title: Learning Output Kernels for Multi-Task Problems

abstract:
  Simultaneously solving multiple related learning tasks is beneficial under a
variety of circumstances, but the prior knowledge necessary to correctly model
task relationships is rarely available in practice. In this paper, we develop a
novel kernel-based multi-task learning technique that automatically reveals
structural inter-task relationships. Building over the framework of output
kernel learning (OKL), we introduce a method that jointly learns multiple
functions and a low-rank multi-task kernel by solving a non-convex
regularization problem. Optimization is carried out via a block coordinate
descent strategy, where each subproblem is solved using suitable conjugate
gradient (CG) type iterative methods for linear operator equations. The
effectiveness of the proposed approach is demonstrated on pharmacological and
collaborative filtering data.


title: Lebesgue Component in Spectrum for Tensor Product of Generic Multiplies

abstract:
  Generic (rigid) measure-preserving transformations with Lebesgue component in
spectrum of their tensor product, two rigid Gaussian systems and two rigid
Poisson suspensions with similar spectral interactions are presented.


title: Cassini UVIS Observations of the Io Plasma Torus: I. Initial Results

abstract:
  During the Cassini spacecraft's flyby of Jupiter (October, 2000-March, 2001),
the Ultraviolet Imaging Spectrograph (UVIS) produced an extensive dataset
consisting of 3,349 spectrally dispersed images of the Io plasma torus. Here we
present an example of the raw data and representative EUV spectra
(561{\AA}-1181{\AA}) of the torus, obtained on October 1, 2000 and November 14,
2000. For most of the flyby period, the entire Io torus fit within the UVIS
field-of-view, enabling the measurement of the total power radiated from the
torus in the extreme ultraviolet. A typical value for the total power radiated
in the wavelength range of 580{\AA}-1181{\AA} is 1.7x10^12 W, with observed
variations of up to 25%. Several brightening events were observed. These events
lasted for roughly 20 hours, during which time the emitted power increased
rapidly by ~20% before slowly returning to the pre-event level. Observed
variations in the relative intensities of torus spectral features provide
strong evidence for compositional changes in the torus plasma with time.
Spatial profiles of the EUV emission show no evidence for a sharply peaked
"ribbon" feature. The ratio of the brightness of the dusk ansa to the
brightness of the dawn ansa is observed to be highly variable, with an average
value of 1.30. Weak longitudinal variations in the brightness of the torus
ansae were observed at the 2% level.


title:
On timelike and spacelike deeply virtual Compton scattering at next to
  leading order

abstract:
  We study timelike and spacelike virtual Compton scattering in the generalized
Bjorken scaling regime at next to leading order in the strong coupling
constant, in the medium energy range which will be studied intensely at JLab12
and in the COMPASS-II experiment at CERN. We show that the Born amplitudes get
sizeable O(\alpha_s) corrections and, even at moderate energies, the gluonic
contributions are by no means negligible. We stress that the timelike and
spacelike cases are complementary and that their difference deserves much
special attention.


title: Determination of the Cosmic Antideuteron Flux in a Monte Carlo approach

abstract:
  We investigate in this paper the antideuteron flux produced in high energy
collisions of cosmic rays with the interstellar matter. We employ the Monte
Carlo generator DPMJET-III together with the coalescence model to simulate, in
an event-by-event basis, the antideuteron production in cosmic ray collisions
in our Galaxy. Then, we use a diffusion model to calculate the expected flux.
We find a secondary antideuteron flux at the Earth which is approximately a
factor of two smaller than in previous calculations.


title: Coarse-grained simulations of RNA and DNA duplexes

abstract:
  Although RNAs play many cellular functions little is known about the dynamics
and thermodynamics of these molecules. In principle, all-atom molecular
dynamics simulations can investigate these issues, but with current computer
facilities, these simulations have been limited to small RNAs and to short
times.
  HiRe-RNA, a recently proposed high-resolution coarse-grained for RNA that
captures many geometric details such as base pairing and stacking, is able to
fold RNA molecules to near-native structures in a short computational time. So
far it had been applied to simple hairpins, and here we present its application
to duplexes of a couple dozen nucleotides and show how with our model and with
Replica Exchange Molecular Dynamics (REMD) we can easily predict the correct
double helix from a completely random configuration and study the dissociation
curve. To show the versatility of our model, we present an application to a
double stranded DNA molecule as well.
  A reconstruction algorithm allows us to obtain full atom structures from the
coarse-grained model. Through atomistic Molecular Dynamics (MD) we can compare
the dynamics starting from a representative structure of a low temperature
replica or from the experimental structure, and show how the two are
statistically identical, highlighting the validity of a coarse-grained approach
for structured RNAs and DNAs.


title: Anomalous dimensions of the double parton fragmentation functions

abstract:
  Double parton fragmentation is a process in which a pair of partons produced
in the short-distance process hadronize into the final state hadron. This
process is important for quarkonium production when the transverse momentum is
much greater than the quark mass. Resummation of logarithms of the ratio of
these two scales requires the evolution equations for double parton
fragmentation functions (DPFF). In this paper we complete the one-loop
evaluation of the anomalous dimensions for the DPFF. We also consider possible
mixing between the DPFF and single parton power suppressed gluon fragmentation
and show that such effects are sub-leading.


title: Portfolio Management Approach in Trade Credit Decision Making

abstract:
  The basic financial purpose of an enterprise is maximization of its value.
Trade credit management should also contribute to realization of this
fundamental aim. Many of the current asset management models that are found in
financial management literature assume book profit maximization as the basic
financial purpose. These book profitbased models could be lacking in what
relates to another aim (i.e., maximization of enterprise value). The enterprise
value maximization strategy is executed with a focus on risk and uncertainty.
This article presents the consequences that can result from operating risk that
is related to purchasers using payment postponement for goods and/or services.
The present article offers a method that uses portfolio management theory to
determine the level of accounts receivable in a firm. An increase in the level
of accounts receivables in a firm increases both net working capital and the
costs of holding and managing accounts receivables. Both of these decrease the
value of the firm, but a liberal policy in accounts receivable coupled with the
portfolio management approach could increase the value. Efforts to assign ways
to manage these risks were also undertaken; among them, special attention was
paid to adapting assumptions from portfolio theory as well as gauging the
potential effect on the firm value.


title:
Planning Optimal From the Firm Value Creation Perspective Levels of
  Operating Cash Investments

abstract:
  The basic financial purpose of corporation is creation of its value.
Liquidity management should also contribute to realization of this fundamental
aim. Many of the current asset management models that are found in financial
management literature assume book profit maximization as the basic financial
purpose. These book profit based models could be lacking in what relates to
another aim like maximization of enterprise value. The corporate value creation
strategy is executed with a focus on risk and uncertainty. Firms hold cash for
a variety of reasons. Generally, cash balances held in a firm can be called
considered, precautionary, speculative, transactional and intentional. The
first are the result of management anxieties. Managers fear the negative part
of the risk and hold cash to hedge against it. Second, cash balances are held
to use chances that are created by the positive part of the risk equation.
Next, cash balances are the result of the operating needs of the firm. In this
article, we analyze the relation between these types of cash balances and risk.
This article presents the discussion about relations between firm net working
investment policy and as result operating cash balances and firm value. This
article also contains propositions for marking levels of precautionary cash
balances and speculative cash balances. Application of these propositions
should help managers to make better decisions to maximize the value of a firm.


title: Polish and Silesian Non-Profit Organizations Liquidity Strategies

abstract:
  The kind of realized mission inflows the sensitivity to risk. Among other
factors, the risk results from decision about liquid assets investment level
and liquid assets financing. The higher the risk exposure, the higher the level
of liquid assets. If the specific risk exposure is smaller, the more aggressive
could be the net liquid assets strategy. The organization choosing between
various solutions in liquid assets needs to decide what level of risk is
acceptable for her owners (or donors) and / or capital suppliers. The paper
shows how, in authors opinion, decisions, about liquid assets management
strategy inflow the risk of the organizations and its economical results during
realization of main mission. Comparison of theoretical model with empirical
data for over 450 Silesian nonprofit organization results suggests that
nonprofit organization managing teams choose more risky aggressive liquid
assets solutions than for-profit firms.


title: Value-Based Inventory Management

abstract:
  The basic financial purpose of a firm is to maximize its value. An inventory
management system should also contribute to realization of this basic aim. Many
current asset management models currently found in financial management
literature were constructed with the assumption of book profit maximization as
basic aim. However these models could lack what relates to another aim, i.e.,
maximization of enterprise value. This article presents a modified value-based
inventory management model.


title:
Experimental Observations of Group Synchrony in a System of Chaotic
  Optoelectronic Oscillators

abstract:
  We experimentally demonstrate group synchrony in a network of four nonlinear
optoelectronic oscillators with time-delayed coupling. We divide the nodes into
two groups of two each, by giving each group different parameters and by
enabling only inter-group coupling. When coupled in this fashion, the two
groups display different dynamics, with no isochronal synchrony between them,
but the nodes in a single group are isochronally synchronized, even though
there is no intra-group coupling. We compare experimental behavior with
theoretical and numerical results.


title: Kalman filtering techniques for focal plane electric field estimation

abstract:
  For a coronagraph to detect faint exoplanets, it will require focal plane
wavefront control techniques to continue reaching smaller angular separations
and higher contrast levels. These correction algorithms are iterative and the
control methods need an estimate of the electric field at the science camera,
which requires nearly all of the images taken for the correction. The best way
to make such algorithms the least disruptive to science exposures is to reduce
the number required to estimate the field. We demonstrate a Kalman filter
estimator that uses prior knowledge to create the estimate of the electric
field, dramatically reducing the number of exposures required to estimate the
image plane electric field while stabilizing the suppression against poor
signal-to-noise (SNR). In addition to a significant reduction in exposures, we
discuss the relative merit of this algorithm to estimation schemes that do not
incorporate prior state estimate history, particularly in regard to estimate
error and covariance. Ultimately the filter will lead to an adaptive algorithm
which can estimate physical parameters in the laboratory for robustness to
variance in the optical train.


title: T_c of disordered superconductors near the Anderson transition

abstract:
  According to the Anderson theorem, the critical temperature T_c of a
disordered superconductor is determined by the average density of states and
does not change at the localization threshold. This statement is valid under
assumption of a self-averaging order parameter, which can be violated in the
strong localization region. Stimulating by statements on the essential increase
of T_c near the Anderson transition, we carried out the systematic
investigation of possible violations of self-averaging. Strong deviations from
the Anderson theorem are possible due to resonances at the quasi-discrete
levels, resulting in localization of the order parameter at the atomic scale.
This effect is determined by the properties of individual impurities and has no
direct relation to the Anderson transition. In particular, we see no reasons to
say on "fractal superconductivity" near the localization threshold.


title:
Rationality of the probabilistic zeta function of finitely generated
  profinite groups

abstract:
  We discuss whether finiteness properties of a profinite group $G$ can be
deduced from the probabilistic zeta function $P_G(s)$. In particular we prove
that if $P_G(s)$ is rational and all but finitely many nonabelian composition
factors of $G$ are groups of Lie type in a fixed characteristic, then $G$
contains only finitely many maximal subgroups.


title:
Perspectives on effectively constraining the location of a massive
  trans-Plutonian object with the New Horizons spacecraft: a sensitivity
  analysis

abstract:
  The radio tracking apparatus of the New Horizons spacecraft, currently
traveling to the Pluto system where its arrival is scheduled for July 2015,
should be able to reach an accuracy of 10 m (range) and 0.1 mm s^-1
(range-rate) over distances up to 50 au. This should allow to effectively
constrain the location of a putative trans-Plutonian massive object, dubbed
Planet X (PX) hereafter, whose existence has recently been postulated for a
variety of reasons connected with, e.g., the architecture of the Kuiper belt
and the cometary flux from the Oort cloud. Traditional scenarios involve a
rock-ice planetoid with mX = 0.7mE at some 100 - 200 au, or a Jovian body with
mX = 5mJ at about 10,000 - 20,000 au; as a result of our preliminary
sensitivity analysis, they should be detectable by New Horizons since they
would impact its range at a km level or so over a time span six years long.
Conversely, range residuals statistically compatible with zero having an
amplitude of 10 m would imply that PX, if it exists, could not be located at
less than about 4,500 au (mX = 0.7mE) or 60,000 au (mX = 5mJ), thus making a
direct detection quite demanding with the present-day technologies. As a
consequence, it would be appropriate to rename such a remote body as Telisto.
Also fundamental physics would benefit from this analysis since certain subtle
effects predicted by MOND for the deep Newtonian regions of our Solar System
are just equivalent to those of a distant pointlike mass.


title:
A Complete Calculus for Possibilistic Logic Programming with Fuzzy
  Propositional Variables

abstract:
  In this paper we present a propositional logic programming language for
reasoning under possibilistic uncertainty and representing vague knowledge.
Formulas are represented by pairs (A, c), where A is a many-valued proposition
and c is value in the unit interval [0,1] which denotes a lower bound on the
belief on A in terms of necessity measures. Belief states are modeled by
possibility distributions on the set of all many-valued interpretations. In
this framework, (i) we define a syntax and a semantics of the general
underlying uncertainty logic; (ii) we provide a modus ponens-style calculus for
a sublanguage of Horn-rules and we prove that it is complete for determining
the maximum degree of possibilistic belief with which a fuzzy propositional
variable can be entailed from a set of formulas; and finally, (iii) we show how
the computation of a partial matching between fuzzy propositional variables, in
terms of necessity measures for fuzzy sets, can be included in our logic
programming system.


title: Reversible Jump MCMC Simulated Annealing for Neural Networks

abstract:
  We propose a novel reversible jump Markov chain Monte Carlo (MCMC) simulated
annealing algorithm to optimize radial basis function (RBF) networks. This
algorithm enables us to maximize the joint posterior distribution of the
network parameters and the number of basis functions. It performs a global
search in the joint space of the parameters and number of parameters, thereby
surmounting the problem of local minima. We also show that by calibrating a
Bayesian model, we can obtain the classical AIC, BIC and MDL model selection
criteria within a penalized likelihood framework. Finally, we show
theoretically and empirically that the algorithm converges to the modes of the
full posterior distribution in an efficient way.


title: Perfect Tree-Like Markovian Distributions

abstract:
  We show that if a strictly positive joint probability distribution for a set
of binary random variables factors according to a tree, then vertex separation
represents all and only the independence relations enclosed in the
distribution. The same result is shown to hold also for multivariate strictly
positive normal distributions. Our proof uses a new property of conditional
independence that holds for these two classes of probability distributions.


title: A Principled Analysis of Merging Operations in Possibilistic Logic

abstract:
  Possibilistic logic offers a qualitative framework for representing pieces of
information associated with levels of uncertainty of priority. The fusion of
multiple sources information is discussed in this setting. Different classes of
merging operators are considered including conjunctive, disjunctive,
reinforcement, adaptive and averaging operators. Then we propose to analyse
these classes in terms of postulates. This is done by first extending the
postulate for merging classical bases to the case where priorites are avaialbe.


title: The Complexity of Decentralized Control of Markov Decision Processes

abstract:
  Planning for distributed agents with partial state information is considered
from a decision- theoretic perspective. We describe generalizations of both the
MDP and POMDP models that allow for decentralized control. For even a small
number of agents, the finite-horizon problems corresponding to both of our
models are complete for nondeterministic exponential time. These complexity
results illustrate a fundamental difference between centralized and
decentralized control of Markov processes. In contrast to the MDP and POMDP
problems, the problems we consider provably do not admit polynomial-time
algorithms and most likely require doubly exponential time to solve in the
worst case. We have thus provided mathematical evidence corresponding to the
intuition that decentralized planning problems cannot easily be reduced to
centralized problems and solved exactly using established techniques.


title: Dynamic Bayesian Multinets

abstract:
  In this work, dynamic Bayesian multinets are introduced where a Markov chain
state at time t determines conditional independence patterns between random
variables lying within a local time window surrounding t. It is shown how
information-theoretic criterion functions can be used to induce sparse,
discriminative, and class-conditional network structures that yield an optimal
approximation to the class posterior probability, and therefore are useful for
the classification task. Using a new structure learning heuristic, the
resulting models are tested on a medium-vocabulary isolated-word speech
recognition task. It is demonstrated that these discriminatively structured
dynamic Bayesian multinets, when trained in a maximum likelihood setting using
EM, can outperform both HMMs and other dynamic Bayesian networks with a similar
number of parameters.


title: Variational Relevance Vector Machines

abstract:
  The Support Vector Machine (SVM) of Vapnik (1998) has become widely
established as one of the leading approaches to pattern recognition and machine
learning. It expresses predictions in terms of a linear combination of kernel
functions centred on a subset of the training data, known as support vectors.
  Despite its widespread success, the SVM suffers from some important
limitations, one of the most significant being that it makes point predictions
rather than generating predictive distributions. Recently Tipping (1999) has
formulated the Relevance Vector Machine (RVM), a probabilistic model whose
functional form is equivalent to the SVM. It achieves comparable recognition
accuracy to the SVM, yet provides a full predictive distribution, and also
requires substantially fewer kernel functions.
  The original treatment of the RVM relied on the use of type II maximum
likelihood (the `evidence framework') to provide point estimates of the
hyperparameters which govern model sparsity. In this paper we show how the RVM
can be formulated and solved within a completely Bayesian paradigm through the
use of variational inference, thereby giving a posterior distribution over both
parameters and hyperparameters. We demonstrate the practicality and performance
of the variational RVM using both synthetic and real world examples.


title: Approximately Optimal Monitoring of Plan Preconditions

abstract:
  Monitoring plan preconditions can allow for replanning when a precondition
fails, generally far in advance of the point in the plan where the precondition
is relevant. However, monitoring is generally costly, and some precondition
failures have a very small impact on plan quality. We formulate a model for
optimal precondition monitoring, using partially-observable Markov decisions
processes, and describe methods for solving this model efficitively, though
approximately. Specifically, we show that the single-precondition monitoring
problem is generally tractable, and the multiple-precondition monitoring
policies can be efficitively approximated using single-precondition soultions.


title:
Utilities as Random Variables: Density Estimation and Structure
  Discovery

abstract:
  Decision theory does not traditionally include uncertainty over utility
functions. We argue that the a person's utility value for a given outcome can
be treated as we treat other domain attributes: as a random variable with a
density function over its possible values. We show that we can apply
statistical density estimation techniques to learn such a density function from
a database of partially elicited utility functions. In particular, we define a
Bayesian learning framework for this problem, assuming the distribution over
utilities is a mixture of Gaussians, where the mixture components represent
statistically coherent subpopulations. We can also extend our techniques to the
problem of discovering generalized additivity structure in the utility
functions in the population. We define a Bayesian model selection criterion for
utility function structure and a search procedure over structures. The
factorization of the utilities in the learned model, and the generalization
obtained from density estimation, allows us to provide robust estimates of
utilities using a significantly smaller number of utility elicitation
questions. We experiment with our technique on synthetic utility data and on a
real database of utility functions in the domain of prenatal diagnosis.


title:
Computational Investigation of Low-Discrepancy Sequences in Simulation
  Algorithms for Bayesian Networks

abstract:
  Monte Carlo sampling has become a major vehicle for approximate inference in
Bayesian networks. In this paper, we investigate a family of related simulation
approaches, known collectively as quasi-Monte Carlo methods based on
deterministic low-discrepancy sequences. We first outline several theoretical
aspects of deterministic low-discrepancy sequences, show three examples of such
sequences, and then discuss practical issues related to applying them to belief
updating in Bayesian networks. We propose an algorithm for selecting direction
numbers for Sobol sequence. Our experimental results show that low-discrepancy
sequences (especially Sobol sequence) significantly improve the performance of
simulation algorithms in Bayesian networks compared to Monte Carlo sampling.


title: A Decision Theoretic Approach to Targeted Advertising

abstract:
  A simple advertising strategy that can be used to help increase sales of a
product is to mail out special offers to selected potential customers. Because
there is a cost associated with sending each offer, the optimal mailing
strategy depends on both the benefit obtained from a purchase and how the offer
affects the buying behavior of the customers. In this paper, we describe two
methods for partitioning the potential customers into groups, and show how to
perform a simple cost-benefit analysis to decide which, if any, of the groups
should be targeted. In particular, we consider two decision-tree learning
algorithms. The first is an "off the shelf" algorithm used to model the
probability that groups of customers will buy the product. The second is a new
algorithm that is similar to the first, except that for each group, it
explicitly models the probability of purchase under the two mailing scenarios:
(1) the mail is sent to members of that group and (2) the mail is not sent to
members of that group. Using data from a real-world advertising experiment, we
compare the algorithms to each other and to a naive mail-to-all strategy.


title: Bayesian Classification and Feature Selection from Finite Data Sets

abstract:
  Feature selection aims to select the smallest subset of features for a
specified level of performance. The optimal achievable classification
performance on a feature subset is summarized by its Receiver Operating Curve
(ROC). When infinite data is available, the Neyman- Pearson (NP) design
procedure provides the most efficient way of obtaining this curve. In practice
the design procedure is applied to density estimates from finite data sets. We
perform a detailed statistical analysis of the resulting error propagation on
finite alphabets. We show that the estimated performance curve (EPC) produced
by the design procedure is arbitrarily accurate given sufficient data,
independent of the size of the feature set. However, the underlying likelihood
ranking procedure is highly sensitive to errors that reduces the probability
that the EPC is in fact the ROC. In the worst case, guaranteeing that the EPC
is equal to the ROC may require data sizes exponential in the size of the
feature set. These results imply that in theory the NP design approach may only
be valid for characterizing relatively small feature subsets, even when the
performance of any given classifier can be estimated very accurately. We
discuss the practical limitations for on-line methods that ensures that the NP
procedure operates in a statistically valid region.


title: A Bayesian Method for Causal Modeling and Discovery Under Selection

abstract:
  This paper describes a Bayesian method for learning causal networks using
samples that were selected in a non-random manner from a population of
interest. Examples of data obtained by non-random sampling include convenience
samples and case-control data in which a fixed number of samples with and
without some condition is collected; such data are not uncommon. The paper
describes a method for combining data under selection with prior beliefs in
order to derive a posterior probability for a model of the causal processes
that are generating the data in the population of interest. The priors include
beliefs about the nature of the non-random sampling procedure. Although exact
application of the method would be computationally intractable for most
realistic datasets, efficient special-case and approximation methods are
discussed. Finally, the paper describes how to combine learning under selection
with previous methods for learning from observational and experimental data
that are obtained on random samples of the population of interest. The net
result is a Bayesian methodology that supports causal modeling and discovery
from a rich mixture of different types of data.


title: Separation Properties of Sets of Probability Measures

abstract:
  This paper analyzes independence concepts for sets of probability measures
associated with directed acyclic graphs. The paper shows that epistemic
independence and the standard Markov condition violate desirable separation
properties. The adoption of a contraction condition leads to d-separation but
still fails to guarantee a belief separation property. To overcome this
unsatisfactory situation, a strong Markov condition is proposed, based on
epistemic independence. The main result is that the strong Markov condition
leads to strong independence and does enforce separation properties; this
result implies that (1) separation properties of Bayesian networks do extend to
epistemic independence and sets of probability measures, and (2) strong
independence has a clear justification based on epistemic independence and the
strong Markov condition.


title: Stochastic Logic Programs: Sampling, Inference and Applications

abstract:
  Algorithms for exact and approximate inference in stochastic logic programs
(SLPs) are presented, based respectively, on variable elimination and
importance sampling. We then show how SLPs can be used to represent prior
distributions for machine learning, using (i) logic programs and (ii) Bayes net
structures as examples. Drawing on existing work in statistics, we apply the
Metropolis-Hasting algorithm to construct a Markov chain which samples from the
posterior distribution. A Prolog implementation for this is described. We also
discuss the possibility of constructing explicit representations of the
posterior.


title: A Differential Approach to Inference in Bayesian Networks

abstract:
  We present a new approach for inference in Bayesian networks, which is mainly
based on partial differentiation. According to this approach, one compiles a
Bayesian network into a multivariate polynomial and then computes the partial
derivatives of this polynomial with respect to each variable. We show that once
such derivatives are made available, one can compute in constant-time answers
to a large class of probabilistic queries, which are central to classical
inference, parameter estimation, model validation and sensitivity analysis. We
present a number of complexity results relating to the compilation of such
polynomials and to the computation of their partial derivatives. We argue that
the combined simplicity, comprehensiveness and computational complexity of the
presented framework is unique among existing frameworks for inference in
Bayesian networks.


title: Any-Space Probabilistic Inference

abstract:
  We have recently introduced an any-space algorithm for exact inference in
Bayesian networks, called Recursive Conditioning, RC, which allows one to trade
space with time at increments of X-bytes, where X is the number of bytes needed
to cache a floating point number. In this paper, we present three key
extensions of RC. First, we modify the algorithm so it applies to more general
factorization of probability distributions, including (but not limited to)
Bayesian network factorizations. Second, we present a forgetting mechanism
which reduces the space requirements of RC considerably and then compare such
requirmenets with those of variable elimination on a number of realistic
networks, showing orders of magnitude improvements in certain cases. Third, we
present a version of RC for computing maximum a posteriori hypotheses (MAP),
which turns out to be the first MAP algorithm allowing a smooth time-space
tradeoff. A key advantage of presented MAP algorithm is that it does not have
to start from scratch each time a new query is presented, but can reuse some of
its computations across multiple queries, leading to significant savings in
ceratain cases.


title: Experiments with Random Projection

abstract:
  Recent theoretical work has identified random projection as a promising
dimensionality reduction technique for learning mixtures of Gausians. Here we
summarize these results and illustrate them by a wide variety of experiments on
synthetic and real data.


title: A Two-round Variant of EM for Gaussian Mixtures

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


title: Minimum Message Length Clustering Using Gibbs Sampling

abstract:
  The K-Mean and EM algorithms are popular in clustering and mixture modeling,
due to their simplicity and ease of implementation. However, they have several
significant limitations. Both coverage to a local optimum of their respective
objective functions (ignoring the uncertainty in the model space), require the
apriori specification of the number of classes/clsuters, and are inconsistent.
In this work we overcome these limitations by using the Minimum Message Length
(MML) principle and a variation to the K-Means/EM observation assignment and
parameter calculation scheme. We maintain the simplicity of these approaches
while constructing a Bayesian mixture modeling tool that samples/searches the
model space using a Markov Chain Monte Carlo (MCMC) sampler known as a Gibbs
sampler. Gibbs sampling allows us to visit each model according to its
posterior probability. Therefore, if the model space is multi-modal we will
visit all models and not get stuck in local optima. We call our approach
multiple chains at equilibrium (MCE) MML sampling.


title:
Mix-nets: Factored Mixtures of Gaussians in Bayesian Networks With Mixed
  Continuous And Discrete Variables

abstract:
  Recently developed techniques have made it possible to quickly learn accurate
probability density functions from data in low-dimensional continuous space. In
particular, mixtures of Gaussians can be fitted to data very quickly using an
accelerated EM algorithm that employs multiresolution kd-trees (Moore, 1999).
In this paper, we propose a kind of Bayesian networks in which low-dimensional
mixtures of Gaussians over different subsets of the domain's variables are
combined into a coherent joint probability model over the entire domain. The
network is also capable of modeling complex dependencies between discrete
variables and continuous variables without requiring discretization of the
continuous variables. We present efficient heuristic algorithms for
automatically learning these networks from data, and perform comparative
experiments illustrated how well these networks model real scientific data and
synthetic data. We also briefly discuss some possible improvements to the
networks, as well as possible applications.


title: Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks

abstract:
  Particle filters (PFs) are powerful sampling-based inference/learning
algorithms for dynamic Bayesian networks (DBNs). They allow us to treat, in a
principled way, any type of probability distribution, nonlinearity and
non-stationarity. They have appeared in several fields under such names as
"condensation", "sequential Monte Carlo" and "survival of the fittest". In this
paper, we show how we can exploit the structure of the DBN to increase the
efficiency of particle filtering, using a technique known as
Rao-Blackwellisation. Essentially, this samples some of the variables, and
marginalizes out the rest exactly, using the Kalman filter, HMM filter,
junction tree algorithm, or any other finite dimensional optimal filter. We
show that Rao-Blackwellised particle filters (RBPFs) lead to more accurate
estimates than standard PFs. We demonstrate RBPFs on two problems, namely
non-stationary online regression with radial basis function networks and robot
localization and map building. We also discuss other potential application
areas and provide references to some finite dimensional optimal filters.


title:
Learning Graphical Models of Images, Videos and Their Spatial
  Transformations

abstract:
  Mixtures of Gaussians, factor analyzers (probabilistic PCA) and hidden Markov
models are staples of static and dynamic data modeling and image and video
modeling in particular. We show how topographic transformations in the input,
such as translation and shearing in images, can be accounted for in these
models by including a discrete transformation variable. The resulting models
perform clustering, dimensionality reduction and time-series analysis in a way
that is invariant to transformations in the input. Using the EM algorithm,
these transformation-invariant models can be fit to static data and time
series. We give results on filtering microscopy images, face and facial pose
clustering, handwritten digit modeling and recognition, video clustering,
object tracking, and removal of distractions from video sequences.


title: Likelihood Computations Using Value Abstractions

abstract:
  In this paper, we use evidence-specific value abstraction for speeding
Bayesian networks inference. This is done by grouping variable values and
treating the combined values as a single entity. As we show, such abstractions
can exploit regularities in conditional probability distributions and also the
specific values of observed variables. To formally justify value abstraction,
we define the notion of safe value abstraction and devise inference algorithms
that use it to reduce the cost of inference. Our procedure is particularly
useful for learning complex networks with many hidden variables. In such cases,
repeated likelihood computations are required for EM or other parameter
optimization techniques. Since these computations are repeated with respect to
the same evidence set, our methods can provide significant speedup to the
learning procedure. We demonstrate the algorithm on genetic linkage problems
where the use of value abstraction sometimes differentiates between a feasible
and non-feasible solution.


title: Being Bayesian about Network Structure

abstract:
  In many domains, we are interested in analyzing the structure of the
underlying distribution, e.g., whether one variable is a direct parent of the
other. Bayesian model-selection attempts to find the MAP model and use its
structure to answer these questions. However, when the amount of available data
is modest, there might be many models that have non-negligible posterior. Thus,
we want compute the Bayesian posterior of a feature, i.e., the total posterior
probability of all models that contain it. In this paper, we propose a new
approach for this task. We first show how to efficiently compute a sum over the
exponential number of networks that are consistent with a fixed ordering over
network variables. This allows us to compute, for a given ordering, both the
marginal probability of the data and the posterior of a feature. We then use
this result as the basis for an algorithm that approximates the Bayesian
posterior of a feature. Our approach uses a Markov Chain Monte Carlo (MCMC)
method, but over orderings rather than over network structures. The space of
orderings is much smaller and more regular than the space of structures, and
has a smoother posterior `landscape'. We present empirical results on synthetic
and real-life datasets that compare our approach to full model averaging (when
possible), to MCMC over network structures, and to a non-Bayesian bootstrap
approach.


title: Gaussian Process Networks

abstract:
  In this paper we address the problem of learning the structure of a Bayesian
network in domains with continuous variables. This task requires a procedure
for comparing different candidate structures. In the Bayesian framework, this
is done by evaluating the {em marginal likelihood/} of the data given a
candidate structure. This term can be computed in closed-form for standard
parametric families (e.g., Gaussians), and can be approximated, at some
computational cost, for some semi-parametric families (e.g., mixtures of
Gaussians).
  We present a new family of continuous variable probabilistic networks that
are based on {em Gaussian Process/} priors. These priors are semi-parametric in
nature and can learn almost arbitrary noisy functional relations. Using these
priors, we can directly compute marginal likelihoods for structure learning.
The resulting method can discover a wide range of functional dependencies in
multivariate data. We develop the Bayesian score of Gaussian Process Networks
and describe how to learn them from data. We present empirical results on
artificial data as well as on real-life domains with non-linear dependencies.


title:
A Qualitative Linear Utility Theory for Spohn's Theory of Epistemic
  Beliefs

abstract:
  In this paper, we formulate a qualitative "linear" utility theory for
lotteries in which uncertainty is expressed qualitatively using a Spohnian
disbelief function. We argue that a rational decision maker facing an uncertain
decision problem in which the uncertainty is expressed qualitatively should
behave so as to maximize "qualitative expected utility." Our axiomatization of
the qualitative utility is similar to the axiomatization developed by von
Neumann and Morgenstern for probabilistic lotteries. We compare our results
with other recent results in qualitative decision making.


title: Building a Stochastic Dynamic Model of Application Use

abstract:
  Many intelligent user interfaces employ application and user models to
determine the user's preferences, goals and likely future actions. Such models
require application analysis, adaptation and expansion. Building and
maintaining such models adds a substantial amount of time and labour to the
application development cycle. We present a system that observes the interface
of an unmodified application and records users' interactions with the
application. From a history of such observations we build a coarse state space
of observed interface states and actions between them. To refine the space, we
hypothesize sub-states based upon the histories that led users to a given
state. We evaluate the information gain of possible state splits, varying the
length of the histories considered in such splits. In this way, we
automatically produce a stochastic dynamic model of the application and of how
it is used. To evaluate our approach, we present models derived from real-world
application usage data.


title: Maximum Entropy and the Glasses You Are Looking Through

abstract:
  We give an interpretation of the Maximum Entropy (MaxEnt) Principle in
game-theoretic terms. Based on this interpretation, we make a formal
distinction between different ways of {em applying/} Maximum Entropy
distributions. MaxEnt has frequently been criticized on the grounds that it
leads to highly representation dependent results. Our distinction allows us to
avoid this problem in many cases.


title: Inference for Belief Networks Using Coupling From the Past

abstract:
  Inference for belief networks using Gibbs sampling produces a distribution
for unobserved variables that differs from the correct distribution by a
(usually) unknown error, since convergence to the right distribution occurs
only asymptotically. The method of "coupling from the past" samples from
exactly the correct distribution by (conceptually) running dependent Gibbs
sampling simulations from every possible starting state from a time far enough
in the past that all runs reach the same state at time t=0. Explicitly
considering every possible state is intractable for large networks, however. We
propose a method for layered noisy-or networks that uses a compact, but often
imprecise, summary of a set of states. This method samples from exactly the
correct distribution, and requires only about twice the time per step as
ordinary Gibbs sampling, but it may require more simulation steps than would be
needed if chains were tracked exactly.


title: Dependency Networks for Collaborative Filtering and Data Visualization

abstract:
  We describe a graphical model for probabilistic relationships---an
alternative to the Bayesian network---called a dependency network. The graph of
a dependency network, unlike a Bayesian network, is potentially cyclic. The
probability component of a dependency network, like a Bayesian network, is a
set of conditional distributions, one for each node given its parents. We
identify several basic properties of this representation and describe a
computationally efficient procedure for learning the graph and probability
components from data. We describe the application of this representation to
probabilistic inference, collaborative filtering (the task of predicting
preferences), and the visualization of acausal predictive relationships.


title: YGGDRASIL - A Statistical Package for Learning Split Models

abstract:
  There are two main objectives of this paper. The first is to present a
statistical framework for models with context specific independence structures,
i.e., conditional independences holding only for sepcific values of the
conditioning variables. This framework is constituted by the class of split
models. Split models are extension of graphical models for contigency tables
and allow for a more sophisticiated modelling than graphical models. The
treatment of split models include estimation, representation and a Markov
property for reading off those independencies holding in a specific context.
The second objective is to present a software package named YGGDRASIL which is
designed for statistical inference in split models, i.e., for learning such
models on the basis of data.


title:
Probabilistic Arc Consistency: A Connection between Constraint Reasoning
  and Probabilistic Reasoning

abstract:
  We document a connection between constraint reasoning and probabilistic
reasoning. We present an algorithm, called {em probabilistic arc consistency},
which is both a generalization of a well known algorithm for arc consistency
used in constraint reasoning, and a specialization of the belief updating
algorithm for singly-connected networks. Our algorithm is exact for singly-
connected constraint problems, but can work well as an approximation for
arbitrary problems. We briefly discuss some empirical results, and related
methods.


title: Feature Selection and Dualities in Maximum Entropy Discrimination

abstract:
  Incorporating feature selection into a classification or regression method
often carries a number of advantages. In this paper we formalize feature
selection specifically from a discriminative perspective of improving
classification/regression accuracy. The feature selection method is developed
as an extension to the recently proposed maximum entropy discrimination (MED)
framework. We describe MED as a flexible (Bayesian) regularization approach
that subsumes, e.g., support vector classification, regression and exponential
family models. For brevity, we restrict ourselves primarily to feature
selection in the context of linear classification/regression methods and
demonstrate that the proposed approach indeed carries substantial improvements
in practice. Moreover, we discuss and develop various extensions of feature
selection, including the problem of dealing with example specific but
unobserved degrees of freedom -- alignments or invariants.


title: Marginalization in Composed Probabilistic Models

abstract:
  Composition of low-dimensional distributions, whose foundations were laid in
the papaer published in the Proceeding of UAI'97 (Jirousek 1997), appeared to
be an alternative apparatus to describe multidimensional probabilistic models.
In contrast to Graphical Markov Models, which define multidomensinoal
distributions in a declarative way, this approach is rather procedural.
Ordering of low-dimensional distributions into a proper sequence fully defines
the resepctive computational procedure; therefore, a stury of different type of
generating sequences is one fo the central problems in this field. Thus, it
appears that an important role is played by special sequences that are called
perfect. Their main characterization theorems are presetned in this paper.
However, the main result of this paper is a solution to the problem of
margnialization for general sequences. The main theorem describes a way to
obtain a generating sequence that defines the model corresponding to the
marginal of the distribution defined by an arbitrary genearting sequence. From
this theorem the reader can see to what extent these comutations are local;
i.e., the sequence consists of marginal distributions whose computation must be
made by summing up over the values of the variable eliminated (the paper deals
with finite model).


title: Fast Planning in Stochastic Games

abstract:
  Stochastic games generalize Markov decision processes (MDPs) to a multiagent
setting by allowing the state transitions to depend jointly on all player
actions, and having rewards determined by multiplayer matrix games at each
state. We consider the problem of computing Nash equilibria in stochastic
games, the analogue of planning in MDPs. We begin by providing a generalization
of finite-horizon value iteration that computes a Nash strategy for each player
in generalsum stochastic games. The algorithm takes an arbitrary Nash selection
function as input, which allows the translation of local choices between
multiple Nash equilibria into the selection of a single global Nash
equilibrium.
  Our main technical result is an algorithm for computing near-Nash equilibria
in large or infinite state spaces. This algorithm builds on our finite-horizon
value iteration algorithm, and adapts the sparse sampling methods of Kearns,
Mansour and Ng (1999) to stochastic games. We conclude by descrbing a
counterexample showing that infinite-horizon discounted value iteration, which
was shown by shaplely to converge in the zero-sum case (a result we give extend
slightly here), does not converge in the general-sum case.


title: Making Sensitivity Analysis Computationally Efficient

abstract:
  To investigate the robustness of the output probabilities of a Bayesian
network, a sensitivity analysis can be performed. A one-way sensitivity
analysis establishes, for each of the probability parameters of a network, a
function expressing a posterior marginal probability of interest in terms of
the parameter. Current methods for computing the coefficients in such a
function rely on a large number of network evaluations. In this paper, we
present a method that requires just a single outward propagation in a junction
tree for establishing the coefficients in the functions for all possible
parameters; in addition, an inward propagation is required for processing
evidence. Conversely, the method requires a single outward propagation for
computing the coefficients in the functions expressing all possible posterior
marginals in terms of a single parameter. We extend these results to an n-way
sensitivity analysis in which sets of parameters are studied.


title: Policy Iteration for Factored MDPs

abstract:
Many large MDPs can be represented compactly using a dynamic Bayesian
network. Although the structure of the value function does not retain the
structure of the process, recent work has shown that value functions in
factored MDPs can often be approximated well using a decomposed value function:
a linear combination of restricted basis functions, each of which refers
only to a small subset of variables. An approximate value function for a
particular policy can be computed using approximate dynamic programming, but
this approach (and others) can only produce an approximation relative to a
distance metric which is weighted by the stationary distribution of the current
policy. This type of weighted projection is ill-suited to policy improvement.
We present a new approach to value determination, that uses a simple
closed-form computation to directly compute a least-squares decomposed
approximation to the value function for any weights. We then use this
value determination algorithm as a subroutine in a policy iteration process. We
show that, under reasonable restrictions, the policies induced by a factored
value function are compactly represented, and can be manipulated efficiently in
a policy iteration process. We also present a method for computing error bounds
for decomposed value functions using a variable-elimination algorithm for
function optimization. The complexity of all of our algorithms depends on the
factorization of system dynamics and of the approximate value function.


title: Game Networks

abstract:
  We introduce Game networks (G nets), a novel representation for multi-agent
decision problems. Compared to other game-theoretic representations, such as
strategic or extensive forms, G nets are more structured and more compact; more
fundamentally, G nets constitute a computationally advantageous framework for
strategic inference, as both probability and utility independencies are
captured in the structure of the network and can be exploited in order to
simplify the inference process. An important aspect of multi-agent reasoning is
the identification of some or all of the strategic equilibria in a game; we
present original convergence methods for strategic equilibrium which can take
advantage of strategic separabilities in the G net structure in order to
simplify the computations. Specifically, we describe a method which identifies
a unique equilibrium as a function of the game payoffs, and one which
identifies all equilibria.


title:
Combinatorial Optimization by Learning and Simulation of Bayesian
  Networks

abstract:
  This paper shows how the Bayesian network paradigm can be used in order to
solve combinatorial optimization problems. To do it some methods of structure
learning from data and simulation of Bayesian networks are inserted inside
Estimation of Distribution Algorithms (EDA). EDA are a new tool for
evolutionary computation in which populations of individuals are created by
estimation and simulation of the joint probability distribution of the selected
individuals. We propose new approaches to EDA for combinatorial optimization
based on the theory of probabilistic graphical models. Experimental results are
also presented.


title: Causal Mechanism-based Model Construction

abstract:
  We propose a framework for building graphical causal model that is based on
the concept of causal mechanisms. Causal models are intuitive for human users
and, more importantly, support the prediction of the effect of manipulation. We
describe an implementation of the proposed framework as an interactive model
construction module, ImaGeNIe, in SMILE (Structural Modeling, Inference, and
Learning Engine) and in GeNIe (SMILE's Windows user interface).


title: Credal Networks under Maximum Entropy

abstract:
  We apply the principle of maximum entropy to select a unique joint
probability distribution from the set of all joint probability distributions
specified by a credal network. In detail, we start by showing that the unique
joint distribution of a Bayesian tree coincides with the maximum entropy model
of its conditional distributions. This result, however, does not hold anymore
for general Bayesian networks. We thus present a new kind of maximum entropy
models, which are computed sequentially. We then show that for all general
Bayesian networks, the sequential maximum entropy model coincides with the
unique joint distribution. Moreover, we apply the new principle of sequential
maximum entropy to interval Bayesian networks and more generally to credal
networks. We especially show that this application is equivalent to a number of
small local entropy maximizations.


title: Risk Agoras: Dialectical Argumentation for Scientific Reasoning

abstract:
  We propose a formal framework for intelligent systems which can reason about
scientific domains, in particular about the carcinogenicity of chemicals, and
we study its properties. Our framework is grounded in a philosophy of
scientific enquiry and discourse, and uses a model of dialectical
argumentation. The formalism enables representation of scientific uncertainty
and conflict in a manner suitable for qualitative reasoning about the domain.


title: Tractable Bayesian Learning of Tree Belief Networks

abstract:
  In this paper we present decomposable priors, a family of priors over
structure and parameters of tree belief nets for which Bayesian learning with
complete observations is tractable, in the sense that the posterior is also
decomposable and can be completely determined analytically in polynomial time.
This follows from two main results: First, we show that factored distributions
over spanning trees in a graph can be integrated in closed form. Second, we
examine priors over tree parameters and show that a set of assumptions similar
to (Heckerman and al. 1995) constrain the tree parameter priors to be a
compactly parameterized product of Dirichlet distributions. Beside allowing for
exact Bayesian learning, these results permit us to formulate a new class of
tractable latent variable models in which the likelihood of a data point is
computed through an ensemble average over tree structures.


title: Probabilistic Models for Agents' Beliefs and Decisions

abstract:
  Many applications of intelligent systems require reasoning about the mental
states of agents in the domain. We may want to reason about an agent's beliefs,
including beliefs about other agents; we may also want to reason about an
agent's preferences, and how his beliefs and preferences relate to his
behavior. We define a probabilistic epistemic logic (PEL) in which belief
statements are given a formal semantics, and provide an algorithm for asserting
and querying PEL formulas in Bayesian networks. We then show how to reason
about an agent's behavior by modeling his decision process as an influence
diagram and assuming that he behaves rationally. PEL can then be used for
reasoning from an agent's observed actions to conclusions about other aspects
of the domain, including unobserved domain variables and the agent's mental
states.


title:
The Anchors Hierachy: Using the triangle inequality to survive high
  dimensional data

abstract:
  This paper is about metric data structures in high-dimensional or
non-Euclidean space that permit cached sufficient statistics accelerations of
learning algorithms.
  It has recently been shown that for less than about 10 dimensions, decorating
kd-trees with additional "cached sufficient statistics" such as first and
second moments and contingency tables can provide satisfying acceleration for a
very wide range of statistical learning tasks such as kernel regression,
locally weighted regression, k-means clustering, mixture modeling and Bayes Net
learning.
  In this paper, we begin by defining the anchors hierarchy - a fast data
structure and algorithm for localizing data based only on a
triangle-inequality-obeying distance metric. We show how this, in its own
right, gives a fast and effective clustering of data. But more importantly we
show how it can produce a well-balanced structure similar to a Ball-Tree
(Omohundro, 1991) or a kind of metric tree (Uhlmann, 1991; Ciaccia, Patella, &
Zezula, 1997) in a way that is neither "top-down" nor "bottom-up" but instead
"middle-out". We then show how this structure, decorated with cached sufficient
statistics, allows a wide variety of statistical learning algorithms to be
accelerated even in thousands of dimensions.


title: PEGASUS: A Policy Search Method for Large MDPs and POMDPs

abstract:
  We propose a new approach to the problem of searching a space of policies for
a Markov decision process (MDP) or a partially observable Markov decision
process (POMDP), given a model. Our approach is based on the following
observation: Any (PO)MDP can be transformed into an "equivalent" POMDP in which
all state transitions (given the current state and action) are deterministic.
This reduces the general problem of policy search to one in which we need only
consider POMDPs with deterministic transitions. We give a natural way of
estimating the value of all policies in these transformed POMDPs. Policy search
is then simply performed by searching for a policy with high estimated value.
We also establish conditions under which our value estimates will be good,
recovering theoretical results similar to those of Kearns, Mansour and Ng
(1999), but with "sample complexity" bounds that have only a polynomial rather
than exponential dependence on the horizon time. Our method applies to
arbitrary POMDPs, including ones with infinite state and action spaces. We also
present empirical results for our approach on a small discrete problem, and on
a complex continuous state/continuous action problem involving learning to ride
a bicycle.


title: Representing and Solving Asymmetric Bayesian Decision Problems

abstract:
  This paper deals with the representation and solution of asymmetric Bayesian
decision problems. We present a formal framework, termed asymmetric influence
diagrams, that is based on the influence diagram and allows an efficient
representation of asymmetric decision problems. As opposed to existing
frameworks, the asymmetric influece diagram primarily encodes asymmetry at the
qualitative level and it can therefore be read directly from the model. We give
an algorithm for solving asymmetric influence diagrams. The algorithm initially
decomposes the asymmetric decision problem into a structure of symmetric
subproblems organized as a tree. A solution to the decision problem can then be
found by propagating from the leaves toward the root using existing evaluation
methods to solve the sub-problems.


title:
Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as
  an Example

abstract:
  When using Bayesian networks for modelling the behavior of man-made
machinery, it usually happens that a large part of the model is deterministic.
For such Bayesian networks deterministic part of the model can be represented
as a Boolean function, and a central part of belief updating reduces to the
task of calculating the number of satisfying configurations in a Boolean
function. In this paper we explore how advances in the calculation of Boolean
functions can be adopted for belief updating, in particular within the context
of troubleshooting. We present experimental results indicating a substantial
speed-up compared to traditional junction tree propagation.


title: Evaluating Influence Diagrams using LIMIDs

abstract:
  We present a new approach to the solution of decision problems formulated as
influence diagrams. The approach converts the influence diagram into a simpler
structure, the LImited Memory Influence Diagram (LIMID), where only the
requisite information for the computation of optimal policies is depicted.
Because the requisite information is explicitly represented in the diagram, the
evaluation procedure can take advantage of it. In this paper we show how to
convert an influence diagram to a LIMID and describe the procedure for finding
an optimal strategy. Our approach can yield significant savings of memory and
computational time when compared to traditional methods.


title: Adaptive Importance Sampling for Estimation in Structured Domains

abstract:
  Sampling is an important tool for estimating large, complex sums and
integrals over high dimensional spaces. For instance, important sampling has
been used as an alternative to exact methods for inference in belief networks.
Ideally, we want to have a sampling distribution that provides optimal-variance
estimators. In this paper, we present methods that improve the sampling
distribution by systematically adapting it as we obtain information from the
samples. We present a stochastic-gradient-descent method for sequentially
updating the sampling distribution based on the direct minization of the
variance. We also present other stochastic-gradient-descent methods based on
the minimizationof typical notions of distance between the current sampling
distribution and approximations of the target, optimal distribution. We finally
validate and compare the different methods empirically by applying them to the
problem of action evaluation in influence diagrams.


title: Conversation as Action Under Uncertainty

abstract:
  Conversations abound with uncetainties of various kinds. Treating
conversation as inference and decision making under uncertainty, we propose a
task independent, multimodal architecture for supporting robust continuous
spoken dialog called Quartet. We introduce four interdependent levels of
analysis, and describe representations, inference procedures, and decision
strategies for managing uncertainties within and between the levels. We
highlight the approach by reviewing interactions between a user and two spoken
dialog systems developed using the Quartet architecture: Prsenter, a prototype
system for navigating Microsoft PowerPoint presentations, and the Bayesian
Receptionist, a prototype system for dealing with tasks typically handled by
front desk receptionists at the Microsoft corporate campus.


title:
Probabilistic Models for Query Approximation with Large Sparse Binary
  Datasets

abstract:
  Large sparse sets of binary transaction data with millions of records and
thousands of attributes occur in various domains: customers purchasing
products, users visiting web pages, and documents containing words are just
three typical examples. Real-time query selectivity estimation (the problem of
estimating the number of rows in the data satisfying a given predicate) is an
important practical problem for such databases.
  We investigate the application of probabilistic models to this problem. In
particular, we study a Markov random field (MRF) approach based on frequent
sets and maximum entropy, and compare it to the independence model and the
Chow-Liu tree model. We find that the MRF model provides substantially more
accurate probability estimates than the other methods but is more expensive
from a computational and memory viewpoint. To alleviate the computational
requirements we show how one can apply bucket elimination and clique tree
approaches to take advantage of structure in the models and in the queries. We
provide experimental results on two large real-world transaction datasets.


title:
Collaborative Filtering by Personality Diagnosis: A Hybrid Memory- and
  Model-Based Approach

abstract:
  The growth of Internet commerce has stimulated the use of collaborative
filtering (CF) algorithms as recommender systems. Such systems leverage
knowledge about the known preferences of multiple users to recommend items of
interest to other users. CF methods have been harnessed to make recommendations
about such items as web pages, movies, books, and toys. Researchers have
proposed and evaluated many approaches for generating recommendations. We
describe and evaluate a new method called emph{personality diagnosis (PD)}.
Given a user's preferences for some items, we compute the probability that he
or she is of the same "personality type" as other users, and, in turn, the
probability that he or she will like new items. PD retains some of the
advantages of traditional similarity-weighting techniques in that all data is
brought to bear on each prediction and new data can be added easily and
incrementally. Additionally, PD has a meaningful probabilistic interpretation,
which may be leveraged to justify, explain, and augment results. We report
empirical results on the EachMovie database of movie ratings, and on user
profile data collected from the CiteSeer digital library of Computer Science
research papers. The probabilistic framework naturally supports a variety of
descriptive measurements - in particular, we consider the applicability of a
value of information (VOI) computation.


title: Compact Securities Markets for Pareto Optimal Reallocation of Risk

abstract:
  The emph{securities market} is the fundamental theoretical framework in
economics and finance for resource allocation under uncertainty. Securities
serve both to reallocate risk and to disseminate probabilistic information.
emph{Complete} securities markets - which contain one security for every
possible state of nature - support Pareto optimal allocations of risk. Complete
markets suffer from the same exponential dependence on the number of underlying
events as do joint probability distributions. We examine whether markets can be
structured and "compacted" in the same manner as Bayesian network
representations of joint distributions. We show that, if all agents'
risk-neutral independencies agree with the independencies encoded in the market
structure, then the market is emph{operationally complete}: risk is still
Pareto optimally allocated, yet the number of securities can be exponentially
smaller. For collections of agents of a certain type, agreement on Markov
independencies is sufficient to admit compact and operationally complete
markets.


title: Value-Directed Belief State Approximation for POMDPs

abstract:
  We consider the problem belief-state monitoring for the purposes of
implementing a policy for a partially-observable Markov decision process
(POMDP), specifically how one might approximate the belief state. Other schemes
for belief-state approximation (e.g., based on minimixing a measures such as
KL-diveregence between the true and estimated state) are not necessarily
appropriate for POMDPs. Instead we propose a framework for analyzing
value-directed approximation schemes, where approximation quality is determined
by the expected error in utility rather than by the error in the belief state
itself. We propose heuristic methods for finding good projection schemes for
belief state estimation - exhibiting anytime characteristics - given a POMDP
value fucntion. We also describe several algorithms for constructing bounds on
the error in decision quality (expected utility) associated with acting in
accordance with a given belief state approximation.


title: Probabilistic State-Dependent Grammars for Plan Recognition

abstract:
  Techniques for plan recognition under uncertainty require a stochastic model
of the plan-generation process. We introduce Probabilistic State-Dependent
Grammars (PSDGs) to represent an agent's plan-generation process. The PSDG
language model extends probabilistic context-free grammars (PCFGs) by allowing
production probabilities to depend on an explicit model of the planning agent's
internal and external state. Given a PSDG description of the plan-generation
process, we can then use inference algorithms that exploit the particular
independence properties of the PSDG language to efficiently answer
plan-recognition queries. The combination of the PSDG language model and
inference algorithms extends the range of plan-recognition domains for which
practical probabilistic inference is possible, as illustrated by applications
in traffic monitoring and air combat.


title: Pivotal Pruning of Trade-offs in QPNs

abstract:
  Qualitative probabilistic networks have been designed for probabilistic
reasoning in a qualitative way. Due to their coarse level of representation
detail, qualitative probabilistic networks do not provide for resolving
trade-offs and typically yield ambiguous results upon inference. We present an
algorithm for computing more insightful results for unresolved trade-offs. The
algorithm builds upon the idea of using pivots to zoom in on the trade-offs and
identifying the information that would serve to resolve them.


title: Monte Carlo Inference via Greedy Importance Sampling

abstract:
  We present a new method for conducting Monte Carlo inference in graphical
models which combines explicit search with generalized importance sampling. The
idea is to reduce the variance of importance sampling by searching for
significant points in the target distribution. We prove that it is possible to
introduce search and still maintain unbiasedness. We then demonstrate our
procedure on a few simple inference tasks and show that it can improve the
inference quality of standard MCMC methods, including Gibbs sampling,
Metropolis sampling, and Hybrid Monte Carlo. This paper extends previous work
which showed how greedy importance sampling could be correctly realized in the
one-dimensional case.


title: Combining Feature and Prototype Pruning by Uncertainty Minimization

abstract:
  We focus in this paper on dataset reduction techniques for use in k-nearest
neighbor classification. In such a context, feature and prototype selections
have always been independently treated by the standard storage reduction
algorithms. While this certifying is theoretically justified by the fact that
each subproblem is NP-hard, we assume in this paper that a joint storage
reduction is in fact more intuitive and can in practice provide better results
than two independent processes. Moreover, it avoids a lot of distance
calculations by progressively removing useless instances during the feature
pruning. While standard selection algorithms often optimize the accuracy to
discriminate the set of solutions, we use in this paper a criterion based on an
uncertainty measure within a nearest-neighbor graph. This choice comes from
recent results that have proven that accuracy is not always the suitable
criterion to optimize. In our approach, a feature or an instance is removed if
its deletion improves information of the graph. Numerous experiments are
presented in this paper and a statistical analysis shows the relevance of our
approach, and its tolerance in the presence of noise.


title: Nash Convergence of Gradient Dynamics in Iterated General-Sum Games

abstract:
  Multi-agent games are becoming an increasing prevalent formalism for the
study of electronic commerce and auctions. The speed at which transactions can
take place and the growing complexity of electronic marketplaces makes the
study of computationally simple agents an appealing direction. In this work, we
analyze the behavior of agents that incrementally adapt their strategy through
gradient ascent on expected payoff, in the simple setting of two-player,
two-action, iterated general-sum games, and present a surprising result. We
show that either the agents will converge to Nash equilibrium, or if the
strategies themselves do not converge, then their average payoffs will
nevertheless converge to the payoffs of a Nash equilibrium.


title: A Knowledge Acquisition Tool for Bayesian-Network Troubleshooters

abstract:
  This paper describes a domain-specific knowledge acquisition tool for
intelligent automated troubleshooters based on Bayesian networks. No Bayesian
network knowledge is required to use the tool, and troubleshooting information
can be specified as natural and intuitive as possible. Probabilities can be
specified in the direction that is most natural to the domain expert. Thus, the
knowledge acquisition efficiently removes the traditional knowledge acquisition
bottleneck of Bayesian networks.


title: On the Use of Skeletons when Learning in Bayesian Networks

abstract:
  In this paper, we present a heuristic operator which aims at simultaneously
optimizing the orientations of all the edges in an intermediate Bayesian
network structure during the search process. This is done by alternating
between the space of directed acyclic graphs (DAGs) and the space of skeletons.
The found orientations of the edges are based on a scoring function rather than
on induced conditional independences. This operator can be used as an extension
to commonly employed search strategies. It is evaluated in experiments with
artificial and real-world data.


title:
Dynamic Trees: A Structured Variational Method Giving Efficient
  Propagation Rules

abstract:
  Dynamic trees are mixtures of tree structured belief networks. They solve
some of the problems of fixed tree networks at the cost of making exact
inference intractable. For this reason approximate methods such as sampling or
mean field approaches have been used. However, mean field approximations assume
a factorized distribution over node states. Such a distribution seems unlickely
in the posterior, as nodes are highly correlated in the prior. Here a
structured variational approach is used, where the posterior distribution over
the non-evidential nodes is itself approximated by a dynamic tree. It turns out
that this form can be used tractably and efficiently. The result is a set of
update rules which can propagate information through the network to obtain both
a full variational approximation, and the relevant marginals. The progagtion
rules are more efficient than the mean field approach and give noticeable
quantitative and qualitative improvement in the inference. The marginals
calculated give better approximations to the posterior than loopy propagation
on a small toy problem.


title: An Uncertainty Framework for Classification

abstract:
  We define a generalized likelihood function based on uncertainty measures and
show that maximizing such a likelihood function for different measures induces
different types of classifiers. In the probabilistic framework, we obtain
classifiers that optimize the cross-entropy function. In the possibilistic
framework, we obtain classifiers that maximize the interclass margin.
Furthermore, we show that the support vector machine is a sub-class of these
maximum-margin classifiers.


title: A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks

abstract:
  This paper extends the work in [Suzuki, 1996] and presents an efficient
depth-first branch-and-bound algorithm for learning Bayesian network
structures, based on the minimum description length (MDL) principle, for a
given (consistent) variable ordering. The algorithm exhaustively searches
through all network structures and guarantees to find the network with the best
MDL score. Preliminary experiments show that the algorithm is efficient, and
that the time complexity grows slowly with the sample size. The algorithm is
useful for empirically studying both the performance of suboptimal heuristic
search algorithms and the adequacy of the MDL principle in learning Bayesian
networks.


title: Probabilities of Causation: Bounds and Identification

abstract:
  This paper deals with the problem of estimating the probability that one
event was a cause of another in a given scenario. Using structural-semantical
definitions of the probabilities of necessary or sufficient causation (or
both), we show how to optimally bound these quantities from data obtained in
experimental and observational studies, making minimal assumptions concerning
the data-generating process. In particular, we strengthen the results of Pearl
(1999) by weakening the data-generation assumptions and deriving theoretically
sharp bounds on the probabilities of causation. These results delineate
precisely how empirical data can be used both in settling questions of
attribution and in solving attribution-related problems of decision making.


title: Model-Based Hierarchical Clustering

abstract:
  We present an approach to model-based hierarchical clustering by formulating
an objective function based on a Bayesian analysis. This model organizes the
data into a cluster hierarchy while specifying a complex feature-set
partitioning that is a key component of our model. Features can have either a
unique distribution in every cluster or a common distribution over some (or
even all) of the clusters. The cluster subsets over which these features have
such a common distribution correspond to the nodes (clusters) of the tree
representing the hierarchy. We apply this general model to the problem of
document clustering for which we use a multinomial likelihood function and
Dirichlet priors. Our algorithm consists of a two-stage process wherein we
first perform a flat clustering followed by a modified hierarchical
agglomerative merging process that includes determining the features that will
have common distributions over the merged clusters. The regularization induced
by using the marginal likelihood automatically determines the optimal model
structure including number of clusters, the depth of the tree and the subset of
features to be modeled as having a common distribution at each node. We present
experimental results on both synthetic data and a real document collection.


title: Conditional Independence and Markov Properties in Possibility Theory

abstract:
  Conditional independence and Markov properties are powerful tools allowing
expression of multidimensional probability distributions by means of
low-dimensional ones. As multidimensional possibilistic models have been
studied for several years, the demand for analogous tools in possibility theory
seems to be quite natural. This paper is intended to be a promotion of de
Cooman's measure-theoretic approcah to possibility theory, as this approach
allows us to find analogies to many important results obtained in probabilistic
framework. First, we recall semi-graphoid properties of conditional
possibilistic independence, parameterized by a continuous t-norm, and find
sufficient conditions for a class of Archimedean t-norms to have the graphoid
property. Then we introduce Markov properties and factorization of possibility
distrubtions (again parameterized by a continuous t-norm) and find the
relationships between them. These results are accompanied by a number of
conterexamples, which show that the assumptions of specific theorems are
substantial.


title:
Variational Approximations between Mean Field Theory and the Junction
  Tree Algorithm

abstract:
  Recently, variational approximations such as the mean field approximation
have received much interest. We extend the standard mean field method by using
an approximating distribution that factorises into cluster potentials. This
includes undirected graphs, directed acyclic graphs and junction trees. We
derive generalized mean field equations to optimize the cluster potentials. We
show that the method bridges the gap between the standard mean field
approximation and the exact junction tree algorithm. In addition, we address
the problem of how to choose the graphical structure of the approximating
distribution. From the generalised mean field equations we derive rules to
simplify the structure of the approximating distribution in advance without
affecting the quality of the approximation. We also show how the method fits
into some other variational approximations that are currently popular.


title: Model Criticism of Bayesian Networks with Latent Variables

abstract:
  The application of Bayesian networks (BNs) to cognitive assessment and
intelligent tutoring systems poses new challenges for model construction. When
cognitive task analyses suggest constructing a BN with several latent
variables, empirical model criticism of the latent structure becomes both
critical and complex. This paper introduces a methodology for criticizing
models both globally (a BN in its entirety) and locally (observable nodes), and
explores its value in identifying several kinds of misfit: node errors, edge
errors, state errors, and prior probability errors in the latent structure. The
results suggest the indices have potential for detecting model misfit and
assisting in locating problematic components of the model.


title:
Exploiting Qualitative Knowledge in the Learning of Conditional
  Probabilities of Bayesian Networks

abstract:
  Algorithms for learning the conditional probabilities of Bayesian networks
with hidden variables typically operate within a high-dimensional search space
and yield only locally optimal solutions. One way of limiting the search space
and avoiding local optima is to impose qualitative constraints that are based
on background knowledge concerning the domain. We present a method for
integrating formal statements of qualitative constraints into two learning
algorithms, APN and EM. In our experiments with synthetic data, this method
yielded networks that satisfied the constraints almost perfectly. The accuracy
of the learned networks was consistently superior to that of corresponding
networks learned without constraints. The exploitation of qualitative
constraints therefore appears to be a promising way to increase both the
interpretability and the accuracy of learned Bayesian networks with known
structure.


title: Numerical simulation on a cell polarisation model: the polar case

abstract:
  When it is polarised, a cell develops an asymmetric distribution of specific
molecular markers, cytoskeleton and cell membrane shape. Polarisation can occur
spontaneously or be triggered by external signals, like gradients of signalling
molecules... In this work, we use the published models of cell polarisation and
we set a numerical analysis for these models. They are based on nonlinear
convection-diffusion equations and the nonlinearity in the transport term
expresses the positive loop between the level of protein concentration
localised in a small area of the cell membrane and the number of new proteins
that will be convected to the same area. We perform numerical simulations and
we illustrate that these models are rich enough to describe the apparition of a
polarisome.


title: Feedback-regulated star formation in molecular clouds and galactic discs

abstract:
  We present a two-zone theory for feedback-regulated star formation in
galactic discs, consistently connecting the galaxy-averaged star formation law
with star formation proceeding in giant molecular clouds (GMCs). Our focus is
on galaxies with gas surface density Sigma_g>~100 Msun pc^-2. In our theory,
the galactic disc consists of Toomre-mass GMCs embedded in a volume-filling
ISM. Radiation pressure on dust disperses GMCs and most supernovae explode in
the volume-filling medium. A galaxy-averaged star formation law is derived by
balancing the momentum input from supernova feedback with the gravitational
weight of the disc gas. This star formation law is in good agreement with
observations for a CO conversion factor depending continuously on Sigma_g. We
argue that the galaxy-averaged star formation efficiency per free fall time,
epsilon_ff^gal, is only a weak function of the efficiency with which GMCs
convert their gas into stars. This is possible because the rate limiting step
for star formation is the rate at which GMCs form: for large efficiency of star
formation in GMCs, the Toomre Q parameter obtains a value slightly above unity
so that the GMC formation rate is consistent with the galaxy-averaged star
formation law. We contrast our results with other theories of
turbulence-regulated star formation and discuss predictions of our model. Using
a compilation of data from the literature, we show that the galaxy-averaged
star formation efficiency per free fall time is non-universal and increases
with increasing gas fraction, as predicted by our model. We also predict that
the fraction of the disc gas mass in bound GMCs decreases for increasing values
of the GMC star formation efficiency. This is qualitatively consistent with the
smooth molecular gas distribution inferred in local ultra-luminous infrared
galaxies and the small mass fraction in giant clumps in high-redshift galaxies.


title: Radio light curves during the passage of cloud G2 near Sgr A*

abstract:
  We calculate radio light curves produced by the bow shock that is likely to
form in front of the G2 cloud when it penetrates the accretion disk of Sgr A*.
The shock acceleration of the radio-emitting electrons is captured
self-consistently by means of first-principles particle-in-cell simulations. We
show that the radio luminosity is expected to reach maximum in early 2013,
roughly a month after the bow shock crosses the orbit pericenter. We estimate
the peak radio flux at 1.4 GHz to be 1.4 - 22 Jy depending on the assumed orbit
orientation and parameters. We show that the most promising frequencies for
radio observations are in the 0.1<nu<1 GHz range, for which the bow shock
emission will be much stronger than the intrinsic radio flux for all the models
considered.


title: The failures of the standard model of cosmology require a new paradigm

abstract:
  Cosmological models that invoke warm or cold dark matter can not explain
observed regularities in the properties of dwarf galaxies, their highly
anisotropic spatial distributions, nor the correlation between observed mass
discrepancies and acceleration. These problems with the standard model of
cosmology have deep implications, in particular in combination with the
observation that the data are excellently described by Modified Newtonian
Dynamics (MOND). MOND is a classical dynamics theory which explains the mass
discrepancies in galactic systems, and in the universe at large, without
invoking dark entities. MOND introduces a new universal constant of nature with
the dimensions of acceleration, a0, such that the pre-MONDian dynamics is valid
for accelerations a >> a0, and the deep MONDian regime is obtained for a << a0,
where space-time scale invariance is invoked. Remaining challenges for MOND are
(i) explaining fully the observed mass discrepancies in galaxy clusters, and
(ii) the development of a relativistic theory of MOND that will satisfactorily
account for cosmology. The universal constant a0 turns out to have an
intriguing connection with cosmology: \bar a0 == 2 pi a0 \approx c H_0 \approx
c^2(Lambda/3)^{1/2}. This may point to a deep connection between cosmology and
internal dynamics of local systems.


title: Relaxation of weakly interacting electrons in one dimension

abstract:
  We consider the problem of relaxation in a one-dimensional system of
interacting electrons. In the limit of weak interactions, we calculate the
decay rate of a single-electron excitation, accounting for the nonlinear
dispersion. The leading processes that determine the relaxation involve
scattering of three particles. We elucidate how particular forms of Coulomb
interaction, unscreened and screened, lead to different results for the decay
rates and identify the dominant scattering processes responsible for relaxation
of excitations of different energies. Interestingly, temperatures much smaller
than the excitation energy strongly affect the rate. At higher temperatures the
quasiparticle relaxes by exciting copropagating electron-hole pairs, whereas at
lowest temperatures the relaxation proceeds via excitations of both
copropagating and counterpropagating pairs.


title: Supernovae and Other Transients in the OGLE-IV Magellanic Bridge Data

abstract:
  We analyze two years (mid-2010 to mid-2012) of OGLE-IV data covering ~65
deg^2 of the Magellanic Bridge (the area between the Magellanic Clouds) and
find 130 transient events including 126 supernovae (SNe), two foreground dwarf
novae and another two SNe-like transients that turned out to be active galactic
nuclei (AGNs). We show our SNe detection efficiency as a function of SN peak
magnitude based on available SNe rate estimates. It is 100% for SNe peak
magnitudes I<18.8 mag and drops to 50% at I=~19.7 mag. With our current
observing area between and around the Magellanic Clouds (~600 deg^2), we expect
to find 24 SNe peaking above I<18 mag, 100 above I<19 mag, and 340 above I<20
mag, annually. We briefly introduce our on-line near-real-time detection system
for SNe and other transients, the OGLE Transient Detection System.


title:
Hybrid methods in planetesimal dynamics: Formation of protoplanetary
  systems and the mill condition

abstract:
  The formation and evolution of protoplanetary discs remains a challenge from
both a theoretical and numerical standpoint. In this work we first perform a
series of tests of our new hybrid algorithm presented in Glaschke, Amaro-Seoane
and Spurzem 2011 (henceforth Paper I) that combines the advantages of high
accuracy of direct-summation N-body methods with a statistical description for
the planetesimal disc based on Fokker-Planck techniques. We then address the
formation of planets, with a focus on the formation of protoplanets out of
planetesimals. We find that the evolution of the system is driven by encounters
as well as direct collisions and requires a careful modelling of the evolution
of the velocity dispersion and the size distribution over a large range of
sizes. The simulations show no termination of the protoplanetary accretion due
to gap formation, since the distribution of the planetesimals is only subjected
to small fluctuations. We also show that these features are weakly correlated
with the positions of the protoplanets. The exploration of different impact
strengths indicates that fragmentation mainly controls the overall mass loss,
which is less pronounced during the early runaway growth. We prove that the
fragmentation in combination with the effective removal of collisional
fragments by gas drag sets an universal upper limit of the protoplanetary mass
as a function of the distance to the host star, which we refer to as the mill
condition.


title: Estimation of distances within the Milky Way using tidal streams

abstract:
  During the past 20 years, numerous stellar streams have been discovered in
both the Milky Way and the Local Group. These streams have been tidally torn
from orbiting systems, which suggests that most of them should roughly trace
the orbit of their progenitors around the Galaxy. As a consequence, they play a
fundamental role in understanding the formation and evolution of our Galaxy.
This project is based on the possibility of applying a technique developed by
Binney in 2008 to various tidal streams and overdensities in the Galaxy. The
aim is to develop an efficient method to constrain the Galactic gravitational
potential, to determine its mass distribution, and to test distance
measurements. Here we apply the technique to the Grillmair & Dionatos cold
stellar stream. In the case of noise-free data, the results show that the
technique provides excellent discrimination against incorrect potentials and
that it is possible to predict the heliocentric distance very accurately. This
changes dramatically when errors are taken into account, which wash out most of
the results. Nevertheless, it is still possible to rule out spherical
potentials and set constraints on the distance of a given stream.


title: The host-galaxy response to the afterglow of GRB 100901A

abstract:
  For Gamma-Ray Burst 100901A, we have obtained Gemini-North and Very Large
Telescope optical afterglow spectra at four epochs: one hour, one day, three
days and one week after the burst, thanks to the afterglow remaining unusually
bright at late times. Apart from a wealth of metal resonance lines, we also
detect lines arising from fine-structure levels of the ground state of Fe II,
and from metastable levels of Fe II and Ni II at the host redshift (z =
1.4084). These lines are found to vary significantly in time. The combination
of the data and modelling results shows that we detect the fall of the Ni II 4
F9/2 metastable level population, which to date has not been observed. Assuming
that the population of the excited states is due to the UV-radiation of the
afterglow, we estimate an absorber distance of a few hundred pc. This appears
to be a typical value when compared to similar studies. We detect two
intervening absorbers (z = 1.3147, 1.3179). Despite the wide temporal range of
the data, we do not see significant variation in the absorption lines of these
two intervening systems.


title: A Study of the Diverse T Dwarf Population Revealed by WISE

abstract:
  We report the discovery of 87 new T dwarfs uncovered with the Wide-field
Infrared Survey Explorer (WISE) and three brown dwarfs with extremely red
near-infrared colors that exhibit characteristics of both L and T dwarfs. Two
of the new T dwarfs are likely binaries with L7+/-1 primaries and mid-type T
secondaries. In addition, our follow-up program has confirmed 10 previously
identified T dwarfs and four photometrically-selected L and T dwarf candidates
in the literature. This sample, along with the previous WISE discoveries,
triples the number of known brown dwarfs with spectral types later than T5.
Using the WISE All-Sky Source Catalog we present updated color-color and
color-type diagrams for all the WISE-discovered T and Y dwarfs. Near-infrared
spectra of the new discoveries are presented, along with spectral
classifications. To accommodate later T dwarfs we have modified the integrated
flux method of determining spectral indices to instead use the median flux.
Furthermore, a newly defined J-narrow index differentiates the early-type Y
dwarfs from late-type T dwarfs based on the J-band continuum slope. The K/J
indices for this expanded sample show that 32% of late-type T dwarfs have
suppressed K-band flux and are blue relative to the spectral standards, while
only 11% are redder than the standards. Comparison of the Y/J and K/J index to
models suggests diverse atmospheric conditions and supports the possible
re-emergence of clouds after the L/T transition. We also discuss peculiar brown
dwarfs and candidates that were found not to be substellar, including two Young
Stellar Objects and two Active Galactic Nuclei. The coolest WISE-discovered
brown dwarfs are the closest of their type and will remain the only sample of
their kind for many years to come.