title: Time, Chance, and Action

abstract:
  To operate intelligently in the world, an agent must reason about its
actions. The consequences of an action are a function of both the state of the
world and the action itself. Many aspects of the world are inherently
stochastic, so a representation for reasoning about actions must be able to
express chances of world states as well as indeterminacy in the effects of
actions and other events. This paper presents a propositional temporal
probability logic for representing and reasoning about actions. The logic can
represent the probability that facts hold and events occur at various times. It
can represent the probability that actions and other events affect the future.
It can represent concurrent actions and conditions that hold or change during
execution of an action. The model of probability relates probabilities over
time. The logical language integrates both modal and probabilistic constructs
and can thus represent and distinguish between possibility, probability, and
truth. Several examples illustrating the use of the logic are given.


title: A Dynamic Approach to Probabilistic Inference

abstract:
  In this paper we present a framework for dynamically constructing Bayesian
networks. We introduce the notion of a background knowledge base of schemata,
which is a collection of parameterized conditional probability statements.
These schemata explicitly separate the general knowledge of properties an
individual may have from the specific knowledge of particular individuals that
may have these properties. Knowledge of individuals can be combined with this
background knowledge to create Bayesian networks, which can then be used in any
propagation scheme. We discuss the theory and assumptions necessary for the
implementation of dynamic Bayesian networks, and indicate where our approach
may be useful.


title: Approximations in Bayesian Belief Universe for Knowledge Based Systems

abstract:
  When expert systems based on causal probabilistic networks (CPNs) reach a
certain size and complexity, the "combinatorial explosion monster" tends to be
present. We propose an approximation scheme that identifies rarely occurring
cases and excludes these from being processed as ordinary cases in a CPN-based
expert system. Depending on the topology and the probability distributions of
the CPN, the numbers (representing probabilities of state combinations) in the
underlying numerical representation can become very small. Annihilating these
numbers and utilizing the resulting sparseness through data structuring
techniques often results in several orders of magnitude of improvement in the
consumption of computer resources. Bounds on the errors introduced into a
CPN-based expert system through approximations are established. Finally,
reports on empirical studies of applying the approximation scheme to a
real-world CPN are given.


title: Robust Inference Policies

abstract:
  A series of monte carlo studies were performed to assess the extent to which
different inference procedures robustly output reasonable belief values in the
context of increasing levels of judgmental imprecision. It was found that, when
compared to an equal-weights linear model, the Bayesian procedures are more
likely to deduce strong support for a hypothesis. But, the Bayesian procedures
are also more likely to strongly support the wrong hypothesis. Bayesian
techniques are more powerful, but are also more error prone.


title: Minimum Error Tree Decomposition

abstract:
  This paper describes a generalization of previous methods for constructing
tree-structured belief network with hidden variables. The major new feature of
the described method is the ability to produce a tree decomposition even when
there are errors in the correlation data among the input variables. This is an
important extension of existing methods since the correlational coefficients
usually cannot be measured with precision. The technique involves using a
greedy search algorithm that locally minimizes an error function.


title:
A Polynomial Time Algorithm for Finding Bayesian Probabilities from
  Marginal Constraints

abstract:
  A method of calculating probability values from a system of marginal
constraints is presented. Previous systems for finding the probability of a
single attribute have either made an independence assumption concerning the
evidence or have required, in the worst case, time exponential in the number of
attributes of the system. In this paper a closed form solution to the
probability of an attribute given the evidence is found. The closed form
solution, however does not enforce the (non-linear) constraint that all terms
in the underlying distribution be positive. The equation requires O(r^3) steps
to evaluate, where r is the number of independent marginal constraints
describing the system at the time of evaluation. Furthermore, a marginal
constraint may be exchanged with a new constraint, and a new solution
calculated in O(r^2) steps. This method is appropriate for calculating
probabilities in a real time expert system


title: Computation of Variances in Causal Networks

abstract:
  The causal (belief) network is a well-known graphical structure for
representing independencies in a joint probability distribution. The exact
methods and the approximation methods, which perform probabilistic inference in
causal networks, often treat the conditional probabilities which are stored in
the network as certain values. However, if one takes either a subjectivistic or
a limiting frequency approach to probability, one can never be certain of
probability values. An algorithm for probabilistic inference should not only be
capable of reporting the inferred probabilities; it should also be capable of
reporting the uncertainty in these probabilities relative to the uncertainty in
the probabilities which are stored in the network. In section 2 of this paper a
method is given for determining the prior variances of the probabilities of all
the nodes. Section 3 contains an approximation method for determining the
variances in inferred probabilities.


title: A Sensitivity Analysis of Pathfinder

abstract:
  Knowledge elicitation is one of the major bottlenecks in expert system
design. Systems based on Bayes nets require two types of information--network
structure and parameters (or probabilities). Both must be elicited from the
domain expert. In general, parameters have greater opacity than structure, and
more time is spent in their refinement than in any other phase of elicitation.
Thus, it is important to determine the point of diminishing returns, beyond
which further refinements will promise little (if any) improvement. Sensitivity
analyses address precisely this issue--the sensitivity of a model to the
precision of its parameters. In this paper, we report the results of a
sensitivity analysis of Pathfinder, a Bayes net based system for diagnosing
pathologies of the lymph system. This analysis is intended to shed some light
on the relative importance of structure and parameters to system performance,
as well as the sensitivity of a system based on a Bayes net to noise in its
assessed parameters.


title: IDEAL: A Software Package for Analysis of Influence Diagrams

abstract:
  IDEAL (Influence Diagram Evaluation and Analysis in Lisp) is a software
environment for creation and evaluation of belief networks and influence
diagrams. IDEAL is primarily a research tool and provides an implementation of
many of the latest developments in belief network and influence diagram
evaluation in a unified framework. This paper describes IDEAL and some lessons
learned during its development.


title: On the Equivalence of Causal Models

abstract:
  Scientists often use directed acyclic graphs (days) to model the qualitative
structure of causal theories, allowing the parameters to be estimated from
observational data. Two causal models are equivalent if there is no experiment
which could distinguish one from the other. A canonical representation for
causal models is presented which yields an efficient graphical criterion for
deciding equivalence, and provides a theoretical basis for extracting causal
structures from empirical data. This representation is then extended to the
more general case of an embedded causal model, that is, a dag in which only a
subset of the variables are observable. The canonical representation presented
here yields an efficient algorithm for determining when two embedded causal
models reflect the same dependency information. This algorithm leads to a model
theoretic definition of causation in terms of statistical dependencies.


title:
Application of Confidence Intervals to the Autonomous Acquisition of
  High-level Spatial Knowledge

abstract:
  Objects in the world usually appear in context, participating in spatial
relationships and interactions that are predictable and expected. Knowledge of
these contexts can be used in the task of using a mobile camera to search for a
specified object in a room. We call this the object search task. This paper is
concerned with representing this knowledge in a manner facilitating its
application to object search while at the same time lending itself to
autonomous learning by a robot. The ability for the robot to learn such
knowledge without supervision is crucial due to the vast number of possible
relationships that can exist for any given set of objects. Moreover, since a
robot will not have an infinite amount of time to learn, it must be able to
determine an order in which to look for possible relationships so as to
maximize the rate at which new knowledge is gained. In effect, there must be a
"focus of interest" operator that allows the robot to choose which examples are
likely to convey the most new information and should be examined first. This
paper demonstrates how a representation based on statistical confidence
intervals allows the construction of a system that achieves the above goals. An
algorithm, based on the Highest Impact First heuristic, is presented as a means
for providing a "focus of interest" with which to control the learning process,
and examples are given.


title: Directed Reduction Algorithms and Decomposable Graphs

abstract:
  In recent years, there have been intense research efforts to develop
efficient methods for probabilistic inference in probabilistic influence
diagrams or belief networks. Many people have concluded that the best methods
are those based on undirected graph structures, and that those methods are
inherently superior to those based on node reduction operations on the
influence diagram. We show here that these two approaches are essentially the
same, since they are explicitly or implicity building and operating on the same
underlying graphical structures. In this paper we examine those graphical
structures and show how this insight can lead to an improved class of directed
reduction methods.


title: Optimal Decomposition of Belief Networks

abstract:
  In this paper, optimum decomposition of belief networks is discussed. Some
methods of decomposition are examined and a new method - the method of Minimum
Total Number of States (MTNS) - is proposed. The problem of optimum belief
network decomposition under our framework, as under all the other frameworks,
is shown to be NP-hard. According to the computational complexity analysis, an
algorithm of belief network decomposition is proposed in (Wee, 1990a) based on
simulated annealing.


title: Pruning Bayesian Networks for Efficient Computation

abstract:
  This paper analyzes the circumstances under which Bayesian networks can be
pruned in order to reduce computational complexity without altering the
computation for variables of interest. Given a problem instance which consists
of a query and evidence for a set of nodes in the network, it is possible to
delete portions of the network which do not participate in the computation for
the query. Savings in computational complexity can be large when the original
network is not singly connected. Results analogous to those described in this
paper have been derived before [Geiger, Verma, and Pearl 89, Shachter 88] but
the implications for reducing complexity of the computations in Bayesian
networks have not been stated explicitly. We show how a preprocessing step can
be used to prune a Bayesian network prior to using standard algorithms to solve
a given problem instance. We also show how our results can be used in a
parallel distributed implementation in order to achieve greater savings. We
define a computationally equivalent subgraph of a Bayesian network. The
algorithm developed in [Geiger, Verma, and Pearl 89] is modified to construct
the subgraphs described in this paper with O(e) complexity, where e is the
number of edges in the Bayesian network. Finally, we define a minimal
computationally equivalent subgraph and prove that the subgraphs described are
minimal.


title:
On Heuristics for Finding Loop Cutsets in Multiply-Connected Belief
  Networks

abstract:
  We introduce a new heuristic algorithm for the problem of finding minimum
size loop cutsets in multiply connected belief networks. We compare this
algorithm to that proposed in [Suemmondt and Cooper, 1988]. We provide lower
bounds on the performance of these algorithms with respect to one another and
with respect to optimal. We demonstrate that no heuristic algorithm for this
problem cam be guaranteed to produce loop cutsets within a constant difference
from optimal. We discuss experimental results based on randomly generated
networks, and discuss future work and open questions.


title:
A Combination of Cutset Conditioning with Clique-Tree Propagation in the
  Pathfinder System

abstract:
  Cutset conditioning and clique-tree propagation are two popular methods for
performing exact probabilistic inference in Bayesian belief networks. Cutset
conditioning is based on decomposition of a subset of network nodes, whereas
clique-tree propagation depends on aggregation of nodes. We describe a means to
combine cutset conditioning and clique- tree propagation in an approach called
aggregation after decomposition (AD). We discuss the application of the AD
method in the Pathfinder system, a medical expert system that offers assistance
with diagnosis in hematopathology.


title: Possibility as Similarity: the Semantics of Fuzzy Logic

abstract:
  This paper addresses fundamental issues on the nature of the concepts and
structures of fuzzy logic, focusing, in particular, on the conceptual and
functional differences that exist between probabilistic and possibilistic
approaches. A semantic model provides the basic framework to define
possibilistic structures and concepts by means of a function that quantifies
proximity, closeness, or resemblance between pairs of possible worlds. The
resulting model is a natural extension, based on multiple conceivability
relations, of the modal logic concepts of necessity and possibility. By
contrast, chance-oriented probabilistic concepts and structures rely on
measures of set extension that quantify the proportion of possible worlds where
a proposition is true. Resemblance between possible worlds is quantified by a
generalized similarity relation: a function that assigns a number between O and
1 to every pair of possible worlds. Using this similarity relation, which is a
form of numerical complement of a classic metric or distance, it is possible to
define and interpret the major constructs and methods of fuzzy logic:
conditional and unconditioned possibility and necessity distributions and the
generalized modus ponens of Zadeh.


title:
Integrating Case-Based and Rule-Based Reasoning: the Possibilistic
  Connection

abstract:
  Rule based reasoning (RBR) and case based reasoning (CBR) have emerged as two
important and complementary reasoning methodologies in artificial intelligence
(Al). For problem solving in complex, real world situations, it is useful to
integrate RBR and CBR. This paper presents an approach to achieve a compact and
seamless integration of RBR and CBR within the base architecture of rules. The
paper focuses on the possibilistic nature of the approximate reasoning
methodology common to both CBR and RBR. In CBR, the concept of similarity is
casted as the complement of the distance between cases. In RBR the transitivity
of similarity is the basis for the approximate deductions based on the
generalized modus ponens. It is shown that the integration of CBR and RBR is
possible without altering the inference engine of RBR. This integration is
illustrated in the financial domain of mergers and acquisitions. These ideas
have been implemented in a prototype system called MARS.


title: Credibility Discounting in the Theory of Approximate Reasoning

abstract:
  We are concerned with the problem of introducing credibility type information
into reasoning systems. The concept of credibility allows us to discount
information provided by agents. An important characteristic of this kind of
procedure is that a complete lack of credibility rather than resulting in the
negation of the information provided results in the nullification of the
information provided. We suggest a representational scheme for credibility
qualification in the theory of approximate reasoning. We discuss the concept of
relative credibility. By this idea we mean to indicate situations in which the
credibility of a piece of evidence is determined by its compatibility with
higher priority evidence. This situation leads to structures very much in the
spirit of nonmonotonic reasoning.


title:
Updating with Belief Functions, Ordinal Conditioning Functions and
  Possibility Measures

abstract:
  This paper discusses how a measure of uncertainty representing a state of
knowledge can be updated when a new information, which may be pervaded with
uncertainty, becomes available. This problem is considered in various
framework, namely: Shafer's evidence theory, Zadeh's possibility theory,
Spohn's theory of epistemic states. In the two first cases, analogues of
Jeffrey's rule of conditioning are introduced and discussed. The relations
between Spohn's model and possibility theory are emphasized and Spohn's
updating rule is contrasted with the Jeffrey-like rule of conditioning in
possibility theory. Recent results by Shenoy on the combination of ordinal
conditional functions are reinterpreted in the language of possibility theory.
It is shown that Shenoy's combination rule has a well-known possibilistic
counterpart.


title: A New Approach to Updating Beliefs

abstract:
  We define a new notion of conditional belief, which plays the same role for
Dempster-Shafer belief functions as conditional probability does for
probability functions. Our definition is different from the standard definition
given by Dempster, and avoids many of the well-known problems of that
definition. Just as the conditional probability Pr (lB) is a probability
function which is the result of conditioning on B being true, so too our
conditional belief function Bel (lB) is a belief function which is the result
of conditioning on B being true. We define the conditional belief as the lower
envelope (that is, the inf) of a family of conditional probability functions,
and provide a closed form expression for it. An alternate way of understanding
our definition of conditional belief is provided by considering ideas from an
earlier paper [Fagin and Halpern, 1989], where we connect belief functions with
inner measures. In particular, we show here how to extend the definition of
conditional probability to non measurable sets, in order to get notions of
inner and outer conditional probabilities, which can be viewed as best
approximations to the true conditional probability, given our lack of
information. Our definition of conditional belief turns out to be an exact
analogue of our definition of inner conditional probability.


title:
The Transferable Belief Model and Other Interpretations of
  Dempster-Shafer's Model

abstract:
  Dempster-Shafer's model aims at quantifying degrees of belief But there are
so many interpretations of Dempster-Shafer's theory in the literature that it
seems useful to present the various contenders in order to clarify their
respective positions. We shall successively consider the classical probability
model, the upper and lower probabilities model, Dempster's model, the
transferable belief model, the evidentiary value model, the provability or
necessity model. None of these models has received the qualification of
Dempster-Shafer. In fact the transferable belief model is our interpretation
not of Dempster's work but of Shafer's work as presented in his book (Shafer
1976, Smets 1988). It is a ?purified' form of Dempster-Shafer's model in which
any connection with probability concept has been deleted. Any model for belief
has at least two components: one static that describes our state of belief, the
other dynamic that explains how to update our belief given new pieces of
information. We insist on the fact that both components must be considered in
order to study these models. Too many authors restrict themselves to the static
component and conclude that Dempster-Shafer theory is the same as some other
theory. But once the dynamic component is considered, these conclusions break
down. Any comparison based only on the static component is too restricted. The
dynamic component must also be considered as the originality of the models
based on belief functions lies in its dynamic component.


title: Valuation-Based Systems for Discrete Optimization

abstract:
  This paper describes valuation-based systems for representing and solving
discrete optimization problems. In valuation-based systems, we represent
information in an optimization problem using variables, sample spaces of
variables, a set of values, and functions that map sample spaces of sets of
variables to the set of values. The functions, called valuations, represent the
factors of an objective function. Solving the optimization problem involves
using two operations called combination and marginalization. Combination tells
us how to combine the factors of the joint objective function. Marginalization
is either maximization or minimization. Solving an optimization problem can be
simply described as finding the marginal of the joint objective function for
the empty set. We state some simple axioms that combination and marginalization
need to satisfy to enable us to solve an optimization problem using local
computation. For optimization problems, the solution method of valuation-based
systems reduces to non-serial dynamic programming. Thus our solution method for
VBS can be regarded as an abstract description of dynamic programming. And our
axioms can be viewed as conditions that permit the use of dynamic programming.


title: Computational Aspects of the Mobius Transform

abstract:
  In this paper we associate with every (directed) graph G a transformation
called the Mobius transformation of the graph G. The Mobius transformation of
the graph (O) is of major significance for Dempster-Shafer theory of evidence.
However, because it is computationally very heavy, the Mobius transformation
together with Dempster's rule of combination is a major obstacle to the use of
Dempster-Shafer theory for handling uncertainty in expert systems. The major
contribution of this paper is the discovery of the 'fast Mobius
transformations' of (O). These 'fast Mobius transformations' are the fastest
algorithms for computing the Mobius transformation of (O). As an easy but
useful application, we provide, via the commonality function, an algorithm for
computing Dempster's rule of combination which is much faster than the usual
one.


title: Using Dempster-Shafer Theory in Knowledge Representation

abstract:
  In this paper, we suggest marrying Dempster-Shafer (DS) theory with Knowledge
Representation (KR). Born out of this marriage is the definition of
"Dempster-Shafer Belief Bases", abstract data types representing uncertain
knowledge that use DS theory for representing strength of belief about our
knowledge, and the linguistic structures of an arbitrary KR system for
representing the knowledge itself. A formal result guarantees that both the
properties of the given KR system and of DS theory are preserved. The general
model is exemplified by defining DS Belief Bases where First Order Logic and
(an extension of) KRYPTON are used as KR systems. The implementation problem is
also touched upon.


title:
A Hierarchical Approach to Designing Approximate Reasoning-Based
  Controllers for Dynamic Physical Systems

abstract:
  This paper presents a new technique for the design of approximate reasoning
based controllers for dynamic physical systems with interacting goals. In this
approach, goals are achieved based on a hierarchy defined by a control
knowledge base and remain highly interactive during the execution of the
control task. The approach has been implemented in a rule-based computer
program which is used in conjunction with a prototype hardware system to solve
the cart-pole balancing problem in real-time. It provides a complementary
approach to the conventional analytical control methodology, and is of
substantial use where a precise mathematical model of the process being
controlled is not available.


title:
Evidence Combination and Reasoning and Its Application to Real-World
  Problem-Solving

abstract:
  In this paper a new mathematical procedure is presented for combining
different pieces of evidence which are represented in the interval form to
reflect our knowledge about the truth of a hypothesis. Evidences may be
correlated to each other (dependent evidences) or conflicting in supports
(conflicting evidences). First, assuming independent evidences, we propose a
methodology to construct combination rules which obey a set of essential
properties. The method is based on a geometric model. We compare results
obtained from Dempster-Shafer's rule and the proposed combination rules with
both conflicting and non-conflicting data and show that the values generated by
proposed combining rules are in tune with our intuition in both cases.
Secondly, in the case that evidences are known to be dependent, we consider
extensions of the rules derived for handling conflicting evidence. The
performance of proposed rules are shown by different examples. The results show
that the proposed rules reasonably make decision under dependent evidences


title:
On Some Equivalence Relations between Incidence Calculus and
  Dempster-Shafer Theory of Evidence

abstract:
  Incidence Calculus and Dempster-Shafer Theory of Evidence are both theories
to describe agents' degrees of belief in propositions, thus being appropriate
to represent uncertainty in reasoning systems. This paper presents a
straightforward equivalence proof between some special cases of these theories.


title:
Using Belief Functions for Uncertainty Management and Knowledge
  Acquisition: An Expert Application

abstract:
  This paper describes recent work on an ongoing project in medical diagnosis
at the University of Guelph. A domain on which experts are not very good at
pinpointing a single disease outcome is explored. On-line medical data is
available over a relatively short period of time. Belief Functions
(Dempster-Shafer theory) are first extracted from data and then modified with
expert opinions. Several methods for doing this are compared and results show
that one formulation statistically outperforms the others, including a method
suggested by Shafer. Expert opinions and statistically derived information
about dependencies among symptoms are also compared. The benefits of using
uncertainty management techniques as methods for knowledge acquisition from
data are discussed.


title: An Architecture for Probabilistic Concept-Based Information Retrieval

abstract:
  While concept-based methods for information retrieval can provide improved
performance over more conventional techniques, they require large amounts of
effort to acquire the concepts and their qualitative and quantitative
relationships. This paper discusses an architecture for probabilistic
concept-based information retrieval which addresses the knowledge acquisition
problem. The architecture makes use of the probabilistic networks technology
for representing and reasoning about concepts and includes a knowledge
acquisition component which partially automates the construction of concept
knowledge bases from data. We describe two experiments that apply the
architecture to the task of retrieving documents about terrorism from a set of
documents from the Reuters news service. The experiments provide positive
evidence that the architecture design is feasible and that there are advantages
to concept-based methods.


title: Amplitude-Based Approach to Evidence Accumulation

abstract:
  We point out the need to use probability amplitudes rather than probabilities
to model evidence accumulation in decision processes involving real physical
sensors. Optical information processing systems are given as typical examples
of systems that naturally gather evidence in this manner. We derive a new,
amplitude-based generalization of the Hough transform technique used for object
recognition in machine vision. We argue that one should use complex Hough
accumulators and square their magnitudes to get a proper probabilistic
interpretation of the likelihood that an object is present. Finally, we suggest
that probability amplitudes may have natural applications in connectionist
models, as well as in formulating knowledge-based reasoning problems.


title: A Probabilistic Reasoning Environment

abstract:
  A framework is presented for a computational theory of probabilistic
argument. The Probabilistic Reasoning Environment encodes knowledge at three
levels. At the deepest level are a set of schemata encoding the system's domain
knowledge. This knowledge is used to build a set of second-level arguments,
which are structured for efficient recapture of the knowledge used to construct
them. Finally, at the top level is a Bayesian network constructed from the
arguments. The system is designed to facilitate not just propagation of beliefs
and assimilation of evidence, but also the dynamic process of constructing a
belief network, evaluating its adequacy, and revising it when necessary.


title: On Non-monotonic Conditional Reasoning

abstract:
  This note is concerned with a formal analysis of the problem of non-monotonic
reasoning in intelligent systems, especially when the uncertainty is taken into
account in a quantitative way. A firm connection between logic and probability
is established by introducing conditioning notions by means of formal
structures that do not rely on quantitative measures. The associated
conditional logic, compatible with conditional probability evaluations, is
non-monotonic relative to additional evidence. Computational aspects of
conditional probability logic are mentioned. The importance of this development
lies on its role to provide a conceptual basis for various forms of evidence
combination and on its significance to unify multi-valued and non-monotonic
logics


title:
Decisions with Limited Observations over a Finite Product Space: the
  Klir Effect

abstract:
  Probability estimation by maximum entropy reconstruction of an initial
relative frequency estimate from its projection onto a hypergraph model of the
approximate conditional independence relations exhibited by it is investigated.
The results of this study suggest that use of this estimation technique may
improve the quality of decisions that must be made on the basis of limited
observations over a decomposable finite product space.


title: Fine-Grained Decision-Theoretic Search Control

abstract:
  Decision-theoretic control of search has previously used as its basic unit.
of computation the generation and evaluation of a complete set of successors.
Although this simplifies analysis, it results in some lost opportunities for
pruning and satisficing. This paper therefore extends the analysis of the value
of computation to cover individual successor evaluations. The analytic
techniques used may prove useful for control of reasoning in more general
settings. A formula is developed for the expected value of a node, k of whose n
successors have been evaluated. This formula is used to estimate the value of
expanding further successors, using a general formula for the value of a
computation in game-playing developed in earlier work. We exhibit an improved
version of the MGSS* algorithm, giving empirical results for the game of
Othello.


title: Rules, Belief Functions and Default Logic

abstract:
  This paper describes a natural framework for rules, based on belief
functions, which includes a repre- sentation of numerical rules, default rules
and rules allowing and rules not allowing contraposition. In particular it
justifies the use of the Dempster-Shafer Theory for representing a particular
class of rules, Belief calculated being a lower probability given certain
independence assumptions on an underlying space. It shows how a belief function
framework can be generalised to other logics, including a general Monte-Carlo
algorithm for calculating belief, and how a version of Reiter's Default Logic
can be seen as a limiting case of a belief function formalism.


title: Combination of Evidence Using the Principle of Minimum Information Gain

abstract:
  One of the most important aspects in any treatment of uncertain information
is the rule of combination for updating the degrees of uncertainty. The theory
of belief functions uses the Dempster rule to combine two belief functions
defined by independent bodies of evidence. However, with limited dependency
information about the accumulated belief the Dempster rule may lead to
unsatisfactory results. The present study suggests a method to determine the
accumulated belief based on the premise that the information gain from the
combination process should be minimum. This method provides a mechanism that is
equivalent to the Bayes rule when all the conditional probabilities are
available and to the Dempster rule when the normalization constant is equal to
one. The proposed principle of minimum information gain is shown to be
equivalent to the maximum entropy formalism, a special case of the principle of
minimum cross-entropy. The application of this principle results in a monotonic
increase in belief with accumulation of consistent evidence. The suggested
approach may provide a more reasonable criterion for identifying conflicts
among various bodies of evidence.


title:
Probabilistic Evaluation of Candidates and Symptom Clustering for
  Multidisorder Diagnosis

abstract:
  This paper derives a formula for computing the conditional probability of a
set of candidates, where a candidate is a set of disorders that explain a given
set of positive findings. Such candidate sets are produced by a recent method
for multidisorder diagnosis called symptom clustering. A symptom clustering
represents a set of candidates compactly as a cartesian product of differential
diagnoses. By evaluating the probability of a candidate set, then, a large set
of candidates can be validated or pruned simultaneously. The probability of a
candidate set is then specialized to obtain the probability of a single
candidate. Unlike earlier results, the equation derived here allows the
specification of positive, negative, and unknown symptoms and does not make
assumptions about disorders not in the candidate.


title: Extending Term Subsumption systems for Uncertainty Management

abstract:
  A major difficulty in developing and maintaining very large knowledge bases
originates from the variety of forms in which knowledge is made available to
the KB builder. The objective of this research is to bring together two
complementary knowledge representation schemes: term subsumption languages,
which represent and reason about defining characteristics of concepts, and
proximate reasoning models, which deal with uncertain knowledge and data in
expert systems. Previous works in this area have primarily focused on
probabilistic inheritance. In this paper, we address two other important issues
regarding the integration of term subsumption-based systems and approximate
reasoning models. First, we outline a general architecture that specifies the
interactions between the deductive reasoner of a term subsumption system and an
approximate reasoner. Second, we generalize the semantics of terminological
language so that terminological knowledge can be used to make plausible
inferences. The architecture, combined with the generalized semantics, forms
the foundation of a synergistic tight integration of term subsumption systems
and approximate reasoning models.


title: Refinement and Coarsening of Bayesian Networks

abstract:
  In almost all situation assessment problems, it is useful to dynamically
contract and expand the states under consideration as assessment proceeds.
Contraction is most often used to combine similar events or low probability
events together in order to reduce computation. Expansion is most often used to
make distinctions of interest which have significant probability in order to
improve the quality of the assessment. Although other uncertainty calculi,
notably Dempster-Shafer [Shafer, 1976], have addressed these operations, there
has not yet been any approach of refining and coarsening state spaces for the
Bayesian Network technology. This paper presents two operations for refining
and coarsening the state space in Bayesian Networks. We also discuss their
practical implications for knowledge acquisition.


title: Second Order Probabilities for Uncertain and Conflicting Evidence

abstract:
  In this paper the elicitation of probabilities from human experts is
considered as a measurement process, which may be disturbed by random
'measurement noise'. Using Bayesian concepts a second order probability
distribution is derived reflecting the uncertainty of the input probabilities.
The algorithm is based on an approximate sample representation of the basic
probabilities. This sample is continuously modified by a stochastic simulation
procedure, the Metropolis algorithm, such that the sequence of successive
samples corresponds to the desired posterior distribution. The procedure is
able to combine inconsistent probabilities according to their reliability and
is applicable to general inference networks with arbitrary structure.
Dempster-Shafer probability mass functions may be included using specific
measurement distributions. The properties of the approach are demonstrated by
numerical experiments.


title: Computing Probability Intervals Under Independency Constraints

abstract:
  Many AI researchers argue that probability theory is only capable of dealing
with uncertainty in situations where a full specification of a joint
probability distribution is available, and conclude that it is not suitable for
application in knowledge-based systems. Probability intervals, however,
constitute a means for expressing incompleteness of information. We present a
method for computing such probability intervals for probabilities of interest
from a partial specification of a joint probability distribution. Our method
improves on earlier approaches by allowing for independency relationships
between statistical variables to be exploited.


title:
An Empirical Analysis of Likelihood-Weighting Simulation on a Large,
  Multiply-Connected Belief Network

abstract:
  We analyzed the convergence properties of likelihood- weighting algorithms on
a two-level, multiply connected, belief-network representation of the QMR
knowledge base of internal medicine. Specifically, on two difficult diagnostic
cases, we examined the effects of Markov blanket scoring, importance sampling,
demonstrating that the Markov blanket scoring and self-importance sampling
significantly improve the convergence of the simulation on our model.


title: Towards a Normative Theory of Scientific Evidence

abstract:
  A scientific reasoning system makes decisions using objective evidence in the
form of independent experimental trials, propositional axioms, and constraints
on the probabilities of events. As a first step towards this goal, we propose a
system that derives probability intervals from objective evidence in those
forms. Our reasoning system can manage uncertainty about data and rules in a
rule based expert system. We expect that our system will be particularly
applicable to diagnosis and analysis in domains with a wealth of experimental
evidence such as medicine. We discuss limitations of this solution and propose
future directions for this research. This work can be considered a
generalization of Nilsson's "probabilistic logic" [Nil86] to intervals and
experimental observations.


title: A Model for Non-Monotonic Reasoning Using Dempster's Rule

abstract:
  Considerable attention has been given to the problem of non-monotonic
reasoning in a belief function framework. Earlier work (M. Ginsberg) proposed
solutions introducing meta-rules which recognized conditional independencies in
a probabilistic sense. More recently an e-calculus formulation of default
reasoning (J. Pearl) shows that the application of Dempster's rule to a
non-monotonic situation produces erroneous results. This paper presents a new
belief function interpretation of the problem which combines the rules in a way
which is more compatible with probabilistic results and respects conditions of
independence necessary for the application of Dempster's combination rule. A
new general framework for combining conflicting evidence is also proposed in
which the normalization factor becomes modified. This produces more intuitively
acceptable results.


title: Default Reasoning and the Transferable Belief Model

abstract:
  Inappropriate use of Dempster's rule of combination has led some authors to
reject the Dempster-Shafer model, arguing that it leads to supposedly
unacceptable conclusions when defaults are involved. A most classic example is
about the penguin Tweety. This paper will successively present: the origin of
the miss-management of the Tweety example; two types of default; the correct
solution for both types based on the transferable belief model (our
interpretation of the Dempster-Shafer model (Shafer 1976, Smets 1988)); Except
when explicitly stated, all belief functions used in this paper are simple
support functions, i.e. belief functions for which only one proposition (the
focus) of the frame of discernment receives a positive basic belief mass with
the remaining mass being given to the tautology. Each belief function will be
described by its focus and the weight of the focus (e.g. m(A)=.9). Computation
of the basic belief masses are always performed by vacuously extending each
belief function to the product space built from all variables involved,
combining them on that space by Dempster's rule of combination, and projecting
the result to the space corresponding to each individual variable.


title: Separable and transitive graphoids

abstract:
  We examine three probabilistic formulations of the sentence a and b are
totally unrelated with respect to a given set of variables U. First, two
variables a and b are totally independent if they are independent given any
value of any subset of the variables in U. Second, two variables are totally
uncoupled if U can be partitioned into two marginally independent sets
containing a and b respectively. Third, two variables are totally disconnected
if the corresponding nodes are disconnected in every belief network
representation. We explore the relationship between these three formulations of
unrelatedness and explain their relevance to the process of acquiring
probabilistic knowledge from human experts.


title: Analysis in HUGIN of Data Conflict

abstract:
  After a brief introduction to causal probabilistic networks and the HUGIN
approach, the problem of conflicting data is discussed. A measure of conflict
is defined, and it is used in the medical diagnostic system MUNIN. Finally, it
is discussed how to distinguish between conflicting data and a rare case.


title: Evaporation phenomena in f(T) gravity

abstract:
  We formulate evaporation phenomena in a generic model of generalized
teleparallel gravity in Weitzenbock spacetime with diagonal and non-diagonal
tetrads basis. We also perform the perturbation analysis around the constant
torsion scalar solution named Nariai spacetime which is an exact solution of
field equations as the limiting case of the Schwarzschild-de Sitter and in the
limit where two back hole and their cosmological horizons coincide. By a
carefully analysis of the horizon perturbation equation, we show that
(anti)evaporation can not happen if we use a diagonal tetrad basis. This result
implies that a typical black hole in any generic form of generalized
teleparallel gravity is frozen in its initial state if we use the diagonal
tetrads. But in the case of non-diagonal tetrads the analysis is completely
different. By a suitable non trivial non-diagonal tetrad basis we investigate
the linear stability of the model under perturbations of the metric and torsion
simultaneously. We observe that in spite of the diagonal case, both evaporation
and anti evaporation can happen. The phenomena depend on the initial phase of
the horizon perturbation. In the first mode when we restrict ourselves to the
first lower modes the (anti)evaporation happens. So, in non-diagonal case the
physical phenomena is reasonable. This is an important advantage of using
non-diagonal tetrads instead of the diagonal ones. We also see that this is an
universal feature, completely independent from the form of the model.


title:
Free algebras, amalgamation, and a theorem of Vaught for many valued
  logics

abstract:
  We investigate atomicity of free algebras and various forms of amalgamation
for BL and MV algebras, and also Heyting algebras, though the latter algebras
may not be linearly ordered, so strictly speaking their corresponding
intuitionistic logic does not belong to many valued logic. Generalizing results
of Comer proved in the classical first order case; working out a sheaf duality
on the Zarski topology defined on the prime spectrum of such algebras, we infer
several definability theorems, and obtain a representation theorem for theories
as continuous sections of Sheaves. We also prove an omitting types theorem for
fuzzy logic, and formulate and prove several of its consequences (in classical
model theory) adapted to our case; that has to do with existence and uniqueness
of prime and atomic models.


title: On some open problems in Algebraic logic

abstract: Several open problems in algebraic logic are solved.


title:
Protoplanetary disk evolution and stellar parameters of T Tauri binaries
  in Chamaeleon I

abstract:
  This study aims to determine the impact of stellar binary companions on the
lifetime and evolution of circumstellar disks in the Chamaeleon I (Cha I)
star-forming region by measuring the frequency and strength of accretion and
circumstellar dust signatures around the individual components of T Tauri
binary stars. We used high-angular resolution adaptive optics JHKL'-band
photometry and 1.5-2.5mu spectroscopy of 19 visual binary and 7 triple stars in
Cha I - including one newly discovered tertiary component - with separations
between ~25 and ~1000au. The data allowed us to infer stellar component masses
and ages and, from the detection of near-infrared excess emission and the
strength of Brackett-gamma emission, the presence of ongoing accretion and hot
circumstellar dust of the individual stellar component of each binary. Of all
the stellar components in close binaries with separations of 25-100au,
10(+15-5)% show signs of accretion. This is less than half of the accretor
fraction found in wider binaries, which itself appears significantly reduced
(~44%) compared with previous measurements of single stars in Cha I. Hot dust
was found around 50(+30-15)% of the target components, a value that is
indistinguishable from that of Cha I single stars. Only the closest binaries
(<25au) were inferred to have a significantly reduced fraction (<~25%) of
components that harbor hot dust. Accretors were exclusively found in binary
systems with unequal component masses M_secondary/M_primary < 0.8, implying
that the detected accelerated disk dispersal is a function of mass-ratio. This
agrees with the finding that only one accreting secondary star was found, which
is also the weakest accretor in the sample. The results imply that disk
dispersal is more accelerated the stronger the dynamical disk truncation, i.e.,
the smaller the inferred radius of the disk. (abridged)


title: Determining Triple Gauge Boson Couplings from Higgs Data

abstract:
  In the framework of effective Lagrangians with the SU(2)_L x U(1)_Y symmetry
linearly realized, modifications of the couplings of the Higgs field to the
electroweak gauge bosons are related to anomalous triple gauge couplings
(TGCs). Here, we show that the analysis of the latest Higgs boson production
data at the LHC and Tevatron give rise to strong bounds on TGCs that are
complementary to those from direct TGC analysis. We present the constraints on
TGCs obtained by combining all available data on direct TGC studies and on
Higgs production analysis. Note added: The analysis has been updated with all
the public data available as November 2013. Updates of this analysis are
provided at http://hep.if.usp.br/Higgs


title:
The role of relativistic jets in the heaviest and most active
  supermassive black holes at high redshift

abstract:
  In powerful radio-quiet active galactic nuclei (AGN), black holes heavier
than one billion solar masses form at a redshift ~1.5-2. Supermassive black
holes in jetted radio-loud AGN seems to form earlier, at a redshift close to 4.
The ratio of active radio-loud to radio-quiet AGN hosting heavy black holes is
therefore a rather a strong function of redshift. We report on some recent
evidence supporting this conclusion, gathered from the Burst Alert Telescope
(BAT, onboard Swift) and by the Large Area Telescope (LAT, onboard Fermi). We
suggest that the more frequent occurrence of relativistic jets in the most
massive black holes at high redshifts, compared to later times, could be due to
the average black hole spin being greater in the distant past, or else to the
jet helping a fast accretion rate (or some combination of the two scenarios).
We emphasize that the large total accretion efficiency of rapidly spinning
black holes inhibits a fast growth, unless a large fraction of the available
gravitational energy of the accreted mass is not converted into radiation, but
used to form and maintain a powerful jet.


title:
The VMC survey VII. Reddening map of the 30 Doradus field and the
  structure of the cold interstellar medium

abstract:
  Context: The details of how galaxies have evolved are imprinted in their star
formation history, chemical enrichment and morpho-kinematic structure.
Parameters behind these can be measured by combining photometric techniques
with modelling. However, there are uncertainties from the ambiguity of colour
and magnitude and the effects of interstellar reddening.
  Aims: In this paper we present a detailed reddening map of the central 30
Doradus region of the Large Magellanic Cloud; for community use and to test the
methods used. The reddening, a measurement of dust extinction, acts as a tracer
of the interstellar medium (ISM).
  Methods: Near infrared photometry of red clump stars are used to measure
reddening as extinction is the main cause of their colour and magnitude
variance. The star formation history is used to convert colour to reddening
values which are subsequently converted to visual extinction.
  Results: Presented is a dust map for the 30 Doradus field. This map samples a
region of 1x1.5 deg, containing ~1.5x10^5 red clump stars which probe reddening
up to AV=6 mag. We compare our map with maps from the literature, including
optical extinction maps and radio, mid- and far-infrared maps of atomic
hydrogen and dust emission. Through estimation of column density we locate
molecular clouds.
  Conclusions: This new reddening map shows correlation with equivalent maps in
the literature, validating the method of red clump star selection. We make our
reddening map available for community use. In terms of ISM the red clump stars
appear to be more affected by the cooler dust measured by 70 micron emission
because there is stronger correlation between increasing emission and
extinction due to red clump stars not being located near hot stars that would
heat the dust. The transition from atomic hydrogen to molecular hydrogen occurs
between densities of NH=4x10^21 to 6x10^21 cm^-2.


title:
The star formation rate and stellar mass limits for submillimetre
  galaxies implied by recent interferometric observations

abstract:
  Explaining the observed number counts of submillimetre (submm) galaxies
(SMGs) has been a longstanding challenge for theoretical models. Surprisingly,
recent observations have suggested that the brightest SMGs are almost
exclusively multiple fainter sources blended into a single source in the
single-dish surveys. This result is in contrast with the predictions of our
previously presented theoretical model, which includes some effects of
blending. In this Letter, we consider the implications of an upper limit on the
submm flux density for the demographics of the SMG population. Using a relation
amongst submm flux, star formation rate (SFR), and dust mass (M_d) from our
previous work, we infer the maximum SFR for a range of flux density limits. For
M_d = 2 x 10^9 (5 x 10^8) M_sun, the SFR limit that corresponds to an
870-{\mu}m flux density (S_870) limit in the range 9 - 12.5 mJy is in the range
~630 - 1400 (3600 - 7700) M_sun/yr. The SFR limit implies a correspondingly
sharp, redshift-dependent cutoff in the stellar mass (M_star) function, the
value of which we predict using the S_870-M_star relation predicted by our
model. The M_star limit decreases with increasing redshift: for an S_870 limit
of 9 - 12.5 mJy, the M_star limit ranges from ~4 - 7 x 10^12 M_sun at z ~ 1 to
~3 - 5 x 10^11 M_sun at z ~ 6. We discuss the few interferometrically detected
SMGs that may be brighter than the proposed cutoff. Although such objects are
certainly interesting, inferences based on such objects may not apply to most
SMGs.


title: Duality and Decay of Macroscopic F-Strings

abstract:
  We study the decay of fundamental string loops of arbitrary size
L/min(n,m)>>\sqrt{\alpha'}, labelled by (n,m;\lambda_n,\bar{\lambda}_m), where
n,m correspond to left- and right-mover harmonics and \lambda_n,\bar{\lambda}_m
to polarisation tensors, and find that a description in terms of the recent
coherent vertex operator construction of Hindmarsh and Skliros is
computationally very efficient. We primarily show that the decay rates and mass
shifts of vertex operators (n,m;\lambda_n,\bar{\lambda}_m) and their "duals"
(n,m;\lambda_n,\bar{\lambda}_m^*) are equal to leading order in the string
coupling, implying for instance that decay rates of epicycloids equal those of
hypocycloids. We then compute the power and decay rates associated to massless
IR radiation for the trajectory (1,1;\lambda_1,\bar{\lambda}_1), and find that
it is precisely reproduced by the low energy effective theory of Dabholkar and
Harvey. Guided by this correspondence, we conjecture the result for arbitrary
trajectories (n,m;\lambda_n,\bar{\lambda}_m) and discover a curious relation
between gravitational and axion plus dilaton radiation. It is now possible to
start exploring string evolution in regimes where a low energy effective
description is less useful, such as in the vicinity of cusps.


title: Rotating Black Droplet

abstract:
  We construct the gravitational dual, in the Unruh state, of the "jammed"
phase of a CFT at strong coupling and infinite N on a fixed five-dimensional
rotating Myers-Perry black hole with equal angular momenta. When the angular
momenta are all zero, the solution corresponds to the five-dimensional
generalization of the solution first studied by Figueras, Lucietti, and
Wiseman. In the extremal limit, when the angular momenta of the Myers-Perry
black hole are maximum, the Unruh, Boulware and Hartle-Hawking states
degenerate. We give a detailed analysis of the corresponding holographic stress
energy tensor for all values of the angular momenta, finding it to be regular
at the horizon in all cases. We compare our results with existent literature on
thermal states of free field theories on black hole backgrounds.


title:
On the Possibility of Habitable Moons in the System of HD 23079: Results
  from Orbital Stability Studies

abstract:
  The aim of our study is to investigate the possibility of habitable moons
orbiting the giant planet HD 23079b, a Jupiter-mass planet, which follows a
low-eccentricity orbit in the outer region of HD 23079's habitable zone. We
show that HD 23079b is able to host habitable moons in prograde and retrograde
orbits, as expected, noting that the outer stability limit for retrograde
orbits is increased by nearly 90% compared to that of prograde orbits, a result
consistent with previous generalized studies. For the targeted parameter space
it was found that the outer stability limit for habitable moons varies between
0.05236 and 0.06955 AU (prograde orbits) and between 0.1023 and 0.1190 AU
(retrograde orbits) depending on the orbital parameters of the Jupiter-type
planet if a minimum mass is assumed. These intervals correspond to 0.306 and
0.345 (prograde orbits) and 0.583 and 0.611 (retrograde orbits) of the planet's
Hill radius. Larger stability limits are obtained if an increased value for the
planetary mass m_p is considered; they are consistent with the theoretically
deduced relationship of m_p^{1/3}. Finally, we compare our results to the
statistical formulae of Domingos et al. (2006) [MNRAS 373, 1227], indicating
both concurrence and limitations.


title: Localization protected quantum order

abstract:
  Closed quantum systems with quenched randomness exhibit many-body localized
regimes wherein they do not equilibrate even though prepared with macroscopic
amounts of energy above their ground states. We show that such localized
systems can order in that individual many-body eigenstates can break symmetries
or display topological order in the infinite volume limit. Indeed, isolated
localized quantum systems can order even at energy densities where the
corresponding thermally equilibrated system is disordered, i.e.: localization
protects order. In addition, localized systems can move between ordered and
disordered localized phases via non-thermodynamic transitions in the properties
of the many-body eigenstates. We give evidence that such transitions may
proceed via localized critical points. We note that localization provides
protection against decoherence that may allow experimental manipulation of
macroscopic quantum states. We also identify a `spectral transition' involving
a sharp change in the spectral statistics of the many-body Hamiltonian.

categories: cond-mat.stat-mech cond-mat.dis-nn cond-mat.mes-hall cond-mat.quant-gas cond-mat.str-el


title: Dark Matter Detection in Focus Point Supersymmetry

abstract:
  We determine the prospects for direct and indirect detection of thermal relic
neutralinos in supersymmetric theories with multi-TeV squarks and sleptons. We
consider the concrete example of the focus point region of minimal
supergravity, but our results are generically valid for all models with
decoupled scalars and mixed Bino-Higgsino or Higgsino-like dark matter. We
determine the parameter space consistent with a 125 GeV Higgs boson including
3-loop corrections in the calculation of the Higgs mass. These corrections
increase m_h by 1-3 GeV, lowering the preferred scalar mass scale and
decreasing the fine-tuning measure in these scenarios. We then systematically
examine prospects for dark matter direct and indirect detection. Direct
detection constraints do not exclude these models, especially for \mu < 0. At
the same time, the scenario generically predicts spin-independent signals just
beyond current bounds. We also consider indirect detection with neutrinos,
gamma rays, anti-protons, and anti-deuterons. Current IceCube neutrino
constraints are competitive with direct detection, implying bright prospects
for complementary searches with both direct and indirect detection.


title:
Gravito-Electromagnetic Perturbations of Kerr-Newman Black Holes:
  Stability and Isospectrality in the Slow-Rotation Limit

abstract:
  The most general stationary black-hole solution of Einstein-Maxwell theory in
vacuum is the Kerr-Newman metric, specified by three parameters: mass M, spin J
and charge Q. Within classical general relativity, the most important and
challenging open problem in black-hole perturbation theory is the study of
gravitational and electromagnetic fields in the Kerr-Newman geometry, because
of the indissoluble coupling of the perturbation functions. Here we circumvent
this long-standing problem by working in the slow-rotation limit. We compute
the quasinormal modes up to linear order in J for any value of Q and provide
the first, fully-consistent stability analysis of the Kerr-Newman metric. For
scalar perturbations the quasinormal modes can be computed exactly, and we
demonstrate that the method is accurate within 3% for spins J/Jmax<~0.5, where
Jmax is the maximum allowed spin for any value of Q. Quite remarkably, we find
numerical evidence that the axial and polar sectors of the
gravito-electromagnetic perturbations are isospectral to linear order in the
spin. The extension of our results to nonasymptotically flat space-times could
be useful in the context of gauge/gravity dualities and string theory.


title:
Towards a Renormalization Group Approach to Density Functional Theory -
  General Formalism and Case Studies -

abstract:
  We discuss a two-point particle irreducible (2PPI) approach to many-body
physics which relies on a renormalization group (RG) flow equation for the
associated effective action. In particular, the general structure and
properties of this RG flow equation are analyzed in detail. Moreover, we
discuss how our 2PPI RG approach relates to Density Functional Theory and argue
that it can in principle be used to study ground-state properties of
non-relativistic many-body systems from microscopic interactions, such as
(heavy) nuclei. For illustration purposes, we use our formalism to compute the
ground-state properties of two toy models.


title: CFTs in rotating black hole backgrounds

abstract:
  We use AdS/CFT to construct the gravitational dual of a 5D CFT in the
background of a non-extremal rotating black hole. Our boundary conditions are
such that the vacuum state of the dual CFT corresponds to the Unruh state. We
extract the expectation value of the stress tensor of the dual CFT using
holographic renormalisation and show that it is stationary and regular on both
the future and the past event horizons. The energy density of the CFT is found
to be negative everywhere in our domain and we argue that this can be
understood as a vacuum polarisation effect. We construct the solutions by
numerically solving the elliptic Einstein--DeTurck equation for stationary
Lorentzian spacetimes with Killing horizons.


title:
Comparative investigation of the freezing phenomena for quantum
  correlations under nondissipative decoherence

abstract:
  We show that the phenomenon of frozen discord, exhibited by specific classes
of two-qubit states under local nondissipative decoherent evolutions, is a
common feature of all known bona fide measures of general quantum correlations.
All those measures, despite inducing typically inequivalent orderings on the
set of nonclassically correlated states, return a constant value in the
considered settings. Every communication protocol which relies on quantum
correlations as resource will run with a performance completely unaffected by
noise in the specified dynamical conditions. We provide a geometric
interpretation of this

categories: quant-ph cond-mat.stat-mech math-ph math.MP physics.optics


title: Waves and distributions connected to systems of interacting populations

abstract:
  We discuss two cases that can be connected to the dynamics of interacting
populations: (I.) density waves for the case or negligible random fluctuations
of the populations densities, and (II.) probability distributions connected to
the model equations for of spatially averaged populations densities for the
case of significant random fluctuations of the independent quantity that can be
associated with the population density. For the case (I.) we consider model
equations containing polynomial nonlinearities. Such nonlinearities can arise
as a consequence of interaction among the populations (for the case of large
population densities) or as a result of a Taylor series expansion (for the case
of small density of interacting populations). In the both cases we can apply
the modified method of simplest equation to obtain exact traveling-wave
solutions connected to migration of population members. Such solutions are
obtained for systems consisting of 1 or 3 populations respectively. For the
case (II.) we discuss model equations of the Fokker-Planck kind for the
evolution of the statistical distributions of population densities. We derive
several stationary distributions for the population density and calculate the
expected exit time connected to the extinction of the studied population.


title:
Characterizing the Cool KOIs. V. KOI-256: A Mutually Eclipsing
  Post-Common Envelope Binary

abstract:
  We report that Kepler Object of Interest 256 (KOI-256) is a mutually
eclipsing post-common envelope binary (ePCEB), consisting of a cool white dwarf
(M = 0.592 +/- 0.089 MSun, R = 0.01345 +/- 0.00091 RSun, Teff = 7100 +/- 700 K)
and an active M3 dwarf (M = 0.51 +/- 0.16 MSun, R = 0.540 +/- 0.014 RSun, Teff
= 3450 +/- 50 K) with an orbital period of 1.37865 +/- 0.00001 days. KOI-256 is
listed as hosting a transiting planet-candidate by Borucki et al. and Batalha
et al.; here we report that the planet-candidate transit signal is in fact the
occultation of a white dwarf as it passes behind the M dwarf. We combine
publicly-available long- and short-cadence Kepler light curves with
ground-based measurements to robustly determine the system parameters. The
occultation events are readily apparent in the Kepler light curve, as is
spin-orbit synchronization of the M dwarf, and we detect the transit of the
white dwarf in front of the M dwarf halfway between the occultation events. The
size of the white dwarf with respect to the Einstein ring during transit (REin
= 0.00473 +/- 0.00055 RSun) causes the transit depth to be shallower than
expected from pure geometry due to gravitational lensing. KOI-256 is an old,
long-period ePCEB and serves as a benchmark object for studying the evolution
of binary star systems as well as white dwarfs themselves, thanks largely to
the availability of near-continuous, ultra-precise Kepler photometry.


title:
Decay coupling constant sum rules for tetraquarks
  $\bm{T[(\bar{Q}q)(Q\bar{q})]}$ with broken SU(3) symmetry

abstract:
  For tetraquarks of the form $T[(\bar{Q}q)(Q\bar{q})]$ we give sum rules for
their decay coupling constants, taking into account the SU(3) symmetry breaking
interactions to first order.


title: Gravitational collapse in Ho\v{r}ava-Lifshitz theory

abstract:
  We study gravitational collapse of a spherical fluid in nonrelativistic
general covariant theory of the Ho\v{r}ava-Lifshitz gravity with the
projectability condition and an arbitrary coupling constant $\lambda$, where
$|\lambda - 1|$ characterizes the deviation of the theory from general
relativity in the infrared limit. The junction conditions across the surface of
a collapsing star are derived under the (minimal) assumption that the junctions
be mathematically meaningful in terms of distribution theory. When the
collapsing star is made of a homogeneous and isotropic perfect fluid, and the
external region is described by a stationary spacetime, the problem reduces to
the matching of six independent conditions. If the perfect fluid is
pressureless (a dust fluid), it is found that the matching is also possible. In
particular, in the case $\lambda = 1$, the external spacetime is described by
the Schwarzschild (anti-) de Sitter solution written in Painlev\'e-Gullstrand
coordinates. In the case $\lambda \not= 1$, the external spacetime is static
but not asymptotically flat. Our treatment can be easily generalized to other
versions of Ho\v{r}ava-Lifshitz gravity or, more generally, to any theory of
higher-order derivative gravity.


title:
On the $L^p$-estimates for Beurling-Ahlfors and Riesz transforms on
  Riemannian manifolds

abstract:
  In our previous papers \cite{Li2008, Li2011}, we proved some martingale
transform representation formulas for the Riesz transforms and the
Beurling-Ahlfors transforms on complete Riemannian manifolds, and proved some
explicit $L^p$-norm estimates for these operators on complete Riemannian
manifolds with suitable curvature conditions. In this paper we correct a gap
contained in \cite{Li2008, Li2011} and prove that the $L^p$-norm of the Riesz
transforms $R_a(L)=\nabla(a-L)^{-1/2}$ can be explicitly bounded by
$C(p^*-1)^{3/2}$ if $Ric+\nabla^2\phi\geq -a$ for $a\geq 0$, and the $L^p$-norm
of the Riesz transform $R_0(L)=\nabla(-L)^{-1/2}$ is bounded by $2(p^*-1)$ if
$Ric+\nabla^2\phi=0$. We also prove that the $L^p$-norm estimates for the
Beurling-Ahlfors transforms obtained in \cite{Li2011} remain valid. Moreover,
we prove the time reversal martingale transform representation formulas for the
Riesz transforms and the Beurling-Ahlfors transforms on complete Riemannian
manifolds.


title: Bruhat and balanced graphs

abstract:
  We generalize chain enumeration in graded partially ordered sets by relaxing
the graded, poset and Eulerian requirements. The resulting balanced digraphs,
which include the classical Eulerian posets having an $R$-labeling, imply the
existence of the (non-homogeneous) ${\bf cd}$-index, a key invariant for
studying inequalities for the flag vector of polytopes. Mirroring Alexander
duality for Eulerian posets, we show an analogue of Alexander duality for
balanced digraphs. For Bruhat graphs of Coxeter groups, an important family of
balanced graphs, our theory gives elementary proofs of the existence of the
complete ${\bf cd}$-index and its properties. We also introduce the rising and
falling quasisymmetric functions of a labeled acyclic digraph and show they are
Hopf algebra homomorphisms mapping balanced digraphs to the Stembridge peak
algebra. We conjecture nonnegativity of the ${\bf cd}$-index for acyclic
digraphs having a balanced linear edge labeling.


title: Detection of a Nearby Halo Debris Stream in the WISE and 2MASS Surveys

abstract:
  Combining the Wide-Field Infrared Survey Explorer All-Sky Release with the
2MASS Point Source Catalog, we detect a nearby, moderately metal-poor stellar
debris stream spanning 24 degrees across the southern sky. The stream, which we
designate Alpheus, is at an estimated distance of ~1.9 kpc. Its position,
orientation, width, estimated metallicity, and to some extent its distance, are
in approximate agreement with what one might expect of the leading tidal tail
of the southern globular cluster NGC 288.


title: Editorial: Statistical Mechanics and Social Sciences

abstract:
  This editorial opens the special issues that the Journal of Statistical
Physics has dedicated to the growing field of statistical physics modeling of
social dynamics. The issues include contributions from physicists and social
scientists, with the goal of fostering a better communication between these two
communities.


title: Interaction of escaping cosmic rays with molecular clouds

abstract:
  The study of the gamma-ray radiation produced by cosmic rays that escape
their accelerators is of paramount importance for (at least) two reasons:
first, the detection of those gamma-ray photons can serve to identify the
sources of cosmic rays and, second, the characteristics of that radiation give
us constraints on the way in which cosmic rays propagate in the interstellar
medium. This paper reviews the present status of the field.


title: Superluminous X-rays from a superluminous supernova

abstract:
  The discovery of a population of superluminous supernovae (SLSNe), with peak
luminosities a factor of ~100 brighter than normal SNe (typically SLSNe have
M_V <-21), has shown an unexpected diversity in core-collapse supernova
properties. Numerous models have been postulated for the nature of these
events, including a strong interaction of the shockwave with a dense
circumstellar environment, a re-energizing of the outflow via a central engine,
or an origin in the catastrophic destruction of the star following a loss of
pressure due to pair production in an extremely massive stellar core (so-called
pair instability supernovae). Here we consider constraints that can be placed
on the explosion mechanism of Hydrogen-poor SLSNe (SLSNe-I) via X-ray
observations, with XMM-Newton, Chandra and Swift, and show that at least one
SLSNe-I is likely the brightest X-ray supernovae ever observed, with L_X ~
10^45 ergs/s, ~150 days after its initial discovery. This is a luminosity 3
orders of magnitude higher than seen in other X-ray supernovae powered via
circumstellar interactions. Such high X-ray luminosities are sufficient to
ionize the ejecta and markedly reduce the optical depth, making it possible to
see deep into the ejecta and any source of emission that resides there.
Alternatively, an engine could have powered a moderately relativistic jet
external to the ejecta, similar to those seen in gamma-ray bursts. If the
detection of X-rays does require an engine it implies that these SNe do create
compact objects, and that the stars are not completely destroyed in a pair
instability event. Future observations will determine which, if any, of these
mechanisms are at play in superluminous supernovae.


title:
Variational Monte Carlo study of spin polarization stability of
  fractional quantum Hall states against realistic effects in half-filled
  Landau levels

abstract:
  We compare ground state energies by variational Monte Carlo of the spin
unpolarized Halperin 331 and the spin polarized Moore-Read (MR) Pfaffian
fractional quantum Hall states at half-filling of the lowest Landau level (LLL)
and the second Landau level (SLL) as a function of small deviations around the
Coulomb point via the finite thickness effect and direct alterations to the the
first two Haldane pseudopotentials. In the comparison we find that in the LLL,
either the 331 state or the MR Pfaffian may be lower in energy depending on the
deviations. In the SLL, however, the MR Pfaffian is consistently lower in
energy except for large deviations. These results suggest that even under
moderate deviations in the interaction potential (through various physical
processes such as finite thickness, Landau level mixing, etc.), the MR Pfaffian
description is more energetically favorable than the Halperin 331 state in the
half-filled SLL (i.e. $\nu = 5/2$), consistent with recent experimental
investigations.


title: Development of an electrical model of a resistive micromegas

abstract:
  We have developped a model to simulate the behavior of a resistive micromegas
(MICROMEsh GAseous Structure) detector to a discharge using an electronic
software (Virtuoso).


title:
Time-of-flight patterns of ultra-cold bosons in optical lattices in
  various Abelian artificial magnetic field gauges

abstract:
  We calculate the time-of-flight patterns of strongly interacting bosons
confined in two-dimensional square lattice in the presence of an artificial
magnetic field using quantum rotor model that is inherently combined with the
Bogolyubov approach. We consider various geometries of the magnetic flux, which
are expected to be realizable, or have already been implemented in experimental
settings. The flexibility of the method let us to study cases of the artificial
magnetic field being uniform, staggered or forming a checkerboard
configuration. Effects of additional temporal modulation of the optical
potential that results from application of Raman lasers driving particle
transitions between lattice sites are also included. The presented
time-of-flight patterns may serve as a verification of chosen gauge in
experiments, but also provide important hints on unconventional, non-zero
momentum condensates, or possibility of observing graphene-like physics
resulting from occurrence of Dirac cones in artificial magnetic fields in
systems of ultra-cold bosons in optical lattices. Also, we elucidate on
differences between effects of magnetic field in solids and the artificial
magnetic field in optical lattices, which can be controlled on much higher
level leading to effects not possible in condensed matter physics.


title:
Sum rules for tetraquark decay coupling constants with broken SU(3)
  symmetry

abstract:
  We give sum rules for tetraquark decay coupling constants, taking into
account the SU(3) symmetry breaking interactions to first order.


title:
Evidence for Two Distinct Stellar Initial Mass Functions : Revisiting
  the Effects of Cluster Dynamical Evolution

abstract:
  We measure the velocity dispersions of six, galactic globular clusters using
spatially integrated spectra, to test for the effects of internal dynamical
evolution in the stellar mass-to-light ratios, Upsilon_*, of star clusters. In
particular, we revisit whether the low values of Upsilon_* we found in our
previous study, from which we concluded that there are at least two population
of stellar clusters with distinct stellar initial mass functions, are
artificially depressed by relaxation driven mass loss. The combination of our
previous sample of five old clusters and these six now provides an order of
magnitude range in cluster mass with which to explore this issue. We find no
relationship between cluster mass, or relaxation time, and Upsilon_*. Because
relaxation is mass dependent, we conclude that the values of Upsilon_* for
these clusters are not strongly affected by dynamical effects, and so confirm
the presence of the population of clusters with low Upsilon_*.


title: Ballistic to diffusive crossover of heat flow in graphene ribbons

abstract:
  Heat flow in nanomaterials is an important area of study, with both
fundamental and technological implications. However, little is known about heat
flow in two-dimensional (2D) devices or interconnects with dimensions
comparable to the phonon mean free path (mfp). Here, we find that short,
quarter-micron graphene samples reach ~35% of the ballistic heat conductance
limit up to room temperature, enabled by the relatively large phonon mfp (~100
nm) in substrate-supported graphene. In contrast, patterning similar samples
into nanoribbons (GNRs) leads to a diffusive heat flow regime that is
controlled by ribbon width and edge disorder. In the edge-controlled regime,
the GNR thermal conductivity scales with width approximately as ~W^{1.8+/-0.3},
being about 100 W/m/K in 65-nm-wide GNRs, at room temperature. Manipulation of
device dimensions on the scale of the phonon mfp can be used to achieve full
control of their heat-carrying properties, approaching fundamentally limited
upper or lower bounds.


title:
Laboratory demonstration of a mid-infrared AGPM vector vortex
  coronagraph

abstract:
  Coronagraphy is a powerful technique to achieve high contrast imaging and
hence to image faint companions around bright targets. Various concepts have
been used in the visible and near-infrared regimes, while coronagraphic
applications in the mid-infrared remain nowadays largely unexplored. Vector
vortex phase masks based on concentric subwavelength gratings show great
promise for such applications. We aim at producing and validating the first
high-performance broadband focal plane phase mask coronagraphs for applications
in the mid-infrared regime, and in particular the L band with a fractional
bandwidth of ~16% (3.5-4.1 \mu m). Based on rigorous coupled wave analysis, we
designed an annular groove phase mask (AGPM) producing a vortex effect in the L
band, and etched it onto a series of diamond substrates. The grating parameters
were measured by means of scanning electron microscopy. The resulting
components were then tested on a mid-infrared coronagraphic test bench. A
broadband raw null depth of 2 x 10^{-3} was obtained for our best L-band AGPM
after only a few iterations between design and manufacturing. This corresponds
to a raw contrast of about 6 x 10^{-5} (10.5 mag) at 2\lambda/D. This result is
fully in line with our projections based on rigorous coupled wave analysis
modeling, using the measured grating parameters. The sensitivity to tilt and
focus has also been evaluated. After years of technological developments,
mid-infrared vector vortex coronagraphs finally become a reality and live up to
our expectations. Based on their measured performance, our L-band AGPMs are now
ready to open a new parameter space in exoplanet imaging at major ground-based
observatories.


title: Inflationary Super-Hubble Waves and the Size of the Universe

abstract:
  The effect of the scalar spectral index on inflationary super-Hubble waves is
to amplify/damp large wavelengths according to whether the spectrum is red
($n_{s}<1$) or blue ($n_{s}>1$). As a consequence, the large-scale temperature
correlation function will unavoidably change sign at some angle if our spectrum
is red, while it will always be positive if it is blue. We show that this
inflationary filtering property also affects our estimates of the size of the
homogeneous patch of the universe through the Grishchuk-Zel'dovich effect.
Using the recent quadrupole measurement of ESA's Planck mission, we find that
the homogeneous patch of universe is at least 87 times bigger than our visible
universe if we accept Planck's best fit value $n_{s}=0.9624$. An independent
estimation of the size of the universe could be used to independently constrain
$n_{s}$, thus narrowing the space of inflationary models.


title:
Nonlinear Schr\"odinger equation on graphs: recent results and open
  problems

abstract:
  In the present paper an introduction to the new subject of nonlinear
dispersive hamiltonian equations on graphs is given. The focus is on recently
established properties of solutions in the case of nonlinear Schr\"odinger
equation. Special consideration is given to existence and behaviour of solitary
solutions. Two subjects are discussed in some detail concerning NLS equation on
a star graph: the standing waves of NLS equation on a graph with a $\delta$
interaction at the vertex; the scattering of fast solitons through an
Y-junction in the cubic case. The emphasis is on description of concepts and
results and on physical context, without reporting detailed proofs; some
perspectives and more ambitious open problems are discussed.


title: Ripples and Charge Puddles in Graphene on a Metallic Substrate

abstract:
  Graphene on a dielectric substrate exhibits spatial doping inhomogeneities,
forming electron-hole puddles. Understanding and controlling the latter is of
crucial importance for unraveling many of graphene's fundamental properties at
the Dirac point. Here we show the coexistence and correlation of charge puddles
and topographic ripples in graphene decoupled from the metallic substrate it
was grown on. The analysis of interferences of Dirac fermion-like electrons
yields a linear dispersion relation, indicating that graphene on a metal can
recover its intrinsic electronic properties.


title: Constraints on dark matter annihilation from AMS-02 results

abstract:
  We use recently released data on the positron-to-electron ratio in cosmic
rays from the AMS-02 experiment to constrain dark matter annihilation in the
Milky Way. Due to the yet unexplained positron excess, limits are generally
weaker than those obtained using other probes, especially gamma rays. This also
means that explaining the positron excess in terms of dark matter annihilation
is difficult. Only if very conservative assumptions on the dark matter
distribution in the Galactic Center region are adopted, it may be possible to
accommodate dark matter annihilating to leptons with a cross section above
10^-24 cm^3/sec. We comment on several theoretical mechanisms to explain such
large annihilation cross sections.


title: Parameterized Verification of Asynchronous Shared-Memory Systems

abstract:
  We characterize the complexity of the safety verification problem for
parameterized systems consisting of a leader process and arbitrarily many
anonymous and identical contributors. Processes communicate through a shared,
bounded-value register. While each operation on the register is atomic, there
is no synchronization primitive to execute a sequence of operations atomically.
We analyze the complexity of the safety verification problem when processes are
modeled by finite-state machines, pushdown machines, and Turing machines. The
problem is coNP-complete when all processes are finite-state machines, and is
PSPACE-complete when they are pushdown machines. The complexity remains
coNP-complete when each Turing machine is allowed boundedly many interactions
with the register. Our proofs use combinatorial characterizations of
computations in the model, and in case of pushdown-systems, some
language-theoretic constructions of independent interest.


title: Five Misunderstandings About Case-Study Research

abstract:
  This article examines five common misunderstandings about case-study
research: (1) Theoretical knowledge is more valuable than practical knowledge;
(2) One cannot generalize from a single case, therefore the single case study
cannot contribute to scientific development; (3) The case study is most useful
for generating hypotheses, while other methods are more suitable for hypotheses
testing and theory building; (4) The case study contains a bias toward
verification; and (5) It is often difficult to summarize specific case studies.
The article explains and corrects these misunderstandings one by one and
concludes with the Kuhnian insight that a scientific discipline without a large
number of thoroughly executed case studies is a discipline without systematic
production of exemplars, and that a discipline without exemplars is an
ineffective one. Social science may be strengthened by the execution of more
good case studies.


title:
Elasticity Maps of Living Neurons Measured by Combined Fluorescence and
  Atomic Force Microscopy

abstract:
  Detailed knowledge of mechanical parameters such as cell elasticity,
stiffness of the growth substrate, or traction stresses generated during axonal
extensions is essential for understanding the mechanisms that control neuronal
growth. Here we combine Atomic Force Microscopy based force spectroscopy with
Fluorescence Microscopy to produce systematic, high-resolution elasticity maps
for three different types of live neuronal cells: cortical (embryonic rat),
embryonic chick dorsal root ganglion, and P-19 (mouse embryonic carcinoma stem
cells) neurons. We measure how the stiffness of neurons changes both during
neurite outgrowth and upon disruption of microtubules of the cell. We find
reversible local stiffening of the cell during growth, and show that the
increase in local elastic modulus is primarily due to the formation of
microtubules. We also report that cortical and P-19 neurons have similar
elasticity maps, with elastic moduli in the range 0.1-2 kPa, with typical
average values of 0.4 kPa (P-19) and 0.2 kPa (cortical). In contrast, DRG
neurons are stiffer than P-19 and cortical cells, yielding elastic moduli in
the range 0.1-8 kPa, with typical average values of 0.9 kPa. Finally, we report
no measurable influence of substrate protein coating on cell body elasticity
for the three types of neurons.


title: How to Approximate A Set Without Knowing Its Size In Advance

abstract:
  The dynamic approximate membership problem asks to represent a set S of size
n, whose elements are provided in an on-line fashion, supporting membership
queries without false negatives and with a false positive rate at most epsilon.
That is, the membership algorithm must be correct on each x in S, and may err
with probability at most epsilon on each x not in S.
  We study a well-motivated, yet insufficiently explored, variant of this
problem where the size n of the set is not known in advance. Existing optimal
approximate membership data structures require that the size is known in
advance, but in many practical scenarios this is not a realistic assumption.
Moreover, even if the eventual size n of the set is known in advance, it is
desirable to have the smallest possible space usage also when the current
number of inserted elements is smaller than n. Our contribution consists of the
following results:
  - We show a super-linear gap between the space complexity when the size is
known in advance and the space complexity when the size is not known in
advance.
  - We show that our space lower bound is tight, and can even be matched by a
highly efficient data structure.


title: Parallel Electric Field Spectrum of Solar Wind Turbulence

abstract:
  By searching through more than 10 satellite-years of THEMIS and Cluster data,
three reliable examples of parallel electric field turbulence in the
undisturbed solar wind have been found. The perpendicular and parallel electric
field spectra in these examples have similar shapes and amplitudes, even at
large scales (frequencies below the ion gyroscale) where Alfvenic turbulence
with no parallel electric field component is thought to dominate. The spectra
of the parallel electric field fluctuations are power laws with exponents near
-5/3 below the ion scales (~ 0.1 Hz), and with a flattening of the spectrum in
the vicinity of this frequency. At small scales (above a few Hz), the spectra
are steeper than -5/3 with values in the range of -2.1 to -2.8. These steeper
slopes are consistent with expectations for kinetic Alfven turbulence, although
their amplitude relative to the perpendicular fluctuations is larger than
expected.


title: Statistical comparison of quark mass matrices\\ in the physical basis

abstract:
  Using the four best measured moduli of the flavor mixing matrix ($|V_{\rm
ud}|$, $|V_{\rm us}|$, $|V_{\rm cd}|$, $|V_{\rm cs}|$), the Jarlskog invariant
$J(V)$, and the quark masses at $M_Z$ energy scale as experimental constraints,
a statistical comparison of three different types of quark mass matrices in the
physical basis is performed. The mass matrices in question are the
Chaturvedi-Gupta-S\'anchez-Col\'on (CGS), the Fritzsch and the Gupta-Rajpoot
types. With nine parameters the best fits are obtained using a Gupta-Rajpoot
type matrix while with seven parameters the best fits are obtained using the
CGS type matrix. The stability of our analysis with respect to evolution of the
quark masses is also presented.


title:
Wolff's Problem of Ideals in the Multipler Algebra on Weighted Dirichlet
  Space

abstract:
  We establish an analogue of Wolff's theorem on ideals in
$H^{\infty}(\mathbb{D})$ for the multiplier algebra of weighted Dirichlet
space.


title:
Efficient Distance Metric Learning by Adaptive Sampling and Mini-Batch
  Stochastic Gradient Descent (SGD)

abstract:
  Distance metric learning (DML) is an important task that has found
applications in many domains. The high computational cost of DML arises from
the large number of variables to be determined and the constraint that a
distance metric has to be a positive semi-definite (PSD) matrix. Although
stochastic gradient descent (SGD) has been successfully applied to improve the
efficiency of DML, it can still be computationally expensive because in order
to ensure that the solution is a PSD matrix, it has to, at every iteration,
project the updated distance metric onto the PSD cone, an expensive operation.
We address this challenge by developing two strategies within SGD, i.e.
mini-batch and adaptive sampling, to effectively reduce the number of updates
(i.e., projections onto the PSD cone) in SGD. We also develop hybrid approaches
that combine the strength of adaptive sampling with that of mini-batch online
learning techniques to further improve the computational efficiency of SGD for
DML. We prove the theoretical guarantees for both adaptive sampling and
mini-batch based approaches for DML. We also conduct an extensive empirical
study to verify the effectiveness of the proposed algorithms for DML.


title: A new approach to Naturalness in SUSY models

abstract:
  We review recent results that provide a new approach to the old problem of
naturalness in supersymmetric models, without relying on subjective definitions
for the fine-tuning associated with {\it fixing} the EW scale (to its measured
value) in the presence of quantum corrections. The approach can address in a
model-independent way many questions related to this problem. The results show
that naturalness and its measure (fine-tuning) are an intrinsic part of the
likelihood to fit the data that {\it includes} the EW scale. One important
consequence is that the additional {\it constraint} of fixing the EW scale,
usually not imposed in the data fits of the models, impacts on their overall
likelihood to fit the data (or chi^2/ndf, ndf: number of degrees of freedom).
This has negative implications for the viability of currently popular
supersymmetric extensions of the Standard Model.


title:
Strangeness and charmness content of nucleon from overlap fermions on
  2+1-flavor domain-wall fermion configurations

abstract:
  We present a calculation of the strangeness and charmness contents
<N|\bar{s}s|N> and <N|\bar{c}c|N> of the nucleon from dynamical lattice QCD
with 2+1 flavors. The calculation is performed with overlap valence quarks on
2+1-flavor domain-wall fermion gauge configurations. The configurations are
generated by the RBC collaboration on a 24^3*64 lattice with sea quark mass
am_l=0.005, am_s=0.04, and inverse lattice spacing a^{-1}=1.73GeV. Both actions
have chiral symmetry which is essential in avoiding contamination due to the
operator mixing with other flavors. Nucleon propagator and the quark loops are
both computed with stochastic grid sources, while low-mode substitution and
low-mode averaging methods are used respectively which substantially improve
the signal to noise ratio. We obtain the strangeness matrix element f_{T_{s}} =
m_s <N|\bar{s}s|N> / M_N = 0.0334(62), and the charmness content f_{T_{c}} =
m_c <N|\bar{c}c|N> / M_N = 0.094(31) which is resolved from zero by 3\sigma
precision for the first time.


title: Persistence and First-Passage Properties in Non-equilibrium Systems

abstract:
  In this review we discuss the persistence and the related first-passage
properties in extended many-body nonequilibrium systems. Starting with simple
systems with one or few degrees of freedom, such as random walk and random
acceleration problems, we progressively discuss the persistence properties in
systems with many degrees of freedom. These systems include spins models
undergoing phase ordering dynamics, diffusion equation, fluctuating interfaces
etc. Persistence properties are nontrivial in these systems as the effective
underlying stochastic process is non-Markovian. Several exact and approximate
methods have been developed to compute the persistence of such non-Markov
processes over the last two decades, as reviewed in this article. We also
discuss various generalisations of the local site persistence probability.
Persistence in systems with quenched disorder is discussed briefly. Although
the main emphasis of this review is on the theoretical developments on
persistence, we briefly touch upon various experimental systems as well.

categories: cond-mat.stat-mech cond-mat.dis-nn cond-mat.soft math.PR


title:
A structure theorem for subgroups of $GL_n$ over complete local
  Noetherian rings with large residual image

abstract:
  Given a complete local Noetherian ring $(A,\m_A)$ with finite residue field
and a subfield $\pmb{k}$ of $A/\m_A$, we show that every closed subgroup $G$ of
$GL_n(A)$ such that $G\mod{\m_A}\supseteq SL_n(\pmb{k})$ contains a conjugate
of $SL_n(W(\pmb{k})_A)$ under some small restrictions on $\pmb{k}$. Here
$W(\pmb{k})_A$ is the closed subring of $A$ generated by the Teichm\"{u}ller
lifts of elements of the subfield $\pmb{k}$.


title:
Spin relaxometry of single nitrogen-vacancy defects in diamond
  nanocrystals for magnetic noise sensing

abstract:
  We report an experimental study of the longitudinal relaxation time ($T_1$)
of the electron spin associated with single nitrogen-vacancy (NV) defects
hosted in nanodiamonds (ND). We first show that $T_1$ decreases over three
orders of magnitude when the ND size is reduced from 100 to 10 nm owing to the
interaction of the NV electron spin with a bath of paramagnetic centers lying
on the ND surface. We next tune the magnetic environment by decorating the ND
surface with Gd$^{3+}$ ions and observe an efficient $T_{1}$-quenching, which
demonstrates magnetic noise sensing with a single electron spin. We estimate a
sensitivity down to $\approx 14$ electron spins detected within 10 s, using a
single NV defect hosted in a 10-nm-size ND. These results pave the way towards
$T_1$-based nanoscale imaging of the spin density in biological samples.


title: Orthogonal Invariance and Identifiability

abstract:
  Orthogonally invariant functions of symmetric matrices often inherit
properties from their diagonal restrictions: von Neumann's theorem on matrix
norms is an early example. We discuss the example of "identifiability", a
common property of nonsmooth functions associated with the existence of a
smooth manifold of approximate critical points. Identifiability (or its
synonym, "partial smoothness") is the key idea underlying active set methods in
optimization. Polyhedral functions, in particular, are always partly smooth,
and hence so are many standard examples from eigenvalue optimization.


title: The distribution of calibrated likelihood-ratios in speaker recognition

abstract:
  This paper studies properties of the score distributions of calibrated
log-likelihood-ratios that are used in automatic speaker recognition. We derive
the essential condition for calibration that the log likelihood ratio of the
log-likelihood-ratio is the log-likelihood-ratio. We then investigate what the
consequence of this condition is to the probability density functions (PDFs) of
the log-likelihood-ratio score. We show that if the PDF of the non-target
distribution is Gaussian, then the PDF of the target distribution must be
Gaussian as well. The means and variances of these two PDFs are interrelated,
and determined completely by the discrimination performance of the recognizer
characterized by the equal error rate. These relations allow for a new way of
computing the offset and scaling parameters for linear calibration, and we
derive closed-form expressions for these and show that for modern i-vector
systems with PLDA scoring this leads to good calibration, comparable to
traditional logistic regression, over a wide range of system performance.