  We perform a detailed study of the relaxation towards equilibrium in the
Hamiltonian Mean-Field (HMF) model, a prototype for long-range interactions in
$N$-particle dynamics. In particular, we point out the role played by the
infinity of stationary states of the associated $N ~$ Vlasov dynamics. In this
context, we derive a new general criterion for the stability of any spatially
homogeneous distribution, and compare its analytical predictions with numerical
simulations of the Hamiltonian, finite $N$, dynamics. We then propose and
verify numerically a scenario for the relaxation process, relying on the Vlasov
equation. When starting from a non stationary or a Vlasov unstable stationary
initial state, the system shows initially a rapid convergence towards a stable
stationary state of the Vlasov equation via non stationary states: we
characterize numerically this dynamical instability in the finite $N$ system by
introducing appropriate indicators. This first step of the evolution towards
Boltzmann-Gibbs equilibrium is followed by a slow quasi-stationary process,
that proceeds through different stable stationary states of the Vlasov
equation. If the finite $N$ system is initialized in a Vlasov stable homogenous
state, it remains trapped in a quasi-stationary state for times that increase
with the nontrivial power law $N^{1.7}$. Single particle momentum distributions
in such a quasi-stationary regime do not have power-law tails, and hence cannot
be fitted by the $q$-exponential distributions derived from Tsallis statistics.


  We calculate the new physics contributions to the neutral $B_d^0$ and $B_s^0$
meson mass splitting $\Delta M_d$ and $\Delta M_s$ induced by the box diagrams
involving the charged-Higgs bosons in the top quark two-Higgs doublet model
(T2HDM). Using the precision data, we obtain the bounds on the parameter space
of the T2HDM: (a) for fixed $M_H=400$ GeV and $\delta=[0^\circ,60^\circ]$, the
upper bound on $\tan{\beta}$ is $\tan \beta \leq 30$ after the inclusion of
major theoretical uncertainties; (b) for the case of $\tan{\beta} \leq 20$, a
light charged Higgs boson with a mass around 300 GeV is allowed; and (c) the
bounds on $\tan{\beta}$ and $M_H$ are strongly correlated: a smaller (larger)
$\tan{\beta}$ means a lighter (heavier) charged Higgs boson.


  Combination of a construction of unambiguous quantum conditions out of the
conventional one and a simultaneous quantization of the positions, momenta,
angular momenta and Hamiltonian leads to the geometric potential given by the
so-called thin-lay quantization.


  Autonomous driving is a challenging task that has gained broad attention from
both academia and industry. Current solutions using convolutional neural
networks require large amounts of computational resources, leading to high
power consumption. Spiking neural networks (SNNs) provide an alternative
computation model to process information and make decisions. This biologically
plausible model has the advantage of low latency and energy efficiency. Recent
work using SNNs for autonomous driving mostly focused on simple tasks like lane
keeping in simplified simulation environments. This project studies SNNs on
photo-realistic driving scenes in the CARLA simulator, which is an important
step toward using SNNs on real vehicles. The efficacy and generalizability of
the method will be investigated.


  We provide evidence of an intermediate Haldane phase in a spin-2 quantum
chain. By combining effective field theory and numerical approaches, we show
that the phase diagram of the proposed model includes SO(5) Haldane,
intermediate Haldane, and large-D phases. We determine the characteristic
properties of these phases, including edge states, string order parameters, and
degeneracies in the entanglement spectrum. The symmetries responsible for the
degeneracy patterns observed in the entanglement spectrum are also discussed.


  As the field of image generation rapidly advances, traditional diffusion
models and those integrated with multimodal large language models (LLMs) still
encounter limitations in interpreting complex prompts and preserving image
consistency pre and post-editing. To tackle these challenges, we present an
innovative image editing framework that employs the robust Chain-of-Thought
(CoT) reasoning and localizing capabilities of multimodal LLMs to aid diffusion
models in generating more refined images. We first meticulously design a CoT
process comprising instruction decomposition, region localization, and detailed
description. Subsequently, we fine-tune the LISA model, a lightweight
multimodal LLM, using the CoT process of Multimodal LLMs and the mask of the
edited image. By providing the diffusion models with knowledge of the generated
prompt and image mask, our models generate images with a superior understanding
of instructions. Through extensive experiments, our model has demonstrated
superior performance in image generation, surpassing existing state-of-the-art
models. Notably, our model exhibits an enhanced ability to understand complex
prompts and generate corresponding images, while maintaining high fidelity and
consistency in images before and after generation.


  In this paper we construct six-dimensional compact non-K\"ahler Hamiltonian
circle manifolds which satisfy the strong Lefschetz property themselves but
nevertheless have a non-Lefschetz symplectic quotient. This provides the first
known counter examples to the question whether the strong Lefschetz property
descends to the symplectic quotient. We also give examples of Hamiltonian
strong Lefschetz circle manifolds which have a non-Lefschetz fixed point
submanifold. In addition, we establish a sufficient and necessary condition for
a finitely presentable group to be the fundamental group of a strong Lefschetz
manifold. We then use it to show the existence of Lefschetz four-manifolds with
non-Lefschetz finite covering spaces.


  The development of techniques for manipulation of quantum information has
opened the door to a variety of protocols for accomplishing unprecedented
tasks. In particular, a new protocol of quantum teleportation was proposed in
2008 to achieve effective energy transportation simply via local operations and
classical communication without breaking any known physical laws. This is
achieved by extracting zero-point energy of entangled many-body systems by
local operations dependent on information obtained by a distant measurement.
The concept is reviewed from an introductory viewpoint herein.


  Recent literature has demonstrated that the use of per-channel energy
normalization (PCEN), has significant performance improvements over traditional
log-scaled mel-frequency spectrograms in acoustic sound event detection (SED)
in a multi-class setting with overlapping events. However, the configuration of
PCEN's parameters is sensitive to the recording environment, the
characteristics of the class of events of interest, and the presence of
multiple overlapping events. This leads to improvements on a class-by-class
basis, but poor cross-class performance. In this article, we experiment using
PCEN spectrograms as an alternative method for SED in urban audio using the
UrbanSED dataset, demonstrating per-class improvements based on parameter
configuration. Furthermore, we address cross-class performance with PCEN using
a novel method, Multi-Rate PCEN (MRPCEN). We demonstrate cross-class SED
performance with MRPCEN, demonstrating improvements to cross-class performance
compared to traditional single-rate PCEN.


  In Grids scheduling decisions are often made on the basis of jobs being
either data or computation intensive: in data intensive situations jobs may be
pushed to the data and in computation intensive situations data may be pulled
to the jobs. This kind of scheduling, in which there is no consideration of
network characteristics, can lead to performance degradation in a Grid
environment and may result in large processing queues and job execution delays
due to site overloads. In this paper we describe a Data Intensive and Network
Aware (DIANA) meta-scheduling approach, which takes into account data,
processing power and network characteristics when making scheduling decisions
across multiple sites. Through a practical implementation on a Grid testbed, we
demonstrate that queue and execution times of data-intensive jobs can be
significantly improved when we introduce our proposed DIANA scheduler. The
basic scheduling decisions are dictated by a weighting factor for each
potential target location which is a calculated function of network
characteristics, processing cycles and data location and size. The job
scheduler provides a global ranking of the computing resources and then selects
an optimal one on the basis of this overall access and execution cost. The
DIANA approach considers the Grid as a combination of active network elements
and takes network characteristics as a first class criterion in the scheduling
decision matrix along with computation and data. The scheduler can then make
informed decisions by taking into account the changing state of the network,
locality and size of the data and the pool of available processing cycles.


  We study one-dimensional insulators obeying a chiral symmetry in the
single-particle picture. The Fermi level is assumed to lie in a mobility gap.
Topological indices are defined for infinite (bulk) or half-infinite (edge)
systems, and it is shown that for a given Hamiltonian with nearest neighbor
hopping the two indices are equal. We also give a new formulation of the index
in terms of the Lyapunov exponents of the zero energy Schr\"odinger equation,
which illustrates the conditions for a topological phase transition occurring
in the mobility gap regime.


  Let $Q$ and $Q'$ be two monomial primary ideals of a polynomial algebra $S$
over a field. We give an upper bound for the Stanley depth of $S/(Q\cap Q')$
which is reached if $Q$,$Q'$ are irreducible. Also we show that Stanley's
Conjecture holds for $Q_1\cap Q_2$, $S/(Q_1\cap Q_2\cap Q_3)$, $(Q_i)_i$ being
some irreducible monomial ideals of $S$.


  While the performance of recent learned intra and sequential video
compression models exceed that of respective traditional codecs, the
performance of learned B-frame compression models generally lag behind
traditional B-frame coding. The performance gap is bigger for complex scenes
with large motions. This is related to the fact that the distance between the
past and future references vary in hierarchical B-frame compression depending
on the level of hierarchy, which causes motion range to vary. The inability of
a single B-frame compression model to adapt to various motion ranges causes
loss of performance. As a remedy, we propose controlling the motion range for
flow prediction during inference (to approximately match the range of motions
in the training data) by downsampling video frames adaptively according to
amount of motion and level of hierarchy in order to compress all B-frames using
a single flexible-rate model. We present state-of-the-art BD rate results to
demonstrate the superiority of our proposed single-model motion-adaptive
inference approach to all existing learned B-frame compression models.


  Recently Dekking conjectured the form of the subword complexity function for
the Fibonacci-Thue-Morse sequence. In this note we prove his conjecture by
purely computational means, using the free software Walnut.


  We study the off-diagonal Goldberger-Treiman relation (ODGTR) and its
discrepancy (ODGTD) in the N, Delta, pi sector through O(p^2) using heavy
baryon chiral perturbation theory. To this order, the ODGTD and axial vector N
to Delta transition radius are determined solely by low energy constants. Loop
corrections appear at O(p^4). For low-energy constants of natural size, the
ODGTD would represent a ~ 2% correction to the ODGTR. We discuss the
implications of the ODGTR and ODGTD for lattice and quark model calculations of
the transition form factors and for parity-violating electroexcitation of the
Delta.


  We prove that any flat family $(\mathcal{ F}_u)_{u\in U}$ of rank 2
torsion-free sheaves on a Gauduchon surface defines a continuous map on the
semi-stable locus $U^{\mathrm {ss}}:=\{u\in U \ |\ \mathcal{ F}_u\hbox{ is
slope semi-stable}\}$ with values in the Donaldson-Uhlenbeck compactification
of the corresponding instanton moduli space. In the general (possibly
non-K\"ahlerian) case, the Donaldson-Uhlenbeck compactification is not a
complex space, and the set $U^{\mathrm {ss}}$ can be a complicated subset of
the base space $U$ that is neither open or closed in the classical topology,
nor locally closed in the Zariski topology. This result provides an efficient
tool for the explicit description of Donaldson-Uhlenbeck compactifications on
arbitrary Gauduchon surfaces.


  We study the optical properties of the solar gravitational lens (SGL) while
treating the Sun as an extended, axisymmetric and rotating body. The
gravitational field of the Sun is represented using a set of zonal harmonics.
We develop an analytical description of the intensity of light that is observed
in the image plane in the strong interference region of a realistic SGL. This
formalism makes it possible to model not only the point-spread function of
point sources, but also actual observables, images that form in the focal plane
of an imaging telescope positioned in the image plane. Perturbations of the
monopole gravitational field of the Sun are dominated by the solar quadrupole
moment, which results in forming an astroid caustic on the image plane.
Consequently, an imaging telescope placed inside the astroid caustic observes
four bright spots, forming the well-known pattern of an Einstein cross. The
relative intensities and positions of these spots change as the telescope is
moved in the image plane, with spots merging into bright arcs when the
telescope approaches the caustic boundary. Outside the astroid caustic, only
two spots remain and the observed pattern eventually becomes indistinguishable
from the imaging pattern of a monopole lens at greater distances from the
optical axis. We present results from extensive numerical simulations, forming
the basis of our ongoing study of prospective exoplanet imaging with the SGL.
These results are also applicable to describe a large class of gravitational
lensing scenarios involving axisymmetric lenses that can be represented using
zonal harmonics.


  Lead pipe remediation budgets are limited and ought to maximize public health
impact. This goal implies a non-trivial optimization problem; lead service
lines connect water mains to individual houses, but any realistic replacement
strategy must batch replacements at a larger scale. Additionally, planners
typically lack a principled method for comparing the relative public health
value of potential interventions and often plan projects based on non-health
factors. This paper describes a simple process for estimating child health
impact at a parcel level by cleaning and synthesizing municipal datasets that
are commonly available but seldom joined due to data quality issues. Using
geocoding as the core record linkage mechanism, parcel-level toxicity data can
be combined with school enrollment records to indicate where young children and
lead lines coexist. A harm metric of estimated exposure-years is described at
the parcel level, which can then be aggregated to the project level and
minimized globally by posing project selection as a 0/1 knapsack problem.
Simplifying further for use by non-experts, the implied linear programming
relaxation is solved intuitively with the greedy algorithm; ordering projects
by benefit cost ratio produces a priority list which planners can then consider
holistically alongside harder to quantify factors. A case study demonstrates
the successful application of this framework to a small U.S. city's existing
data to prioritize federal infrastructure funding. While this paper focuses on
lead in drinking water, the approach readily generalizes to other sources of
residential toxicity with disproportionate impact on children.


  I review some of the scientific work of George Papadopoulos in the context of
the Greek cultural tradition and modern theoretical physics. The main emphasis
is on his works on path integrals and their applications. The review is closed
by an excursus on the polaron physics, where the path-integral approach has
been proven to be a method of excellence.


  RF data-driven device fingerprinting through the use of deep learning has
recently surfaced as a possible method for enabling secure device
identification and authentication. Traditional approaches are commonly
susceptible to the domain adaptation problem where a model trained on data
collected under one domain performs badly when tested on data collected under a
different domain. Some examples of a domain change include varying the location
or environment of the device and varying the time or day of the data
collection. In this work, we propose using multifractal analysis and the
variance fractal dimension trajectory (VFDT) as a data representation input to
the deep neural network to extract device fingerprints that are domain
generalizable. We analyze the effectiveness of the proposed VFDT representation
in detecting device-specific signatures from hardware-impaired IQ (in-phase and
quadrature) signals, and we evaluate its robustness in real-world settings,
using an experimental testbed of 30 WiFi-enabled Pycom devices. Our
experimental results show that the proposed VFDT representation improves the
scalability, robustness and generalizability of the deep learning models
significantly compared to when using IQ data samples.


  Unpaired image-to-image translation methods aim at learning a mapping of
images from a source domain to a target domain. Recently, these methods proved
to be very useful in biological applications to display subtle phenotypic cell
variations otherwise invisible to the human eye. However, current models
require a large number of images to be trained, while mostmicroscopy
experiments remain limited in the number of images they can produce. In this
work, we present an improved CycleGAN architecture that employs self-supervised
discriminators to alleviate the need for numerous images. We demonstrate
quantitatively and qualitatively that the proposed approach outperforms the
CycleGAN baseline, including when it is combined with differentiable
augmentations. We also provide results obtained with small biological datasets
on obvious and non-obvious cell phenotype variations, demonstrating a
straightforward application of this method.


  The structural solution problem can be a daunting and time consuming task.
Especially in the presence of impurity phases, current methods such as indexing
become more unstable. In this work, we apply the novel approach of
semi-supervised learning towards the problem of identifying the Bravais lattice
and the space group of inorganic crystals. Our semi-supervised generative deep
learning model can train on both labeled data -- diffraction patterns with the
associated crystal structure -- and unlabeled data, diffraction patterns that
lack this information. This approach allows our models to take advantage of the
troves of unlabeled data that current supervised learning approaches cannot,
which should result in models that can more accurately generalize to real data.
In this work, we classify powder diffraction patterns into all 14 Bravais
lattices and 144 space groups (we limit the number due to sparse coverage in
crystal structure databases), which covers more crystal classes than other
studies. Our models also drastically outperform current deep learning
approaches for both space group and Bravais Lattice classification using less
training data.


  A fundamental question in Riemannian geometry is to find canonical metrics on
a given smooth manifold. In the 1980s, R. Hamilton proposed an approach to this
question based on parabolic partial differential equations. The goal is to
start from a given initial metric and deform it to a canonical metric by means
of an evolution equation. There are various natural evolution equations for
Riemannian metrics, including the Ricci flow and the conformal Yamabe flow. In
this survey, we discuss the global behavior of the solutions to these
equations. In particular, we describe how these techniques can be used to prove
the Differentiable Sphere Theorem.
  This article is based on the Takagi Lectures delivered by the author at the
Research Institute for Mathematical Sciences, Kyoto University, on June 4,
2011.


  Self-interacting massive particles with spin $\geq 1$ unavoidably violate
unitarity; the question is at what scale. For spin-$1$ the strong coupling
scale (at which perturbative unitarity is lost) cannot be raised by any finite
tuning of the interactions, while for spin-$2$ there exists a special tuning of
the Wilson coefficients which can raise this scale (and enjoys numerous special
properties such as ghost-freedom). Here, we fill in the missing piece by
describing how the self-interactions of a massive spin-$3/2$ field, or "massive
gravitino", become strongly coupled at high energies. We show that while
several different structures appear in the leading order potential, the strong
coupling scale cannot be raised (in the absence of additional fields). At the
level of the off-shell Lagrangian, it is always the non-linear symmetries of
the longitudinal Stuckelberg mode that dictate the strong coupling, and we show
that in general it is only possible to parametrically raise the strong coupling
scale if Wess-Zumino structures exist. We complement this off-shell approach
with a first analysis of positivity bounds for a massive spin-$3/2$ particle,
showing that any potential self-interaction which contributes to an on-shell
2-to-2 elastic process at tree level must vanish if this low-energy theory is
to have a standard UV completion. We identify the mixing between the
longitudinal mode and the transverse modes as the main obstacle to positivity,
and clarify how the non-Abelian nature of non-linear (dRGT) massive gravity
allows it to satisfy positivity where all known spin $\geq 3/2$ Abelian
theories fail. Our results imply that a massive gravitino cannot appear alone
in a controlled EFT---it must be accompanied by other particles, e.g.~as part
of a supermultiplet. Together with the spin-$1$ and spin-$2$ cases, we suggest
features which will persist in even higher spin massive theories.


  Natural language interfaces (NLIs) enable users to flexibly specify
analytical intentions in data visualization. However, diagnosing the
visualization results without understanding the underlying generation process
is challenging. Our research explores how to provide explanations for NLIs to
help users locate the problems and further revise the queries. We present XNLI,
an explainable NLI system for visual data analysis. The system introduces a
Provenance Generator to reveal the detailed process of visual transformations,
a suite of interactive widgets to support error adjustments, and a Hint
Generator to provide query revision hints based on the analysis of user queries
and interactions. Two usage scenarios of XNLI and a user study verify the
effectiveness and usability of the system. Results suggest that XNLI can
significantly enhance task accuracy without interrupting the NLI-based analysis
process.


  We verify that the dilaton together with one exactly marginal field, form a
moduli space of marginal deformations of closed bosonic string field theory to
polynomial order five. We use the results of this successful check in order to
find the best functional form of a fit of quintic amplitudes. We then use this
fit in order to accurately compute the tachyon and dilaton effective potential
in the limit of infinite level. We observe that to order four, the effective
potential gives unexpectedly accurate results for the vacuum. We are thus led
to conjecture that the effective potential, to a given order, is a good
approximation to the whole potential including all interactions from the
vertices up to this order from the untruncated string field. We then go on and
compute the effective potential to order five. We analyze its vacuum structure
and find that it has several saddle points, including the Yang-Zwiebach vacuum,
but also a local minimum. We discuss the possible physical meanings of these
vacua.


  We perform a renormalization group transformation to construct a lattice
theory of chiral fermions. The field variables of the continuum theory are
averaged over hypercubes to define lattice fields. Integrating out the
continuum variables in perturbation theory we derive a chirally invariant
effective action for the lattice fields. This is consistent with the
Nielsen-Niniomiya theorem because the effective action is nonlocal. We also
construct the axial current on the lattice and we show that the axial anomaly
of the continuum theory is reproduced in the Schwinger model. This shows that
chiral fermions can be regularized on the lattice.


  In atomic Bose-Einstein condensates in optical lattices, mean-field energy
can support the existence of period-doubled density waves, which are similar to
Bloch waves but have the double periodicity of the underlying lattice
potentials. However, they are dynamically unstable. Here, we propose to use the
spin-orbit coupling to stabilize the period-doubled density waves. The
stabilization mechanism is revealed to relate to interaction-induced
spontaneous symmetry breaking of the spin-flip parity symmetry.


  Consider $N$ bosons in a finite box $\Lambda= [0,L]^3\subset \mathbf R^3$
interacting via a two-body smooth repulsive short range potential. We construct
a variational state which gives the following upper bound on the ground state
energy per particle \[\bar\lim_{\rho\to0} \bar \lim_{L \to \infty,
  N/L^3 \to \rho} (\frac{e_0(\rho)- 4 \pi a \rho}{(4 \pi
a)^{5/2}(\rho)^{3/2}})\leq \frac{16}{15\pi^2}, \] where $a$ is the scattering
length of the potential. Previously, an upper bound of the form $C 16/15\pi^2$
for some constant $C > 1$ was obtained in \cite{ESY}. Our result proves the
upper bound of the the prediction by Lee-Yang \cite{LYang} and Lee-Huang-Yang
\cite{LHY}.


  The existence of a static lengthscale that grows in accordance with the
dramatic slowing down observed at the glass transition is a subject of intense
interest. A recent publication compared two proposals for this length scale,
one based on the point-to-set correlation technique and the other on the scale
where the lowest eigenvalue of the Hessian matrix becomes sensitive to
disorder. The conclusion was that both approaches lead to the same lengthscale,
but the former is easier to measure at higher temperatures and the latter at
lower temperatures. But even after using both methods together, the range of
increase in the observed lengthscales was limited by the relaxation times
reachable by standard molecular dynamics techniques (i.e. about 4-5 orders of
magnitude). In this paper we therefore attempt to explore the typical scale at
even lower temperatures, testing for this purpose two approaches, one based on
the idea of vapor deposition and the other on a swap Monte Carlo technique. We
conclude that the first approach does not help in getting to lower
temperatures, but the second one does so quite effectively. We can reach a
typical lengthscale that grows in accordance with at least $15$ orders of
magnitude increase in the relaxation time, competing with the best experimental
conditions. We conclude by discussing the relationship between the observed
lengthscale and various models of the relaxation time.


  Scientists and engineers are often interested in learning the number of
subpopulations (or components) present in a data set. A common suggestion is to
use a finite mixture model (FMM) with a prior on the number of components. Past
work has shown the resulting FMM component-count posterior is consistent; that
is, the posterior concentrates on the true, generating number of components.
But consistency requires the assumption that the component likelihoods are
perfectly specified, which is unrealistic in practice. In this paper, we add
rigor to data-analysis folk wisdom by proving that under even the slightest
model misspecification, the FMM component-count posterior diverges: the
posterior probability of any particular finite number of components converges
to 0 in the limit of infinite data. Contrary to intuition, posterior-density
consistency is not sufficient to establish this result. We develop novel
sufficient conditions that are more realistic and easily checkable than those
common in the asymptotics literature. We illustrate practical consequences of
our theory on simulated and real data.


  Germanium-based alloys hold great promise for future spintronics
applications, due to their potential for integration with conventional Si-based
electronics. Mn5Ge3 exhibits strong ferromagnetism up to the Curie temperature
Tc~295K. We use Point Contact Andreev Reflection (PCAR) spectroscopy to measure
the spin polarization of Mn5Ge3 epilayers grown by solid phase epitaxy on
Ge(111). In addition, we calculate the spin polarization of bulk Mn5Ge3 in the
diffusive and ballistic regimes using density-functional theory. The measured
spin polarization, Pc=43+/-5% is compared to our theoretical estimates,
PDFT=10+/-5% and 35+/-5% in the ballistic and diffusive limits respectively.


  The bidirectional Fano algorithm (BFA) can achieve at least two times
decoding throughput compared to the conventional unidirectional Fano algorithm
(UFA). In this paper, bidirectional Fano decoding is examined from the queuing
theory perspective. A Discrete Time Markov Chain (DTMC) is employed to model
the BFA decoder with a finite input buffer. The relationship between the input
data rate, the input buffer size and the clock speed of the BFA decoder is
established. The DTMC based modelling can be used in designing a high
throughput parallel BFA decoding system. It is shown that there is a tradeoff
between the number of BFA decoders and the input buffer size, and an optimal
input buffer size can be chosen to minimize the hardware complexity for a
target decoding throughput in designing a high throughput parallel BFA decoding
system.


  The vortex penetration field of the multilayer coating model with a single
superconductor layer and a single insulator layer formed on a bulk
superconductor are derived. The same formula can be applied to a model with a
superconductor layer formed on a bulk superconductor without an insulator
layer.


  Wilson loop diagrams are an important tool in studying scattering amplitudes
of SYM $N=4$ theory and are known by previous work to be associated to
positroids. In this paper we study the structure of the associated positroids,
as well as the structure of the denominator of the integrand defined by each
diagram. We give an algorithm to derive the Grassmann necklace of the
associated positroid directly from the Wilson loop diagram, and a recursive
proof that the dimension of these cells is thrice the number of propagators in
the diagram. We also show that the ideal generated by the denominator in the
integrand is the radical of the ideal generated by the product of Grassmann
necklace minors.


  Regulators and academics are increasingly interested in the causal effect
that algorithmic actions of a digital platform have on consumption. We
introduce a general causal inference problem we call the steerability of
consumption that abstracts many settings of interest. Focusing on observational
designs and exploiting the structure of the problem, we exhibit a set of
assumptions for causal identifiability that significantly weaken the often
unrealistic overlap assumptions of standard designs. The key novelty of our
approach is to explicitly model the dynamics of consumption over time, viewing
the platform as a controller acting on a dynamical system. From this dynamical
systems perspective, we are able to show that exogenous variation in
consumption and appropriately responsive algorithmic control actions are
sufficient for identifying steerability of consumption. Our results illustrate
the fruitful interplay of control theory and causal inference, which we
illustrate with examples from econometrics, macroeconomics, and machine
learning.


  A scintillating-fiber beam profile detector has been designed, built and
tested, for the monitoring of the position and size of the electron beam of the
DAFNE, the recently commissioned electron beam-test facility at the Frascati
LNF. A description of the detector construction and assembly, together with the
results achieved during the 2003-2004 run, are here reported.


  The cosmic web structure is studied with the concepts and methods of fractal
geometry, employing the adhesion model of cosmological dynamics as a basic
reference. The structures of matter clusters and cosmic voids in cosmological
N-body simulations or the Sloan Digital Sky Survey are elucidated by means of
multifractal geometry. A non-lacunar multifractal geometry can encompass three
fundamental descriptions of the cosmic structure, namely, the web structure,
hierarchical clustering, and halo distributions. Furthermore, it explains our
present knowledge of cosmic voids. In this way, a unified theory of the
large-scale structure of the universe seems to emerge. The multifractal
spectrum that we obtain significantly differs from the one of the adhesion
model and conforms better to the laws of gravity. The formation of the cosmic
web is best modeled as a type of turbulent dynamics, generalizing the known
methods of Burgers turbulence.


  This proceedings contribution gives a brief experimental update of the `Any
light particle search (ALPS) -II' at DESY which will be sensitive to sub-eV,
very weakly coupled particles beyond the Standard Model. First data on hidden
sector photon parameter space through photon-hidden photon oscillations in
vacuum is expected in 2014. Axion-like particle search (implying the
installation of superconducting HERA magnets) could be realized in 2017.


  We consider the kinetics of the imperfect narrow escape problem, i.e. the
time it takes for a particle diffusing in a confined medium of generic shape to
reach and to be adsorbed by a small, imperfectly reactive patch embedded in the
boundary of the domain, in two or three dimensions. Imperfect reactivity is
modeled by an intrinsic surface reactivity $\kappa$ of the patch, giving rise
to Robin boundary conditions. We present a formalism to calculate the exact
asymptotics of the mean reaction time in the limit of large volume of the
confining domain. We obtain exact explicit results in the two limits of large
and small reactivities of the reactive patch, and a semi-analytical expression
in the general case. Our approach reveals an anomalous scaling of the mean
reaction time as the inverse square root of the reactivity in the large
reactivity limit, valid for an initial position near the extremity of the
reactive patch. We compare our exact results with those obtained within the
``constant flux approximation''; we show that this approximation turns out to
give exactly the next-to-leading order term of the small reactivity limit, and
provides a good approximation of the reaction time far from the reactive patch
for all reactivities, but not in the vicinity of the boundary of the reactive
patch due to the above mentioned anomalous scaling. These results thus provide
a general framework to quantify the mean reaction times for the imperfect
narrow escape problem.


  Decoding sequences that stem from multiple transmissions of a codeword over
an insertion, deletion, and substitution channel is a critical component of
efficient deoxyribonucleic acid (DNA) data storage systems. In this paper, we
consider a concatenated coding scheme with an outer nonbinary low-density
parity-check code or a polar code and either an inner convolutional code or a
time-varying block code. We propose two novel decoding algorithms for inference
from multiple received sequences, both combining the inner code and channel to
a joint hidden Markov model to infer symbolwise a posteriori probabilities
(APPs). The first decoder computes the exact APPs by jointly decoding the
received sequences, whereas the second decoder approximates the APPs by
combining the results of separately decoded received sequences and has a
complexity that is linear with the number of sequences. Using the proposed
algorithms, we evaluate the performance of decoding multiple received sequences
by means of achievable information rates and Monte-Carlo simulations. We show
significant performance gains compared to a single received sequence. In
addition, we succeed in improving the performance of the aforementioned coding
scheme by optimizing both the inner and outer codes.


  A general mathematical framework for quantum key distribution based on the
concepts of quantum channel and Turing machine is suggested. The security for
its special case is proved. The assumption is that the adversary can perform
only individual (in essence, classical) attacks. For this case an advantage of
quantum key distribution over classical one is shown.


  We explore in some detail the hypothesis that the generation of a primordial
lepton-antilepton asymmetry (Leptogenesis) early on in the history of the
Universe is the root cause for the origin of matter. After explaining the
theoretical conditions for producing a matter-antimatter asymmetry in the
Universe we detail how, through sphaleron processes, it is possible to
transmute a lepton asymmetry -- or, more precisely, a (B-L)-asymmetry -- into a
baryon asymmetry. Because Leptogenesis depends in detail on properties of the
neutrino spectrum, we review briefly existing experimental information on
neutrinos as well as the seesaw mechanism, which offers a theoretical
understanding of why neutrinos are so light. The bulk of the review is devoted
to a discussion of thermal Leptogenesis and we show that for the neutrino
spectrum suggested by oscillation experiments one obtains the observed value
for the baryon to photon density ratio in the Universe, independently of any
initial boundary conditions. In the latter part of the review we consider how
well Leptogenesis fits with particle physics models of dark matter. Although
axionic dark matter and Leptogenesis can be very naturally linked, there is a
potential clash between Leptogenesis and models of supersymmetric dark matter
because the high temperature needed for Leptogenesis leads to an overproduction
of gravitinos, which alter the standard predictions of Big Bang
Nucleosynthesis. This problem can be resolved, but it constrains the
supersymmetric spectrum at low energies and the nature of the lightest
supersymmetric particle (LSP). Finally, as an illustration of possible other
options for the origin of matter, we discuss the possibility that Leptogenesis
may occur as a result of non-thermal processes.


  It is shown that in 2D system of the fermions with simplest indirect
boson-induced attraction (through the Einstein phonon exchange as an example)
along with the normal and superconducting phases there arises a new (called
"abnormal normal" or pseudogap) one where the absolute value of the order
parameter is finite but its phase is a random quantity. It is important that
this new phase really exists at low carriers density only, i.e. it shrinks when
doping increases. The relevance of the results obtained with dependence of
pseudogap on doping in high-temperature superconductors is speculated.


  We consider the observational signature of the dynamical effects on the
luminosity function of globular clusters. For the three best studied systems,
in Milky Way, M31, and M87, there is a statistically significant difference
between the inner and outer population of globular clusters. In all cases the
inner clusters are on average brighter than the outer clusters ($0.26 < \Delta
m_0 < 0.84$) and have a smaller dispersion in magnitudes ($0.04 < \Delta \sigma
< 0.53$), with the larger differences for the local, better observed samples.
The differences are of the type that would be expected if the inner population
had been depleted by tidal shocks. The results suggest that the inner
population suffers substantial evolution from its initial distribution and
cannot not be used as a standard candle without correction for dynamical
evolution.


  The package aITALC has been developed for fully automated calculations of two
fermion production at e+ e- collider and other similar reactions. We emphasize
the connection and interoperability between the different modules required for
the calculation and the external tools Diana, Form and LoopTools. Results for
e+ e- -> f anti-f, e+ e- are presented.


  In this letter, we present a novel and extremely fast steganalysis method of
Voice over IP (VoIP) streams, driven by the need for a quick and accurate
detection of possible steganography in VoIP streams. We firstly analyzed the
correlations in carriers. To better exploit the correlation in code-words, we
mapped vector quantization code-words into a semantic space. In order to
achieve high detection efficiency, only one hidden layer is utilized to extract
the correlations between these code-words. Finally, based on the extracted
correlation features, we used the softmax classifier to categorize the input
stream carriers. To boost the performance of this proposed model, we
incorporate a simple knowledge distillation framework into the training
process. Experimental results show that the proposed method achieves
state-of-the-art performance both in detection accuracy and efficiency. In
particular, the processing time of this method on average is only about 0.05\%
when sample length is as short as 0.1s, attaching strong practical value to
online serving of steganography monitor.


  We show that every Fell bundle B over a locally compact group G is "proper"
in a sense recently introduced by Ng. Combining our results with those of Ng we
show that if B satisfies the "approximation property" then it is amenable in
the sense that the full and reduced cross-sectional C*-algebras coincide.


  We show that any disjunctive sum of Hackenbush Flowers $G$ has as evil twin
$G^* \in {G, G+*}$ such that the outcomes of $G$ under normal and mis\`ere play
are the same as the outcomes of $G^*$ under mis\`ere and normal play
respectively. We also show that, under mis\`ere play, any Green Hackenbush
position that has a single edge incident with the ground is equivalent to a
nim-heap.


  Deep neural networks (DNNs) have been widely and successfully applied to
various applications, but they require large amounts of memory and
computational power. This severely restricts their deployment on
resource-limited devices. To address this issue, many efforts have been made on
training low-bit weight DNNs. In this paper, we focus on training ternary
weight \{-1, 0, +1\} networks which can avoid multiplications and dramatically
reduce the memory and computation requirements. A ternary weight network can be
considered as a sparser version of the binary weight counterpart by replacing
some -1s or 1s in the binary weights with 0s, thus leading to more efficient
inference but more memory cost. However, the existing approaches to training
ternary weight networks cannot control the sparsity (i.e., percentage of 0s) of
the ternary weights, which undermines the advantage of ternary weights. In this
paper, we propose to our best knowledge the first sparsity-control approach
(SCA) to training ternary weight networks, which is simply achieved by a weight
discretization regularizer (WDR). SCA is different from all the existing
regularizer-based approaches in that it can control the sparsity of the ternary
weights through a controller $\alpha$ and does not rely on gradient estimators.
We theoretically and empirically show that the sparsity of the trained ternary
weights is positively related to $\alpha$. SCA is extremely simple,
easy-to-implement, and is shown to consistently outperform the state-of-the-art
approaches significantly over several benchmark datasets and even matches the
performances of the full-precision weight counterparts.


  We report on a comparison of two possible $\Lambda_c^+$ spin hypotheses,
$J=\frac{1}{2}$ and $\frac{3}{2}$, via the process
$e^+e^-\to\Lambda_c^+\bar\Lambda_c^-$, using the angular distributions of
$\Lambda_c^+$ decays into $pK_S^0$, $\Lambda\pi^+$, $\Sigma^0\pi^+$, and
$\Sigma^+\pi^0$. The data were recorded at $\sqrt s = 4.6$ GeV with the BESIII
detector and correspond to an integrated luminosity of 587 pb$^{-1}$. The
$\Lambda_c^+$ spin is determined to be $J=\frac{1}{2}$, with this value favored
over the $\frac{3}{2}$ hypothesis with a significance corresponding to more
than 6 Gaussian standard deviations.


  The tracking-by-detection framework requires a set of positive and negative
training samples to learn robust tracking models for precise localization of
target objects. However, existing tracking models mostly treat different
samples independently while ignores the relationship information among them. In
this paper, we propose a novel structure-aware deep neural network to overcome
such limitations. In particular, we construct a graph to represent the pairwise
relationships among training samples, and additionally take the natural
language as the supervised information to learn both feature representations
and classifiers robustly. To refine the states of the target and re-track the
target when it is back to view from heavy occlusion and out of view, we
elaborately design a novel subnetwork to learn the target-driven visual
attentions from the guidance of both visual and natural language cues.
Extensive experiments on five tracking benchmark datasets validated the
effectiveness of our proposed method.


  The muon-capture reactions 2H(\mu^-,\nu_\mu)nn and 3He(\mu^-,\nu_\mu)3H are
studied with nuclear strong-interaction potentials and charge-changing weak
currents, derived in chiral effective field theory. The low-energy constants
(LEC's) c_D and c_E, present in the three-nucleon potential and (c_D)
axial-vector current, are constrained to reproduce the A=3 binding energies and
the triton Gamow-Teller matrix element. The vector weak current is related to
the isovector component of the electromagnetic current via the
conserved-vector-current constraint, and the two LEC's entering the contact
terms in the latter are constrained to reproduce the A=3 magnetic moments. The
muon capture rates on deuteron and 3He are predicted to be 399(3) sec^{-1} and
1494 (21) sec^{-1}, respectively, where the spread accounts for the cutoff
sensitivity as well as uncertainties in the LEC's and electroweak radiative
corrections. By comparing the calculated and precisely measured rates on 3He, a
value for the induced pseudoscalar form factor is obtained in good agreement
with the chiral perturbation theory prediction.


  During the past few decades, missing-data problems have been studied
extensively, with a focus on the ignorable missing case, where the missing
probability depends only on observable quantities. By contrast, research into
non-ignorable missing data problems is quite limited. The main difficulty in
solving such problems is that the missing probability and the regression
likelihood function are tangled together in the likelihood presentation, and
the model parameters may not be identifiable even under strong parametric model
assumptions. In this paper we discuss a semiparametric model for non-ignorable
missing data and propose a maximum full semiparametric likelihood estimation
method, which is an efficient combination of the parametric conditional
likelihood and the marginal nonparametric biased sampling likelihood. The extra
marginal likelihood contribution can not only produce efficiency gain but also
identify the underlying model parameters without additional assumptions. We
further show that the proposed estimators for the underlying parameters and the
response mean are semiparametrically efficient. Extensive simulations and a
real data analysis demonstrate the advantage of the proposed method over
competing methods.


  The Lamb shift (2P_{1/2}-2S_{1/2}) in the muonic helium ion (mu ^4_2He)^+ is
calculated with the account of contributions of orders alpha^3, alpha^4,
alpha^5 and alpha^6. Special attention is given to corrections of the electron
vacuum polarization, the nuclear structure and recoil effects. The obtained
numerical value of the Lamb shift 1379.028 meV can be considered as a reliable
estimate for the comparison with experimental data.


  A self-similar solution for time evolution of isothermal, self-gravitating
viscous disks is found under the condition that $\alpha' \equiv \alpha (H/r)$
is constant in space (where $\alpha$ is the viscosity parameter and $H/r$ is
the ratio of a half-thickness to radius of the disk). This solution describes a
homologous collapse of a disk via self-gravity and viscosity. The disk
structure and evolution is distinct in the inner and outer parts. There is a
constant mass inflow in the outer portions so that the disk has flat rotation
velocity, constant accretion velocity, and surface density decreasing outward
as $\Sigma \propto r^{-1}$. In the inner portions, in contrast, mass is
accumulated near the center owing to the boundary condition of no radial
velocity at the origin, thereby a strong central concentration being produced;
surface density varies as $\Sigma \propto r^{-5/3}$. Moreover, the transition
radius separating the inner and outer portions increases linearly with time.
The consequence of such a high condensation is briefly discussed in the context
of formation of a quasar black hole.


  For surface groups and right-angled Artin groups, we prove lower bounds on
the shortest word in the generators representing a nontrivial element of the
kth term of the lower central series.


  The availability of high-intensity, heavy-ion beams coupled to sensitive,
large solid-angleacceptance spectrometers has enabled a detailed examination of
the fission fragments produced in induced-fission reactions. The
abrasion-fission process involves the formation of projectile-like prefragments
in violent nuclear collisions at relative energies in excess of 100 MeV/u. At
intermediate energies below this threshold, experiments suggest a change in the
prefragment kinematic qualities. Information regarding the influence of this
transitional phase upon the evolution of nuclei approaching the point of
scission is scarce. In this article, data are presented for over 200 nuclei
from nickel to palladium produced in abrasion-fission reactions of a 80 MeV/u
238U beam. Cross sections were obtained following yield measurements performed
for the principal charge states of the identified fission fragments and a
detailed analysis of the ion transmission. A full kinematic analysis of the
fission fragments has been performed using the LISE++ software package, where
the trajectory of an ion passing through a spectrometer can be reconstructed
based upon measurements at the focal plane. The results obtained at the S800
spectrograph are compared with predictions obtained with a three-fission
progenitor (3EER) model. Systematic studies of fission-fragment properties
continue to provide a valuable experimental benchmark for theoretical efforts
directed toward describing this complex decay channel, that is important in the
context of planning experiments to explore the neutron-rich region of the
nuclear chart at rare-isotope beam facilities.


  Fiji National University is encountering many difficulties with its current
administrative systems. These difficulties include accessibility, scalability,
performance, flexibility and integration. We propose a new campus information
system, FNU-CIS to addresses these difficulties. FNU-CIS has the potential to
provide wide range of the services for students and staffs at the university.
In order to assist in the design and implementation of proposed FNU-CIS, we
present an overview, software architecture and prototype implementation of our
proposed system. We discuss the key properties of our system, compare it with
other similar systems available and outline our future plans for research in
FNU-CIS implementation.


  To optimise the design of the light readout in the ArDM 1-ton liquid argon
dark matter detector, a range of reflector and WLS coating combinations were
investigated in several small setups, where argon scintillation light was
generated by radioactive sources in gas at normal temperature and pressure and
shifted into the blue region by tetraphenyl butadiene (TPB). Various
thicknesses of TPB were deposited by spraying and vacuum evaporation onto
specular 3M{\small\texttrademark}-foil and diffuse
Tetratex{\small\textregistered} (TTX) substrates. Light yields of each
reflector and TPB coating combination were compared. Reflection coefficients of
TPB coated reflectors were independently measured using a spectroradiometer in
a wavelength range between 200 and 650 nm. WLS coating on the PMT window was
also studied. These measurements were used to define the parameters of the
light reflectors of the ArDM experiment. Fifteen large $120\times 25$ cm$^2$
TTX sheets were coated and assembled in the detector. Measurements in argon gas
are reported providing good evidence of fulfilling the light collection
requirements of the experiment.


  A theory of equilibrium states of electrons above a liquid helium surface in
the presence of an external clamping field is built based on the first
principles of quantum statistics for the system of many identical
Fermi-particles. The approach is based on the variation principle modified for
the considered system and on Thomas-Fermi model. In terms of the developed
theory we obtain the self-consistency equations that connect the parameters of
the system description, i.e., the potential of a static electric field, the
distribution function of electrons and the surface profile of a liquid
dielectric. The equations are used to study the phase transition of the system
to a spatially periodic state. To demonstrate the capabilities of the proposed
method, the characteristics of the phase transition of the system to a
spatially periodic state of a trough type are analyzed.


  We present the results of the application of the Dispersion Matrix approach
to exclusive semileptonic $B$-meson decays. This method allows to determine the
hadronic form factors in a non-perturbative and completely model-independent
way. Starting from lattice results available at large values of the momentum
transfer, the behaviour of the form factors in their whole kinematical range is
obtained without introducing any parameterization of their momentum dependence.
We will focus on the determination of the Cabibbo-Kobayashi-Maskawa matrix
elements $\vert V_{cb} \vert$ and $\vert V_{ub} \vert$ through the analysis of
$B_{(s)} \to D_{(s)}^{(*)} \ell \nu$ and $B_{(s)} \to \pi(K) \ell \nu$ decays.
New theoretical determinations of the Lepton Flavour Universality ratios
relevant for these transitions will be also presented.


  We consider a family of singular Volterra integral equations that appear in
the study of monotone travelling-wave solutions for a family of
diffusion-convection-reaction equations involving the $p$-Laplacian operator.
Our results extend the ones due to B.\,Gilding for the case $p=2$. The fact
that $p\neq2$ modifies the nature of the singularity in the integral equation,
and introduces the need to develop some new tools for the analysis. The results
for the integral equation are then used to study the existence and properties
of travelling-wave solutions for doubly nonlinear diffusion-reaction equations
in terms of the constitutive functions of the problem.


  We introduce trimmed NURBS surfaces with accurate boundary control, briefly
called ABC-surfaces, as a solution to the notorious problem of constructing
watertight or smooth ($G^1$ and $G^2)$ multi-patch surfaces within the function
range of standard CAD/CAM systems and the associated file exchange formats. Our
construction is based on the appropriate blend of a base surface, which traces
out the intended global shape, and a series of reparametrized ribbons, which
dominate the shape near the boundary.


  In the thin-wall approximation, the decay of a gravitating false vacuum to a
lower-energy state is affected by the cosmological horizon structure in both
spaces. The nucleation radius of a bubble of true vacuum depends on the surface
tension of its boundary and equals the false vacuum cosmological horizon at a
critical tension. We argue that there is no tunneling instanton solution beyond
the critical tension and argue that there is therefore a bound on allowed
membrane tension in theories which rely on semiclassical tunneling to relax the
cosmological constant.


  For complex segmentation tasks, the achievable accuracy of fully automated
systems is inherently limited. Specifically, when a precise segmentation result
is desired for a small amount of given data sets, semi-automatic methods
exhibit a clear benefit for the user. The optimization of human computer
interaction (HCI) is an essential part of interactive image segmentation.
Nevertheless, publications introducing novel interactive segmentation systems
(ISS) often lack an objective comparison of HCI aspects. It is demonstrated,
that even when the underlying segmentation algorithm is the same throughout
interactive prototypes, their user experience may vary substantially. As a
result, users prefer simple interfaces as well as a considerable degree of
freedom to control each iterative step of the segmentation. In this article, an
objective method for the comparison of ISS is proposed, based on extensive user
studies. A summative qualitative content analysis is conducted via abstraction
of visual and verbal feedback given by the participants. A direct assessment of
the segmentation system is executed by the users via the system usability scale
(SUS) and AttrakDiff-2 questionnaires. Furthermore, an approximation of the
findings regarding usability aspects in those studies is introduced, conducted
solely from the system-measurable user actions during their usage of
interactive segmentation prototypes. The prediction of all questionnaire
results has an average relative error of 8.9%, which is close to the expected
precision of the questionnaire results themselves. This automated evaluation
scheme may significantly reduce the resources necessary to investigate each
variation of a prototype's user interface (UI) features and segmentation
methodologies.


  A hybrid quantum system consisting of spatially separated two-level atoms is
studied. Two atoms do not interact directly, but they are coupled via an
intermediate system which is consisting of a superconducting flux qubit
interacting with a mechanical and an electrical resonator which are coupled to
one of the atoms. Moreover, the superconducting flux qubit is driven by a
classical microwave field. Applying the adiabatic elimination an effective
Hamiltonian for the atomic subsystem is obtained. Our results demonstrate that
the entanglement degradation decay as well as the fidelity decay in the
dispersive regime are faster. Moreover, the driven field amplitude possesses an
important role in the entanglement and fidelity evolution.


  Confinement effects of Aharonov-Bohm (AB) flux and magnetic fields with
topological defect on CO diatomic molecule modeled by screened modified Kratzer
potential is investigated in this paper. The all-encompassing effects of the
fields and topological defect result in a strongly repulsive system. We
discover that the collective effect of the fields and defect is intense than
the lone and dual effect and consequently there is a substantial shift in the
bound state energy of the system. We also find that to sustain a low-energy
medium for the molecule modeled by SMKP, the topological defect and weak AB
field are required, whereas the Magnetic field can be used as a control
parameter or enhancer. The effects of the topological defect and magnetic and
AB fields on the thermal and magnetic properties of the system are duly
analyzed. We observe that the system tends to exhibit both a paramagnetic and
diamagnetic behavior for weak and intense magnetic field respectively and some
sort of saturation at large magnetic field. To further validate our findings,
we map our result to 3D and a comparison of our results with what obtains in
literature reveals an excellent agreement.


  Natural Language Processing (NLP) systems are increasingly taking the form of
multi-stage pipelines involving multiple distinct language models (LMs) and
prompting strategies. Here we address the question of how to fine-tune such
systems to improve their performance. We cast this as a problem of optimizing
the underlying LM weights and the prompting strategies together, and consider a
challenging but highly realistic scenario in which we have no gold labels for
any intermediate stages in the pipeline. To address this challenge, we evaluate
approximate optimization strategies in which we bootstrap training labels for
all pipeline stages and use these to optimize the pipeline's prompts and
fine-tune its weights alternatingly. In experiments with multi-hop QA,
mathematical reasoning, and feature-based classification, we find that simple
approaches for optimizing the prompts and weights together outperform directly
optimizing weights alone and prompts alone by up to 65% and 5%, respectively,
on average across LMs and tasks. We will release our new optimizers in DSPy at
http://dspy.ai


  Nontrivial examples of Teichm\"uller curves have been studied systematically
with notions of combinatorics invariant under affine homeomorphisms. An origami
(square-tiled surface) induces a Teichm\"uller curve for which the absolute
Galois group acts on the embedded curve in the moduli space. In this paper, we
study general origamis not admitting pure half-translation structure. Such a
flat surface is given by a cut-and-paste construction from origami that is a
translation surface. We present an algorithm for the simultaneous calculation
of the Veech groups of origamis of given degree. We have calculated the
equivalence classes, the $PSL(2,\mathbb{Z})$-orbits, and some Galois invariants
for all the patterns of origamis of degree $d\leq 7$.


  Building on recent constructions of Quantum Cross Subspace Alignment (QCSA)
codes, this work develops a coding scheme for QEBXSTPIR, i.e., classical
private information retrieval with $X$-secure storage and $T$-private queries,
over a quantum multiple access channel, that is resilient to any set of up to
$E$ erased servers (equivalently known as unresponsive servers, or stragglers)
together with any set of up to $B$ Byzantine servers. The scheme is accordingly
labeled QEBCSA, with the `E' and `B' indicating resilience to erased and
Byzantine servers respectively. The QEBCSA code structure may be broadly useful
for problems such as quantum coded secure distributed computation, where
security, straggler resilience, and distributed superdense coding gains are
simultaneously required. The $X$-security property is further exploited to
improve the communication rate when $\epsilon$-error decoding is allowed.


  We show that the derived category of any singularity over a field of
characteristic 0 can be embedded fully and faithfully into a smooth
triangulated category which has a semiorthogonal decomposition with components
equivalent to derived categories of smooth varieties. This provides a
categorical resolution of the singularity.


  Segmentation of lymphoma lesions is challenging due to their varied sizes and
locations in whole-body PET scans. This work presents a fully-automated
segmentation technique using a multi-center dataset of diffuse large B-cell
lymphoma (DLBCL) with heterogeneous characteristics. We utilized a dataset of
[18F]FDG-PET scans (n=194) from two different imaging centers, including cases
with primary mediastinal large B-cell lymphoma (PMBCL) (n=104). Automated brain
and bladder removal approaches were utilized as preprocessing steps to tackle
false positives caused by normal hypermetabolic uptake in these organs. Our
segmentation model is a convolutional neural network (CNN) based on a 3D U-Net
architecture that includes squeeze and excitation (SE) modules. Hybrid
distribution, region, and boundary-based losses (Unified Focal and Mumford-Shah
(MS)) were utilized that showed the best performance compared to other
combinations (p<0.05). Cross-validation between different centers, DLBCL and
PMBCL cases, and three random splits were applied on train/validation data. The
ensemble of these six models achieved a Dice similarity coefficient (DSC) of
0.77 +- 0.08 and Hausdorff distance (HD) of 16.5 +-12.5. Our 3D U-net model
with SE modules for segmentation with hybrid loss performed significantly
better (p<0.05) as compared to the 3D U-Net (without SE modules) using the same
loss function (Unified Focal and MS loss) (DSC= 0.64 +-0.21 and HD= 26.3 +-
18.7). Our model can facilitate a fully automated quantification pipeline in a
multi-center context that opens the possibility for routine reporting of total
metabolic tumor volume (TMTV) and other metrics shown useful for the management
of lymphoma.


  In robotic domains, learning and planning are complicated by continuous state
spaces, continuous action spaces, and long task horizons. In this work, we
address these challenges with Neuro-Symbolic Relational Transition Models
(NSRTs), a novel class of models that are data-efficient to learn, compatible
with powerful robotic planning methods, and generalizable over objects. NSRTs
have both symbolic and neural components, enabling a bilevel planning scheme
where symbolic AI planning in an outer loop guides continuous planning with
neural models in an inner loop. Experiments in four robotic planning domains
show that NSRTs can be learned after only tens or hundreds of training
episodes, and then used for fast planning in new tasks that require up to 60
actions and involve many more objects than were seen during training. Video:
https://tinyurl.com/chitnis-nsrts


  We show how to calculate the finite-state dimension (equivalently, the
finite-state compressibility) of a saturated sets $X$ consisting of {\em all}
infinite sequences $S$ over a finite alphabet $\Sigma_m$ satisfying some given
condition $P$ on the asymptotic frequencies with which various symbols from
$\Sigma_m$ appear in $S$. When the condition $P$ completely specifies an
empirical probability distribution $\pi$ over $\Sigma_m$, i.e., a limiting
frequency of occurrence for {\em every} symbol in $\Sigma_m$, it has been known
since 1949 that the Hausdorff dimension of $X$ is precisely $\CH(\pi)$, the
Shannon entropy of $\pi$, and the finite-state dimension was proven to have
this same value in 2001.
  The saturated sets were studied by Volkmann and Cajar decades ago. It got
attention again only with the recent developments in multifractal analysis by
Barreira, Saussol, Schmeling, and separately Olsen. However, the powerful
methods they used -- ergodic theory and multifractal analysis -- do not yield a
value for the finite-state (or even computable) dimension in an obvious manner.
  We give a pointwise characterization of finite-state dimensions of saturated
sets. Simultaneously, we also show that their finite-state dimension and strong
dimension coincide with their Hausdorff and packing dimension respectively,
though the techniques we use are completely elementary. Our results
automatically extend to less restrictive effective settings (e.g.,
constructive, computable, and polynomial-time dimensions).


  We report on the design and performance of a spark-resistant bulk-micromegas
chamber. The principle of this design lends itself to the construction of
large-area muon chambers for the upgrade of the detectors at the Large Hadron
Collider at CERN for luminosities in excess of 10**34/cm2/s or other high-rate
applications.


  It is known that optical activities can perform rotations. It is shown that
the rotation, if modulated by attenuations, can perform symmetry operations of
Wigner's little group which dictates the internal space-time symmetries of
elementary particles.


  We report on the development of a trapped-ion, microwave frequency standard
based on the 12.6 GHz hyperfine transition in laser-cooled ytterbium-171 ions.
The entire system fits into a 6U 19-inch rack unit ($51\times49\times28$ cm)
and comprises laser, electronics, and physics package subsystems. The
performance of this development system is evaluated; the fractional frequency
instability was measured to be $3.6\times10^{-12}/\surd\tau$ for averaging
times between 30 s and 1500 s.


  In recent years, advanced deep neural networks have required a large number
of parameters for training. Therefore, finding a method to reduce the number of
parameters has become crucial for achieving efficient training. This work
proposes a training scheme for classical neural networks (NNs) that utilizes
the exponentially large Hilbert space of a quantum system. By mapping a
classical NN with $M$ parameters to a quantum neural network (QNN) with
$O(\text{polylog} (M))$ rotational gate angles, we can significantly reduce the
number of parameters. These gate angles can be updated to train the classical
NN. Unlike existing quantum machine learning (QML) methods, the results
obtained from quantum computers using our approach can be directly used on
classical computers. Numerical results on the MNIST and Iris datasets are
presented to demonstrate the effectiveness of our approach. Additionally, we
investigate the effects of deeper QNNs and the number of measurement shots for
the QNN, followed by the theoretical perspective of the proposed method. This
work opens a new branch of QML and offers a practical tool that can greatly
enhance the influence of QML, as the trained QML results can benefit classical
computing in our daily lives.


  An explicit lattice point realization is provided for the primary components
of an arbitrary binomial ideal in characteristic zero. This decomposition is
derived from a characteristic-free combinatorial description of certain primary
components of binomial ideals in affine semigroup rings, namely those that are
associated to faces of the semigroup. These results are intimately connected to
hypergeometric differential equations in several variables.


  We examined the use of the Ising model as an $L_0$ regularization method for
field-aware factorization machines (FFM). This approach improves generalization
performance and has the advantage of simultaneously determining the best
feature combinations for each of several groups. We can deepen the
interpretation and understanding of the model from the similarities and
differences in the features selected in each group.


  We revisit the classical problem due to Weyl, as well as its generalisations,
concerning the isometric immersions of $\mathbb{S}^2$ into simply-connected
$3$-dimensional Riemannian manifolds with non-negative Gauss curvature. A
sufficient condition is exhibited for the existence of global
$C^{1,1}$-isometric immersions. Our developments are based on the framework
\`{a} la Labourie (Immersions isom\'{e}triques elliptiques et courbes
pseudo-holomorphes, J. Diff. Geom. 30 (1989), 395--424) of studying isometric
immersions using $J$-holomorphic curves. We obtain along the way a
generalisation of a classical theorem due to Heinz and Pogorelov.


  We prove that, for every modulus $\mathfrak{q}$, every class of the narrow
ray class group $H_{\mathfrak{q}}(\mathbf{K})$ of an arbitrary number field
$\mathbf{K}$ contains a product of three unramified prime ideals $\mathfrak{p}$
of degree one with $\mathfrak{N}\mathfrak{p}\le
(t(\mathbf{K})\mathfrak{N}\mathfrak{q})^3$, where $t(\mathbf{K})$ is an
explicit function of $\mathbf{K}$ described in the paper. To achieve this
result, we first obtain a sharp explicit Brun-Titchmarsh Theorem for ray
classes and then an equally explicit improved Brun-Titchmarsh Theorem for large
subgroups of narrow ray class groups. En route, we deduce an explicit upper
bound for the least prime ideal in a quadratic subgroup of a narrow ray class
group and also for the size of the least ideal that is a product of degree one
primes in any given class of $H_\mathfrak{q}(\mathbf{K})$.


  We consider the problem of estimating means of two Gaussians in a 2-Gaussian
mixture, which is not balanced and is corrupted by noise of an arbitrary
distribution. We present a robust algorithm to estimate the parameters,
together with upper bounds on the numbers of samples required for the estimate
to be correct, where the bounds are parametrised by the dimension, ratio of the
mixing coefficients, a measure of the separation of the two Gaussians, related
to Mahalanobis distance, and a condition number of the covariance matrix. In
theory, this is the first sample-complexity result for imbalanced mixtures
corrupted by adversarial noise. In practice, our algorithm outperforms the
vanilla Expectation-Maximisation (EM) algorithm in terms of estimation error.


  We have used the Sloan Digital Sky Survey (SDSS) to undertake an
investigation of lopsidedness in a sample of ~25,000 nearby galaxies (z <
0.06). We use the m=1 azimuthal Fourier mode between the 50% and 90% light
radii as our measure of lopsidedness. The SDSS spectra are used to measure the
properties of the stars, gas, and black hole in the central-most few-kpc-scale
region. We show that there is a strong link between lopsidedness in the outer
parts of the galactic disk and the youth of the stellar population in the
central region. This link is independent of the other structural properties of
the galaxy. These results provide a robust statistical characterization of the
connections between accretion/interactions/mergers and the resulting star
formation. We also show that residuals in the galaxy mass-metallicity relation
correlate with lopsidedness (at fixed mass, the more metal-poor galaxies are
more lopsided). This suggests that the events causing lopsidedness and enhanced
star formation deliver lower metallicity gas into the galaxy's central region.
Finally, we find that there is a trend for the more powerful active galactic
nuclei to be hosted by more lopsided galaxies (at fixed galaxy mass, density,
or concentration). However if we compare samples matched to have both the same
structures and central stellar populations, we then find no difference in
lopsidedness between active and non-active galaxies. This leads to the
following picture. The presence of cold gas in the central region of a galaxy
(irrespective of its origin) is essential for both star-formation and black
hole growth. The delivery of cold gas is aided by the processes that produce
lopsidedness. Other processes on scales smaller than we can probe with our data
are required to transport the gas to the black hole.


  In many applications, maintaining a consistent dense map of the environment
is key to enabling robotic platforms to perform higher level decision making.
Several works have addressed the challenge of creating precise dense 3D maps
from visual sensors providing depth information. However, during operation over
longer missions, reconstructions can easily become inconsistent due to
accumulated camera tracking error and delayed loop closure. Without explicitly
addressing the problem of map consistency, recovery from such distortions tends
to be difficult. We present a novel system for dense 3D mapping which addresses
the challenge of building consistent maps while dealing with scalability.
Central to our approach is the representation of the environment as a
collection of overlapping TSDF subvolumes. These subvolumes are localized
through feature-based camera tracking and bundle adjustment. Our main
contribution is a pipeline for identifying stable regions in the map, and to
fuse the contributing subvolumes. This approach allows us to reduce map growth
while still maintaining consistency. We demonstrate the proposed system on a
publicly available dataset and simulation engine, and demonstrate the efficacy
of the proposed approach for building consistent and scalable maps. Finally we
demonstrate our approach running in real-time on-board a lightweight MAV.


  We investigate G-invariant symplectic structures on the cotangent bundle T*P
of a principal G-bundle P(M,G) which are canonically related to automorphisms
of the tangent bundle TP covering the identity map of P and commuting with the
action of TG on TP. The symplectic structures corresponding to connections on
P(M,G) are also investigated. The Marsden-Weinstein reduction procedure for
these symplectic structures is discussed.


  The dynamic mode decomposition (DMD) is a broadly applicable dimensionality
reduction algorithm that approximates a matrix containing time-series data by
the outer product of a matrix of exponentials, representing Fourier-like time
dynamics, and a matrix of coefficients, representing spatial structures. This
interpretable spatio-temporal decomposition is commonly computed using linear
algebraic techniques in its simplest formulation or a nonlinear optimization
procedure within the variable projection framework. For data with sparse
outliers or data which are not well-represented by exponentials in time, the
standard Frobenius norm fit of the data creates significant biases in the
recovered time dynamics. As a result, practitioners are left to clean such
defects from the data manually or to use a black-box cleaning approach like
robust PCA. As an alternative, we propose a framework and a set of algorithms
for incorporating robust features into the nonlinear optimization used to
compute the DMD itself. The algorithms presented are flexible, allowing for
regu- larizers and constraints on the optimization, and scalable, using a
stochastic approach to decrease the computational cost for data in high
dimensional space. Both synthetic and real data examples are provided.


  We study invariant random subgroups (IRSs) of semidirect products $G = A
\rtimes \Gamma$. In particular, we characterize all IRSs of parabolic subgroups
of $\mathrm{SL}_d(\mathbb{R})$, and show that all ergodic IRSs of $\mathbb{R}^d
\rtimes \mathrm{SL}_d(\mathbb{R})$ are either of the form $\mathbb{R}^d \rtimes
K$ for some IRS of $\mathrm{SL}_d(\mathbb{R})$, or are induced from IRSs of
$\Lambda \rtimes \mathrm{SL}(\Lambda)$, where $\Lambda < \mathbb{R}^d$ is a
lattice.


  With the rise of stochastic generative models in robot policy learning,
end-to-end visuomotor policies are increasingly successful at solving complex
tasks by learning from human demonstrations. Nevertheless, since real-world
evaluation costs afford users only a small number of policy rollouts, it
remains a challenge to accurately gauge the performance of such policies. This
is exacerbated by distribution shifts causing unpredictable changes in
performance during deployment. To rigorously evaluate behavior cloning
policies, we present a framework that provides a tight lower-bound on robot
performance in an arbitrary environment, using a minimal number of experimental
policy rollouts. Notably, by applying the standard stochastic ordering to robot
performance distributions, we provide a worst-case bound on the entire
distribution of performance (via bounds on the cumulative distribution
function) for a given task. We build upon established statistical results to
ensure that the bounds hold with a user-specified confidence level and
tightness, and are constructed from as few policy rollouts as possible. In
experiments we evaluate policies for visuomotor manipulation in both simulation
and hardware. Specifically, we (i) empirically validate the guarantees of the
bounds in simulated manipulation settings, (ii) find the degree to which a
learned policy deployed on hardware generalizes to new real-world environments,
and (iii) rigorously compare two policies tested in out-of-distribution
settings. Our experimental data, code, and implementation of confidence bounds
are open-source.


  GPT-2 and BERT demonstrate the effectiveness of using pre-trained language
models (LMs) on various natural language processing tasks. However, LM
fine-tuning often suffers from catastrophic forgetting when applied to
resource-rich tasks. In this work, we introduce a concerted training framework
(CTNMT) that is the key to integrate the pre-trained LMs to neural machine
translation (NMT). Our proposed CTNMT consists of three techniques: a)
asymptotic distillation to ensure that the NMT model can retain the previous
pre-trained knowledge; b) a dynamic switching gate to avoid catastrophic
forgetting of pre-trained knowledge; and c) a strategy to adjust the learning
paces according to a scheduled policy. Our experiments in machine translation
show CTNMT gains of up to 3 BLEU score on the WMT14 English-German language
pair which even surpasses the previous state-of-the-art pre-training aided NMT
by 1.4 BLEU score. While for the large WMT14 English-French task with 40
millions of sentence-pairs, our base model still significantly improves upon
the state-of-the-art Transformer big model by more than 1 BLEU score. The code
and model can be downloaded from https://github.com/bytedance/neurst/
tree/master/examples/ctnmt.


  Since the standard inflationary paradigm is based on quantum field theory on
classical space-times, it excludes the Planck era. Using techniques from loop
quantum gravity, the paradigm is extended to a self-consistent theory from the
Planck scale to the onset of slow roll inflation, covering some 11 orders of
magnitude in energy density and curvature. This pre-inflationary dynamics also
opens a small window for novel effects, e.g. a source for non-Gaussianities,
which could extend the reach of cosmological observations to the deep Planck
regime of the early universe.


  It is pointed out that in a general class of flavour models one can identify
certain universally present FCNC operators, induced by the exchange of heavy
flavour messengers. Their coefficients depend on the rotation angles that
connect flavour and fermion mass basis. The lower bounds on the messenger scale
are derived using updated experimental constraints on the FCNC operators. The
obtained bounds are different for different operators and in addition they
depend on the chosen set of rotations. Given the sensitivity expected in the
forthcoming experiments, the present analysis suggests interesting room for
discovering new physics. As the highlights emerge the leptonic processes,
$\mu\rightarrow e\gamma$, $\mu\rightarrow eee$ and $\mu\rightarrow e$
conversion in nuclei.


  In this talk I will review the "state of the art" of the calculations of the
nuclear matrix elements (NME) of the neutrinoless double beta decays for the
nuclei 48Ca, 76Ge, 82Se, 124Sn, 128Te, 130Te and 136Xe in the framework of the
Interacting Shell Model (ISM), and compare them with the NME's obtained using
the Quasi-particle RPA approach (QRPA). I will also discuss the effect of the
competition between the pairing and quadrupole correlations in the value of
these NME's. In particular I will show that, as the difference in deformation
between parent and grand daughter grows, the NME's of both the neutrinoless and
the two neutrino modes decrease rapidly.


  There is a lot of research on probabilistic transition systems. There are not
many studies in probabilistic process models. The lack of investigation into
the interactive aspect of probabilistic processes is mainly due to the
difficulty caused by the discrepancy between probabilistic actions and
nondeterministic behaviours. The paper proposes a uniform approach to
probabilistic process models and a bisimulation congruence for probabilistic
concurrency.


  The fragmentation functions for gluons to split into P-wave heavy quarkonium
states are calculated to leading order in the QCD coupling constant.
Long-distance effects are factored into two nonperturbative parameters: the
derivative of the radial wavefunction at the origin and a second parameter
related to the probability for a heavy-quark-antiquark pair that is produced in
a color-octet S-wave state to form a color-singlet P-wave bound state. The
fragmentation probabilities for a high transverse momentum gluon to split into
the P-wave charmonium states $\chi_{c0}$, $\chi_{c1}$, and $\chi_{c2}$ are
estimated to be $0.4 \times 10^{-4}$, $1.8 \times 10^{-4}$, and $2.4 \times
10^{-4}$, respectively. This fragmentation process may account for a
significant fraction of the rate for the inclusive production of $\chi_{cJ}$ at
large transverse momentum in $p \bar p$ colliders.


  We develop reduced, stochastic models for high dimensional, dissipative
dynamical systems that relax very slowly to equilibrium and can encode long
term memory. We present a variety of empirical and first principles approaches
for model reduction, and build a mathematical framework for analyzing the
reduced models. We introduce the notions of universal and asymptotic filters to
characterize `optimal' model reductions for sloppy linear models. We illustrate
our methods by applying them to the practically important problem of modeling
evaporation in oil spills.


  Recent results on top quark physics with the D0 experiment in pbar-p
collisions at sqrt(s) = 1.8 TeV for an integrated luminosity of 125 pb**-1 are
reported. The direct measurement of the top quark mass uses single lepton and
dilepton events, giving the result m(top) = 172.1 +/- 7.1 GeV/c**2. The
measurement of the t-tbar production cross section includes analyses from 9 top
decay channels: dilepton (t-tbar --> e+mu, e+e, and mu+mu), electron and
neutrino (t-tbar --> e+nu), single leptons (t-tbar --> e + jets, t-tbar --> mu
+ jets) with and without b tagging, and all-jets (t-tbar --> 6 jets). We
measure the t-tbar production cross section to be 5.9 +/- 1.7 pb at m(top) =
172.1 GeV/c**2. Combining the D0 and the CDF measurements of the top quark mass
and combining their t-tbar cross sections, in both cases taking into account
error correlations, yields unofficially sigma(t-tbar) = 6.7 +/- 1.3 pb at an
averaged top quark mass of m(top) = 173.8 +/- 5.2 GeV/c**2. Preliminary results
on a search for charged Higgs production in top events, t --> Higgs + b, are
presented.


  Quaternions, introduced by Hamilton in 1843 as a generalization of complex
numbers, have found, in more recent years, a wealth of applications in a number
of different areas which motivated the design of efficient methods for
numerically approximating the zeros of quaternionic polynomials. In fact, one
can find in the literature recent contributions to this subject based on the
use of complex techniques, but numerical methods relying on quaternion
arithmetic remain scarce. In this paper we propose a Weierstrass-like method
for finding simultaneously {\sl all} the zeros of unilateral quaternionic
polynomials. The convergence analysis and several numerical examples
illustrating the performance of the method are also presented.


  Deep learning is changing many areas in molecular physics, and it has shown
great potential to deliver new solutions to challenging molecular modeling
problems. Along with this trend arises the increasing demand of expressive and
versatile neural network architectures which are compatible with molecular
systems. A new deep neural network architecture, Molecular Configuration
Transformer (Molecular CT), is introduced for this purpose. Molecular CT is
composed of a relation-aware encoder module and a computationally universal
geometry learning unit, thus able to account for the relational constraints
between particles meanwhile scalable to different particle numbers and
invariant with respect to the trans-rotational transforms. The computational
efficiency and universality make Molecular CT versatile for a variety of
molecular learning scenarios and especially appealing for transferable
representation learning across different molecular systems. As examples, we
show that Molecular CT enables representational learning for molecular systems
at different scales, and achieves comparable or improved results on common
benchmarks using a more light-weighted structure compared to baseline models.


  We construct a unilateral lattice tiling of $\mathbb{R}^n$ into hypercubes of
two differnet side lengths $p$ or $q$. This generalizes the Pythagorean tiling
in $\mathbb{R}^2$. We also show that this tiling is unique up to symmetries,
which proves a variation of a conjecture by B\"olcskei from 2001. For positive
integers $p$ and $q$ this tiling also provides a tiling of
$(\mathbb{Z}/(p^n+q^n)\mathbb{Z})^n$.


  A path-integral quantization method is proposed for dynamical systems whose
classical equations of motion do \textit{not} necessarily follow from the
action principle. The key new notion behind this quantization scheme is the
Lagrange structure which is more general than the Lagrangian formalism in the
same sense as Poisson geometry is more general than the symplectic one. The
Lagrange structure is shown to admit a natural BRST description which is used
to construct an AKSZ-type topological sigma-model. The dynamics of this
sigma-model in $d+1$ dimensions, being localized on the boundary, are proved to
be equivalent to the original theory in $d$ dimensions. As the topological
sigma-model has a well defined action, it is path-integral quantized in the
usual way that results in quantization of the original (not necessarily
Lagrangian) theory. When the original equations of motion come from the action
principle, the standard BV path-integral is explicitly deduced from the
proposed quantization scheme. The general quantization scheme is exemplified by
several models including the ones whose classical dynamics are not variational.


  FRIPON is an efficient ground-based network for the detection and
characterization of fireballs, which was initiated in France in 2016 with over
one hundred cameras and which has been very successfully extended to Europe and
Canada with one hundred more stations. After seven successful years of
operation in the northern hemisphere, it seems necessary to extend this network
towards the southern hemisphere - where the lack of detection is evident - to
obtain an exhaustive view of fireball activity. The task of extending the
network to any region outside the northern hemisphere presents the challenge of
a new installation process, where the recommended and tested version of the
several sub-systems that compose a station had to be replaced due to regional
availability and compatibility considerations, as well as due to constant
software and hardware obsolescence and updates. In Chile, we have a unique
geography, with a vast extension in latitude, as well as desert regions, which
have generated the need to evaluate the scientific and technical performance of
the network under special conditions, prioritizing the optimization of a set of
factors related to the deployment process, as well as the feasible and
achievable versions of the required components, the geographical location of
the stations, and their respective operational, maintenance, safety, and
communication conditions. In this talk, we will present the current status of
this effort, including a brief report on the obstacles and difficulties
encountered and how we have solved them, the current operational status of the
network in Northern Chile, as well as the challenges and prospects for the
densification of the network over South America.


  Our goal here is threefold: [1] To present a new dense-stereo matching
algorithm, tMGM, that by combining the hierarchical logic of tSGM with the
support structure of MGM achieves 6-8\% performance improvement over the
baseline SGM (these performance numbers are posted under tMGM-16 in the
Middlebury Benchmark V3 ); and [2] Through an exhaustive quantitative and
qualitative comparative study, to compare how the major variants of the SGM
approach to dense stereo matching, including the new tMGM, perform in the
presence of: (a) illumination variations and shadows, (b) untextured or weakly
textured regions, (c) repetitive patterns in the scene in the presence of large
stereo rectification errors. [3] To present a novel DEM-Sculpting approach for
estimating initial disparity search bounds for multi-date satellite stereo
pairs. Based on our study, we have found that tMGM generally performs best with
respect to all these data conditions. Both tSGM and MGM improve the density of
stereo disparity maps and combining the two in tMGM makes it possible to
accurately estimate the disparities at a significant number of pixels that
would otherwise be declared invalid by SGM. The datasets we have used in our
comparative evaluation include the Middlebury2014, KITTI2015, and ETH3D
datasets and the satellite images over the San Fernando area from the MVS
Challenge dataset.


  We study the complexity of some fundamental operations for triangular sets in
dimension zero. Using Las-Vegas algorithms, we prove that one can perform such
operations as change of order, equiprojectable decomposition, or quasi-inverse
computation with a cost that is essentially that of modular composition. Over
an abstract field, this leads to a subquadratic cost (with respect to the
degree of the underlying algebraic set). Over a finite field, in a boolean RAM
model, we obtain a quasi-linear running time using Kedlaya and Umans' algorithm
for modular composition. Conversely, we also show how to reduce the problem of
modular composition to change of order for triangular sets, so that all these
problems are essentially equivalent. Our algorithms are implemented in Maple;
we present some experimental results.


  I shall discuss three small systems in which I think room-temperature
superconductivity has been observed. These are: 1. Narrow channels through
films of oxidised atactic polypropylene (OAPP) and other polymers. 2. Some
multiwalled carbon nanotubes or mats of nanotubes. 3. Sandwich structures based
on CdF_2. The main emphasis will be on polymer films.


  We perform exact diagonalization and time evolution of the
Lechner-Hauke-Zoller (LHZ) annealing architecture [Science Advances 1(9),
e1500838 (2015)] for ten physical qubits. Thereby, on a training set consisting
of $2400$ problem instances, we perform the optimization task of tuning the
local fields with the goal to identify a set of fixed optimal annealing
protocols, that outperforms linear protocols on a large class of arbitrary LHZ
problem instances. We show that average ground state fidelities of $0.9$ can be
achieved by applying optimized protocols onto groups of problem instances with
similar energy landscapes. Particularly, the set of optimized annealing
protocols reduces annealing time required to reach a predefined threshold
ground state fidelity by an average of $\sim72\%$, corresponding to a speed-up
of factor $\sim3.5$. Moreover, as these protocols are meant to be readily
applicable in experimental setups, they can be used as a starting point for
problem-specific protocol optimization. In reverse, we discuss how previously
optimized protocols can potentially be used to gauge the instantaneous energy
landscape of a spin glass problem. Albeit simulations were performed on the LHZ
architecture, identification of optimized protocols is not limited to either
simulations or local connectivity only.


  Quantum key distribution (QKD) involving polarized photons could be
vulnerable to a jamming (or denial-of-service) attack, in which a third party
applies an external magnetic field to rotate the plane of polarization of
photons headed toward one of the two intended recipients. Sufficiently large
Faraday rotation of one of the polarized beams would prevent Alice and Bob from
establishing a secure quantum channel. We investigate requirements to induce
such rotation both for free-space transmission and for transmission via optical
fiber, and find reasonable ranges of parameters in which a jamming attack could
be successful against fiber-based QKD, even for systems that implement
automated recalibration for polarization-frame alignment. The jamming attack
could be applied selectively and indefinitely by an adversary without revealing
her presence, and could be further combined with various eavesdropping attacks
to yield unauthorized information.


  We study the regular sets of local energy solutions to the Navier-Stokes
equations in terms of conditions on the initial data. It is shown that if a
weighted $L^2$ norm of the initial data is finite, then all local energy
solutions are regular in a region confined by space-time hypersurfaces
determined by the weight. This result refines and generalizes Theorems C and D
of Caffarelli, Kohn and Nirenberg (Comm. Pure Appl. Math. 35; 1982) and our
recent paper [15] as well.


  Doppler broadening plays an important role in laser rovibrational
spectroscopy of trapped deuterated molecular hydrogen ions (HD$^+$), even at
the millikelvin temperatures achieved through sympathetic cooling by
laser-cooled beryllium ions. Recently, Biesheuvel \textit{et al.}
\cite{Biesheuvel2016} presented a theoretical lineshape model for such
transitions which not only considers linestrengths and Doppler broadening, but
also the finite sample size and population redistribution by blackbody
radiation, which are important in view of the long storage and probe times
achievable in ion traps. Here, we employ the rate equation model developed by
Biesheuvel \textit{et al.} to theoretically study the Doppler-broadened
hyperfine structure of the $(v,L):(0,3)\rightarrow(4,2)$ rovibrational
transition in HD$^+$ at 1442~nm. We observe prominent yet hitherto unrecognized
ghost features in the simulated spectrum, whose positions depend on the Doppler
width, transition rates, and saturation levels of the hyperfine components
addressed by the laser. We explain the origin and behavior of such features,
and we provide a simple quantitative guideline to assess whether ghost features
may appear. As such ghost features may be common to saturated Doppler-broadened
spectra of rotational and vibrational transitions in trapped ions composed of
partly overlapping lines, our work illustrates the necessity to use lineshape
models that take into account all the relevant physics.