title: Light under a matter field microscope

abstract:
  Textbooks state that the successful application of Maxwell's Equations in
physical optics problems requires light to interact with matter where any
inhomogeneities are spaced by less than or equal to the wavelength of light;
the 'dense' limit. This untested statement is proved correct in the experiment
reported here and a precise dense limit is found. A 'matter field microscope'
is used. The dense limit appears exactly when the (approximately fixed) optical
wavenumber coincides with the (rapidly increasing) spatial frequency for a
discontinuous matter field as it develops from a dilute to a concentrated
material system. It is inferred that in the dilute material system examined
here plane wave methods are only valid over a well-defined, but restricted,
transverse dimension which is introduced here as the 'wavewidth'. This appears
to reveal a natural sub-wavelength mode structure for light. This result then
allows the application of the uncertainty relation to describe a longitudinal
spatial restriction of the light when crossing matter density interfaces.
Physical optics using 'spatial' rays and 'spatial' photons can then be
described on this model for light.


title:
Helioseismology of Pre-Emerging Active Regions II: Average Emergence
  Properties

abstract:
  We report on average subsurface properties of pre-emerging active regions as
compared to areas where no active region emergence was detected. Helioseismic
holography is applied to samples of the two populations (pre-emergence and
without emergence), each sample having over 100 members, which were selected to
minimize systematic bias, as described in Leka et al. We find that there are
statistically significant signatures (i.e., difference in the means of more
than a few standard errors) in the average subsurface flows and the apparent
wave speed that precede the formation of an active region. The measurements
here rule out spatially extended flows of more than about 15 m/s in the top 20
Mm below the photosphere over the course of the day preceding the start of
visible emergence. These measurements place strong constraints on models of
active region formation.


title: Grating monochromator for soft X-ray self-seeding the European XFEL

abstract:
  Self-seeding is a promising approach to significantly narrow the SASE
bandwidth of XFELs to produce nearly transform-limited pulses. The
implementation of this method in the soft X-ray wavelength range necessarily
involves gratings as dispersive elements. We study a very compact self-seeding
scheme with a grating monochromator originally designed at SLAC, which can be
straightforwardly installed in the SASE3 type undulator beamline at the
European XFEL. The monochromator design is based on a toroidal VLS grating
working at a fixed incidence angle mounting without entrance slit. It covers
the spectral range from 300 eV to 1000 eV. The optical system was studied using
wave optics method (in comparison with ray tracing) to evaluate the performance
of the self-seeding scheme. Our wave optics analysis takes into account the
actual beam wavefront of the radiation from the coherent FEL source, third
order aberrations, and errors from each optical element. Wave optics is the
only method available, in combination with FEL simulations, for the design of a
self-seeding monochromator without exit slit. We show that, without exit slit,
the self-seeding scheme is distinguished by the much needed experimental
simplicity, and can practically give the same resolving power (about 7000) as
with an exit slit. Wave optics is also naturally applicable to calculations of
the self-seeding scheme efficiency, which include the monochromator
transmittance and the effect of the mismatching between seed beam and electron
beam. Simulations show that the FEL power reaches 1 TW and that the spectral
density for a TW pulse is about two orders of magnitude higher than that for
the SASE pulse at saturation.


title: Quantum mechanics on profinite groups and partial order

abstract:
  Inverse limits and profinite groups are used in a quantum mechanical context.
Two cases are considered. A quantum system with positions in the profinite
group ${\mathbb Z}_p$ and momenta in the group ${\mathbb Q}_p/{\mathbb Z}_p$;
and a quantum system with positions in the profinite group ${\hat {\mathbb Z}}$
and momenta in the group ${\mathbb Q}/{\mathbb Z}$. The corresponding
Schwatz-Bruhat spaces of wavefunctions and the Heisenberg-Weyl groups are
discussed. The sets of subsystems of these systems are studied from the point
of view of partial order theory. It is shown that they are directed-complete
partial orders. It is also shown that they are topological spaces with $T_0$
topologies, and this is used to define continuity of various physical
quantities. The physical meaning of profinite groups, non-Archimedean metrics,
partial orders and $T_0$ topologies, in a quantum mechanical context, is
discussed.


title: Galaxy Rotation Curves in Covariant Horava-Lifshitz Gravity

abstract:
  Using the multiplicity of solutions for the projectable case of the covariant
extension of Horava-Lifshitz Gravity, we show that an appropriate choice for
the auxiliary field allows for an effective description of galaxy rotation
curves. This description is based on static and spherically symmetric solutions
of covariant Horava-Lifshitz Gravity and does not require Dark Matter.


title: A stack and a pop stack in series

abstract:
  We study sorting machines consisting of a stack and a pop stack in series,
with or without a queue between them. While there are, a priori, four such
machines, only two are essentially different: a pop stack followed directly by
a stack, and a pop stack followed by a queue and then by a stack. In the former
case, we obtain complete answers for the basis and enumeration of the sortable
permutations. In the latter case, we present several conjectures.


title: Space-time S-matrix and Flux-tube S-matrix at Finite Coupling

abstract:
  We propose a non-perturbative formulation of planar scattering amplitudes in
N=4 SYM or, equivalently, polygonal Wilson loops. The construction is based on
the OPE approach and introduces a new decomposition of the Wilson loop in terms
of fundamental building blocks named Pentagon transitions. These transitions
satisfy a simple relation to the worldsheet S-matrix on top of the so called
Gubser-Klebanov-Polyakov vacuum which allows us to bootstrap them at any value
of the coupling. In this letter we present a subsector of the full solution to
scattering amplitudes which we call the gluonic part. We match our results with
both weak and strong coupling data available in the literature. For example,
the strong coupling Y-system can be understood in this approach.


title:
A complete O(alpha_S^2) calculation of the signal-background
  interference for the Higgs diphoton decay channel

abstract:
  We present the full ${\cal O}(\as^2)$ computation of the interference effects
between the Higgs diphoton signal and the continuum background at the LHC.
While the main contribution to the interference originates on the $gg$ partonic
subprocess, we find that the corrections from the $qg$ and $q\bar{q}$ channels
amount up to 35% of it. We discuss the effect of these new subprocesses in the
shift of the diphoton invariant mass peak recently reported by S. Martin in
Ref.\cite{Martin:2012xc}.


title: Adaptive pumping for spectral control of random lasers

abstract:
  A laser is not necessarily a sophisticated device: Pumping energy into an
amplifying medium randomly filled with scatterers, a powder for instance, makes
a perfect "random laser." In such a laser, the absence of mirrors greatly
simplifies laser design, but control over emission directionality or frequency
tunability is lost, seriously hindering prospects for this otherwise simple
laser. Lately, we proposed a novel approach to harness random lasers, inspired
by spatial shaping methods recently employed for coherent light control in
complex media. Here, we experimentally implement this method in an optofluidic
random laser where scattering is weak and modes extend spatially and strongly
overlap, making individual selection a priori impossible. We show that control
over laser emission can indeed be regained even in this extreme case by
actively shaping the spatial profile of the optical pump. This unique degree of
freedom, which has never been exploited, allows selection of any desired
wavelength and shaping of lasing modes, without prior knowledge of their
spatial distribution. Mode selection is achieved with spectral selectivity down
to 0.06nm and more than 10dB side-lobe rejection. This experimental method
paves the way towards fully tunable and controlled random lasers and can be
transferred to other class of lasers.


title: Reachability via Compositionality in Petri nets

abstract:
  We introduce a novel technique for checking reachability in Petri nets that
relies on a recently introduced compositional algebra of nets. We prove that
the technique is correct, and discuss our implementation. We report promising
experimental results on some well-known examples.


title:
The importance of warm, AGN-driven outflows in the nuclear regions of
  nearby ULIRGs

abstract:
  We present an optical spectroscopic study of a 90% complete sample of 17
nearby ULIRGs with optical Seyfert nuclei, with the aim of investigating the
nature of the nuclear warm gas outflows. A high proportion (94%) of our sample
show disturbed emission line kinematics in the form of broad (FWHM > 500 km
s-1) and/or strongly blueshifted (\Delta V < -150 km s-1) emission line
components. This proportion is significantly higher than found in a comparison
sample of non-Sy ULIRGs (19%). We also find evidence that the [OII]5007,4959
emission lines in Sy-ULIRGs are broader and more asymmetric that in samples of
non-ULIRG Seyferts.
  The Sy-ULIRG sample encompasses a wide diversity of emission line profiles.
In most individual objects we are able to fit the profiles of all the emission
lines with a kinematic model derived from the strong [OIII]4959,5007 lines,
using between 2 and 5 Gaussian components. From these fits we derive diagnostic
line ratios that are used to investigate the ionization mechanisms for the
different kinematic components. We show that, in general, the line ratios are
consistent with gas of super-solar abundance photoionized by a combination of
AGN and starburst activity, with an increasing contribution from the AGN with
increasing FWHM of the individual kinematic components, and the AGN
contribution dominating for the broadest components. However, shock ionization
cannot be ruled out in some cases. Our derived upper limits on the mass
outflows rates and kinetic powers of the emission line outflows show that they
can be as energetically significant as the neutral and molecular outflows in
ULIRGs-consistent with the requirements of the hydrodynamic simulations that
include AGN feedback. However, the uncertainties are large, and more accurate
estimates of the radii, densities and reddening of the outflows are required to
put these results on a firmer footing.


title: Elliptic Yang-Mills Flow Theory

abstract:
  We lay the foundations of a Morse homology on the space of connections on a
principal $G$-bundle over a compact manifold $Y$, based on a newly defined
gauge-invariant functional $\mathcal J$. While the critical points of $\mathcal
J$ correspond to Yang-Mills connections on $P$, its $L^2$-gradient gives rise
to a novel system of elliptic equations. This contrasts previous approaches to
a study of the Yang-Mills functional via a parabolic gradient flow. We carry
out the complete analytical details of our program in the case of a compact
two-dimensional base manifold $Y$. We furthermore discuss its relation to the
well-developed parabolic Morse homology of Riemannian surfaces. Finally, an
application of our elliptic theory is given to three-dimensional product
manifolds $Y=\Sigma\times S^1$.


title:
Observation of inhibited electron-ion coupling in strongly heated
  graphite

abstract:
  Creating non-equilibrium states of matter with highly unequal electron and
lattice temperatures allows unsurpassed insight into the dynamic coupling
between electrons and ions through time-resolved energy relaxation
measurements. Recent studies on low-temperature laser-heated graphite suggest a
complex energy exchange when compared to other materials. To avoid problems
related to surface preparation, crystal quality and poor understanding of the
energy deposition and transport mechanisms, we apply a different energy
deposition mechanism, via laser-accelerated protons, to isochorically and
non-radiatively heat macroscopic graphite samples up to temperatures close to
the melting threshold. Using time-resolved x ray diffraction, we show clear
evidence of a very small electron-ion energy transfer, yielding approximately
three times longer relaxation times than previously reported. This is
indicative of the existence of an energy transfer bottleneck in non-equilibrium
warm dense matter.


title:
Dispersive Media Subcell Averaging in the FDTD Method using Corrective
  Surface Currents

abstract:
  We present a corrective subcell averaging technique that improves on the
accuracy of the volume-averaged finite-difference time-domain (FDTD) method in
the presence of dispersive material interfaces. The method is based on an
alternative effective-medium formulation that captures field discontinuities at
interfaces as electric and magnetic surface currents. In calculating the
spectra of strongly dispersive Mie scatterers we demonstrate that the derived
FDTD algorithm is both highly efficient and able to approximately restore
second order accuracy.


title:
Main conjectures for CM fields and a Yager-type theorem for Rubin-Stark
  elements

abstract:
  In this article, we study the p-ordinary Iwasawa theory of the (conjectural)
Rubin-Stark elements defined over abelian extensions of a CM field F and
develop a rank-g Euler/Kolyvagin system machinery (where 2g is the degree of
F), refining and generalizing Perrin-Riou's theory and the author's prior work.
This has several important arithmetic consequences: Using the recent results of
Hida and Hsieh on the CM main conjectures, we prove a natural extension of a
theorem of Yager for the CM field F, where we relate the Rubin-Stark elements
to the several-variable Katz p-adic L-function. Furthermore, beyond the cases
covered by Hida and Hsieh, we are able to reduce the p-ordinary CM main
conjectures to a local statement about the Rubin-Stark elements. We discuss
applications of our results in the arithmetic of CM abelian varieties.


title: Plastic flow in a sheared polycrystalline solid using phase field model

abstract:
  Plastic deformation in solids induced by external shear stress is of huge
practical interest. Presence of local crystalline order in polycrystals,
consisting of many grains, distinguishes its deformation pattern from that of
amorphous materials. Despite strong anisotropy, induced by external stress, the
plastic flow and the consequent deformation field show strong dynamical
heterogeneity. The distribution $P(u)$ of particle displacements ($u$) shows
three distinct regimes including a power law scaling regime at moderate
displacements. Using a phase field simulation we show how polycrystals generate
saddle and vortex like flow patterns, which hitherto have been termed as
elementary plastic events in the context of amorphous materials. Interestingly,
such events here find natural explanation in terms of the underlying
dislocation dynamics. We also characterize the spatial distribution of the flow
field using Okubo-Weiss measure.


title: The VERITAS Dark Matter Program

abstract:
  The VERITAS array of Cherenkov telescopes, designed for the detection of
gamma-rays in the 100 GeV-10 TeV energy range, performs dark matter searches
over a wide variety of targets. VERITAS continues to carry out focused
observations of dwarf spheroidal galaxies in the Local Group, of the Milky Way
galactic center, and of Fermi-LAT unidentified sources. This report presents
our extensive observations of these targets, new statistical techniques, and
current constraints on dark matter particle physics derived from these
observations.


title: Deformations of Kolyvagin systems

abstract:
  Ochiai has previously proved that the Beilinson-Kato Euler systems for
modular forms interpolate in nearly-ordinary $p$-adic families (Howard has
obtained a similar result for Heegner points), based on which he was able to
prove a half of the two-variable main conjectures. The principal goal of this
article is to generalize Ochiai's work in the level of Kolyvagin systems so as
to prove that Kolyvagin systems associated to Beilinson-Kato elements
interpolate in the full deformation space (in particular, beyond the
nearly-ordinary locus) and use what we call universal Kolyvagin systems to
attempt a main conjecture over the eigencurve. Along the way, we utilize these
objects in order to define a quasicoherent sheaf on the eigencurve that behaves
like a $p$-adic $L$-function (in a certain sense of the word, in
$3$-variables).


title: Junction conditions for F(R)-gravity and their consequences

abstract:
  I present the junction conditions for F(R) theories of gravity and their
implications: the generalized Israel conditions and equations. These junction
conditions are necessary to construct global models of stars, galaxies, etc.,
where a vacuum region surrounds a finite body in equilibrium, as well as to
describe shells of matter and braneworlds, and they are stricter than in
General Relativity in both cases. For the latter case, I obtain the field
equations for the energy-momentum tensor on the shell/brane, and they turn out
to be, remarkably, the same as in General Relativity. An exceptional case for
quadratic F(R), previously overlooked in the literature, is shown to arise
allowing for a discontinuous R, and leading to an energy-momentum content on
the shell with unexpected properties, such as non-vanishing components normal
to the shell and a new term resembling classical dipole distributions. For the
former case, they do not only require the agreement of the first and second
fundamental forms on both sides of the matching hypersurface, but also that the
scalar curvature R and its first derivative agree there too. I argue that, as a
consequence, matched solutions in General Relativity are not solutions of
F(R)-models generically. Several relevant examples are analyzed.


title:
Emergence of time in power-counting renormalizable Riemannian theory of
  gravity

abstract:
  We suggest a new scenario of gravitation in which gravity at the fundamental
level is described by a Riemannian (i.e. locally Euclidean) theory without the
notion of time. The Lorentzian metric structure and the notion of time emerge
as effective properties at long distances. On the other hand, at short
distances, higher derivative terms compatible with the Riemannian
diffeomorphism become important and the system is described by a power-counting
renormalizable Riemannian theory.


title:
The self-actuating InAs nanowire-based nanoelectromechanical Josephson
  junction

abstract:
  Half a century ago Brian Josephson made a series of striking predictions
related to a tunnelling barrier sandwiched between two superconductors. One
particular prediction, later became known as the a.c.-Josephson effect, said
that under a finite d.c. bias the tunnelling current contains an a.c.
supercurrent component, oscillating at microwave frequency. This prediction was
experimentally verified through observation of the junction current-voltage
characteristics modified by the interaction with the electromagnetic radiation,
and direct detection of the microwave radiation emitted from the junction
itself. It had also been established that the behaviour of the d.c.
current-voltage characteristic is determined by the high-frequency dynamics of
the Josephson junction, and can be used to study various systems such as atoms
in the electron-spin resonance, optical phonons in high-Tc superconductors, and
vibrating Nb molecules. Here we present an InAs nanowire Josephson junction
device, where a vibrating nanowire weak link plays the role of a
nanoelectromechanical resonator. The flow of the a.c.- Josephson current
through the junction enabled actuation and detection of vibrational modes of
the resonator by means of simple d.c. transport measurements.


title: Efficient Decomposition of Single-Qubit Gates into $V$ Basis Circuits

abstract:
  We develop the first constructive algorithms for compiling single-qubit
unitary gates into circuits over the universal $V$ basis. The $V$ basis is an
alternative universal basis to the more commonly studied $\{H,T\}$ basis. We
propose two classical algorithms for quantum circuit compilation: the first
algorithm has expected polynomial time (in precision $\log(1/\epsilon)$) and
offers a depth/precision guarantee that improves upon state-of-the-art methods
for compiling into the $\{H,T\}$ basis by factors ranging from 1.86 to
$\log_2(5)$. The second algorithm is analogous to direct search and yields
circuits a factor of 3 to 4 times shorter than our first algorithm, and
requires time exponential in $\log(1/\epsilon)$; however, we show that in
practice the runtime is reasonable for an important range of target precisions.


title: The Theory of Electrodynamics in a Linear Dielectric

abstract:
  We adopt the continuum limit of a linear, isotropic, homogeneous,
transparent, dispersion-negligible dielectric of refractive index $n$ and
examine the consequences of the effective speed of light in a stationary
dielectric, $c/n$, for D'Alembert's principle and the Lagrange equations. The
principles of dynamics in the dielectric-filled space are then applied to the
electromagnetic Lagrangian and we derive equations of motion for the
macroscopic fields. A direct derivation of the total energy--momentum tensor
from the field strength tensor for the electromagnetic field in a dielectric is
used to demonstrate the utility of the new theory by resolving the century-old
Abraham--Minkowski electromagnetic momentum controversy in a way that preserves
the principles of conservation of energy, conservation of linear momentum, and
conservation of angular momentum.


title:
Non-commutative/non-associative IIA (IIB) Q- and R-branes and their
  intersections

abstract:
  In this paper we discuss the construction of non-geometric Q- and R-branes as
sources of non-geometric Q- and R-fluxes in string compactifications. The
non-geometric Q-branes, being obtained via T-duality from the NS 5-brane or
respectively from the KK-monopole, are still local solutions of the standard NS
action, where however the background fields G and B possess non-geometric
global monodromy properties. We show that using double field theory, redefined
background fields tilde G and beta as well as their corresponding effective
action, the Q-branes are locally and globally well behaved solutions.
Furthermore the R-brane solution can be at least formally constructed using
dual coordinates. We derive the associated non-geometric Q- and R-fluxes and
discuss that closed strings moving in the space transversal to the
world-volumes of the non-geometric branes see a non-commutative or a
non-associative geometry.
  In the second part of the paper we construct intersecting Q- and R-brane
configurations as completely supersymmetric solutions of type IIA/B
supergravity with certain SU(3) x SU(3) group structures. In the near horizon
limit the intersecting brane configurations lead to type II backgrounds of the
form AdS4 x M6, where the six-dimensional compact space M6 is a torus fibration
with various non-geometric Q- and R-fluxes in the compact directions. It
exhibits an interesting non-commutative and non-associate geometric structure.
Furthermore we also determine some of the effective four-dimensional
superpotentials originating from the non-geometric fluxes.


title:
Dynamical influence processes on networks: General theory and
  applications to social contagion

abstract:
  We study binary state dynamics on a network where each node acts in response
to the average state of its neighborhood. Allowing varying amounts of
stochasticity in both the network and node responses, we find different
outcomes in random and deterministic versions of the model. In the limit of a
large, dense network, however, we show that these dynamics coincide. We
construct a general mean field theory for random networks and show this
predicts that the dynamics on the network are a smoothed version of the average
response function dynamics. Thus, the behavior of the system can range from
steady state to chaotic depending on the response functions, network
connectivity, and update synchronicity. As a specific example, we model the
competing tendencies of imitation and non-conformity by incorporating an
off-threshold into standard threshold models of social contagion. In this way
we attempt to capture important aspects of fashions and societal trends. We
compare our theory to extensive simulations of this "limited imitation
contagion" model on Poisson random graphs, finding agreement between the
mean-field theory and stochastic simulations.


title: Solitons in Schr\"odinger-Maxwell equations

abstract:
  In this paper we study the Nonlinear Schr\"odinger-Maxwell equations (NSM).
We are interested to analyse the existence of solitons, namely of finite energy
solutions which exhibit stability properties. This paper is divided in two
parts. In the first, we give an abstract definition of soliton and we develope
an abstract existence theory. In the second, we apply this theory to NSM.


title:
Analytical approximation of Blasius' similarity solution with rigorous
  error bounds

abstract:
  We use a recently developed method \cite{Costinetal}, \cite{Dubrovin} to find
accurate analytic approximations with rigorous error bounds for the classic
similarity solution of Blasius of the boundary layer equation in fluid
mechanics, the two point boundary value problem $f^{\prime \prime \prime} + f
f^{\prime \prime} =0$ with $f(0)=f^\prime (0)=0$ and $\lim_{x \rightarrow
\infty} f^\prime (x) =1$. The approximation is given in terms of a polynomial
in $[0, \frac{5}{2}]$ and in terms of the error function in $[\frac{5}{2},
\infty)$. The two representations for the solution in different domains match
at $x=\frac{5}{2}$ determining all free parameters in the problem, in
particular $f^{\prime \prime} (0) =0.469600 \pm 0.000022 $ at the wall The
method can in principle provide approximations to any desired accuracy for this
or wide classes of linear or nonlinear differential equations with initial or
boundary value conditions. The analysis relies on controlling the errors in the
approximation through contraction mapping arguments, using energy bounds for
the Green's function of the linearized problem.


title: Inter-Cloud Data Security Strategies

abstract:
  Cloud computing is a complex infrastructure of software, hardware,
processing, and storage that is available as a service. Cloud computing offers
immediate access to large numbers of the world's most sophisticated
supercomputers and their corresponding processing power, interconnected at
various locations around the world, proffering speed in the tens of trillions
of computations per second. Information in databases and software scattered
around the Internet. There are many service providers in the internet, we can
call each service as a cloud, each cloud service will exchange data with other
cloud, so when the data is exchanged between the clouds, there exist the
problem of security. Security is an important issue for cloud computing, both
in terms of legal compliance and user trust, and needs to be considered at
every phase of design. In contrast to traditional solutions, where the IT
services are under proper physical, logical and personnel controls, Cloud
Computing moves the application software and databases to the large data
centers, where the management of the data and services may not be trustworthy.
This unique attribute, however, poses many new security challenges. Cloud
computing seems to offer some incredible benefits for communicators.


title:
Joint Ultra-wideband and Signal Strength-based Through-building Tracking
  for Tactical Operations

abstract:
  Accurate device free localization (DFL) based on received signal strength
(RSS) measurements requires placement of radio transceivers on all sides of the
target area. Accuracy degrades dramatically if sensors do not surround the
area. However, law enforcement officers sometimes face situations where it is
not possible or practical to place sensors on all sides of the target room or
building. For example, for an armed subject barricaded in a motel room, police
may be able to place sensors in adjacent rooms, but not in front of the room,
where the subject would see them. In this paper, we show that using two
ultra-wideband (UWB) impulse radios, in addition to multiple RSS sensors,
improves the localization accuracy, particularly on the axis where no sensors
are placed (which we call the x-axis). We introduce three methods for combining
the RSS and UWB data. By using UWB radios together with RSS sensors, it is
still possible to localize a person through walls even when the devices are
placed only on two sides of the target area. Including the data from the UWB
radios can reduce the localization area of uncertainty by more than 60%.


title: Statistical Proof Pattern Recognition: Automated or Interactive?

abstract:
  In this paper, we compare different existing approaches employed in data
mining of big proof libraries in automated and interactive theorem proving.


title: Verifying a platform for digital imaging: a multi-tool strategy

abstract:
  Fiji is a Java platform widely used by biologists and other experimental
scientists to process digital images. In particular, in our research - made
together with a biologists team; we use Fiji in some pre-processing steps
before undertaking a homological digital processing of images. In a previous
work, we have formalised the correctness of the programs which use homological
techniques to analyse digital images. However, the verification of Fiji's
pre-processing step was missed. In this paper, we present a multi-tool approach
filling this gap, based on the combination of Why/Krakatoa, Coq and ACL2.


title: On the Distributional Hessian of the Distance Function

abstract:
  We describe the precise structure of the distributional Hessian of the
distance function from a point of a Riemannian manifold. In doing this we also
discuss some geometrical properties of the cutlocus of a point and we compare
some different weak notions of Hessian and Laplacian.


title:
i-R-Cd (R = Gd - Tm, Y): A new family of binary magnetic icosahedral
  quasicrystals

abstract:
  "Seek and ye shall find, the unsought shall go undetected." This adage,
attributed to Aristophanes' can be considered one of the defining mantras of
new materials research; if you don't look, you certainly will not discover. As
a result of our recent discovery of the binary quasicrystalline phase
i-Sc12Zn88 we proposed that there may well be other binary quasicrystalline
phases lurking nearby known crystalline approximants, perhaps as peritectally
forming compounds with very limited liquidus surfaces, offering greatly reduced
ranges of composition/temperature for primary solidification. Here we report
that, as the adage goes, we have found. Indeed, adjacent to the RCd6, cubic
approximate structure, we have discovered the long sought after model system
for magnetic quasicrystals: a new family of at least seven rare earth
icosahedral quasicrystals: i-R-Cd for R = Gd - Tm, Y, six of which now form the
known set of moment bearing, binary quasicrystals.


title: Short-time Evolution of Alkane-in-Water Nanoemulsions

abstract:
  The stability of alkane-in-water nanoemulsions during the sub-stationary
regime is studied by means of Emulsion Stability Simulations (ESS). The effects
of Ostwald ripening, flocculation, coalescence, gravity, and hydration forc- es
are considered. According to these calculations flocculation and coalescence
are predominant during the first few seconds after the preparation of the
emulsion. This favors the generation of a right-skewed Drop Size Distribu- tion
(DSD). As the system evolves, the drops grow larger and more repulsive causing
a slow down of the flocculation process. In the case of dodecane (C12) and
hexadecane (C16) the referred phenomena, reinforce the ripening trend to
subvert the initial DSD variation, producing a meta-stable distribution which
is preserved during several minutes. After this time, Ostwald ripening
dominates: the skirt of the distribution changes progressively from
right-skewed to left-skewed. Consistent with these changes, the cube average
radius of the emulsion increases rapidly at first, but progressively diminishes
generating a concave-downward curve that stabilizes asymptotically. In the case
of dodecane and hexadecane the complete dissolution of the drops promoted by
ripening is prevented at all times due to coalescence. In the case of octane
(C8) a substantial amount of drops is lost by dissolution, forbidding the
attain- ment of a stable DSD. In all cases the molecular exchange only favors a
decrease of the average radius as a function of time. It is the elimination of
drops either by dissolution or coalescence which causes an increase of the
average radius of the emulsion.


title: A II$_1$ factor approach to the Kadison-Singer problem

abstract:
  We show that the Kadison-Singer problem, asking whether the pure states of
the diagonal subalgebra $\ell^\infty\Bbb N\subset \Cal B(\ell^2\Bbb N)$ have
unique state extensions to $\Cal B(\ell^2\Bbb N)$, is equivalent to a similar
statement in II$_1$ factor framework, concerning the ultrapower inclusion
$D^\omega \subset R^\omega$, where $D$ is the Cartan subalgebra of the
hyperfinite II$_1$ factor $R$, and $\omega$ is a free ultraflter. While we do
not settle the problem in this latter form, we prove that if $A$ is any
singular maximal abelian subalgebra of $R$, then the inclusion $A^\omega
\subset R^\omega$ does satisfy the Kadison-Singer property.


title: Laser-assisted nuclear photoeffect reexamined

abstract:
  The S-matrix element and the cross section of the laser-assisted nuclear
photoeffect are recalculated in a gauge invariant manner taking into account
the effect of the Coulomb field of the remainder nucleus. The \gamma-photon
energy dependence of the laser free cross section obtained in the plane wave
and long-wavelength Coulomb-Volkov approximations are compared. Numerically the
laser-assisted partial cross sections with laser photon energy 2 keV and some
different polarization states of \gamma-photon of energy 3 MeV are
investigated.


title: Bricks and urbanism in the Indus Valley rise and decline

abstract:
  The Indus Civilization, often denoted by its major city Harappa, spanned
almost two millennia from 3200 to 1300 BC. Its tradition reaches back to 7000
BC: a 5000 year long expansion of villages and towns, of trading activity, and
of technological advancements culminates between 2600 and 1900 BC in the
build-up of large cities, writing, and political authority; it emerges as one
of the first great civilizations in history. During the ensuing 600 years,
however, key technologies fall out of use, urban centers are depopulated, and
people emigrate from former core settlement areas. Although many different
hypotheses have been put forward to explain this deurbanization, a conclusive
causal chain has not yet been established. We here combine literature estimates
on brick typology, and on urban area for individual cities. In the context of
the existing extensive data on Harappan artifact find sites and put in their
chronological context, the combined narratives told by bricks, cities, and
spatial extent can provide a new point of departure for discussing the possible
reasons for the mysterious "decline".


title: On partitions of G-spaces and G-lattices

abstract:
  Given a $G$-space $X$ and a non-trivial $G$-invariant ideal $I$ of subsets of
$X$, we prove that for every partition $X=A_1\cup\dots\cup A_n$ of $X$ into
$n\ge 2$ pieces there is a piece $A_i$ of the partition and a finite set
$F\subset G$ of cardinality $|F|\le
\phi(n+1):=\max_{1<x<n+1}\frac{x^{n+1-x}-1}{x-1}$ such that $G=F\cdot
\Delta(A_i)$ where $\Delta(A_i)=\{g\in G:gA_i\cap A_i\notin I\}$ is the
difference set of the set $A_i$. Also we investigate the growth of the sequence
$\phi(n)=\max_{1<x<n}\frac{x^{n-x}-1}{x-1}$ and show that $\ln
\phi(n)=nW(ne)-2n+\frac{n}{W(ne)}+\frac{W(ne)}{n}+O\big(\frac{\ln n}n\big)$
where $W(x)$ is the Lambert W-function, defined implicitly as $W(x)e^{W(x)}=x$.
This shows that $\phi(n)$ grows faster that any exponent $a^n$ but slower than
the sequence of factorials $n!$.


title:
Representation invariant Geometrothermodynamics: applications to
  ordinary thermodynamic systems

abstract:
  In this work we employ a recently devised metric within the
Geometrothermodynamics program to study ordinary thermodynamic systems. The new
feature of this metric is that, in addition to Legendre symmetry, it exhibits
invariance under a change of representation. This metric was derived in a
previous work by the authors while addressing the problem of the conformal
structure of the thermodynamic metrics for different representations. Here, we
present an thorough analysis for the ideal gas, the van der Waals fluid, the
one dimensional Ising model and some other systems of cosmological interest.


title: Double core hole production in N2: Beating the Auger clock

abstract:
  We investigate the creation of double K-shell holes in N2 molecules via
sequential absorption of two photons on a timescale shorter than the core-hole
lifetime by using intense x-ray pulses from the Linac Coherent Light Source
free electron laser. The production and decay of these states is characterized
by photoelectron spectroscopy and Auger electron spectroscopy. In molecules,
two types of double core holes are expected, the first with two core holes on
the same N atom, and the second with one core hole on each N atom. We report
the first direct observations of the former type of core hole in a molecule, in
good agreement with theory, and provide an experimental upper bound for the
relative contribution of the latter type.


title: On Squeezed Limits in Single-Field Inflation - Part I

abstract:
  The n-point correlation functions in single-field inflation obey a set of
consistency conditions in the exact squeezed limit which are not present in
multi-field models, and thus are powerful tools to distinguish between the two.
However, these consistency conditions may be violated for a finite range of
scales in single-field models, for example by departures from the Bunch-Davies
state. These it excited states may be the consequence of interactions during
inflation, or may be a remnant of the era that preceded inflation. In this
paper we analyze the bispectrum, and show that in the regime of theoretical
control the resulting signal in the squeezed limit remains undetectably small
in all known models which continuously excite the state. We also show that the
signal remains undetectably small if the initial state is related to the
Bunch-Davies state by a Bogoliubov transformation and the energy density in the
state is small enough so that the usual slow-roll conditions are obeyed.
Bogoliubov states that lead to violations of the slow-roll conditions, as well
as more general excited states, require more careful treatment and will be
discussed in a separate publication.


title: Atmospheric leptons, the search for a prompt component

abstract:
  The flux of high-energy (>GeV) neutrinos consists primarily of those produced
by cosmic-ray interactions in the atmosphere. The contribution from
extraterrestrial sources is still unknown. Current limits suggest that the
observed spectrum is dominated by atmospheric neutrinos up to at least 100 TeV.
The contribution of charmed hadrons to the flux of atmospheric neutrinos is
important in the context of the search for astrophysical neutrinos because the
spectrum of such "prompt" neutrinos is harder than that of "conventional"
neutrinos from decay of pions and kaons. The prompt component therefore becomes
increasingly important as energy increases. This paper reviews the status of
the search for prompt muons and neutrinos with emphasis on the complementary
aspects of muons, electron neutrinos and muon neutrinos.


title: Towards a replicator dynamics model of age structured populations

abstract:
  In this paper we present a new modelling framework combining replicator
dynamics (which is the standard model of frequency dependent selection) with
the model of an age-structured population. The new framework allows for the
modelling of populations consisting of competing strategies carried by
individuals who change across their life cycle. Firstly the discretization of
the McKendrick von Foerster model is derived. It is shown that the Euler--Lotka
equation is satisfied when the new model reaches a steady state (i.e. stable
frequencies between the age classes). This discretization consists of the unit
age classes and the timescale is chosen that only a fraction of individuals
play single game round. This implies linear dynamics within single time unit
when individuals not killed during game round are moved from one age class to
another. Since its local linear behaviour the system is equivalent to large
Bernadelli-Lewis-Leslie matrix. Then the methodology of multipopulation games
is used for the derivation of two, mutually equivalent systems of equations.
The first contains equations describing the evolution of the strategy
frequencies in the whole population completed by subsystems of equations
describing the evolution of the age structure for each strategy. The second
system contains equations describing the changes of the general population's
age structure, completed with subsystems of equations describing the selection
of the strategies within each age class. Then the obtained system of replicator
dynamics is presented in the form of the mixed ODE-PDE system. The obtained
results are illustrated by example of the sex ratio model which shows that when
different mortalities of both sexes are assumed, the sex ratio of 0.5 is
obtained but that Fisher's mechanism driven by the reproductive value of the
different sexes is not in equilibrium.


title:
Helioseismology of Pre-Emerging Active Regions I: Overview, Data, and
  Target Selection Criteria

abstract:
  This first paper in a series describes the design of a study testing whether
pre-appearance signatures of solar magnetic active regions were detectable
using various tools of local helioseismology. The ultimate goal is to
understand flux-emergence mechanisms by setting observational constraints on
pre-appearance subsurface changes, for comparison with results from simulation
efforts. This first paper provides details of the data selection and
preparation of the samples, each containing over 100 members, of two
populations: regions on the Sun that produced a numbered NOAA active region,
and a "control" sample of areas that did not. The seismology is performed on
data from the GONG network; accompanying magnetic data from SOHO/MDI are used
for co-temporal analysis of the surface magnetic field. Samples are drawn from
2001 -- 2007, and each target is analyzed for 27.7 hr prior to an objectively
determined time of emergence. The results of two analysis approaches are
published separately: one based on averages of the seismology- and
magnetic-derived signals over the samples, another based on Discriminant
Analysis of these signals, for a statistical test of detectable differences
between the two populations. We include here descriptions of a new
potential-field calculation approach and the algorithm for matching sample
distributions over multiple variables. We describe known sources of bias and
the approaches used to mitigate them. We also describe unexpected bias sources
uncovered during the course of the study and include a discussion of
refinements that should be included in future work on this topic.


title: Inequality and Network Formation Games

abstract:
  This paper addresses the matter of inequality in network formation games. We
employ a quantity that we are calling the Nash Inequality Ratio (NIR), defined
as the maximal ratio between the highest and lowest costs incurred to
individual agents in a Nash equilibrium strategy, to characterize the extent to
which inequality is possible in equilibrium. We give tight upper bounds on the
NIR for the network formation games of Fabrikant et al. (PODC '03) and Ehsani
et al. (SPAA '11). With respect to the relationship between equality and social
efficiency, we show that, contrary to common expectations, efficiency does not
necessarily come at the expense of increased inequality.


title: Nonparametric functionals as generalized functions

abstract:
  The paper considers probability distribution, density, conditional
distribution and density and conditional moments as well as their kernel
estimators in spaces of generalized functions. This approach does not require
restrictions on classes of distributions common in nonparametric estimation.
Density in usual function spaces is not well-posed; this paper establishes
existence and well-posedness of the generalized density function. It also
demonstrates root-n convergence of the kernel density estimator in the space of
generalized functions. It is shown that the usual kernel estimator of the
conditional distribution converges at a parametric rate as a random process in
the space of generalized functions to a limit Gaussian process regardless of
pointwise existence of the conditional distribution. Conditional moments such
as conditional mean are also be characterized via generalized functions.
Convergence of the kernel estimators to the limit Gaussian process is shown to
hold as long as the appropriate moments exist.


title:
Concepts and a case study for a flexible class of graphical Markov
  models

abstract:
  With graphical Markov models, one can investigate complex dependences,
summarize some results of statistical analyses with graphs and use these graphs
to understand implications of well-fitting models. The models have a rich
history and form an area that has been intensively studied and developed in
recent years. We give a brief review of the main concepts and describe in more
detail a flexible subclass of models, called traceable regressions. These are
sequences of joint response regressions for which regression graphs permit one
to trace and thereby understand pathways of dependence. We use these methods to
reanalyze and interpret data from a prospective study of child development, now
known as the Mannheim Study of Children at Risk. The two related primary
features concern cognitive and motor development, at the age of 4.5 and 8 years
of a child. Deficits in these features form a sequence of joint responses.
Several possible risks are assessed at birth of the child and when the child
reached age 3 months and 2 years.


title:
Dark signal correction for a lukecold frame transfer CCD. Application to
  the SODISM solar telescope on board the PICARD space mission

abstract:
  When Charge Coupled Devices are used for scientific observations, their dark
signal is a hindrance. In their pristine state, most CCD pixels are `cool';
they exhibit low, quasi uniform dark current, which can be estimated and
corrected for. In space, after having been hit by an energetic particle, pixels
can turn `hot'. They start delivering excessive, less predictable, dark
current. The hot pixels need therefore to be flagged so that subsequent
analysis may ignore them. The image data of the PICARD SODISM solar telescope
(Meftah et al. 2013) require dark signal correction and hot pixel
identification. Its frame transfer E2V 42-80 CCD operates at -7{\deg}C. Both
image and memory zones thus accumulate dark current during, respectively,
integration and readout time. These two components must be separated to
estimate the dark signal for any observation. This is the purpose of the Dark
Signal Model presented in this paper. The dark signal time series of every
pixel is processed by the Unbalanced Haar Technique (Fryzlewicz 2007) in order
to timestamp when its dark signal is expected to change. In-between those
instants, both components are assumed constant and a robust linear regression
vs. integration time provides first estimates and a quality coefficient. The
latter serves to assign definitive estimates. Our model is part of the SODISM
Level 1 data production scheme. To check its reliability, we verify on dark
frames that it leaves a negligible residual bias (5 e-), and generates a small
RMS error (25 e- rms). The cool pixel level is found to be 4 e-/pxl/s, in
agreement with the predicted value. The emergence rate of hot pixels is
investigated too. It legitimates a threshold criterion at 50 e-/pxl/s. The
growth rate is found to be 4% of the frame area per year. Aspects of the method
(adaptation of the Unbalanced Haar Technique, dedicated robust linear
regression) have a generic interest.


title: ARPES: A probe of electronic correlations

abstract:
  Angle-resolved photoemission spectroscopy (ARPES) is one of the most direct
methods of studying the electronic structure of solids. By measuring the
kinetic energy and angular distribution of the electrons photoemitted from a
sample illuminated with sufficiently high-energy radiation, one can gain
information on both the energy and momentum of the electrons propagating inside
a material. This is of vital importance in elucidating the connection between
electronic, magnetic, and chemical structure of solids, in particular for those
complex systems which cannot be appropriately described within the
independent-particle picture. Among the various classes of complex systems, of
great interest are the transition metal oxides, which have been at the center
stage in condensed matter physics for the last four decades. Following a
general introduction to the topic, we will lay the theoretical basis needed to
understand the pivotal role of ARPES in the study of such systems. After a
brief overview on the state-of-the-art capabilities of the technique, we will
review some of the most interesting and relevant case studies of the novel
physics revealed by ARPES in 3d-, 4d- and 5d-based oxides.


title: Inclusive and exclusive dilepton photoproduction at high energies

abstract:
  In this work we investigate the inclusive and exclusive photoproduction of
dileptons, which is relevant for the physics programme to be studied in the
proposed electron-proton collider, the LHeC. In the inclusive case, the process
is sensitive to the parton distribution functions in the photon whereas the
exclusive channel is connected to the small-$x$ QCD dynamics. For the latter,
we investigate the role played by saturation physics at a very high energy
scenario. The estimates for production cross sections and the number of events
are presented.


title: Non-Gaussianity from Excited Initial Inflationary States

abstract:
  We study squeezed limit $f_{NL}$ generation by excited initial inflationary
states in a model independent way. We restrict "excited" to mean a Bogoliubov
transformation of the Bunch Davies state. We simultaneously impose the
constraints that the observable power spectrum is nearly scale invariant over
at least three decades and that the observable modes today be subhorizon at the
beginning of inflation while not causing significant backreaction. We show that
most excited initial inflationary states for single field inflationary models
with negligible superhorizon evolution do not produce an observable squeezed
limit $f_{NL}$. The case in which one mode is in the Bunch Davies state while
the other two modes are in an excited state with $0.01<|\beta_k|\leq0.1$ may
generate a squeezed limit $f_{NL}$ which is detectable with future experiments.


title: A Hybrid Approach to Extract Keyphrases from Medical Documents

abstract:
  Keyphrases are the phrases, consisting of one or more words, representing the
important concepts in the articles. Keyphrases are useful for a variety of
tasks such as text summarization, automatic indexing,
clustering/classification, text mining etc. This paper presents a hybrid
approach to keyphrase extraction from medical documents. The keyphrase
extraction approach presented in this paper is an amalgamation of two methods:
the first one assigns weights to candidate keyphrases based on an effective
combination of features such as position, term frequency, inverse document
frequency and the second one assign weights to candidate keyphrases using some
knowledge about their similarities to the structure and characteristics of
keyphrases available in the memory (stored list of keyphrases). An efficient
candidate keyphrase identification method as the first component of the
proposed keyphrase extraction system has also been introduced in this paper.
The experimental results show that the proposed hybrid approach performs better
than some state-of-the art keyphrase extraction approaches.


title:
Surveillance of the Incidence of Noncommunicable Diseases (NCDs) with
  Prevalence Data: Theory and Application to Diabetes in Denmark

abstract:
  Secular trends of the incidence of NCDs are especially important as they
indicate changes of the risk profile of a population. The article describes a
method for detecting secular trends in the incidence from a series of
prevalence data - without requiring costly follow-up studies or running a
register. After describing the theory, the method is applied to the incidence
of diabetes in Denmark.


title: Extended global symmetries for 4d N=1 SQCD theories

abstract:
  In arXiv:0811.1909 Spiridonov and Vartanov, using the superconformal index
technique, found that 4-dimensional N=1 SQCD theory with SU(2) gauge group and
four flavors has 72 dual representations. Recently in arXiv:1209.1404 the
authors showed that these dual theories, when coupled to 5d hypermultiplets
with specific boundary conditions have an extended E_7 global symmetry. In this
work we find that for a reduced theory with 3 flavors the explicit SU(6) global
symmetry is enhanced to an E_6 symmetry in the presence of 5d hypermultiplets.
We also show connections between indices of different theories in 3 and 4
dimensions.


title: The Black Hole Quantum Entropy and Its Minimal Value

abstract:
  In the paper it is demonstrated that the Schwarzschild black-hole quantum
entropy computed within the scope of the Generalized Uncertainty Principle has
a nonzero minimum under the assumption that for a radius of the black hole the
lower limit is placed, whose value is twice the minimal length. Such a limit is
quite natural when using, as a proper deformation parameter in a quantum theory
with a minimal length, the dimensionless small parameter introduced previously
by one of the authors in co-authorship with his colleagues and caused by
modification of the density matrix at Planck scales. The results obtained have
been compared to the results of other authors and analyzed from the viewpoint
of their compatibility with the well-known facts and the holographic principle
in particular.


title:
Constrained Willmore Tori and Elastic Curves in 2-Dimensional Space
  Forms

abstract:
  In this paper we consider two special classes of constrained Willmore tori in
the 3-sphere. The first class is given by the rotation of closed elastic curves
in the upper half plane - viewed as the hyperbolic plane - around the x-axis.
The second is given as the preimage of closed constrained elastic curves, i.e.,
elastic curve with enclosed area constraint, in the round 2-sphere under the
Hopf fibration. We show that all conformal types can be isometrically immersed
into S^3 as constrained Willmore (Hopf) tori and write down all constrained
elastic curves in H^2 and S^2 in terms of the Weierstrass elliptic functions.
Further, we determine the closing condition for the curves and compute the
Willmore energy and the conformal type of the resulting tori.


title:
Phase diagram of doped BaFe$_2$As$_2$ superconductor under broken $C_4$
  symmetry

abstract:
  We develop a minimal multiorbital tight-binding model with realistic hopping
parameters. The model breaks the symmetry of the tetragonal point group by
lowering it from $C_4$ to $D_{2d}$, which accurately describes the Fermi
surface evolution of the electron-doped BaFe$_{2-x}$Co$_x$As$_2$ and hole-doped
Ba$_{1-y}$K$_y$Fe$_2$As$_2$ compounds. An investigation of the phase diagram
with a mean-field $t$-$U$-$V$ Bogoliubov-de Gennes Hamiltonian results in
agreement with the experimentally observed electron- and hole-doped phase
diagram with only one set of $t$, $U$ and $V$ parameters. Additionally, the
self-consistently calculated superconducting order parameter exhibits
$s^\pm$-wave pairing symmetry with a small d-wave pairing admixture in the
entire doping range, % The superconducting $s^\pm + d$-wave order parameter
which is the subtle result of the weakly broken symmetry and competing
interactions in the multiorbital mean-field Hamiltonian.


title: Restoring Unitarity in the q-Deformed World-Sheet S-Matrix

abstract:
  The world-sheet S-matrix of the string in AdS5 x S5 has been shown to admit a
q-deformation that relates it to the S-matrix of a generalization of the
sine-Gordon theory, which arises as the Pohlmeyer reduction of the superstring.
Whilst this is a fascinating development the resulting S-matrix is not
explicitly unitary. The problem has been known for a long time in the context
of S-matrices related to quantum groups. A braiding relation often called
"unitarity" actually only corresponds to quantum field theory unitarity when
the S-matrix is Hermitian analytic and quantum group S-matrices manifestly
violate this. On the other hand, overall consistency of the S-matrix under the
bootstrap requires that the deformation parameter is a root of unity and
consequently one is forced to perform the "vertex" to IRF, or SOS,
transformation on the states to truncate the spectrum consistently. In the IRF
formulation unitarity is now manifest and the string S-matrix and the S-matrix
of the generalised sine-Gordon theory are recovered in two different limits. In
the latter case, expanding the Yang-Baxter equation we find that the tree-level
S-matrix of the Pohlmeyer-reduced string should satisfy a modified classical
Yang-Baxter equation explaining the apparent anomaly in the perturbative
computation. We show that the IRF form of the S-matrix meshes perfectly with
the bootstrap equations.


title: Minimal models, GT-action and formality of the little disk operad

abstract:
  We give a new proof of formality of the operad of little disks. The proof
makes use of an operadic version of a simple formality criterion for
commutative differential graded algebras due to Sullivan. We see that formality
is a direct consequence of the fact that the Grothendieck-Teichm\"uller group
operates on the chain operad of little disks.


title: Hybrid r-Vacua in N=2 Supersymmetric QCD: Universal Condensate Formula

abstract:
  We derive an exact unified formula for all condensates (quark and monopole)
in the hybrid r vacua in N=2 supersymmetric QCD slightly deformed by a \mu A^2
term. The gauge group is assumed to be U(N) and the number of the quark flavors
N_f subject to the condition N< N_f < 2N. In the r vacua r quarks and N-r-1
monopoles from non-overlapping subgroups of U(N) develop vacuum expectation
values (r<N). We then briefly review possible dynamical regimes (confinement,
screening, and "instead of confinement") in the hybrid r vacua in \mu-deformed
N=2 SQCD (the small-\mu limit).


title: Magnetic energy production by turbulence in binary neutron star mergers

abstract:
  The simultaneous detection of electromagnetic and gravitational wave emission
from merging neutron star binaries would aid greatly in their discovery and
interpretation. By studying turbulent amplification of magnetic fields in local
high-resolution simulations of neutron star merger conditions, we demonstrate
that magnetar-level (~10^16) G fields are present throughout the merger
duration. We find that the small-scale turbulent dynamo converts 60% of the
randomized kinetic energy into magnetic fields on a merger time scale. Since
turbulent magnetic energy dissipates through reconnection events which
accelerate relativistic electrons, turbulence may facilitate the conversion of
orbital kinetic energy into radiation. If 10^-4 of the ~ 10^53 erg of orbital
kinetic available gets processed through reconnection, and creates radiation in
the 15-150 keV band, then the fluence at 200 Mpc would be 10^-7 erg/cm^2,
potentially rendering most merging neutron stars in the advanced LIGO and Virgo
detection volumes detectable by Swift BAT.


title:
Nanotube Quantum Dot Transport With Spin-Orbit Coupling and Interacting
  Leads

abstract:
  We analyzed the effects of a spin voltage as well as a conventionally applied
voltage in a QD system with a different number of quantum states in the dot
region in presence of Coulombic interaction between the quantum dot and two
leads. We extended the NEGF treatment developed for noninteracting leads onto
the case of four quantum states $m =\{\sigma,\lambda\}=\{\pm,\pm\}$ interacting
with leads. Our derivation is based on the equation-of-motion technique and
Langreth's theorem. For a Coulombic repulsion between the contacts and QD we
obtain an expression for the current through QD for the four quantum states. To
determine the parameters of the model Hamiltonian we used our previous
calculations [1] of the electronic properties of a symmetrical nanotube QD
(5,5)/(10,0)\_1/(5,5) in a tight binding model, where \_1 denotes the length of
the middle QD segment of a (10,0) zigzag nanotube. We calculated the density of
electronic states with spin up and down for the case of a single QD without
pseudospin states for an infinite Coulomb repulsion, in good agreement with the
calculations of Yuan Li, et al. [2]. Our calculation showed that the position
of the conductance peaks nearest to zero is not affected by the strength of the
QD-lead Coulombic interaction parameters. We also demonstrated that this
interaction shifts the density of states to higher energies. The interplay
between the Kondo effect and the bias is highly temperature-dependent and
becomes significant only at low temperatures. Lastly, we found that the
existence of four quantum states $m=\{\sigma,\lambda\}$ leads to abrupt changes
in the density of states. In this case the values of the current are
approximately ten times lower than for QD with only two quantum states $m
=\{\sigma\}=\{\pm\}$. However, in the case of a conventional bias the current
amplitudes in both cases are approximately the same.


title: Baryon Asymmetry and Dark Matter Through the Vector-Like Portal

abstract:
  A possible connection between the cosmological baryon asymmetry, dark matter
and vector-like fermions is investigated. In this scenario an asymmetry
generated through baryogenesis or leptogenesis (in the vector-like matter
sector) connects the baryon asymmetry to the dark matter density. We present
explicit renormalizable models where this connection occurs. These models have
asymmetric dark matter and a significant invisible Higgs decay width to dark
matter particles is possible. We refer to this type of scenario as the
vector-like portal. In some asymmetric dark matter models there are potential
naturalness issues for the low energy effective theory. We address that issue
in the models we consider by starting with a Lagrangian that is the most
general renormalizable one consistent with the gauge (and discrete) symmetries
and showing the low energy effective theory automatically has the required form
as a consequence of the symmetries of the full theory. We show that the mass of
the dark matter candidate is predicted in these scenarios.


title:
Rate of tunneling nonequilibrium quasiparticles in superconducting
  qubits

abstract:
  In superconducting qubits the lifetime of quantum states cannot be prolonged
arbitrarily by decreasing temperature. At low temperature quasiparticles
tunneling between electromagnetic environment and superconducting islands takes
the condensate state out of equilibrium due to charge imbalance. We obtain the
tunneling rate from a phenomenological model of non-equilibrium, where
nonequilibrium quasiparticle tunnelling stimulates a temperature-dependent
chemical potential shift in superconductor. As a result we obtain a
non-monotonic behavior for relaxation rate as function of temperature.
Depending on the fabrication parameters for some qubits the lowest tunneling
rate of nonequilibrium quasiparticles can take place only near the onset
temperature below which nonequilibrium quasiparticles dominate over equilibrium
one. Our theory also indicates that such tunnelings can influence the
probability of transitions in qubits through a coupling to the zero-point
energy of phase fluctuations.


title: Causality in Bayesian Belief Networks

abstract:
  We address the problem of causal interpretation of the graphical structure of
Bayesian belief networks (BBNs). We review the concept of causality explicated
in the domain of structural equations models and show that it is applicable to
BBNs. In this view, which we call mechanism-based, causality is defined within
models and causal asymmetries arise when mechanisms are placed in the context
of a system. We lay the link between structural equations models and BBNs
models and formulate the conditions under which the latter can be given causal
interpretation.


title: From Conditional Oughts to Qualitative Decision Theory

abstract:
  The primary theme of this investigation is a decision theoretic account of
conditional ought statements (e.g., "You ought to do A, if C") that rectifies
glaring deficiencies in classical deontic logic. The resulting account forms a
sound basis for qualitative decision theory, thus providing a framework for
qualitative planning under uncertainty. In particular, we show that adding
causal relationships (in the form of a single graph) as part of an epistemic
state is sufficient to facilitate the analysis of action sequences, their
consequences, their interaction with observations, their expected utilities
and, hence, the synthesis of plans and strategies under uncertainty.


title:
A Probabilistic Algorithm for Calculating Structure: Borrowing from
  Simulated Annealing

abstract:
  We have developed a general Bayesian algorithm for determining the
coordinates of points in a three-dimensional space. The algorithm takes as
input a set of probabilistic constraints on the coordinates of the points, and
an a priori distribution for each point location. The output is a
maximum-likelihood estimate of the location of each point. We use the extended,
iterated Kalman filter, and add a search heuristic for optimizing its solution
under nonlinear conditions. This heuristic is based on the same principle as
the simulated annealing heuristic for other optimization problems. Our method
uses any probabilistic constraints that can be expressed as a function of the
point coordinates (for example, distance, angles, dihedral angles, and
planarity). It assumes that all constraints have Gaussian noise. In this paper,
we describe the algorithm and show its performance on a set of synthetic data
to illustrate its convergence properties, and its applicability to domains such
ng molecular structure determination.


title:
A Study of Scaling Issues in Bayesian Belief Networks for Ship
  Classification

abstract:
  The problems associated with scaling involve active and challenging research
topics in the area of artificial intelligence. The purpose is to solve real
world problems by means of AI technologies, in cases where the complexity of
representation of the real world problem is potentially combinatorial. In this
paper, we present a novel approach to cope with the scaling issues in Bayesian
belief networks for ship classification. The proposed approach divides the
conceptual model of a complex ship classification problem into a set of small
modules that work together to solve the classification problem while preserving
the functionality of the original model. The possible ways of explaining sensor
returns (e.g., the evidence) for some features, such as portholes along the
length of a ship, are sometimes combinatorial. Thus, using an exhaustive
approach, which entails the enumeration of all possible explanations, is
impractical for larger problems. We present a network structure (referred to as
Sequential Decomposition, SD) in which each observation is associated with a
set of legitimate outcomes which are consistent with the explanation of each
observed piece of evidence. The results show that the SD approach allows one to
represent feature-observation relations in a manageable way and achieve the
same explanatory power as an exhaustive approach.


title: Tradeoffs in Constructing and Evaluating Temporal Influence Diagrams

abstract:
  This paper addresses the tradeoffs which need to be considered in reasoning
using probabilistic network representations, such as Influence Diagrams (IDs).
In particular, we examine the tradeoffs entailed in using Temporal Influence
Diagrams (TIDs) which adequately capture the temporal evolution of a dynamic
system without prohibitive data and computational requirements. Three
approaches for TID construction which make different tradeoffs are examined:
(1) tailoring the network at each time interval to the data available (rather
then just copying the original Bayes Network for all time intervals); (2)
modeling the evolution of a parsimonious subset of variables (rather than all
variables); and (3) model selection approaches, which seek to minimize some
measure of the predictive accuracy of the model without introducing too many
parameters, which might cause "overfitting" of the model. Methods of evaluating
the accuracy/efficiency of the tradeoffs are proposed.


title: End-User Construction of Influence Diagrams for Bayesian Statistics

abstract:
  Influence diagrams are ideal knowledge representations for Bayesian
statistical models. However, these diagrams are difficult for end users to
interpret and to manipulate. We present a user-based architecture that enables
end users to create and to manipulate the knowledge representation. We use the
problem of physicians' interpretation of two-arm parallel randomized clinical
trials (TAPRCT) to illustrate the architecture and its use. There are three
primary data structures. Elements of statistical models are encoded as
subgraphs of a restricted class of influence diagram. The interpretations of
those elements are mapped into users' language in a domain-specific, user-based
semantic interface, called a patient-flow diagram, in the TAPRCT problem.
Pennitted transformations of the statistical model that maintain the semantic
relationships of the model are encoded in a metadata-state diagram, called the
cohort-state diagram, in the TAPRCT problem. The algorithm that runs the system
uses modular actions called construction steps. This framework has been
implemented in a system called THOMAS, that allows physicians to interpret the
data reported from a TAPRCT.


title: On Considering Uncertainty and Alternatives in Low-Level Vision

abstract:
  In this paper we address the uncertainty issues involved in the low-level
vision task of image segmentation. Researchers in computer vision have worked
extensively on this problem, in which the goal is to partition (or segment) an
image into regions that are homogeneous or uniform in some sense. This
segmentation is often utilized by some higher level process, such as an object
recognition system. We show that by considering uncertainty in a Bayesian
formalism, we can use statistical image models to build an approximate
representation of a probability distribution over a space of alternative
segmentations. We give detailed descriptions of the various levels of
uncertainty associated with this problem, discuss the interaction of prior and
posterior distributions, and provide the operations for constructing this
representation.


title: Forecasting Sleep Apnea with Dynamic Network Models

abstract:
  Dynamic network models (DNMs) are belief networks for temporal reasoning. The
DNM methodology combines techniques from time series analysis and probabilistic
reasoning to provide (1) a knowledge representation that integrates
noncontemporaneous and contemporaneous dependencies and (2) methods for
iteratively refining these dependencies in response to the effects of exogenous
influences. We use belief-network inference algorithms to perform forecasting,
control, and discrete event simulation on DNMs. The belief network formulation
allows us to move beyond the traditional assumptions of linearity in the
relationships among time-dependent variables and of normality in their
probability distributions. We demonstrate the DNM methodology on an important
forecasting problem in medicine. We conclude with a discussion of how the
methodology addresses several limitations found in traditional time series
analyses.


title: Normative Engineering Risk Management Systems

abstract:
  This paper describes a normative system design that incorporates diagnosis,
dynamic evolution, decision making, and information gathering. A single
influence diagram demonstrates the design's coherence, yet each activity is
more effectively modeled and evaluated separately. Application to offshore oil
platforms illustrates the design. For this application, the normative system is
embedded in a real-time expert system.


title: Diagnosis of Multiple Faults: A Sensitivity Analysis

abstract:
  We compare the diagnostic accuracy of three diagnostic inference models: the
simple Bayes model, the multimembership Bayes model, which is isomorphic to the
parallel combination function in the certainty-factor model, and a model that
incorporates the noisy OR-gate interaction. The comparison is done on 20
clinicopathological conference (CPC) cases from the American Journal of
Medicine-challenging cases describing actual patients often with multiple
disorders. We find that the distributions produced by the noisy OR model agree
most closely with the gold-standard diagnoses, although substantial differences
exist between the distributions and the diagnoses. In addition, we find that
the multimembership Bayes model tends to significantly overestimate the
posterior probabilities of diseases, whereas the simple Bayes model tends to
significantly underestimate the posterior probabilities. Our results suggest
that additional work to refine the noisy OR model for internal medicine will be
worthwhile.


title: Additive Belief-Network Models

abstract:
  The inherent intractability of probabilistic inference has hindered the
application of belief networks to large domains. Noisy OR-gates [30] and
probabilistic similarity networks [18, 17] escape the complexity of inference
by restricting model expressiveness. Recent work in the application of
belief-network models to time-series analysis and forecasting [9, 10] has given
rise to the additive belief network model (ABNM). We (1) discuss the nature and
implications of the approximations made by an additive decomposition of a
belief network, (2) show greater efficiency in the induction of additive models
when available data are scarce, (3) generalize probabilistic inference
algorithms to exploit the additive decomposition of ABNMs, (4) show greater
efficiency of inference, and (5) compare results on inference with a simple
additive belief network.


title: Parameter Adjustment in Bayes Networks. The generalized noisy OR-gate

abstract:
  Spiegelhalter and Lauritzen [15] studied sequential learning in Bayesian
networks and proposed three models for the representation of conditional
probabilities. A forth model, shown here, assumes that the parameter
distribution is given by a product of Gaussian functions and updates them from
the _ and _r messages of evidence propagation. We also generalize the noisy
OR-gate for multivalued variables, develop the algorithm to compute probability
in time proportional to the number of parents (even in networks with loops) and
apply the learning model to this gate.


title:
A fuzzy relation-based extension of Reggia's relational model for
  diagnosis handling uncertain and incomplete information

abstract:
  Relational models for diagnosis are based on a direct description of the
association between disorders and manifestations. This type of model has been
specially used and developed by Reggia and his co-workers in the late eighties
as a basic starting point for approaching diagnosis problems. The paper
proposes a new relational model which includes Reggia's model as a particular
case and which allows for a more expressive representation of the observations
and of the manifestations associated with disorders. The model distinguishes,
i) between manifestations which are certainly absent and those which are not
(yet) observed, and ii) between manifestations which cannot be caused by a
given disorder and manifestations for which we do not know if they can or
cannot be caused by this disorder. This new model, which can handle uncertainty
in a non-probabilistic way, is based on possibility theory and so-called
twofold fuzzy sets, previously introduced by the authors.


title: Dialectic Reasoning with Inconsistent Information

abstract:
  From an inconsistent database non-trivial arguments may be constructed both
for a proposition, and for the contrary of that proposition. Therefore,
inconsistency in a logical database causes uncertainty about which conclusions
to accept. This kind of uncertainty is called logical uncertainty. We define a
concept of "acceptability", which induces a means for differentiating
arguments. The more acceptable an argument, the more confident we are in it. A
specific interest is to use the acceptability classes to assign linguistic
qualifiers to propositions, such that the qualifier assigned to a propositions
reflects its logical uncertainty. A more general interest is to understand how
classes of acceptability can be defined for arguments constructed from an
inconsistent database, and how this notion of acceptability can be devised to
reflect different criteria. Whilst concentrating on the aspects of assigning
linguistic qualifiers to propositions, we also indicate the more general
significance of the notion of acceptability.


title: Causal Independence for Knowledge Acquisition and Inference

abstract:
  I introduce a temporal belief-network representation of causal independence
that a knowledge engineer can use to elicit probabilistic models. Like the
current, atemporal belief-network representation of causal independence, the
new representation makes knowledge acquisition tractable. Unlike the atemproal
representation, however, the temporal representation can simplify inference,
and does not require the use of unobservable variables. The representation is
less general than is the atemporal representation, but appears to be useful for
many practical applications.


title: Utility-Based Abstraction and Categorization

abstract:
  We take a utility-based approach to categorization. We construct
generalizations about events and actions by considering losses associated with
failing to distinguish among detailed distinctions in a decision model. The
utility-based methods transform detailed states of the world into more abstract
categories comprised of disjunctions of the states. We show how we can cluster
distinctions into groups of distinctions at progressively higher levels of
abstraction, and describe rules for decision making with the abstractions. The
techniques introduce a utility-based perspective on the nature of concepts, and
provide a means of simplifying decision models used in automated reasoning
systems. We demonstrate the techniques by describing the capabilities and
output of TUBA, a program for utility-based abstraction.


title: Sensitivity Analysis for Probability Assessments in Bayesian Networks

abstract:
  When eliciting probability models from experts, knowledge engineers may
compare the results of the model with expert judgment on test scenarios, then
adjust model parameters to bring the behavior of the model more in line with
the expert's intuition. This paper presents a methodology for analytic
computation of sensitivity values to measure the impact of small changes in a
network parameter on a target probability value or distribution. These values
can be used to guide knowledge elicitation. They can also be used in a gradient
descent algorithm to estimate parameter values that maximize a measure of
goodness-of-fit to both local and holistic probability assessments.


title: Causal Modeling

abstract:
  Causal Models are like Dependency Graphs and Belief Nets in that they provide
a structure and a set of assumptions from which a joint distribution can, in
principle, be computed. Unlike Dependency Graphs, Causal Models are models of
hierarchical and/or parallel processes, rather than models of distributions
(partially) known to a model builder through some sort of gestalt. As such,
Causal Models are more modular, easier to build, more intuitive, and easier to
understand than Dependency Graph Models. Causal Models are formally defined and
Dependency Graph Models are shown to be a special case of them. Algorithms
supporting inference are presented. Parsimonious methods for eliciting
dependent probabilities are presented.


title: Some Complexity Considerations in the Combination of Belief Networks

abstract:
  One topic that is likely to attract an increasing amount of attention within
the Knowledge-base systems research community is the coordination of
information provided by multiple experts. We envision a situation in which
several experts independently encode information as belief networks. A
potential user must then coordinate the conclusions and recommendations of
these networks to derive some sort of consensus. One approach to such a
consensus is the fusion of the contributed networks into a single, consensus
model prior to the consideration of any case-specific data (specific
observations, test results). This approach requires two types of combination
procedures, one for probabilities, and one for graphs. Since the combination of
probabilities is relatively well understood, the key barriers to this approach
lie in the realm of graph theory. This paper provides formal definitions of
some of the operations necessary to effect the necessary graphical
combinations, and provides complexity analyses of these procedures. The paper's
key result is that most of these operations are NP-hard, and its primary
message is that the derivation of ?good? consensus networks must be done
heuristically.


title:
Deriving a Minimal I-map of a Belief Network Relative to a Target
  Ordering of its Nodes

abstract:
  This paper identifies and solves a new optimization problem: Given a belief
network (BN) and a target ordering on its variables, how can we efficiently
derive its minimal I-map whose arcs are consistent with the target ordering? We
present three solutions to this problem, all of which lead to directed acyclic
graphs based on the original BN's recursive basis relative to the specified
ordering (such a DAG is sometimes termed the boundary DAG drawn from the given
BN relative to the said ordering [5]). Along the way, we also uncover an
important general principal about arc reversals: when reordering a BN according
to some target ordering, (while attempting to minimize the number of arcs
generated), the sequence of arc reversals should follow the topological
ordering induced by the original belief network's arcs to as great an extent as
possible. These results promise to have a significant impact on the derivation
of consensus models, as well as on other algorithms that require the
reconfiguration and/or combination of BN's.


title:
Probabilistic Conceptual Network: A Belief Representation Scheme for
  Utility-Based Categorization

abstract:
  Probabilistic conceptual network is a knowledge representation scheme
designed for reasoning about concepts and categorical abstractions in
utility-based categorization. The scheme combines the formalisms of abstraction
and inheritance hierarchies from artificial intelligence, and probabilistic
networks from decision analysis. It provides a common framework for
representing conceptual knowledge, hierarchical knowledge, and uncertainty. It
facilitates dynamic construction of categorization decision models at varying
levels of abstraction. The scheme is applied to an automated machining problem
for reasoning about the state of the machine at varying levels of abstraction
in support of actions for maintaining competitiveness of the plant.


title:
Reasoning about the Value of Decision-Model Refinement: Methods and
  Application

abstract:
  We investigate the value of extending the completeness of a decision model
along different dimensions of refinement. Specifically, we analyze the expected
value of quantitative, conceptual, and structural refinement of decision
models. We illustrate the key dimensions of refinement with examples. The
analyses of value of model refinement can be used to focus the attention of an
analyst or an automated reasoning system on extensions of a decision model
associated with the greatest expected value.


title:
Mixtures of Gaussians and Minimum Relative Entropy Techniques for
  Modeling Continuous Uncertainties

abstract:
  Problems of probabilistic inference and decision making under uncertainty
commonly involve continuous random variables. Often these are discretized to a
few points, to simplify assessments and computations. An alternative
approximation is to fit analytically tractable continuous probability
distributions. This approach has potential simplicity and accuracy advantages,
especially if variables can be transformed first. This paper shows how a
minimum relative entropy criterion can drive both transformation and fitting,
illustrating with a power and logarithm family of transformations and mixtures
of Gaussian (normal) distributions, which allow use of efficient influence
diagram methods. The fitting procedure in this case is the well-known EM
algorithm. The selection of the number of components in a fitted mixture
distribution is automated with an objective that trades off accuracy and
computational cost.


title: Valuation Networks and Conditional Independence

abstract:
  Valuation networks have been proposed as graphical representations of
valuation-based systems (VBSs). The VBS framework is able to capture many
uncertainty calculi including probability theory, Dempster-Shafer's
belief-function theory, Spohn's epistemic belief theory, and Zadeh's
possibility theory. In this paper, we show how valuation networks encode
conditional independence relations. For the probabilistic case, the class of
probability models encoded by valuation networks includes undirected graph
models, directed acyclic graph models, directed balloon graph models, and
recursive causal graph models.


title: Relevant Explanations: Allowing Disjunctive Assignments

abstract:
  Relevance-based explanation is a scheme in which partial assignments to
Bayesian belief network variables are explanations (abductive conclusions). We
allow variables to remain unassigned in explanations as long as they are
irrelevant to the explanation, where irrelevance is defined in terms of
statistical independence. When multiple-valued variables exist in the system,
especially when subsets of values correspond to natural types of events, the
over specification problem, alleviated by independence-based explanation,
resurfaces. As a solution to that, as well as for addressing the question of
explanation specificity, it is desirable to collapse such a subset of values
into a single value on the fly. The equivalent method, which is adopted here,
is to generalize the notion of assignments to allow disjunctive assignments. We
proceed to define generalized independence based explanations as maximum
posterior probability independence based generalized assignments (GIB-MAPs).
GIB assignments are shown to have certain properties that ease the design of
algorithms for computing GIB-MAPs. One such algorithm is discussed here, as
well as suggestions for how other algorithms may be adapted to compute
GIB-MAPs. GIB-MAP explanations still suffer from instability, a problem which
may be addressed using ?approximate? conditional independence as a condition
for irrelevance.


title: A Generalization of the Noisy-Or Model

abstract:
  The Noisy-Or model is convenient for describing a class of uncertain
relationships in Bayesian networks [Pearl 1988]. Pearl describes the Noisy-Or
model for Boolean variables. Here we generalize the model to nary input and
output variables and to arbitrary functions other than the Boolean OR function.
This generalization is a useful modeling aid for construction of Bayesian
networks. We illustrate with some examples including digital circuit diagnosis
and network reliability analysis.


title:
Using First-Order Probability Logic for the Construction of Bayesian
  Networks

abstract:
  We present a mechanism for constructing graphical models, specifically
Bayesian networks, from a knowledge base of general probabilistic information.
The unique feature of our approach is that it uses a powerful first-order
probabilistic logic for expressing the general knowledge base. This logic
allows for the representation of a wide range of logical and probabilistic
information. The model construction procedure we propose uses notions from
direct inference to identify pieces of local statistical information from the
knowledge base that are most appropriate to the particular event we want to
reason about. These pieces are composed to generate a joint probability
distribution specified as a Bayesian network. Although there are fundamental
difficulties in dealing with fully general knowledge, our procedure is
practical for quite rich knowledge bases and it supports the construction of a
far wider range of networks than allowed for by current template technology.


title:
Representing and Reasoning With Probabilistic Knowledge: A Bayesian
  Approach

abstract:
  PAGODA (Probabilistic Autonomous Goal-Directed Agent) is a model for
autonomous learning in probabilistic domains [desJardins, 1992] that
incorporates innovative techniques for using the agent's existing knowledge to
guide and constrain the learning process and for representing, reasoning with,
and learning probabilistic knowledge. This paper describes the probabilistic
representation and inference mechanism used in PAGODA. PAGODA forms theories
about the effects of its actions and the world state on the environment over
time. These theories are represented as conditional probability distributions.
A restriction is imposed on the structure of the theories that allows the
inference mechanism to find a unique predicted distribution for any action and
world state description. These restricted theories are called uniquely
predictive theories. The inference mechanism, Probability Combination using
Independence (PCI), uses minimal independence assumptions to combine the
probabilities in a theory to make probabilistic predictions.


title: Graph-Grammar Assistance for Automated Generation of Influence Diagrams

abstract:
  One of the most difficult aspects of modeling complex dilemmas in
decision-analytic terms is composing a diagram of relevance relations from a
set of domain concepts. Decision models in domains such as medicine, however,
exhibit certain prototypical patterns that can guide the modeling process.
Medical concepts can be classified according to semantic types that have
characteristic positions and typical roles in an influence-diagram model. We
have developed a graph-grammar production system that uses such inherent
interrelationships among medical terms to facilitate the modeling of medical
decisions.


title: Using Causal Information and Local Measures to Learn Bayesian Networks

abstract:
  In previous work we developed a method of learning Bayesian Network models
from raw data. This method relies on the well known minimal description length
(MDL) principle. The MDL principle is particularly well suited to this task as
it allows us to tradeoff, in a principled way, the accuracy of the learned
network against its practical usefulness. In this paper we present some new
results that have arisen from our work. In particular, we present a new local
way of computing the description length. This allows us to make significant
improvements in our search algorithm. In addition, we modify our algorithm so
that it can take into account partial domain information that might be provided
by a domain expert. The local computation of description length also opens the
door for local refinement of an existent network. The feasibility of our
approach is demonstrated by experiments involving networks of a practical size.


title: Minimal Assumption Distribution Propagation in Belief Networks

abstract:
  As belief networks are used to model increasingly complex situations, the
need to automatically construct them from large databases will become
paramount. This paper concentrates on solving a part of the belief network
induction problem: that of learning the quantitative structure (the conditional
probabilities), given the qualitative structure. In particular, a theory is
presented that shows how to propagate inference distributions in a belief
network, with the only assumption being that the given qualitative structure is
correct. Most inference algorithms must make at least this assumption. The
theory is based on four network transformations that are sufficient for any
inference in a belief network. Furthermore, the claim is made that contrary to
popular belief, error will not necessarily grow as the inference chain grows.
Instead, for QBN belief nets induced from large enough samples, the error is
more likely to decrease as the size of the inference chain increases.


title:
An Algorithm for the Construction of Bayesian Network Structures from
  Data

abstract:
  Previous algorithms for the construction of Bayesian belief network
structures from data have been either highly dependent on conditional
independence (CI) tests, or have required an ordering on the nodes to be
supplied by the user. We present an algorithm that integrates these two
approaches - CI tests are used to generate an ordering on the nodes from the
database which is then used to recover the underlying Bayesian network
structure using a non CI based method. Results of preliminary evaluation of the
algorithm on two networks (ALARM and LED) are presented. We also discuss some
algorithm performance issues and open problems.


title:
A Construction of Bayesian Networks from Databases Based on an MDL
  Principle

abstract:
  This paper addresses learning stochastic rules especially on an
inter-attribute relation based on a Minimum Description Length (MDL) principle
with a finite number of examples, assuming an application to the design of
intelligent relational database systems. The stochastic rule in this paper
consists of a model giving the structure like the dependencies of a Bayesian
Belief Network (BBN) and some stochastic parameters each indicating a
conditional probability of an attribute value given the state determined by the
other attributes' values in the same record. Especially, we propose the
extended version of the algorithm of Chow and Liu in that our learning
algorithm selects the model in the range where the dependencies among the
attributes are represented by some general plural number of trees.


title:
Knowledge-Based Decision Model Construction for Hierarchical Diagnosis:
  A Preliminary Report

abstract:
  Numerous methods for probabilistic reasoning in large, complex belief or
decision networks are currently being developed. There has been little research
on automating the dynamic, incremental construction of decision models. A
uniform value-driven method of decision model construction is proposed for the
hierarchical complete diagnosis. Hierarchical complete diagnostic reasoning is
formulated as a stochastic process and modeled using influence diagrams. Given
observations, this method creates decision models in order to obtain the best
actions sequentially for locating and repairing a fault at minimum cost. This
method construct decision models incrementally, interleaving probe actions with
model construction and evaluation. The method treats meta-level and baselevel
tasks uniformly. That is, the method takes a decision-theoretic look at the
control of search in causal pathways and structural hierarchies.


title:
A Synthesis of Logical and Probabilistic Reasoning for Program
  Understanding and Debugging

abstract:
  We describe the integration of logical and uncertain reasoning methods to
identify the likely source and location of software problems. To date, software
engineers have had few tools for identifying the sources of error in complex
software packages. We describe a method for diagnosing software problems
through combining logical and uncertain reasoning analyses. Our preliminary
results suggest that such methods can be of value in directing the attention of
software engineers to paths of an algorithm that have the highest likelihood of
harboring a programming error.


title:
An Implementation of a Method for Computing the Uncertainty in Inferred
  Probabilities in Belief Networks

abstract:
  In recent years the belief network has been used increasingly to model
systems in Al that must perform uncertain inference. The development of
efficient algorithms for probabilistic inference in belief networks has been a
focus of much research in AI. Efficient algorithms for certain classes of
belief networks have been developed, but the problem of reporting the
uncertainty in inferred probabilities has received little attention. A system
should not only be capable of reporting the values of inferred probabilities
and/or the favorable choices of a decision; it should report the range of
possible error in the inferred probabilities and/or choices. Two methods have
been developed and implemented for determining the variance in inferred
probabilities in belief networks. These methods, the Approximate Propagation
Method and the Monte Carlo Integration Method are discussed and compared in
this paper.