title:
Mechanisms of Multi-strain Coexistence in Host-Phage Systems with Nested
  Infection Networks

abstract:
  Bacteria and their viruses ("bacteriophages") coexist in natural environments
forming complex infection networks. Recent empirical findings suggest that
phage-bacteria infection networks often possess a nested structure such that
there is a hierarchical relationship among who can infect whom. Here we
consider how nested infection networks may affect phage and bacteria dynamics
using a multi-type Lotka-Volterra framework with cross-infection. Analysis of
similar models have, in the past, assumed simpler interaction structures as a
first step towards tractability. We solve the proposed model, finding trade-off
conditions on the life-history traits of both bacteria and viruses that allow
coexistence in communities with nested infection networks. First, we find that
bacterial growth rate should decrease with increasing defense against
infection. Second, we find that the efficiency of viral infection should
decrease with host range. Next, we establish a relationship between relative
densities and the curvature of life history trade-offs. We compare and contrast
the current findings to the "Kill-the-Winner" model of multi-species
phage-bacteria communities. Finally, we discuss a suite of testable hypotheses
stemming from the current model concerning relationships between infection
range, life history traits and coexistence in complex phage-bacteria
communities.


title:
ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process
  Mixtures

abstract:
  The Dirichlet process (DP) is a fundamental mathematical tool for Bayesian
nonparametric modeling, and is widely used in tasks such as density estimation,
natural language processing, and time series modeling. Although MCMC inference
methods for the DP often provide a gold standard in terms asymptotic accuracy,
they can be computationally expensive and are not obviously parallelizable. We
propose a reparameterization of the Dirichlet process that induces conditional
independencies between the atoms that form the random measure. This conditional
independence enables many of the Markov chain transition operators for DP
inference to be simulated in parallel across multiple cores. Applied to mixture
modeling, our approach enables the Dirichlet process to simultaneously learn
clusters that describe the data and superclusters that define the granularity
of parallelization. Unlike previous approaches, our technique does not require
alteration of the model and leaves the true posterior distribution invariant.
It also naturally lends itself to a distributed software implementation in
terms of Map-Reduce, which we test in cluster configurations of over 50
machines and 100 cores. We present experiments exploring the parallel
efficiency and convergence properties of our approach on both synthetic and
real-world data, including runs on 1MM data vectors in 256 dimensions.


title: Altered Maxwell equations in the length gauge

abstract:
  The length gauge uses a scalar potential to describe a laser field, thus
treating it as a longitudinal field rather than as a transverse field. This
distinction is revealed in the fact that the Maxwell equations that relate to
the length gauge are not the same as those for transverse fields. In
particular, a source term is necessary in the length-gauge Maxwell equations,
whereas the Coulomb-gauge description of plane waves possesses the basic
property of transverse fields that they propagate with no source terms at all.
This difference is shown to be importantly consequential in some previously
unremarked circumstances; and it explains why the G\"oppert-Mayer gauge
transformation does not provide the security that might be expected of full
gauge equivalence.


title:
Interplay of three kinds of motion in the disk counterpart of type II
  spicules: up-flow, transversal and torsional motions

abstract:
  Recently, it was shown that the complex dynamical behaviour of spicules has
to be interpreted as the result of simultaneous action of three kinds of
motion: (1) field aligned flows, (2) swaying motions, and (3) torsional
motions. We use high-quality observations from CRISP at the SST to investigate
signs of these different kinetic modes in spicules on the disk. Earlier, rapid
blue-shifted excursions (RBEs), short-lived absorption features in the blue
wing of chromospheric spectral lines, were identified as the disk counterpart
of type II spicules. Here we report the existence of similar absorption
features in the red wing of the Ca II 8542 and Halpha lines: rapid red-shifted
excursions (RREs). RREs are found over the whole solar disk and are located in
the same regions as RBEs: in the vicinity of magnetic field concentrations.
RREs have similar characteristics as RBEs: they have similar lengths, widths,
lifetimes, and average Doppler velocity. The striking similarity of RREs to
RBEs implies that RREs are a manifestation of the same physical phenomenon and
that spicules harbour motions that can result in a net red-shift when observed
on-disk. We find that RREs are less abundant than RBEs and we interpret the
higher number of RBEs and the decreased imbalance towards the limb as an
indication that field-aligned up-flows have a significant contribution to the
net Dopplershift. Most RREs and RBEs are observed in isolation but we find many
examples of parallel and touching RRE/RBE pairs which appear to be part of the
same spicule. We interpret the existence of these RRE/RBE pairs as signs that
torsional motion is an important characteristic of spicules. The fact that most
RBEs and RREs are observed in isolation agrees with the idea that transversal
swaying motion is another important kinetic mode. We find examples of
transitions from RRE to RBE and vice versa.


title: Black-hole solution without curvature singularity

abstract:
  An exact solution of the vacuum Einstein field equations over a
nonsimply-connected manifold is presented. This solution is spherically
symmetric and has no curvature singularity. It can be considered to be a
regularization of the Schwarzschild solution over a simply-connected manifold,
which has a curvature singularity at the center. Spherically symmetric collapse
of matter in R^4 may result in this nonsingular black-hole solution, if
quantum-gravity effects allow for topology change near the center.


title: Nonthermal emission model of isolated X-ray pulsar RX J0420.0-5022

abstract:
  In the present paper an alternative theoretical interpretation to the
generally assumed thermal emission models of the observed X-ray spectrum of
isolated pulsar RX J0420.0-5022 is presented. It is well known that the
distribution function of relativistic particles is one-dimensional at the
pulsar surface. However, cyclotron instability causes an appearance of
transverse momenta of relativistic electrons, which as a result, start to
radiate in the synchrotron regime. On the basis of the Vlasov's kinetic
equation we study the process of the quasi-linear diffusion (QLD) developed by
means of the cyclotron instability. This mechanism provides generation of
optical and X-ray emission on the light cylinder lengthscales. The analysis of
the three archival XMM-Newton observations of RX J0420.0-5022 is performed.
Considering a different approach of the synchrotron emission theory, the
spectral energy distribution is obtained that is in a good agreement with the
observational data. A fit to the X-ray spectrum is performed using both the
present synchrotron emission model spectrum absorbed by cold interstellar
matter and generally assumed absorbed black-body model.


title:
Elliptic Flow of Protons and Antiprotons in Au+Au Collisions at
  $\sqrt{s_{NN}}$ = 7.7--62.4 GeV within Alternative Scenarios of Three-Fluid
  Dynamics

abstract:
  Analysis of elliptic flow of protons and antiprotons in Au+Au collisions is
performed in a wide range of incident energies $\sqrt{s_{NN}}$ = 7.7--62.4 GeV.
Simulations has been done within the three-fluid model employing a purely
hadronic equation of state (EoS) and two versions of the EoS involving
deconfinement transition: an EoS with the first-order phase transition and that
with a smooth crossover transition. It is found that the proton data are
reproduced approximately to the same extent within all of the scenarios,
including the hadronic one, while the deconfinement scenarios look certainly
preferable for the antiproton elliptic flow. The fact that difference between
elliptic flows of protons and antiprotons decreases with the incident energy
rise is a consequence of reducing baryon stopping rather than an onset of
deconfinement.


title: Hunting for GeV emission from the gamma-ray binary HESS J0632+057

abstract:
  In the last decade Cherenkov telescopes on the ground and space-based
gamma-ray instruments have identified a new sub-class of high mass X-ray
binaries (HMXB), whose emission is dominated by gamma rays. To date only five
of these systems have been definitively identified. However at GeV energies
there is still one, HESS J0632+057, that has no reported detection by the Fermi
LAT. A deep search for gamma-ray emission of HESS J0632+057 has been performed
using more than 3.5 years of Fermi-LAT data. We present the results of this
search in the context of the other known gamma-ray binary systems.


title: Dilepton Spectroscopy of QCD Matter at Collider Energies

abstract:
  Low-mass dilepton spectra as measured in high-energy heavy-ion collisions are
a unique tool to obtain spectroscopic information about the strongly
interacting medium produced in these reactions. Specifically, in-medium
modifications of the vector spectral function, which is well known in the
vacuum, can be deduced from the thermal radiation off the expanding QCD
fireball. This, in particular, allows to investigate the fate of the $\rho$
resonance in the dense medium, and possibly infer from it signatures of the
(partial) restoration of chiral symmetry, which is spontaneously broken in the
QCD vacuum. After briefly reviewing calculations of thermal dilepton emission
rates from hot QCD matter, utilizing effective hadronic theory, lattice QCD or
resummed perturbative QCD, we focus on applications to dilepton spectra at
heavy-ion collider experiments at RHIC and LHC. This includes invariant-mass
spectra at full RHIC energy with transverse-momentum dependencies and azimuthal
asymmetries, as well as a systematic investigation of the excitation function
down to fixed-target energies, thus making contact to previous precision
measurements at the SPS. Furthermore, predictions for the energy frontier at
the LHC are presented in both dielectron and dimuon channels.


title:
Embedding of Blink Frequency in Electrooculography Signal using
  Difference Expansion based Reversible Watermarking Technique

abstract:
  In the past few years, like other fields, rapid expansion of digitization and
globalization has influenced the medical field as well. For progress of
diagnostic results most of the reputed hospitals and diagnostic centres all
over the world have started exchanging medical information. In this proposed
method, the calculated diagnostic parametric values of the original
Electrooculography (EOG) signal are embedded as a watermark by using Difference
Expansion (DE) algorithm based reversible watermarking technique. The extracted
watermark provides the required parametric values at the recipient end without
any post computation of the recovered EOG signal. By computing the parametric
values from the recovered signal, the integrity of the extracted watermark can
be validated. The time domain features of EOG signal are calculated for the
generation of watermark. In the current work, various features are studied and
two major features related to blink frequency are used to generate the
watermark. The high Signal to Noise Ratio (SNR) and the Bit Error Rate (BER)
claim the robustness of the proposed method.


title:
Nonlinear Charged Black Holes in Anti-de Sitter Quasi-Topological
  Gravity

abstract:
  In this paper, we present the static charged solutions of quartic
quasitopological gravity in the presence of a nonlinear electromagnetic field.
Two branches of these solutions present black holes with one or two horizons or
a naked singularity depending on the charge and mass of the black hole. The
entropy of the charged black holes of fourth order quasitopological gravity
through the use of Wald formula is computed and the mass, temperature and the
charge of these black holes are found as well. We show that black holes with
spherical, flat and hyperbolical horizon in quasitopological gravity are stable
for any allowed quasitopological parameters.


title:
Parametrization of white matter manifold-like structures using principal
  surfaces

abstract:
  In this manuscript, we are concerned with data generated from a diffusion
tensor imaging (DTI) experiment. The goal is to parameterize manifold-like
white matter tracts, such as the corpus callosum, using principal surfaces. We
approach the problem by finding a geometrically motivated surface-based
representation of the corpus callosum and visualize the fractional anisotropy
(FA) values projected onto the surface; the method applies to any other
diffusion summary as well as to other white matter tracts. We provide an
algorithm that 1) constructs the principal surface of a corpus callosum; 2)
flattens the surface into a parametric 2D map; 3) projects associated FA values
on the map. The algorithm was applied to a longitudinal study containing 466
diffusion tensor images of 176 multiple sclerosis (MS) patients observed at
multiple visits. For each subject and visit the study contains a registered DTI
scan of the corpus callosum at roughly 20,000 voxels. Extensive simulation
studies demonstrate fast convergence and robust performance of the algorithm
under a variety of challenging scenarios.


title: On Differentially Private Filtering for Event Streams

abstract:
  Rigorous privacy mechanisms that can cope with dynamic data are required to
encourage a wider adoption of large-scale monitoring and decision systems
relying on end-user information. A promising approach to develop these
mechanisms is to specify quantitative privacy requirements at design time
rather than as an afterthought, and to rely on signal processing techniques to
achieve satisfying trade-offs between privacy and performance specifications.
This paper discusses, from the signal processing point of view, an event stream
analysis problem introduced in the database and cryptography literature. A
discrete-valued input signal describes the occurrence of events contributed by
end-users, and a system is supposed to provide some output signal based on this
information, while preserving the privacy of the participants. The notion of
privacy adopted here is that of event-level differential privacy, which
provides strong privacy guarantees and has important operational advantages.
Several mechanisms are described to provide differentially private output
signals while minimizing the impact on performance. These mechanisms
demonstrate the benefits of leveraging system theoretic techniques to provide
privacy guarantees for dynamic systems.


title: A Modification of LLR

abstract:
  The Lucas-Lehmer (LL) primality test for Mersenne numbers is the fastest
known primality test. In 1969, Hans Riesel published a modification of LL to
test numbers of the form $N = h \cdot 2^n - 1$, where $h < 2^n$ is an odd
integer and $n \ge 2$ \cite{Riesel}. This test is now known as the
Lucas-Lehmer-Riesel (LLR) primality test. In Algorithm
\ref{PrimalityAlgorithm}, we present a modification of LLR which works for any
odd integer $N$. A probabilistic version of our algorithm runs in expected time
$\tilde{O}(\log^3 N)$, and a deterministic version in expected
$\tilde{O}(\log^4 N)$. We conclude with a conjecture which, if true, would
imply that there exists a polynomial time algorithm for factoring integers.


title: Generalized Elliptic Integrals and Applications

abstract:
  We use some general properties, presented in previous work, to evaluate
special cases of integrals relating Rogers-Ramanujan continued fraction, eta
function and elliptic integrals.


title:
Experimental Neutron-Induced Fission Fragment Mass Yields of 232Th and
  238U at Energies from 10 to 33 MeV

abstract:
  Development of nuclear energy applications requires data for neutron-induced
reactions for actinides in a wide neutron energy range. Here we describe
measurements of pre-neutron emission fission fragment mass yields of 232Th and
238U at incident neutron energies from 10 to 33 MeV. The measurements were done
at the quasi-monoenergetic neutron beam of the Louvain-la-Neuve cyclotron
facility CYCLONE; a multi-section twin Frisch-gridded ionization chamber was
used to detect fission fragments. For the peak neutron energies at 33, 45 and
60 MeV, the details of the data analysis and the experimental results have been
published before and in this work we present data analysis in the low-energy
tail of the neutron energy spectra. The preliminary measurement results are
compared with available experimental data and theoretical predictions.


title: Local Radiation-Driven Instabilities in Post-Main Sequence Massive Stars

abstract:
  Late in their evolution, massive stars may undergo periods of violent
instability and mass loss, but the mechanism responsible for these episodes has
not been identified. We study one potential contributor: the development of
local radiation-driven instabilities in the outer layers of main sequence (MS)
and post-MS massive stars. We construct a sequence of massive stellar evolution
models and investigate where they are subject to local radiative instabilities,
both in the presence of magnetic fields and without them,and at a range of
metallicities. We find that these types of instabilities do not occur in
solar-metallicity MS stars up to 100\,M$_\odot$, but they set in immediately
post-MS for stars heavier than $\sim 25$\,M$_\odot$. Once an instability
appears, it involves a significant amount of mass in the star's upper layers
(up to $\sim 1$ per cent of the initial stellar mass), suggesting that
radiation-driven instabilities are a potentially viable mechanism for dynamic
mass loss. We find that the presence of magnetic fields at strengths low enough
not to disturb the hydrostatic balance of the star does not alter these
results. Stars with sub-Solar metallicity also show instability, but their
instabilities involve less mass and appear later in the star's evolution.


title: Examples of backreaction of small scale inhomogeneities in cosmology

abstract:
  In previous work, we introduced a new framework to treat large scale
backreaction effects due to small scale inhomogeneities in general relativity.
We considered one-parameter families of spacetimes for which such backreaction
effects can occur, and we proved that, provided the weak energy condition on
matter is satisfied, the leading effect of small scale inhomogeneities on large
scale dynamics is to produce a traceless effective stress-energy tensor that
itself satisfies the weak energy condition. In this work, we illustrate the
nature of our framework by providing two explicit examples of one-parameter
families with backreaction. The first, based on previous work of Berger, is a
family of polarized vacuum Gowdy spacetimes on a torus, which satisfies all of
the assumptions of our framework. As the parameter approaches its limiting
value, the metric uniformly approaches a smooth background metric, but
spacetime derivatives of the deviation of the metric from the background metric
do not converge uniformly to zero. The limiting metric has nontrivial
backreaction from the small scale inhomogeneities, with an effective
stress-energy that is traceless and satisfies the weak energy condition, in
accord with our theorems. Our second one-parameter family consists of metrics
which have a uniform Friedmann-Lemaitre-Robertson-Walker limit. This family
satisfies all of our assumptions with the exception of the weak energy
condition for matter. In this case, the limiting metric has an effective
stress-energy tensor which is not traceless. We emphasize the importance of
imposing energy conditions on matter in studies of backreaction.


title:
Vortex matter in low dimensional systems with proximity induced
  superconductivity

abstract:
  We study theoretically the vortex matter structure in low dimensional (LD)
systems with superconducting order induced by proximity to a bulk
superconductor. We analyze the effects of microscopic coupling mechanisms
between the two systems and the effects of possible mismatch in the band
structures of these materials on the energy spectrum of vortex-core electrons.
The unusual structure of vortex cores is discussed in the context of recent
tunneling microscopy/spectroscopy experiments.


title: One-Dimensional Navier-Stokes Finite Element Flow Model

abstract:
  This technical report documents the theoretical, computational, and practical
aspects of the one-dimensional Navier-Stokes finite element flow model. The
document is particularly useful to those who are interested in implementing,
validating and utilizing this relatively-simple and widely-used model.


title: Dark energy properties from large future galaxy surveys

abstract:
  We perform a detailed forecast on how well a {\sc Euclid}-like survey will be
able to constrain dark energy and neutrino parameters from a combination of its
cosmic shear power spectrum, galaxy power spectrum, and cluster mass function
measurements. We find that the combination of these three probes vastly
improves the survey's potential to measure the time evolution of dark energy.
In terms of a dark energy figure-of-merit defined as $(\sigma(w_{\mathrm p})
\sigma(w_a))^{-1}$, we find a value of 690 for {\sc Euclid}-like data combined
with {\sc Planck}-like measurements of the cosmic microwave background (CMB)
anisotropies in a 10-dimensional cosmological parameter space, assuming a
$\Lambda$CDM fiducial cosmology. For the more commonly used 7-parameter model,
we find a figure-of-merit of 1900 for the same data combination. We consider
also the survey's potential to measure dark energy perturbations in models
wherein the dark energy is parameterised as a fluid with a nonstandard
non-adiabatic sound speed, and find that in an \emph{optimistic} scenario in
which $w_0$ deviates by as much as is currently observationally allowed from
$-1$, models with $\hat{c}_\mathrm{s}^2 = 10^{-6}$ and $\hat{c}_\mathrm{s}^2 =
1$ can be distinguished at more than $2\sigma$ significance. We emphasise that
constraints on the dark energy sound speed from cluster measurements are
strongly dependent on the modelling of the cluster mass function; significantly
weaker sensitivities ensue if we modify our model to include fewer features of
nonlinear dark energy clustering. Finally, we find that the sum of neutrino
masses can be measured with a $1 \sigma$ precision of 0.015~eV, (abridged)


title:
Coherent Josephson qubit suitable for scalable quantum integrated
  circuits

abstract:
  We demonstrate a planar, tunable superconducting qubit with energy relaxation
times up to 44 microseconds. This is achieved by using a geometry designed to
both minimize radiative loss and reduce coupling to materials-related defects.
At these levels of coherence, we find a fine structure in the qubit energy
lifetime as a function of frequency, indicating the presence of a sparse
population of incoherent, weakly coupled two-level defects. This is supported
by a model analysis as well as experimental variations in the geometry. Our
`Xmon' qubit combines facile fabrication, straightforward connectivity, fast
control, and long coherence, opening a viable route to constructing a
chip-based quantum computer.


title: Determinacy and J\'onsson cardinals in $L(\mathbb{R})$

abstract:
  Assume $\mathsf{ZF}+\mathsf{AD}+V=L(\mathbb{R})$ and let $\kappa<\Theta$ be
an uncountable cardinal. We show that $\kappa$ is J\'onsson, and that if
$\mathrm{cof}(\kappa)=\omega$ then $\kappa$ is Rowbottom. We also establish
some other partition properties.


title: Phase Space Formulation of Population Dynamics in Ecology

abstract:
  A phase space theory for population dynamics in Ecology is presented. This
theory applies for a certain class of dynamical systems, that will be called
M-systems, for which a conserved quantity, the M-function, can be defined in
phase space. This M-function is the generator of time displacements and
contains all the dynamical information of the system. In this sense the
M-function plays the role of the hamiltonian function for mechanical systems.
In analogy with Hamilton theory we derive equations of motion as derivatives
over the resource function in phase space. A M-bracket is defined which allows
one to perform a geometrical approach in analogy to Poisson bracket of
hamiltonian systems. We show that the equations of motion can be derived from a
variational principle over a functional J of the trajectories. This functional
plays for M-systems the same role than the action S for hamiltonian systems.
Finally, three important systems in population dynamics, namely,
Lotka-Volterra, self-feeding and logistic evolution, are shown to be M-systems.


title: Terahertz Conductivity of the Heavy-Fermion State in CeCoIn5

abstract:
  The optical properties of thin films of the heavy-fermion compound CeCoIn5,
which were deposited by molecular beam epitaxy onto MgF2 substrates, have been
studied at frequencies 7 to 45 cm^{-1} (corresponding to 0.2 to 1.3 THz) and
temperatures 2 to 300 K. We observe an electrodynamic behavior which is typical
for heavy fermions, namely Drude-like conductivity with a relaxation rate at
rather low frequencies. This relaxation rate increases almost linearly with
temperature up to at least 30 K. The coherent heavy-fermion state,
characterized by an increase of the effective mass, continuously evolves upon
cooling and is not fully developed for temperatures as low as 5 K.


title:
Sharing of Semantically Enhanced Information for the Adaptive Execution
  of Business Processes

abstract:
  Motivated from the Context Aware Computing, and more particularly from the
Data-Driven Process Adaptation approach, we propose the Semantic Context Space
(SCS) Engine which aims to facilitate the provision of adaptable business
processes. The SCS Engine provides a space which stores semantically annotated
data and it is open to other processes, systems, and external sources for
information exchange. The specified implementation is inspired from the
Semantic TupleSpace and uses the JavaSpace Service of the Jini Framework
(changed to Apache River lately) as an underlying basis. The SCS Engine
supplies an interface where a client can execute the following operations: (i)
write: which inserts in the space available information along with its
respective meta-information, (ii) read: which retrieves from the space
information which meets specific meta-information constrains, and (iii) take:
which retrieves and simultaneously deletes from the space information which
meets specific meta-information constrains. In terms of this thesis the
available types of meta-information are based on ontologies described in RDFS
or WSML. The applicability of the SCS Engine implementation in the context of
data-driven process adaptation has been ensured by an experimental evaluation
of the provided operations. Ultimately, we refer to open issues which could be
addressed to enrich the proposed Engine with additional features.


title:
Controlling the position of traveling waves in reaction-diffusion
  systems

abstract:
  We present a method to control the position as a function of time of
one-dimensional traveling wave solutions to reaction-diffusion systems
according to a pre-specified protocol of motion. Given this protocol, the
control function is found as the solution of a perturbatively derived integral
equation. Two cases are considered. First, we derive an analytical expression
for the space ($x$) and time ($t$) dependent control function
$f\left(x,t\right)$ that is valid for arbitrary protocols and many
reaction-diffusion systems. These results are close to numerically computed
optimal controls. Second, for stationary control of traveling waves in
one-component systems, the integral equation reduces to a Fredholm integral
equation of the first kind. In both cases, the control can be expressed in
terms of the uncontrolled wave profile and its propagation velocity, rendering
detailed knowledge of the reaction kinetics unnecessary.


title: Duality of Entanglement Norms

abstract:
  We consider four norms on tensor product spaces that have appeared in quantum
information theory and demonstrate duality relationships between them. We show
that the product numerical radius is dual to the robustness of entanglement,
and we similarly show that the S(k)-norm is dual to the projective tensor norm.
We show that, analogous to how the product numerical radius and the S(k)-norm
characterize k-block positivity of operators, there is a natural version of the
projective tensor norm that characterizes Schmidt number. In this way we obtain
an elementary new proof of the cross norm criterion for separability, and we
also generalize both the cross norm and realignment criteria to the case of
arbitrary Schmidt number.


title: Socially optimal charging strategies for electric vehicles

abstract:
  Electric vehicles represent a promising technology for reducing emissions and
dependence on fossil fuels and have started entering different automotive
markets. In order to bolster their adoption by consumers and hence enhance
their penetration rate, a charging station infrastructure needs to be deployed.
This paper studies decentralized policies that assign electric vehicles to a
network of charging stations with the goal to achieve little to no queueing.
This objective is especially important for electric vehicles, whose charging
times are fairly long. The social optimality of the proposed policies is
established in the many-server regime, where each station is equipped with
multiple charging slots. Further, convergence issues of the algorithm that
achieves the optimal policy are examined. Finally, the results provide insight
on how to address questions related to the optimal location deployment of the
infrastructure.


title:
Determining neutron star masses and radii using energy-resolved
  waveforms of X-ray burst oscillations

abstract:
  Simultaneous, precise measurements of the mass $M$ and radius $R$ of neutron
stars can yield uniquely valuable information about the still uncertain
properties of cold matter at several times the density of nuclear matter. One
method that could be used to measure $M$ and $R$ is to analyze the
energy-dependent waveforms of the X-ray flux oscillations seen during some
thermonuclear bursts from some neutron stars. These oscillations are thought to
be produced by X-ray emission from hotter regions on the surface of the star
that are rotating at or near the spin frequency of the star. Here we explore
how well $M$ and $R$ could be determined by generating, and analyzing using
Bayesian techniques, synthetic energy-resolved X-ray data that we produce
assuming a future space mission having 2--30 keV energy coverage and an
effective area of 10 m$^2$, such as the proposed \textit{LOFT} or
\textit{AXTAR} missions.
  We find that if the hot spot is within 10$^\circ$ of the rotation equator,
both $M$ and $R$ can usually be determined with an uncertainty of about 10% if
there are $10^6$ total counts from the spot, whereas waveforms from spots
within 20$^\circ$ of the rotation pole provide no useful constraints. These
constraints can usually be achieved even if the burst oscillations vary with
time and data from multiple bursts must be used to obtain 10$^6$ counts from
the hot spot. This is therefore a promising method to constrain $M$ and $R$
tightly enough to discriminate strongly between competing models of cold,
high-density matter.


title: The PAV algorithm optimizes binary proper scoring rules

abstract:
  There has been much recent interest in application of the
pool-adjacent-violators (PAV) algorithm for the purpose of calibrating the
probabilistic outputs of automatic pattern recognition and machine learning
algorithms. Special cost functions, known as proper scoring rules form natural
objective functions to judge the goodness of such calibration. We show that for
binary pattern classifiers, the non-parametric optimization of calibration,
subject to a monotonicity constraint, can be solved by PAV and that this
solution is optimal for all regular binary proper scoring rules. This extends
previous results which were limited to convex binary proper scoring rules. We
further show that this result holds not only for calibration of probabilities,
but also for calibration of log-likelihood-ratios, in which case optimality
holds independently of the prior probabilities of the pattern classes.


title:
Semiclassical description of collapses and revivals of quantum wave
  packets in bounded domains

abstract:
  We study a special kind of semiclassical limit of quantum dynamics on a
circle and in a box (infinite potential well with hard walls) as the Planck
constant tends to zero and time tends to infinity. The results give detailed
information about all stages of evolution of quantum wave packets:
semiclassical motion, collapses, revivals, as well as intermediate stages. In
particular, we rigorously justify the fact that the spatial distribution of a
wave packet is most of the time close to uniform. This fact was previously
known only from numerical calculations.
  We apply the obtained results to a problem of classical mechanics: deciding
whether recently suggested functional classical mechanics is preferable to
traditional Newtonian one from the quantum-mechanical point of view. To do
this, we study the semiclassical limit of the Husimi functions of quantum
states. We show that functional mechanics remains valid at larger time scales
than Newtonian one and, therefore, is preferable.
  Finally, we analyse the quantum dynamics in a box in case when the size of
the box is known with a random error. We show that, in this case, the
probability distribution of the position of a quantum particle is not almost
periodic, but tends to a limit distribution as time indefinitely increases.


title: A primer on information theory, with applications to neuroscience

abstract:
  Given the constant rise in quantity and quality of data obtained from neural
systems on many scales ranging from molecular to systems',
information-theoretic analyses became increasingly necessary during the past
few decades in the neurosciences. Such analyses can provide deep insights into
the functionality of such systems, as well as a rigid mathematical theory and
quantitative measures of information processing in both healthy and diseased
states of neural systems. This chapter will present a short introduction to the
fundamentals of information theory, especially suited for people having a less
firm background in mathematics and probability theory. To begin, the
fundamentals of probability theory such as the notion of probability,
probability distributions, and random variables will be reviewed. Then, the
concepts of information and entropy (in the sense of Shannon), mutual
information, and transfer entropy (sometimes also referred to as conditional
mutual information) will be outlined. As these quantities cannot be computed
exactly from measured data in practice, estimation techniques for
information-theoretic quantities will be presented. The chapter will conclude
with the applications of information theory in the field of neuroscience,
including questions of possible medical applications and a short review of
software packages that can be used for information-theoretic analyses of neural
data.


title:
Hybrid Textures of Majorana Neutrino Mass Matrix and Current
  Experimental Tests

abstract:
  Motivated by recent measurements of a relatively large \theta_{13} in the
Daya Bay and RENO reactor neutrino experiments, we carry out a systematic
analysis of the hybrid textures of Majorana neutrino mass matrix $M_\nu$, which
contain one texture zero and two equal nonzero matrix elements. We show that
three neutrino masses (m_1, m_2, m_3) and three leptonic CP-violating phases
(\delta, \rho, \sigma) can fully be determined from two neutrino mass-squared
differences (\delta m^2, \Delta m^2) and three flavor mixing angles
(\theta_{12}, \theta_{23}, \theta_{13}). Out of sixty logically possible
patterns of $M_\nu$, thirty-nine are found to be compatible with current
experimental data at the 3\sigma level. We demonstrate that the texture zero of
$M_\nu$ is stable against one-loop quantum corrections, while the equality
between two independent elements not. Phenomenological implications of $M_\nu$
for the neutrinoless double-beta decay and leptonic CP violation are discussed,
and a realization of the texture zero and equality by means of discrete flavor
symmetries is illustrated.


title: On weights for relative motives with integral coefficients

abstract:
  The goal of this paper is to define a certain Chow weight structure for the
category of Voevodsky's motivic complexes with integral coefficients (as
described by Cisinski and Deglise) over any excellent finite-dimensional
separated scheme $S$. Our results are parallel to (though substantially weaker
than) the corresponding 'rational coefficient' statements proved by D. Hebert
and the author.
  As an immediate consequence of the existence of 'weights', we obtain certain
(Chow)-weight spectral sequences and filtrations for any (co)homology of
$S$-motives.


title: One-shot lossy quantum data compression

abstract:
  We provide a framework for one-shot quantum rate distortion coding, in which
the goal is to determine the minimum number of qubits required to compress
quantum information as a function of the probability that the distortion
incurred upon decompression exceeds some specified level. We obtain a one-shot
characterization of the minimum qubit compression size for an
entanglement-assisted quantum rate-distortion code in terms of the smooth
max-information, a quantity previously employed in the one-shot quantum reverse
Shannon theorem. Next, we show how this characterization converges to the known
expression for the entanglement-assisted quantum rate distortion function for
asymptotically many copies of a memoryless quantum information source. Finally,
we give a tight, finite blocklength characterization for the
entanglement-assisted minimum qubit compression size of a memoryless isotropic
qubit source subject to an average symbol-wise distortion constraint.


title:
On inferring extinction laws in z~6 quasars as signatures of supernova
  dust

abstract:
  Unusual extinction curves of high-redshift QSOs have been taken as evidence
that dust is primarily produced by supernovae at high redshift. In particular,
the 3000 A Todini-Ferrara-Maiolino kink in the extinction curve of the z = 6.20
SDSS J1048+4637 has been attributed to supernova dust. Here we discuss the
challenges in inferring robust extinction curves of high-redshift QSOs and
critically assess previous claims of detection of supernova dust. In
particular, we address the sensitivity to the choice of intrinsic QSO spectrum,
the need for a long wavelength baseline, and the drawbacks in fitting
theoretical extinction curves. In a sample of 21 QSOs at z ~ 6 we detect
significant ultraviolet extinction using existing broad-band optical,
near-infrared, and Spitzer photometry. The median extinction curve is
consistent with a Small Magellanic Cloud curve with A_1450 ~ 0.7 mag and does
not exhibit any conspicuous (restframe) 2175 A or 3000 A features. For two
QSOs, SDSS J1044-0125 at z = 5.78 and SDSS J1030+0524 at z = 6.31, we further
present X-shooter spectra covering the wavelength range 0.9-2.5 um. The
resulting non-parametric extinction curves do not exhibit the 3000 A kink.
Finally, in a re-analysis of literature spectra of SDSS J1048+4637, we do not
find evidence for a conspicuous kink. We conclude that the existing evidence
for a 3000 A feature is weak and that the overall dust properties at high and
low redshift show no significant differences. This, however, does not preclude
supernovae from dominating the dust budget at high redshift.


title:
An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected
  Graphs, and its Multicommodity Generalizations

abstract:
  In this paper, we introduce a new framework for approximately solving flow
problems in capacitated, undirected graphs and apply it to provide
asymptotically faster algorithms for the maximum $s$-$t$ flow and maximum
concurrent multicommodity flow problems. For graphs with $n$ vertices and $m$
edges, it allows us to find an $\epsilon$-approximate maximum $s$-$t$ flow in
time $O(m^{1+o(1)}\epsilon^{-2})$, improving on the previous best bound of
$\tilde{O}(mn^{1/3} poly(1/\epsilon))$. Applying the same framework in the
multicommodity setting solves a maximum concurrent multicommodity flow problem
with $k$ commodities in $O(m^{1+o(1)}\epsilon^{-2}k^2)$ time, improving on the
existing bound of $\tilde{O}(m^{4/3} poly(k,\epsilon^{-1})$.
  Our algorithms utilize several new technical tools that we believe may be of
independent interest:
  - We give a non-Euclidean generalization of gradient descent and provide
bounds on its performance. Using this, we show how to reduce approximate
maximum flow and maximum concurrent flow to the efficient construction of
oblivious routings with a low competitive ratio.
  - We define and provide an efficient construction of a new type of flow
sparsifier. In addition to providing the standard properties of a cut
sparsifier our construction allows for flows in the sparse graph to be routed
(very efficiently) in the original graph with low congestion.
  - We give the first almost-linear-time construction of an
$O(m^{o(1)})$-competitive oblivious routing scheme. No previous such algorithm
ran in time better than $\tilde{{\Omega}}(mn)$.
  We also note that independently Jonah Sherman produced an almost linear time
algorithm for maximum flow and we thank him for coordinating submissions.


title: The structure of Bayes nets for vision recognition

abstract:
  This paper is part of a study whose goal is to show the effciency of using
Bayes networks to carry out model based vision calculations. [Binford et al.
1987] Recognition proceeds by drawing up a network model from the object's
geometric and functional description that predicts the appearance of an object.
Then this network is used to find the object within a photographic image. Many
existing and proposed techniques for vision recognition resemble the
uncertainty calculations of a Bayes net. In contrast, though, they lack a
derivation from first principles, and tend to rely on arbitrary parameters that
we hope to avoid by a network model. The connectedness of the network depends
on what independence considerations can be identified in the vision problem.
Greater independence leads to easier calculations, at the expense of the net's
expressiveness. Once this trade-off is made and the structure of the network is
determined, it should be possible to tailor a solution technique for it. This
paper explores the use of a network with multiply connected paths, drawing on
both techniques of belief networks [Pearl 86] and influence diagrams. We then
demonstrate how one formulation of a multiply connected network can be solved.


title: Summary of A New Normative Theory of Probabilistic Logic

abstract:
  By probabilistic logic I mean a normative theory of belief that explains how
a body of evidence affects one's degree of belief in a possible hypothesis. A
new axiomatization of such a theory is presented which avoids a finite
additivity axiom, yet which retains many useful inference rules. Many of the
examples of this theory--its models do not use numerical probabilities. Put
another way, this article gives sharper answers to the two questions: 1.What
kinds of sets can used as the range of a probability function? 2.Under what
conditions is the range set of a probability function isomorphic to the set of
real numbers in the interval 10,1/ with the usual arithmetical operations?


title: Probability Distributions Over Possible Worlds

abstract:
  In Probabilistic Logic Nilsson uses the device of a probability distribution
over a set of possible worlds to assign probabilities to the sentences of a
logical language. In his paper Nilsson concentrated on inference and associated
computational issues. This paper, on the other hand, examines the probabilistic
semantics in more detail, particularly for the case of first-order languages,
and attempts to explain some of the features and limitations of this form of
probability logic. It is pointed out that the device of assigning probabilities
to logical sentences has certain expressive limitations. In particular,
statistical assertions are not easily expressed by such a device. This leads to
certain difficulties with attempts to give probabilistic semantics to default
reasoning using probabilities assigned to logical sentences.


title: Hierarchical Evidence and Belief Functions

abstract:
  Dempster/Shafer (D/S) theory has been advocated as a way of representing
incompleteness of evidence in a system's knowledge base. Methods now exist for
propagating beliefs through chains of inference. This paper discusses how rules
with attached beliefs, a common representation for knowledge in automated
reasoning systems, can be transformed into the joint belief functions required
by propagation algorithms. A rule is taken as defining a conditional belief
function on the consequent given the antecedents. It is demonstrated by example
that different joint belief functions may be consistent with a given set of
rules. Moreover, different representations of the same rules may yield
different beliefs on the consequent hypotheses.


title:
Decision-Theoretic Control of Problem Solving: Principles and
  Architecture

abstract:
  This paper presents an approach to the design of autonomous, real-time
systems operating in uncertain environments. We address issues of problem
solving and reflective control of reasoning under uncertainty in terms of two
fundamental elements: l) a set of decision-theoretic models for selecting among
alternative problem-solving methods and 2) a general computational architecture
for resource-bounded problem solving. The decisiontheoretic models provide a
set of principles for choosing among alternative problem-solving methods based
on their relative costs and benefits, where benefits are characterized in terms
of the value of information provided by the output of a reasoning activity. The
output may be an estimate of some uncertain quantity or a recommendation for
action. The computational architecture, called Schemer-ll, provides for
interleaving of and communication among various problem-solving subsystems.
These subsystems provide alternative approaches to information gathering,
belief refinement, solution construction, and solution execution. In
particular, the architecture provides a mechanism for interrupting the
subsystems in response to critical events. We provide a decision theoretic
account for scheduling problem-solving elements and for critical-event-driven
interruption of activities in an architecture such as Schemer-II.


title:
Induction and Uncertainty Management Techniques Applied to Veterinary
  Medical Diagnosis

abstract:
  This paper discusses a project undertaken between the Departments of
Computing Science, Statistics, and the College of Veterinary Medicine to design
a medical diagnostic system. On-line medical data has been collected in the
hospital database system for several years. A number of induction methods are
being used to extract knowledge from the data in an attempt to improve upon
simple diagnostic charts used by the clinicians. They also enhance the results
of classical statistical methods - finding many more significant variables. The
second part of the paper describes an essentially Bayesian method of evidence
combination using fuzzy events at an initial step. Results are presented and
comparisons are made with other methods.


title: KNET: Integrating Hypermedia and Bayesian Modeling

abstract:
  KNET is a general-purpose shell for constructing expert systems based on
belief networks and decision networks. Such networks serve as graphical
representations for decision models, in which the knowledge engineer must
define clearly the alternatives, states, preferences, and relationships that
constitute a decision basis. KNET contains a knowledge-engineering core written
in Object Pascal and an interface that tightly integrates HyperCard, a
hypertext authoring tool for the Apple Macintosh computer, into a novel
expert-system architecture. Hypertext and hypermedia have become increasingly
important in the storage management, and retrieval of information. In broad
terms, hypermedia deliver heterogeneous bits of information in dynamic,
extensively cross-referenced packages. The resulting KNET system features a
coherent probabilistic scheme for managing uncertainty, an objectoriented
graphics editor for drawing and manipulating decision networks, and HyperCard's
potential for quickly constructing flexible and friendly user interfaces. We
envision KNET as a useful prototyping tool for our ongoing research on a
variety of Bayesian reasoning problems, including tractable representation,
inference, and explanation.


title: A Method for Using Belief Networks as Influence Diagrams

abstract:
  This paper demonstrates a method for using belief-network algorithms to solve
influence diagram problems. In particular, both exact and approximation
belief-network algorithms may be applied to solve influence-diagram problems.
More generally, knowing the relationship between belief-network and
influence-diagram problems may be useful in the design and development of more
efficient influence diagram algorithms.


title: Process, Structure, and Modularity in Reasoning with Uncertainty

abstract:
  Computational mechanisms for uncertainty management must support interactive
and incremental problem formulation, inference, hypothesis testing, and
decision making. However, most current uncertainty inference systems
concentrate primarily on inference, and provide no support for the larger
issues. We present a computational approach to uncertainty management which
provides direct support for the dynamic, incremental aspect of this task, while
at the same time permitting direct representation of the structure of
evidential relationships. At the same time, we show that this approach responds
to the modularity concerns of Heckerman and Horvitz [Heck87]. This paper
emphasizes examples of the capabilities of this approach. Another paper
[D'Am89] details the representations and algorithms involved.


title: Probabilistic Causal Reasoning

abstract:
  Predicting the future is an important component of decision making. In most
situations, however, there is not enough information to make accurate
predictions. In this paper, we develop a theory of causal reasoning for
predictive inference under uncertainty. We emphasize a common type of
prediction that involves reasoning about persistence: whether or not a
proposition once made true remains true at some later time. We provide a
decision procedure with a polynomial-time algorithm for determining the
probability of the possible consequences of a set events and initial
conditions. The integration of simple probability theory with temporal
projection enables us to circumvent problems that nonmonotonic temporal
reasoning schemes have in dealing with persistence. The ideas in this paper
have been implemented in a prototype system that refines a database of causal
rules in the course of applying those rules to construct and carry out plans in
a manufacturing domain.


title:
Modeling uncertain and vague knowledge in possibility and evidence
  theories

abstract:
  This paper advocates the usefulness of new theories of uncertainty for the
purpose of modeling some facets of uncertain knowledge, especially vagueness,
in AI. It can be viewed as a partial reply to Cheeseman's (among others)
defense of probability.


title:
A Temporal Logic for Uncertain Events and An Outline of A Possible
  Implementation in An Extension of PROLOG

abstract:
  There is uncertainty associated with the occurrence of many events in real
life. In this paper we develop a temporal logic to deal with such uncertain
events and outline a possible implementation in an extension of PROLOG. Events
are represented as fuzzy sets with the membership function giving the
possibility of occurrence of the event in a given interval of time. The
developed temporal logic is simple but powerful. It can determine effectively
the various temporal relations between uncertain events or their combinations.
PROLOG provides a uniform substrate on which to effectively implement such a
temporal logic for uncertain events


title: Uncertainty Management for Fuzzy Decision Support Systems

abstract:
  A new approach for uncertainty management for fuzzy, rule based decision
support systems is proposed: The domain expert's knowledge is expressed by a
set of rules that frequently refer to vague and uncertain propositions. The
certainty of propositions is represented using intervals [a, b] expressing that
the proposition's probability is at least a and at most b. Methods and
techniques for computing the overall certainty of fuzzy compound propositions
that have been defined by using logical connectives 'and', 'or' and 'not' are
introduced. Different inference schemas for applying fuzzy rules by using modus
ponens are discussed. Different algorithms for combining evidence that has been
received from different rules for the same proposition are provided. The
relationship of the approach to other approaches is analyzed and its problems
of knowledge acquisition and knowledge representation are discussed in some
detail. The basic concepts of a rule-based programming language called PICASSO,
for which the approach is a theoretical foundation, are outlined.


title: Probability as a Modal Operator

abstract:
  This paper argues for a modal view of probability. The syntax and semantics
of one particularly strong probability logic are discussed and some examples of
the use of the logic are provided. We show that it is both natural and useful
to think of probability as a modal operator. Contrary to popular belief in AI,
a probability ranging between 0 and 1 represents a continuum between
impossibility and necessity, not between simple falsity and truth. The present
work provides a clear semantics for quantification into the scope of the
probability operator and for higher-order probabilities. Probability logic is a
language for expressing both probabilistic and logical concepts.


title: Truth Maintenance Under Uncertainty

abstract:
  This paper addresses the problem of resolving errors under uncertainty in a
rule-based system. A new approach has been developed that reformulates this
problem as a neural-network learning problem. The strength and the fundamental
limitations of this approach are explored and discussed. The main result is
that neural heuristics can be applied to solve some but not all problems in
rule-based systems.


title: Bayesian Assessment of a Connectionist Model for Fault Detection

abstract:
  A previous paper [2] showed how to generate a linear discriminant network
(LDN) that computes likely faults for a noisy fault detection problem by using
a modification of the perceptron learning algorithm called the pocket
algorithm. Here we compare the performance of this connectionist model with
performance of the optimal Bayesian decision rule for the example that was
previously described. We find that for this particular problem the
connectionist model performs about 97% as well as the optimal Bayesian
procedure. We then define a more general class of noisy single-pattern boolean
(NSB) fault detection problems where each fault corresponds to a single
:pattern of boolean instrument readings and instruments are independently
noisy. This is equivalent to specifying that instrument readings are
probabilistic but conditionally independent given any particular fault. We
prove:
  1. The optimal Bayesian decision rule for every NSB fault detection problem
is representable by an LDN containing no intermediate nodes. (This slightly
extends a result first published by Minsky & Selfridge.) 2. Given an NSB fault
detection problem, then with arbitrarily high probability after sufficient
iterations the pocket algorithm will generate an LDN that computes an optimal
Bayesian decision rule for that problem. In practice we find that a reasonable
number of iterations of the pocket algorithm produces a network with good, but
not optimal, performance.


title: On the Logic of Causal Models

abstract:
  This paper explores the role of Directed Acyclic Graphs (DAGs) as a
representation of conditional independence relationships. We show that DAGs
offer polynomially sound and complete inference mechanisms for inferring
conditional independence relationships from a given causal set of such
relationships. As a consequence, d-separation, a graphical criterion for
identifying independencies in a DAG, is shown to uncover more valid
independencies then any other criterion. In addition, we employ the Armstrong
property of conditional independence to show that the dependence relationships
displayed by a DAG are inherently consistent, i.e. for every DAG D there exists
some probability distribution P that embodies all the conditional
independencies displayed in D and none other.


title: The Optimality of Satisficing Solutions

abstract:
  This paper addresses a prevailing assumption in single-agent heuristic search
theory- that problem-solving algorithms should guarantee shortest-path
solutions, which are typically called optimal. Optimality implies a metric for
judging solution quality, where the optimal solution is the solution with the
highest quality. When path-length is the metric, we will distinguish such
solutions as p-optimal.


title: An Empirical Comparison of Three Inference Methods

abstract:
  In this paper, an empirical evaluation of three inference methods for
uncertain reasoning is presented in the context of Pathfinder, a large expert
system for the diagnosis of lymph-node pathology. The inference procedures
evaluated are (1) Bayes' theorem, assuming evidence is conditionally
independent given each hypothesis; (2) odds-likelihood updating, assuming
evidence is conditionally independent given each hypothesis and given the
negation of each hypothesis; and (3) a inference method related to the
Dempster-Shafer theory of belief. Both expert-rating and decision-theoretic
metrics are used to compare the diagnostic accuracy of the inference methods.


title: Parallel Belief Revision

abstract:
  This paper describes a formal system of belief revision developed by Wolfgang
Spohn and shows that this system has a parallel implementation that can be
derived from an influence diagram in a manner similar to that in which Bayesian
networks are derived. The proof rests upon completeness results for an
axiomatization of the notion of conditional independence, with the Spohn system
being used as a semantics for the relation of conditional independence.


title: Stochastic Sensitivity Analysis Using Fuzzy Influence Diagrams

abstract:
  The practice of stochastic sensitivity analysis described in the decision
analysis literature is a testimonial to the need for considering deviations
from precise point estimates of uncertainty. We propose the use of Bayesian
fuzzy probabilities within an influence diagram computational scheme for
performing sensitivity analysis during the solution of probabilistic inference
and decision problems. Unlike other parametric approaches, the proposed scheme
does not require resolving the problem for the varying probability point
estimates. We claim that the solution to fuzzy influence diagrams provides as
much information as the classical point estimate approach plus additional
information concerning stochastic sensitivity. An example based on diagnostic
decision making in microcomputer assembly is used to illustrate this idea. We
claim that the solution to fuzzy influence diagrams provides as much
information as the classical point estimate approach plus additional interval
information that is useful for stochastic sensitivity analysis.


title: A Representation of Uncertainty to Aid Insight into Decision Models

abstract:
  Many real world models can be characterized as weak, meaning that there is
significant uncertainty in both the data input and inferences. This lack of
determinism makes it especially difficult for users of computer decision aids
to understand and have confidence in the models. This paper presents a
representation for uncertainty and utilities that serves as a framework for
graphical summary and computer-generated explanation of decision models. The
application described that tests the methodology is a computer decision aid
designed to enhance the clinician-patient consultation process for patients
with angina (chest pain due to lack of blood flow to the heart muscle). The
angina model is represented as a Bayesian decision network. Additionally, the
probabilities and utilities are treated as random variables with probability
distributions on their range of possible values. The initial distributions
represent information on all patients with anginal symptoms, and the approach
allows for rapid tailoring to more patientspecific distributions. This
framework provides a metric for judging the importance of each variable in the
model dynamically.


title: Rational Nonmonotonic Reasoning

abstract:
  Nonmonotonic reasoning is a pattern of reasoning that allows an agent to make
and retract (tentative) conclusions from inconclusive evidence. This paper
gives a possible-worlds interpretation of the nonmonotonic reasoning problem
based on standard decision theory and the emerging probability logic. The
system's central principle is that a tentative conclusion is a decision to make
a bet, not an assertion of fact. The system is rational, and as sound as the
proof theory of its underlying probability log.


title:
A Comparison of Decision Analysis and Expert Rules for Sequential
  Diagnosis

abstract:
  There has long been debate about the relative merits of decision theoretic
methods and heuristic rule-based approaches for reasoning under uncertainty. We
report an experimental comparison of the performance of the two approaches to
troubleshooting, specifically to test selection for fault diagnosis. We use as
experimental testbed the problem of diagnosing motorcycle engines. The first
approach employs heuristic test selection rules obtained from expert mechanics.
We compare it with the optimal decision analytic algorithm for test selection
which employs estimated component failure probabilities and test costs. The
decision analytic algorithm was found to reduce the expected cost (i.e. time)
to arrive at a diagnosis by an average of 14% relative to the expert rules.
Sensitivity analysis shows the results are quite robust to inaccuracy in the
probability and cost estimates. This difference suggests some interesting
implications for knowledge acquisition.


title: Multiple decision trees

abstract:
  This paper describes experiments, on two domains, to investigate the effect
of averaging over predictions of multiple decision trees, instead of using a
single tree. Other authors have pointed out theoretical and commonsense reasons
for preferring the multiple tree approach. Ideally, we would like to consider
predictions from all trees, weighted by their probability. However, there is a
vast number of different trees, and it is difficult to estimate the probability
of each tree. We sidestep the estimation problem by using a modified version of
the ID3 algorithm to build good trees, and average over only these trees. Our
results are encouraging. For each domain, we managed to produce a small number
of good trees. We find that it is best to average across sets of trees with
different structure; this usually gives better performance than any of the
constituent trees, including the ID3 tree.


title: Probabilistic Inference and Probabilistic Reasoning

abstract:
  Uncertainty enters into human reasoning and inference in at least two ways.
It is reasonable to suppose that there will be roles for these distinct uses of
uncertainty also in automated reasoning.


title: Probabilistic and Non-Monotonic Inference

abstract:
  (l) I have enough evidence to render the sentence S probable. (la) So,
relative to what I know, it is rational of me to believe S. (2) Now that I have
more evidence, S may no longer be probable. (2a) So now, relative to what I
know, it is not rational of me to believe S. These seem a perfectly ordinary,
common sense, pair of situations. Generally and vaguely, I take them to embody
what I shall call probabilistic inference. This form of inference is clearly
non-monotonic. Relatively few people have taken this form of inference, based
on high probability, to serve as a foundation for non-monotonic logic or for a
logical or defeasible inference. There are exceptions: Jane Nutter [16] thinks
that sometimes probability has something to do with non-monotonic reasoning.
Judea Pearl [ 17] has recently been exploring the possibility. There are any
number of people whom one might call probability enthusiasts who feel that
probability provides all the answers by itself, with no need of help from
logic. Cheeseman [1], Henrion [5] and others think it useful to look at a
distribution of probabilities over a whole algebra of statements, to update
that distribution in the light of new evidence, and to use the latest updated
distribution of probability over the algebra as a basis for planning and
decision making. A slightly weaker form of this approach is captured by Nilsson
[15], where one assumes certain probabilities for certain statements, and
infers the probabilities, or constraints on the probabilities of other
statement. None of this corresponds to what I call probabilistic inference. All
of the inference that is taking place, either in Bayesian updating, or in
probabilistic logic, is strictly deductive. Deductive inference, particularly
that concerned with the distribution of classical probabilities or chances, is
of great importance. But this is not to say that there is no important role for
what earlier logicians have called "ampliative" or "inductive" or "scientific"
inference, in which the conclusion goes beyond the premises, asserts more than
do the premises. This depends on what David Israel [6] has called "real rules
of inference". It is characteristic of any such logic or inference procedure
that it can go wrong: that statements accepted at one point may be rejected at
a later point. Research underlying the results reported here has been partially
supported by the Signals Warfare Center of the United States Army.


title: Epistemological Relevance and Statistical Knowledge

abstract:
  For many years, at least since McCarthy and Hayes (1969), writers have
lamented, and attempted to compensate for, the alleged fact that we often do
not have adequate statistical knowledge for governing the uncertainty of
belief, for making uncertain inferences, and the like. It is hardly ever
spelled out what "adequate statistical knowledge" would be, if we had it, and
how adequate statistical knowledge could be used to control and regulate
epistemic uncertainty.


title: Utility-Based Control for Computer Vision

abstract:
  Several key issues arise in implementing computer vision recognition of world
objects in terms of Bayesian networks. Computational efficiency is a driving
force. Perceptual networks are very deep, typically fifteen levels of
structure. Images are wide, e.g., an unspecified-number of edges may appear
anywhere in an image 512 x 512 pixels or larger. For efficiency, we dynamically
instantiate hypotheses of observed objects. The network is not fixed, but is
created incrementally at runtime. Generation of hypotheses of world objects and
indexing of models for recognition are important, but they are not considered
here [4,11]. This work is aimed at near-term implementation with parallel
computation in a radar surveillance system, ADRIES [5, 15], and a system for
industrial part recognition, SUCCESSOR [2]. For many applications, vision must
be faster to be practical and so efficiently controlling the machine vision
process is critical. Perceptual operators may scan megapixels and may require
minutes of computation time. It is necessary to avoid unnecessary sensor
actions and computation. Parallel computation is available at several levels of
processor capability. The potential for parallel, distributed computation for
high-level vision means distributing non-homogeneous computations. This paper
addresses the problem of task control in machine vision systems based on
Bayesian probability models. We separate control and inference to extend the
previous work [3] to maximize utility instead of probability. Maximizing
utility allows adopting perceptual strategies for efficient information
gathering with sensors and analysis of sensor data. Results of controlling
machine vision via utility to recognize military situations are presented in
this paper. Future work extends this to industrial part recognition for
SUCCESSOR.


title: Evidential Reasoning in a Network Usage Prediction Testbed

abstract:
  This paper reports on empirical work aimed at comparing evidential reasoning
techniques. While there is prima facie evidence for some conclusions, this i6
work in progress; the present focus is methodology, with the goal that
subsequent results be meaningful. The domain is a network of UNIX* cycle
servers, and the task is to predict properties of the state of the network from
partial descriptions of the state. Actual data from the network are taken and
used for blindfold testing in a betting game that allows abstention. The focal
technique has been Kyburg's method for reasoning with data of varying relevance
to a particular query, though the aim is to be able eventually to compare
various uncertainty calculi. The conclusions are not novel, but are
instructive. 1. All of the calculi performed better than human subjects, so
unbiased access to sample experience is apparently of value. 2. Performance
depends on metric: (a) when trials are repeated, net = gains - losses favors
methods that place many bets, if the probability of placing a correct bet is
sufficiently high; that is, it favors point-valued formalisms; (b) yield =
gains/(gains + lossee) favors methods that bet only when sure to bet correctly;
that is, it favors interval-valued formalisms. 3. Among the calculi, there were
no clear winners or losers. Methods are identified for eliminating the bias of
the net as a performance criterion and for separating the calculi effectively:
in both cases by posting odds for the betting game in the appropriate way.


title: Justifying the Principle of Interval Constraints

abstract:
  When knowledge is obtained from a database, it is only possible to deduce
confidence intervals for probability values. With confidence intervals
replacing point values, the results in the set covering model include interval
constraints for the probabilities of mutually exclusive and exhaustive
explanations. The Principle of Interval Constraints ranks these explanations by
determining the expected values of the probabilities based on distributions
determined from the interval, constraints. This principle was developed using
the Classical Approach to probability. This paper justifies the Principle of
Interval Constraints with a more rigorous statement of the Classical Approach
and by defending the concept of probabilities of probabilities.


title: Probabilistic Semantics and Defaults

abstract:
  There is much interest in providing probabilistic semantics for defaults but
most approaches seem to suffer from one of two problems: either they require
numbers, a problem defaults were intended to avoid, or they generate peculiar
side effects. Rather than provide semantics for defaults, we address the
problem defaults were intended to solve: that of reasoning under uncertainty
where numeric probability distributions are not available. We describe a
non-numeric formalism called an inference graph based on standard probability
theory, conditional independence and sentences of favouring where a favours b -
favours(a, b) - p(a|b) > p(a). The formalism seems to handle the examples from
the nonmonotonic literature. Most importantly, the sentences of our system can
be verified by performing an appropriate experiment in the semantic domain.


title: Decision Making with Linear Constraints on Probabilities

abstract:
  Techniques for decision making with knowledge of linear constraints on
condition probabilities are examined. These constraints arise naturally in many
situations: upper and lower condition probabilities are known; an ordering
among the probabilities is determined; marginal probabilities or bounds on such
probabilities are known, e.g., data are available in the form of a
probabilistic database (Cavallo and Pittarelli, 1987a); etc. Standard
situations of decision making under risk and uncertainty may also be
characterized by linear constraints. Each of these types of information may be
represented by a convex polyhedron of numerically determinate condition
probabilities. A uniform approach to decision making under risk, uncertainty,
and partial uncertainty based on a generalized version of a criterion of
Hurwicz is proposed, Methods for processing marginal probabilities to improve
decision making using any of the criteria discussed are presented.


title: Maintenance in Probabilistic Knowledge-Based Systems

abstract:
  Recent developments using directed acyclical graphs (i.e., influence diagrams
and Bayesian networks) for knowledge representation have lessened the problems
of using probability in knowledge-based systems (KBS). Most current research
involves the efficient propagation of new evidence, but little has been done
concerning the maintenance of domain-specific knowledge, which includes the
probabilistic information about the problem domain. By making use of
conditional independencies represented in she graphs, however, probability
assessments are required only for certain variables when the knowledge base is
updated. The purpose of this study was to investigate, for those variables
which require probability assessments, ways to reduce the amount of new
knowledge required from the expert when updating probabilistic information in a
probabilistic knowledge-based system. Three special cases (ignored outcome,
split outcome, and assumed constraint outcome) were identified under which many
of the original probabilities (those already in the knowledge-base) do not need
to be reassessed when maintenance is required.


title: A Linear Approximation Method for Probabilistic Inference

abstract:
  An approximation method is presented for probabilistic inference with
continuous random variables. These problems can arise in many practical
problems, in particular where there are "second order" probabilities. The
approximation, based on the Gaussian influence diagram, iterates over linear
approximations to the inference problem.


title: An Axiomatic Framework for Bayesian and Belief-function Propagation

abstract:
  In this paper, we describe an abstract framework and axioms under which exact
local computation of marginals is possible. The primitive objects of the
framework are variables and valuations. The primitive operators of the
framework are combination and marginalization. These operate on valuations. We
state three axioms for these operators and we derive the possibility of local
computation from the axioms. Next, we describe a propagation scheme for
computing marginals of a valuation when we have a factorization of the
valuation on a hypertree. Finally we show how the problem of computing
marginals of joint probability distributions and joint belief functions fits
the general framework.


title: A General Non-Probabilistic Theory of Inductive Reasoning

abstract:
  Probability theory, epistemically interpreted, provides an excellent, if not
the best available account of inductive reasoning. This is so because there are
general and definite rules for the change of subjective probabilities through
information or experience; induction and belief change are one and same topic,
after all. The most basic of these rules is simply to conditionalize with
respect to the information received; and there are similar and more general
rules. 1 Hence, a fundamental reason for the epistemological success of
probability theory is that there at all exists a well-behaved concept of
conditional probability. Still, people have, and have reasons for, various
concerns over probability theory. One of these is my starting point:
Intuitively, we have the notion of plain belief; we believe propositions2 to be
true (or to be false or neither). Probability theory, however, offers no formal
counterpart to this notion. Believing A is not the same as having probability 1
for A, because probability 1 is incorrigible3; but plain belief is clearly
corrigible. And believing A is not the same as giving A a probability larger
than some 1 - c, because believing A and believing B is usually taken to be
equivalent to believing A & B.4 Thus, it seems that the formal representation
of plain belief has to take a non-probabilistic route. Indeed, representing
plain belief seems easy enough: simply represent an epistemic state by the set
of all propositions believed true in it or, since I make the common assumption
that plain belief is deductively closed, by the conjunction of all propositions
believed true in it. But this does not yet provide a theory of induction, i.e.
an answer to the question how epistemic states so represented are changed
tbrough information or experience. There is a convincing partial answer: if the
new information is compatible with the old epistemic state, then the new
epistemic state is simply represented by the conjunction of the new information
and the old beliefs. This answer is partial because it does not cover the quite
common case where the new information is incompatible with the old beliefs. It
is, however, important to complete the answer and to cover this case, too;
otherwise, we would not represent plain belief as conigible. The crucial
problem is that there is no good completion. When epistemic states are
represented simply by the conjunction of all propositions believed true in it,
the answer cannot be completed; and though there is a lot of fruitful work, no
other representation of epistemic states has been proposed, as far as I know,
which provides a complete solution to this problem. In this paper, I want to
suggest such a solution. In [4], I have more fully argued that this is the only
solution, if certain plausible desiderata are to be satisfied. Here, in section
2, I will be content with formally defining and intuitively explaining my
proposal. I will compare my proposal with probability theory in section 3. It
will turn out that the theory I am proposing is structurally homomorphic to
probability theory in important respects and that it is thus equally easily
implementable, but moreover computationally simpler. Section 4 contains a very
brief comparison with various kinds of logics, in particular conditional logic,
with Shackle's functions of potential surprise and related theories, and with
the Dempster - Shafer theory of belief functions.


title:
Generating Decision Structures and Causal Explanations for Decision
  Making

abstract:
  This paper examines two related problems that are central to developing an
autonomous decision-making agent, such as a robot. Both problems require
generating structured representafions from a database of unstructured
declarative knowledge that includes many facts and rules that are irrelevant in
the problem context. The first problem is how to generate a well structured
decision problem from such a database. The second problem is how to generate,
from the same database, a well-structured explanation of why some possible
world occurred. In this paper it is shown that the problem of generating the
appropriate decision structure or explanation is intractable without
introducing further constraints on the knowledge in the database. The paper
proposes that the problem search space can be constrained by adding knowledge
to the database about causal relafions between events. In order to determine
the causal knowledge that would be most useful, causal theories for
deterministic and indeterministic universes are proposed. A program that uses
some of these causal constraints has been used to generate explanations about
faulty plans. The program shows the expected increase in efficiency as the
causal constraints are introduced.


title: Updating Probabilities in Multiply-Connected Belief Networks

abstract:
  This paper focuses on probability updates in multiply-connected belief
networks. Pearl has designed the method of conditioning, which enables us to
apply his algorithm for belief updates in singly-connected networks to
multiply-connected belief networks by selecting a loop-cutset for the network
and instantiating these loop-cutset nodes. We discuss conditions that need to
be satisfied by the selected nodes. We present a heuristic algorithm for
finding a loop-cutset that satisfies these conditions.


title: Handling uncertainty in a system for text-symbol context analysis

abstract:
  In pattern analysis, information regarding an object can often be drawn from
its surroundings. This paper presents a method for handling uncertainty when
using context of symbols and texts for analyzing technical drawings. The method
is based on Dempster-Shafer theory and possibility theory.


title: Causal Networks: Semantics and Expressiveness

abstract:
  Dependency knowledge of the form "x is independent of y once z is known"
invariably obeys the four graphoid axioms, examples include probabilistic and
database dependencies. Often, such knowledge can be represented efficiently
with graphical structures such as undirected graphs and directed acyclic graphs
(DAGs). In this paper we show that the graphical criterion called d-separation
is a sound rule for reading independencies from any DAG based on a causal input
list drawn from a graphoid. The rule may be extended to cover DAGs that
represent functional dependencies as well as conditional dependencies.


title: MCE Reasoning in Recursive Causal Networks

abstract:
  A probabilistic method of reasoning under uncertainty is proposed based on
the principle of Minimum Cross Entropy (MCE) and concept of Recursive Causal
Model (RCM). The dependency and correlations among the variables are described
in a special language BNDL (Belief Networks Description Language). Beliefs are
propagated among the clauses of the BNDL programs representing the underlying
probabilistic distributions. BNDL interpreters in both Prolog and C has been
developed and the performance of the method is compared with those of the
others.


title: Nonmonotonic Reasoning via Possibility Theory

abstract:
  We introduce the operation of possibility qualification and show how. this
modal-like operator can be used to represent "typical" or default knowledge in
a theory of nonmonotonic reasoning. We investigate the representational power
of this approach by looking at a number of prototypical problems from the
nonmonotonic reasoning literature. In particular we look at the so called Yale
shooting problem and its relation to priority in default reasoning.


title:
Predicting the Likely Behaviors of Continuous Nonlinear Systems in
  Equilibrium

abstract:
  This paper introduces a method for predicting the likely behaviors of
continuous nonlinear systems in equilibrium in which the input values can vary.
The method uses a parameterized equation model and a lower bound on the input
joint density to bound the likelihood that some behavior will occur, such as a
state variable being inside a given numeric range. Using a bound on the density
instead of the density itself is desirable because often the input density's
parameters and shape are not exactly known. The new method is called SAB after
its basic operations: split the input value space into smaller regions, and
then bound those regions' possible behaviors and the probability of being in
them. SAB finds rough bounds at first, and then refines them as more time is
given. In contrast to other researchers' methods, SAB can (1) find all the
possible system behaviors, and indicate how likely they are, (2) does not
approximate the distribution of possible outcomes without some measure of the
error magnitude, (3) does not use discretized variable values, which limit the
events one can find probability bounds for, (4) can handle density bounds, and
(5) can handle such criteria as two state variables both being inside a numeric
range.


title: Generalizing the Dempster-Shafer Theory to Fuzzy Sets

abstract:
  With the desire to apply the Dempster-Shafer theory to complex real world
problems where the evidential strength is often imprecise and vague, several
attempts have been made to generalize the theory. However, the important
concept in the D-S theory that the belief and plausibility functions are lower
and upper probabilities is no longer preserved in these generalizations. In
this paper, we describe a generalized theory of evidence where the degree of
belief in a fuzzy set is obtained by minimizing the probability of the fuzzy
set under the constraints imposed by a basic probability assignment. To
formulate the probabilistic constraint of a fuzzy focal element, we decompose
it into a set of consonant non-fuzzy focal elements. By generalizing the
compatibility relation to a possibility theory, we are able to justify our
generalization to Dempster's rule based on possibility distribution. Our
generalization not only extends the application of the D-S theory but also
illustrates a way that probability theory and fuzzy set theory can be combined
to deal with different kinds of uncertain information in AI systems.


title: Logical Fuzzy Optimization

abstract:
  We present a logical framework to represent and reason about fuzzy
optimization problems based on fuzzy answer set optimization programming. This
is accomplished by allowing fuzzy optimization aggregates, e.g., minimum and
maximum in the language of fuzzy answer set optimization programming to allow
minimization or maximization of some desired criteria under fuzzy environments.
We show the application of the proposed logical fuzzy optimization framework
under the fuzzy answer set optimization programming to the fuzzy water
allocation optimization problem.


title:
Simplicity of the Lie algebra of skew symmetric elements of a Leavitt
  path algebra

abstract:
  For a field $F$ of characteristic not 2 and a directed row-finite graph
$\Gamma$ let $L(\Gamma)$ be the Leavitt path algebra with the standard
involution $*.$ We study the Lie algebra $K=K(L(\Gamma),*)$ of
$*-$skew-symmetric elements and find necessary and sufficient conditions for
the Lie algebra $[K,K]$ to be simple.


title: Percolation description of the global topography of Earth and Moon

abstract:
  Remarkable global correlations exist between geometrical features of
terrestrial surface on the Earth, current mean sea level and its geological
internal processes whose origins have remained an essential goal in the Earth
sciences. Theoretical modeling of the ubiquitous self-similar fractal patterns
observed on the Earth and their underlying rules is indeed of great importance.
Here I present a percolation description of the global topography of the Earth
in which the present mean sea level is automatically singled out as a critical
level in the model. This finding elucidates the origins of the appearance of
scale invariant patterns on the Earth. The criticality is shown to be
accompanied by a continental aggregation, unraveling an important correlation
between the water and long-range topographic evolution. To have a comparison
point in hand, I apply such analysis onto the lunar topography which reveals
various characteristic features of the Moon.


title:
Blind Interference Suppression and Power Adjustment with Alternating
  Optimization for Cooperative DS-CDMA Networks

abstract:
  This work presents blind joint interference suppression and power allocation
algorithms for DS-CDMA networks with multiple relays and decode and forward
protocols. A scheme for joint allocation of power levels across the relays
subject to group-based power constraints and the design of linear receivers for
interference suppression is proposed. A code-constrained constant modulus (CCM)
design for the receive filters and the power allocation vectors is devised
along with a blind channel estimator. In order to solve the proposed
optimization efficiently, an alternating optimization strategy is presented
with recursive least squares (RLS)-type algorithms for estimating the
parameters of the receiver, the power allocation and the channels. Simulations
show that the proposed algorithms obtain significant gains in capacity and
performance over existing schemes.


title:
Joint Iterative Power Adjustment and Interference Suppression Algorithms
  for Cooperative DS-CDMA Networks

abstract:
  This work presents joint iterative power allocation and interference
suppression algorithms for DS-CDMA networks which employ multiple relays and
the amplify and forward cooperation strategy. We propose a joint constrained
optimization framework that considers the allocation of power levels across the
relays subject to individual and global power constraints and the design of
linear receivers for interference suppression. We derive constrained minimum
mean-squared error (MMSE) expressions for the parameter vectors that determine
the optimal power levels across the relays and the parameters of the linear
receivers. In order to solve the proposed optimization problems efficiently, we
develop recursive least squares (RLS) algorithms for adaptive joint iterative
power allocation, and receiver and channel parameter estimation. Simulation
results show that the proposed algorithms obtain significant gains in
performance and capacity over existing schemes.


title: The Fulde-Ferrell-Larkin-Ovchinnikov state in pnictides

abstract:
  Fe-based superconductors (FeSC) exhibit all the properties of systems that
allow the formation of a superconducting phase with oscillating order
parameter, called the Fulde--Ferrell--Larkin--Ovchinnikov (FFLO) phase. By the
analysis of the Cooper pair susceptibility in two-band FeSC, such systems are
shown to support the existence of a FFLO phase, regardless of the exhibited
order parameter symmetry. We also show the state with nonzero Cooper pair
momentum, in superconducting FeSC with $\sim \cos(k_{x}) \cdot \cos (k_{y})$
symmetry, to be the ground state of the system in a certain parameter range.


title: Higher laminations, webs and N=2 line operators

abstract:
  A detailed study of half-BPS line operators of higher rank 4d N=2 theory
engineered from six dimensional A_{N-1} (2,0) theory on a bordered Riemann
surface with full marked points is performed. Geometrically, each 4d UV line
operator is represented by an irreducible bipartite web formed by three
junctions on Riemann surface, and such web structure is called higher
lamination. Algebraically, the space of UV line operators is identified with
the integral tropical a coordinates of the corresponding PGL(N,C) local system,
and the space of IR line operator is identified with the cluster X coordinates
of SL(N.C) local system. The expectation value of UV line operator at Coulomb
branch parameterized by X coordinates is calculated, and the result is a
positive Laurent polynomial in X. Using the expectation values, we calculate
the operator product expansion (OPE) between the line operators, which is then
represented geometrically by higher rank Skein relations. We also calculate the
Poisson brackets of these line operators, and Frenchel-Nielson type coordinates
are constructed for Higher Teichmuller space, etc.


title: The Assembly of Milky Way-like Galaxies Since z~2.5

abstract:
  Galaxies with the mass of the Milky Way dominate the stellar mass density of
the Universe but it is uncertain how and when they were assembled. Here we
study progenitors of these galaxies out to z=2.5, using data from the 3D-HST
and CANDELS Treasury surveys. We find that galaxies with present-day stellar
masses of log(M)~10.7 built ~90% of their stellar mass since z=2.5, with most
of the star formation occurring before z=1. In marked contrast to the assembly
history of massive elliptical galaxies, mass growth is not limited to large
radii: the mass in the central 2 kpc of the galaxies increased by a factor of
3.2+-0.8 between z=2.5 and z=1. We therefore rule out simple models in which
bulges were fully assembled at high redshift and disks gradually formed around
them. Instead, bulges (and black holes) likely formed in lockstep with disks,
through bar instabilities, migration, or other processes. We find that after
z=1 the growth in the central regions gradually stopped and the disk continued
to build, consistent with recent studies of the gas distributions in z~1
galaxies and the properties of many spiral galaxies today.


title:
MOST Observations of sigma Ori E: Challenging the Centrifugal Breakout
  Narrative

abstract:
  We present results from three weeks' photometric monitoring of the magnetic
helium-strong star sigma Ori E using the MOST microsatellite. The star's light
curve is dominated by twice-per-rotation eclipse-like dimmings arising when
magnetospheric clouds transit across and occult the stellar disk. However, no
evidence is found for any abrupt centrifugal breakout of plasma from the
magnetosphere, either in the residual flux or in the depths of the light
minima. Motivated by this finding we compare the observationally inferred
magnetospheric mass against that predicted by a breakout analysis. The large
discrepancy between the values leads us to argue that centrifugal breakout does
not play a significant role in establishing the magnetospheric mass budget of
sigma Ori E.


title:
Enabling Non-Parametric Strong Lensing Models to Derive Reliable Cluster
  Mass Distributions. WSLAP+

abstract:
  In the strong lensing regime non-parametric lens models struggle to achieve
sufficient angular resolution for a meaningful derivation of the central
cluster mass distribution. The problem lies mainly with cluster members which
perturb lensed images and generate additional images, requiring high resolution
modeling, even though we mainly wish to understand the relatively smooth
cluster component. The required resolution is not achievable because the
separation between lensed images is several times larger than the deflection
angles by member galaxies, even for the deepest data. Here we bypass this
limitation by incorporating a simple physical prior for member galaxies, using
their observed positions and their luminosity scaled masses. This galaxy
contribution is added to a relatively coarse Gaussian pixel grid for modeling
the cluster mass distribution, extending our established WSLAP code (Diego et
al. 2007). We test this new code with a simulation based on A1689, using the
pixels belonging to multiply-lensed images and the observed member galaxies.
Dealing with the cluster members this way leads to convergent solutions,
without resorting to regularization, reproducing well the input cluster and
substructures. We highlight the ability of this method to recover dark
sub-components of the cluster, unrelated to member galaxies. Such anomalies can
provide clues to the nature of invisible dark matter, but are hard to discover
using parametrized models where substructures are defined by the visible data.
With our increased resolution and stability we show, for the first time, that
non-parametric models can be made sufficiently precise to locate
multiply-lensed systems, thereby achieving fully self-consistent solutions
without reliance on input systems from less objective means.


title:
Jet Sampling: Improving Event Reconstruction through Multiple
  Interpretations

abstract:
  The classification of events involving jets as signal-like or background-like
can depend strongly on the jet algorithm used and its parameters. This is
partly due to the fact that standard jet algorithms yield a single partition of
the particles in an event into jets, even if no particular choice stands out
from the others. As an alternative, we propose that one should consider
multiple interpretations of each event, generalizing the Qjets procedure to
event-level analysis. With multiple interpretations, an event is no longer
restricted to either satisfy cuts or not satisfy them - it can be assigned a
weight between 0 and 1 based on how well it satisfies the cuts. These
cut-weights can then be used to improve the discrimination power of an analysis
or reduce the uncertainty on mass or cross-section measurements. For example,
using this approach on a Higgs plus Z boson sample, with h->bb we find an 28%
improvement in significance can be realized at the 8 TeV LHC. Through a number
of other examples, we show various ways in which having multiple
interpretations can be useful on the event level.


title:
The Structural Evolution of Milky Way-like Star Forming Galaxies since
  z~1.3

abstract:
  We follow the structural evolution of star forming galaxies (SFGs) like the
Milky Way by selecting progenitors to z~1.3 based on the stellar mass growth
inferred from the evolution of the star forming sequence. We select our sample
from the 3D-HST survey, which utilizes spectroscopy from the HST WFC3 G141
near-IR grism and enables precise redshift measurements for our sample of SFGs.
Structural properties are obtained from Sersic profile fits to CANDELS WFC3
imaging. The progenitors of z=0 SFGs with stellar mass M=10^{10.5} Msun are
typically half as massive at z~1. This late-time stellar mass assembly is
consistent with recent studies that employ abundance matching techniques. The
descendant SFGs at z~0 have grown in half-light radius by a factor of ~1.4
since z~1. The half-light radius grows with stellar mass as r_e M^{0.29}. While
most of the stellar mass is clearly assembling at large radii, the mass surface
density profiles reveal ongoing mass growth also in the central regions where
bulges and pseudobulges are common features in present day late-type galaxies.
Some portion of this growth in the central regions is due to star formation as
recent observations of H-alpha maps for SFGs at z~1 are found to be extended
but centrally peaked. Connecting our lookback study with galactic archeology,
we find the stellar mass surface density at R=8 kpc to have increased by a
factor of ~2 since z~1, in good agreement with measurements derived for the
solar neighborhood of the Milky Way.