  The fundamental structure of the 4-dimensional spacetime is assumed to be the
lorentzian CR-structure (LCR-structure), which contains two correlated
3-dimensional CR-structures. It is defined by explicit Frobenius integrable
relations characterized by "left" and "right" CP(3) points. This LCR-structure
is invariant under a very restrictive tetrad-Weyl symmetry, which permits a
unique special second order partial differential equation applied to a
Yang-Mills field, identified with the gluon field. A class of metrics with the
corresponding self-dual forms are defined. After partially fixing the
tetrad-Weyl symmetry, the electroweak connection is also defined, which is
directly related with the class of LCR-tetrads of the structure. The "free
electron" LCR-structure is identified, which has gravitational and electroweak
potentials (dressings) with fermionic gyromagnetic ratio g=2. The corresponding
massless "neutrino" LCR-structure is also found. These two solitonic
configurations constitute the first leptonic generation identified with the
Petrov type D LCR-structure. The muon and tau leptonic generations are
identified with the Petrov type II and I respectively. Using the electron
LCR-structure, I compute the corresponding quark having an additional gluonic
potential and providing the explication of the lepton-quark correspondence. The
standard model is implied via the causal Bogoliubov, Epstein-Glaser and Scharf
procedure viewed as a targeted harmonic analysis in the rigged Hilbert-Fock
space of precise Poincare representations of the computed geometric structures.


  We show that a collection of lossy multi-chromatically modulated qubits can
be used to dissipatively engineer arbitrary Gaussian states of a set of bosonic
modes. Our ideas are especially suited to superconducting-circuit
architectures, where all the required ingredients are experimentally available.
The generation of such multimode Gaussian states is necessary for many
applications, most notably measurement-based quantum computation. We build upon
some of our previous proposals, where we showed how to generate single-mode and
two-mode squeezed states through cooling and lasing. Special care must be taken
when extending these ideas to many bosonic modes, and we discuss here how to
overcome all the limitations and hurdles that naturally appear. We illustrate
our ideas with a fully worked out example consisting of GHZ states, but have
also tested several other examples such as cluster states. All these examples
allow us to show that it is possible to use a set of N lossy qubits to cool
down a bosonic chain of N modes to any desired Gaussian state.


  We report on optical observations on GRB 000210 obtained with the 2.56-m
Nordic Optical Telescope and the 1.54-m Danish Telescope starting 12.4 hours
after the gamma-ray event. The content of the X-ray error box determined by the
Chandra satellite is discussed.


  Correlation matrices are the sub-class of positive definite real matrices
with all entries on the diagonal equal to unity. Earlier work has exhibited a
parametrisation of the corresponding Cholesky factorisation in terms of partial
correlations, and also in terms of hyperspherical co-ordinates. We show how the
two are relating, starting from the definition of the partial correlations in
terms of the Schur complement. We extend this to the generalisation of
correlation matrices to the cases of complex and quaternion entries. As in the
real case, we show how the hyperspherical parametrisation leads naturally to a
distribution on the space of correlation matrices $\{R\}$ with probability
density function proportional to $( \det R)^a$. For certain $a$, a construction
of random correlation matrices realising this distribution is given in terms of
rectangular standard Gaussian matrices.


  In the paper we consider the Jacobian locus $\overline{J_g}$ and the Prym
locus $\overline{P_{g+1}}$, in the moduli space $A_g$ of principally polarized
abelian varieties of dimension $g$, for $g\geq 7$, and we study the extrinsic
geometry of $\overline{J_g}\subset \overline{P_{g+1}}$, under the inclusion
provided by the theory of generalized Prym varieties as introduced by
Beauville. More precisely, we study certain geodesic curves with respect to the
Siegel metric of $A_g$, starting at a Jacobian variety $[JC]\in A_g$ of a curve
$[C]\in M_g$ and with direction $\zeta\in T_{[JC]}J_g$. We prove that for a
general $JC$, any geodesic of this kind is not contained in $\overline{J_g}$
and even in $\overline{P_{g+1}}$, if $\zeta$ has rank $k<\Cliff C-3$, where
$\Cliff C$ denotes the Clifford index of $C$.


  We have mapped the Auriga/California molecular cloud with the Herschel PACS
and SPIRE cameras and the Bolocam 1.1 mm camera on the Caltech Submillimeter
Observatory (CSO) with the eventual goal of quantifying the star formation and
cloud structure in this Giant Molecular Cloud (GMC) that is comparable in size
and mass to the Orion GMC, but which appears to be forming far fewer stars. We
have tabulated 60 compact 70/160um sources that are likely pre-main-sequence
objects and correlated those with Spitzer and WISE mid-IR sources. At 1.1 mm we
find 18 cold, compact sources and discuss their properties. The most important
result from this part of our study is that we find a modest number of
additional compact young objects beyond those identified at shorter wavelengths
with Spitzer. We also describe the dust column density and temperature
structure derived from our photometric maps. The column density peaks at a few
x 10^22 cm^-2 (N_H2) and is distributed in a clear filamentary structure along
which nearly all the pre-main-sequence objects are found. We compare the YSO
surface density to the gas column density and find a strong non-linear
correlation between them. The dust temperature in the densest parts of the
filaments drops to ~10K from values ~ 14--15K in the low density parts of the
cloud. We also derive the cumulative mass fraction and probability density
function of material in the cloud which we compare with similar data on other
star-forming clouds.


  We study the k-dependence of the gap function of a bilayer superconductor,
using standard mean-field techniques applied to a 2D extended Hubbard model, in
the presence of coherent interlayer pair-tunneling and quenched coherent
single-particle tunneling. The intralayer pairing potential thus defined is
expandable in a finite number of basis functions for the irreducible
representations of the point-group of the perfectly square lattice, C_{4v}.
  This gives rise to a competition between s- and d-wave symmetry, as the
chemical potential is increased from the bottom to the top of a realistic band
for most cuprates. It allows for mixed-symmetry paired state at temperatures
below T_c, but never at T_c on a square lattice. Inclusion of the interlayer
pair-tunneling into the effective pairing potential leads to highly non-trivial
k-space structures, such as pronounced maxima along the Fermi line not seen in
the absence of interlayer pair-tunneling.
  We show how such a gap structure evolves with temperature and with band
filling, and how it affects various observables. In particular, a nonuniversal
value of the normalized jump in the specific heat at T_c will be evidenced, at
variance with the conventional universal BCS result.


  Residual stress is an important factor governing evaluating and controlling
the quality of metal materials in industrial products. X-ray measurements
provide one of the most effective means of evaluating residual stress without
destruction. In such measurements, the effects of residual stress on the
crystal structure can be observed through the Debye ring deformation.
  In previous studies, we developed a residual stress measurement system based
on the $cos \alpha$ method, using a two-dimensional (2D) silicon-on-insulator
pixel (SOIPIX) detector known as INTPIX4. In a typical laboratory setup, this
system requires only 1 second to measure a specified point. This is drastically
faster than the conventional system based on the $sin^{2} \psi$ method, which
requires more than 10 min, and the $cos \alpha$-based system using an imaging
plate, which requires 1 min. Compared to other systems, it can evaluate the 2D
distribution of residual stress faster and provide more detailed information
for evaluating materials. We first attempted to measure the 2D distribution in
a laboratory setup with a Cr X-ray tube (Cr K$\alpha$ 5.4 keV) and obtained
satisfactory results. We subsequently took measurements using synchrotron
monochromatic X-rays to determine the fine accuracy and fine sampling pitch
distribution. In this paper, we report the results of the initial synchrotron
experiment, including the residual stress distribution of the standard specimen
obtained by the first prototype setup. Furthermore, we compare the synchrotron
measurements with those from the laboratory.


  We prove a few results concerning the notions of finite dimensionality of
mixed Tate motives in the sense of Kimura and O'Sullivan. It is shown that
being oddly or evenly finite dimensional is equivalent to vanishing of certain
cohomology groups defined by means of Levine weight filtration. We then explain
the relation to the Grothendieck group of the triangulated category D of mixed
Tate motives. This naturally gives rise to a \lambda-ring structure on K_0(D).


  This theoretical paper introduces a new way to view and characterize teaching
and learning astronomy. It describes a framework, based on results from
empirical data, analyzed through standard qualitative research methodology, in
which a theoretical model for vital competencies of learning astronomy is
proposed: Reading the Sky. This model takes into account not only disciplinary
knowledge but also disciplinary discernment and extrapolating
three-dimensionality. Together, these constitute the foundation for the
competency referred to as Reading the Sky. In this paper, I describe these
concepts and how I see them being connected and intertwined to form a new
competency model for learning astronomy and how this can be used to inform
astronomy education to better match the challenges students face when entering
the discipline of astronomy: The Spiral of Teaching and Learning. Two examples
are presented to highlight how this model can be used in teaching situations.


  We have started since 1997 the Meudon Multicolor Survey of Outer Solar System
Objects with the aim of collecting a large and homogeneous set of color data
for Trans-Neptunian and Centaurs objects [...] We have a combined sample of 52
B-R color measurements for 8 Centaurs, 22 Classicals, 13 Plutinos, 8 Scattered
objects and 1 object with unidentified dynamical class. This dataset is the
largest single and homogeneous published dataset to date [...]. A strong
(color) correlation with mean excitation velocity points toward a space
weathering/impact origin for the color diversity. However, thorough modeling of
the collisional/dynamical environment in the Edgeworth-Kuiper belt needs to be
done in order to confirm this scenario. We found also that the Classical TNOs
consist in the superposition of two distinct populations: the dynamically Cold
Classical TNOs (red colors, low i, small sizes) and the dynamically Hot
Classical TNOs (diverse colors, moderate and high i, larger sizes). [...] Our
specific observation strategy [...] permitted us to highlight a few objects
suspected to have true compositional and/or texture variation on their
surfaces. These are 1998 HK151, 1999 DF9, 1999 OY3, 2000 GP183, 2000 OK67, and
2001 KA77 and should be prime targets for further observations [...]. Our
survey has also highlighted 1998 SN165 whose colors and dynamical properties
puts it in a new dynamical class distinct from the Classicals, its previously
assigned dynamical class.


  We define local fibration structures for real map germs with strictly
positive dimensional discriminant: a local fibration structure over the
complement of the discriminant, and a complete local fibration structure which
includes the stratified discriminant into the picture. We provide new classes
of map germs endowed with such local fibration structures.


  Demand forecasting applications have immensely benefited from the
state-of-the-art Deep Learning methods used for time series forecasting.
Traditional uni-modal models are predominantly seasonality driven which attempt
to model the demand as a function of historic sales along with information on
holidays and promotional events. However, accurate and robust sales forecasting
calls for accommodating multiple other factors, such as natural calamities,
pandemics, elections, etc., impacting the demand for products and product
categories in general. We propose a multi-modal sales forecasting network that
combines real-life events from news articles with traditional data such as
historical sales and holiday information. Further, we fuse information from
general product trends published by Google trends. Empirical results show
statistically significant improvements in the SMAPE error metric with an
average improvement of 7.37% against the existing state-of-the-art sales
forecasting techniques on a real-world supermarket dataset.


  Despite numerous efforts, it is still unclear whether lenticular galaxies
(S0s) evolve from spirals whose star formation was suppressed, or formed trough
mergers or disk instabilities. In this paper we present a pilot study of 21 S0
galaxies in extreme environments (field and cluster), and compare their
spatially-resolved kinematics and global stellar populations. Our aim is to
identify whether there are different mechanisms that form S0s in different
environments. Our results show that the kinematics of S0 galaxies in field and
cluster are, indeed, different. Lenticulars in the cluster are more
rotationally supported, suggesting that they are formed through processes that
involve the rapid consumption or removal of gas (e.g. starvation, ram pressure
stripping). In contrast, S0s in the field are more pressure supported,
suggesting that minor mergers served mostly to shape their kinematic
properties. These results are independent of total mass, luminosity, or
disk-to-bulge ratio. On the other hand, the mass-weighted age, metallicity, and
star formation time-scale of the galaxies correlate more with mass than with
environment, in agreement with known relations from previous work such as the
one between mass and metallicity. Overall, our results re-enforce the idea that
there are multiple mechanisms that produce S0s, and that both mass $and$
environment play key roles. A larger sample is highly desirable to confirm or
refute the results and the interpretation of this pilot study.


  We present a model for a system involving a photon gauge field and a scalar
field at quantum criticality in the frame of a Lifthitz-type non-relativistic
Maxwell theory. We will show this model gives rise to Lorentz and CPT violation
which leads to a frequency-dependent rotation of polarization plane of
radiations, and so leaves potential signals on the cosmic microwave background
temperature and polarization anisotropies.


  We explore value-based solutions for multi-agent reinforcement learning
(MARL) tasks in the centralized training with decentralized execution (CTDE)
regime popularized recently. However, VDN and QMIX are representative examples
that use the idea of factorization of the joint action-value function into
individual ones for decentralized execution. VDN and QMIX address only a
fraction of factorizable MARL tasks due to their structural constraint in
factorization such as additivity and monotonicity. In this paper, we propose a
new factorization method for MARL, QTRAN, which is free from such structural
constraints and takes on a new approach to transforming the original joint
action-value function into an easily factorizable one, with the same optimal
actions. QTRAN guarantees more general factorization than VDN or QMIX, thus
covering a much wider class of MARL tasks than does previous methods. Our
experiments for the tasks of multi-domain Gaussian-squeeze and modified
predator-prey demonstrate QTRAN's superior performance with especially larger
margins in games whose payoffs penalize non-cooperative behavior more
aggressively.


  In this paper we study some classes of second order non-homogeneous nonlinear
differential equations allowing a specific representation for nonlinear Green's
function. In particular, we show that if the nonlinear term possesses a special
multiplicativity property, then its Green's function is represented as the
product of the Heaviside function and the general solution of the corresponding
homogeneous equations subject to non-homogeneous Cauchy conditions. Hierarchies
of specific non-linearities admitting this representation are derived. The
nonlinear Green's function solution is numerically justified for the
sinh-Gordon and Liouville equations. We also list two open problems leading to
a more thorough characterizations of non-linearities admitting the obtained
representation for the nonlinear Green's function.


  It is shown that the time-reversal symmetry is broken during the phase
transition causing orbital ordering in a crystal with Jahn-Teller (J-T) ions.
This means that such phase transitions are magnetic ones. As an example we have
considered the spinel crystals containing $T_{2g}$ ions: $NiCr_2O_4$
($T_{orb}=310$ K, $T_c=70$ K) and $CuCr_2O_4$ ($T_{orb}=860$ K, $T_c=135$ K),
where the orbital ordering precedes the spin ordering, which occurs at lower
temperatures. The spin-ordering transition is naturally included into our
general group-theoretical scheme of the phase transition. Based on the symmetry
group of the parent phase, the symmetry groups for possible orbital- and
spin-ordered phases are found. We present arguments in favor of the idea that
the orbital ordering occurs due to the superexchange between J-T ions, and the
spin ordering is caused by the spin-orbital interaction. At the same time, the
displacements of atoms of the coordination polyhedron do not lead to the
removal of the orbital degeneracy, and they are merely an accompanying effect.


  Both dilepton and charm production at RHIC are considered to be important
signatures for Quark-Gluon Plasma production. Recently it was argued by
S.Gavin, P.L.McGaughey,P.V. Ruuskanen and R.Vogt that the background from
semileptonic correlated charm decays is so large that it makes dilepton
measurements virtually impossible. We show that this conclusion in fact is in
fact reversed if the energy loss due to secondary interaction of charmed quarks
is included.


  We extend temporal-difference (TD) learning in order to obtain
risk-sensitive, model-free reinforcement learning algorithms. This extension
can be regarded as modification of the Rescorla-Wagner rule, where the
(sigmoidal) stimulus is taken to be either the event of over- or
underestimating the TD target. As a result, one obtains a stochastic
approximation rule for estimating the free energy from i.i.d. samples generated
by a Gaussian distribution with unknown mean and variance. Since the Gaussian
free energy is known to be a certainty-equivalent sensitive to the mean and the
variance, the learning rule has applications in risk-sensitive decision-making.


  For two decades now, the importance of microstructure in the mechanical
behaviour of a large collection of grains has been a topic of intense research.
Many approaches have been developed to study the microstructure in granular
materials, based on local or mesoscopic measures or using ideas from diverse
fields like percolation theory, complex networks and persistent homology.
However, we do not fully understand the role of microstructure in the
mechanical behaviour of simple and commonplace packings like a pile of sand or
box of grains. Here, by characterizing the spatial variation of local
microstructure, we uncover intriguing large-scale spatial patterns in particle
packings deposited under the influence of gravity. We detail the emergence of
these patterns, and provide a unified fundamental explanation of classic and
puzzling mechanical behaviours of granular materials like central stress
minimum and Janssen effect. Further, we show the striking dependence of spatial
structure on the history of preparation, size distribution of particles and
boundary conditions. Our study reveals the existence of global spatial
structure in a locally disordered media, and elucidates its significance in the
mechanical behaviour of granular materials.


  Using the in-viscid two-field reduced MHD model, a new analytical theory is
developed to unify the Hahm-Kulsrud-Taylor (HKT) linear solution and the
Rutherford quasi-linear regime. Adopting a quasi-linear approach, we obtain a
closed system of equations for plasma response in a static plasma in slab
geometry. An integral form of analytical solution is obtained for the forced
magnetic reconnection, uniformly valid throughout the entire regimes from the
HKT linear solution to the Rutherford quasi-linear solution. In particular, the
quasi-linear effect can be described by a single coefficient $K_s\propto
S^{8/5} \psi_c^2$, where $S=\frac{\tau_R}{\tau_A}$ and $\psi_c$ are the
Lunquist number and amplitude of external magnetic perturbation, respectively.
The HKT linear solution for response can be recovered when the index
$K_s\rightarrow 0$. On the other hand, the quasi-linear effect plays a key role
in the island growth when $K_s\sim1$. Our new analytical solution has also been
compared with reduced MHD simulations with agreement.


  A variable combination of realistic and random two-body interactions allows
the study of collective properties, such as the energy spectra and B(E2)
transition strengths in 44Ti, 48Cr and 24Mg. It is found that the average
energies of the yrast band states maintain the ordering for any degree of
randomness, but the B(E2) values lose their quadrupole collectivity when
randomness dominates the Hamiltonian. The high probability of the yrast band to
be ordered in the presence of pure random forces exhibits the strong
correlations between the different members of the band.


  We demonstrate and verify the in-situ addition of a collecting lens for
electroluminescence experiments to an existing scanning tunneling microscope.
We fabricate a simple clip-on lens that we reversibly attach at the sample
plate via regular sample transfer tools to collimate the light emitted from a
plasmonic tunneling junction to the viewport ordinarily used for optical
access. The proximity of the lens to the tunneling junction allows us to
exploit the full numerical aperture which helps us achieve good collection
efficiencies, demonstrating the quick turnaround of converting an existing
setup with optical access into a practical scanning luminescence microscope. We
verify the function of the clip-on lens by measuring the bias dependent plasmon
of Au, Ag, and spatial luminescence maps.


  We study scalar field perturbations on the background of non-supersymmetric
black rings and of supersymmetric black rings. In the infinite-radius limit of
these geometries, we are able to separate the wave equation, and to study wave
phenomena in its vicinities. In this limit, we show that (i) both geometries
are stable against scalar field perturbations, (ii) the absorption
cross-section for scalar fields is equal to the area of the event horizon in
the supersymmetric case, and proportional to it in the non-supersymmetric
situation.


  Second-order information has proven to be very effective in determining the
redundancy of neural network weights and activations. Recent paper proposes to
use Hessian traces of weights and activations for mixed-precision quantization
and achieves state-of-the-art results. However, prior works only focus on
selecting bits for each layer while the redundancy of different channels within
a layer also differ a lot. This is mainly because the complexity of determining
bits for each channel is too high for original methods. Here, we introduce
Channel-wise Hessian Aware trace-Weighted Quantization (CW-HAWQ). CW-HAWQ uses
Hessian trace to determine the relative sensitivity order of different channels
of activations and weights. What's more, CW-HAWQ proposes to use deep
Reinforcement learning (DRL) Deep Deterministic Policy Gradient (DDPG)-based
agent to find the optimal ratios of different quantization bits and assign bits
to channels according to the Hessian trace order. The number of states in
CW-HAWQ is much smaller compared with traditional AutoML based mix-precision
methods since we only need to search ratios for the quantization bits. Compare
CW-HAWQ with state-of-the-art shows that we can achieve better results for
multiple networks.


  We propose a unified stochastic SIR model driven by L\'{e}vy noise. The model
is structural enough to allow for time-dependency, nonlinearity, discontinuity,
demography and environmental disturbances. We present concise results on the
existence and uniqueness of positive global solutions and investigate the
extinction and persistence of the novel model. Examples and simulations are
provided to illustrate the main results.


  The energy-conserving sampling and weighting (ECSW) method is a
hyperreduction method originally developed for accelerating the performance of
Galerkin projection-based reduced-order models (PROMs) associated with
large-scale finite element models, when the underlying projected operators need
to be frequently recomputed as in parametric and/or nonlinear problems. In this
paper, this hyperreduction method is extended to Petrov-Galerkin PROMs where
the underlying high-dimensional models can be associated with arbitrary finite
element, finite volume, and finite difference semi-discretization methods. Its
scope is also extended to cover local PROMs based on piecewise-affine
approximation subspaces, such as those designed for mitigating the Kolmogorov
$n$-width barrier issue associated with convection-dominated flow problems. The
resulting ECSW method is shown in this paper to be robust and accurate. In
particular, its offline phase is shown to be fast and parallelizable, and the
potential of its online phase for large-scale applications of industrial
relevance is demonstrated for turbulent flow problems with $O(10^7)$ and
$O(10^8)$ degrees of freedom. For such problems, the online part of the ECSW
method proposed in this paper for Petrov-Galerkin PROMs is shown to enable
wall-clock time and CPU time speedup factors of several orders of magnitude
while delivering exceptional accuracy.


  We develop the theory of groupoid graded semisimple rings. Our rings are
neither unital nor one-sided artinian. Instead, they exhibit a strong version
of having local units and being locally artinian, and we call them
$\Gamma_0$-artinian. One of our main results is a groupoid graded version of
the Wedderburn-Artin Theorem, where we characterize groupoid graded semisimple
rings as direct sums of graded simple $\Gamma_0$-artinian rings and we exhibit
the structure of this latter class of rings. In this direction, we also prove a
groupoid graded version of Jacobson-Chevalley density theorem. We need to
define and study properties of groupoid gradings on matrix rings (possibly of
infinite size) over groupoid graded rings, and specially over groupoid graded
division rings. Because of that, we study groupoid graded division rings and
their graded modules. We consider a natural notion of freeness for groupoid
graded modules that, when specialized to group graded rings, gives the usual
one, and show that for a groupoid graded division ring all graded modules are
free (in this sense). Contrary to the group graded case, there are groupoid
graded rings for which all graded modules are free according to our definition,
but they are not graded division rings. We exhibit an easy example of this kind
of rings and characterize such class among groupoid graded semisimple rings. We
also relate groupoid graded semisimple rings with the notion of semisimple
category defined by B. Mitchell. For that, we show the link between functors
from a preadditive category to abelian groups and graded modules over the
groupoid graded ring associated to this category, generalizing a result of P.
Gabriel. We characterize simple artinian categories and categories for which
every functor from them to abelian groups is free in the sense of B. Mitchell.


  Alzheimer s disease (AD) pathophysiology is still imperfectly understood and
current paradigms have not led to curative outcome. Omics technologies offer
great promises for improving our understanding and generating new hypotheses.
However, integration and interpretation of such data pose major challenges,
calling for adequate knowledge models. AlzPathway is a disease map that gives a
detailed and broad account of AD pathophysiology. However, AlzPathway lacks
formalism, which can lead to ambiguity and misinterpretation. Ontologies are an
adequate framework to overcome this limitation, through their axiomatic
definitions and logical reasoning properties. We introduce the AD Map Ontology
(ADMO) an ontological upper model based on systems biology terms. We then
propose to convert AlzPathway into an ontology and to integrate it into ADMO.
We demonstrate that it allows one to deal with issues related to redundancy,
naming, consistency, process classification and pathway relationships. Further,
it opens opportunities to expand the model using elements from other resources,
such as generic pathways from Reactome or clinical features contained in the
ADO (AD Ontology). A version of the ontology will be made freely available to
the community on Bioportal at the time of the confer-ence.


  In this paper, we consider an inference problem for the first order
autoregressive process with non-zero mean driven by a long memory stationary
Gaussian process. Suppose that the covariance function of the noise can be
expressed as $|k|^{2H-2}$ times a positive constant when $k$ tends to infinity,
and the fractional Gaussian noise and the fractional ARIMA model are special
examples that satisfy this assumption. We propose moment estimators and prove
the strong consistency, the asymptotic normality and joint asymptotic
normality.


  The nearest neighbor spacing distribution of levels of deformed even-even
nuclei classified according to their quadrupole deformation parameter is
investigated. The results suggest that the oblate deformed nuclei have more
regular spectra than prolate ones.


  The full optimization of the design and operation of instruments whose
functioning relies on the interaction of radiation with matter is a super-human
task, given the large dimensionality of the space of possible choices for
geometry, detection technology, materials, data-acquisition, and
information-extraction techniques, and the interdependence of the related
parameters. On the other hand, massive potential gains in performance over
standard, "experience-driven" layouts are in principle within our reach if an
objective function fully aligned with the final goals of the instrument is
maximized by means of a systematic search of the configuration space. The
stochastic nature of the involved quantum processes make the modeling of these
systems an intractable problem from a classical statistics point of view, yet
the construction of a fully differentiable pipeline and the use of deep
learning techniques may allow the simultaneous optimization of all design
parameters.
  In this document we lay down our plans for the design of a modular and
versatile modeling tool for the end-to-end optimization of complex instruments
for particle physics experiments as well as industrial and medical applications
that share the detection of radiation as their basic ingredient. We consider a
selected set of use cases to highlight the specific needs of different
applications.


  We introduce a new foundation rank based in the relation of dividing between
partial types. We call DU to this rank. We also introduce a new way to define
the D rank over formulas as a foundation rank. In this way, SU, DU and D are
foundation ranks based in the relation of dividing. We study the properties and
the relations between these ranks. Next, we discuss the possible definitions of
a supersimple type. This is a concept that it is not clear in the previous
literature. In this paper we give solid arguments to set up a concrete
definition of this concept and its properties. We also see that DU
characterizes supersimplicity, while D not.


  We study the dissipative Rabi model under deep strong far-off-resonant
driving when the driving frequency and strength exceed significantly the qubit
transition frequency. We find analytical expressions for the density matrix,
the steady-state first-order field correlation function and the multiphoton
emission spectrum. This spectrum consists of a series of narrow delta-like
lines, caused by coherent processes and of Lorenzian peaks resulted from the
incoherent scattering of photons. It is shown that the width of emission lines
as well as the decay rate of the time oscillations in the population difference
have oscillating dependence on the driving strength. We show how the features
of this dependence are controlled by the relative rates of energetic relaxation
and pure dephasing.


  We propose a new method for producing color images from sketches. Current
solutions in sketch colorization either necessitate additional user instruction
or are restricted to the "paired" translation strategy. We leverage semantic
image segmentation from a general-purpose panoptic segmentation network to
generate an additional adversarial loss function. The proposed loss function is
compatible with any GAN model. Our method is not restricted to datasets with
segmentation labels and can be applied to unpaired translation tasks as well.
Using qualitative, and quantitative analysis, and based on a user study, we
demonstrate the efficacy of our method on four distinct image datasets. On the
FID metric, our model improves the baseline by up to 35 points. Our code,
pretrained models, scripts to produce newly introduced datasets and
corresponding sketch images are available at
https://github.com/giddyyupp/AdvSegLoss.


  The Lambert W function gives the solutions of a simple exponential
polynomial. The generalized Lambert W function was defined by Mez\"{o} and
Baricz, and has found applications in delay differential equations and physics.
In this article we describe an even more general function, the inverse of a
product of powers of linear functions and one exponential term. We show that
the coefficients of the Taylor series for these functions can be described by
multivariable hypergeometric functions of the parameters. We also present a
surprising conjecture for the radius of convergence of the Taylor series, with
a rough proof.


  Gamma-ray blazars are among the most extreme astrophysical sources, harboring
phenomena far more energetic than those attainable by terrestrial accelerators.
These galaxies are understood to be active galactic nuclei that are powered by
accretion onto supermassive black holes and have relativistic jets pointed
along the Earth line of sight. The emission displayed is variable at all
wavelengths and timescales probed thus far, necessitating contemporaneous
broadband observations to disentangle the details of the emission processes
within the relativistic jets. The very high energy (VHE; $E\ge$100 GeV) photons
emitted by these sources are detectable with ground-based imaging atmospheric
Cherenkov telescopes such as VERITAS. As these photons propagate extragalactic
distances, the interaction with the diffuse starlight that pervades the entire
Universe results in a distance and energy dependent gamma-ray opacity, offering
a unique method for probing photon densities on cosmological scales. These
galaxies have also been postulated to be potential sources of ultra-high-energy
cosmic rays, a theory which can be examined through deep gamma-ray observations
of sources which probe moderate gamma-ray opacities. Within this work, I will
highlight ongoing research regarding the broadband emission from
VERITAS-observed VHE blazars, as well as the potential to use them for
cosmological insight.


  We report the recent results of proton and anti-proton yields as a function
of centrality and p_T in Au+Au collisions at sqrt(s_NN) = 200 GeV, measured by
the PHENIX experiment at RHIC. In central collisions at intermediate transverse
momenta (1.5 < p_T < 4.5 GeV/c) a significant fraction of all produced
particles is protons and anti-protons. They show a different scaling behavior
from that of pions. The pbar/pi and p/pi ratios are enhanced compared to
peripheral Au+Au, p+p and e+e- collisions. This enhancement is limited to p_T <
5 GeV/c as deduced from the ratio of charged hadrons to pi^0 measured in the
range 1.5 < p_T < 9 GeV/c.


  Computational topology is an area that revisits topological problems from an
algorithmic point of view, and develops topological tools for improved
algorithms. We survey results in computational topology that are concerned with
graphs drawn on surfaces. Typical questions include representing surfaces and
graphs embedded on them computationally, deciding whether a graph embeds on a
surface, solving computational problems related to homotopy, optimizing curves
and graphs on surfaces, and solving standard graph algorithm problems more
efficiently in the case of surface-embedded graphs.


  Here we consider a gravitational action having local Poincare invariance
which is given by the dimensional continuation of the Euler density in ten
dimensions. It is shown that the local supersymmetric extension of this action
requires the algebra to be the maximal extension of the N=1 super-Poincare
algebra. The resulting action is shown to describe a gauge theory for the
M-algebra, and is not the eleven-dimensional supergravity theory of
Cremmer-Julia-Scherk. The theory admits a class of vacuum solutions of the form
S^{10-d} x Y_{d+1}, where Y_{d+1} is a warped product of R with a d-dimensional
spacetime. It is shown that a nontrivial propagator for the graviton exists
only for d=4 and positive cosmological constant. Perturbations of the metric
around this solution reproduce linearized General Relativity around
four-dimensional de Sitter spacetime.


  Charged surfaces in contact with liquids containing ions are accompanied in
equilibrium by an electric double layer consisting of a layer of electric
charge on the surface that is screened by a diffuse ion cloud in the bulk
fluid. This screening cloud determines not only the interactions between
charged colloidal particles or polyelectrolytes and their self-assembly into
ordered structures, but it is also pivotal in understanding energy storage
devices, such as electrochemical cells and supercapacitors. However, little is
known to what spatial complexity the electric double layers can be designed.
Here, we show that electric double layers of non-trivial topology and geometry
-including tori, multi-tori and knots- can be realised in charged topological
colloidal particles, using numerical modelling within a mean-field
Poisson-Boltzmann theory. We show that the complexity of double layers
-including geometry and topology- can be tuned by changing the Debye screening
length of the medium, or by changing the shape and topology of the (colloidal)
particle. More generally, this work is an attempt to introduce concepts of
topology in the field of charged colloids, which could lead to novel exciting
material design paradigms.


  Patients in the intensive care unit (ICU) require constant and close
supervision. To assist clinical staff in this task, hospitals use monitoring
systems that trigger audiovisual alarms if their algorithms indicate that a
patient's condition may be worsening. However, current monitoring systems are
extremely sensitive to movement artefacts and technical errors. As a result,
they typically trigger hundreds to thousands of false alarms per patient per
day - drowning the important alarms in noise and adding to the exhaustion of
clinical staff. In this setting, data is abundantly available, but obtaining
trustworthy annotations by experts is laborious and expensive. We frame the
problem of false alarm reduction from multivariate time series as a
machine-learning task and address it with a novel multitask network
architecture that utilises distant supervision through multiple related
auxiliary tasks in order to reduce the number of expensive labels required for
training. We show that our approach leads to significant improvements over
several state-of-the-art baselines on real-world ICU data and provide new
insights on the importance of task selection and architectural choices in
distantly supervised multitask learning.


  Tracks events at the IceCube Observatory are characterized by an energetic
muon crossing several kilometers before decaying. Such muons are dominantly
produced in charged current (CC) muon neutrino - hadron interactions. However,
muons are also produced through the $W$ - boson production and in the decay of
tau leptons and heavy mesons created in neutral and charged current
interactions induced by all neutrino flavors. In this paper, we investigate the
contribution of these subleading channels to events characterized as tracks at
the IceCube. Our results indicate that these channels correspond to a non -
negligible fraction of the HESE track events. In addition, we show that its
contributions are concentrated in muons that are less energetic than those
arising from muonic neutrino CC interactions for the same visible energies of
the process. Finally, we investigate the impact of these additional channels on
the description of the astrophysical neutrino flux, and we find that the
inclusion of these subleading processes are important in determining the
parameters of the astrophysical neutrino flux.


  This paper compares and generalizes Berge's maximum theorem for noncompact
image sets established in Feinberg, Kasyanov and Voorneveld (2014) and the
local maximum theorem established in Bonnans and Shapiro (2000).


  This article, in memory of Professor Hiroshi Suura, is devoted to the effects
of particles in loops, ranging from quantum electrodynamics to precise tests of
the electroweak theory and CP violation.


  In this paper we study the effect of stochastic perturbations on a common
type of moving boundary value PDE's which endorse Stefan boundary conditions,
or Stefan problems, and show the existence and uniqueness of the solutions to a
number of stochastic equations of this kind. Moreover we also derive the space
and time regularities of the solutions and the associated boundaries via
Kolmogorov's Continuity Theorem in an appropriately defined normed space.
  The paper first conveys our previous results where randomness is smoothly
correlated in space and Brownian in time, then introduces a new methodology
that enables us to prove the existence and uniqueness of the solution to the
standard heat equation driven by a space-time Brownian sheet as well as its
boundary regularity, and finally extends it to the stochastic moving boundary
partial PDE's driven by the same type of randomness.


  In the realm of cerebrovascular monitoring, primary metrics typically include
blood pressure, which influences cerebral blood flow (CBF) and is contingent
upon vessel radius. Measuring CBF non-invasively poses a persistent challenge,
primarily attributed to the difficulty of accessing and obtaining signal from
the brain. This study aims to introduce a compact speckle visibility
spectroscopy (SVS) device designed for non-invasive CBF measurements, offering
cost-effectiveness and scalability while tracking CBF with remarkable
sensitivity and temporal resolution. The wearable hardware has a modular design
approach consisting solely of a laser diode as the source and a meticulously
selected board camera as the detector. They both can be easily placed on the
head of a subject to measure CBF with no additional optical elements. The SVS
device can achieve a sampling rate of 80 Hz with minimal susceptibility to
external disturbances. The device also achieves better SNR compared with
traditional fiber-based SVS devices, capturing about 70 times more signal and
showing superior stability and reproducibility. It is designed to be paired and
distributed in multiple configurations around the head, and measure signals
that exceed the quality of prior optical CBF measurement techniques. Given its
cost-effectiveness, scalability, and simplicity, this laser-centric tool offers
significant potential in advancing non-invasive cerebral monitoring
technologies.


  We study the integral transform which appeared in a different form in
Akhiezer's textbook "Lectures on Integral Transforms".


  The Linked Data Benchmark Council's Social Network Benchmark (LDBC SNB) is an
effort intended to test various functionalities of systems used for graph-like
data management. For this, LDBC SNB uses the recognizable scenario of operating
a social network, characterized by its graph-shaped data. LDBC SNB consists of
two workloads that focus on different functionalities: the Interactive workload
(interactive transactional queries) and the Business Intelligence workload
(analytical queries). This document contains the definition of both workloads.
This includes a detailed explanation of the data used in the LDBC SNB, a
detailed description for all queries, and instructions on how to generate the
data and run the benchmark with the provided software.


  We study the complexity of classical constraint satisfaction problems on a 2D
grid. Specifically, we consider the complexity of function versions of such
problems, with the additional restriction that the constraints are
translationally invariant, namely, the variables are located at the vertices of
a 2D grid and the constraint between every pair of adjacent variables is the
same in each dimension. The only input to the problem is thus the size of the
grid. This problem is equivalent to one of the most interesting problems in
classical physics, namely, computing the lowest energy of a classical system of
particles on the grid. We provide a tight characterization of the complexity of
this problem, and show that it is complete for the class $FP^{NEXP}$. Gottesman
and Irani (FOCS 2009) also studied classical translationally-invariant
constraint satisfaction problems; they show that the problem of deciding
whether the cost of the optimal solution is below a given threshold is
NEXP-complete. Our result is thus a strengthening of their result from the
decision version to the function version of the problem. Our result can also be
viewed as a generalization to the translationally invariant setting, of
Krentel's famous result from 1988, showing that the function version of SAT is
complete for the class $FP^{NP}$. An essential ingredient in the proof is a
study of the complexity of a gapped variant of the problem. We show that it is
NEXP-hard to approximate the cost of the optimal assignment to within an
additive error of $\Omega(N^{1/4})$, for an $N \times N$ grid. To the best of
our knowledge, no gapped result is known for CSPs on the grid, even in the
non-translationally invariant case. As a byproduct of our results, we also show
that a decision version of the optimization problem which asks whether the cost
of the optimal assignment is odd or even is also complete for $P^{NEXP}$.


  A new method for the construction of Fock-adapted operator Markovian cocycles
is outlined, and its use is illustrated by application to a number of examples
arising in physics and probability. The construction uses the Trotter-Kato
Theorem and a recent characterisation of such cocycles in terms of an
associated family of contraction semigroups.


  We report results of experiments with liquid $^3$He confined in a high
porosity anisotropic nanostructure which we call planar aerogel. This aerogel
consists of nanofibers (with diameters $\sim10$ nm) which are randomly oriented
in the plane normal to the specific axis. We used two samples of planar aerogel
prepared using different techniques. We have found that on cooling from the
normal phase of $^3$He the superfluid transition in both samples occurs into an
equal spin pairing superfluid phase. NMR properties of this phase qualitatively
agree with the properties of the superfluid A phase in the anisotropic
Larkin-Imry-Ma state. We have observed differences between results obtained in
the presence and absence of solid paramagnetic $^3$He on the aerogel strands.
We propose that these differences may be due, at least in part, to a magnetic
scattering channel which appears in the presence of solid paramagnetic $^3$He.


  We consider the structure group of a non-degenerate symmetric (non-trivial)
set-theoretical solution of the quantum Yang-Baxter equation. This is a
Bieberbach group and also a Garside group. We show this group is not
bi-orderable, that is it does not admit a total order which is invariant under
left and right multiplication. Regarding the existence of a left invariant
total ordering, there is a great diversity. There exist structure groups with
space of left orders homeomorphic to the Cantor set and all left orders
Conradian, while there exist others that are even not unique product groups.


  Vertically stacked and coupled InAs/GaAs self-assembled quantum dots (SADs)
are predicted to exhibit a strong non-parabolic dependence of the interband
transition energy on the electric field, which is not encountered in single SAD
structures nor in other types of quantum structures. Our study based on an
eight-band strain-dependent ${\bf k}\cdot{\bf p}$ Hamiltonian indicates that
this anomalous quantum confined Stark effect is caused by the three-dimensional
strain field distribution which influences drastically the hole states in the
stacked SAD structures.


  This paper proposes an adaptive near-hover position controller for
quadcopters, which can be deployed to quadcopters of very different mass, size
and motor constants, and also shows rapid adaptation to unknown disturbances
during runtime. The core algorithmic idea is to learn a single policy that can
adapt online at test time not only to the disturbances applied to the drone,
but also to the robot dynamics and hardware in the same framework. We achieve
this by training a neural network to estimate a latent representation of the
robot and environment parameters, which is used to condition the behaviour of
the controller, also represented as a neural network. We train both networks
exclusively in simulation with the goal of flying the quadcopters to goal
positions and avoiding crashes to the ground. We directly deploy the same
controller trained in the simulation without any modifications on two
quadcopters in the real world with differences in mass, size, motors, and
propellers with mass differing by 4.5 times. In addition, we show rapid
adaptation to sudden and large disturbances up to one-third of the mass of the
quadcopters. We perform an extensive evaluation in both simulation and the
physical world, where we outperform a state-of-the-art learning-based adaptive
controller and a traditional PID controller specifically tuned to each platform
individually. Video results can be found at https://youtu.be/U-c-LbTfvoA.


  The formation dynamics is studied for a singular profile of a surface of an
ideal conducting fluid in an electric field. Self-similar solutions of
electrohydrodynamic equations describing the fundamental process of formation
of surface conic cusps with angles close to the Taylor cone angle 98.6 are
obtained. The behavior of physical quantities (field strength, fluid velocity,
surface curvature) near the singularity is established.


  We have developed a mean-field model to describe the dynamics of a
non-equilibrium Bose-Einstein condensate of exciton-polaritons in a
semiconductor microcavity. The spectrum of elementary excitations around the
stationary state is analytically studied in different geometries. A diffusive
behaviour of the Goldstone mode is found in the spatially homogeneous case and
new features are predicted for the Josephson effect in a two-well geometry.


  The field content of the two dimensional string theory consists of the
dynamical tachyon field and some nonpropagating fields which consist in the
topological sector of this theory. We propose in this paper to study this
topological sector as a spacetime gauge theory with a simple centrally extended
$w_\infty$ algebra. This $w_\infty$ algebra appears in both the world sheet
BRST analysis and the matrix model approach. Since the two dimensional
centrally extended Poincar\'e algebra is naturally embedded in the centrally
extended $w_\infty$ algebra, the low energy action for the metric and dilaton
appears naturally when the model is truncated at this level. We give a
plausible explanation of emergence of discrete states in this formulation. This
theory is again the effective theory at zero slope limit. To include higher
order $\alpha'$ corrections, we speculate that the whole theory is a gauge
theory of a deformed $w_\infty$ algebra, and the deformation parameter is just
$\alpha'$.


  Knowledge tracing---where a machine models the knowledge of a student as they
interact with coursework---is a well established problem in computer supported
education. Though effectively modeling student knowledge would have high
educational impact, the task has many inherent challenges. In this paper we
explore the utility of using Recurrent Neural Networks (RNNs) to model student
learning. The RNN family of models have important advantages over previous
methods in that they do not require the explicit encoding of human domain
knowledge, and can capture more complex representations of student knowledge.
Using neural networks results in substantial improvements in prediction
performance on a range of knowledge tracing datasets. Moreover the learned
model can be used for intelligent curriculum design and allows straightforward
interpretation and discovery of structure in student tasks. These results
suggest a promising new line of research for knowledge tracing and an exemplary
application task for RNNs.


  Time-dependent density matrix renormalization group method with a matrix
product ansatz is employed for explicit computation of non-equilibrium steady
state density operators of several integrable and non-integrable quantum spin
chains, which are driven far from equilibrium by means of Markovian couplings
to external baths at the two ends. It is argued that even though the
time-evolution can not be simulated efficiently due to fast entanglement
growth, the steady states in and out of equilibrium can be typically accurately
approximated, so that chains of length of the order n ~ 100 are accessible. Our
results are demonstrated by performing explicit simulations of steady states
and calculations of energy/spin densities/currents in several problems of heat
and spin transport in quantum spin chains. Previously conjectured relation
between quantum chaos and normal transport is re-confirmed with high acurracy
on much larger systems.


  In this paper we study the formation of bound states in the continuum in a
quantum dot molecule coupled to leads. Based in the combination of bound states
in the continuum and Fano effect, we propose a new design of a spin-dependent
polarizer. By lifting the spin degeneracy of the carriers in the quantum dots
by means of a magnetic field the system can be used as a spin-polarized device.
A detailed analysis of the spin-dependent conductance and spin polarization as
a function of the applied magnetic field and gate voltages is carried out.


  In the episodic accretion scenario, a large fraction of the protostellar mass
accretes during repeated and large bursts of accretion. Since outbursts on
protostars are typically identified at specific wavelengths, interpreting these
outbursts requires converting this change in flux to a change in total
luminosity. The Class I young stellar object EC 53 in the Serpens Main cloud
has undergone repeated increases in brightness at 850 $\mu$m that are likely
caused by bursts of accretion. In this study, we perform two- and
three-dimensional continuum radiative transfer modeling to quantify the
internal luminosity rise in EC 53 that corresponds to the factor of $\sim$1.5
enhancement in flux at 850 $\mu$m. We model the spectral energy distribution
and radial intensity profile in both the quiescent and outburst phases. The
internal luminosity in the outburst phase is $\sim 3.3$ times brighter than the
luminosity in the quiescent phase. The radial intensity profile analysis
demonstrates that the detected sub-mm flux variation of EC 53 comes from the
heated envelope by the accretion burst. We also find that the role of external
heating of the EC 53 envelope by the interstellar radiation field is
insignificant.


  The idea of the meson cloud approach in the chiral quark model has been
extended to include gluon cloud in order to achieve the parton densities in the
nucleon, based on the constitute quark framework. The splitting function of the
quark to the quark-meson and quark-gluon at low $Q^2$ value are used to obtain
parton densities in the constituent quark. The phenomenological constituent
model is employed to extract the parton distributions in the proton at low
$Q^2$ value. Since we have access to the parton densities at low $Q^2$, we are
able to obtain $F_{2}(x,Q^2)$ structure function at low $Q^2$ value. The result
is in good agreement with available experimental data and some theoretical
models. To confirm the validity of our calculations, the fraction of total
momentum of proton which is carried by gluon at high $Q^2$ and also the
Gottfried sum rule are computed. The results are in good agreement with what
are expected.


  Recent measurements of the correlation between the spin of the top and the
spin of the anti-top quark produced in proton anti-proton scattering at a
centre of mass energy of 1.96 TeV by the CDF and D0 collaborations are
discussed. Using up to 4.3 fb^-1 of data taken with the CDF and D0 detectors
the spin correlation parameter C, the degree to which the spins are correlated,
is measured in dileptonic and semileptonic final states. The measurements are
found to be in agreement with Standard Model predictions.


  Let $S$ denote the graded polynomial ring $\C[x_1,...,x_m]$. We interpret a
chain complex of free $S$-modules having finite length homology modules as an
$S^1$-equivariant map $\C^m\sm\{0\} \to X$, where $X$ is a moduli space of
exact sequences. By computing the cohomology of such spaces $X$ we obtain
obstructions to such maps, including a slight generalization of the
Herzog-K\"uhl equations.


  During big bang nucleosynthesis, any injection of extra neutrons around the
time of the $^7$Be formation, i.e. at a temperature of order $T \simeq 50$~keV,
can reduce the predicted freeze-out amount of $^7$Be + $^7$Li that otherwise
remains in sharp contradiction with the Spite plateau value inferred from the
observations of Pop II stars. However, the growing confidence in the primordial
D/H determinations puts a strong constraint on any such scenario. We address
this issue in detail, analyzing different temporal patterns of neutron
injection, such as decay, annihilation, resonant annihilation, and oscillation
between mirror and standard model world neutrons. For this latter case, we
derive the realistic injection pattern taking into account thermal effects
(damping and refraction) in the primordial plasma. If the extra neutron supply
is the sole non-standard mechanism operating during the BBN, the suppression of
lithium abundance below Li/H~$\leq 1.9 \times 10^{-10}$ always leads to the
overproduction of deuterium, D/H~$\geq 3.6 \times 10^{-5}$, well outside the
error bars suggested by recent observations.


  It was suggested in the literature that the self-diffusion coefficient of
simple fluids can be approximated as a ratio of the squared thermal velocity of
the atoms to the "fluid Einstein frequency," which can thus serve as a rough
estimate of the friction (momentum transfer) rate in the dense fluid phase. In
this article we test this suggestion using a single-component Yukawa fluid as a
reference system. The available simulation data on self-diffusion in Yukawa
fluids, complemented with new data for Yukawa melts (Yukawa fluids near the
freezing phase transition), are carefully analyzed. It is shown that although
not exact, this earlier suggestion nevertheless provides a very sensible way of
normalization of the self-diffusion constant. Additionally, we demonstrate that
certain quantitative properties of self-diffusion in Yukawa melts are also
shared by systems like one-component plasma and liquid metals at freezing,
providing support to an emerging dynamical freezing indicator for simple soft
matter systems. The obtained results are also briefly discussed in the context
of the theory of momentum transfer in complex (dusty) plasmas.


  We learn optimal user association policies for traffic from different
locations to Access Points(APs), in the presence of unknown dynamic traffic
demand. We aim at minimizing a broad family of $\alpha$-fair cost functions
that express various objectives in load assignment in the wireless downlink,
such as total load or total delay minimization. Finding an optimal user
association policy in dynamic environments is challenging because traffic
demand fluctuations over time are non-stationary and difficult to characterize
statistically, which obstructs the computation of cost-efficient associations.
Assuming arbitrary traffic patterns over time, we formulate the problem of
online learning of optimal user association policies using the Online Convex
Optimization (OCO) framework. We introduce a periodic benchmark for OCO
problems that generalizes state-of-the-art benchmarks. We exploit inherent
properties of the online user association problem and propose PerOnE, a simple
online learning scheme that dynamically adapts the association policy to
arbitrary traffic demand variations. We compare PerOnE against our periodic
benchmark and prove that it enjoys the no-regret property, with additional
sublinear dependence of the network size. To the best of our knowledge, this is
the first work that introduces a periodic benchmark for OCO problems and a
no-regret algorithm for the online user association problem. Our theoretical
findings are validated through results on a real-trace dataset.


  We study the shared processor scheduling problem with a single shared
processor where a unit time saving (weight) obtained by processing a job on the
shared processor depends on the job. A polynomial-time optimization algorithm
has been given for the problem with equal weights in the literature. This paper
extends that result by showing an $O(n \log n)$ optimization algorithm for a
class of instances in which non-decreasing order of jobs with respect to
processing times provides a non-increasing order with respect to weights ---
this instance generalizes the unweighted case of the problem. This algorithm
also leads to a $\frac{1}{2}$-approximation algorithm for the general weighted
problem. The complexity of the weighted problem remains open.


  In Evolutionary Robotics a population of solutions is evolved to optimize
robots that solve a given task. However, in traditional Evolutionary
Algorithms, the population of solutions tends to converge to local optima when
the problem is complex or the search space is large, a problem known as
premature convergence. Quality Diversity algorithms try to overcome premature
convergence by introducing additional measures that reward solutions for being
different while not necessarily performing better. In this paper we compare a
single objective Evolutionary Algorithm with two diversity promoting search
algorithms; a Multi-Objective Evolutionary Algorithm and MAP-Elites a Quality
Diversity algorithm, for the difficult problem of evolving control and
morphology in modular robotics. We compare their ability to produce high
performing solutions, in addition to analyze the evolved morphological
diversity. The results show that all three search algorithms are capable of
evolving high performing individuals. However, the Quality Diversity algorithm
is better adept at filling all niches with high-performing solutions. This
confirms that Quality Diversity algorithms are well suited for evolving modular
robots and can be an important means of generating repertoires of high
performing solutions that can be exploited both at design- and runtime.


  Recently, the ever-increasing capacity of large-scale annotated datasets has
led to profound progress in stereo matching. However, most of these successes
are limited to a specific dataset and cannot generalize well to other datasets.
The main difficulties lie in the large domain differences and unbalanced
disparity distribution across a variety of datasets, which greatly limit the
real-world applicability of current deep stereo matching models. In this paper,
we propose CFNet, a Cascade and Fused cost volume based network to improve the
robustness of the stereo matching network. First, we propose a fused cost
volume representation to deal with the large domain difference. By fusing
multiple low-resolution dense cost volumes to enlarge the receptive field, we
can extract robust structural representations for initial disparity estimation.
Second, we propose a cascade cost volume representation to alleviate the
unbalanced disparity distribution. Specifically, we employ a variance-based
uncertainty estimation to adaptively adjust the next stage disparity search
space, in this way driving the network progressively prune out the space of
unlikely correspondences. By iteratively narrowing down the disparity search
space and improving the cost volume resolution, the disparity estimation is
gradually refined in a coarse-to-fine manner. When trained on the same training
images and evaluated on KITTI, ETH3D, and Middlebury datasets with the fixed
model parameters and hyperparameters, our proposed method achieves the
state-of-the-art overall performance and obtains the 1st place on the stereo
task of Robust Vision Challenge 2020. The code will be available at
https://github.com/gallenszl/CFNet.


  In this work we are concerned with solutions to the linear Schr\"odinger type
system with mixed dispersion, the so-called biharmonic Schr\"odinger equation.
Precisely, we are able to prove an exact control property for these solutions
with the control in the energy space posed on an oriented star graph structure
$\mathcal{G}$ for $T>T_{min}$, with $$T_{min}=\sqrt{ \frac{ \overline{L}
(L^2+\pi^2)}{\pi^2\varepsilon(1- \overline{L} \varepsilon)}},$$ when the
couplings and the controls appear only on the Neumann boundary conditions.


  We present some computational simulations of graphene-based nanoribbons with
a number of half-twists varying from 0 to 4 and two types of defects obtained
by removing a single carbon atom from two different sites. Optimized geometries
are found by using a mix of classical-quantum semiempirical computations.
According with the simulations results, the local curvature of the nanoribbons
increases at the defect sites, specially for a higher number of half-twists.
The HOMO-LUMO energy gap of the nanostructures has significant variation when
the number of half-twists increases for the defective nanoribbons. At the
quantum semiempirical level, the first optically active transitions and
oscillator strengths are calculated using the full configuration interaction
(CI) framework, and the optical absorption in the UV/Visible range (electronic
transitions) and in the infrared (vibrational transitions) are achieved.
Distinct nanoribbons show unique spectral signatures in the UV/Visible range,
with the first absorption peaks in wavelengths ranging from the orange to the
violet. Strong absorption is observed in the ultraviolet region, although
differences in their infrared spectra are hardly discernible.


  Since thin-film silicon solar cells have limited optical absorption, we
explore the effect of a nanostructured back reflector to recycle the unabsorbed
light. As a back reflector we investigate a 3D photonic band gap crystal made
from silicon that is readily integrated with the thin films. We numerically
obtain the optical properties by solving the 3D time-harmonic Maxwell equations
using the finite-element method, and model silicon with experimentally
determined optical constants. The absorption enhancement relevant for
photovoltaics is obtained by weighting the absorption spectra with the AM 1.5
standard solar spectrum. We study thin films either thicker ($L_{Si} = 2400$
nm) or much thinner ($L_{Si} = 80$ nm) than the wavelength of light. At $L_{Si}
= 2400$ nm, the 3D photonic band gap crystal enhances the spectrally averaged
($\lambda = 680$ nm to $880$ nm) silicon absorption by $2.22$x (s-pol.) to
$2.45$x (p-pol.), which exceeds the enhancement of a perfect metal back
reflector ($1.47$ to $1.56$x). The absorption is enhanced by the (i) broadband
angle and polarization-independent reflectivity in the 3D photonic band gap,
and (ii) the excitation of many guided modes in the film by the crystal's
surface diffraction leading to enhanced path lengths. At $L_{Si} = 80$ nm, the
photonic crystal back reflector yields a striking average absorption
enhancement of $9.15$x, much more than $0.83$x for a perfect metal, which is
due to a remarkable guided mode confined within the combined thickness of the
thin film and the photonic crystal's Bragg attenuation length. The broad
bandwidth of the 3D photonic band gap leads to the back reflector's Bragg
attenuation length being much shorter than the silicon absorption length.
Consequently, light is confined inside the thin film and the absorption
enhancements are not due to the additional thickness of the photonic crystal
back reflector.


  The transport properties of unsubstituted and amino-substituted butanethiol
molecules sandwiched between Au electrodes are investigated by using
first-principles approaches. New states are observed around the Fermi levels
when -NH2 is substituted for -H in the bridging butanethiol. The
amino-substituted states lead to a sharp increase of the current which is
credited to the resonant tunneling in the junction. We observe a novel
conductance peak at VSD = 0.1 V and negative differential resistance (NDR) in a
certain range of source-drain bias. In addition to the I-VSD characteristics,
we also investigate the current as a function of gate voltage (I-VG) and find
that, for a fixed source-drain bias (VSD = 0.01 V), the gate voltage can
modulate the conductance by up to 30 times in the amino-substituted butanethiol
junction. These I-VG characteristics suggest that the amino-substituted
butanethiol molecular junction may be a promising candidate for field-effect
transistors.


  In 1981 W.L. Edge discovered and studied a pencil $\mathcal{C}$ of highly
symmetric genus $6$ projective curves with remarkable properties. Edge's work
was based on an 1895 paper of A. Wiman. Both papers were written in the
satisfying style of 19th century algebraic geometry. In this paper and its
sequel [FL], we consider $\mathcal{C}$ from a more modern, conceptual
perspective, whereby explicit equations are reincarnated as geometric objects.


  There are many AI tasks involving multiple interacting agents where agents
should learn to cooperate and collaborate to effectively perform the task. Here
we develop and evaluate various multi-agent protocols to train agents to
collaborate with teammates in grid soccer. We train and evaluate our
multi-agent methods against a team operating with a smart hand-coded policy. As
a baseline, we train agents concurrently and independently, with no
communication. Our collaborative protocols were parameter sharing, coordinated
learning with communication, and counterfactual policy gradients. Against the
hand-coded team, the team trained with parameter sharing and the team trained
with coordinated learning performed the best, scoring on 89.5% and 94.5% of
episodes respectively when playing against the hand-coded team. Against the
parameter sharing team, with adversarial training the coordinated learning team
scored on 75% of the episodes, indicating it is the most adaptable of our
methods. The insights gained from our work can be applied to other domains
where multi-agent collaboration could be beneficial.


  Bochner's theorem gives the necessary and sufficient conditions on a function
such that its Fourier transform corresponds to a true probability density
function. In the Wigner phase space picture, quantum Bochner's theorem gives
the necessary and sufficient conditions on a function such that it is a quantum
characteristic function of a valid (and possibly mixed) quantum state and such
that its Fourier transform is a true probability density. We extend this
theorem to discrete phase space representations which possess enough symmetry.
More precisely, we show that discrete phase space representations that are
built on projective unitary representations of abelian groups, with a slight
restriction on admissible 2-cocycles, enable a quantum Bochner's theorem.


  We consider the problem of designing policies for partially observable Markov
decision processes (POMDPs) with dynamic coherent risk objectives. Synthesizing
risk-averse optimal policies for POMDPs requires infinite memory and thus
undecidable. To overcome this difficulty, we propose a method based on bounded
policy iteration for designing stochastic but finite state (memory)
controllers, which takes advantage of standard convex optimization methods.
Given a memory budget and optimality criterion, the proposed method modifies
the stochastic finite state controller leading to sub-optimal solutions with
lower coherent risk.


  Intelligent agents are often faced with the problem of trying to merge
possibly conflicting pieces of information obtained from different sources into
a consistent view of the world. We propose a framework for the modelling of
such merging operations with roots in the work of Spohn (1988, 1991). Unlike
most approaches we focus on the merging of epistemic states, not knowledge
bases. We construct a number of plausible merging operations and measure them
against various properties that merging operations ought to satisfy. Finally,
we discuss the connection between merging and the use of infobases Meyer (1999)
and Meyer et al. (2000).


  A planar orthogonal drawing $\Gamma$ of a planar graph $G$ is a geometric
representation of $G$ such that the vertices are drawn as distinct points of
the plane, the edges are drawn as chains of horizontal and vertical segments,
and no two edges intersect except at their common end-points. A bend of
$\Gamma$ is a point of an edge where a horizontal and a vertical segment meet.
$\Gamma$ is bend-minimum if it has the minimum number of bends over all
possible planar orthogonal drawings of $G$. This paper addresses a long
standing, widely studied, open question: Given a planar 3-graph $G$ (i.e., a
planar graph with vertex degree at most three), what is the best computational
upper bound to compute a bend-minimum planar orthogonal drawing of $G$ in the
variable embedding setting? In this setting the algorithm can choose among the
exponentially many planar embeddings of $G$ the one that leads to an orthogonal
drawing with the minimum number of bends. We answer the question by describing
an $O(n)$-time algorithm that computes a bend-minimum planar orthogonal drawing
of $G$ with at most one bend per edge, where $n$ is the number of vertices of
$G$. The existence of an orthogonal drawing algorithm that simultaneously
minimizes the total number of bends and the number of bends per edge was
previously unknown.


  In this paper, we introduce two robust, nonparametric methods for multiple
change-point detection in the variability of a multivariate sequence of
observations. We demonstrate that changes in ranks generated from data depth
functions can be used to detect changes in the variability of a sequence of
multivariate observations. In order to detect more than one change, the first
algorithm uses methods similar to that of wild-binary segmentation. The second
algorithm estimates change-points by maximizing a penalized version of the
classical Kruskal Wallis ANOVA test statistic. We show that this objective
function can be maximized via the well-known PELT algorithm. Under mild,
nonparametric assumptions both of these algorithms are shown to be consistent
for the correct number of change-points and the correct location(s) of the
change-point(s). We demonstrate the efficacy of these methods with a simulation
study, where we compare our new methods to several competing methods. We show
our methods outperform existing methods in this problem setting, and our
methods can estimate changes accurately when the data are heavy tailed or
skewed.


  The analysis of complex networks has revealed patterns of organization in a
variety of natural and artificial systems, including neuronal networks of the
brain at multiple scales. In this paper, we describe a novel analysis of the
large-scale connectivity between regions of the mammalian cerebral cortex,
utilizing a set of hierarchical measurements proposed recently. We examine
previously identified functional clusters of brain regions in macaque visual
cortex and cat cortex and find significant differences between such clusters in
terms of several hierarchical measures, revealing differences in how these
clusters are embedded in the overall cortical architecture. For example, the
ventral cluster of visual cortex maintains structurally more segregated, less
divergent connections than the dorsal cluster, which may point to functionally
different roles of their constituent brain regions.


  The observed angular correlation function of the cosmic microwave background
has previously been reported to be anomalous, particularly when measured in
regions of the sky uncontaminated by Galactic emission. Recent work by
Efstathiou et al. presents a Bayesian comparison of isotropic theories, casting
doubt on the significance of the purported anomaly. We extend this analysis to
all anisotropic Gaussian theories with vanishing mean (<delta T> = 0), using
the much wider class of models to confirm that the anomaly is not likely to
point to new physics. On the other hand if there is any new physics to be
gleaned, it results from low-l alignments which will be better quantified by a
full-sky statistic.
  We also consider quadratic maximum likelihood power spectrum estimators that
are constructed assuming isotropy. The underlying assumptions are therefore
false if the ensemble is anisotropic. Nonetheless we demonstrate that, for
theories compatible with the observed sky, these estimators (while no longer
optimal) remain statistically superior to pseudo-C_l power spectrum estimators.


  This paper focuses on hadron mass effects in calculations of semi-inclusive
kaon production in lepton-Deuteron deeply inelastic scattering at HERMES and
COMPASS kinematics. In the collinear factorization framework, the corresponding
cross section is shown to factorize, at leading order and leading twist, into
products of parton distributions and fragmentation functions evaluated in terms
of kaon- and nucleon-mass-dependent scaling variables, and to respect gauge
invariance. It is found that hadron mass corrections for integrated kaon
multiplicities sizeably reduce the apparent large discrepancy between
measurements of $K^+ + K^-$ multiplicities performed by the two collaborations,
and fully reconcile their $K^+/K^-$ ratios.


  Over the last years, there has been an unprecedented proliferation of fake
news. As a consequence, we are more susceptible to the pernicious impact that
misinformation and disinformation spreading can have in different segments of
our society. Thus, the development of tools for automatic detection of fake
news plays and important role in the prevention of its negative effects. Most
attempts to detect and classify false content focus only on using textual
information. Multimodal approaches are less frequent and they typically
classify news either as true or fake. In this work, we perform a fine-grained
classification of fake news on the Fakeddit dataset, using both unimodal and
multimodal approaches. Our experiments show that the multimodal approach based
on a Convolutional Neural Network (CNN) architecture combining text and image
data achieves the best results, with an accuracy of 87%. Some fake news
categories such as Manipulated content, Satire or False connection strongly
benefit from the use of images. Using images also improves the results of the
other categories, but with less impact. Regarding the unimodal approaches using
only text, Bidirectional Encoder Representations from Transformers (BERT) is
the best model with an accuracy of 78%. Therefore, exploiting both text and
image data significantly improves the performance of fake news detection.


  We study exclusive vector meson photoproduction, $\gamma p \to V + p$ with
$V=J/\psi$ or $\Upsilon$, at NLO in collinear factorisation, in order to
examine what may be learnt about the gluon distribution at very low $x$. We
examine the factorisation scale dependence of the predictions. We argue that,
using knowledge of the NLO corrections, terms enhanced by a large $\ln(1/\xi)$
can be reabsorbed in the LO part by a choice of the factorisation scale. (In
these exclusive processes $\xi$ takes the role of Bjorken-$x$.) Then, the scale
dependence coming from the remaining NLO contributions has no $\ln(1/\xi)$
enhancements. As a result, we find that predictions for the amplitude of
$\Upsilon$ production are stable to within about $\pm 15\%$. This will allow
data for the exclusive process $p p \to p\Upsilon p$ at the LHC, particularly
from LHCb, to be included in global parton analyses to constrain the gluon PDF
down to $x\sim 10^{-5}$. Moreover, the study of exclusive $J/\psi$
photoproduction indicates that the gluon density found in the recent global PDF
analyses is too small at low $x$ and low scales.


  The vacuum is filled with complex scalar fields, such as the Higgs field.
These fields serve as order parameters for superfluidity (quantum phase
coherence over macroscopic distances), making the entire universe a superfluid.
We review a mathematical model consisting of two aspects: (a) emergence of the
superfluid during the big bang; (b) observable manifestations of superfluidity
in the present universe. The creation aspect requires a self-interacting scalar
field that is asymptotically free, i.e., the interaction must grow from zero
during the big bang, and this singles out the Halpern-Huang potential, which
has exponential behavior for large fields. It leads to an equivalent
cosmological constant that decays like a power law, and this gives dark energy
without "fine-tuning". Quantum turbulence (chaotic vorticity) in the early
universe was able to create all the matter in the universe, fulfilling the
inflation scenario. In the present universe, the superfluid can be
phenomenologically described by a nonlinear Klein-Gordon equation. It predicts
halos around galaxies with higher superfluid density, which is perceived as
dark matter through gravitational lensing. In short, dark energy is the energy
density of the cosmic superfluid, and dark matter arises from local
fluctuations of the superfluid density.


  The stochastic gravitational wave background (SGWB) is a rich resource of
cosmological information, encoded both in its source statistics and
anisotropies induced by propagation effects. We provide a theoretical
description of it, without employing theoretical tools which rely on the
geometric optics limit. Our formalism is based on the so-called {\it classical
matter approximation} and it is able to capture wave-optics effects, such as
interference and diffraction. We show that the interaction between the
gravitational waves and the cosmic structures along the line-of-sight produce
observable scalar and vector polarization modes, on top of modulating the
tensorial ones. We build the two point correlation function describing the
statistics of the SGWB, and introduce the intensity and gravitational Stokes
parameters for all its components. In the case of an unpolarized, Gaussian and
statistically homogeneous SGWB, we show that the interaction with matter
modulates its intensity and does not generate a net difference between left-
and right- helicity tensor and vector modes, as expected. We demonstrate that,
in order to produce $Q_T$- and $U_T$- tensor polarization modes the background
must posses an hexadecapole anisotropy, while a quandrupole anisotropy can
source the vector $Q_V$- and $U_V$- Stokes parameters.


  We present an interesting Sunyaev-Zel'dovich (SZ) detection in the first of
the Arcminute Microkelvin Imager (AMI) 'blind', degree-square fields to have
been observed down to our target sensitivity of 100{\mu}Jy/beam. In follow-up
deep pointed observations the SZ effect is detected with a maximum peak
decrement greater than 8 \times the thermal noise. No corresponding emission is
visible in the ROSAT all-sky X-ray survey and no cluster is evident in the
Palomar all-sky optical survey. Compared with existing SZ images of distant
clusters, the extent is large (\approx 10') and complex; our analysis favours a
model containing two clusters rather than a single cluster. Our Bayesian
analysis is currently limited to modelling each cluster with an ellipsoidal or
spherical beta-model, which do not do justice to this decrement. Fitting an
ellipsoid to the deeper candidate we find the following. (a) Assuming that the
Evrard et al. (2002) approximation to Press & Schechter (1974) correctly gives
the number density of clusters as a function of mass and redshift, then, in the
search area, the formal Bayesian probability ratio of the AMI detection of this
cluster is 7.9 \times 10^4:1; alternatively assuming Jenkins et al. (2001) as
the true prior, the formal Bayesian probability ratio of detection is 2.1
\times 10^5:1. (b) The cluster mass is MT,200 = 5.5+1.2\times 10^14h-1M\odot.
(c) Abandoning a physical model with num- -1.3 70 ber density prior and instead
simply modelling the SZ decrement using a phenomenological {\beta}-model of
temperature decrement as a function of angular distance, we find a central SZ
temperature decrement of -295+36 {\mu}K - this allows for CMB primary
anisotropies, receiver -15 noise and radio sources. We are unsure if the
cluster system we observe is a merging system or two separate clusters.


  Coded computing has proved to be useful in distributed computing. We have
observed that almost all coded computing systems studied so far consider a
setup of one master and some workers. However, recently emerging technologies
such as blockchain, internet of things, and federated learning introduce new
requirements for coded computing systems. In these systems, data is generated
in a distributed manner, so central encoding/decoding by a master is not
feasible and scalable. This paper presents a fully distributed coded computing
system that consists of $k\in\mathbb{N}$ data owners and $N\in\mathbb{N}$
workers, where data owners employ workers to do some computations on their
data, as specified by a target function $f$ of degree $d\in\mathbb{N}$. As
there is no central encoder, workers perform encoding themselves, prior to
computation phase. The challenge in this system is the presence of adversarial
data owners that do not know the data of honest data owners but cause
discrepancies by sending different data to different workers, which is
detrimental to local encodings in workers. There are at most
$\beta\in\mathbb{N}$ adversarial data owners, and each sends at most
$v\in\mathbb{N}$ different versions of data. Since the adversaries and their
possibly colluded behavior are not known to workers and honest data owners,
workers compute tags of their received data, in addition to their main
computational task, and send them to data owners to help them in decoding. We
introduce a tag function that allows data owners to partition workers into sets
that previously had received the same data from all data owners. Then, we
characterize the fundamental limit of the system, $t^*$, which is the minimum
number of workers whose work can be used to correctly calculate the desired
function of data of honest data owners. We show that $t^*=v^{\beta}d(K-1)+1$,
and present converse and achievable proofs.


  Results are presented for simulations of electron-positron pair production in
relativistic heavy-ion collisions leading to electron capture and positron
ejection. We apply a two-center relativistic continuum distorted-wave model to
represent the electron/positron dynamics during the collision process. The
results are compared with experimental cross-section data for ${\rm La}^{57+}$
and ${\rm Au}^{79+}$ impact on Gold, Silver and Copper targets. The theory is
in very good agreement with experiment, verifying the result that the process
increases in importance with both collision energy and target atomic number,
and improves upon previous simulations of this process.


  The Carina Nebula represents one of the largest and most active star forming
regions known in our Galaxy with numerous very massive stars.Our recently
obtained Herschel PACS & SPIRE far-infrared maps cover the full area (about 8.7
deg^2) of the Carina Nebula complex and reveal the population of deeply
embedded young stellar objects, most of which are not yet visible in the mid-
or near-infrared.We study the properties of the 642 objects that are
independently detected as point-like sources in at least two of the five
Herschel bands.For those objects that can be identified with apparently single
Spitzer counterparts, we use radiative transfer models to derive information
about the basic stellar and circumstellar parameters.We find that about 75% of
the Herschel-detected YSOs are Class 0 protostars.The luminosities of the
Herschel-detected YSOs with SED fits are restricted to values of <=5400 Lsun,
their masses (estimated from the radiative transfer modeling) range from about
1 Msun to 10 Msun.Taking the observational limits into account and
extrapolating the observed number of Herschel-detected protostars over the IMF
suggest that the star formation rate of the CNC is about 0.017 Msun/yr.The
spatial distribution of the Herschel YSO candidates is highly inhomogeneous and
does not follow the distribution of cloud mass.Most Herschel YSO candidates are
found at the irradiated edges of clouds and pillars.This provides support to
the picture that the formation of this latest stellar generation is triggered
by the advancing ionization fronts.The currently ongoing star formation process
forms only low-mass and intermediate-mass stars, but no massive stars.The
far-infrared fluxes of the famous object EtaCar are about a factor of two lower
than expected from observations with the ISO obtained 15 years ago; this may be
due to dynamical changes in the circumstellar dust in the Homunculus Nebula.


  Magneto-optical traps (MOTs) are widely used for laser cooling of atoms. We
have developed a high-flux compact cold-atom source based on a pyramid MOT with
a unique adjustable aperture that is highly suitable for portable quantum
technology devices, including space-based experiments. The adjustability
enabled an investigation into the previously unexplored impact of aperture size
on the atomic flux, and optimisation of the aperture size allowed us to
demonstrate a higher flux than any reported cold-atom sources that use a
pyramid, LVIS, 3D-MOT or grating MOT. We achieved 2.0(1)x10^10 atoms/s of 87-Rb
with a mean velocity of 32(1)m/s, FWHM of 27.6(9)m/s and divergence of
58(3)mrad. Halving the total optical power to 195mW caused only a 26% reduction
of the flux, and a 33% decrease in mean velocity. Methods to further decrease
the velocity as required have been identified. The low power consumption and
small size make this design suitable for a wide range of cold-atom
technologies.


  We provide a critical perspective on the collection of low-temperature
transport phenomena in low-density two-dimensional semiconductor systems often
referred to as the 2D metal-insulator transition. We discuss the physical
mechanisms underlying the anomalous behavior of the two-dimensional effective
metallic phase and the metal-insulator transition itself. We argue that a key
feature of the 2D MIT physics is the long-range bare Coulombic disorder arising
from the random distribution of charged impurities in the low-density 2D
semiconductor structures.


  This is the first of a series of papers based on sensitive 610 MHz
observations of the ELAIS N1 field using the Giant Metrewave Radio Telescope.
We describe the observations, processing and source catalogue extraction from a
deep image with area of 1.86\,deg$^{2}$ and minimum noise of
$\sim$7.1\,$\mu$Jy/beam. We compile a catalogue of 4290 sources with flux
densities in the range 28.9\,$\mu$Jy - 0.503\,Jy, and derive the
Euclidean-normalized differential source counts for sources with flux densities
brighter than $\rm{35.5\,\mu\,Jy}$. Our counts show a flattening at 610 MHz
flux densities below 1 mJy. Below the break the counts are higher than previous
observations at this frequency, but generally consistent with recent models of
the low-frequency source population. The radio catalogue is cross-matched
against multi-wavelength data leading to identifications for 92\% and reliable
redshifts for 72\% of our sample, with 19\% of the redshifts based on
spectroscopy. For the sources with redshifts we use radio and X-ray luminosity,
optical spectroscopy and mid-infrared colours to search for evidence of the
presence of an Active Galactic Nucleus (AGN). We compare our identifications to
predictions of the flux density distributions of star forming galaxies (SFGs)
and AGN, and find a good agreement assuming the majority of the sources without
redshifts are SFGs. We derive spectral index distributions for a sub-sample.
The majority of the sources are steep spectra, with a median spectral index
that steepens with frequency;
$\mathrm{\alpha^{325}_{610}\,=\,-0.80\,\pm\,0.29}$,
$\mathrm{\alpha^{610}_{1400}\,=\,-0.83\,\pm\,0.31}$ and
$\mathrm{\alpha^{610}_{5000}\,=\,-1.12\,\pm\,0.15}$.


  We relate the MSW effect to the efective absorption of the electronic
collective motion energy by retaining the imaginary part of the index of
refraction associated with the charged-current scattering and show that the
small angle MSW solution to the solar neutrino anomaly can be used as a probe
of the physical conditions of the solar interior if it is correct. We find that
the constraint on the absorption imposed by the small angle MSW solution and
the theoretical estimate of the absorption by the Boltzmann kinetic theory are
consistent, which shows that a consistent theoretical picture can be developed
when plasma absorption processes are taken into account.


  Using the fact that flat space with a boundary is related by a Weyl
transformation to anti-de Sitter (AdS) space, one may study observables in
boundary conformal field theory (BCFT) by placing a CFT in AdS. In addition to
correlation functions of local operators, a quantity of interest is the free
energy of the CFT computed on the AdS space with hyperbolic ball metric, i.e.
with a spherical boundary. It is natural to expect that the AdS free energy can
be used to define a quantity that decreases under boundary renormalization
group (RG) flows. We test this idea by discussing in detail the case of the
large $N$ critical $O(N)$ model in general dimension $d$, as well as its
perturbative descriptions in the epsilon-expansion. Using the AdS approach, we
recover the various known boundary critical behaviors of the model, and we
compute the free energy for each boundary fixed point, finding results which
are consistent with the conjectured $F$-theorem in a continuous range of
dimensions. Finally, we also use the AdS setup to compute correlation functions
and extract some of the BCFT data. In particular, we show that using the bulk
equations of motion, in conjunction with crossing symmetry, gives an efficient
way to constrain bulk two-point functions and extract anomalous dimensions of
boundary operators.


  Although pre-trained language models~(PLMs) have recently advanced the
research progress in mathematical reasoning, they are not specially designed as
a capable multi-task solver, suffering from high cost for multi-task deployment
(\eg a model copy for a task) and inferior performance on complex mathematical
problems in practical applications. To address these issues, in this paper, we
propose \textbf{JiuZhang~2.0}, a unified Chinese PLM specially for multi-task
mathematical problem solving. Our idea is to maintain a moderate-sized model
and employ the \emph{cross-task knowledge sharing} to improve the model
capacity in a multi-task setting. Specially, we construct a
Mixture-of-Experts~(MoE) architecture for modeling mathematical text, so as to
capture the common mathematical knowledge across tasks. For optimizing the MoE
architecture, we design \emph{multi-task continual pre-training} and
\emph{multi-task fine-tuning} strategies for multi-task adaptation. These
training strategies can effectively decompose the knowledge from the task data
and establish the cross-task sharing via expert networks. In order to further
improve the general capacity of solving different complex tasks, we leverage
large language models~(LLMs) as complementary models to iteratively refine the
generated solution by our PLM, via in-context learning. Extensive experiments
have demonstrated the effectiveness of our model.


  Symmetry plays a fundamental role in our understanding of both conventional
symmetry breaking phases and the more exotic quantum and topological phases of
matter. We explore the experimental signatures of symmetry enriched U(1)
quantum spin liquids (QSLs) on the pyrochlore lattice. We point out that the Ce
local moment of the newly discovered pyrochlore QSL candidate
Ce$_2$Sn$_2$O$_7$, is a dipole-octupole doublet. The generic model for these
unusual doublets supports two distinct symmetry enriched U(1) QSL ground states
in the corresponding quantum spin ice regimes. These two U(1) QSLs are dubbed
dipolar U(1) QSL and octupolar U(1) QSL. While the dipolar U(1) QSL has been
discussed in many contexts, the octupolar U(1) QSL is rather unique. Based on
the symmetry properties of the dipole-octupole doublets, we predict the
peculiar physical properties of the octupolar U(1) QSL, elucidating the unique
spectroscopic properties in the externalmagnetic fields. We further predict the
Anderson-Higgs transition from the octupolar U(1) QSL driven by the external
magnetic fields. We identify the experimental relevance with the candidate
material Ce$_2$Sn$_2$O$_7$ and other dipole-octupole doublet systems.