzy Logic and Probability

abstract:
  In this paper we deal with a new approach to probabilistic reasoning in a
logical framework. Nearly almost all logics of probability that have been
proposed in the literature are based on classical two-valued logic. After
making clear the differences between fuzzy logic and probability theory, here
we propose a {em fuzzy} logic of probability for which completeness results (in
a probabilistic sense) are provided. The main idea behind this approach is that
probability values of crisp propositions can be understood as truth-values of
some suitable fuzzy propositions associated to the crisp ones. Moreover,
suggestions and examples of how to extend the formalism to cope with
conditional probabilities and with other uncertainty formalisms are also
provided.


title: Probabilistic Temporal Reasoning with Endogenous Change

abstract:
  This paper presents a probabilistic model for reasoning about the state of a
system as it changes over time, both due to exogenous and endogenous
influences. Our target domain is a class of medical prediction problems that
are neither so urgent as to preclude careful diagnosis nor progress so slowly
as to allow arbitrary testing and treatment options. In these domains there is
typically enough time to gather information about the patient's state and
consider alternative diagnoses and treatments, but the temporal interaction
between the timing of tests, treatments, and the course of the disease must
also be considered. Our approach is to elicit a qualitative structural model of
the patient from a human expert---the model identifies important attributes,
the way in which exogenous changes affect attribute values, and the way in
which the patient's condition changes endogenously. We then elicit
probabilistic information to capture the expert's uncertainty about the effects
of tests and treatments and the nature and timing of endogenous state changes.
This paper describes the model in the context of a problem in treating vehicle
accident trauma, and suggests a method for solving the model based on the
technique of sequential imputation. A complementary goal of this work is to
understand and synthesize a disparate collection of research efforts all using
the name ?probabilistic temporal reasoning.? This paper analyzes related work
and points out essential differences between our proposed model and other
approaches in the literature.


title:
Toward a Characterization of Uncertainty Measure for the Dempster-Shafer
  Theory

abstract:
  This is a working paper summarizing results of an ongoing research project
whose aim is to uniquely characterize the uncertainty measure for the
Dempster-Shafer Theory. A set of intuitive axiomatic requirements is presented,
some of their implications are shown, and the proof is given of the minimality
of recently proposed measure AU among all measures satisfying the proposed
requirements.


title: A Definition and Graphical Representation for Causality

abstract:
  We present a precise definition of cause and effect in terms of a fundamental
notion called unresponsiveness. Our definition is based on Savage's (1954)
formulation of decision theory and departs from the traditional view of
causation in that our causal assertions are made relative to a set of
decisions. An important consequence of this departure is that we can reason
about cause locally, not requiring a causal explanation for every dependency.
Such local reasoning can be beneficial because it may not be necessary to
determine whether a particular dependency is causal to make a decision. Also in
this paper, we examine the graphical encoding of causal relationships. We show
that influence diagrams in canonical form are an accurate and efficient
representation of causal relationships. In addition, we establish a
correspondence between canonical form and Pearl's causal theory.


title:
Learning Bayesian Networks: A Unification for Discrete and Gaussian
  Domains

abstract:
  We examine Bayesian methods for learning Bayesian networks from a combination
of prior knowledge and statistical data. In particular, we unify the approaches
we presented at last year's conference for discrete and Gaussian domains. We
derive a general Bayesian scoring metric, appropriate for both domains. We then
use this metric in combination with well-known statistical facts about the
Dirichlet and normal--Wishart distributions to derive our metrics for discrete
and Gaussian domains.


title: A Bayesian Approach to Learning Causal Networks

abstract:
  Whereas acausal Bayesian networks represent probabilistic independence,
causal Bayesian networks represent causal relationships. In this paper, we
examine Bayesian methods for learning both types of networks. Bayesian methods
for learning acausal networks are fairly well developed. These methods often
employ assumptions to facilitate the construction of priors, including the
assumptions of parameter independence, parameter modularity, and likelihood
equivalence. We show that although these assumptions also can be appropriate
for learning causal networks, we need additional assumptions in order to learn
causal networks. We introduce two sufficient assumptions, called {em mechanism
independence} and {em component independence}. We show that these new
assumptions, when combined with parameter independence, parameter modularity,
and likelihood equivalence, allow us to apply methods for learning acausal
networks to learn causal networks.


title: Display of Information for Time-Critical Decision Making

abstract:
  We describe methods for managing the complexity of information displayed to
people responsible for making high-stakes, time-critical decisions. The
techniques provide tools for real-time control of the configuration and
quantity of information displayed to a user, and a methodology for designing
flexible human-computer interfaces for monitoring applications. After defining
a prototypical set of display decision problems, we introduce the expected
value of revealed information (EVRI) and the related measure of expected value
of displayed information (EVDI). We describe how these measures can be used to
enhance computer displays used for monitoring complex systems. We motivate the
presentation by discussing our efforts to employ decision-theoretic control of
displays for a time-critical monitoring application at the NASA Mission Control
Center in Houston.


title:
Reasoning, Metareasoning, and Mathematical Truth: Studies of Theorem
  Proving under Limited Resources

abstract:
  In earlier work, we introduced flexible inference and decision-theoretic
metareasoning to address the intractability of normative inference. Here,
rather than pursuing the task of computing beliefs and actions with decision
models composed of distinctions about uncertain events, we examine methods for
inferring beliefs about mathematical truth before an automated theorem prover
completes a proof. We employ a Bayesian analysis to update belief in truth,
given theorem-proving progress, and show how decision-theoretic methods can be
used to determine the value of continuing to deliberate versus taking immediate
action in time-critical situations.


title: Improved Sampling for Diagnostic Reasoning in Bayesian Networks

abstract:
  Bayesian networks offer great potential for use in automating large scale
diagnostic reasoning tasks. Gibbs sampling is the main technique used to
perform diagnostic reasoning in large richly interconnected Bayesian networks.
Unfortunately Gibbs sampling can take an excessive time to generate a
representative sample. In this paper we describe and test a number of heuristic
strategies for improving sampling in noisy-or Bayesian networks. The strategies
include Monte Carlo Markov chain sampling techniques other than Gibbs sampling.
Emphasis is put on strategies that can be implemented in distributed systems.


title: Cautious Propagation in Bayesian Networks

abstract:
  Consider the situation where some evidence e has been entered to a Bayesian
network. When performing conflict analysis, sensitivity analysis, or when
answering questions like "What if the finding on X had been y instead of x?"
you need probabilities P (e'| h), where e' is a subset of e, and h is a
configuration of a (possibly empty) set of variables. Cautious propagation is a
modification of HUGIN propagation into a Shafer-Shenoy-like architecture. It is
less efficient than HUGIN propagation; however, it provides easy access to P
(e'| h) for a great deal of relevant subsets e'.


title: Information/Relevance Influence Diagrams

abstract:
  In this paper we extend the influence diagram (ID) representation for
decisions under uncertainty. In the standard ID, arrows into a decision node
are only informational; they do not represent constraints on what the decision
maker can do. We can represent such constraints only indirectly, using arrows
to the children of the decision and sometimes adding more variables to the
influence diagram, thus making the ID more complicated. Users of influence
diagrams often want to represent constraints by arrows into decision nodes. We
represent constraints on decisions by allowing relevance arrows into decision
nodes. We call the resulting representation information/relevance influence
diagrams (IRIDs). Information/relevance influence diagrams allow for direct
representation and specification of constrained decisions. We use a combination
of stochastic dynamic programming and Gibbs sampling to solve IRIDs. This
method is especially useful when exact methods for solving IDs fail.


title: Estimating Continuous Distributions in Bayesian Classifiers

abstract:
  When modeling a probability distribution with a Bayesian network, we are
faced with the problem of how to handle continuous variables. Most previous
work has either solved the problem by discretizing, or assumed that the data
are generated by a single Gaussian. In this paper we abandon the normality
assumption and instead use statistical methods for nonparametric density
estimation. For a naive Bayesian classifier, we present experimental results on
a variety of natural and artificial domains, comparing two methods of density
estimation: assuming normality and modeling each conditional distribution with
a single Gaussian; and using nonparametric kernel density estimation. We
observe large reductions in error on several natural and artificial data sets,
which suggests that kernel estimation is a useful tool for learning Bayesian
models.


title: Stochastic Simulation Algorithms for Dynamic Probabilistic Networks

abstract:
  Stochastic simulation algorithms such as likelihood weighting often give
fast, accurate approximations to posterior probabilities in probabilistic
networks, and are the methods of choice for very large networks. Unfortunately,
the special characteristics of dynamic probabilistic networks (DPNs), which are
used to represent stochastic temporal processes, mean that standard simulation
algorithms perform very poorly. In essence, the simulation trials diverge
further and further from reality as the process is observed over time. In this
paper, we present simulation algorithms that use the evidence observed at each
time step to push the set of trials back towards reality. The first algorithm,
"evidence reversal" (ER) restructures each time slice of the DPN so that the
evidence nodes for the slice become ancestors of the state variables. The
second algorithm, called "survival of the fittest" sampling (SOF),
"repopulates" the set of trials at each time step using a stochastic
reproduction rate weighted by the likelihood of the evidence according to each
trial. We compare the performance of each algorithm with likelihood weighting
on the original network, and also investigate the benefits of combining the ER
and SOF methods. The ER/SOF combination appears to maintain bounded error
independent of the number of time steps in the simulation.


title: Probabilistic Exploration in Planning while Learning

abstract:
  Sequential decision tasks with incomplete information are characterized by
the exploration problem; namely the trade-off between further exploration for
learning more about the environment and immediate exploitation of the accrued
information for decision-making. Within artificial intelligence, there has been
an increasing interest in studying planning-while-learning algorithms for these
decision tasks. In this paper we focus on the exploration problem in
reinforcement learning and Q-learning in particular. The existing exploration
strategies for Q-learning are of a heuristic nature and they exhibit limited
scaleability in tasks with large (or infinite) state and action spaces.
Efficient experimentation is needed for resolving uncertainties when possible
plans are compared (i.e. exploration). The experimentation should be sufficient
for selecting with statistical significance a locally optimal plan (i.e.
exploitation). For this purpose, we develop a probabilistic hill-climbing
algorithm that uses a statistical selection procedure to decide how much
exploration is needed for selecting a plan which is, with arbitrarily high
probability, arbitrarily close to a locally optimal one. Due to its generality
the algorithm can be employed for the exploration strategy of robust
Q-learning. An experiment on a relatively complex control task shows that the
proposed exploration strategy performs better than a typical exploration
strategy.


title:
On the Detection of Conflicts in Diagnostic Bayesian Networks Using
  Abstraction

abstract:
  An important issue in the use of expert systems is the so-called brittleness
problem. Expert systems model only a limited part of the world. While the
explicit management of uncertainty in expert systems itigates the brittleness
problem, it is still possible for a system to be used, unwittingly, in ways
that the system is not prepared to address. Such a situation may be detected by
the method of straw models, first presented by Jensen et al. [1990] and later
generalized and justified by Laskey [1991]. We describe an algorithm, which we
have implemented, that takes as input an annotated diagnostic Bayesian network
(the base model) and constructs, without assistance, a bipartite network to be
used as a straw model. We show that in some cases this straw model is better
that the independent straw model of Jensen et al., the only other straw model
for which a construction algorithm has been designed and implemented.


title: HUGS: Combining Exact Inference and Gibbs Sampling in Junction Trees

abstract:
  Dawid, Kjaerulff and Lauritzen (1994) provided a preliminary description of a
hybrid between Monte-Carlo sampling methods and exact local computations in
junction trees. Utilizing the strengths of both methods, such hybrid inference
methods has the potential of expanding the class of problems which can be
solved under bounded resources as well as solving problems which otherwise
resist exact solutions. The paper provides a detailed description of a
particular instance of such a hybrid scheme; namely, combination of exact
inference and Gibbs sampling in discrete Bayesian networks. We argue that this
combination calls for an extension of the usual message passing scheme of
ordinary junction trees.


title:
Sensitivities: An Alternative to Conditional Probabilities for Bayesian
  Belief Networks

abstract:
  We show an alternative way of representing a Bayesian belief network by
sensitivities and probability distributions. This representation is equivalent
to the traditional representation by conditional probabilities, but makes
dependencies between nodes apparent and intuitively easy to understand. We also
propose a QR matrix representation for the sensitivities and/or conditional
probabilities which is more efficient, in both memory requirements and
computational speed, than the traditional representation for computer-based
implementations of probabilistic inference. We use sensitivities to show that
for a certain class of binary networks, the computation time for approximate
probabilistic inference with any positive upper bound on the error of the
result is independent of the size of the network. Finally, as an alternative to
traditional algorithms that use conditional probabilities, we describe an exact
algorithm for probabilistic inference that uses the QR-representation for
sensitivities and updates probability distributions of nodes in a network
according to messages from the neighbors.


title: Is There a Role for Qualitative Risk Assessment?

abstract:
  Classically, risk is characterized by a point value probability indicating
the likelihood of occurrence of an adverse effect. However, there are domains
where the attainability of objective numerical risk characterizations is
increasingly being questioned. This paper reviews the arguments in favour of
extending classical techniques of risk assessment to incorporate meaningful
qualitative and weak quantitative risk characterizations. A technique in which
linguistic uncertainty terms are defined in terms of patterns of argument is
then proposed. The technique is demonstrated using a prototype computer-based
system for predicting the carcinogenic risk due to novel chemical compounds.


title: On the Complexity of Solving Markov Decision Problems

abstract:
  Markov decision problems (MDPs) provide the foundations for a number of
problems of interest to AI researchers studying automated planning and
reinforcement learning. In this paper, we summarize results regarding the
complexity of solving MDPs and the running time of MDP solution algorithms. We
argue that, although MDPs can be solved efficiently in theory, more study is
needed to reveal practical algorithms for solving large problems quickly. To
encourage future research, we sketch some alternative methods of analysis that
rely on the structure of MDPs.


title: Causal Inference and Causal Explanation with Background Knowledge

abstract:
  This paper presents correct algorithms for answering the following two
questions; (i) Does there exist a causal explanation consistent with a set of
background knowledge which explains all of the observed independence facts in a
sample? (ii) Given that there is such a causal explanation what are the causal
relationships common to every such causal explanation?


title: Strong Completeness and Faithfulness in Bayesian Networks

abstract:
  A completeness result for d-separation applied to discrete Bayesian networks
is presented and it is shown that in a strong measure-theoretic sense almost
all discrete distributions for a given network structure are faithful; i.e. the
independence facts true of the distribution are all and only those entailed by
the network structure.


title:
A Theoretical Framework for Context-Sensitive Temporal Probability Model
  Construction with Application to Plan Projection

abstract:
  We define a context-sensitive temporal probability logic for representing
classes of discrete-time temporal Bayesian networks. Context constraints allow
inference to be focused on only the relevant portions of the probabilistic
knowledge. We provide a declarative semantics for our language. We present a
Bayesian network construction algorithm whose generated networks give sound and
complete answers to queries. We use related concepts in logic programming to
justify our approach. We have implemented a Bayesian network construction
algorithm for a subset of the theory and demonstrate it's application to the
problem of evaluating the effectiveness of treatments for acute cardiac
conditions.


title: Refining Reasoning in Qualitative Probabilistic Networks

abstract:
  In recent years there has been a spate of papers describing systems for
probabilisitic reasoning which do not use numerical probabilities. In some
cases the simple set of values used by these systems make it impossible to
predict how a probability will change or which hypothesis is most likely given
certain evidence. This paper concentrates on such situations, and suggests a
number of ways in which they may be resolved by refining the representation.


title:
On the Testability of Causal Models with Latent and Instrumental
  Variables

abstract:
  Certain causal models involving unmeasured variables induce no independence
constraints among the observed variables but imply, nevertheless, inequality
contraints on the observed distribution. This paper derives a general formula
for such instrumental variables, that is, exogenous variables that directly
affect some variables but not all. With the help of this formula, it is
possible to test whether a model involving instrumental variables may account
for the data, or, conversely, whether a given variables can be deemed
instrumental.


title:
Probabilistic Evaluation of Sequential Plans from Causal Models with
  Hidden Variables

abstract:
  The paper concerns the probabilistic evaluation of plans in the presence of
unmeasured variables, each plan consisting of several concurrent or sequential
actions. We establish a graphical criterion for recognizing when the effects of
a given plan can be predicted from passive observations on measured variables
only. When the criterion is satisfied, a closed-form expression is provided for
the probability that the plan will achieve a specified goal.


title:
Exploiting the Rule Structure for Decision Making within the Independent
  Choice Logic

abstract:
  This paper introduces the independent choice logic, and in particular the
"single agent with nature" instance of the independent choice logic, namely
ICLdt. This is a logical framework for decision making uncertainty that extends
both logic programming and stochastic models such as influence diagrams. This
paper shows how the representation of a decision problem within the independent
choice logic can be exploited to cut down the combinatorics of dynamic
programming. One of the main problems with influence diagram evaluation
techniques is the need to optimise a decision for all values of the 'parents'
of a decision variable. In this paper we show how the rule based nature of the
ICLdt can be exploited so that we only make distinctions in the values of the
information available for a decision that will make a difference to utility.


title:
Abstraction in Belief Networks: The Role of Intermediate States in
  Diagnostic Reasoning

abstract:
  Bayesian belief networks are bing increasingly used as a knowledge
representation for diagnostic reasoning. One simple method for conducting
diagnostic reasoning is to represent system faults and observations only. In
this paper, we investigate how having intermediate nodes-nodes other than fault
and observation nodes affects the diagnostic performance of a Bayesian belief
network. We conducted a series of experiments on a set of real belief networks
for medical diagnosis in liver and bile disease. We compared the effects on
diagnostic performance of a two-level network consisting just of disease and
finding nodes with that of a network which models intermediate
pathophysiological disease states as well. We provide some theoretical evidence
for differences observed between the abstracted two-level network and the full
network.


title:
Accounting for Context in Plan Recognition, with Application to Traffic
  Monitoring

abstract:
  Typical approaches to plan recognition start from a representation of an
agent's possible plans, and reason evidentially from observations of the
agent's actions to assess the plausibility of the various candidates. A more
expansive view of the task (consistent with some prior work) accounts for the
context in which the plan was generated, the mental state and planning process
of the agent, and consequences of the agent's actions in the world. We present
a general Bayesian framework encompassing this view, and focus on how context
can be exploited in plan recognition. We demonstrate the approach on a problem
in traffic monitoring, where the objective is to induce the plan of the driver
from observation of vehicle movements. Starting from a model of how the driver
generates plans, we show how the highway context can appropriately influence
the recognizer's interpretation of observed driver behavior.


title: A New Pruning Method for Solving Decision Trees and Game Trees

abstract:
  The main goal of this paper is to describe a new pruning method for solving
decision trees and game trees. The pruning method for decision trees suggests a
slight variant of decision trees that we call scenario trees. In scenario
trees, we do not need a conditional probability for each edge emanating from a
chance node. Instead, we require a joint probability for each path from the
root node to a leaf node. We compare the pruning method to the traditional
rollback method for decision trees and game trees. For problems that require
Bayesian revision of probabilities, a scenario tree representation with the
pruning method is more efficient than a decision tree representation with the
rollback method. For game trees, the pruning method is more efficient than the
rollback method.


title: Directed Cyclic Graphical Representations of Feedback Models

abstract:
  The use of directed acyclic graphs (DAGs) to represent conditional
independence relations among random variables has proved fruitful in a variety
of ways. Recursive structural equation models are one kind of DAG model.
However, non-recursive structural equation models of the kinds used to model
economic processes are naturally represented by directed cyclic graphs with
independent errors, a characterization of conditional independence errors, a
characterization of conditional independence constraints is obtained, and it is
shown that the result generalizes in a natural way to systems in which the
error variables or noises are statistically dependent. For non-linear systems
with independent errors a sufficient condition for conditional independence of
variables in associated distributions is obtained.


title: Causal Inference in the Presence of Latent Variables and Selection Bias

abstract:
  We show that there is a general, informative and reliable procedure for
discovering causal relations when, for all the investigator knows, both latent
variables and selection bias may be at work. Given information about
conditional independence and dependence relations between measured variables,
even when latent variables and selection bias may be present, there are
sufficient conditions for reliably concluding that there is a causal path from
one variable to another, and sufficient conditions for reliably concluding when
no such causal path exists.


title: Modeling Failure Priors and Persistence in Model-Based Diagnosis

abstract:
  Probabilistic model-based diagnosis computes the posterior probabilities of
failure of components from the prior probabilities of component failure and
observations of system behavior. One problem with this method is that such
priors are almost never directly available. One of the reasons is that the
prior probability estimates include an implicit notion of a time interval over
which they are specified -- for example, if the probability of failure of a
component is 0.05, is this over the period of a day or is this over a week? A
second problem facing probabilistic model-based diagnosis is the modeling of
persistence. Say we have an observation about a system at time t_1 and then
another observation at a later time t_2. To compute posterior probabilities
that take into account both the observations, we need some model of how the
state of the system changes from time t_1 to t_2. In this paper, we address
these problems using techniques from Reliability theory. We show how to compute
the failure prior of a component from an empirical measure of its reliability
-- the Mean Time Between Failure (MTBF). We also develop a scheme to model
persistence when handling multiple time tagged observations.


title:
A Polynomial Algorithm for Computing the Optimal Repair Strategy in a
  System with Independent Component Failures

abstract:
  The goal of diagnosis is to compute good repair strategies in response to
anomalous system behavior. In a decision theoretic framework, a good repair
strategy has low expected cost. In a general formulation of the problem, the
computation of the optimal (lowest expected cost) repair strategy for a system
with multiple faults is intractable. In this paper, we consider an interesting
and natural restriction on the behavior of the system being diagnosed: (a) the
system exhibits faulty behavior if and only if one or more components is
malfunctioning. (b) The failures of the system components are independent.
Given this restriction on system behavior, we develop a polynomial time
algorithm for computing the optimal repair strategy. We then go on to introduce
a system hierarchy and the notion of inspecting (testing) components before
repair. We develop a linear time algorithm for computing an optimal repair
strategy for the hierarchical system which includes both repair and inspection.


title:
Exploiting System Hierarchy to Compute Repair Plans in Probabilistic
  Model-based Diagnosis

abstract:
  The goal of model-based diagnosis is to isolate causes of anomalous system
behavior and recommend inexpensive repair actions in response. In general,
precomputing optimal repair policies is intractable. To date, investigators
addressing this problem have explored approximations that either impose
restrictions on the system model (such as a single fault assumption) or compute
an immediate best action with limited lookahead. In this paper, we develop a
formulation of repair in model-based diagnosis and a repair algorithm that
computes optimal sequences of actions. This optimal approach is costly but can
be applied to precompute an optimal repair strategy for compact systems. We
show how we can exploit a hierarchical system specification to make this
approach tractable for large systems. When introducing hierarchy, we also
consider the tradeoff between simply replacing a component and decomposing it
to repair its subcomponents. The hierarchical repair algorithm is suitable for
off-line precomputation of an optimal repair strategy. A modification of the
algorithm takes advantage of an iterative deepening scheme to trade off
inference time and the quality of the computed strategy.


title: Path Planning under Time-Dependent Uncertainty

abstract:
  Standard algorithms for finding the shortest path in a graph require that the
cost of a path be additive in edge costs, and typically assume that costs are
deterministic. We consider the problem of uncertain edge costs, with potential
probabilistic dependencies among the costs. Although these dependencies violate
the standard dynamic-programming decomposition, we identify a weaker stochastic
consistency condition that justifies a generalized dynamic-programming approach
based on stochastic dominance. We present a revised path-planning algorithm and
prove that it produces optimal paths under time-dependent uncertain costs. We
test the algorithm by applying it to a model of stochastic bus networks, and
present empirical performance results comparing it to some alternatives.
Finally, we consider extensions of these concepts to a more general class of
problems of heuristic search under uncertainty.


title:
Defaults and Infinitesimals: Defeasible Inference by Nonarchimedean
  Entropy-Maximization

abstract:
  We develop a new semantics for defeasible inference based on extended
probability measures allowed to take infinitesimal values, on the
interpretation of defaults as generalized conditional probability constraints
and on a preferred-model implementation of entropy maximization.


title: An Order of Magnitude Calculus

abstract:
  This paper develops a simple calculus for order of magnitude reasoning. A
semantics is given with soundness and completeness results. Order of magnitude
probability functions are easily defined and turn out to be equivalent to kappa
functions, which are slight generalizations of Spohn's Natural Conditional
Functions. The calculus also gives rise to an order of magnitude decision
theory, which can be used to justify an amended version of Pearl's decision
theory for kappa functions, although the latter is weaker and less expressive.


title: A Method for Implementing a Probabilistic Model as a Relational Database

abstract:
  This paper discusses a method for implementing a probabilistic inference
system based on an extended relational data model. This model provides a
unified approach for a variety of applications such as dynamic programming,
solving sparse linear equations, and constraint propagation. In this framework,
the probability model is represented as a generalized relational database.
Subsequent probabilistic requests can be processed as standard relational
queries. Conventional database management systems can be easily adopted for
implementing such an approximate reasoning system.


title:
Optimization of Inter-Subnet Belief Updating in Multiply Sectioned
  Bayesian Networks

abstract:
  Recent developments show that Multiply Sectioned Bayesian Networks (MSBNs)
can be used for diagnosis of natural systems as well as for model-based
diagnosis of artificial systems. They can be applied to single-agent oriented
reasoning systems as well as multi-agent distributed probabilistic reasoning
systems. Belief propagation between a pair of subnets plays a central role in
maintenance of global consistency in a MSBN. This paper studies the operation
UpdateBelief, presented originally with MSBNs, for inter-subnet propagation. We
analyze how the operation achieves its intended functionality, which provides
hints as for how its efficiency can be improved. We then define two new
versions of UpdateBelief that reduce the computation time for inter-subnet
propagation. One of them is optimal in the sense that the minimum amount of
computation for coordinating multi-linkage belief propagation is required. The
optimization problem is solved through the solution of a graph-theoretic
problem: the minimum weight open tour in a tree.


title: Generating Explanations for Evidential Reasoning

abstract:
  In this paper, we present two methods to provide explanations for reasoning
with belief functions in the valuation-based systems. One approach, inspired by
Strat's method, is based on sensitivity analysis, but its computation is
simpler thus easier to implement than Strat's. The other one is to examine the
impact of evidence on the conclusion based on the measure of the information
content in the evidence. We show the property of additivity for the pieces of
evidence that are conditional independent within the context of the
valuation-based systems. We will give an example to show how these approaches
are applied in an evidential network.


title: Inference with Causal Independence in the CPSC Network

abstract:
  This paper reports experiments with the causal independence inference
algorithm proposed by Zhang and Poole (1994b) on the CPSC network created by
Pradhan et al. (1994). It is found that the algorithm is able to answer 420 of
the 422 possible zero-observation queries, 94 of 100 randomly generated
five-observation queries, 87 of 100 randomly generated ten-observation queries,
and 69 of 100 randomly generated twenty-observation queries.


title:
Chiral symmetry breaking from two-loop effective potential of the
  holographic non-local NJL model

abstract:
  We calculate the two-loop effective potential of the non-local NJL model
derived from the Sakai-Sugimoto model in string theory. In contrast to
conventional NJL with 4-fermion contact interaction, the chiral symmetry was
previously found to be dynamically broken for arbitrary weak coupling at the
one-loop level. As a confirmation, the approximate numerical solutions to the
gap equation at one loop are explicitly demonstrated for weak couplings. We
then calculate the one and two-loop contribution to the effective potential of
the non-local NJL model and found that the two-loop contribution is negative.
The two-loop potential for the chiral symmetric vacuum is also negative but
larger than the combined effective potential of the chiral broken vacuum at the
two-loop level. The chiral symmetry breaking thus persists for arbitrary weak
coupling at the two-loop level.


title:
Action of the Cremona group on foliations on $\mathbb{P}^2_\mathbb{C}$:
  some curious facts

abstract:
  The Cremona group of birational transformations of $\mathbb{P}^2_\mathbb{C}$
acts on the space $\mathbb{F}(2)$ of holomorphic foliations on the complex
projective plane. Since this action is not compatible with the natural
graduation of $\mathbb{F}(2)$ by the degree, its description is complicated.
The fixed points of the action are essentially described by Cantat-Favre in
\cite{CF}. In that paper we are interested in problems of "aberration of the
degree" that is pairs
$(\phi,\mathcal{F})\in\mathrm{Bir}(\mathbb{P}^2_\mathbb{C})\times\mathbb{F}(2)$
for which $\deg\phi^*\mathcal{F}<(\deg\mathcal{F}+1)\deg\phi+\deg\phi-2$, the
generic degree of such pull-back. We introduce the notion of numerical
invariance ($\deg\phi^*\mathcal{F}=\deg\mathcal{F}$) and relate it in small
degrees to the existence of transversal structure for the considered
foliations.


title:
Validation of the effective masses calculated using finite difference
  method on a five-point stencil for inorganic and organic semiconductors

abstract:
  We present a code to calculate effective masses from energy dispersion
relation using finite difference method on a five-point stencil. We validate
our code against inorganic (GaAs and InP) and organic (pentacene and rubrene)
crystals.


title:
Probability of the emergence of helical precipitation patterns in the
  wake of reaction-diffusion fronts

abstract:
  Helical and helicoidal precipitation patterns emerging in the wake of
reaction-diffusion fronts are studied. In our experiments, these chiral
structures arise with well-defined probabilities P_H controlled by conditions
such as e.g., the initial concentration of the reagents. We develop a model
which describes the observed experimental trends. The results suggest that P_H
is determined by a delicate interplay among the time and length scales related
to the front and to the unstable precipitation modes and, furthermore, the
noise amplitude also plays a quantifiable role.


title:
Can AGN feedback-driven star formation explain the size evolution of
  massive galaxies?

abstract:
  Observations indicate that massive galaxies at z~2 are more compact than
galaxies of comparable mass at z~0, with effective radii evolving by a factor
of ~3-5. This implies that galaxies grow significantly in size but relatively
little in mass over the past ~10 Gyr. Two main physical models have been
proposed in order to explain the observed evolution of massive galaxies:
"mergers" and "puffing-up" scenarios. Here we introduce another possibility,
and discuss the potential role of the central active galactic nucleus (AGN)
feedback on the evolution of its host galaxy. We consider triggering of star
formation, due to AGN feedback, with radiation pressure on dusty gas as the
driving feedback mechanism. In this picture, stars are formed in the
feedback-driven outflow at increasingly larger radii and build up the outer
regions of the host galaxy. The resulting increase in size and stellar mass can
be compared with the observed growth of massive galaxies. Star formation in the
host galaxy is likely obscured due to dust extinction and reddening. We suggest
a number of observational predictions of our model, and discuss possible
implications for AGN feedback-driven star formation.


title:
A certain critical density property for invariant Harnack inequalities
  in H-type groups

abstract:
  We consider second order linear degenerate-elliptic operators which are
elliptic with respect to horizontal directions generating a stratified algebra
of H-type. Extending a result by Guti\'errez and Tournier for the Heisenberg
group, we prove a critical density estimate by assuming a condition of
Cordes-Landis type. We then deduce an invariant Harnack inequality for the
non-negative solutions from a result by Di Fazio, Guti\'errez, and Lanconelli.


title:
Inverse scattering with the data at fixed energy and fixed incident
  direction

abstract:
  Consider the Schr\"odinger operator $-\nabla^2+q$ $ $q$, $q=q(x), x \in
\mathbf{R}^3$. Let $A(\beta,\alpha, k)$ be the corresponding scattering
amplitude, $k^2$ be the energy, $\alpha \in S^2$ be the incident direction,
$\beta \in S^2$ be the direction of scattered wave, $S^2$ be the unit sphere in
$\mathbf{R}^3$. Assume that $k=k_0 >0$ is fixed, and $\alpha=\alpha_0$ is
fixed. Then the scattering data are $A(\beta)= A(\beta,\alpha_0,
k_0)=A_q(\beta)$ is a function on $S^2$. The following invers$ \textit{IP:
Given an arbitrary $f \in L^2(S^2)$ and an arbitrary small number $$ $q \in
C_0^{\infty}(D)$, where $D \in \mathbf{R}^3$ is an arbitrary fixed domai$
$||A_q(\beta)-f(\beta)||_{L^2(S^2)} < \epsilon$?} A positive answer to this
question is given. A method for constructing such a $q$ is proposed. There are
infinitely many such $q$, not necessarily real-valued.


title:
Coulomb scattering in the massless Nelson model I. Foundations of
  two-electron scattering

abstract:
  We construct two-electron scattering states and verify their tensor product
structure in the infrared-regular massless Nelson model. The proof follows the
lines of Haag-Ruelle scattering theory: Scattering state approximants are
defined with the help of two time-dependent renormalized creation operators of
the electrons acting on the vacuum. They depend on ground state wave functions
of the (single-electron) fiber Hamiltonians with infrared cut-off. Convergence
of these approximants as $t\to \infty$ is shown with the help of Cook's method
combined with a non-stationary phase argument. Removal of the infrared cut-off
in the limit $t\to \infty$ requires sharp estimates on the derivatives of these
ground state wave functions w.r.t. electron and photon momenta, with mild
dependence on the infrared cut-off. These key estimates, which carry
information about the localization of electrons in space, are obtained in a
companion paper with the help of iterative analytic perturbation theory. Our
results hold in the weak coupling regime.


title:
Asymptotic Data Rates of Receive-Diversity Systems with MMSE Estimation
  and Spatially Correlated Interferers

abstract:
  An asymptotic technique is presented to characterize the bits/symbol
achievable on a representative wireless link in a spatially distributed network
with active interferers at correlated positions, N receive diversity branches,
and linear Minimum-Mean-Square-Error (MMSE) receivers. This framework is then
applied to systems including analogs to Matern type I and type II networks
which are useful to model systems with Medium-Access Control (MAC), cellular
uplinks with orthogonal transmissions and frequency reuse, and Boolean cluster
networks. It is found that for our network models, with moderately large N, the
correlation between interferer positions does not significantly influence the
bits/symbol resulting in simple approximations for the data rates achievable in
such networks which are known to be difficult to analyze and for which few
analytical results are available.


title:
Interpretation of Infrared Vibration-Rotation Spectra of Interstellar
  and Circumstellar Molecules

abstract:
  Infrared vibration-rotation lines can be valuable probes of interstellar and
circumstellar molecules, especially symmetric molecules, which have no pure
rotational transitions. But most such observations have been interpreted with
an isothermal absorbing slab model, which leaves out important radiative
transfer and molecular excitation effects. A more realistic non-LTE and
non-isothermal radiative transfer model has been constructed. The results of
this model are in much better agreement with the observations, including cases
where lines in one branch of a vibration-rotation band are in absorption and
another in emission. In general, conclusions based on the isothermal absorbing
slab model can be very misleading, but the assumption of LTE may not lead to
such large errors, particularly if the radiation field temperature is close to
the gas temperature.


title: Quadro-quadric special birational transformations of projective spaces

abstract:
  Special birational transformations $\Phi:\p^r\da Z$ defined by quadric
hypersurfaces are studied by means of the variety of lines $\mathcal
L_z\subset\p^{r-1}$ passing through a general point $z\in Z$. Classification
results are obtained when $Z$ is either a Grassmannian of lines, or the
10-dimensional spinor variety, or the $E_6$-variety. In the particular case of
quadro-quadric transformations, we extend the well-known classification of Ein
and Shepherd-Barron coming from Zak's classification of Severi varieties to a
wider class of prime Fano manifolds $Z$. Combining both results, we get a
classification of special birational transformations $\Phi:\p^r\da Z$ defined
by quadric hypersurfaces onto (a linear setion of) a rational homogeneous
variety different from a projective space and a quadric hypersurface.


title:
Canonical forms of Order-$k$ ($k = 2, 3, 4$) Symmetric Tensors of Format
  $3 \times \dots \times 3$ Over Prime Fields

abstract:
  We consider symmetric tensors of format: $3 \times 3$ over $\mathbb{F}_p$ for
$p = 2, 3, 5$; $3 \times 3 \times 3$ over $\mathbb{F}_p$ for $p = 2, 3$; and $3
\times 3 \times 3 \times 3$ over $\mathbb{F}_p$ for $p = 2, 3$. In each case we
compute their equivalence classes under the action of the general linear group
$GL_3 (\mathbb{F}_p )$. We use computer algebra to determine the set of tensors
of each symmetric rank, then we compute the orbit of the group action. We
determine the maximum symmetric rank of these tensors and compare it with the
maximum rank.


title: An IMF Study of the Dwarf Starburst Galaxy NGC 4214

abstract:
  The production rate of ionizing photons in young < 8 Myr, unresolved stellar
clusters in the nearby irregular galaxy NGC 4214 is probed using
multi-wavelength Hubble Space Telescope WFC3 data. We normalize the ionizing
photon rate by the cluster mass to investigate the upper end of the stellar
initial mass function (IMF). We have found that within the uncertainties the
upper end of the stellar IMF appears to be universal in this galaxy, and that
deviations from a universal IMF can be attributed to stochastic sampling of
stars in clusters with masses < 10^3 M_sun. Furthermore, we have found that
there does not seem to be a dependence of the maximum stellar mass on the
cluster mass. We have also found that for massive clusters, feedback may cause
an underrepresentation in Ha luminosities, which needs to be taken into account
when conducting this type of analysis.


title: Spike train statistics and Gibbs distributions

abstract:
  This paper is based on a lecture given in the LACONEU summer school,
Valparaiso, January 2012. We introduce Gibbs distribution in a general setting,
including non stationary dynamics, and present then three examples of such
Gibbs distributions, in the context of neural networks spike train statistics:
(i) Maximum entropy model with spatio-temporal constraints; (ii) Generalized
Linear Models; (iii) Conductance based Inte- grate and Fire model with chemical
synapses and gap junctions.


title:
Classical homogeneous multidimensional continued fraction algorithms are
  ergodic

abstract:
  Homogeneous continued fraction algorithms are multidimensional
generalizations of the classical Euclidean algorithm, the dissipative map $$
(x_1,x_2) \in \mathbb{R}_+^2 \longmapsto \left\{\begin{array}{ll}
  (x_1 - x_2, x_2), & \mbox{if $x_1 \geq x_2$}
  (x_1, x_2 - x_1), & \mbox{otherwise.} \end{array} \right. $$ We focus on
those which act piecewise linearly on finitely many copies of positive cones
which we call Rauzy induction type algorithms.
  In particular, a variation Selmer algorithm belongs to this class. We prove
that Rauzy induction type algorithms, as well as Selmer algorithms, are ergodic
with respect to Lebesgue measure.


title:
The phase diagram and critical behavior of the three-state majority-vote
  model

abstract:
  The three-state majority-vote model with noise on Erdos-Renyi's random graphs
has been studied. Using Monte Carlo simulations we obtain the phase diagram,
along with the critical exponents. Exact results for limiting cases are
presented, and shown to be in agreement with numerical values. We find that the
critical noise qc is an increasing function of the mean connectivity z of the
graph. The critical exponents beta/nu, gamma/nu and 1/nu are calculated for
several values of connectivity. We also study the globally connected network,
which corresponds to the mean-field limit z = N-1 -> infinity. Our numerical
results indicate that the correlation length scales with the number of nodes in
the graph, which is consistent with an effective dimensionality equal to unity.


title:
Matching Pursuit LASSO Part II: Applications and Sparse Recovery over
  Batch Signals

abstract:
  Matching Pursuit LASSIn Part I \cite{TanPMLPart1}, a Matching Pursuit LASSO
({MPL}) algorithm has been presented for solving large-scale sparse recovery
(SR) problems. In this paper, we present a subspace search to further improve
the performance of MPL, and then continue to address another major challenge of
SR -- batch SR with many signals, a consideration which is absent from most of
previous $\ell_1$-norm methods. As a result, a batch-mode {MPL} is developed to
vastly speed up sparse recovery of many signals simultaneously. Comprehensive
numerical experiments on compressive sensing and face recognition tasks
demonstrate the superior performance of MPL and BMPL over other methods
considered in this paper, in terms of sparse recovery ability and efficiency.
In particular, BMPL is up to 400 times faster than existing $\ell_1$-norm
methods considered to be state-of-the-art.O Part II: Applications and Sparse
Recovery over Batch Signals


title:
Global Star Formation Rates and Dust Emission Over the Galaxy
  Interaction Sequence

abstract:
  We measured and modeled the spectral energy distributions (SEDs) in 28 bands
from the ultraviolet to the far-infrared (FIR) for 31 interacting galaxies in
14 systems. The sample is drawn from the Spitzer Interacting Galaxy Survey,
which probes a range of galaxy interaction parameters at multiple wavelengths
with an emphasis on the infrared bands. The subset presented in this paper
consists of all galaxies for which FIR Herschel SPIRE observations are publicly
available. Our SEDs combine the Herschel photometry with multi-wavelength data
from Spitzer, GALEX, Swift UVOT, and 2MASS. While the shapes of the SEDs are
broadly similar across our sample, strongly interacting galaxies typically have
more mid-infrared emission relative to their near-infrared and FIR emission
than weakly or moderately interacting galaxies. We modeled the full SEDs to
derive host galaxy star formation rates (SFR), specific star formation rates
(sSFR), stellar masses, dust temperatures, dust luminosities, and dust masses.
We find increases in the dust luminosity and mass, SFR, and cold (15-25 K) dust
temperature as the interaction progresses from moderately to strongly
interacting and between non- interacting and strongly interacting galaxies. We
also find increases in the SFR between weakly and strongly interacting
galaxies. In contrast, the sSFR remains unchanged across all the interaction
stages. The SFR derived from the SED modeling agrees well with rates estimated
by proportionality relations that depend on infrared emission. (abridged)


title:
Coulomb scattering in the massless Nelson model II. Regularity of ground
  states

abstract:
  For the massless Nelson model we provide detailed information about the
dependence of the normalized ground states $\check{\psi}_{P,\sigma}$ of the
fiber single-electron Hamiltonians $H_{P,\sigma}$ on the total momentum $P$ and
the infrared cut-off $\sigma$. This information is obtained with the help of
the iterative analytic perturbation theory. In particular, we derive bounds of
the form \[ \|\partial_{P_i}\check{\psi}_{P,\sigma}\|,\ \| \partial_{P_i}
\partial_{P_j}\check{\psi}_{P,\sigma} \|\leq
\frac{c}{\sigma^{\delta_{\lambda_0}}}, \] for some constant $c$ and a function
of the maximal admissible coupling constant $\lambda_0\mapsto
\delta_{\lambda_0}$ s.t. $\lim_{\lambda_0\to 0}\delta_{\lambda_0}=0$. These
results hold both in the infrared-regular and infrared-singular case. They are
exploited in part I of this series to construct the two-electron scattering
states in the infrared-regular massless Nelson model (in the absence of an
infrared cut-off) along the lines of Haag-Ruelle scattering theory. They should
also be relevant for the problem of scattering of two infraparticles in the
infrared-singular Nelson model, whose solution is the goal of this series of
papers. Although a part of a larger investigation, the present work is written
in a self-contained fashion.


title:
Is Eddington-Born-Infeld theory really free of cosmological
  singularities?

abstract:
  The Eddington-inspired-Born-Infeld (EiBI) theory has been recently
resurrected. Such a theory is characterized by being equivalent to Einstein
theory in vacuum but differing from it in the presence of matter. One of the
virtues of the theory is to avoid the Big Bang singularity for a radiation
filled universe. In this paper, we analyze singularity avoidance in this kind
of model. More precisely, we analyze the behavior of a homogeneous and
isotropic universe filled with phantom energy in addition to the dark and
baryonic matter. Unlike the Big Bang singularity that can be avoided in this
kind of model through a bounce or a loitering effect on the physical metric, we
find that the Big Rip singularity is unavoidable in the EiBI phantom model even
though it can be postponed towards a slightly further future cosmic time as
compared with the same singularity in other models based on the standard
general relativity and with the same matter content described above.


title:
Masses and age of the Chemically Peculiar double-lined binary
  $\chi$~Lupi

abstract:
  We aim at measuring the stellar parameters of the two Chemically Peculiar
components of the B9.5Vp HgMn + A2 Vm double-lined spectroscopic binary
HD141556, whose period is 15.25 days. We combined historical radial velocity
measurements with new spatially resolved astrometric observations from
PIONIER/VLTI to reconstruct the three-dimensional orbit of the binary, and thus
obtained the individual masses. We fit the available photometric points
together with the flux ratios provided by interferometry to constrain the
individual sizes, which we compared to predictions from evolutionary models.The
individual masses of the components are $\Ma = 2.84 \pm 0.12\ \Msun$ and $\Mb =
1.94 \pm 0.09\ \Msun$. The dynamical distance is compatible with the Hipparcos
parallax. We find linear stellar radii of $\Ra=2.85 \pm 0.15\ \Rsun$ and
$\Rb=1.75 \pm 0.18\ \Rsun$. This result validates a posteriori the flux ratio
used in previous detailed abundance studies. We determine a sub-solar initial
metallicity $Z=0.012\pm0.003$ and an age of $(2.8\pm0.3)\times10^8\ $years. Our
results imply that the primary rotates more slowly than its synchronous
velocity, while the secondary is probably synchronous. We show that strong
tidal coupling during the pre-main sequence evolution followed by a full
decoupling at zero-age main sequence provides a plausible explanation for these
very low rotation rates.


title: Sur l'uniformisation des orbifolds K\"ahl\'eriens compacts

abstract:
  This article generalises to K\"ahler orbifolds general results on
uniformisation of compact K\"ahler manifolds such as the Shafarevich conjecture
for linear fundamental groups.


title:
Repr\'esentations lin\'eaires des groupes k\"ahl\'eriens :
  Factorisations et conjecture de Shafarevich lin\'eaire

abstract:
  We extend to compact K\"ahler manifolds some classical results on linear
representation of fundamental groups of complex projective manifolds. Our
approach based on an interversion lemma for fibrations with tori versus general
type manifolds as fibers gives a refinement of the classical work of Zuo. We
extend to the kahler case some general results on holomorphic convexity of
coverings such as the linear shafarevich conjecture. In the first version, the
proof of the statement that a linear Kahler group is virtually
complex-projective was wrong. We removed it from this new version. The proof
will be given in a forthcoming work.


title:
Experimental evidence and theoretical modeling of two-photon absorption
  dynamics in the reduction of intensity noise of solid-state Er:Yb lasers

abstract:
  A theoretical and experimental investigation of the intensity noise reduction
induced by two-photon absorption in a Er,Yb:Glass laser is reported. The time
response of the two-photon absorption mechanism is shown to play an important
role on the behavior of the intensity noise spectrum of the laser. A model
including an additional rate equation for the two-photon-absorption losses is
developed and allows the experimental observations to be predicted.


title: On simple zeros of the Riemann zeta-function

abstract:
  We show that at least 19/27 of the zeros of the Riemann zeta-function are
simple, assuming the Riemann Hypothesis (RH). This was previously established
by Conrey, Ghosh and Gonek [Proc. London Math. Soc. 76 (1998), 497--522] under
the additional assumption of the Generalised Lindel\"of Hypothesis (GLH). We
are able to remove this hypothesis by careful use of the generalised Vaughan
identity.


title:
On the fate of the matter reinserted within young nuclear stellar
  clusters

abstract:
  This paper presents a hydrodynamical model describing the evolution of the
gas reinserted by stars within a rotating young nuclear star cluster (NSC). We
explicitly consider the impact of the stellar component to the flow by means of
a uniform insertion of mass and energy within the stellar cluster. The model
includes the gravity force of the stellar component and a central supermassive
black hole (SMBH), and accounts for the heating from the central source of
radiation and the radiative cooling of the thermalized gas. By using a set of
parameters typical for NSCs and SMBHs in Seyfert galaxies our simulations show
that a filamentary/clumpy structure is formed in the inner part of the cluster.
This "torus" is Compton thick and covers a large fraction of the sky (as seen
from the SMBH). In the outer parts of the cluster a powerful wind is produced,
that inhibits the infall of matter from larger scales and thus the NSC-SMBH
interplay occurs in isolation.


title:
The toric h-vector of a cubical complex in terms of noncrossing
  partition statistics

abstract:
  This paper introduces a new and simple statistic on noncrossing partitions
that expresses each coordinate of the toric $h$-vector of a cubical complex,
written in the basis of the Adin $h$-vector entries, as the total weight of all
noncrossing partitions. The same model may also be used to obtain a very simple
combinatorial interpretation of the contribution of a cubical shelling
component to the toric $h$-vector. In this model, a strengthening of the
symmetry expressed by the Dehn-Sommerville equations may be derived from the
self-duality of the noncrossing partition lattice, exhibited by the involution
of Simion and Ullman.


title: Linear Coding Schemes for the Distributed Computation of Subspaces

abstract:
  Let $X_1, ..., X_m$ be a set of $m$ statistically dependent sources over the
common alphabet $\mathbb{F}_q$, that are linearly independent when considered
as functions over the sample space. We consider a distributed function
computation setting in which the receiver is interested in the lossless
computation of the elements of an $s$-dimensional subspace $W$ spanned by the
elements of the row vector $[X_1, \ldots, X_m]\Gamma$ in which the $(m \times
s)$ matrix $\Gamma$ has rank $s$. A sequence of three increasingly refined
approaches is presented, all based on linear encoders.
  The first approach uses a common matrix to encode all the sources and a
Korner-Marton like receiver to directly compute $W$. The second improves upon
the first by showing that it is often more efficient to compute a carefully
chosen superspace $U$ of $W$. The superspace is identified by showing that the
joint distribution of the $\{X_i\}$ induces a unique decomposition of the set
of all linear combinations of the $\{X_i\}$, into a chain of subspaces
identified by a normalized measure of entropy. This subspace chain also
suggests a third approach, one that employs nested codes. For any joint
distribution of the $\{X_i\}$ and any $W$, the sum-rate of the nested code
approach is no larger than that under the Slepian-Wolf (SW) approach. Under the
SW approach, $W$ is computed by first recovering each of the $\{X_i\}$. For a
large class of joint distributions and subspaces $W$, the nested code approach
is shown to improve upon SW. Additionally, a class of source distributions and
subspaces are identified, for which the nested-code approach is sum-rate
optimal.


title: Spectral gap for quantum graphs and their connectivity

abstract:
  The spectral gap for Laplace operators on metric graphs is investigated in
relation to graph's connectivity, in particular what happens if an edge is
added to (or deleted from) a graph. It is shown that in contrast to discrete
graphs connection between the connectivity and the spectral gap is not
one-to-one. The size of the spectral gap depends not only on the topology of
the metric graph but on its geometric properties as well. It is shown that
adding sufficiently large edges as well as cutting away sufficiently small
edges leads to a decrease of the spectral gap. Corresponding explicit criteria
are given.


title: Lorentz Violation in the Higgs Sector and Noncommutative Standard Model

abstract:
  The noncommutative standard model apparently violates the Lorentz symmetry.
We compare the Lorentz violating terms in the Higgs sector of the
noncommutative standard model with their counterparts in the standard model
extension. We show that the Lorentz violating parameters in the Higgs sector
can be expressed directly in terms of the noncommutative parameter without any
background field. The absence of the background field enhances the obtained
bounds on the noncommutative parameter from the standard model extension.


title:
Closed string Ramond-Ramond proposed higher derivative interactions on
  fermionic amplitudes in IIB

abstract:
  The complete form of the amplitude of one closed string Ramond-Ramond (RR),
two fermionic strings and one scalar field in IIB superstring theory has been
computed in detail. Deriving $<V_{C}V_{\bar\psi}V_{\psi} V_{\phi}>$ by using
suitable gauge fixing, we discover some new vertices and their higher
derivative corrections. We investigate both infinite gauge and scalar
$u-$channel poles of this amplitude. In particular, by using the fact that the
kinetic term of fermion fields has no correction, employing Born-Infeld action,
the Wess-Zumino terms and their higher derivative corrections, we discover all
infinite $t,s-$channel fermion poles. The couplings between one RR and two
fermions and all their infinite higher derivative corrections have been
explored. In order to look for all infinite $(s+t+u)-$ channel scalar/gauge
poles for $p+2=n,p=n$ cases, we obtain the couplings between two fermions-two
scalars and two fermions, one scalar and one gauge field as well as all their
infinite higher derivative corrections in type IIB. Specifically we make
various comments based on arXiv:1205.5079 in favor of universality conjecture
for all order higher derivative corrections (with or without low energy
expansion) and the relation of open/closed string that is responsible for all
superstring scattering amplitudes in IIA,IIB.


title: The actin cortex as an active wetting layer

abstract:
  Using active gel theory we study theoretically the properties of the cortical
actin layer of animal cells. The cortical layer is described as a
non-equilibrium wetting film on the cell membrane. The actin density is
approximately constant in the layer and jumps to zero at its edge. The layer
thickness is determined by the ratio of the polymerization velocity and the
depolymerization rate of actin.


title: On a singular heat equation with dynamic boundary conditions

abstract:
  In this paper we analyze a nonlinear parabolic equation characterized by a
singular diffusion term describing very fast diffusion effects. The equation is
settled in a smooth bounded three-dimensional domain and complemented with a
general boundary condition of dynamic type. This type of condition prescribes
some kind of mass conservation; hence extinction effects are not expected for
solutions that emanate from strictly positive initial data. Our main results
regard existence of weak solutions, instantaneous regularization properties,
long-time behavior, and, under special conditions, uniqueness.


title:
Tunable g factor and phonon-mediated hole spin relaxation in Ge/Si
  nanowire quantum dots

abstract:
  We theoretically consider g factor and spin lifetimes of holes in a
longitudinal Ge/Si core/shell nanowire quantum dot that is exposed to external
magnetic and electric fields. For the ground states, we find a large anisotropy
of the g factor which is highly tunable by applying electric fields. This
tunability depends strongly on the direction of the electric field with respect
to the magnetic field. We calculate the single-phonon hole spin relaxation
times T1 for zero and small electric fields and propose an optimal setup in
which very large T1 of the order of tens of milliseconds can be reached.
Increasing the relative shell thickness or the longitudinal confinement length
further prolongs T1. In the absence of electric fields, the dephasing vanishes
and the decoherence time T2 is determined by T2 = 2 T1.


title:
Fingerprints of Orbital Physics in Magnetic Resonant Inelastic X-ray
  Scattering

abstract:
  Orbital degrees of freedom play a major role in the physics of many strongly
correlated transition metal compounds. However, they are still very difficult
to access experimentally, in particular by neutron scattering. We propose here
how to reveal orbital occupancies of the system ground state by magnetic
resonant inelastic x-ray scattering (RIXS). This is possible because, unlike in
neutron scattering, the intensity of the magnetic excitations in RIXS depends
essentially on the symmetry of the orbitals where the spins are in.


title:
The Relationship of Dynamical Heterogeneity to the Adam-Gibbs and Random
  First-Order Transition Theories of Glass Formation

abstract:
  We carefully examine common measures of dynamical heterogeneity for a model
polymer melt and test how these scales compare with those hypothesized by the
Adam and Gibbs (AG) and random first-order transition (RFOT) theories of
relaxation in glass-forming liquids. To this end, we first analyze clusters of
highly mobile particles, the string-like collective motion of these mobile
particles, and clusters of relative low mobility. We show that the time scale
of the high-mobility clusters and strings is associated with a diffusive time
scale, while the low-mobility particles' time scale relates to a structural
relaxation time. We find a high degree of similarity in the geometrical
structure of these particle clusters. In particular, we show that the fractal
dimensions of these clusters are consistent with those of swollen branched
polymers or branched polymers with screened excluded-volume interactions. In
contrast, the fractal dimension of the strings crosses over from that of
self-avoiding walks for small strings, to simple random walks for longer, {more
strongly interacting, strings, corresponding to flexible polymers with screened
excluded-volume interactions. We examine the appropriateness of identifying the
size scales of either mobile particle clusters or strings with the size of
cooperatively rearranging regions (CRR) in the AG and RFOT theories. We find
that the string size appears to be the most consistent measure of CRR for both
the AG and RFOT models. Identifying strings or clusters with the "mosaic"
length of the RFOT model relaxes the conventional assumption that the "entropic
droplets" are compact. We also confirm the validity of the entropy formulation
of the AG theory, constraining the exponent values of the RFOT theory. This
constraint, together with the analysis of size scales, enables us to estimate
the characteristic exponents of RFOT.


title: Microscopic theory of photonic band gaps in optical lattices

abstract:
  We propose a microscopic model to describe the scattering of light by atoms
in optical lattices. The model is shown to efficiently capture Bragg
scattering, spontaneous emission and photonic band gaps. A connection to the
transfer matrix formalism is established in the limit of a one-dimensional
optical lattice, and we find the two theories to yield results in good
agreement. The advantage of the microscopic model is, however, that it suits
better for studies of finite-size and disorder effects.


title:
Some recent progress in understanding exclusive double charmonium
  production at $B$ factories

abstract:
  We review some recent progress in understanding various exclusive double
charmonium production processes at $B$ factories, within the nonrelativistic
QCD factorization framework. First we investigate the impact of the joint
perturbative and relativistic correction on the process that has attracted a
great amount of attention in the past decade, $e^+e^-\to J/\psi+\eta_c$. We
then briefly discuss the phenomenological implication of the next-to-leading
order perturbative correction to the processes $e^+e^-\to J/\psi+\chi_{c0,1,2}
(\eta_{c2})$. We further emphasize a novel theoretical challenge, which is
recently discovered by applying the NRQCD factorization approach to the
helicity-suppressed hard exclusive reactions involving heavy quarkonium.


title: A hybrid Euler-Hadamard product and moments of \zeta'(\rho)

abstract:
  Keating and Snaith modeled the Riemann zeta-function \zeta(s) by
characteristic polynomials of random NxN unitary matrices, and used this to
conjecture the asymptotic main term for the 2k-th moment of \zeta(1/2+it) when
k>-1/2. However, an arithmetical factor, widely believed to be part of the
leading term coefficient, had to be inserted in an ad hoc manner. Gonek, Hughes
and Keating later developed a hybrid formula for \zeta(s) that combines a
truncation of its Euler product with a product over its zeros. Using it, they
recovered the moment conjecture of Keating and Snaith in a way that naturally
includes the arithmetical factor. Here we use the hybrid formula to recover a
conjecture of Hughes, Keating and O'Connell concerning discrete moments of the
derivative of the Riemann zeta-function averaged over the zeros of \zeta(s),
incorporating the arithmetical factor in a natural way.


title: A Weyl Creation Algebra Approach to the Riemann Hypothesis

abstract:
  We sketch a Weyl creation operator approach to the Riemann Hypothesis;
i.e.,arithmetic on the Weyl algebras with ergodic theory to transport
operators. We prove that finite Hasse-Dirichlet alternating zeta functions or
eta functions can be induced from a product of "creation operators". The latter
idea is the result of considering complex quantum mechanics with complex
time-space related concepts. Then we overview these ideas, with variations,
which may provide a pathway that may settle RH; e.g., a Euler Factor analysis
to study the quantum behavior of the integers as one pushes up the critical
line near a macro zeta function zero.


title:
A Correction to "Dynkin Games Via Dirichlet Forms and Singular Control
  of One-Dimensional Diffusion"

abstract:
  In the paper "Dynkin Games Via Dirichlet Forms and Singular Control of
One-Dimensional Diffusion", the authors tried to show the existences of a
smooth value function and an optimal policy to a one-dimensional stochastic
singular control problem, where the underlying process is a generalized
diffusion process given by dX_t=\mu(X_t)dt+\sigma(X_t)dw_t, in which w_t is a
Wiener process. It is found that either a condition on \mu(x) and \sigma(x)
should be added, or a different diffusion process should be considered and as a
result, the main theorem of this paper should be amended.


title: Collision Assisted Zeeman Cooling with Multiple Types of Atoms

abstract:
  Through a combination of spin-exchange collisions in a magnetic field and
optical pumping, it is possible to cool a gas of atoms without requiring the
loss of atoms from the gas. We investigated this technique, Collision Assisted
Zeeman Cooling, using 85Rb and 87Rb. We experimentally confirmed that our
measured two isotope CAZ cooling rate agreed with a cooling rate predicted
though a simple analytic model. As part of the measured cooling rate, we
quantitatively characterized the heating rates associated with our actual
implementation of this cooling technique and found hyperfine-changing
collisions to be a significant limitation for the 85/87Rb gas mixture. We
comment on the prospects for improving the cooling performance beyond that
presented in this work.


title: Electron-Electron Interactions and Plasmon Dispersion in Graphene

abstract:
  Plasmons in two-dimensional electron systems with nonparabolic bands, such as
graphene, feature strong dependence on electron-electron interactions. We use a
many-body approach to relate plasmon dispersion at long wavelengths to Landau
Fermi-liquid interactions and quasiparticle velocity. An identical
renormalization is shown to arise for the magnetoplasmon resonance. For a model
with N>>1 fermion species, this approach predicts a power-law dependence for
plasmon frequency vs. carrier concentration, valid in a wide range of doping
densities, both high and low. Gate tunability of plasmons in graphene can be
exploited to directly probe the effects of electron-electron interaction.


title: A binary scenario for the pre-explosion outburst of the supernova 2010mc

abstract:
  I raise the possibility that the pre-explosion outburst (PEO) of the type IIn
supernova 2010mc (PTF 10tel) was energized by mass accretion onto an O
main-sequence stellar companion. According to this suggestion the SN progenitor
suffered a rapid expansion within months before explosion. The expansion was
driven by leakage of energy from the core where vigorous oxygen nuclear burning
takes place within a year prior to explosion. This expansion triggered mass
transfer onto the secondary star. Most of the extra energy of the outburst
comes from the accretion of ~0.1Mo onto the secondary star. As well, the gas
outflowing at v~2000 km/s was launched from the accreting secondary star, most
likely in a bipolar outflow. The binary model can account for the slower
circumstellar medium that was ejected at earlier times, and explain the
red-shifted peak of the Halpha emission at 5.8 days past explosion. I compare
some properties of the PEO of SN 2010mc to those of other stellar eruptions,
such as the stellar merger event V838 Monocerotis and the nineteenth century
Great Eruption of the massive stellar binary system Eta Carinae. I speculate
that all Type IIn supernovae owe their dense circumstellar gas to binary
interaction.


title:
On the relation between connectivity, independence and generalized
  caterpillars

abstract:
  A spanning generalized caterpillar is a spanning tree in which all vertices
of degree more than two are on a path. In this note, we find a relation between
the existence of spanning generalized caterpillar and the independence and
connectivity number in a graph. We also point out to an error in a "theorem" in
the paper "Spanning spiders and light-splitting switches", by L. Gargano et al.
in Discrete Math. (2004), and find out a relation between another mentioned
theorem and the existence of spanning generalized caterpillar.


title: Cognitive Interference Alignment for OFDM Two-tiered Networks

abstract:
  In this contribution, we introduce an interference alignment scheme that
allows the coexistence of an orthogonal frequency division multiplexing (OFDM)
macro-cell and a cognitive small-cell, deployed in a two-tiered structure and
transmitting over the same bandwidth. We derive the optimal linear strategy for
the single antenna secondary base station, maximizing the spectral efficiency
of the opportunistic link, accounting for both signal sub-space structure and
power loading strategy. Our analytical and numerical findings prove that the
precoder structure proposed is optimal for the considered scenario in the face
of Rayleigh and exponential decaying channels.


title: Dyson-Schwinger equations in the theory of computation

abstract:
  Following Manin's approach to renormalization in the theory of computation,
we investigate Dyson-Schwinger equations on Hopf algebras, operads and
properads of flow charts, as a way of encoding self-similarity structures in
the theory of algorithms computing primitive and partial recursive functions
and in the halting problem.


title:
Magnetic form factor, field map and field distribution for a BCS type-II
  superconductor near its Bc2(T) phase boundary

abstract:
  We review the magnetic form factor deduced by Delrieu from the Gorkov's
equation for a Bardeen-Cooper-Schrieffer (BCS) type-II superconductor near its
Bc2 phase boundary, i.e. when its magnetization is small. A numerical study of
the form factor, field map, and field distribution follows. The characteristics
of the transition from the low-temperature BCS to the high-temperature
Ginzburg-Landau vortex lattices is studied. The exotic shape of the component
field distribution and the form factor at low temperature and as a function of
the external field intensity are discussed. Our numerical work should be
helpful for the analysing of small angle neutron scattering and muon spin
rotation vortex-lattice data recorded for BCS superconductors and maybe other
superconductors in the clean limit.


title:
Early Science Results from SOFIA, the World's Largest Airborne
  Observatory

abstract:
  The Stratospheric Observatory For Infrared Astronomy, or SOFIA, is the
largest flying observatory ever built,consisting of a 2.7-meter diameter
telescope embedded in a modified Boeing 747-SP aircraft. SOFIA is a joint
project between NASA and the German Aerospace Center Deutsches Zentrum fur Luft
und-Raumfahrt (DLR). By flying at altitudes up to 45000 feet, the observatory
gets above 99.9 percent of the infrared-absorbing water vapor in the Earth's
atmosphere. This opens up an almost uninterrupted wavelength range from
0.3-1600 microns that is in large part obscured from ground based
observatories. Since its 'Initial Science Flight' in December 2010, SOFIA has
flown several dozen science flights, and has observed a wide array of objects
from Solar System bodies, to stellar nurseries, to distant galaxies. This paper
reviews a few of the exciting new science results from these first flights
which were made by three instruments: the mid-infrared camera FORCAST, the
far-infrared heterodyne spectrometer GREAT, and the optical occultation
photometer HIPO.


title: Road to room-temperature superconductivity: A universal model

abstract:
  In a semiclassical view superconductivity is attributed exclusively to the
advance of atoms' outer s electrons through the nuclei of neighbor atoms in a
solid. The necessary progression of holes in the opposite direction has the
electric and magnetic effect as if two electrons were advancing instead of each
actual one. Superconductivity ceases when the associated lateral oscillation of
the outer s electrons extends between neighbor atoms. If such overswing occurs
already at T = 0, then the material is a normal conductor. Otherwise, lateral
overswing can be caused by lattice vibrations at a critical temperature Tc or
by a critical magnetic field Bc. Lateral electron oscillations are reduced -
and Tc is increased - when the atoms of the outer s electrons are squeezed, be
it in the bulk crystal, in a thin film, or under external pressure on the
sample. The model is applied to alkali metals and alkali-doped fullerenes.
Aluminum serves as an example of a simple metal with superconductivity.
Application of the model to transition metals, intertransitional alloys and
compounds of transition metals with other elements sheds light on the pattern
of their critical temperature. More examples of the squeeze effect are provided
by the superconductivity of PdH, MgB2, borocarbides, ferropnictides, and
organic charge-transfer salts. The model also provides the superconduction
mechanism in the oxide superconductors, exemplified by YBa2Cu3O7. Finally the
model suggests which steps to take in order to reach superconductivity at room
temperature and above.


title:
On the spectral theory of Gesztesy-\v{S}eba realizations of 1-D Dirac
  operators with point interactions on a discrete set

abstract:
  We investigate spectral properties of Gesztesy-\v{S}eba realizations
D_{X,\alpha} and D_{X,\beta} of the 1-D Dirac differential expression D with
point interactions on a discrete set $X=\{x_n\}_{n=1}^\infty\subset
\mathbb{R}.$ Here $\alpha := \{\alpha_{n}\}_{n=1}^\infty$ and \beta
:=\{\beta_{n}\}_{n=1}^\infty \subset\mathbb{R}.
  The Gesztesy-\v{S}eba realizations $D_{X,\alpha}$ and $D_{X,\beta}$ are the
relativistic counterparts of the corresponding Schr\"odinger operators
$H_{X,\alpha}$ and $H_{X,\beta}$ with $\delta$- and $\delta'$-interactions,
respectively.
  We define the minimal operator D_X as the direct sum of the minimal Dirac
operators on the intervals $(x_{n-1}, x_n)$. Then using the regularization
procedure for direct sum of boundary triplets we construct an appropriate
boundary triplet for the maximal operator $D_X^*$ in the case
$d_*(X):=\inf\{|x_i-x_j| \,, i\not=j\} = 0$. It turns out that the boundary
operators $B_{X,\alpha}$ and $B_{X,\beta}$ parameterizing the realizations
D_{X,\alpha} and D_{X,\beta} are Jacobi matrices. These matrices substantially
differ from the ones appearing in spectral theory of Schr\"odinger operators
with point interactions. We show that certain spectral properties of the
operators $D_{X,\alpha}$ and $D_{X,\beta}$ correlate with the corresponding
spectral properties of the Jacobi matrices $B_{X,\alpha}$ and $B_{X,\beta}$,
respectively. Using this connection we investigate spectral properties
(self-adjointness, discreteness, absolutely continuous and singular spectra) of
Gesztesy--{\vS}eba realizations. Moreover, we investigate the non-relativistic
limit as the velocity of light $c\to\infty$. Most of our results are new even
in the case $d_*(X)> 0.$


title: Do we understand elastic scattering up to LHC energies?

abstract:
  The measurements of high energy \bar p p and pp elastic at ISR, SPS and
Tevatron colliders have provided usefull informations on the behavior of the
scattering amplitude. A large step in energy domain is accomplished with the
LHC collider presently running, giving a unique opportunity to improve our
knowledge on the asymptotic regime of the elastic scattering amplitude and to
verify the validity of our theoretical approach, to describe the total cross
section \sigma_{tot}(s), the total elastic cross section \sigma_{el}(s), the
ratio of the real to imaginary parts of the forward amplitude \rho(s) and the
differential cross section d\sigma (s,t)/dt.


title: Singularities of Noncompact Charged Objects

abstract:
  We formulate a model of noncompact spherical charged objects in the framework
of noncommutative field theory. The Einstein-Maxwell field equations are solved
with charged anisotropic fluid. We choose the forms of mass and charge
densities which belong to two parameter family of density distribution
functions instead of densities as Gaussian width length. It is found that the
corresponding densities and the Ricci scalar are singular at origin whereas the
metric is nonsingular indicating a spacelike singularity. The numerical
solution of the horizon equation implies that there are either two or one or no
horizon depending on the mass. We also evaluate the Hawking temperature which
implies that a black hole with two horizons is evaporated to an extremal black
hole with one horizon.


title:
Parity Violating Deep Inelastic Electron-Deuteron Scattering: Higher
  Twist and Parton Angular Momentum

abstract:
  We study the effect of parton angular momentum on the twist-four correction
to the left-right asymmetry in the electron-deuteron parity-violating deep
inelastic scattering (PVDIS). We show that this higher-twist correction is
transparent to the dynamics of parton angular momentum needed to account for
the Sivers and Boer-Mulders functions and spin-independent parton distribution
functions. A sufficiently precise measurement of the PVDIS asymmetry may, thus,
provide additional information about the parton dynamics responsible for
nucleon spin.


title: Proton Electrodynamics in Liquid Water

abstract:
  The dielectric spectrum of liquid water, $10^{4} - 10^{11}$ Hz, is
interpreted in terms of diffusion of charges, formed as a result of
self-ionization of H$_{2}$O molecules. This approach explains the Debye
relaxation and the dc conductivity as two manifestations of this diffusion. The
Debye relaxation is due to the charge diffusion with a fast recombination rate,
$1/\tau_{2}$, while the dc conductivity is a manifestation of the diffusion
with a much slower recombination rate, $1/\tau_{1}$. Applying a simple model
based on Brownian-like diffusion, we find $\tau_{2} \simeq 10^{-11}$ s and
$\tau_{1} \simeq 10^{-6}$ s, and the concentrations of the charge carriers,
involved in each of the two processes, $N_{2} \simeq 5 \times 10^{26}$ m$^{-3}$
and $N_{1} \simeq 10^{14}$ m$^{-3}$. Further, we relate $N_{2}$ and $N_{1}$ to
the total concentration of H$_{3}$O$^{+}$--OH$^{-}$ pairs and to the pH index,
respectively, and find the lifetime of a single water molecule, $\tau_{0}
\simeq 10^{-9}$ s. Finally, we show that the high permittivity of water results
mostly from flickering of separated charges, rather than from reorientations of
intact molecular dipoles.


title: Chiral Majorana edge states in HgTe quantum wells

abstract:
  HgTe-based quantum wells (QWs) recently attracted a lot of attention for the
realization of a two-dimensional topological insulator with protected helical
edge states. Another class of topological systems are topological
superconductors (TSCs) with Majorana edge states. In this paper, we show how
proximity induced s-wave superconductivity in the bulk of HgTe-QWs and in the
presence of a Zeeman field can exhibit a TSC with chiral Majorana edge states.
We calculate the topological invariants and the corresponding Majorana edge
states explicitly within a four-band model accounting for inversion symmetry
breaking terms due to Rashba spin-orbit coupling and bulk inversion asymmetry
present in these QWs.


title:
Wavefunction transformation due to changing group and phase velocities:
  an expose

abstract:
  If a time-dependent Hamiltonian is allowed to evolve adiabatically, and if it
returns to its original form, then the ground state wavefunction must have
picked up the dynamic or(and) the geometric phase factor(s) due to some
interaction during the above evolution. Here, (i) we invoke the Pancharatnam's
notion of phase retardation to show that the microscopic origin of Berry's
phase is due to a change in the phase velocity (of a wavefunction), and (ii)
the dynamic phase factor is shown to have its origin in the group velocity of a
wavefunction. We also expose that the emitted photon's polarization during an
electronic transition has its origin in the changes of the phase velocity of
the electron wavefunction. Here, we prove the above statements, (i) and (ii),
such that they are not due to some interpretational issues, hence, an
expos${\rm \acute{e}}$.