title:
Deconfining the rotational Goldstone mode: the superconducting nematic
  liquid crystal in 2+1D

abstract:
  The Goldstone theorem states that there should be a massless mode for each
spontaneously broken symmetry generator. There is no such rotational mode in
crystals, however superconducting quantum nematics should carry rotational
Goldstone modes. By generalization of thermal 2D defect mediated melting theory
into a 2+1D quantum duality, the emergence of the rotational mode at the
quantum phase transition from the solid to the nematic arises as a
deconfinement phenomenon, with the unusual property that the stiffness of the
rotational mode originates entirely in the dual dislocation condensate.


title: The ontology of General Relativity

abstract:
  I discuss the ontological assumptions and implications of General Relativity.
I maintain that General Relativity is a theory about gravitational fields, not
about space-time. The latter is a more basic ontological category, that emerges
from physical relations among all existents. I also argue that there are no
physical singularities in space-time. Singular space-time models do not belong
to the ontology of the world: they are not things but concepts, i.e. defective
solutions of Einstein's field equations. I briefly discuss the actual
implication of the so-called singularity theorems in General Relativity and
some problems related to ontological assumptions of Quantum Gravity.


title: V.M. Slipher and the Development of the Nebular Spectrograph

abstract:
  Vesto Melvin Slipher was the first astronomer to clearly define the factors
that determine the "speed" of a nebular spectrograph. This brief historical
summary recounts the way these ideas developed and how Slipher's early work on
galaxy Doppler shifts was so quickly extended in the 1930's when Milton Humason
and Edwin Hubble at Mt. Wilson Observatory began to push the velocity-distance
relationship to such a depth that no one could doubt its cosmological
significance.


title: Calculating effective gun policies

abstract:
  Following recent shootings in the USA, a debate has erupted, one side
favoring stricter gun control, the other promoting protection through more
weapons. We provide a scientific foundation to inform this debate, based on
mathematical, epidemiological models that quantify the dependence of
firearm-related death rates of people on gun policies. We assume a shooter
attacking a single individual or a crowd. Two strategies can minimize deaths in
the model, depending on parameters: either a ban of private firearms
possession, or a policy allowing the general population to carry guns. In
particular, the outcome depends on the fraction of offenders that illegally
possess a gun, on the degree of protection provided by gun ownership, and on
the fraction of the population who take up their right to own a gun and carry
it with them when attacked, parameters that can be estimated from statistical
data. With the measured parameters, the model suggests that if the gun law is
enforced at a level similar to that in the United Kingdom, gun-related deaths
are minimized if private possession of firearms is banned. If such a policy is
not practical or possible due to constitutional or cultural constraints, the
model and parameter estimation indicate that a partial reduction in firearm
availability can lead to a reduction in gun-induced death rates, even if they
are not minimized. Most importantly, our analysis identifies the crucial
parameters that determine which policy reduces the death rates, providing
guidance for future statistical studies that will be necessary for more refined
quantitative predictions.


title:
Phase fluctuations versus Gaussian fluctuations in optimally-doped
  YBa$_2$Cu$_3$O$_7$

abstract:
  We analyze recent torque measurements of the magnetization $M_d$ vs. magnetic
field $H$ in optimally doped YBa$_2$Cu$_3$O$_{7-y}$ (OPT YBCO) to argue against
a recent proposal by Rey et al that the magnetization results above $T_c$ are
consistent with Gaussian fluctuations. We find that, despite its strong
interlayer coupling, OPT YBCO displays an anomalous non-monotonic dependence of
$M_d$ on $H$ which represents direct evidence for the locking of the pair
wavefunction phase $\theta_n$ at $T_c$ and the subsequent unlocking by a
relatively weak $H$. These unusual features characterize the unusual nature of
the transition to the Meissner state in cuprate superconductors. They are
absent in low-$T_c$ superconductors to our knowledge. We also stress the
importance of the vortex liquid state, as well as the profiles of the melting
field $H_m(T)$ and the upper critical field curve $H_{c2}(T)$ in the $T$-$H$
plane. Contrary to the claims of Rey et al, we show that the curves of the
magnetization and the Nernst signal illustrate the inaccessibility of the
$H_{c2}$ line near $T_c$. The prediction of the $H_{c2}$ line by Rey et al is
shown to be invalid in OPT YBCO.


title: Environmental Influence on Dental Morphology

abstract:
  Development is a complex phenomenon where the forming phenotype interacts
with genetic and environmental inputs. Teeth are an important model for
developmental studies and their development has been thoroughly investigated.
However, despite of a large literature on the genetics of dental development,
no studies have focused yet on the environmental influences on dental
morphology. Here, we aim to test whether and to what extent the environment
plays a role in producing morphological variation in human teeth. We selected a
sample of modern human skulls and we used dental enamel hypoplasia as an
environmental stress marker in order to identify two groups with different
stress level, referred to as SG ("stressed" group) and NSG ("non-stressed"
group). We collected data on the occurrence and the relative development of 15
morphological traits (5 for each molar) on upper molars using a standard
methodology commonly used in dental anthropology (ASU-DAS system) and then we
compared the frequencies of the traits in the two groups. Overall, the results
suggest that: (i) stressors like malnutrition and/or systemic diseases have a
significant effect on upper molar morphology; (ii) stress generates a
developmental response which increases the morphological variability of the SG;
(iii) the increase of variability is directional, since individuals belonging
to the SG have increased cusps dimensions and number. These results are
consistent with the expectations of the morphodynamic model of dental
development (Jernvall, 1995).


title: Fractional Helical Liquids and Non-Abelian Anyons in Quantum Wires

abstract:
  We study one dimensional wires with spin-orbit coupling. We show that in the
presence of Zeeman field and strong electron-electron interaction a clean wire
may form fractional helical liquid states with phenomenology similar to
fractional quantum Hall liquids. Most notably, the wire's two terminal
conductance is predicted to show fractional quantized conductance plateaus at
low electron density. When the system is proximity-coupled to a superconductor,
fractional Majorana bound states may be stabilized. We discuss how disorder
destabilizes these fractional phases. Possible experimental realizations of
similar states in double wire systems are discussed.


title: On the significance of B-decays to radially excited D

abstract:
  We discuss the possibilities of testing the recent claims of a relatively
large value of the B --> D' transition form factor through B0--> D' pi+ and B-
--> D' pi- decay modes. To estimate the width of the latter we need the decay
constant of the radially excited D-meson that we computed in lattice QCD with
Nf=2 dynamical flavors and after taking the continuum limit we found
f(D')=117(25) MeV. We also provide an update for the values of the D-meson
decay constants obtained by using maximally twisted mass QCD.


title:
Observation of Asymmetric Transport in Structures with Active
  Nonlinearities

abstract:
  A mechanism for asymmetric transport based on the interplay between the
fundamental symmetries of parity (P) and time (T) with nonlinearity is
presented. We experimentally demonstrate and theoretically analyze the
phenomenon using a pair of coupled van der Pol oscillators, as a reference
system, one with anharmonic gain and the other with complementary anharmonic
loss; connected to two transmission lines. An increase of the gain/loss
strength or the number of PT-symmetric nonlinear dimers in a chain, can
increase both the asymmetry and transmittance intensities.


title: BPS one-branes in five dimensions

abstract:
  In a recent paper, we studied the scalar fields of the five dimensional N=2
hypermultiplets using the method of symplectic covariance. For static
spherically symmetric backgrounds, we showed that exactly two possibilities
exist and detailed one of them. In this paper, we present the second case:
Bogomol'nyi-Prasad-Sommerfield (BPS) one-branes carrying charge under the
hypermultiplets. We find an explicitly symplectic solution of the fields in
this background and derive the conditions required for a full spacetime
understanding.


title:
As vacancies, Ga antisites and Au impurities in Zincblende and Wurtzite
  GaAs nanowire segments from first principles

abstract:
  In this paper some specific issues related to point defects in GaAs nanowires
are addressed with the help of density functional theory calculations. These
issues mainly arise from the growth of nanowires under conditions different
from those used for thin films or bulk GaAs, such as the co-existence of
zincblende and wurtzite polytypes, the use of gold particles as catalyst, and
the arsenic-limited growth regime. Hence, we carry out density-functional
calculations for As vacancies, Ga_As antisites, and Au impurities in ZB and WZ
GaAs crystals. Our results show that As vacancies can diffuse within in a ZB
GaAs crystal with migration barriers of ~1.9 eV. Within WZ GaAs, As vacancy
diffusion is found to be anisotropic, with low barriers of 1.60 up to 1.79 eV
(depending on doping conditions) in the ab-plane, while there are higher
barriers of 2.07 to 2.44 eV to diffuse along the c-axis. The formation energy
of Au impurities is found to be generally much lower than those of arsenic
vacancies or Ga_As antisites. Thus, Au impurities will be the dominant defects
formed in Au-catalyzed nanowire growth. Moreover, we find that it is
energetically more favorable by 1 to 2 eV for an Au impurity to replace a
lattice Ga atom than a lattice As atom in GaAs. An Au substitutional defect for
a lattice Ga atom in ZB GaAs is found to create a charge transfer level in the
lower half of the band gap. While our calculations locate this level at E_v +
0.22eV, taking into account the inaccuracy of the density functional that ought
to be corrected by a downshift of E_v by about 0.2eV results in good agreement
with the experimental result of E_v + 0.4eV.


title: Tip-surface interactions in dynamic atomic force microscopy

abstract:
  In atomic force microscopy (AFM) tip-surface interactions are usually
considered as functions of the tip position only, so-called force curves.
However, tip-surface interactions often depend on the tip velocity and the past
tip trajectory. Here, we introduce a compact and general description of these
interactions appropriate to dynamic AFM where the measurement of force is
restricted to a narrow frequency band. We represent the tip-surface interaction
in terms of a force disk in the phase space of position and velocity.
Determination of the amplitude dependence of tip-surface forces at a fixed
static probe height allows for a comprehensive treatment of conservative and
dissipative interactions. We illuminate the fundamental limitations of force
reconstruction with narrow band dynamic AFM and we show how the amplitude
dependence of the Fourier component of the force at the tip oscillation
frequency, gives qualitative insight into the detailed nature of the
tip-surface interaction. With minimal assumptions this amplitude dependence
force spectroscopy allows for a quantitative reconstruction of the effective
conservative tip-surface force as well as a position-dependent damping factor.
We demonstrate this reconstruction on simulated intermodulation AFM data.


title:
Continuum Variability of Deeply Embedded Protostars as a Probe of
  Envelope Structure

abstract:
  Stars may be assembled in large growth spurts, however the evidence for this
hypothesis is circumstantial. Directly studying the accretion at the earliest
phases of stellar growth is challenging because young stars are deeply embedded
in optically thick envelopes, which have spectral energy distributions that
peak in the far-IR, where observations are difficult. In this paper, we
consider the feasibility of detecting accretion outbursts from these younger
stars by investigating the timescales for how the protostellar envelope
responds to changes in the emission properties of the central source. The
envelope heats up in response to an outburst, brightening at all wavelengths
and with the emission peak moving to shorter wavelengths. The timescale for
this change depends on the time for dust grains to heat and re-emit photons and
the time required for the energy to escape the inner, optically-thick portion
of the envelope. We find that the dust response time is much shorter than the
photon propagation time and thus the timescale over which the emission varies
is set by time delays imposed by geometry. These times are hours to days near
the peak of the spectral energy distribution and weeks to months in the sub-mm.
The ideal location to quickly detect continuum variability is therefore in the
mid- to far-IR, near the peak of the spectral energy distribution, where the
change in emission amplitude is largest. Searching for variability in sub-mm
continuum emission is also feasible, though with a longer time separation and a
weaker relationship between the amount of detected emission amplitude and
change in central source luminosity. Such observations would constrain
accretion histories of protostars and would help to trace the disk/envelope
instabilities that lead to stellar growth.


title: PXIE: Project X Injector Experiment

abstract:
  A multi-MW proton facility, Project X has been proposed and is currently
under development at Fermilab. As part of this development program, we are
constructing a prototype of the front end of the Project X linac at Fermilab.
The construction and successful operations of this facility will validate the
concept for the Project X front end, thereby minimizing the primary technical
risk element within the Project. The Project X Injector Experiment (PXIE) can
be constructed over the period FY12-16 and will include an H- ion source, a CW
2.1-MeV RFQ and two SC cryomodules providing up to 30 MeV energy gain at an
average beam current of 1 mA. Successful operations of the facility will
demonstrate the viability of novel front end technologies that will find
applications beyond Project X in the longer term.


title:
Tickling the CMB damping tail: scrutinizing the tension between the ACT
  and SPT experiments

abstract:
  The Atacama Cosmology Telescope (ACT) and the South Pole Telescope (SPT) have
recently provided new, very precise measurements of the cosmic microwave
background (CMB) anisotropy damping tail. The values of the cosmological
parameters inferred from these measurements, while broadly consistent with the
expectations of the standard cosmological model, are providing interesting
possible indications for new physics that are definitely worth of
investigation. The ACT results, while compatible with the standard expectation
of three neutrino families, indicate a level of CMB lensing, parametrized by
the lensing amplitude parameter A_L, that is about 70% higher than expected. If
not a systematic, this anomalous lensing amplitude could be produced by
modifications of general relativity or coupled dark energy. Vice-versa, the SPT
experiment, while compatible with a standard level of CMB lensing, prefers an
excess of dark radiation, parametrized by the effective number of relativistic
degrees of freedom N_eff. Here we perform a new analysis of these experiments
allowing simultaneous variations in both these, non-standard, parameters. We
also combine these experiments, for the first time in the literature, with the
recent WMAP9 data, one at a time. Including the Hubble Space Telescope (HST)
prior on the Hubble constant and information from baryon acoustic oscillations
(BAO) surveys provides the following constraints from ACT: N_eff=3.23\pm0.47,
A_L=1.65\pm0.33 at 68% c.l., while for SPT we have N_eff=3.76\pm0.34,
A_L=0.81\pm0.12 at 68% c.l.. In particular, the A_L estimates from the two
experiments, even when a variation in N_eff is allowed, are in tension at more
than 95% c.l..


title: PXIE Optics and Layout

abstract:
  The Project X Injector Experiment (PXIE) will serve as a prototype for the
Project X front end. The aim is to validate the Project-X design and to
decrease technical risks, known to be mainly related to the front end. PXIE
will accelerate a 1 mA CW beam to about 25 MeV. It will consist of an ion
source, LEBT, CW RFQ, MEBT, two SC cryomodules, a diagnostic section and a beam
dump. A bunch-by-bunch chopper located in the MEBT section will allow formation
of an arbitrary bunch structure. PXIE deviates somewhat from the current
Project-X front end concept in that it provides additional instrumentation and
relies on a reduced number of kickers for bunch chopping; the diagnostic
section also include an RF separator to allow studying extinction of removed
bunches. The paper discusses the main requirements and constraints motivating
the facility layout and optics. Final adjustments to the Project X front end
design, if needed, will be based on operational experience gained with PXIE.


title: Codes on Lattices for Random SAF Routing

abstract:
  In this paper, a construction of constant weight codes based on the unique
decomposition of elements in lattices is presented. The conditions for unique
primary decomposition and unique irreducible decomposition in lattices are
discussed and connections with the decomposition of ideals in Noetherian
commutative rings established. In this context it is shown, drawing on the
definitive works of Dilworth, Ward and others, that, as opposed to Noetherian
commutative rings, the existence of unique irreducible decomposition in
lattices does not guarantee unique primary decomposition. The source alphabet
in our proposed construction is a set of uniquely decomposable elements
constructed from a chosen subset of irreducible or primary elements of the
appropriate lattice. The distance function between two lattice elements is
based on the symmetric distance between sets of constituent elements. It is
known that constructing such constant weight codes is equivalent to
constructing a Johnson graph with appropriate parameters. Some bounds on the
code sizes are also presented and a method to obtain codes of optimal size,
utilizing the Johnson graph description of the codes, is discussed. As an
application we show how these codes can be used for error and erasure
correction in random networks employing store-and-forward (SAF) routing.


title: Further refinements of the Heinz inequality

abstract:
  The celebrated Heinz inequality asserts that $ 2|||A^{1/2}XB^{1/2}|||\leq
|||A^{\nu}XB^{1-\nu}+A^{1-\nu}XB^{\nu}|||\leq |||AX+XB|||$ for $X \in
\mathbb{B}(\mathscr{H})$, $A,B\in \+$, every unitarily invariant norm
$|||\cdot|||$ and $\nu \in [0,1]$. In this paper, we present several
improvement of the Heinz inequality by using the convexity of the function
$F(\nu)=|||A^{\nu}XB^{1-\nu}+A^{1-\nu}XB^{\nu}|||$, some integration techniques
and various refinements of the Hermite--Hadamard inequality. In the setting of
matrices we prove that \begin{eqnarray*}
&&\hspace{-0.5cm}\left|\left|\left|A^{\frac{\alpha+\beta}{2}}XB^{1-\frac{\alpha+\beta}{2}}+A^{1-\frac{\alpha+\beta}{2}}XB^{\frac{\alpha+\beta}{2}}\right|\right|\right|\leq\frac{1}{|\beta-\alpha|}
\left|\left|\left|\int_{\alpha}^{\beta}\left(A^{\nu}XB^{1-\nu}+A^{1-\nu}XB^{\nu}\right)d\nu\right|\right|\right|\nonumber\\
&&\qquad\qquad\leq
\frac{1}{2}\left|\left|\left|A^{\alpha}XB^{1-\alpha}+A^{1-\alpha}XB^{\alpha}+A^{\beta}XB^{1-\beta}+A^{1-\beta}XB^{\beta}\right|\right|\right|\,,
\end{eqnarray*} for real numbers $\alpha, \beta$.


title: C*-algebras associated with topological group quivers II: K-groups

abstract:
  Topological quivers generalize the notion of directed graphs in which the
sets of vertices and edges are locally compact (second countable) Hausdorff
spaces. Associated to a topological quiver $Q$ is a $C^*$-correspondence, and
in turn, a Cuntz-Pimsner algebra $C^*(Q).$ Given $\Gamma$ a locally compact
group and $\alpha$ and $\beta$ endomorphisms on $\Gamma,$ one may construct a
topological quiver $Q_{\alpha,\beta}(\Gamma)$ with vertex set $\Gamma,$ and
edge set $\Omega_{\alpha,\beta}(\Gamma)= \{(x,y)\in\Gamma\times\Gamma\st
\alpha(y)=\beta(x)\}.$ In \cite{Mc1}, the author examined the Cuntz-Pimsner
algebra $\cO_{\alpha,\beta}(\Gamma):=C^*(Q_{\alpha,\beta}(\Gamma))$ and found
generators (and their relations) of $\cO_{\alpha,\beta}(\Gamma).$ In this
paper, the author uses this information to create a six term exact sequence in
order to calculate the $K$-groups of $\cO_{\alpha,\beta}(\Gamma).$


title:
On the black hole from merging binary neutron stars: how fast can it
  spin?

abstract:
  The merger of two neutron stars will in general lead to the formation of a
torus surrounding a black hole whose rotational energy can be tapped to
potentially power a short gamma-ray burst. We have studied the merger of
equal-mass binaries with spins aligned with the orbital angular momentum to
determine the maximum spin the black hole can reach. Our initial data consists
of irrotational binaries to which we add various amounts of rotation to
increase the total angular momentum. Although the initial data violates the
constraint equations, the use of the constraint-damping CCZ4 formulation yields
evolutions with violations smaller than those with irrotational initial data
and standard formulations. Interestingly, we find that a limit of $J/M^2 \simeq
0.89$ exists for the dimensionless spin and that any additional angular
momentum given to the binary ends up in the torus rather than in the black
hole, thus providing another nontrivial example supporting the cosmic
censorship hypothesis.


title: Non-commutative f-divergence functional

abstract:
  We introduce the non-commutative $f$-divergence functional
$\Theta(\widetilde{A},\widetilde{B}):=\int_TB_t^{\frac{1}{2}}f\left(B_t^{-\frac{1}{2}}
A_tB_t^{-\frac{1}{2}}\right)B_t^{\frac{1}{2}}d\mu(t)$ for an operator convex
function $f$, where $\widetilde{A}=(A_t)_{t\in T}$ and
$\widetilde{B}=(B_t)_{t\in T}$ are continuous fields of Hilbert space operators
and study its properties. We establish some relations between the perspective
of an operator convex function $f$ and the non-commutative $f$-divergence
functional. In particular, an operator extension of Csisz\'{a}r's result
regarding $f$-divergence functional is presented. As some applications, we
establish a refinement of the Choi--Davis--Jensen operator inequality, obtain
some unitarily invariant norm inequalities and give some results related to the
Kullback--Leibler distance.


title: Alignment and Aperture Scan at the Fermilab Booster

abstract:
  The Fermilab booster has an intensity upgrade plan called the Proton
Improvement plan (PIP). The flux throughput goal is 2E17 protons/hour, which is
almost double the current operation at 1.1E17 protons/hour. The beam loss in
the machine is going to be the source of issues. The booster accelerates beam
from 400 MeV to 8 GeV and extracts to the Main Injector. Several percent of the
beam is lost within 3 msec after the injection. The aperture at injection
energy was measured and compared with the survey data. The magnets are going to
be realigned in March 2012 in order to increase the aperture. The beam studies,
analysis of the scan and alignment data, and the result of the magnet moves
will be discussed in this paper.


title:
Why quantum computing is hard - and quantum cryptography is not provably
  secure

abstract:
  Despite high hopes for quantum computation in the 1990s, progress in the past
decade has been slow; we still cannot perform computation with more than about
three qubits and are no closer to solving problems of real interest than a
decade ago. Separately, recent experiments in fluid mechanics have demonstrated
the emergence of a full range of quantum phenomena from completely classical
motion. We present two specific hypotheses. First, Kuramoto theory may give a
basis for geometrical thinking about entanglement. Second, we consider a recent
soliton model of the electron, in which the quantum-mechanical wave function is
a phase modulation of a carrier wave. Both models are consistent with one
another and with observation. Both models suggest how entanglement and
decoherence may be related to device geometry. Both models predict that it will
be difficult to maintain phase coherence of more than three qubits in the
plane, or four qubits in a three-dimensional structure. The soliton model also
shows that the experimental work which appeared to demonstrate a violation of
Bell's inequalities might not actually do so; regardless of whether it is a
correct description of the world, it exposes a flaw in the logic of the Bell
tests. Thus the case for the security of EPR-based quantum cryptography has
just not been made. We propose experiments in quantum computation to test this.
Finally, we examine two possible interpretations of such soliton models: one is
consistent with the transactional interpretation of quantum mechanics, while
the other is an entirely classical model in which we do not have to abandon the
idea of a single world where action is local and causal.


title: Propagators in de Sitter space

abstract:
  In a spacetime with no global timelike Killing vector, we do not have a
natural choice for the vacuum state of matter fields, leading to an ambiguity
in defining the Feynman propagators. In this paper, taking the vacuum state to
be the instantaneous ground state of the Hamiltonian at each moment, we develop
a method for calculating wave functions associated with the vacuum and the
corresponding in-in and in-out propagators. We apply this method to free scalar
field theory in de Sitter space and obtain de Sitter invariant propagators in
various coordinate patches. We show that the in-out propagator in the Poincare
patch has a finite massless limit in a de Sitter invariant form. We argue and
numerically check that our in-out propagators agree with those obtained by a
path integral with the standard i\epsilon prescription, and identify the
condition on a foliation of spacetime under which such coincidence can happen
for the foliation. We also show that the in-out propagators satisfy Polyakov's
composition law. Several applications of our framework are also discussed.


title: Momentum Cogging at the Fermilab Booster

abstract:
  The Fermilab booster has an intensity upgrade plan called the Proton
Improvement plan (PIP). The flux throughput goal is 2E17 protons/hour which is
almost double the current operation at 1.1E17 protons/hour. The beam loss in
the machine is going to be an issue. The booster accelerates beam from 400 MeV
to 8GeV and extracts to The Main Injector (MI). Cogging is the process that
synchronizes the extraction kicker gap to the MI by changing radial position of
the beam during the cycle. The gap creation occurs at about 700MeV which is
6msec into the cycle. The variation of the revolution frequency from cycle to
cycle is larger at lower energy and it is hard to control by changing the
radial position because of aperture limitations. Momentum cogging is able to
move the gap creation earlier by using dipole correctors and radial position
feedback, and controlling the revolution frequency and radial position at the
same time. The new cogging is going to save energy loss and aperture. The
progress of the momentum cogging system development is going to be discussed in
this paper.


title: Use of the Discrete Variable Representation Basis in Nuclear Physics

abstract:
  The discrete variable representation (DVR) basis is nearly optimal for
numerically representing wave functions in nuclear physics: Suitable problems
enjoy exponential convergence, yet the Hamiltonian remains sparse. We show that
one can often use smaller basis sets than with the traditional harmonic
oscillator basis, and still benefit from the simple analytic properties of the
DVR bases which requires no overlap integrals, simply permit using various
Jacobi coordinates, and admit straightforward analyses of the ultraviolet and
infrared convergence properties.


title: Protected edge modes without symmetry

abstract:
  We discuss the question of when a gapped 2D electron system without any
symmetry has a protected gapless edge mode. While it is well known that systems
with a nonzero thermal Hall conductance, $K_H \neq 0$, support such modes, here
we show that robust modes can also occur when $K_H = 0$ -- if the system has
quasiparticles with fractional statistics. We show that some types of
fractional statistics are compatible with a gapped edge, while others are
fundamentally incompatible. More generally, we give a criterion for when an
electron system with abelian statistics and $K_H = 0$ can support a gapped
edge: we show that a gapped edge is possible if and only if there exists a
subset of quasiparticle types $M$ such that (1) all the quasiparticles in $M$
have trivial mutual statistics, and (2) every quasiparticle that is not in $M$
has nontrivial mutual statistics with at least one quasiparticle in $M$. We
derive this criterion using three different approaches: a microscopic analysis
of the edge, a general argument based on braiding statistics, and finally a
conformal field theory approach that uses constraints from modular invariance.
We also discuss the analogous result for 2D boson systems.


title: Fractional Perfect b-Matching Polytopes. I: General Theory

abstract:
  The fractional perfect b-matching polytope of an undirected graph G is the
polytope of all assignments of nonnegative real numbers to the edges of G such
that the sum of the numbers over all edges incident to any vertex v is a
prescribed nonnegative number b_v. General theorems which provide conditions
for nonemptiness, give a formula for the dimension, and characterize the
vertices, edges and face lattices of such polytopes are obtained. Many of these
results are expressed in terms of certain spanning subgraphs of G which are
associated with subsets or elements of the polytope. For example, it is shown
that an element u of the fractional perfect b-matching polytope of G is a
vertex of the polytope if and only if each component of the graph of u either
is acyclic or else contains exactly one cycle with that cycle having odd
length, where the graph of u is defined to be the spanning subgraph of G whose
edges are those at which u is positive.


title:
Comparison Theory for Markov Chains on Different State Spaces and
  Application to Random Walk on Derangements

abstract:
  Let $X_{t}$ and $Y_{t}$ be two Markov chains, on state spaces $\Omega \subset
\hat{\Omega}$. In this paper, we discuss how to prove bounds on the spectrum of
$X_{t}$ based on bounds on the spectrum of $Y_{t}$. This generalizes work of
Diaconis, Saloff-Coste, Yuen and others on comparison of chains in the case
$\Omega = \hat{\Omega}$. The main tool is the extension of functions from the
smaller space to the larger, which allows comparison of the entire spectrum of
the two chains. The theory is used to give quick analyses of several chains
without symmetry. The main application is to a `random transposition' walk on
derangements.


title: On the Acceptability of Arguments in Preference-Based Argumentation

abstract:
  Argumentation is a promising model for reasoning with uncertain knowledge.
The key concept of acceptability enables to differentiate arguments and
counterarguments: The certainty of a proposition can then be evaluated through
the most acceptable arguments for that proposition. In this paper, we
investigate different complementary points of view: - an acceptability based on
the existence of direct counterarguments, - an acceptability based on the
existence of defenders. Pursuing previous work on preference-based
argumentation principles, we enforce both points of view by taking into account
preference orderings for comparing arguments. Our approach is illustrated in
the context of reasoning with stratified knowldge bases.


title: Merging Uncertain Knowledge Bases in a Possibilistic Logic Framework

abstract:
  This paper addresses the problem of merging uncertain information in the
framework of possibilistic logic. It presents several syntactic combination
rules to merge possibilistic knowledge bases, provided by different sources,
into a new possibilistic knowledge base. These combination rules are first
described at the meta-level outside the language of possibilistic logic. Next,
an extension of possibilistic logic, where the combination rules are inside the
language, is proposed. A proof system in a sequent form, which is sound and
complete with respect to the possibilistic logic semantics, is given.


title:
A Hybrid Algorithm to Compute Marginal and Joint Beliefs in Bayesian
  Networks and Its Complexity

abstract:
  There exist two general forms of exact algorithms for updating probabilities
in Bayesian Networks. The first approach involves using a structure, usually a
clique tree, and performing local message based calculation to extract the
belief in each variable. The second general class of algorithm involves the use
of non-serial dynamic programming techniques to extract the belief in some
desired group of variables. In this paper we present a hybrid algorithm based
on the latter approach yet possessing the ability to retrieve the belief in all
single variables. The technique is advantageous in that it saves a NP-hard
computation step over using one algorithm of each type. Furthermore, this
technique re-enforces a conjecture of Jensen and Jensen [JJ94] in that it still
requires a single NP-hard step to set up the structure on which inference is
performed, as we show by confirming Li and D'Ambrosio's [LD94] conjectured
NP-hardness of OFP.


title: Structured Reachability Analysis for Markov Decision Processes

abstract:
  Recent research in decision theoretic planning has focussed on making the
solution of Markov decision processes (MDPs) more feasible. We develop a family
of algorithms for structured reachability analysis of MDPs that are suitable
when an initial state (or set of states) is known. Using compact, structured
representations of MDPs (e.g., Bayesian networks), our methods, which vary in
the tradeoff between complexity and accuracy, produce structured descriptions
of (estimated) reachable states that can be used to eliminate variables or
variable values from the problem description, reducing the size of the MDP and
making it easier to solve. One contribution of our work is the extension of
ideas from GRAPHPLAN to deal with the distributed nature of action
representations typically embodied within Bayes nets and the problem of
correlated action effects. We also demonstrate that our algorithm can be made
more complete by using k-ary constraints instead of binary constraints. Another
contribution is the illustration of how the compact representation of
reachability constraints can be exploited by several existing (exact and
approximate) abstraction algorithms for MDPs.


title: Tractable Inference for Complex Stochastic Processes

abstract:
  The monitoring and control of any dynamic system depends crucially on the
ability to reason about its current status and its future trajectory. In the
case of a stochastic system, these tasks typically involve the use of a belief
state- a probability distribution over the state of the process at a given
point in time. Unfortunately, the state spaces of complex processes are very
large, making an explicit representation of a belief state intractable. Even in
dynamic Bayesian networks (DBNs), where the process itself can be represented
compactly, the representation of the belief state is intractable. We
investigate the idea of maintaining a compact approximation to the true belief
state, and analyze the conditions under which the errors due to the
approximations taken over the lifetime of the process do not accumulate to make
our answers completely irrelevant. We show that the error in a belief state
contracts exponentially as the process evolves. Thus, even with multiple
approximations, the error in our process remains bounded indefinitely. We show
how the additional structure of a DBN can be used to design our approximation
scheme, improving its performance significantly. We demonstrate the
applicability of our ideas in the context of a monitoring task, showing that
orders of magnitude faster inference can be achieved with only a small
degradation in accuracy.


title: Empirical Analysis of Predictive Algorithms for Collaborative Filtering

abstract:
  Collaborative filtering or recommender systems use a database about user
preferences to predict additional topics or products a new user might like. In
this paper we describe several algorithms designed for this task, including
techniques based on correlation coefficients, vector-based similarity
calculations, and statistical Bayesian methods. We compare the predictive
accuracy of the various methods in a set of representative problem domains. We
use two basic classes of evaluation metrics. The first characterizes accuracy
over a set of individual predictions in terms of average absolute deviation.
The second estimates the utility of a ranked list of suggested items. This
metric uses an estimate of the probability that a user will see a
recommendation in an ordered list. Experiments were run for datasets associated
with 3 application areas, 4 experimental protocols, and the 2 evaluation
metrics for the various algorithms. Results indicate that for a wide range of
conditions, Bayesian networks with decision trees at each node and correlation
methods outperform Bayesian-clustering and vector-similarity methods. Between
correlation and Bayesian networks, the preferred method depends on the nature
of the dataset, nature of the application (ranked versus one-by-one
presentation), and the availability of votes with which to make predictions.
Other considerations include the size of database, speed of predictions, and
learning time.


title:
Query Expansion in Information Retrieval Systems using a Bayesian
  Network-Based Thesaurus

abstract:
  Information Retrieval (IR) is concerned with the identification of documents
in a collection that are relevant to a given information need, usually
represented as a query containing terms or keywords, which are supposed to be a
good description of what the user is looking for. IR systems may improve their
effectiveness (i.e., increasing the number of relevant documents retrieved) by
using a process of query expansion, which automatically adds new terms to the
original query posed by an user. In this paper we develop a method of query
expansion based on Bayesian networks. Using a learning algorithm, we construct
a Bayesian network that represents some of the relationships among the terms
appearing in a given document collection; this network is then used as a
thesaurus (specific for that collection). We also report the results obtained
by our method on three standard test collections.


title:
Dealing with Uncertainty in Situation Assessment: towards a Symbolic
  Approach

abstract:
  The situation assessment problem is considered, in terms of object,
condition, activity, and plan recognition, based on data coming from the
real-word {em via} various sensors. It is shown that uncertainty issues are
linked both to the models and to the matching algorithm. Three different types
of uncertainties are identified, and within each one, the numerical and the
symbolic cases are distinguished. The emphasis is then put on purely symbolic
uncertainties: it is shown that they can be dealt with within a purely symbolic
framework resulting from a transposition of classical numerical estimation
tools.


title: Marginalizing in Undirected Graph and Hypergraph Models

abstract:
  Given an undirected graph G or hypergraph X model for a given set of
variables V, we introduce two marginalization operators for obtaining the
undirected graph GA or hypergraph HA associated with a given subset A c V such
that the marginal distribution of A factorizes according to GA or HA,
respectively. Finally, we illustrate the method by its application to some
practical examples. With them we show that hypergraph models allow defining a
finer factorization or performing a more precise conditional independence
analysis than undirected graph models.


title: Utility Elicitation as a Classification Problem

abstract:
  We investigate the application of classification techniques to utility
elicitation. In a decision problem, two sets of parameters must generally be
elicited: the probabilities and the utilities. While the prior and conditional
probabilities in the model do not change from user to user, the utility models
do. Thus it is necessary to elicit a utility model separately for each new
user. Elicitation is long and tedious, particularly if the outcome space is
large and not decomposable. There are two common approaches to utility function
elicitation. The first is to base the determination of the users utility
function solely ON elicitation OF qualitative preferences.The second makes
assumptions about the form AND decomposability OF the utility function.Here we
take a different approach: we attempt TO identify the new USERs utility
function based on classification relative to a database of previously collected
utility functions. We do this by identifying clusters of utility functions that
minimize an appropriate distance measure. Having identified the clusters, we
develop a classification scheme that requires many fewer and simpler
assessments than full utility elicitation and is more robust than utility
elicitation based solely on preferences. We have tested our algorithm on a
small database of utility functions in a prenatal diagnosis domain and the
results are quite promising.


title: Irrelevance and Independence Relations in Quasi-Bayesian Networks

abstract:
  This paper analyzes irrelevance and independence relations in graphical
models associated with convex sets of probability distributions (called
Quasi-Bayesian networks). The basic question in Quasi-Bayesian networks is, How
can irrelevance/independence relations in Quasi-Bayesian networks be detected,
enforced and exploited? This paper addresses these questions through Walley's
definitions of irrelevance and independence. Novel algorithms and results are
presented for inferences with the so-called natural extensions using fractional
linear programming, and the properties of the so-called type-1 extensions are
clarified through a new generalization of d-separation.


title: Dynamic Jointrees

abstract:
  It is well known that one can ignore parts of a belief network when computing
answers to certain probabilistic queries. It is also well known that the
ignorable parts (if any) depend on the specific query of interest and,
therefore, may change as the query changes. Algorithms based on jointrees,
however, do not seem to take computational advantage of these facts given that
they typically construct jointrees for worst-case queries; that is, queries for
which every part of the belief network is considered relevant. To address this
limitation, we propose in this paper a method for reconfiguring jointrees
dynamically as the query changes. The reconfiguration process aims at
maintaining a jointree which corresponds to the underlying belief network after
it has been pruned given the current query. Our reconfiguration method is
marked by three characteristics: (a) it is based on a non-classical definition
of jointrees; (b) it is relatively efficient; and (c) it can reuse some of the
computations performed before a jointree is reconfigured. We present
preliminary experimental results which demonstrate significant savings over
using static jointrees when query changes are considerable.


title: On the Semi-Markov Equivalence of Causal Models

abstract:
  The variability of structure in a finite Markov equivalence class of causally
sufficient models represented by directed acyclic graphs has been fully
characterized. Without causal sufficiency, an infinite semi-Markov equivalence
class of models has only been characterized by the fact that each model in the
equivalence class entails the same marginal statistical dependencies. In this
paper, we study the variability of structure of causal models within a
semi-Markov equivalence class and propose a systematic approach to construct
models entailing any specific marginal statistical dependencies.


title: Comparative Uncertainty, Belief Functions and Accepted Beliefs

abstract:
  This paper relates comparative belief structures and a general view of belief
management in the setting of deductively closed logical representations of
accepted beliefs. We show that the range of compatibility between the classical
deductive closure and uncertain reasoning covers precisely the nonmonotonic
'preferential' inference system of Kraus, Lehmann and Magidor and nothing else.
In terms of uncertain reasoning any possibility or necessity measure gives
birth to a structure of accepted beliefs. The classes of probability functions
and of Shafer's belief functions which yield belief sets prove to be very
special ones.


title: Qualitative Decision Theory with Sugeno Integrals

abstract:
  This paper presents an axiomatic framework for qualitative decision under
uncertainty in a finite setting. The corresponding utility is expressed by a
sup-min expression, called Sugeno (or fuzzy) integral. Technically speaking,
Sugeno integral is a median, which is indeed a qualitative counterpart to the
averaging operation underlying expected utility. The axiomatic justification of
Sugeno integral-based utility is expressed in terms of preference between acts
as in Savage decision theory. Pessimistic and optimistic qualitative utilities,
based on necessity and possibility measures, previously introduced by two of
the authors, can be retrieved in this setting by adding appropriate axioms.


title: The Bayesian Structural EM Algorithm

abstract:
  In recent years there has been a flurry of works on learning Bayesian
networks from data. One of the hard problems in this area is how to effectively
learn the structure of a belief network from incomplete data- that is, in the
presence of missing values or hidden variables. In a recent paper, I introduced
an algorithm called Structural EM that combines the standard Expectation
Maximization (EM) algorithm, which optimizes parameters, with structure search
for model selection. That algorithm learns networks based on penalized
likelihood scores, which include the BIC/MDL score and various approximations
to the Bayesian score. In this paper, I extend Structural EM to deal directly
with Bayesian model selection. I prove the convergence of the resulting
algorithm and show how to apply it for learning a large class of probabilistic
models, including Bayesian networks and some variants thereof.


title: Learning the Structure of Dynamic Probabilistic Networks

abstract:
  Dynamic probabilistic networks are a compact representation of complex
stochastic processes. In this paper we examine how to learn the structure of a
DPN from data. We extend structure scoring rules for standard probabilistic
networks to the dynamic case, and show how to search for structure when some of
the variables are hidden. Finally, we examine two applications where such a
technology might be useful: predicting and classifying dynamic behaviors, and
learning causal orderings in biological processes. We provide empirical results
that demonstrate the applicability of our methods in both domains.


title: Learning by Transduction

abstract:
  We describe a method for predicting a classification of an object given
classifications of the objects in the training set, assuming that the pairs
object/classification are generated by an i.i.d. process from a continuous
probability distribution. Our method is a modification of Vapnik's
support-vector machine; its main novelty is that it gives not only the
prediction itself but also a practicable measure of the evidence found in
support of that prediction. We also describe a procedure for assigning degrees
of confidence to predictions made by the support vector machine. Some
experimental results are presented, and possible extensions of the algorithms
are discussed.


title: Graphical Models and Exponential Families

abstract:
  We provide a classification of graphical models according to their
representation as subfamilies of exponential families. Undirected graphical
models with no hidden variables are linear exponential families (LEFs),
directed acyclic graphical models and chain graphs with no hidden variables,
including Bayesian networks with several families of local distributions, are
curved exponential families (CEFs) and graphical models with hidden variables
are stratified exponential families (SEFs). An SEF is a finite union of CEFs
satisfying a frontier condition. In addition, we illustrate how one can
automatically generate independence and non-independence constraints on the
distributions over the observable variables implied by a Bayesian network with
hidden variables. The relevance of these results for model selection is
examined.


title:
Psychological and Normative Theories of Causal Power and the
  Probabilities of Causes

abstract:
  This paper (1)shows that the best supported current psychological theory
(Cheng, 1997) of how human subjects judge the causal power or influence of
variations in presence or absence of one feature on another, given data on
their covariation, tacitly uses a Bayes network which is either a noisy or gate
(for causes that promote the effect) or a noisy and gate (for causes that
inhibit the effect); (2)generalizes Chengs theory to arbitrary acyclic networks
of noisy or and noisy and gates; (3)gives various sufficient conditions for the
estimation of the parameters in such networks when there are independent,
unobserved causes; (4)distinguishes direct causal influence of one feature on
another (influence along a path with one edge) from total influence (influence
along all paths from one variable to another) and gives sufficient conditions
for estimating each when there are unobserved causes of the outcome variable;
(5)describes the relation between Cheng models and a simplified version of the
Rubin framework for representing causal relations.


title: Minimum Encoding Approaches for Predictive Modeling

abstract:
  We analyze differences between two information-theoretically motivated
approaches to statistical inference and model selection: the Minimum
Description Length (MDL) principle, and the Minimum Message Length (MML)
principle. Based on this analysis, we present two revised versions of MML: a
pointwise estimator which gives the MML-optimal single parameter model, and a
volumewise estimator which gives the MML-optimal region in the parameter space.
Our empirical results suggest that with small data sets, the MDL approach
yields more accurate predictions than the MML estimators. The empirical results
also demonstrate that the revised MML estimators introduced here perform better
than the original MML estimator suggested by Wallace and Freeman.


title:
Towards Case-Based Preference Elicitation: Similarity Measures on
  Preference Structures

abstract:
  While decision theory provides an appealing normative framework for
representing rich preference structures, eliciting utility or value functions
typically incurs a large cost. For many applications involving interactive
systems this overhead precludes the use of formal decision-theoretic models of
preference. Instead of performing elicitation in a vacuum, it would be useful
if we could augment directly elicited preferences with some appropriate default
information. In this paper we propose a case-based approach to alleviating the
preference elicitation bottleneck. Assuming the existence of a population of
users from whom we have elicited complete or incomplete preference structures,
we propose eliciting the preferences of a new user interactively and
incrementally, using the closest existing preference structures as potential
defaults. Since a notion of closeness demands a measure of distance among
preference structures, this paper takes the first step of studying various
distance measures over fully and partially specified preference structures. We
explore the use of Euclidean distance, Spearmans footrule, and define a new
measure, the probabilistic distance. We provide computational techniques for
all three measures.


title: Solving POMDPs by Searching in Policy Space

abstract:
  Most algorithms for solving POMDPs iteratively improve a value function that
implicitly represents a policy and are said to search in value function space.
This paper presents an approach to solving POMDPs that represents a policy
explicitly as a finite-state controller and iteratively improves the controller
by search in policy space. Two related algorithms illustrate this approach. The
first is a policy iteration algorithm that can outperform value iteration in
solving infinitehorizon POMDPs. It provides the foundation for a new heuristic
search algorithm that promises further speedup by focusing computational effort
on regions of the problem space that are reachable, or likely to be reached,
from a start state.


title: Hierarchical Solution of Markov Decision Processes using Macro-actions

abstract:
  We investigate the use of temporally abstract actions, or macro-actions, in
the solution of Markov decision processes. Unlike current models that combine
both primitive actions and macro-actions and leave the state space unchanged,
we propose a hierarchical model (using an abstract MDP) that works with
macro-actions only, and that significantly reduces the size of the state space.
This is achieved by treating macroactions as local policies that act in certain
regions of state space, and by restricting states in the abstract MDP to those
at the boundaries of regions. The abstract MDP approximates the original and
can be solved more efficiently. We discuss several ways in which macro-actions
can be generated to ensure good solution quality. Finally, we consider ways in
which macro-actions can be reused to solve multiple, related MDPs; and we show
that this can justify the computational overhead of macro-action generation.


title:
Inferring Informational Goals from Free-Text Queries: A Bayesian
  Approach

abstract:
  People using consumer software applications typically do not use technical
jargon when querying an online database of help topics. Rather, they attempt to
communicate their goals with common words and phrases that describe software
functionality in terms of structure and objects they understand. We describe a
Bayesian approach to modeling the relationship between words in a user's query
for assistance and the informational goals of the user. After reviewing the
general method, we describe several extensions that center on integrating
additional distinctions and structure about language usage and user goals into
the Bayesian models.


title: Evaluating Las Vegas Algorithms - Pitfalls and Remedies

abstract:
  Stochastic search algorithms are among the most sucessful approaches for
solving hard combinatorial problems. A large class of stochastic search
approaches can be cast into the framework of Las Vegas Algorithms (LVAs). As
the run-time behavior of LVAs is characterized by random variables, the
detailed knowledge of run-time distributions provides important information for
the analysis of these algorithms. In this paper we propose a novel methodology
for evaluating the performance of LVAs, based on the identification of
empirical run-time distributions. We exemplify our approach by applying it to
Stochastic Local Search (SLS) algorithms for the satisfiability problem (SAT)
in propositional logic. We point out pitfalls arising from the use of improper
empirical methods and discuss the benefits of the proposed methodology for
evaluating and comparing LVAs.


title: An Anytime Algorithm for Decision Making under Uncertainty

abstract:
  We present an anytime algorithm which computes policies for decision problems
represented as multi-stage influence diagrams. Our algorithm constructs
policies incrementally, starting from a policy which makes no use of the
available information. The incremental process constructs policies which
includes more of the information available to the decision maker at each step.
While the process converges to the optimal policy, our approach is designed for
situations in which computing the optimal policy is infeasible. We provide
examples of the process on several large decision problems, showing that, for
these examples, the process constructs valuable (but sub-optimal) policies
before the optimal policy would be available by traditional methods.


title:
The Lumiere Project: Bayesian User Modeling for Inferring the Goals and
  Needs of Software Users

abstract:
  The Lumiere Project centers on harnessing probability and utility to provide
assistance to computer software users. We review work on Bayesian user models
that can be employed to infer a users needs by considering a user's background,
actions, and queries. Several problems were tackled in Lumiere research,
including (1) the construction of Bayesian models for reasoning about the
time-varying goals of computer users from their observed actions and queries,
(2) gaining access to a stream of events from software applications, (3)
developing a language for transforming system events into observational
variables represented in Bayesian user models, (4) developing persistent
profiles to capture changes in a user expertise, and (5) the development of an
overall architecture for an intelligent user interface. Lumiere prototypes
served as the basis for the Office Assistant in the Microsoft Office '97 suite
of productivity applications.


title: Any Time Probabilistic Reasoning for Sensor Validation

abstract:
  For many real time applications, it is important to validate the information
received from the sensors before entering higher levels of reasoning. This
paper presents an any time probabilistic algorithm for validating the
information provided by sensors. The system consists of two Bayesian network
models. The first one is a model of the dependencies between sensors and it is
used to validate each sensor. It provides a list of potentially faulty sensors.
To isolate the real faults, a second Bayesian network is used, which relates
the potential faults with the real faults. This second model is also used to
make the validation algorithm any time, by validating first the sensors that
provide more information. To select the next sensor to validate, and measure
the quality of the results at each stage, an entropy function is used. This
function captures in a single quantity both the certainty and specificity
measures of any time algorithms. Together, both models constitute a mechanism
for validating sensors in an any time fashion, providing at each step the
probability of correct/faulty for each sensor, and the total quality of the
results. The algorithm has been tested in the validation of temperature sensors
of a power plant.


title:
Measure Selection: Notions of Rationality and Representation
  Independence

abstract:
  We take another look at the general problem of selecting a preferred
probability measure among those that comply with some given constraints. The
dominant role that entropy maximization has obtained in this context is
questioned by arguing that the minimum information principle on which it is
based could be supplanted by an at least as plausible "likelihood of evidence"
principle. We then review a method for turning given selection functions into
representation independent variants, and discuss the tradeoffs involved in this
transformation.


title: Implementing Resolute Choice Under Uncertainty

abstract:
  The adaptation to situations of sequential choice under uncertainty of
decision criteria which deviate from (subjective) expected utility raises the
problem of ensuring the selection of a nondominated strategy. In particular,
when following the suggestion of Machina and McClennen of giving up
separability (also known as consequentialism), which requires the choice of a
substrategy in a subtree to depend only on data relevant to that subtree, one
must renounce to the use of dynamic programming, since Bellman's principle is
no longer valid. An interpretation of McClennen's resolute choice, based on
cooperation between the successive Selves of the decision maker, is proposed.
Implementations of resolute choice which prevent Money Pumps negative prices of
information or, more generally, choices of dominated strategies, while
remaining computationally tractable, are proposed.


title: Dealing with Uncertainty on the Initial State of a Petri Net

abstract:
  This paper proposes a method to find the actual state of a complex dynamic
system from information coming from the sensors on the system himself, or on
its environment. The nominal evolution of the system is a priori known and can
be modeled (by an expert, for example), by different methods. In this paper,
the Petri nets have been chosen. Contrary to the usual use of the Petri nets,
the initial state of the system is unknown. So a degree of belief is bound to
each places, or set of places. The theory used to model this uncertainty is the
Dempster-Shafer's one which is well adapted to this type of problems. From the
given Petri net characterizing the nominal evolution of the dynamic system, and
from the observation inputs, the proposed method allows to determine according
to the reliability of the model and the inputs, the state of the system at any
time.


title:
Hierarchical Mixtures-of-Experts for Exponential Family Regression
  Models with Generalized Linear Mean Functions: A Survey of Approximation and
  Consistency Results

abstract:
  We investigate a class of hierarchical mixtures-of-experts (HME) models where
exponential family regression models with generalized linear mean functions of
the form psi(ga+fx^Tfgb) are mixed. Here psi(...) is the inverse link function.
Suppose the true response y follows an exponential family regression model with
mean function belonging to a class of smooth functions of the form psi(h(fx))
where h(...)in W_2^infty (a Sobolev class over [0,1]^{s}). It is shown that the
HME probability density functions can approximate the true density, at a rate
of O(m^{-2/s}) in L_p norm, and at a rate of O(m^{-4/s}) in Kullback-Leibler
divergence. These rates can be achieved within the family of HME structures
with no more than s-layers, where s is the dimension of the predictor fx. It is
also shown that likelihood-based inference based on HME is consistent in
recovering the truth, in the sense that as the sample size n and the number of
experts m both increase, the mean square error of the predicted mean response
goes to zero. Conditions for such results to hold are stated and discussed.


title:
Exact Inference of Hidden Structure from Sample Data in Noisy-OR
  Networks

abstract:
  In the literature on graphical models, there has been increased attention
paid to the problems of learning hidden structure (see Heckerman [H96] for
survey) and causal mechanisms from sample data [H96, P88, S93, P95, F98]. In
most settings we should expect the former to be difficult, and the latter
potentially impossible without experimental intervention. In this work, we
examine some restricted settings in which perfectly reconstruct the hidden
structure solely on the basis of observed sample data.


title: Large Deviation Methods for Approximate Probabilistic Inference

abstract:
  We study two-layer belief networks of binary random variables in which the
conditional probabilities Pr[childlparents] depend monotonically on weighted
sums of the parents. In large networks where exact probabilistic inference is
intractable, we show how to compute upper and lower bounds on many
probabilities of interest. In particular, using methods from large deviation
theory, we derive rigorous bounds on marginal probabilities such as
Pr[children] and prove rates of convergence for the accuracy of our bounds as a
function of network size. Our results apply to networks with generic transfer
function parameterizations of the conditional probability tables, such as
sigmoid and noisy-OR. They also explicitly illustrate the types of averaging
behavior that can simplify the problem of inference in large networks.


title: Mixture Representations for Inference and Learning in Boltzmann Machines

abstract:
  Boltzmann machines are undirected graphical models with two-state stochastic
variables, in which the logarithms of the clique potentials are quadratic
functions of the node states. They have been widely studied in the neural
computing literature, although their practical applicability has been limited
by the difficulty of finding an effective learning algorithm. One
well-established approach, known as mean field theory, represents the
stochastic distribution using a factorized approximation. However, the
corresponding learning algorithm often fails to find a good solution. We
conjecture that this is due to the implicit uni-modality of the mean field
approximation which is therefore unable to capture multi-modality in the true
distribution. In this paper we use variational methods to approximate the
stochastic distribution using multi-modal mixtures of factorized distributions.
We present results for both inference and learning to demonstrate the
effectiveness of this approach.


title:
A Comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer
  Architectures for Computing Marginals of Probability Distributions

abstract:
  In the last decade, several architectures have been proposed for exact
computation of marginals using local computation. In this paper, we compare
three architectures - Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer - from
the perspective of graphical structure for message propagation, message-passing
scheme, computational efficiency, and storage efficiency.


title: Incremental Tradeoff Resolution in Qualitative Probabilistic Networks

abstract:
  Qualitative probabilistic reasoning in a Bayesian network often reveals
tradeoffs: relationships that are ambiguous due to competing qualitative
influences. We present two techniques that combine qualitative and numeric
probabilistic reasoning to resolve such tradeoffs, inferring the qualitative
relationship between nodes in a Bayesian network. The first approach
incrementally marginalizes nodes that contribute to the ambiguous qualitative
relationships. The second approach evaluates approximate Bayesian networks for
bounds of probability distributions, and uses these bounds to determinate
qualitative relationships in question. This approach is also incremental in
that the algorithm refines the state spaces of random variables for tighter
bounds until the qualitative relationships are resolved. Both approaches
provide systematic methods for tradeoff resolution at potentially lower
computational cost than application of purely numeric methods.


title: Using Qualitative Relationships for Bounding Probability Distributions

abstract:
  We exploit qualitative probabilistic relationships among variables for
computing bounds of conditional probability distributions of interest in
Bayesian networks. Using the signs of qualitative relationships, we can
implement abstraction operations that are guaranteed to bound the distributions
of interest in the desired direction. By evaluating incrementally improved
approximate networks, our algorithm obtains monotonically tightening bounds
that converge to exact distributions. For supermodular utility functions, the
tightening bounds monotonically reduce the set of admissible decision
alternatives as well.


title:
Magic Inference Rules for Probabilistic Deduction under Taxonomic
  Knowledge

abstract:
  We present locally complete inference rules for probabilistic deduction from
taxonomic and probabilistic knowledge-bases over conjunctive events. Crucially,
in contrast to similar inference rules in the literature, our inference rules
are locally complete for conjunctive events and under additional taxonomic
knowledge. We discover that our inference rules are extremely complex and that
it is at first glance not clear at all where the deduced tightest bounds come
from. Moreover, analyzing the global completeness of our inference rules, we
find examples of globally very incomplete probabilistic deductions. More
generally, we even show that all systems of inference rules for taxonomic and
probabilistic knowledge-bases over conjunctive events are globally incomplete.
We conclude that probabilistic deduction by the iterative application of
inference rules on interval restrictions for conditional probabilities, even
though considered very promising in the literature so far, seems very limited
in its field of application.


title: Lazy Propagation in Junction Trees

abstract:
  The efficiency of algorithms using secondary structures for probabilistic
inference in Bayesian networks can be improved by exploiting independence
relations induced by evidence and the direction of the links in the original
network. In this paper we present an algorithm that on-line exploits
independence relations induced by evidence and the direction of the links in
the original network to reduce both time and space costs. Instead of
multiplying the conditional probability distributions for the various cliques,
we determine on-line which potentials to multiply when a message is to be
produced. The performance improvement of the algorithm is emphasized through
empirical evaluations involving large real world Bayesian networks, and we
compare the method with the HUGIN and Shafer-Shenoy inference algorithms.


title: Constructing Situation Specific Belief Networks

abstract:
  This paper describes a process for constructing situation-specific belief
networks from a knowledge base of network fragments. A situation-specific
network is a minimal query complete network constructed from a knowledge base
in response to a query for the probability distribution on a set of target
variables given evidence and context variables. We present definitions of query
completeness and situation-specific networks. We describe conditions on the
knowledge base that guarantee query completeness. The relationship of our work
to earlier work on KBMC is also discussed.


title:
Treatment Choice in Heterogeneous Populations Using Experiments without
  Covariate Data (Invited Paper)

abstract:
  I examine the problem of treatment choice when a planner observes (i)
covariates that describe each member of a population of interest and (ii) the
outcomes of an experiment in which subjects randomly drawn from this population
are randomly assigned to treatment groups within which all subjects receive the
same treatment. Covariate data for the subjects of the experiment are not
available. The optimal treatment rule is to divide the population into
subpopulations whose members share the same covariate value, and then to choose
for each subpopulation a treatment that maximizes its mean outcome. However the
planner cannot implement this rule. I draw on my work on nonparametric analysis
of treatment response to address the planner's problem.


title:
An Experimental Comparison of Several Clustering and Initialization
  Methods

abstract:
  We examine methods for clustering in high dimensions. In the first part of
the paper, we perform an experimental comparison between three batch clustering
algorithms: the Expectation-Maximization (EM) algorithm, a winner take all
version of the EM algorithm reminiscent of the K-means algorithm, and
model-based hierarchical agglomerative clustering. We learn naive-Bayes models
with a hidden root node, using high-dimensional discrete-variable data sets
(both real and synthetic). We find that the EM algorithm significantly
outperforms the other methods, and proceed to investigate the effect of various
initialization schemes on the final solution produced by the EM algorithm. The
initializations that we consider are (1) parameters sampled from an
uninformative prior, (2) random perturbations of the marginal distribution of
the data, and (3) the output of hierarchical agglomerative clustering. Although
the methods are substantially different, they lead to learned models that are
strikingly similar in quality.


title: From Likelihood to Plausibility

abstract:
  Several authors have explained that the likelihood ratio measures the
strength of the evidence represented by observations in statistical problems.
This idea works fine when the goal is to evaluate the strength of the available
evidence for a simple hypothesis versus another simple hypothesis. However, the
applicability of this idea is limited to simple hypotheses because the
likelihood function is primarily defined on points (simple hypotheses) of the
parameter space. In this paper we define a general weight of evidence that is
applicable to both simple and composite hypotheses. It is based on the
Dempster-Shafer concept of plausibility and is shown to be a generalization of
the likelihood ratio. Functional models are of a fundamental importance for the
general weight of evidence proposed in this paper. The relevant concepts and
ideas are explained by means of a familiar urn problem and the general analysis
of a real-world medical problem is presented.


title:
A Multivariate Discretization Method for Learning Bayesian Networks from
  Mixed Data

abstract:
In this paper we address the problem of discretization in the context of
learning Bayesian networks (BNs) from data containing both continuous and
discrete variables. We describe a new technique for multivariate
discretization, whereby each continuous variable is discretized while taking
into account its interaction with the other variables. The technique is based
on the use of a Bayesian scoring metric that scores the discretization policy
for a continuous variable given a BN structure and the observed data. Since the
metric is relative to the BN structure currently being evaluated, the
discretization of a variable needs to be dynamically adjusted as the BN
structure changes.


title: Resolving Conflicting Arguments under Uncertainties

abstract:
  Distributed knowledge based applications in open domain rely on common sense
information which is bound to be uncertain and incomplete. To draw the useful
conclusions from ambiguous data, one must address uncertainties and conflicts
incurred in a holistic view. No integrated frameworks are viable without an
in-depth analysis of conflicts incurred by uncertainties. In this paper, we
give such an analysis and based on the result, propose an integrated framework.
Our framework extends definite argumentation theory to model uncertainty. It
supports three views over conflicting and uncertain knowledge. Thus, knowledge
engineers can draw different conclusions depending on the application context
(i.e. view). We also give an illustrative example on strategical decision
support to show the practical usefulness of our framework.


title:
Flexible Decomposition Algorithms for Weakly Coupled Markov Decision
  Problems

abstract:
  This paper presents two new approaches to decomposing and solving large
Markov decision problems (MDPs), a partial decoupling method and a complete
decoupling method. In these approaches, a large, stochastic decision problem is
divided into smaller pieces. The first approach builds a cache of policies for
each part of the problem independently, and then combines the pieces in a
separate, light-weight step. A second approach also divides the problem into
smaller pieces, but information is communicated between the different problem
pieces, allowing intelligent decisions to be made about which piece requires
the most attention. Both approaches can be used to find optimal policies or
approximately optimal policies with provable bounds. These algorithms also
provide a framework for the efficient transfer of knowledge across problems
that share similar structure.


title: Logarithmic Time Parallel Bayesian Inference

abstract:
  I present a parallel algorithm for exact probabilistic inference in Bayesian
networks. For polytree networks with n variables, the worst-case time
complexity is O(log n) on a CREW PRAM (concurrent-read, exclusive-write
parallel random-access machine) with n processors, for any constant number of
evidence variables. For arbitrary networks, the time complexity is O(r^{3w}*log
n) for n processors, or O(w*log n) for r^{3w}*n processors, where r is the
maximum range of any variable, and w is the induced width (the maximum clique
size), after moralizing and triangulating the network.


title: Learning From What You Don't Observe

abstract:
  The process of diagnosis involves learning about the state of a system from
various observations of symptoms or findings about the system. Sophisticated
Bayesian (and other) algorithms have been developed to revise and maintain
beliefs about the system as observations are made. Nonetheless, diagnostic
models have tended to ignore some common sense reasoning exploited by human
diagnosticians; In particular, one can learn from which observations have not
been made, in the spirit of conversational implicature. There are two concepts
that we describe to extract information from the observations not made. First,
some symptoms, if present, are more likely to be reported before others.
Second, most human diagnosticians and expert systems are economical in their
data-gathering, searching first where they are more likely to find symptoms
present. Thus, there is a desirable bias toward reporting symptoms that are
present. We develop a simple model for these concepts that can significantly
improve diagnostic inference.


title: Context-Specific Approximation in Probabilistic Inference

abstract:
  There is evidence that the numbers in probabilistic inference don't really
matter. This paper considers the idea that we can make a probabilistic model
simpler by making fewer distinctions. Unfortunately, the level of a Bayesian
network seems too coarse; it is unlikely that a parent will make little
difference for all values of the other parents. In this paper we consider an
approximation scheme where distinctions can be ignored in some contexts, but
not in other contexts. We elaborate on a notion of a parent context that allows
a structured context-specific decomposition of a probability distribution and
the associated probabilistic inference scheme called probabilistic partial
evaluation (Poole 1997). This paper shows a way to simplify a probabilistic
model by ignoring distinctions which have similar probabilities, a method to
exploit the simpler model, a bound on the resulting errors, and some
preliminary empirical results on simple networks.


title:
Empirical Evaluation of Approximation Algorithms for Probabilistic
  Decoding

abstract:
  It was recently shown that the problem of decoding messages transmitted
through a noisy channel can be formulated as a belief updating task over a
probabilistic network [McEliece]. Moreover, it was observed that iterative
application of the (linear time) Pearl's belief propagation algorithm designed
for polytrees outperformed state of the art decoding algorithms, even though
the corresponding networks may have many cycles. This paper demonstrates
empirically that an approximation algorithm approx-mpe for solving the most
probable explanation (MPE) problem, developed within the recently proposed
mini-bucket elimination framework [Dechter96], outperforms iterative belief
propagation on classes of coding networks that have bounded induced width. Our
experiments suggest that approximate MPE decoders can be good competitors to
the approximate belief updating decoders.


title: Decision Theoretic Foundations of Graphical Model Selection

abstract:
  This paper describes a decision theoretic formulation of learning the
graphical structure of a Bayesian Belief Network from data. This framework
subsumes the standard Bayesian approach of choosing the model with the largest
posterior probability as the solution of a decision problem with a 0-1 loss
function and allows the use of more general loss functions able to trade-off
the complexity of the selected model and the error of choosing an
oversimplified model. A new class of loss functions, called disintegrable, is
introduced, to allow the decision problem to match the decomposability of the
graphical model. With this class of loss functions, the optimal solution to the
decision problem can be found using an efficient bottom-up search strategy.


title: On the Geometry of Bayesian Graphical Models with Hidden Variables

abstract:
  In this paper we investigate the geometry of the likelihood of the unknown
parameters in a simple class of Bayesian directed graphs with hidden variables.
This enables us, before any numerical algorithms are employed, to obtain
certain insights in the nature of the unidentifiability inherent in such
models, the way posterior densities will be sensitive to prior densities and
the typical geometrical form these posterior densities might take. Many of
these insights carry over into more complicated Bayesian networks with
systematic missing data.


title:
Bayes-Ball: The Rational Pastime (for Determining Irrelevance and
  Requisite Information in Belief Networks and Influence Diagrams)

abstract:
  One of the benefits of belief networks and influence diagrams is that so much
knowledge is captured in the graphical structure. In particular, statements of
conditional irrelevance (or independence) can be verified in time linear in the
size of the graph. To resolve a particular inference query or decision problem,
only some of the possible states and probability distributions must be
specified, the "requisite information."
  This paper presents a new, simple, and efficient "Bayes-ball" algorithm which
is well-suited to both new students of belief networks and state of the art
implementations. The Bayes-ball algorithm determines irrelevant sets and
requisite information more efficiently than existing methods, and is linear in
the size of the graph for belief networks and influence diagrams.


title: Switching Portfolios

abstract:
  A constant rebalanced portfolio is an asset allocation algorithm which keeps
the same distribution of wealth among a set of assets along a period of time.
Recently, there has been work on on-line portfolio selection algorithms which
are competitive with the best constant rebalanced portfolio determined in
hindsight. By their nature, these algorithms employ the assumption that high
returns can be achieved using a fixed asset allocation strategy. However, stock
markets are far from being stationary and in many cases the wealth achieved by
a constant rebalanced portfolio is much smaller than the wealth achieved by an
ad-hoc investment strategy that adapts to changes in the market. In this paper
we present an efficient Bayesian portfolio selection algorithm that is able to
track a changing market. We also describe a simple extension of the algorithm
for the case of a general transaction cost, including the transactions cost
models recently investigated by Blum and kalai. We provide a simple analysis of
the competitiveness of the algorithm and check its performance on real stock
data from the New York Stock Exchange accumulated during a 22-year period.


title: Bayesian Networks from the Point of View of Chain Graphs

abstract:
  AThe paper gives a few arguments in favour of the use of chain graphs for
description of probabilistic conditional independence structures. Every
Bayesian network model can be equivalently introduced by means of a
factorization formula with respect to a chain graph which is Markov equivalent
to the Bayesian network. A graphical characterization of such graphs is given.
The class of equivalent graphs can be represented by a distinguished graph
which is called the largest chain graph. The factorization formula with respect
to the largest chain graph is a basis of a proposal of how to represent the
corresponding (discrete) probability distribution in a computer (i.e.
parametrize it). This way does not depend on the choice of a particular
Bayesian network from the class of equivalent networks and seems to be the most
efficient way from the point of view of memory demands. A separation criterion
for reading independency statements from a chain graph is formulated in a
simpler way. It resembles the well-known d-separation criterion for Bayesian
networks and can be implemented locally.


title: Learning Mixtures of DAG Models

abstract:
  We describe computationally efficient methods for learning mixtures in which
each component is a directed acyclic graphical model (mixtures of DAGs or
MDAGs). We argue that simple search-and-score algorithms are infeasible for a
variety of problems, and introduce a feasible approach in which parameter and
structure search is interleaved and expected data is treated as real data. Our
approach can be viewed as a combination of (1) the Cheeseman--Stutz asymptotic
approximation for model posterior probability and (2) the
Expectation--Maximization algorithm. We evaluate our procedure for selecting
among MDAGs on synthetic and real examples.


title: Probabilistic Inference in Influence Diagrams

abstract:
  This paper is about reducing influence diagram (ID) evaluation into Bayesian
network (BN) inference problems. Such reduction is interesting because it
enables one to readily use one's favorite BN inference algorithm to efficiently
evaluate IDs. Two such reduction methods have been proposed previously (Cooper
1988, Shachter and Peot 1992). This paper proposes a new method. The BN
inference problems induced by the mew method are much easier to solve than
those induced by the two previous methods.


title:
Planning with Partially Observable Markov Decision Processes: Advances
  in Exact Solution Method

abstract:
  There is much interest in using partially observable Markov decision
processes (POMDPs) as a formal model for planning in stochastic domains. This
paper is concerned with finding optimal policies for POMDPs. We propose several
improvements to incremental pruning, presently the most efficient exact
algorithm for solving POMDPs.


title: Flexible and Approximate Computation through State-Space Reduction

abstract:
  In the real world, insufficient information, limited computation resources,
and complex problem structures often force an autonomous agent to make a
decision in time less than that required to solve the problem at hand
completely. Flexible and approximate computations are two approaches to
decision making under limited computation resources. Flexible computation helps
an agent to flexibly allocate limited computation resources so that the overall
system utility is maximized. Approximate computation enables an agent to find
the best satisfactory solution within a deadline. In this paper, we present two
state-space reduction methods for flexible and approximate computation:
quantitative reduction to deal with inaccurate heuristic information, and
structural reduction to handle complex problem structures. These two methods
can be applied successively to continuously improve solution quality if more
computation is available. Our results show that these reduction methods are
effective and efficient, finding better solutions with less computation than
some existing well-known methods.


title: Cosmological behavior in extended nonlinear massive gravity

abstract:
  We perform a detailed dynamical analysis of various cosmological scenarios in
extended (varying-mass) nonlinear massive gravity. Due to the enhanced freedom
in choosing the involved free functions, this cosmological paradigm allows for
a huge variety of solutions that can attract the universe at late times,
comparing to scalar-field cosmology or usual nonlinear massive gravity. Amongst
others, it accepts quintessence, phantom, or cosmological-constant-like
late-time solutions, which moreover can alleviate the coincidence problem.
These features seem to be general and non-sensitive to the imposed ansantzes
and model parameters, and thus extended nonlinear massive gravity can be a good
candidate for the description of nature.


title:
From Andreev bound states to Majorana fermions in topological wires on
  superconducting substrates : a story of mutation

abstract:
  We study the proximity effect in a topological nanowire tunnel coupled to an
s-wave superconducting substrate. We use a general Green's function approach
that allows us to study the evolution of the Andreev bound states in the wire
into Majorana fermions. We show that the strength of the tunnel coupling
induces a topological transition in which the Majorana fermionic states can be
destroyed when the coupling is very strong. Moreover, we provide a
phenomenologial study of the effects of disorder in the superconductor on the
formation of Majorana fermions. We note a non-trivial effect of a quasiparticle
broadening term which can take the wire from a topological into a
non-topological phase in certain ranges of parameters. Our results have also
direct consequences for a nanowire coupled to an inhomogenous superconductor.


title:
CFHTLenS: The Environmental Dependence of Galaxy Halo Masses from Weak
  Lensing

abstract:
  We use weak gravitational lensing to analyse the dark matter halos around
satellite galaxies in galaxy groups in the CFHTLenS dataset. This dataset is
derived from the CFHTLS-Wide survey, and encompasses 154 sq. deg of
high-quality shape data. Using the photometric redshifts, we divide the sample
of lens galaxies with stellar masses in the range 10^9 Msun to 10^10.5 Msun
into those likely to lie in high-density environments (HDE) and those likely to
lie in low-density environments (LDE). Through comparison with galaxy
catalogues extracted from the Millennium Simulation, we show that the sample of
HDE galaxies should primarily (~61%) consist of satellite galaxies in groups,
while the sample of LDE galaxies should consist of mostly (~87%) non-satellite
(field and central) galaxies. Comparing the lensing signals around samples of
HDE and LDE galaxies matched in stellar mass, the lensing signal around HDE
galaxies clearly shows a positive contribution from their host groups on their
lensing signals at radii of ~500--1000 kpc, the typical separation between
satellites and group centres. More importantly, the subhalos of HDE galaxies
are less massive than those around LDE galaxies by a factor 0.65 +/- 0.12,
significant at the 2.9 sigma level. A natural explanation is that the halos of
satellite galaxies are stripped through tidal effects in the group environment.
Our results are consistent with a typical tidal truncation radius of ~40 kpc.


title: Comparing the Ancient Star Formation Histories of the Magellanic Clouds

abstract:
  We present preliminary results from a new HST archival program aimed at
tightly constraining the ancient (>4 Gyr ago) star formation histories (SFHs)
of the field populations of the SMC and LMC. We demonstrate the quality of the
archival data by constructing HST/WFPC2-based color-magnitude diagrams (CMDs;
M_{F555W} ~ +8) for 7 spatially diverse fields in the SMC and 8 fields in the
LMC. The HST-based CMDs are >2 magnitudes deeper than any from ground based
observations, and are particularly superior in high surface brightness regions,
e.g., the LMC bar, which contain a significant fraction of star formation and
are crowding limited from ground based observations. To minimize systematic
uncertainties, we derive the SFH of each field using an identical maximum
likelihood CMD fitting technique. We then compute an approximate mass weighted
average SFH for each galaxy. We find that both galaxies lack a dominant burst
of early star formation, which suggests either a suppression or an
under-fueling of early star formation. From 10-12 Gyr ago, the LMC experienced
a period of enhanced stellar mass growth relative to the SMC. Similar to some
previous studies, we find two notable peaks in the SFH of the SMC at ~4.5 and 9
Gyr ago, which could be due to repeated close passages with the LMC, implying
an interaction history that has persisted for at least 9 Gyr. We find little
evidence for strong periodic behavior in the lifetime SFHs of both MCs,
suggesting that repeated encounters with the Milky Way are unlikely. Beginning
~3.5 Gyr ago, both galaxies show increases in their SFHs, in agreement with
previous studies, and thereafter, track each other remarkably well. (abridged)


title:
Comparing results for a global metric from analytical perturbation
  theory and a numerical code

abstract:
  We compare the results obtained from analytical perturbation theory and the
AKM numerical code for an axistationary spacetime built from matching a
rotating perfect fluid interior with the equation of state $\epsilon-3p=4B$ of
the simple MIT bag model and an asymptotically flat exterior. We discuss the
behaviour of the error in the metric components of the analytical approximation
going to higher orders. Additionally, we check and comment the errors in
multipole moments, central pressure and some other physical properties of the
spacetime.


title: Three-dimensional Spontaneous Magnetic Reconnection

abstract:
  Magnetic reconnection is best known from observations of the Sun where it
causes solar flares. Observations estimate the reconnection rate a small, but
non-negligible fraction of the Alfv\'en speed, so-called fast reconnection.
Until recently, the prevailing pictures of reconnection were referring to
either resistivity or plasma microscopic effects, which was contradictory to
the observed rates. The alternative picture was either reconnection due to the
stochasticity of magnetic field lines in turbulence or the tearing instability
of the thin current sheet. In this paper I simulated long-term
three-dimensional nonlinear evolution of a thin, planar current sheet subject
to fast oblique tearing instability using direct numerical simulations of
resistive-viscous MHD. The late-time evolution resembles generic turbulence
with -5/3 power spectrum and scale-dependent anisotropy, so I conclude that the
tearing-driven reconnection becomes turbulent reconnection. The turbulence is
local in scale, so microscopic diffusivity should not affect large-scale
quantities. This is confirmed by convergence of the reconnection rate towards
$\sim 0.015 v_A$ with increasing Lundquist number. In this spontaneous
reconnection with mean field and without driving the dissipation rate per unit
area also converge to $\sim 0.006 \rho v_A^3$, the dimensionless constants
$0.015$ and $0.006$ are governed only by self-driven nonlinear dynamics of the
sheared magnetic field. Remarkably, this also means that thin current sheet has
a universal fluid resistance depending only on its length to width ratio and to
$v_A/c$.


title: Comment on Perez et al [PRX 2, 041005 (2012), arXiv:1209.2011]

abstract:
  Recently Perez et al [arXiv:1209.2011] wrote on the spectral slope of MHD
turbulence claiming that it is consistent with -3/2. This work contains a
number of errors, factual inaccuracies and puzzling methods in the
interpretation of numerics. We argue that the numerical evidence that the
authors presented is actually against -3/2 slope and the assumption of
universal alignment, opposite to what they claim. Perhaps the most puzzling is
the Fig.8 that claims that the authors measured the inertial range length and
that it is consistent with Re^2/3 scaling. At a close inspection it appears
that the datapoints are not the result of a measurement, but rather were
calculated by a formula.


title: How closely do baryons follow dark matter on large scales?

abstract:
  We investigate the large-scale clustering and gravitational interaction of
baryons and dark matter (DM) over cosmic time using a set of collisionless
N-body simulations. Both components, baryons and DM, are evolved from distinct
primordial density and velocity power spectra as predicted by early-universe
physics. We first demonstrate that such two-component simulations require an
unconventional match between force and mass resolution (i.e. force softening on
at least the mean particle separation scale). Otherwise, the growth on any
scale is not correctly recovered because of a spurious coupling between the two
species at the smallest scales. With these simulations, we then demonstrate how
the primordial differences in the clustering of baryons and DM are
progressively diminished over time. In particular, we explicitly show how the
BAO signature is damped in the spatial distribution of baryons and imprinted in
that of DM. This is a rapid process, yet it is still not fully completed at low
redshifts. On large scales, the overall shape of the correlation function of
baryons and DM differs by 2% at z = 9 and by 0.2% at z = 0. The differences in
the amplitude of the BAO peak are approximately a factor of 5 larger: 10% at z
= 9 and 1% at z = 0. These discrepancies are, however, smaller than effects
expected to be introduced by galaxy formation physics in both the shape of the
power spectrum and in the BAO peak, and are thus unlikely to be detected given
the precision of the next generation of galaxy surveys. Hence, our results
validate the standard practice of modelling the observed galaxy distribution
using predictions for the total mass clustering in the Universe.