  We analyze a discontinuous Galerkin FEM-BEM scheme for a second order
elliptic transmission problem posed in the three-dimensional space. The
symmetric variational formulation is discretized by nonconforming
Raviart-Thomas finite elements on a general partition of the interior domain
coupled with discontinuous boundary elements on an independent quasi-uniform
mesh of the transmission interface. We prove (almost) quasi-optimal convergence
of the method and confirm the theory by a numerical experiment. In addition, we
consider the case when continuous rather than discontinuous boundary elements
are used.


  In this paper we present experimental data on the frequency domain response
of a SQUID ring (a Josephson weak link enclosed by a thick superconducting
ring) coupled to a radio frequency (rf) tank circuit resonator. We show that
with the ring weakly hysteretic the resonance lineshape of this coupled system
can display opposed fold bifurcations that appear to touch (pinch off). We
demonstrate that for appropriate circuit parameters these pinch off lineshapes
exist as solutions of the non-linear equations of motion for the system.


  We study the asymptotic distribution of CM points on the moduli space of
elliptic curves over $\mathbb{C}_p$, as the discriminant of the underlying
endomorphism ring varies. In contrast with the complex case, we show that there
is no uniform distribution. In this paper we characterize all the sequences of
discriminants for which the corresponding CM points converge towards the Gauss
point of the Berkovich affine line. We also give an analogous characterization
for Hecke orbits. In the companion paper we characterize all the remaining
limit measures of CM points and Hecke orbits.


  Conventional techniques that measure rapid time variations are inefficient or
inadequate to discover and observe rapidly pulsating astronomical sources. It
is therefore conceivable that there exist some classes of objects pulsating
with extremely short periods that have not yet been discovered. This article
starts from the fact that rapid flux variations generate a spectral modulation
that can be detected in the beat spectrum of the output current fluctuations of
a quadratic detector. The telescope could observe at any frequency, although
shorter frequencies would have the advantage of lower photon noise. The
techniques would allow us to find and observe extremely fast time variations,
opening up a new time window in Astronomy. The current fluctuation technique,
like intensity interferometers, uses second-order correlation effects and fits
into the current renewal of interest in intensity interferometry. An
interesting aspect it shares with intensity interferometry is that it can use
inexpensive large telescope that have low-quality mirrors, like Cherenkov
telescopes. It has other advantages over conventional techniques that measure
time variations, foremost of which is its simplicity. Consequently, it could be
used for extended monitoring of astronomical sources, something that is
difficult to do with conventional telescopes. Arguably, the most interesting
scientific justification for the technique comes from Serendipity


  For numerous parameter and state estimation problems, assimilating new data
as they become available can help produce accurate and fast inference of
unknown quantities. While most existing algorithms for solving those kind of
ill-posed inverse problems can only be used with a single instance of the
observed data, in this work we propose a new framework that enables existing
algorithms to invert multiple instances of data in a sequential fashion.
Specifically we will work with the well-known iteratively regularized
Gauss-Newton method (IRGNM), a variational methodology for solving nonlinear
inverse problems. We develop a theory of convergence analysis for a proposed
dynamic IRGNM algorithm in the presence of Gaussian white noise. We combine
this algorithm with the classical IRGNM to deliver a practical (hybrid)
algorithm that can invert data sequentially while producing fast estimates. Our
work includes the proof of well-definedness of the proposed iterative scheme,
as well as various error bounds that rely on standard assumptions for nonlinear
inverse problems. We use several numerical experiments to verify our
theoretical findings, and to highlight the benefits of incorporating sequential
data. The context of the numerical experiments comprises various parameter
identification problems including a Darcy flow elliptic PDE example, and that
of electrical impedance tomography.


  We investigate the semi-ringed topos obtained by extension of scalars from
the arithmetic site of our previous work, by replacing the smallest Boolean
semifield by the tropical semifield of real numbers with the max-plus
operations. The obtained site is the semi-direct product of the Euclidean
half-line by the action of the monoid of positive integers by multiplication.
Its points are the same as the points of the arithmetic site over the tropical
semifield of real numbers, and coincide with the quotient of the adele class
space of Q by the action of the maximal compact subgroup of the idele class
group. The structure sheaf of the scaling topos endows it with a natural
structure of tropical curve over the arithmetic topos. The restriction of this
structure to the periodic orbits of the scaling flow gives, for each prime p,
an analogue of an elliptic curve whose Jacobian is a cyclic group of order p-1.
The Riemann-Roch formula holds and involves real valued dimensions and real
degrees for divisors.


  In [Adv. Math., 267(2014), 277-306], Cheskidov and Lu develop a new framework
of the evolutionary system that deals directly with the notion of a uniform
global attractor due to Haraux, and by which a trajectory attractor is able to
be defined for the original system under consideration. The notion of a
trajectory attractor was previously established for a system without uniqueness
by considering a family of auxiliary systems including the original one. In
this paper, we further prove the existence of a notion of a strongly compact
strong trajectory attractor if the system is asymptotically compact. As a
consequence, we obtain the strong equicontinuity of all complete trajectories
on global attractor and the finite strong uniform tracking property. Then we
apply the theory to a general nonautonomous reaction-diffusion systems. In
particular, we obtain the structure of uniform global attractors without any
additional condition on nonlinearity other than those guarantee the existence
of a uniform absorbing set. Finally, we construct some interesting examples of
such nonlinearities. It is not known whether they can be handled by previous
frameworks.


  We study the rapidity dependence of the cross section on the rapidity of the
central exclusive production cross sections of C-even mesons in $pA$ and $AA$
collisions, where $A$ is a heavy ion. We observe qualitatively different
behaviour of the contributions arising from $\gamma$-Odderon and
Pomeron-Pomeron fusion mechanisms. This can be used to extract the Odderon
signal from the events of $f_2$ mesons exclusively produced in the forward
region. Estimates, obtained using expected values of the Odderon cross section,
indicate that the $\gamma$-Odderon contribution may exceed by a few times the
Pomeron-induced background in Pb-Pb collisions. Moreover, the Odderon effect
can be clearly seen in terms of the asymmetry in $pA$ and $AA$ collisions with
the beam and target reversed. It is particularly interesting to note that the
asymmetry for $\gamma$-Odderon fusion reaches its maximum value close to 1 in
the forward direction, whereas the asymmetry for the Pomeron-Pomeron fusion
contribution is small. The role of additional interactions of the $f_2$ meson
with nucleons in the heavy ion, and also the contributions from secondary
Reggeons, are estimated. The photon-Odderon contribution has a large
normalisation uncertainty but the enhances cross-section in the forward region
combined with a large asymmetry increases the chance of experimentally
detecting the Odderon.


  Neuromorphic architectures offer great promise for achieving computation
capacities beyond conventional Von Neumann machines. The essential elements for
achieving this vision are highly scalable synaptic mimics that do not undermine
biological fidelity. Here we demonstrate that single solid-state TiO2
memristors can exhibit non-associative plasticity phenomena observed in
biological synapses, supported by their metastable memory state transition
properties. We show that, contrary to conventional uses of solid-state memory,
the existence of rate-limiting volatility is a key feature for capturing
short-term synaptic dynamics. We also show how the temporal dynamics of our
prototypes can be exploited to implement spatio-temporal computation,
demonstrating the memristors full potential for building biophysically
realistic neural processing systems.


  A system vulnerability analysis technique (SVAT) for complex mission critical
systems (CMCS) was developed in response to the need to be able to conduct
penetration testing on large industrial systems which cannot be taken offline
or risk disablement or impairment for conventional penetration testing.
SVAT-CMCS facilitates the use of known vulnerability and exploit information,
incremental testing of system components and data analysis techniques to
identify attack pathways in CMCSs. This data can be utilized for corrective
activities or to target controlled manual follow-up testing. This paper
presents the SVAT-CMCS paradigm and describes its implementation in a software
tool, which was built using the Blackboard Architecture, that can be utilized
for attack pathway identification. The performance of this tool is
characterized using three example models. In particular, it explores the path
generation speed and the impact of link cap restrictions on system operations,
under different levels of network size and complexity. Accurate fact-rule
processing is also tested using these models. The results show significant
decreases in path generation efficiency as the link cap and network complexity
increase; however, rule processing accuracy is not impacted.


  We define the notion of exact completion with respect to an existential
elementary doctrine. We observe that the forgetful functor from the 2-category
exact categories to existential elementary doctrines has a left biadjoint that
can be obtained as a composite of two others. Finally, we conclude how this
notion encompasses both that of the exact completion of a regular category as
well as that of the exact completion of a cartesian category with weak
pullbacks.


  Blockchain has received increasing attention in academia and industry.
However, the increasing transaction volumes and limited on-chain storage
underscore scalability as a key challenge hindering the widespread adoption of
blockchain. Fortunately, off-chain networks that enable transactions outside
the blockchain show promising potential to mitigate the scalability challenge.
Off-chain solutions that address blockchain scalability hurdles, such as
payment channel networks, facilitate secure and fast off-chain transactions,
thus relieving the main chain's strain. In this article, we provide a
comprehensive review of key technologies, solutions, and challenges of
off-chain networks. First, we introduce the background of off-chain networks
encompassing design motivation, framework, overview, and application scenarios.
We then review the key issues and technologies associated with off-chain
networks. Subsequently, we summarize the mainstream solutions for the
corresponding key issues. Finally, we discuss some research challenges and open
issues in this area.


  We consider the propagation of waves in a waveguide with Neumann boundary
conditions. We work at low wavenumber with only one propagating mode in the
leads, all the other modes being evanescent. We assume that the waveguide is
symmetric with respect to an axis orthogonal to the longitudinal direction and
is endowed with a branch of height $L$ whose width coincides with the
wavelength of the propagating modes. In this setting, tuning the parameter $L$,
we prove the existence of simple geometries where the transmission coefficient
is equal to one (perfect invisibility). We also show that these geometries, for
possibly different values of $L$, support so called trapped modes (non zero
solutions of finite energy of the homogeneous problem) associated with
eigenvalues embedded in the continuous spectrum.


  These lecture notes describe the current state of affairs for Manin's
conjecture in the context of del Pezzo surfaces.


  In this pedagogically motivated work, the process of migration in reflection
seismics has been considered from a rigorously mathematical viewpoint. An
inclined subsurface reflector with a constant dipping angle has been shown to
cause a shift in the normal moveout equation, with the peak of the moveout
curve tracing an elliptic locus. Since any subsurface reflector actually has a
non-uniform spatial variation, the use of a more comprehensive principle of
migration, by adopting the wave equation, has been argued to be necessary. By
this approach an expression has been derived for both the amplitude and the
phase of a subsurface wavefield with vertical velocity variation. This
treatment has entailed the application of the WKB approximation, whose
self-consistency has been established by the fact that the logarithmic
variation of the velocity is very slow in the vertical direction, a feature
that is much more strongly upheld at increasingly greater subsurface depths.
Finally, it has been demonstrated that for a planar subsurface wavefield, there
is an equivalence between the constant velocity Stolt Migration algorithm and
the stationary phase approximation method (by which the origin of the reflected
subsurface signals is determined).


  We demonstrate how simultaneously-operated $^{39}$K -- $^{87}$Rb
interferometers exhibiting a high level of correlation can be used to make
competitive tests of the university of free fall. This work provides an
overview of our experimental apparatus and data analysis procedure, including a
detailed study of systematic effects. With a total interrogation time of $2T =
40$ ms in a compact apparatus, we reach a statistical uncertainty on the
measurement of the E\"{o}tv\"{o}s parameter of $7.8 \times 10^{-8}$ after $2.4
\times 10^4$ s of integration. The main limitations to our measurement arise
from a combination of wavefront aberrations, the quadratic Zeeman effect in
$^{39}$K, parasitic interferometers in $^{87}$Rb, and the velocity sensitivity
of our detection system. These systematic errors limit the accuracy of our
measurement to $\eta = 0.9(1.6) \times 10^{-6}$. We discuss prospects for
improvements using ultracold atoms at extended interrogation times.


  We previously showed how the measurements of some eighteen time series of
relative frequency or phase shifts could be combined (1) to cancel the phase
noise of the lasers, (2) to cancel the Doppler fluctuations due to non-inertial
motions of the six optical benches, and (3) to remove the phase noise of the
onboard reference oscillators required to track the photodetector fringes, all
the while preserving signals from passinggravitational waves. Here we analyze
the effect of the additional noise due to the optical modulators used for
removing the phase fluctuations of the onboard reference oscillators. We use a
recently measured noise spectrum of an individual modulator to quantify the
contribution of modulator noise to the first and second-generation Time-Delay
Interferometric (TDI) combinations as a function of the modulation frequency.
We show that modulator noise can be made smaller than the expected proof-mass
acceleration and optical-path noises if the modulation frequencies are larger
than $\approx 682$ MHz in the case of the unequal-arm Michelson TDI combination
$X_1$, $\approx 1.08$ GHz for the Sagnac TDI combination $\alpha_1$, and
$\approx 706$ MHz for the symmetrical Sagnac TDI combination $\zeta_1$. These
modulation frequencies are substantially smaller than previously estimated and
may lead to less stringent requirements on the LISA's oscillator noise
calibration subsystem.


  In the first part of the paper we study the approximation of the $SLE_\kappa$
traces via the Ninomiya-Victoir Splitting Scheme. We prove a strong convergence
in probability \textit{w.r.t.} the $sup$-norm to the distance between the $SLE$
trace and the output of the Ninomiya-Victoir Splitting Scheme when applied in
the context of the Loewner differential equation. We show also that an $L^p$
convergence of the scheme can be achieved under a set of assumptions. In the
last section, we show the uniform convergence of the approximation of the $SLE$
trace obtained using a different scheme that is based on the linear
interpolation of the Brownian driving force.


  The dispersion relation of longitudinal electrostatic oscillations in a
relativistic plasma is studied in the context of the nonextensive statistics
formalism proposed by Tsallis [C. Tsallis, J. Stat. Phys. {\bf 52}, 479
(1988)], where nonextensivity is characterized by a parameter $q$ in Tsallis's
entropy. $q=1$ corresponds to the usual Boltzmann-Gibbs, extensive statistics
formalism. In the nonrelativistic regime, normalizability of the equilibrium
distribution function implies that $-1\leq q\leq\infty$. We show that in the
relativistic regime much tighter constraints must be satisfied, namely $0\leq q
\leq 1+ k_B T/mc^2$, where $k_B$ is the Boltzmann constant, $T$ is the
temperature of the plasma, and $m$ is the particle mass. Then we study
longitudinal oscillations in a proton-electron plasma, assuming immobile
protons, and electrons whose distribution function maximizes Tsallis's entropy.
The dispersion relation of these oscillations is written in integral form for
the long wavelength limit. Explicit expressions in terms of generalized
hypergeometric functions can be found for all possibles values of $q$ in the
ultra-relativistic regime.


  A new nanofibrous material, consisting of a conductive carbon core and an
external layer made of vanadium oxide, has been studied as a cathode for
aluminium-ion batteries. The material enables a mixed-ion intercalation
mechanism, resulting in the alternating insertion of $\text{Al}^{3+}$ and
$\text{AlCl}_4^-$ in the $\text{V}_2\text{O}_5$ and carbon layers,
respectively. This is a highly desirable feature for cathode materials which
may increase the energy density of future batteries by optimising the
utilisation of the electrolyte.


  We present a photometric study of the globular clusters (GCs) in the Virgo
giant elliptical galaxy M86 based on Washington CT1 images. The colors of the
GCs in M86 show a bimodal distribution with a blue peak at (C -T1) = 1.30 and a
red peak at (C -T1) = 1.72. The spatial distribution of the red GCs is
elongated similarly to that of the stellar halo, while that of the blue GCs is
roughly circular. The radial number density profile of the blue GCs is more
extended than that of the red GCs. The radial number density profile of the red
GCs is consistent with the surface brightness profile of the M86 stellar halo.
The GC system has a negative radial color gradient, which is mainly due to the
number ratio of the blue GCs to the red GCs increasing as galactocentric radius
increase. The bright blue GCs in the outer region of M86 show a blue tilt: the
brighter they are, the redder their mean colors get. These results are
discussed in comparison with other Virgo giant elliptical galaxies.


  We study phase structure of mass-deformed ABJM theory which is a three
dimensional $\mathcal{N}=6$ superconformal theory deformed by mass parameters
and has the gauge group $\text{U}(N)\times \text{U}(N)$ with Chern-Simons
levels $(k,-k)$ which may have a gravity dual. We discuss that the mass
deformed ABJM theory on $S^3$ breaks supersymmetry in a large-$N$ limit if the
mass is larger than a critical value. To see some evidence for this conjecture,
we compute the partition function exactly, and numerically by using the Monte
Carlo Simulation for small $N$. We discover that the partition function has
zeroes as a function of the mass deformation parameters if $N\ge k$, which
supports the large-$N$ supersymmetry breaking. We also find a solution to the
large-$N$ saddle point equations, where the free energy is consistent with the
finite $N$ result.


  Current referring video object segmentation (R-VOS) techniques extract
conditional kernels from encoded (low-resolution) vision-language features to
segment the decoded high-resolution features. We discovered that this causes
significant feature drift, which the segmentation kernels struggle to perceive
during the forward computation. This negatively affects the ability of
segmentation kernels. To address the drift problem, we propose a
Spectrum-guided Multi-granularity (SgMg) approach, which performs direct
segmentation on the encoded features and employs visual details to further
optimize the masks. In addition, we propose Spectrum-guided Cross-modal Fusion
(SCF) to perform intra-frame global interactions in the spectral domain for
effective multimodal representation. Finally, we extend SgMg to perform
multi-object R-VOS, a new paradigm that enables simultaneous segmentation of
multiple referred objects in a video. This not only makes R-VOS faster, but
also more practical. Extensive experiments show that SgMg achieves
state-of-the-art performance on four video benchmark datasets, outperforming
the nearest competitor by 2.8% points on Ref-YouTube-VOS. Our extended SgMg
enables multi-object R-VOS, runs about 3 times faster while maintaining
satisfactory performance. Code is available at https://github.com/bo-miao/SgMg.


  Four ongoing microlensing experiments have produced important new results but
also big puzzles, the major one being that the expected classes of lenses
cannot account for the observed distribution of time scales. I discuss future
experiments that could resolve these puzzles. By far the most important would
be to launch a parallax satellite into solar orbit. I also discuss a number of
non-dark-matter applications of microlensing, including searching for planets,
measuring the rotation speeds of giant stars, and imaging a black hole at the
center of a quasar.


  Let M be a 7-manifold with a G2-structure defined by \phi
\in\Omega^{3}_{+}(M). We prove that {\phi} is conformal-Killing with respect to
the associated metric g(\phi) if and only if the G2-structure is nearly
parallel. Let M be an 8-manifold with a Spin7-structure defined by \psi
\in\Omega^{4}_{+}(M). We prove that {\psi} is conformal-Killing with respect to
the associated metric g(\psi) if and only if the Spin7-structure is parallel.


  While the scaling of entanglement in a quantum system can be used to
distinguish many-body quantum phases, it is usually hard to quantify the amount
of entanglement in mixed states of open quantum systems, while measuring
entanglement experimentally, even for the closed systems, requires in general
quantum state tomography. In this work we show how to remedy this situation in
system with a fixed or conserved charge, e.g., density or magnetization, due to
an emerging relation between quantum correlations and coherence. First, we show
how, in these cases, the presence of multipartite entanglement or quantumness
can be faithfully witnessed simply by detecting coherence in the quantum
system, while bipartite entanglement or bipartite quantum discord are implied
by asymmetry (block coherence) in the system. Second, we prove that the
relation between quantum correlations and coherence is also quantitative.
Namely, we establish upper and lower bounds on the amount of multipartite and
bipartite entanglement in a many-body system with a fixed local charge, in
terms of the amount of coherence and asymmetry present in the system.
Importantly, both for pure and mixed quantum states, these bounds are expressed
as closed formulas, and furthermore, for bipartite entanglement, are
experimentally accessible by means of the multiple quantum coherence spectra.
In particular, in one-dimensional systems, our bounds may detect breaking of
the area law of entanglement entropy. We illustrate our results on the example
of a many-body localized system, also in the presence of dephasing.


  The possibility of using electron beam phase space manipulations to support a
free-electron laser accelerator design optimization has motivated our research.
An on-going program demonstrating the exchange of transverse horizontal and
longitudinal emittances at the Fermilab A0 photoinjector has benefited recently
from the upgrade of several of the key diagnostics stations. Accurate
measurements of these properties upstream and downstream of the exchanger
beamline are needed. Improvements in the screen resolution term and reduced
impact of the optical system's depth-of-focus by using YAG:Ce single crystals
normal to the beam direction will be described. The requirement to measure
small energy spreads (<10 keV) in the spectrometer and the exchange process
which resulted in bunch lengths less than 500 fs led to other diagnostics
performance adjustments and upgrades as well. A longitudinal to transverse
exchange example is also reported.


  In transient simulations of particulate Stokes flow, to accurately capture
the interaction between the constituent particles and the confining wall, the
discretization of the wall often needs to be locally refined in the region
approached by the particles. Consequently, standard fast direct solvers lose
their efficiency since the linear system changes at each time step. This
manuscript presents a new computational approach that avoids this issue by
pre-constructing a fast direct solver for the wall ahead of time, computing a
low-rank factorization to capture the changes due to the refinement, and
solving the problem on the refined discretization via a Woodbury formula.
Numerical results illustrate the efficiency of the solver in accelerating
particulate Stokes simulations.


  Image outpainting is a very intriguing problem as the outside of a given
image can be continuously filled by considering as the context of the image.
This task has two main challenges. The first is to maintain the spatial
consistency in contents of generated regions and the original input. The second
is to generate a high-quality large image with a small amount of adjacent
information. Conventional image outpainting methods generate inconsistent,
blurry, and repeated pixels. To alleviate the difficulty of an outpainting
problem, we propose a novel image outpainting method using bidirectional
boundary region rearrangement. We rearrange the image to benefit from the image
inpainting task by reflecting more directional information. The bidirectional
boundary region rearrangement enables the generation of the missing region
using bidirectional information similar to that of the image inpainting task,
thereby generating the higher quality than the conventional methods using
unidirectional information. Moreover, we use the edge map generator that
considers images as original input with structural information and hallucinates
the edges of unknown regions to generate the image. Our proposed method is
compared with other state-of-the-art outpainting and inpainting methods both
qualitatively and quantitatively. We further compared and evaluated them using
BRISQUE, one of the No-Reference image quality assessment (IQA) metrics, to
evaluate the naturalness of the output. The experimental results demonstrate
that our method outperforms other methods and generates new images with
360{\deg}panoramic characteristics.


  In recent years, Reinforcement Learning (RL) has been applied to real-world
problems with increasing success. Such applications often require to put
constraints on the agent's behavior. Existing algorithms for constrained RL
(CRL) rely on gradient descent-ascent, but this approach comes with a caveat.
While these algorithms are guaranteed to converge on average, they do not
guarantee last-iterate convergence, i.e., the current policy of the agent may
never converge to the optimal solution. In practice, it is often observed that
the policy alternates between satisfying the constraints and maximizing the
reward, rarely accomplishing both objectives simultaneously. Here, we address
this problem by introducing Reinforcement Learning with Optimistic
Ascent-Descent (ReLOAD), a principled CRL method with guaranteed last-iterate
convergence. We demonstrate its empirical effectiveness on a wide variety of
CRL problems including discrete MDPs and continuous control. In the process we
establish a benchmark of challenging CRL problems.


  Main-sequence stars with convective envelopes often appear larger and cooler
than predicted by standard models of stellar evolution for their measured
masses. This is believed to be caused by stellar activity. In a recent study,
accurate measurements have been published for the K-type components of the 1.62
day detached eclipsing binary EPIC 219511354, showing the radii and
temperatures for both stars to be affected by these discrepancies. This is a
rare example of a system in which the age and chemical composition are known,
by virtue of being a member of the well-studied open cluster Ruprecht 147 (age
$\sim$ 3 Gyr, [Fe/H] = +0.10). Here we report a detailed study of this system
with non-standard models incorporating magnetic inhibition of convection. We
show that these calculations are able to reproduce the observations largely
within their uncertainties, providing robust estimates of the strength of the
magnetic fields on both stars: $1600 \pm 130$ G and $1830 \pm 150$ G for the
primary and secondary, respectively. Empirical estimates of the magnetic field
strengths based on the measured X-ray luminosity of the system are roughly
consistent with these predictions, supporting this mechanism as a possible
explanation for the radius and temperature discrepancies.


  A good supervised embedding for a specific machine learning task is only
sensitive to changes in the label of interest and is invariant to other
confounding factors. We leverage the concept of repeatability from measurement
theory to describe this property and propose to use the intra-class correlation
coefficient (ICC) to evaluate the repeatability of embeddings. We then propose
a novel regularizer, the ICC regularizer, as a complementary component for
contrastive losses to guide deep neural networks to produce embeddings with
higher repeatability. We use simulated data to explain why the ICC regularizer
works better on minimizing the intra-class variance than the contrastive loss
alone. We implement the ICC regularizer and apply it to three speech tasks:
speaker verification, voice style conversion, and a clinical application for
detecting dysphonic voice. The experimental results demonstrate that adding an
ICC regularizer can improve the repeatability of learned embeddings compared to
only using the contrastive loss; further, these embeddings lead to improved
performance in these downstream tasks.


  Data from atmospheric and solar neutrinos indicate that there are at least
three neutrino types involved in oscillation phenomena. Even if the
corresponding neutrino mass scales are very different, the inevitable reference
to mixing between more than two neutrino types has profound consequences on the
planning of the accelerator experiments suggested by these results. We discuss
the measurement of mixing angles and CP phases in the context of the neutrino
beam emanating from a {\it neutrino factory}: the straight sections of a muon
storage ring. We emphasize the importance of charge identification. The
appearance of wrong sign muons in a long baseline experiment may provide a
powerful test of neutrino oscillations in the mass-difference range indicated
by atmospheric-neutrino observations.


  Knowledge Graph Embedding (KGE) aims to represent entities and relations of
knowledge graph in a low-dimensional continuous vector space. Recent works
focus on incorporating structural knowledge with additional information, such
as entity descriptions, relation paths and so on. However, common used
additional information usually contains plenty of noise, which makes it hard to
learn valuable representation. In this paper, we propose a new kind of
additional information, called entity neighbors, which contain both semantic
and topological features about given entity. We then develop a deep memory
network model to encode information from neighbors. Employing a gating
mechanism, representations of structure and neighbors are integrated into a
joint representation. The experimental results show that our model outperforms
existing KGE methods utilizing entity descriptions and achieves
state-of-the-art metrics on 4 datasets.


  We investigate the problem of guessing a discrete random variable $Y$ under a
privacy constraint dictated by another correlated discrete random variable $X$,
where both guessing efficiency and privacy are assessed in terms of the
probability of correct guessing. We define $h(P_{XY}, \epsilon)$ as the maximum
probability of correctly guessing $Y$ given an auxiliary random variable $Z$,
where the maximization is taken over all $P_{Z|Y}$ ensuring that the
probability of correctly guessing $X$ given $Z$ does not exceed $\epsilon$. We
show that the map $\epsilon\mapsto h(P_{XY}, \epsilon)$ is strictly increasing,
concave, and piecewise linear, which allows us to derive a closed form
expression for $h(P_{XY}, \epsilon)$ when $X$ and $Y$ are connected via a
binary-input binary-output channel. For $(X^n, Y^n)$ being pairs of independent
and identically distributed binary random vectors, we similarly define
$\underline{h}_n(P_{X^nY^n}, \epsilon)$ under the assumption that $Z^n$ is also
a binary vector. Then we obtain a closed form expression for
$\underline{h}_n(P_{X^nY^n}, \epsilon)$ for sufficiently large, but nontrivial
values of $\epsilon$.


  We calculate the structure function and intermittent exponent of the 1.) Keck
data, which consists of 29 high resolution, high signal to noise ratio (S/N)
QSO Ly$\alpha$ absorption spectra, and 2.)the Ly$\alpha$ forest simulation
samples produced via the pseudo hydro scheme for the low density cold dark
matter (LCDM) model and warm dark matter (WDM) model with particle mass
$m_W=300, 600, 800$ and 1000 eV. These two measures detect not only
non-gaussianities, but also the type of non-gaussianty in the the field. We
find that, 1.) the structure functions of the simulation samples are
significantly larger than that of Keck data on scales less than about 100
h$^{-1}$ kpc, 2.) the intermittent exponent of the simulation samples is more
negative than that of Keck data on all redshifts considered, 3.) the
order-dependence of the structure functions of simulation samples are closer to
the intermittency of hierarchical clustering on all scales, while the Keck data
are closer to a lognormal field on small scales. These differences are
independent of noise and show that the intermittent evolution modeled by the
pseudo-hydro simulation is substantially different from observations, even
though they are in good agreement in terms of second and lower order
statistics. (Abridged)


  We showed that the prime gap for a prime number p is less than or equal to
the prime count of the prime number.


  Superconductivity in graphite intercalated compounds has been studied for
more than 40 years and it is still not fully understood, despite the recent
progress and the discovery of relatively high Tc superconductivity in CaC6 and
YbC6. Recent studies now suggest that the electron-phonon coupling is most
likely responsible for pairing and that the intercalant-derived electronic
states and vibrations play the dominant role. Here, we present the first
studies of electronic structure in CaC6, a superconductor with Tc=11.6 K. Using
angle-resolved photoemission spectroscopy, we find that, contrary to
theoretical models, the EPC on the graphene-derived Fermi sheets is
surprisingly strong, reflecting the interaction with high-frequency
graphene-derived vibrations. Thus, in addition to the amazing properties in the
charge-neutral state, graphene sheets also show surprises in the heavily doped
regime: they may support strong pairing interactions and lead to
superconductivity in compounds in which they are building blocks.


  The energy carried in the gravitational wave signal from an eccentric binary
is spread across several harmonics of the orbital frequency. The inclusion of
the harmonics in the analysis of the gravitational wave signal increases the
signal-to-noise ratio of the detected signal for binaries whose fundamental
frequency is below the galactic confusion-limited noise cut-off. This can allow
for an improved angular resolution for sources whose orbital period is greater
than 2000 s. Globular cluster sources include possible binary black holes and
neutron stars which may have high eccentricities. Cluster dynamics may also
enhance the eccentricities of double white dwarf binaries and white
dwarf-neutron star binaries over the galactic sources. Preliminary results of
the expected signal-to-noise ratio for selected globular cluster binaries are
presented.


  We report on the transverse laser cooling of a magnetically guided beam of
ultra cold chromium atoms. Radial compression by a tapering of the guide is
employed to adiabatically heat the beam. Inside the tapered section heat is
extracted from the atom beam by a two-dimensional optical molasses
perpendicular to it, resulting in a significant increase of atomic phase space
density. A magnetic offset field is applied to prevent optical pumping to
untrapped states. Our results demonstrate that by a suitable choice of the
magnetic offset field, the cooling beam intensity and detuning, atom losses and
longitudinal heating can be avoided. Final temperatures below 65 microkelvin
have been achieved, corresponding to an increase of phase space density in the
guided beam by more than a factor of 30.


  In statistical learning, a dataset is often partitioned into two parts: the
training set and the holdout (i.e., testing) set. For instance, the training
set is used to learn a predictor, and then the holdout set is used for
estimating the accuracy of the predictor on the true distribution. However,
often in practice, the holdout dataset is reused and the estimates tested on
the holdout dataset are chosen adaptively based on the results of prior
estimates, leading to that the predictor may become dependent of the holdout
set. Hence, overfitting may occur, and the learned models may not generalize
well to the unseen datasets. Prior studies have established connections between
the stability of a learning algorithm and its ability to generalize, but the
traditional generalization is not robust to adaptive composition. Recently,
Dwork et al. in NIPS, STOC, and Science 2015 show that the holdout dataset from
i.i.d. data samples can be reused in adaptive statistical learning, if the
estimates are perturbed and coordinated using techniques developed for
differential privacy, which is a widely used notion to quantify privacy. Yet,
the results of Dwork et al. are applicable to only the case of i.i.d. samples.
In contrast, correlations between data samples exist because of various
behavioral, social, and genetic relationships between users. Our results in
adaptive statistical learning generalize the results of Dwork et al. for i.i.d.
data samples to arbitrarily correlated data. Specifically, we show that the
holdout dataset from correlated samples can be reused in adaptive statistical
learning, if the estimates are perturbed and coordinated using techniques
developed for Bayesian differential privacy, which is a privacy notion recently
introduced by Yang et al. in SIGMOD 2015 to broaden the application scenarios
of differential privacy when data records are correlated.


  We investigated the sample of galaxies belonging to the Tully groups of
galaxies. We analyzed the orientation of galaxies inside the group. We did not
found significant deviation from isotropy both in orientation of position
angles and angles $\delta_D$ and $\eta$ giving the spatial orientation of
galaxy planes. Moreover we analyzed consequences of different approximation of
"true shape" of galaxies and showed possible influence of this problem for
investigation of spatial orientation of galaxies. Implications of the obtained
results for theory of galaxy formation was discussed as well.


  This paper reports a new continuous 3D loss function for learning depth from
monocular images. The dense depth prediction from a monocular image is
supervised using sparse LIDAR points, which enables us to leverage available
open source datasets with camera-LIDAR sensor suites during training.
Currently, accurate and affordable range sensor is not readily available.
Stereo cameras and LIDARs measure depth either inaccurately or sparsely/costly.
In contrast to the current point-to-point loss evaluation approach, the
proposed 3D loss treats point clouds as continuous objects; therefore, it
compensates for the lack of dense ground truth depth due to LIDAR's sparsity
measurements. We applied the proposed loss in three state-of-the-art monocular
depth prediction approaches DORN, BTS, and Monodepth2. Experimental evaluation
shows that the proposed loss improves the depth prediction accuracy and
produces point-clouds with more consistent 3D geometric structures compared
with all tested baselines, implying the benefit of the proposed loss on general
depth prediction networks. A video demo of this work is available at
https://youtu.be/5HL8BjSAY4Y.


  We prove a vertex isoperimetric inequality for the $n$-dimensional Hamming
ball $\mathcal{B}_n(R)$ of radius $R$. The isoperimetric inequality is sharp up
to a constant factor for sets that are comparable to $\mathcal{B}_n(R)$ in
size. A key step in the proof is a local expansion phenomenon in hypercubes.


  When the mass difference between the lightest slepton, the NLSP, and the
lightest neutralino, the LSP, is smaller than the tau mass, the lifetime of the
lightest slepton increases in many orders of magnitude with respect to typical
lifetimes of other supersymmetric particles. These small mass differences are
possible in the MSSM and, for instance, they correspond to the coannihilation
region of the CMSSM for $M_{1/2} \gsim 700$ GeV. In a general gravity-mediated
MSSM, where the lightest supersymmetric particle is the neutralino, the
lifetime of the lightest slepton is inversely proportional to the square of the
intergenerational mixing in the slepton mass matrices. Such a long-lived
slepton would produce a distinctive signature at LHC and a measurement of its
lifetime would be relatively simple. Therefore, the long-lived slepton scenario
offers an excellent opportunity to study lepton flavour violation at ATLAS and
CMS detectors in the LHC and an improvement of the leptonic mass insertion
bounds by more than five orders of magnitude would be possible.


  We study whether the approach of Deffayet et al. (DPSV) can be adopted for
obtaining a derivative part of quadratic action for scalar perturbations in
beyond Horndeski theories about homogeneous and isotropic backgrounds. We find
that even though the method does remove the second and higher derivatives of
metric perturbations from the linearized Galileon equation, in the same manner
as in the general Horndeski theory, it gives incorrect result for the quadratic
action. We analyse the reasons behind this property and suggest the way of
modifying the approach, so that it gives valid results.


  The intersection between security and continuous software engineering has
been of great interest since the early years of the agile development movement,
and it remains relevant as software development processes are more frequently
guided by agility and the adoption of DevOps. Several authors have contributed
studies about the framing of secure agile development and secure DevOps,
motivating academic contributions to methods and practices, but also
discussions around benefits and challenges. Especially the challenges captured
also our interest since, for the last few years, we are conducting research on
secure continuous software engineering from a more applied, practical
perspective with the overarching aim to introduce solutions that can be adopted
at scale. The short positioning at hands summarizes a relevant part of our
endeavors in which we validated challenges with several practitioners of
different roles. More than framing a set of challenges, we conclude by
presenting four key research directions we identified for practitioners and
researchers to delineate future work.


  This paper outlines two approaches|based on counterexample-guided abstraction
refinement (CEGAR) and counterexample-guided inductive synthesis (CEGIS),
respectively to the automated synthesis of finite-state probabilistic models
and programs. Our CEGAR approach iteratively partitions the design space
starting from an abstraction of this space and refines this by a light-weight
analysis of verification results. The CEGIS technique exploits critical
subsystems as counterexamples to prune all programs behaving incorrectly on
that input. We show the applicability of these synthesis techniques to
sketching of probabilistic programs, controller synthesis of POMDPs, and
software product lines.


  We propose the use of lattice field theory for the study of string field
theory at the non-perturbative quantum level. We identify many potential
obstacles and examine possible resolutions thereof. We then experiment with our
approach in the particularly simple case of a one-dimensional linear dilaton
and analyse the results.


  Using our recent results on eigenvalues of invariants associated to the Lie
superalgebra gl(m|n), we use characteristic identities to derive explicit
matrix element formulae for all gl(m|n) generators, particularly non-elementary
generators, on finite dimensional type 1 unitary irreducible representations.
We compare our results with existing works that deal with only subsets of the
class of type 1 unitary representations, all of which only present explicit
matrix elements for elementary generators. Our work therefore provides an
important extension to existing methods, and thus highlights the strength of
our techniques which exploit the characteristic identities.


  We present a general procedure for constructing new Hilbert spaces for loop
quantum gravity on non-compact spatial manifolds. Given any fixed background
state representing a non-compact spatial geometry, we use the
Gel'fand-Naimark-Segal construction to obtain a representation of the algebra
of observables. The resulting Hilbert space can be interpreted as describing
fluctuation of compact support around this background state. We also give an
example of a state which approximates classical flat space and can be used as a
background state for our construction.


  We describe a new approach to obtaining very high surface densities of
optical spectra in astronomical observations with extremely accurate
subtraction of night sky emission. The observing technique requires that the
telescope is nodded rapidly between targets and adjacent sky positions; object
and sky spectra are recorded on adjacent regions of a low-noise CCD through
charge shuffling. This permits the use of extremely high densities of small
slit apertures (`microslits') since an extended slit is not required for sky
interpolation. The overall multi-object advantage of this technique is as large
as 2.9x that of conventional multi-slit observing for an instrument
configuration which has an underfilled CCD detector and is always >1.5 for high
target densities. The `nod-shuffle' technique has been practically implemented
at the Anglo-Australian Telescope as the `LDSS++ project' and achieves
sky-subtraction accuracies as good as 0.04%, with even better performance
possible. This is a factor of ten better than is routinely achieved with
long-slits. LDSS++ has been used in various observational modes, which we
describe, and for a wide variety of astronomical projects. The nod-shuffle
approach should be of great benefit to most spectroscopic (e.g. long-slit,
fiber, integral field) methods and would allow much deeper spectroscopy on very
large telescopes (10m or greater) than is currently possible. Finally we
discuss the prospects of using nod-shuffle to pursue extremely long
spectroscopic exposures (many days) and of mimicking nod-shuffle observations
with infrared arrays.


  The slow dynamics (10^-6 s - 10^4 s) of the magnetization in the paramagnetic
phase, predicted by Glauber for 1d Ising ferromagnets, has been observed with
ac susceptibility and SQUID magnetometry measurements in a molecular chain
comprising alternating Co{2+} spins and organic radical spins strongly
antiferromagnetically coupled. An Arrhenius behavior with activation energy
Delta=152 K has been observed for ten decades of relaxation time and found to
be consistent with the Glauber model. We have extended this model to take into
account the ferrimagnetic nature of the chain as well as its helicoidal
structure.


  In this paper, we characterize $\ell$-open and $\ell$-closed $C^*$-algebras
and deduce that $\ell$-open $C^*$-algebras are $\ell$-closed, as conjectured by
Blackadar. Moreover, we show that a commutative unital $C^*$-algebra is
$\ell$-open if and only if it is semiprojective.


  We establish the second main theorem with the best truncation level one for
an entire holomorphic curve $f:\C \to A$ into a semi-abelian variety $A$ and an
arbitrary effective reduced divisor $D$ on $A$; the low truncation level is
important for applications. We will actually prove this for the jet lifts of
$f$. Finally we give some applications, including the solution of a problem
posed by Mark Green.


  The coupled phonon-ripplon modes of the quasi-one-dimensional electron chain
on the liquid helium sutface are studied. It is shown that the electron-ripplon
coupling leads to the splitting of the collective modes of the wire with the
appearance of low-frequency modes and high-frequency optical modes starting
from threshold frequencies. The effective masses of an electron plus the
associated dimple for low frequency modes are estimated and the values of the
threshold frequencies are calculated. The results obtained can be used in
experimental attempts to observe the phase transition of the electron wire into
a quasi-ordered phase.


  Volatile molecules containing hydrogen, carbon, and nitrogen are key
components of planetary atmospheres. In the pebble accretion model for rocky
planet formation, these volatile species are accreted during the main planetary
formation phase. For this study, we modelled the partitioning of volatiles
within a growing planet and the outgassing to the surface. The core stores more
than 90\% of the hydrogen and carbon budgets of Earth for realistic values of
the partition coefficients of H and C between metal and silicate melts. The
magma oceans of Earth and Venus are sufficiently deep to undergo oxidation of
ferrous Fe$^{2+}$ to ferric Fe$^{3+}$. This increased oxidation state leads to
the outgassing of primarily CO$_2$ and H$_2$O from the magma ocean of Earth. In
contrast, the oxidation state of Mars' mantle remains low and the main
outgassed hydrogen carrier is H$_2$. This hydrogen easily escapes the
atmosphere due to the irradiation from the young Sun in XUV wavelengths,
dragging with it the majority of the CO, CO$_2$, H$_2$O, and N$_2$ contents of
the atmosphere. A small amount of surface water is maintained on Mars, in
agreement with proposed ancient ocean shorelines, for moderately low values of
the mantle oxidation. Nitrogen partitions relatively evenly between the core
and the atmosphere due to its extremely low solubility in magma; the burial of
large reservoirs of nitrogen in the core is thus not possible. The overall low
N contents of Earth disagree with the high abundance of N in all chondrite
classes and favours a volatile delivery by pebble snow. Our model of rapid
rocky planet formation by pebble accretion displays broad consistency with the
volatile contents of the Sun's terrestrial planets. The diversity of the
terrestrial planets can therefore be used as benchmark cases to calibrate
models of extrasolar rocky planets and their atmospheres.


  This paper presents an algorithm for the unsupervised learning of latent
variable models from unlabeled sets of data. We base our technique on spectral
decomposition, providing a technique that proves to be robust both in theory
and in practice. We also describe how to use this algorithm to learn the
parameters of two well known text mining models: single topic model and Latent
Dirichlet Allocation, providing in both cases an efficient technique to
retrieve the parameters to feed the algorithm. We compare the results of our
algorithm with those of existing algorithms on synthetic data, and we provide
examples of applications to real world text corpora for both single topic model
and LDA, obtaining meaningful results.


  Momentum methods such as Polyak's heavy ball (HB) method, Nesterov's
accelerated gradient (AG) as well as accelerated projected gradient (APG)
method have been commonly used in machine learning practice, but their
performance is quite sensitive to noise in the gradients. We study these
methods under a first-order stochastic oracle model where noisy estimates of
the gradients are available. For strongly convex problems, we show that the
distribution of the iterates of AG converges with the accelerated
$O(\sqrt{\kappa}\log(1/\varepsilon))$ linear rate to a ball of radius
$\varepsilon$ centered at a unique invariant distribution in the 1-Wasserstein
metric where $\kappa$ is the condition number as long as the noise variance is
smaller than an explicit upper bound we can provide. Our analysis also
certifies linear convergence rates as a function of the stepsize, momentum
parameter and the noise variance; recovering the accelerated rates in the
noiseless case and quantifying the level of noise that can be tolerated to
achieve a given performance. In the special case of strongly convex quadratic
objectives, we can show accelerated linear rates in the $p$-Wasserstein metric
for any $p\geq 1$ with improved sensitivity to noise for both AG and HB through
a non-asymptotic analysis under some additional assumptions on the noise
structure. Our analysis for HB and AG also leads to improved non-asymptotic
convergence bounds in suboptimality for both deterministic and stochastic
settings which is of independent interest. To the best of our knowledge, these
are the first linear convergence results for stochastic momentum methods under
the stochastic oracle model. We also extend our results to the APG method and
weakly convex functions showing accelerated rates when the noise magnitude is
sufficiently small.


  We place bounds on anomalous gauge boson couplings from LEP data with
particular emphasis on those couplings which do not contribute to $Z$ decays at
tree level. We use an effective field theory formalism to compute the one-loop
corrections to the $Z\rightarrow {\overline f} f$ decay widths resulting from
non-standard model three and four gauge boson vertices. We find that the
precise measurements at LEP constrain the three gauge boson couplings at a
level comparable to that obtainable at LEPII and LHC.


  The Colouring problem is that of deciding, given a graph $G$ and an integer
$k$, whether $G$ admits a (proper) $k$-colouring. For all graphs $H$ up to five
vertices, we classify the computational complexity of Colouring for
$(\mbox{diamond},H)$-free graphs. Our proof is based on combining known results
together with proving that the clique-width is bounded for $(\mbox{diamond},
P_1+2P_2)$-free graphs. Our technique for handling this case is to reduce the
graph under consideration to a $k$-partite graph that has a very specific
decomposition. As a by-product of this general technique we are also able to
prove boundedness of clique-width for four other new classes of
$(H_1,H_2)$-free graphs. As such, our work also continues a recent systematic
study into the (un)boundedness of clique-width of $(H_1,H_2)$-free graphs, and
our five new classes of bounded clique-width reduce the number of open cases
from 13 to 8.


  An automata network (AN) is a finite graph where each node holds a state from
a finite alphabet and is equipped with a local map defining the evolution of
the state of the node depending on its neighbors. They are studied both from
the dynamical and the computational complexity point of view. Inspired from
well-established notions in the context of cellular automata, we develop a
theory of intrinsic simulations and universality for families of automata
networks. We establish many consequences of intrinsic universality in terms of
complexity of orbits (periods of attractors, transients, etc) as well as
hardness of the standard well-studied decision problems for automata networks
(short/long term prediction, reachability, etc). In the way, we prove
orthogonality results for these problems: the hardness of a single one does not
imply hardness of the others, while intrinsic universality implies hardness of
all of them. As a complement, we develop a proof technique to establish
intrinsic simulation and universality results which is suitable to deal with
families of symmetric networks were connections are non-oriented. It is based
on an operation of glueing of networks, which allows to produce complex orbits
in large networks from compatible pseudo-orbits in small networks. As an
illustration, we give a short proof that the family of networks were each node
obeys the rule of the 'game of life' cellular automaton is strongly universal.
This formalism and proof technique is also applied in a companion paper devoted
to studying the effect of update schedules on intrinsic universality for
concrete symmetric families of automata networks.


  Most interesting problems in robotics (e.g., locomotion and manipulation) are
realized through intermittent contact with the environment. Due to the
perception and modeling errors, assuming an exact time for establishing contact
with the environment is unrealistic. On the other hand, handling uncertainties
in contact timing is notoriously difficult as it gives rise to either handling
uncertain complementarity systems or solving combinatorial optimization
problems at run-time. This work presents a novel optimal control formulation to
find robust control policies under contact timing uncertainties. Our main
novelty lies in casting the stochastic problem to a deterministic optimization
over the uncertainty set that ensures robustness criterion satisfaction of
candidate pre-contact states and optimizes for contact-relevant objectives.
This way, we only need to solve a manageable standard nonlinear programming
problem without complementarity constraints or combinatorial explosion. Our
simulation results on multiple simplified locomotion and manipulation tasks
demonstrate the robustness of our uncertainty-aware formulation compared to the
nominal optimal control formulation.


  Let M be a matroid representable over a (partial) field P and B a matrix
representable over a sub-partial field P' of P. We say that B confines M to P'
if, whenever a P-representation matrix A of M has a submatrix B, A is a scaled
P'-matrix. We show that, under some conditions on the partial fields, on M, and
on B, verifying whether B confines M to P' amounts to a finite check. A
corollary of this result is Whittle's Stabilizer Theorem.
  A combination of the Confinement Theorem and the Lift Theorem from
arXiv:0804.3263 leads to a short proof of Whittle's characterization of the
matroids representable over GF(3) and other fields.
  We also use a combination of the Confinement Theorem and the Lift Theorem to
prove a characterization, in terms of representability over partial fields, of
the 3-connected matroids that have k inequivalent representations over GF(5),
for k = 1, ..., 6.
  Additionally we give, for a fixed matroid M, an algebraic construction of a
partial field P_M and a representation A over P_M such that every
representation of M over a partial field P is equal to f(A) for some
homomorphism f:P_M->P. Using the Confinement Theorem we prove an algebraic
analog of the theory of free expansions by Geelen et al.


  The ontology proposed in this paper is aimed at demonstrating that it is
possible to understand the counter-intuitive predictions of quantum mechanics
while still retaining much of the framework underlying classical physics, the
implication being that it is better to avoid wandering into unnecessarily
speculative realms without the support of conclusive evidence. In particular,
it is argued that it is possible to interpret quantum mechanics as simply
describing an external world consisting of familiar physical entities (e.g.,
particles or fields) residing in classical 3-dimensional space (not
configuration space) with Lorentz covariance maintained.


  Gravitational microlensing has the potential to provide direct gravitational
masses of single, free-floating brown dwarfs, independent of evolutionary and
atmospheric models. The proper motions and parallaxes of nearby brown dwarfs
can be used to predict close future alignments with distant background stars
that cause a microlensing event. Targeted astrometric follow up of the
predicted microlensing events permits the brown dwarf's mass to be measured.
Predicted microlensing events are typically found via searching for a peak
threshold signal using an estimate of the lens mass. We develop a novel method
that finds predicted events that instead will lead to a target lens mass
precision. The main advantage of our method is that it does not require a lens
mass estimate. We use this method to search for predicted astrometric
microlensing events occurring between 2014 - 2032 using a catalog of 1225 low
mass star and brown dwarf lenses in the Solar Neighborhood of spectral type M6
or later and a background source catalog from DECaLS Data Release 9. The
background source catalog extends to $g = $ 23.95, providing a more dense
catalog compared to Gaia. Our search did not reveal any upcoming microlensing
events. We estimate the rate of astrometric microlensing event for brown dwarfs
in the Legacy Survey and find it to be low $\sim10^{-5}$yr$^{-1}$. We recommend
carrying out targeted searches for brown dwarfs in front of the Galactic Bulge
and Plane to find astrometric microlensing events that will allow the masses of
single, free-floating brown dwarfs to be measured.


  Photo-ionization is the accepted mechanism for the propagation of positive
streamers in air though the parameters are not very well known; the efficiency
of this mechanism largely depends on the presence of both nitrogen and oxygen.
But experiments show that streamer propagation is amazingly robust against
changes of the gas composition; even for pure nitrogen with impurity levels
below 1 ppm streamers propagate essentially with the same velocity as in air,
but their minimal diameter is smaller, and they branch more frequently.
Additionally, they move more in a zigzag fashion and sometimes exhibit a
feathery structure. In our simulations, we test the relative importance of
photo-ionization and of the background ionization from pulsed repetitive
discharges, in air as well as in nitrogen with 1 ppm O2 . We also test
reasonable parameter changes of the photo-ionization model. We find that photo-
ionization dominates streamer propagation in air for repetition frequencies of
at least 1 kHz, while in nitrogen with 1 ppm O2 the effect of the repetition
frequency has to be included above 1 Hz. Finally, we explain the feather-like
structures around streamer channels that are observed in experiments in
nitrogen with high purity, but not in air.


  The multipole expansions for massive vector and symmetric tensor fields in
the region outside spatially compact stationary sources are obtained by using
the symmetric and trace-free formalism in terms of the irreducible Cartesian
tensors, and the closed-form expressions for the source multipole moments are
provided. The expansions show a Yukawa-like dependence on the massive
parameters of the fields, and the integrals of the stationary source multipole
moments are all modulated by a common radial factor related to the source
distribution. For stationary massive vector field, there are two types of
``magnetic'' multipole moments, among which one is the generalization of that
of the magnetostatic field, and another, being an additional set of multipole
moments of the stationary massive vector field, can not be transformed away. As
to the stationary massive symmetric tensor field, its multipole expansion is
presented when the trace of its spatial part is specified, where besides the
counterparts of the mass and spin multipole moments of massless symmetric
tensor field, three additional sets of multipole moments also appear. The
multipole expansions of the tensor field under two typical cases are discussed,
where it is shown that if the spatial part of the tensor field is trace-free,
the monopole and dipole moments in the corresponding expansion will vanish.


  In this paper we investigate maximum principles for functionals defined on
solutions to special partial differential equations of elliptic type, extending
results by Payne and Philippin.
  We apply such maximum principles to investigate one overdetermined problem.


  Molecular communication (MC) implemented on Nano networks has extremely
attractive characteristics in terms of energy efficiency, dependability, and
robustness. Even though, the impact of incredibly slow molecule diffusion and
high variability environments remains unknown. Analysis and designs of
communication systems usually rely on developing mathematical models that
describe the communication channel. However, the underlying channel models are
unknown in some systems, such as MC systems, where chemical signals are used to
transfer information. In these cases, a new method to analyze and design is
needed. In this paper, we concentrate on one critical aspect of the MC system,
modelling MC received signal until time t , and demonstrate that using tools
from ML makes it promising to train detectors that can be executed well without
any information about the channel model. Machine learning (ML) is one of the
intelligent methodologies that has shown promising results in the domain. This
paper applies Azure Machine Learning (Azure ML) for flexible pavement
maintenance regressions problems and solutions. For prediction, four parameters
are used as inputs: the receiver radius, transmitter radius, distance between
receiver and transmitter, and diffusion coefficient, while the output is mAP
(mean average precision) of the received signal. Azure ML enables algorithms
that can learn from data and experiences and accomplish tasks without having to
be coded. In the established Azure ML, the regression algorithms such as, boost
decision tree regression, Bayesian linear regression, neural network, and
decision forest regression are selected. The best performance is chosen as an
optimality criterion. Finally, a comparison that shows the potential benefits
of Azure ML tool over programmed based tool (Python), used by developers on
local PCs, is demonstrated


  We study the relationship between online Gaussian process (GP) regression and
kernel least mean squares (KLMS) algorithms. While the latter have no capacity
of storing the entire posterior distribution during online learning, we
discover that their operation corresponds to the assumption of a fixed
posterior covariance that follows a simple parametric model. Interestingly,
several well-known KLMS algorithms correspond to specific cases of this model.
The probabilistic perspective allows us to understand how each of them handles
uncertainty, which could explain some of their performance differences.


  We demonstrate experimentally that the micromagnetic profile of the
out-of-plane component of magnetic induction, B_z, in the crossing lattices
regime of layered superconductors can be manipulated by varying the in-plane
magnetic field, H_{||}. Moving Josephson vortices drag/push pancake vortex
stacks, and the magnetic profile, B_z(x), can be controllably sculpted across
the entire single crystal sample. Depending on the H-history and temperature we
can increase or decrease the flux density at the center and near the edges of
the crystal by as much as 40%, realising both 'convex' and 'concave' magnetic
flux lenses. Our experimental results are well described by molecular dynamics
simulations.


  We show that the class of amalgamated free products of two free groups over a
cyclic subgroup admits amenable, faithful and transitive actions on infinite
countable sets. This work generalizes the results on such actions for doubles
of free group on 2 generators.


  Incorporation of kinetic effects such as Landau damping into a fluid
framework was pioneered by Hammett and Perkins PRL 1990, by obtaining closures
of the fluid hierarchy, where the gyrotropic heat flux fluctuations or the
deviation of the 4th-order gyrotropic fluid moment, are expressed through
lower-order fluid moments. To obtain a closure of a fluid model expanded around
a bi-Maxwellian distribution function, the usual plasma dispersion function
$Z(\zeta)$ that appears in kinetic theory or the associated plasma response
function $R(\zeta)=1 + \zeta Z(\zeta)$, have to be approximated with a suitable
Pad\'e approximant in such a way, that the closure is valid for all $\zeta$
values. Such closures are rare, and the original closures of Hammett and
Perkins are often employed. Here we present a complete mapping of all plausible
Landau fluid closures that can be constructed at the level of 4th-order moments
in the gyrotropic limit and we identify the most precise closures. Furthermore,
by considering 1D closures at higher-order moments, we show that it is possible
to reproduce linear Landau damping in the fluid framework to any desired
precision, thus showing convergence of the fluid and collisionless kinetic
descriptions.


  Three hundred fifty three radio sources from the NRAO VLA Sky Survey (NVSS)
(Condon et al. 1998) and the FIRST Survey (White et al. 1997}, are detected in
the areas of 179 Shakhbazian Compact Groups (ShCGs) of galaxies. Ninety three
of them are identified with galaxies in 74 ShCGs. Six radio sources have
complex structure. The radio spectra of 22 sources are determined. Radio
luminosities of galaxies in ShCGs are in general higher than that of galaxies
in Hickson Compact Groups (HCGs). The comparison of radio (at 1.4 GHz) and FIR
(at 60 $\mu$m) fluxes of ShCG galaxies with that of HCG galaxies shows that
galaxies in ShCGs are relatively stronger emitters at radio wavelengths, while
galaxies in HCGs have relatively stronger FIR emission. The reasons of such
difference is discussed.


  We demonstrate a qubit-readout architecture where the dispersive coupling is
entirely mediated by a kinetic inductance. This allows us to engineer the
dispersive shift of the readout resonator independent of the qubit and
resonator capacitances. We validate the pure kinetic coupling concept and
demonstrate various generalized flux qubit regimes from plasmon to fluxon, with
dispersive shifts ranging from 60 kHz to 2 MHz at the half-flux quantum sweet
spot. We achieve readout performances comparable to conventional architectures
with quantum state preparation fidelities of 99.7 % and 92.7 % for the ground
and excited states, respectively, and below 0.1 % leakage to non-computational
states.


  We calculate small correction terms to gravitational potential near
$p$-branes embedded in a constant NS $B$ field background in the context of
M-theory or string theory. The normalizable wave functions of gravity
fluctuations around the brane describe only massive modes. We compute such wave
functions analytically. We estimate the correction to gravitational potential
for small and long distances, and show that there is an intermediate range of
distances in which we can identify $4D$ gravity on the brane below a crossover
scale given in terms of components of the $B$ field. The $4D$ gravity is
metastable and for distances much larger than the crossover scale the $5D$
gravity is recovered.


  After introducing the fundamental properties of self-gravitating systems, we
present an application of Tsallis' generalized entropy to the analysis of their
thermodynamic nature. By extremizing the Tsallis entropy, we obtain an equation
of state known as the stellar polytrope. For a self-gravitating stellar system
confined within a perfectly reflecting wall, we discuss the thermodynamic
instability caused by its negative specific heat. The role of the extremum as a
quasi-equilibrium is also demonstrated from the results of N-body simulations.


  Due to the statistical complexity of video, the high degree of inherent
stochasticity, and the sheer amount of data, generating natural video remains a
challenging task. State-of-the-art video generation models often attempt to
address these issues by combining sometimes complex, usually video-specific
neural network architectures, latent variable models, adversarial training and
a range of other methods. Despite their often high complexity, these approaches
still fall short of generating high quality video continuations outside of
narrow domains and often struggle with fidelity. In contrast, we show that
conceptually simple autoregressive video generation models based on a
three-dimensional self-attention mechanism achieve competitive results across
multiple metrics on popular benchmark datasets, for which they produce
continuations of high fidelity and realism. We also present results from
training our models on Kinetics, a large scale action recognition dataset
comprised of YouTube videos exhibiting phenomena such as camera movement,
complex object interactions and diverse human movement. While modeling these
phenomena consistently remains elusive, we hope that our results, which include
occasional realistic continuations encourage further research on comparatively
complex, large scale datasets such as Kinetics.


  Adaptive multiple testing with covariates is an important research direction
that has gained major attention in recent years. It has been widely recognized
that leveraging side information provided by auxiliary covariates can improve
the power of false discovery rate (FDR) procedures. Currently, most such
procedures are devised with $p$-values as their main statistics. However, for
two-sided hypotheses, the usual data processing step that transforms the
primary statistics, known as $z$-values, into $p$-values not only leads to a
loss of information carried by the main statistics, but can also undermine the
ability of the covariates to assist with the FDR inference. We develop a
$z$-value based covariate-adaptive (ZAP) methodology that operates on the
intact structural information encoded jointly by the $z$-values and covariates.
It seeks to emulate the oracle $z$-value procedure via a working model, and its
rejection regions significantly depart from those of the $p$-value adaptive
testing approaches. The key strength of ZAP is that the FDR control is
guaranteed with minimal assumptions, even when the working model is
misspecified. We demonstrate the state-of-the-art performance of ZAP using both
simulated and real data, which shows that the efficiency gain can be
substantial in comparison with $p$-value based methods. Our methodology is
implemented in the $\texttt{R}$ package $\texttt{zap}$.


  A subset of positive integers $F$ is a Schreier set if it is non-empty and
$|F|\leqslant \min F$ (here $|F|$ is the cardinality of $F$). For each positive
integer $k$, we define $k\mathcal{S}$ as the collection of all the unions of at
most $k$ Schreier sets. Also, for each positive integer $n$, let
$(k\mathcal{S})^n$ be the collection of all sets in $k\mathcal{S}$ with the
maximum element equal to $n$. It is well-known that the sequence
$(|(1\mathcal{S})^n|)_{n=1}^\infty$ is the Fibbonacci sequence. In particular,
the sequence satisfies a linear recurrence. We generalize this statement,
namely, we show that the sequence $(|(k\mathcal{S})^n|)_{n=1}^\infty$ satisfies
a linear recurrence for every positive $k$.


  Mapping the distribution of neutral atomic hydrogen (HI) in the Universe
through its 21 cm emission line provides a powerful cosmological probe to map
the large-scale structures and shed light on various cosmological phenomena.
The Baryon Acoustic Oscillations at low redshifts can potentially be probed by
sensitive HI intensity mapping experiments and constrain the properties of dark
energy. However, the 21 cm signal detection faces formidable challenges due to
the dominance of various astrophysical foregrounds, which can be several orders
of magnitude stronger. Our current work introduces a novel and
model-independent Internal Linear Combination (ILC) method in harmonic space
using the principal components of the 21 cm signal for accurate foreground
removal and power spectrum estimation. We estimate the principal components by
incorporating prior knowledge of the theoretical 21 cm covariance matrix. We
test our methodology by detailed simulations of radio observations,
incorporating synchrotron emission, free-free radiation, extragalactic point
sources, and thermal noise. We estimate the full sky 21 cm angular power
spectrum after application of a mask on the full sky cleaned 21 cm signal by
using the mode-mode coupling matrix. These full sky estimates of angular
spectra can be directly used to measure the cosmological parameters. For the
first time, we demonstrate the effectiveness of a foreground model-independent
ILC method in harmonic space to reconstruct the 21 cm signal.


  We propose a model of dynamical noncommutative quantum mechanics in which the
noncommutative strengths, describing the properties of the commutation
relations of the coordinate and momenta, respectively, are arbitrary energy
dependent functions. The Schr\"{o}dinger equation in the energy dependent
noncommutative algebra is derived for a two dimensional system for an arbitrary
potential. The resulting equation reduces in the small energy limit to the
standard quantum mechanical one, while for large energies the effects of the
noncommutativity become important. We investigate in detail three cases, in
which the noncommutative strengths are determined by an independent energy
scale, related to the vacuum quantum fluctuations, by the particle energy, and
by a quantum operator representation, respectively. Specifically, in our study
we assume an arbitrary power law energy-dependence of the noncommutative
strength parameters, and of their algebra. In this case, in the quantum
operator representation, the Schr\"{o}dinger equation can be formulated
mathematically as a fractional differential equation. For all our three models
we analyze the quantum evolution of the free particle, and of the harmonic
oscillator, respectively. The general solutions of the noncommutative
Schr\"{o}dinger equation as well as the expressions of the energy levels are
explicitly obtained.


  Looking for an efficient algorithm for the computation of the homology groups
of an algebraic set or even a semi-algebraic set is an important problem in the
effective real algebraic geometry. Recently, Peter Burgisser, Felipe Cucker and
Pierre Lairez wrote a paper [1], which made a step forward by giving an
algorithm of weak exponential time. However, the algorithm is not not
practical. In my thesis, I will introduce my improvement of this algorithm
using an adaptive grid algorithm on the unit sphere.


  We study the interplay between flavor symmetries and leptogenesis in the case
when the scale of flavor symmetry breaking is higher than the scale at which
lepton number is violated. We show that when the heavy Majorana neutrinos
belong to an irreducible representation of the flavor group, all the
leptogenesis CP asymmetries vanish in the limit of exact symmetry. In the case
of reducible representations we identify a general condition that, if
satisfied, guarantees the same result. We then focus on the case of a model in
which an $A_4$ flavor symmetry yields a drastic reduction in the number of free
parameters, implying that at leading order several quantities are only a
function of the lightest neutrino mass $m_l$, which in turn is strongly
constrained. For normal ordering (NO) we find m_l\simeq (0.0044 \div 0.0056) eV
while for inverted ordering (IO) m_l\gtrsim 0.017 eV. For the 0\nu2\beta decay
parameter this yields |m_{ee}|\simeq (0.006\div 0.007) eV (NO) and
|m_{ee}|\gsim 0.017 eV (IO). We show that the leptogenesis CP asymmetries only
depend on m_l, on a single non-hierarchical Yukawa coupling y, and on two
parameters that quantify the flavor symmetry breaking effects, and we argue
that the unflavored regime for leptogenesis is strongly preferred in our model,
thus realizing a rather predictive scenario. Performing a calculation of the
matter-antimatter asymmetry we find that for NO the observed value is easily
reproduced for natural values of the symmetry breaking parameters. For IO
successful leptogenesis is possible for a limited choice of the parameters
implying rather large reheating temperatures T_reh\gtrsim 5*10^13 GeV.


  A machine learning model, under the influence of observed or unobserved
confounders in the training data, can learn spurious correlations and fail to
generalize when deployed. For image classifiers, augmenting a training dataset
using counterfactual examples has been empirically shown to break spurious
correlations. However, the counterfactual generation task itself becomes more
difficult as the level of confounding increases. Existing methods for
counterfactual generation under confounding consider a fixed set of
interventions (e.g., texture, rotation) and are not flexible enough to capture
diverse data-generating processes. Given a causal generative process, we
formally characterize the adverse effects of confounding on any downstream
tasks and show that the correlation between generative factors (attributes) can
be used to quantitatively measure confounding between generative factors. To
minimize such correlation, we propose a counterfactual generation method that
learns to modify the value of any attribute in an image and generate new images
given a set of observed attributes, even when the dataset is highly confounded.
These counterfactual images are then used to regularize the downstream
classifier such that the learned representations are the same across various
generative factors conditioned on the class label. Our method is
computationally efficient, simple to implement, and works well for any number
of generative factors and confounding variables. Our experimental results on
both synthetic (MNIST variants) and real-world (CelebA) datasets show the
usefulness of our approach.


  Unsupervised representation learning with contrastive learning achieved great
success. This line of methods duplicate each training batch to construct
contrastive pairs, making each training batch and its augmented version
forwarded simultaneously and leading to additional computation. We propose a
new jigsaw clustering pretext task in this paper, which only needs to forward
each training batch itself, and reduces the training cost. Our method makes use
of information from both intra- and inter-images, and outperforms previous
single-batch based ones by a large margin. It is even comparable to the
contrastive learning methods when only half of training batches are used.
  Our method indicates that multiple batches during training are not necessary,
and opens the door for future research of single-batch unsupervised methods.
Our models trained on ImageNet datasets achieve state-of-the-art results with
linear classification, outperforming previous single-batch methods by 2.6%.
Models transferred to COCO datasets outperform MoCo v2 by 0.4% with only half
of the training batches. Our pretrained models outperform supervised ImageNet
pretrained models on CIFAR-10 and CIFAR-100 datasets by 0.9% and 4.1%
respectively. Code is available at
https://github.com/Jia-Research-Lab/JigsawClustering


  Microelectromagnet devices, a ring trap and a matrix, were developed for the
microscopic control of biological systems. The ring trap is a circular Au wire
with an insulator on top. The matrix has two arrays of straight Au wires, one
array perpendicular to the other, that are separated and topped by insulating
layers. Microelectromagnets can produce strong magnetic fields to stably
manipulate magnetically tagged biological systems in a fluid. Moreover, by
controlling the currents flowing through the wires, a microelectromagnet matrix
can move a peak in the magnetic field magnitude continuously over the surface
of the device, generate multiple peaks simultaneously and control them
independently. These capabilities of a matrix can be used to trap, continuously
transport, assemble, separate and sort biological samples on micrometer length
scales. Combining microelectromagnets with microfluidic systems, chip-based
experimental systems can be realized for novel applications in biological and
biomedical studies.


  We consider a revenue optimizing seller selling a single item to a buyer, on
whose private value the seller has a noisy signal. We show that, when the
signal is kept private, arbitrarily more revenue could potentially be extracted
than if the signal is leaked or revealed. We then show that, if the seller is
not allowed to make payments to the buyer, the gap between the two is bounded
by a multiplicative factor of 3, if the value distribution conditioning on each
signal is regular. We give examples showing that both conditions are necessary
for a constant bound to hold.
  We connect this scenario to multi-bidder single-item auctions where bidders'
values are correlated. Similarly to the setting above, we show that the revenue
of a Bayesian incentive compatible, ex post individually rational auction can
be arbitrarily larger than that of a dominant strategy incentive compatible
auction, whereas the two are no more than a factor of 5 apart if the auctioneer
never pays the bidders and if each bidder's value conditioning on the others'
is drawn according to a regular distribution. The upper bounds in both settings
degrade gracefully when the distribution is a mixture of a small number of
regular distributions.


  We discuss an extension of our earlier work on the time-dependent
Landauer--B\"uttiker formalism for noninteracting electronic transport. The
formalism can without complication be extended to superconducting central
regions since the Green's functions in the Nambu representation satisfy the
same equations of motion which, in turn, leads to the same closed expression
for the equal-time lesser Green's function, i.e., for the time-dependent
reduced one-particle density matrix. We further write the finite-temperature
frequency integrals in terms of known special functions thereby considerably
speeding up the computation. Numerical simulations in simple normal metal --
superconductor -- normal metal junctions are also presented.


  We present the updates of the Galactic O-Star Catalog (GOSC) that we have
undertaken in the last two years: new spectral types, more objects, additional
information, and coordination with CDS. We also present updates for the
Galactic O-Star Spectroscopic Survey (GOSSS). A new paper (GOSSS-III) has been
published and ~1000 targets have been observed since 2014. Four new setups have
been added to our lineup and for two of them we have already obtained over 100
spectra: with OSIRIS at the 10.4 m GTC we are observing northern dim stars and
with FRODOspec at the 2.0 m Liverpool Telescope we are observing northern
bright stars. Finally, we also make available new versions of MGB, the spectral
classification tool associated with the project, and of the GOSSS grid of
spectroscopic standards.


  This paper focuses on the Data Augmentation for low-resource Natural Language
Understanding (NLU) tasks. We propose Prompt-based D}ata Augmentation model
(PromDA) which only trains small-scale Soft Prompt (i.e., a set of trainable
vectors) in the frozen Pre-trained Language Models (PLMs). This avoids human
effort in collecting unlabeled in-domain data and maintains the quality of
generated synthetic data. In addition, PromDA generates synthetic data via two
different views and filters out the low-quality data using NLU models.
Experiments on four benchmarks show that synthetic data produced by PromDA
successfully boost up the performance of NLU models which consistently
outperform several competitive baseline models, including a state-of-the-art
semi-supervised model using unlabeled in-domain data. The synthetic data from
PromDA are also complementary with unlabeled in-domain data. The NLU models can
be further improved when they are combined for training.


  ARGO-YBJ is an air shower detector array with a fully covered layer of
resistive plate chambers. It is operated with a high duty cycle and a large
field of view. It continuously monitors the northern sky at energies above 0.3
TeV. In this paper, we report a long-term monitoring of Mrk 421 over the period
from 2007 November to 2010 February. This source was observed by the
satellite-borne experiments Rossi X-ray Timing Explorer and Swift in the X-ray
band. Mrk 421 was especially active in the first half of 2008. Many flares are
observed in both X-ray and gamma-ray bands simultaneously. The gamma-ray flux
observed by ARGO-YBJ has a clear correlation with the X-ray flux. No lag
between the X-ray and gamma-ray photons longer than 1 day is found. The
evolution of the spectral energy distribution is investigated by measuring
spectral indices at four different flux levels. Hardening of the spectra is
observed in both X-ray and gamma-ray bands. The gamma-ray flux increases
quadratically with the simultaneously measured X-ray flux. All these
observational results strongly favor the synchrotron self-Compton process as
the underlying radiative mechanism.


  We construct a model that naturally generates mu and B of the same order
without producing large CP violating phases. This is easily accomplished once
one permits these mass scales to be determined independently of the ordinary
gauge-mediated soft masses. The alignment of phases is shown to emerge
dynamically upon coupling to supergravity and is not unique to the model
presented here.


  We describe algorithms for two-stage stochastic linear programming with
recourse and their implementation on a grid computing platform. In particular,
we examine serial and asynchronous versions of the L-shaped method and a
trust-region method. The parallel platform of choice is the dynamic,
heterogeneous, opportunistic platform provided by the Condor system. The
algorithms are of master-worker type (with the workers being used to solve
second-stage problems, and the MW runtime support library (which supports
master-worker computations) is key to the implementation. Computational results
are presented on large sample average approximations of problems from the
literature.


  We suggest a model for data losses in a single node of a packet-switched
network (like the Internet) which reduces to one-dimensional discrete random
walks with unusual boundary conditions. The model shows critical behavior with
an abrupt transition from exponentially small to finite losses as the data
arrival rate increases. The critical point is characterized by strong
fluctuations of the loss rate. Although we consider the packet arrival being a
Markovian process, the loss rate exhibits non-Markovian power-law correlations
in time at the critical point.


  We present a computational model for the semantic interpretation of symmetry
in naturalistic scenes. Key features include a human-centred representation,
and a declarative, explainable interpretation model supporting deep semantic
question-answering founded on an integration of methods in knowledge
representation and deep learning based computer vision. In the backdrop of the
visual arts, we showcase the framework's capability to generate human-centred,
queryable, relational structures, also evaluating the framework with an
empirical study on the human perception of visual symmetry. Our framework
represents and is driven by the application of foundational, integrated Vision
and Knowledge Representation and Reasoning methods for applications in the
arts, and the psychological and social sciences.


  In this paper we show that it is possible to retrieve structural information
about complex block-oriented nonlinear systems, starting from linear
approximations of the nonlinear system around different setpoints.The key idea
is to monitor the movements of the poles and zeros of the linearized models and
to reduce the number of candidate models on the basis of these observations.
Besides the well known open loop single branch Wiener-, Hammerstein-, and
Wiener-Hammerstein systems, we also cover a number of more general structures
like parallel (multi branch) Wiener-Hammerstein models, and closed loop block
oriented models, including linear fractional representation (LFR) models.


  This is the final report on reproducibility@xsede, a one-day workshop held in
conjunction with XSEDE14, the annual conference of the Extreme Science and
Engineering Discovery Environment (XSEDE). The workshop's discussion-oriented
agenda focused on reproducibility in large-scale computational research. Two
important themes capture the spirit of the workshop submissions and
discussions: (1) organizational stakeholders, especially supercomputer centers,
are in a unique position to promote, enable, and support reproducible research;
and (2) individual researchers should conduct each experiment as though someone
will replicate that experiment. Participants documented numerous issues,
questions, technologies, practices, and potentially promising initiatives
emerging from the discussion, but also highlighted four areas of particular
interest to XSEDE: (1) documentation and training that promotes reproducible
research; (2) system-level tools that provide build- and run-time information
at the level of the individual job; (3) the need to model best practices in
research collaborations involving XSEDE staff; and (4) continued work on
gateways and related technologies. In addition, an intriguing question emerged
from the day's interactions: would there be value in establishing an annual
award for excellence in reproducible research?


  The long range spatial and temporal ordering displayed by discrete time
crystals, can become advantageous properties when used for sensing extremely
weak signals. Here, we investigate their performance as quantum sensors of weak
AC-fields and demonstrate, using the quantum Fisher information measure, that
they can overcome the shot noise limit while allowing long interrogation times.
In such systems, collective interactions stabilize their dynamics against noise
making them robust enough to protocol imperfections.


  Recent work [hep-th/0504183,hep-th/0508002] indicates an approach to the
formulation of diffeomorphism invariant quantum field theories (qft's) on the
Groenewold-Moyal (GM) plane. In this approach to the qft's, statistics gets
twisted and the S-matrix in the non-gauge qft's becomes independent of the
noncommutativity parameter theta^{\mu\nu}. Here we show that the noncommutative
algebra has a commutative spacetime algebra as a substructure: the Poincare,
diffeomorphism and gauge groups are based on this algebra in the twisted
approach as is known already from the earlier work of [hep-th/0510059]. It is
natural to base covariant derivatives for gauge and gravity fields as well on
this algebra. Such an approach will in particular introduce no additional gauge
fields as compared to the commutative case and also enable us to treat any
gauge group (and not just U(N)). Then classical gravity and gauge sectors are
the same as those for \theta^{\mu \nu}=0, but their interactions with matter
fields are sensitive to theta^{\mu \nu}. We construct quantum noncommutative
gauge theories (for arbitrary gauge groups) by requiring consistency of twisted
statistics and gauge invariance. In a subsequent paper (whose results are
summarized here), the locality and Lorentz invariance properties of the
S-matrices of these theories will be analyzed, and new non-trivial effects
coming from noncommutativity will be elaborated.
  This paper contains further developments of [hep-th/0608138] and a new
formulation based on its approach.


  We investigate the gravitational collapse of a spherically symmetric, perfect
fluid with equation of state P = (Gamma -1)rho. We restrict attention to the
ultrarelativistic (``kinetic-energy-dominated'', ``scale-free'') limit where
black hole formation is anticipated to turn on at infinitesimal black hole mass
(Type II behavior). Critical solutions (those which sit at the threshold of
black hole formation in parametrized families of collapse) are found by solving
the system of ODEs which result from a self-similar ansatz, and by solving the
full Einstein/fluid PDEs in spherical symmetry. These latter PDE solutions
(``simulations'') extend the pioneering work of Evans and Coleman (Gamma = 4/3)
and verify that the continuously self-similar solutions previously found by
Maison and Hara et al for $1.05 < Gamma < 1.89 are (locally) unique critical
solutions. In addition, we find strong evidence that globally regular critical
solutions do exist for 1.89 < Gamma <= 2$, that the sonic point for Gamma_dn =
1.8896244 is a degenerate node, and that the sonic points for Gamma >Gamma_dn
are nodal points rather than focal points as previously reported. We also find
a critical solution for Gamma = 2, and present evidence that it is continuously
self-similar and Type II. Mass-scaling exponents for all of the critical
solutions are calculated by evolving near-critical initial data, with results
which confirm and extend previous calculations based on linear perturbation
theory. Finally, we comment on critical solutions generated with an ideal-gas
equation of state.


  We present a phenomenological study of a CP-violating two-Higgs-doublet Model
with type-II Yukawa couplings at the Large Hadron Collider (LHC). In the light
of recent LHC data, we focus on the parameter space that survives the current
and past experimental constraints as well as theoretical bounds on the model.
Once the phenomenological scenario is set, we analyse the scope of the LHC in
exploring this model through the discovery of a charged Higgs boson produced in
association with a W boson, with the former decaying into the lightest neutral
Higgs and a second W state, altogether yielding a b\bar b W^+W^- signature, of
which we exploit the W^+W^- semileptonic decays.