  Fix an elliptic curve $E_0$ without CM and a non-isotrivial elliptic scheme
over a smooth irreducible curve, both defined over the algebraic numbers.
Consider the union of all images of a fixed finite-rank subgroup (of arbitrary
rank) of $E_0^g$, also defined over the algebraic numbers, under all isogenies
between $E_0^g$ and some fiber of the $g$-th fibered power $\mathcal{A}$ of the
elliptic scheme, where $g$ is a fixed natural number. As a special case of a
slightly more general result, we characterize the subvarieties (of arbitrary
dimension) inside $\mathcal{A}$ that have potentially Zariski dense
intersection with this set. In the proof, we combine a generalized
Vojta-R\'emond inequality with the Pila-Zannier strategy.


  We remove the global quotient presentation input in the theory of windows in
derived categories of smooth Artin stacks of finite type. As an application, we
use existing results on flipping of strata for wall-crossing of Gieseker
semi-stable torsion-free sheaves of rank two on rational surfaces to produce
semi-orthogonal decompositions relating the different moduli stacks. The
complementary pieces of these semi-orthogonal decompositions are derived
categories of products of Hilbert schemes of points on the surface.


  Let $f$ and $g$ be two Hecke-Maass cusp forms of weight zero for
$SL_2(\mathbb Z)$ with Laplacian eigenvalues $\frac{1}{4}+u^2$ and
$\frac{1}{4}+v^2$, respectively. Then both have real Fourier coefficients say,
$\lambda_f(n)$ and $\lambda_g(n)$, and we may normalize $f$ and $g$ so that
$\lambda_f(1)=1=\lambda_g(1)$. In this article, we first prove that the
sequence $\{\lambda_f(n)\lambda_g(n)\}_{n \in \mathbb{N}}$ has infinitely many
sign changes. Then we derive a bound for the first negative coefficient for the
same sequence in terms of the Laplacian eigenvalues of $f$ and $g$.


  A modern elementary introduction to special relativity for advanced school
children or first-year university students, in Russian. I try to demonstrate
that relativity does not contradict common sense; on the contrary, it follows
from common sense logically. I discuss Minkowski space-time geometry in some
detail. Geometrical approach, with few simple formulas but many pictures, makes
results of the theory intuitively obvious.


  In arXiv:1212.2613, we associated a presheaf \Sigma^A with each unital
C*-algebra A. The spectral presheaf \Sigma^A generalises the Gelfand spectrum
of an abelian unital C*-algebra. In the present article, we consider
one-parameter groups of automorphisms of the spectral presheaf, in particular
those arising from one-parameter groups of inner automorphisms of the algebra.
We interpret the spectral presheaf as a (generalised) state space for a quantum
system and show how one can use flows on the spectral presheaf and on
associated structures to describe the time evolution of non-relativistic
quantum systems, both in the Schr\"odinger picture and the Heisenberg picture.


  We report a detailed calorimetric study on single crystals of the
5$d$-transition metal pnictide SrPt2As2 with a superconducting critical
temperature $T_c$ $\sim$5K. The peculiar field dependence of the electronic
specific heat coefficient $\gamma$ can be decomposed into two linear
components. Moreover, the temperature evolution of the electronic specific heat
below $T_c$ is best described by a two-gap model. These findings suggest that
two energy gaps are associated with the superconductivity. In parallel, we show
that the spin-lattice relaxation time $T_1$, through nuclear magnetic resonance
measurement, obeys the so-called Korringa relation well. This, along with the
$T^2$ dependence of resistivity at low temperatures, points to a Fermi liquid
ground state in this material.


  Neural networks have become a powerful tool in pattern recognition and part
of their success is due to generalization from using large datasets. However,
unlike other domains, time series classification datasets are often small. In
order to address this problem, we propose a novel time series data augmentation
called guided warping. While many data augmentation methods are based on random
transformations, guided warping exploits the element alignment properties of
Dynamic Time Warping (DTW) and shapeDTW, a high-level DTW method based on shape
descriptors, to deterministically warp sample patterns. In this way, the time
series are mixed by warping the features of a sample pattern to match the time
steps of a reference pattern. Furthermore, we introduce a discriminative
teacher in order to serve as a directed reference for the guided warping. We
evaluate the method on all 85 datasets in the 2015 UCR Time Series Archive with
a deep convolutional neural network (CNN) and a recurrent neural network (RNN).
The code with an easy to use implementation can be found at
https://github.com/uchidalab/time_series_augmentation .


  We have performed detailed multifractal analysis on the minutely volatility
of two indexes and 1139 stocks in the Chinese stock markets based on the
partition function approach. The partition function $\chi_q(s)$ scales as a
power law with respect to box size $s$. The scaling exponents $\tau(q)$ form a
nonlinear function of $q$. Statistical tests based on bootstrapping show that
the extracted multifractal nature is significant at the 1% significance level.
The individual securities can be well modeled by the $p$-model in turbulence
with $p = 0.40 \pm 0.02$. Based on the idea of ensemble averaging (including
quenched and annealed average), we treat each stock exchange as a whole and
confirm the existence of multifractal nature in the Chinese stock markets.


  Novel methods for quantitative, transient-state multiparametric imaging are
increasingly being demonstrated for assessment of disease and treatment
efficacy. Here, we build on these by assessing the most common Non-Cartesian
readout trajectories (2D/3D radials and spirals), demonstrating efficient
anti-aliasing with a k-space view-sharing technique, and proposing novel
methods for parameter inference with neural networks that incorporate the
estimation of proton density. Our results show good agreement with gold
standard and phantom references for all readout trajectories at 1.5T and 3T.
Parameters inferred with the neural network were within 6.58% difference from
the parameters inferred with a high-resolution dictionary. Concordance
correlation coefficients were above 0.92 and the normalized root mean squared
error ranged between 4.2% - 12.7% with respect to gold-standard phantom
references for T1 and T2. In vivo acquisitions demonstrate sub-millimetric
isotropic resolution in under five minutes with reconstruction and inference
times < 7 minutes. Our 3D quantitative transient-state imaging approach could
enable high-resolution multiparametric tissue quantification within clinically
acceptable acquisition and reconstruction times.


  Coherent interaction of a quantum system with environment usually induces
quantum decoherence. However, remarkably, in certain configurations the
coherent system-environment coupling can be simultaneously explored to engineer
a specific dark state of the environment that eliminates the decoherence. Here
we report on experimental demonstration of such protocol for suppression of
quantum decoherence by quantum decoherence itself. The protocol is based on
indirect control of the environment via quantum measurements on quantum probes
interacting with the environment prior to the system that should be protected.
No direct manipulation with the environment is required to suppress the
decoherence. In our proof-of-principle experiment, we demonstrate protection of
a single qubit coupled to another single qubit. We implement the required
quantum circuits with linear optics and single photons, which allows us to
maintain very high degree of control and flexibility in the experiment. Our
results clearly confirm the decoherence suppression achieved by the protocol
and pave the way to its application to other physical platforms.


  Mediation analysis is an increasingly popular statistical method for
explaining causal pathways to inform intervention. While methods have
increased, there is still a dearth of robust mediation methods for count
outcomes with excess zeroes. Current mediation methods addressing this issue
are computationally intensive, biased, or challenging to interpret. To overcome
these limitations, we propose a new mediation methodology for zero-inflated
count outcomes using the marginalized zero-inflated Poisson (MZIP) model and
the counterfactual approach to mediation. This novel work gives
population-average mediation effects whose variance can be estimated rapidly
via delta method. This methodology is extended to cases with exposure-mediator
interactions. We apply this novel methodology to explore if diabetes diagnosis
can explain BMI differences in healthcare utilization and test model
performance via simulations comparing the proposed MZIP method to existing
zero-inflated and Poisson methods. We find that our proposed method minimizes
bias and computation time compared to alternative approaches while allowing for
straight-forward interpretations.


  It is well-known that the spectral radius of a connected uniform hypergraph
is an eigenvalue of the hypergraph. However, its algebraic multiplicity remains
unknown. In this paper, we use the Poisson Formula and matching polynomials to
determine the algebraic multiplicity of the spectral radius of a uniform
hypertree.


  We show the existence of solution in the maximal $L_p-L_q$ regularity
framework to a class of symmetric parabolic problems on a uniformly $C^2$
domain in ${\mathcal R}$. Our approach consist in showing ${\mathcal R}$ -
boundedness of families of solution operators to corresponding resolvent
problems first in the whole space, then in half-space, perturbed half-space and
finally, using localization arguments, on the domain. Assuming additionally
boudedness of the domain we also show exponential decay of the solution. In
particular, our approach does not require assuming a priori the uniform
Lopatinskii - Shapiro condition.


  In this paper we try to answer the main question: what is a quantum black
hole?


  We exhibit the invariance of cycle affinities in finite state Markov
processes under various natural probabilistic constructions, for instance under
conditioning and under a new combinatorial construction that we call ``drag and
drop''. We show that cycle affinities have a natural probabilistic meaning
related to first passage non-equilibrium fluctuation relations that we
establish.


  For graphs $G$ and $H$, an $H$-coloring of $G$ is an adjacency preserving map
from the vertices of $G$ to the vertices of $H$. $H$-colorings generalize such
notions as independent sets and proper colorings in graphs. There has been much
recent research on the extremal question of finding the graph(s) among a fixed
family that maximize or minimize the number of $H$-colorings. In this paper, we
prove several results in this area.
  First, we find a class of graphs ${\mathcal H}$ with the property that for
each $H \in {\mathcal H}$, the $n$-vertex tree that minimizes the number of
$H$-colorings is the path $P_n$. We then present a new proof of a theorem of
Sidorenko, valid for large $n$, that for every $H$ the star $K_{1,n-1}$ is the
$n$-vertex tree that maximizes the number of $H$-colorings. Our proof uses a
stability technique which we also use to show that for any non-regular $H$ (and
certain regular $H$) the complete bipartite graph $K_{2,n-2}$ maximizes the
number of $H$-colorings of $n$-vertex $2$-connected graphs. Finally, we show
that the cycle $C_n$ maximizes the number of proper colorings of $n$-vertex
$2$-connected graphs.


  In order for a droplet to rebound rather than coalesce from a liquid surface,
there must be a layer of air persisting throughout the impact. This air layer
acts as a lubricant to the system and permits a pressure transfer between the
two liquid bodies. Through considering the bath, air, and drop regions of
fluid, we introduce a fully coupled reduced dynamic model of two-dimensional
droplets rebounding off liquid baths, which incorporates a evolving lubricating
air layer. Numerical solutions of the lubrication-mediated model are compared
to widely used direct numerical simulation (DNS) of the Navier-Stokes
equations. The reduced model captures rebounding dynamics well in the regime
where it is most relevant: for low speed impacts of small droplets, where
capillary forces are important. Numerically, the reduced model is efficient,
allowing for the computation of multiple rebounds and of long time dynamics of
droplets rebounding on a vibrating bath. Furthermore, the lubrication-mediated
model is able to provide detailed information within the air layer such as
pressure and lubrication-layer geometry, which is often omitted from reduced
models, and typically obtained by higher resolution but higher cost DNS
methods.


  Electrophysiological observation plays a major role in epilepsy evaluation.
However, human interpretation of brain signals is subjective and prone to
misdiagnosis. Automating this process, especially seizure detection relying on
scalp-based Electroencephalography (EEG) and intracranial EEG, has been the
focus of research over recent decades. Nevertheless, its numerous challenges
have inhibited a definitive solution. Inspired by recent advances in deep
learning, we propose a new classification approach for EEG time series based on
Recurrent Neural Networks (RNNs) via the use of Long-Short Term Memory (LSTM)
networks. The proposed deep network effectively learns and models
discriminative temporal patterns from EEG sequential data. Especially, the
features are automatically discovered from the raw EEG data without any
pre-processing step, eliminating humans from laborious feature design task. We
also show that, in the epilepsy scenario, simple architectures can achieve
competitive performance. Using simple architectures significantly benefits in
the practical scenario considering their low computation complexity and reduced
requirement for large training datasets. Using a public dataset, a multi-fold
cross-validation scheme exhibited an average validation accuracy of 95.54\% and
an average AUC of 0.9582 of the ROC curve among all sets defined in the
experiment. This work reinforces the benefits of deep learning to be further
attended in clinical applications and neuroscientific research.


  Using 980.6 $\rm fb^{-1}$ of data collected with the Belle detector operating
at the KEKB asymmetric-energy $e^+e^-$ collider, we present a measurement of
the branching fraction of the singly Cabibbo-suppressed decay $\Lambda_c^+ \to
p \omega$. A clear $\Lambda_c^+$ signal is observed for $\Lambda_c^+ \to p
\omega$ with a statistical significance of 9.1 standard deviations, and we
measure the ratio of branching fractions ${\cal B}(\Lambda_c^+ \to p
\omega)/{\cal B}(\Lambda_c^+ \to p K^- \pi^+) = (1.32 \pm 0.12 (\rm stat) \pm
0.10 (\rm syst))\times 10^{-2}$, from which we infer the branching fraction
${\cal B}(\Lambda_c^+ \to p \omega) = (8.27 \pm 0.75 (\rm stat) \pm 0.62 (\rm
syst) \pm 0.42 (\rm ref))\times 10^{-4}$. The first quoted uncertainty is
statistical, the second systematic, and the third from the reference mode
$\Lambda_c^+ \to p K^- \pi^+$.


  This paper introduces modal independence logic MIL, a modal logic that can
explicitly talk about independence among propositional variables. Formulas of
MIL are not evaluated in worlds but in sets of worlds, so called teams. In this
vein, MIL can be seen as a variant of V\"a\"an\"anen's modal dependence logic
MDL. We show that MIL embeds MDL and is strictly more expressive. However, on
singleton teams, MIL is shown to be not more expressive than usual modal logic,
but MIL is exponentially more succinct. Making use of a new form of
bisimulation, we extend these expressivity results to modal logics extended by
various generalized dependence atoms. We demonstrate the expressive power of
MIL by giving a specification of the anonymity requirement of the dining
cryptographers protocol in MIL. We also study complexity issues of MIL and show
that, though it is more expressive, its satisfiability and model checking
problem have the same complexity as for MDL.


  We study a possibly integrable model of abelian gauge fields on a
two-dimensional surface M, with volume form mu. It has the same phase space as
ideal hydrodynamics, a coadjoint orbit of the volume-preserving diffeomorphism
group of M, SDiff(M,mu). Gauge field Poisson brackets differ from the
Heisenberg algebra, but are reminiscent of Yang-Mills theory on a null surface.
Enstrophy invariants are Casimirs of the Poisson algebra of gauge invariant
observables. Some symplectic leaves of the Poisson manifold are identified. The
Hamiltonian is a magnetic energy, similar to that of electrodynamics, and
depends on a metric whose volume element is not a multiple of mu. The magnetic
field evolves by a quadratically non-linear `Euler' equation, which may also be
regarded as describing geodesic flow on SDiff(M,mu). Static solutions are
obtained. For uniform mu, an infinite sequence of local conserved charges
beginning with the hamiltonian are found. The charges are shown to be in
involution, suggesting integrability. Besides being a theory of a novel kind of
ideal flow, this is a toy-model for Yang-Mills theory and matrix field
theories, whose gauge-invariant phase space is conjectured to be a coadjoint
orbit of the diffeomorphism group of a non-commutative space.


  Estimating species relationship trees, so-called phylogenetic trees, from
aligned sequence data (such as DNA, RNA, or proteins) is one of the main aims
of evolutionary biology. However, tree reconstruction criteria like maximum
parsimony do not necessarily lead to unique trees and in some cases even fail
to recognize the \enquote{correct} tree (i.e., the tree on which the data was
generated).
  On the other hand, a recent study has shown that for an alignment containing
precisely those characters (sites) which require up to two substitutions on a
given tree, this tree will be the unique maximum parsimony tree.
  It is the aim of the present manuscript to generalize this recent result in
the following sense: We show that for a tree with $n$ leaves, as long as $k<
\frac{n}{8}+\frac{6}{5}-\frac{1}{10} \sqrt{\frac{5}{16} n^2+4}$ (or,
equivalently, $n>8 k-\frac{46}{5}+\frac{2}{5} \sqrt{40 k-31} $), the maximum
parsimony tree for the alignment containing all characters which require (up to
or precisely) $k$ substitutions on a given tree $T$ will be unique in the NNI
neighborhood of $T$ and it will coincide with $T$, too. In other words, within
the NNI neighborhood of $T$, $T$ is the unique most parsimonious tree for said
alignment. This partially answers a recently published conjecture
affirmatively.


  We explore the connections between Dickson's lemma and weak Ramsey theory. We
show that a weak version of the Paris--Harrington principle for pairs in $c$
colors and miniaturized Dickson's lemma for $c$-tuples are equivalent over
$\mathsf{RCA}_0^{\ast}$. Furthermore, we look at a cascade of consequences for
several variants of weak Ramsey's theorem.


  We examine the conditions for the existence of bounded dynamical phases for
finite PT-symmetric arrays of split-ring resonators. The dimer (N=2), trimer
(N=3) and pentamer (N=5) cases are solved in closed form, while for $N>5$
results were computed numerically for several gain/loss spatial distributions.
It is found that the parameter stability window decreases monotonically with
the size of the array.


  As floods are a major and growing source of risk in urban areas, there is a
necessity to improve flood risk management frameworks and civil protection
through planning interventions that modify surface flow pathways and introduce
storage. Despite the complexity of densely urbanised areas, modern flood models
can represent urban features and flow characteristics to help researchers,
local authorities, and insurance companies to develop and improve efficient
flood risk frameworks to achieve resilience in cities. A cost-benefit driven
source-receptor flood risk framework is developed in this study to identify (1)
locations contributing to surface flooding (sources), (2) buildings and
locations at high flood risk (receptors), (3) the cost-benefit nexus between
the source and the receptor, and finally (4) ways to mitigate flooding at the
receptor by adding Blue-Green Infrastructure (BGI) in critical locations. The
analysis is based on five steps to identify the source and the receptor in a
study area based on the flood exposure of buildings, damages arising from
flooding and available green spaces with the best potential to add sustainable
and resilient solutions to reduce flooding. The framework was developed using
the detailed hydrodynamic model CityCAT in a case study of the city centre of
Newcastle upon Tyne, UK. The novelty of this analysis is that firstly, multiple
storm magnitudes (i.e. small and large floods) are used combined with a method
to locate the areas and the buildings at flood risk and a prioritized set of
best places to add interventions upstream and downstream. Secondly, planning
decisions are informed by considering the benefit from reduced damages to
properties and the cost to construct resilient BGI options rather than a
restricted hydraulic analysis considering only flood depths and storages in
isolation from real-world economics.


  We theoretically and numerically investigate the ion-acoustic features of
collective Thomson scattering (CTS) in two-stream plasmas. When the electron
distribution functions of two (stationary and moving) components overlap with
each other at the phase velocities corresponding to the two resonant peaks of
the ion-acoustic feature, the theoretical spectrum shows asymmetry because the
rate of electron Landau damping is different for the two peaks. The results of
numerical simulations agree well with the theoretical spectra. We also
demonstrate the effect of a two-stream-type instability in the ion-acoustic
feature. The simulated spectrum in the presence of the instability shows an
asymmetry with the opposite trend to the overlapped case, which results from
the temporal change of the electron distribution function caused by the
instability. Our results show that two-stream plasmas have significant effects
on CTS spectra and that the waves resulting from instabilities can be observed
in the ion-acoustic feature.


  Geological analysis of 3D Digital Outcrop Models (DOMs) for reconstruction of
ancient habitable environments is a key aspect of the upcoming ESA ExoMars 2022
Rosalind Franklin Rover and the NASA 2020 Rover Perseverance missions in
seeking signs of past life on Mars. Geologists measure and interpret 3D DOMs,
create sedimentary logs and combine them in `correlation panels' to map the
extents of key geological horizons, and build a stratigraphic model to
understand their position in the ancient landscape. Currently, the creation of
correlation panels is completely manual and therefore time-consuming, and
inflexible. With InCorr we present a visualization solution that encompasses a
3D logging tool and an interactive data-driven correlation panel that evolves
with the stratigraphic analysis. For the creation of InCorr we closely
cooperated with leading planetary geologists in the form of a design study. We
verify our results by recreating an existing correlation analysis with InCorr
and validate our correlation panel against a manually created illustration.
Further, we conducted a user-study with a wider circle of geologists. Our
evaluation shows that InCorr efficiently supports the domain experts in
tackling their research questions and that it has the potential to
significantly impact how geologists work with digital outcrop representations
in general.


  Dynamic full-field optical coherence tomography (D-FFOCT) has recently
emerged as a label-free imaging tool, capable of resolving cell types and
organelles within 3D live samples, whilst monitoring their activity at tens of
milliseconds resolution. Here, a D-FFOCT module design is presented which can
be coupled to a commercial microscope with a stage top incubator, allowing
non-invasive label-free longitudinal imaging over periods of minutes to weeks
on the same sample. Long term volumetric imaging on human induced pluripotent
stem cell-derived retinal organoids is demonstrated, highlighting tissue and
cell organisation as well as cell shape, motility and division. Imaging on
retinal explants highlights single 3D cone and rod structures. An optimal
workflow for data acquisition, postprocessing and saving is demonstrated,
resulting in a time gain factor of 10 compared to prior state of the art.
Finally, a method to increase D-FFOCT signal-to-noise ratio is demonstrated,
allowing rapid organoid screening.


  Group field theories have recently been shown to admit a 1/N expansion
dominated by so-called `melonic graphs', dual to triangulated spheres. In this
note, we deepen the analysis of this melonic sector. We obtain a combinatorial
formula for the melonic amplitudes in terms of a graph polynomial related to a
higher dimensional generalization of the Kirchhoff tree-matrix theorem. Simple
bounds on these amplitudes show the existence of a phase transition driven by
melonic interaction processes. We restrict our study to the Boulatov-Ooguri
models, which describe topological BF theories and are the basis for the
construction of four dimensional models of quantum gravity.


  We have considered the propagation of gravitational waves (GW) in de Sitter
space time and how a non-zero value of the cosmological constant might affect
their detection in pulsar timing arrays (PTA). If {\Lambda} is different from
zero waves are non-linear in Friedmann-Robertson-Walker coordinates and
although the amount of non-linearity is very small it gives noticeable effects
for GW originating in extragalactic sources such as spiraling black hole
binaries. The results indicate that the timing residuals induced by
gravitational waves from such sources in PTA would show a peculiar angular
dependence with a marked enhancement around a particular value of the angle
subtended by the source and the pulsars, depending mainly on the actual value
of the cosmological constant and the distance to the source. The position of
the peak could represent a gauge of the value of \Lambda. The enhancement that
the new effect brings about could facilitate the first direct detection of
gravitational waves while representing a local measurement of \Lambda.


  Luminescent metal-organic frameworks (MOFs) offer a multifunctional platform
for creating non-invasive sensors and tuneable optoelectronics. However,
fluorochromic materials that are photophysically resilient and show high
sensitivity towards different physical and chemical stimuli are scarce. We
report a facile host-guest nanoconfinement strategy to construct a fluorescent
hybrid material with multiple sensing capabilities. We design and fabricate a
new Guest@MOF material: comprising a zeolitic MOF (ZIF-71) as a nanoporous host
for encapsulating rhodamine B (RhB dye) guest molecules, resulting in the
RhB@ZIF-71 system with mechanochromic, thermochromic, and solvatochromic
sensing response. The fluorochromic sensing properties stem from the
nanoconfinement effect that ZIF-71 imposes on RhB monomers, yielding the H- or
J-type aggregates with tuneable photophysical and photochemical properties. For
mechanochromism, the external pressure causes an emission red shift in a linear
fashion, switching RhB guests from H-type to J-type aggregates through a shear
deformation. For thermochromism, we demonstrate a linear scaling as a function
of temperature due to the spatial restriction imposed on J-type aggregates
incarcerated in ZIF-71 pores. Harnessing the solvatochromism of RhB@ZIF-71, we
identified three diverse groups of volatile organic compounds. The multimodal
sensing response pave the way to smart applications like photonic pressure
sensors, non-invasive thermometers, and ultrasensitive chemosensors.


  We present results from an ongoing calculation of the QCD equation of state
at finite baryon chemical potential $\mu_B$. We use the method of Taylor
expansions to circumvent the sign problem and calculate the expansion
coefficients to sixth order using HISQ fermions. We work at two lattice
spacings, namely $N_\tau=6$ and 8 and, though we do not take the continuum
limit, demonstrate that cutoff effects remain under control. We also use our
results to construct an equation of state along the freeze-out curve. Using our
sixth-order results as a cross-check, we demonstrate that our fourth-order
equation of state is suitable for the modeling of dense matter created in heavy
ion collisions with center-of-mass energies down to $s_{NN}^{1/2}\sim20$ GeV.


  Classifying the symmetries of the scalar sector in multi-Higgs-doublet models
is the main focus of this thesis. We have found certain symmetries that are
always broken in models with more than two doublets, which we name "frustrated
symmetries". In the attempt towards the classification of possible symmetries
in the scalar sector of the NHDM, we find that these symmetry groups are either
subgroups of the maximal torus, or certain finite Abelian groups which are not
subgroups of maximal tori. For the subgroups of the maximal torus, we present
an algorithmic strategy that gives the full list of possible realizable Abelian
symmetries for any given $N$. We extend this strategy to include Abelian
antiunitary symmetries (with generalized CP transformations) in NHDM. We also
show that multi-Higgs-doublet models can naturally accommodate scalar dark
matter candidates protected by the group $\Z_p$, since these groups are
realizable in NHDM. These models do not require any significant fine-tuning and
can lead to a variety of forms of microscopic dynamics among the dark matter
candidates.


  We classify nonrational Fano threefolds $X$ with terminal Gorenstein
singularities such that $\mathrm{\rk}\, \mathrm{\Pic}(X)=1$, $(-K_X)^3\ge 8$,
and $\mathrm{\rk}\, \mathrm{\Cl}(X)\le 2$.


  In this paper, we present some extensions of interpolation between the
arithmetic-geometric means inequality. Among other inequalities, it is shown
that if $A, B, X$ are $n\times n$ matrices, then \begin{align*}
\|AXB^*\|^2\leq\|f_1(A^*A)Xg_1(B^*B)\|\,\|f_2(A^*A)Xg_2(B^*B)\|, \end{align*}
where $f_1,f_2,g_1,g_2$ are non-negative continues functions such that
$f_1(t)f_2(t)=t$ and $g_1(t)g_2(t)=t\,\,(t\geq0)$. We also obtain the
inequality \begin{align*}
\left|\left|\left|AB^*\right|\right|\right|^2\nonumber&\leq
\left|\left|\left|p(A^*A)^{\frac{m}{p}}+
(1-p)(B^*B)^{\frac{s}{1-p}}\right|\right|\right|\,\left|\left|\left|(1-p)(A^*A)^{\frac{n}{1-p}}+
p(B^*B)^{\frac{t}{p}}\right|\right|\right|, \end{align*} in which $m,n,s,t$ are
real numbers such that $m+n=s+t=1$, $|||\cdot|||$ is an arbitrary unitarily
invariant norm and $p\in[0,1]$.


  We report the discovery of the gamma-ray burst GRB 000131 and its optical
afterglow. The optical identification was made with the VLT 84 hours after the
burst following a BATSE detection and an Inter Planetary Network localization.
GRB 000131 was a bright, long-duration GRB, with an apparent precursor signal
62 s prior to trigger. The afterglow was detected in ESO VLT, NTT, and DK1.54m
follow-up observations. Broad-band and spectroscopic observations of the
spectral energy distribution reveals a sharp break at optical wavelengths which
is interpreted as a Ly-alpha absorption edge at 6700 A. This places GRB 000131
at a redshift of 4.500 +/- 0.015. The inferred isotropic energy release in
gamma rays alone was approximately 10^54 erg (depending on the assumed
cosmology). The rapid power-law decay of the afterglow (index alpha=2.25,
similar to bursts with a prior break in the lightcurve), however, indicates
collimated outflow, which relaxes the energy requirements by a factor of < 200.
The afterglow of GRB 000131 is the first to be identified with an 8-m class
telescope.


  We show that the quantum discord and the measurement induced non-locality
(MiN) in a bipartite quantum state is invariant under the action of a local
quantum channel if and only if the channel is invertible. In particular, these
quantities are invariant under a local unitary channel.


  We define a "sutured topological quantum field theory", motivated by the
study of sutured Floer homology of product 3-manifolds, and contact elements.
We study a rich algebraic structure of suture elements in sutured TQFT, showing
that it corresponds to contact elements in sutured Floer homology. We use this
approach to make computations of contact elements in sutured Floer homology
over $\Z$ of sutured manifolds $(D^2 \times S^1, F \times S^1)$ where $F$ is
finite. This generalises previous results of the author over $\Z_2$
coefficients. Our approach elaborates upon the quantum field theoretic aspects
of sutured Floer homology, building a non-commutative Fock space, together with
a bilinear form deriving from a certain combinatorial partial order; we show
that the sutured TQFT of discs is isomorphic to this Fock space.


  In this work we present a hierarchy of generalized contextuality. It refines
the traditional binary distinction between contextual and noncontextual
theories, and facilitates their comparison based on how contextual they are.
Our approach focuses on the contextuality of prepare-and-measure scenarios,
described by general probabilistic theories (GPTs). To motivate the hierarchy,
we define it as the resource ordering of a novel resource theory of
GPT-contextuality. The building blocks of its free operations are classical
systems and GPT-embeddings. The latter are simulations of one GPT by another,
which preserve the operational equivalences and thus cannot generate
contextuality. Noncontextual theories can be recovered as least elements in the
hierarchy. We then define a new contextuality monotone, called classical
excess, given by the minimal error of embedding a GPT within an infinite
classical system. In addition, we show that the optimal success probability in
the parity oblivious multiplexing game also defines a monotone in our resource
theory. We end with a discussion of a potential interpretation of the non-free
operations of the resource theory of GPT-contextuality as expressing a kind of
information erasure.


  We examine the use of two superbeam neutrino oscillation experiments with
baselines $\lsim 1000$ km to resolve parameter degeneracies inherent in the
three-neutrino analysis of such experiments. We find that with appropriate
choices of neutrino energies and baselines two experiments with different
baselines can provide a much better determination of the neutrino mass ordering
than a single experiment alone. Two baselines are especially beneficial when
the mass scale for solar neutrino oscillations $\delta m^2_{\rm sol}$ is $\gsim
5\times10^{-5}$ eV$^2$. We also examine CP violation sensitivity and the
resolution of other parameter degeneracies. We find that the combined data of
superbeam experiments with baselines of 295 and 900 km can provide sensitivity
to both the neutrino mass ordering and CP violation for $\sin^22\theta_{13}$
down to 0.03 for $|\delta m^2_{\rm atm}| \simeq 3\times10^{-3}$ eV$^2$. It
would be highly advantageous to have a 10% determination of $|\delta m^2_{\rm
atm}|$ before the beam energies and baselines are finalized, although if
$|\delta m^2_{\rm atm}|$ is not that well known, the neutrino energies and
baselines can be chosen to give fairly good sensitivity for a range of $|\delta
m^2_{\rm atm}|$.


  Multiple object tracking faces several challenges that may be alleviated with
trajectory information. Knowing the posterior locations of an object helps
disambiguating and solving situations such as occlusions, re-identification,
and identity switching. In this work, we show that trajectory estimation can
become a key factor for tracking, and present TrajE, a trajectory estimator
based on recurrent mixture density networks, as a generic module that can be
added to existing object trackers. To provide several trajectory hypotheses,
our method uses beam search. Also, relying on the same estimated trajectory, we
propose to reconstruct a track after an occlusion occurs. We integrate TrajE
into two state of the art tracking algorithms, CenterTrack [63] and Tracktor
[3]. Their respective performances in the MOTChallenge 2017 test set are
boosted 6.3 and 0.3 points in MOTA score, and 1.8 and 3.1 in IDF1, setting a
new state of the art for the CenterTrack+TrajE configuration


  In this paper, we focus on preserving differential privacy (DP) in continual
learning (CL), in which we train ML models to learn a sequence of new tasks
while memorizing previous tasks. We first introduce a notion of continual
adjacent databases to bound the sensitivity of any data record participating in
the training process of CL. Based upon that, we develop a new DP-preserving
algorithm for CL with a data sampling strategy to quantify the privacy risk of
training data in the well-known Averaged Gradient Episodic Memory (A-GEM)
approach by applying a moments accountant. Our algorithm provides formal
guarantees of privacy for data records across tasks in CL. Preliminary
theoretical analysis and evaluations show that our mechanism tightens the
privacy loss while maintaining a promising model utility.


  We study the effect of long range particle exchange in bosonic arrangements.
We show that by combining the solution of the Heisenberg equations of motion
with matrix product state representation it is possible to investigate the
dynamics as well as the ground state while including particle exchange beyond
next-neighbours sites. These ideas are then applied to study the emergence of
entanglement as a result of scattering in boson chains. We propose a scheme to
generate highly entangled multi-particle states that exploits collision as a
powerful entangling mechanism.


  In this paper, we implement a low-latency rapid-prototyping platform for
signal processing based on software-defined radios (SDRs) and off-the-shelf PC
hardware. This platform allows to evaluate a wide variety of algorithms in
real-time environments, supporting new developments in the fields of classical,
AI-based, and hybrid signal processing. To accomplish this, the streaming
protocol of the used USRP X310 devices is implemented using the Data Plane
Development Kit (DPDK), which allows to handle network communication in
userspace only. This bypasses the kernel and thus avoids the latencies caused
by interrupt handling, scheduling, and context switches. It allows signal
processing to be performed on isolated processor cores that are protected from
interrupts to a great extent. To validate our approach, linear time-invariant
channel emulation has been implemented. For this, an analog-to-analog latency
of 31 microseconds was achieved, demonstrating that our PC-based approach
enables the implementation of rapid-prototyping systems with low latency.


  We extend results on analytic complex measures on the complex unit circle to
a non-commutative multivariate setting. Identifying continuous linear
functionals on a certain self-adjoint subspace of the Cuntz--Toeplitz
$C^*-$algebra, the free disk operator system, with non-commutative (NC)
analogues of complex measures, we refine a previously developed Lebesgue
decomposition for positive NC measures to establish an NC version of the
Frigyes and Marcel Riesz Theorem for `analytic' measures, i.e. complex measures
with vanishing positive moments. The proof relies on novel results on the order
properties of positive NC measures that we develop and extend from classical
measure theory.


  We use the H$41\alpha$ recombination line to create templates of the
millimeter free-free emission in the ALMA-IMF continuum maps, which allows to
separate it from dust emission. This method complements spectral-index
information and extrapolation from centimeter wavelength maps. We use the
derived maps to estimate the properties of up to 34 HII regions across the
ALMA-IMF protoclusters. The hydrogen ionizing-photon rate $Q_0$ and spectral
types follow the evolutionary trend proposed by Motte et al. The youngest
protoclusters lack detectable ionized gas, followed by protoclusters with
increasing numbers of OB stars. The total $Q_0$ increases from $\sim 10^{45}$
s$^{-1}$ to $> 10^{49}$ s$^{-1}$. We used the adjacent He$41\alpha$ line to
measure the relative number abundances of helium, finding values consistent
with the Galactic interstellar medium, although a few outliers are discussed. A
search for sites of maser amplification of the H$41\alpha$ line returned
negative results. We looked for possible correlations between the electron
densities ($n_e$), emission measures (EM), and $Q_0$ with HII region size $D$.
The latter are the better correlated, with $Q_0 \propto D^{2.49\pm0.18}$. This
favors interpretations where smaller ultracompact HII regions are not
necessarily the less dynamically evolved versions of larger ones, but rather
are ionized by less massive stars. Moderate correlations were found between
dynamical width $\Delta V_\mathrm{dyn}$ with $D$ and $Q_0$. $\Delta
V_\mathrm{dyn}$ increases from about one to two times the ionized-gas sound
speed. Finally, an outlier HII region south of W43-MM2 is discussed. We suggest
that this source could harbor an embedded stellar or disk wind.


  Directed acyclic graph (DAG) has been widely employed to represent
directional relationships among a set of collected nodes. Yet, the available
data in one single study is often limited for accurate DAG reconstruction,
whereas heterogeneous data may be collected from multiple relevant studies. It
remains an open question how to pool the heterogeneous data together for better
DAG structure reconstruction in the target study. In this paper, we first
introduce a novel set of structural similarity measures for DAG and then
present a transfer DAG learning framework by effectively leveraging information
from auxiliary DAGs of different levels of similarities. Our theoretical
analysis shows substantial improvement in terms of DAG reconstruction in the
target study, even when no auxiliary DAG is overall similar to the target DAG,
which is in sharp contrast to most existing transfer learning methods. The
advantage of the proposed transfer DAG learning is also supported by extensive
numerical experiments on both synthetic data and multi-site brain functional
connectivity network data.


  This paper deals with the the norm of the weighted Bergman projection
operator $P_\alpha:L^\infty\to \mathcal{B}$ where $\alpha>-1$ and $\mathcal{B}$
is the Bloch space of the unit ball of the complex space $\mathbf{C}^n$. We
consider two Bloch norms, the standard Bloch norm and invariant norm w.r.t.
automorphisms of the unit ball. Our work contains as a special case the main
result of the recent paper \cite{Perala}.


  Quantitative assessment of the uncertainties tainting the results of computer
simulations is nowadays a major topic of interest in both industrial and
scientific communities. One of the key issues in such studies is to get
information about the output when the numerical simulations are expensive to
run. This paper considers the problem of exploring the whole space of
variations of the computer model input variables in the context of a large
dimensional exploration space. Various properties of space filling designs are
justified: interpoint-distance, discrepancy, minimum spanning tree criteria. A
specific class of design, the optimized Latin Hypercube Sample, is considered.
Several optimization algorithms, coming from the literature, are studied in
terms of convergence speed, robustness to subprojection and space filling
properties of the resulting design. Some recommendations for building such
designs are given. Finally, another contribution of this paper is the deep
analysis of the space filling properties of the design 2D-subprojections.


  In the new era of time-domain surveys Type Ia supernovae are being caught
sooner after explosion, which has exposed significant variation in their early
light curves. Two driving factors for early time evolution are the distribution
of nickel in the ejecta and the presence of flux excesses of various causes. We
perform an analysis of the largest young SN Ia sample to date. We compare 115
SN Ia light curves from the Zwicky Transient Facility to the turtls model grid
containing light curves of Chandrasekhar-mass explosions with a range of nickel
masses, nickel distributions and explosion energies. We find that the majority
of our observed light curves are well reproduced by Chandrasekhar-mass
explosion models with a preference for highly extended nickel distributions. We
identify six SNe Ia with an early-time flux excess in our g- and r-band data
(four `blue' and two `red' flux excesses). We find an intrinsic rate of 18+/-11
per cent of early flux excesses in SNe Ia at z < 0.07, based on three detected
flux excesses out of 30 (10 per cent) observed SNe Ia with a simulated
efficiency of 57 per cent. This is comparable to rates of flux excesses in the
literature but also accounts for detection efficiencies. Two of these events
are mostly consistent with CSM interaction, while the other four have longer
lifetimes in agreement with companion interaction and nickel-clump models. We
find a higher frequency of flux excesses in 91T/99aa-like events (44+/-13 per
cent).


  One of the most significant challenges in statistical signal processing and
machine learning is how to obtain a generative model that can produce samples
of large-scale data distribution, such as images and speeches. Generative
Adversarial Network (GAN) is an effective method to address this problem. The
GANs provide an appropriate way to learn deep representations without
widespread use of labeled training data. This approach has attracted the
attention of many researchers in computer vision since it can generate a large
amount of data without precise modeling of the probability density function
(PDF). In GANs, the generative model is estimated via a competitive process
where the generator and discriminator networks are trained simultaneously. The
generator learns to generate plausible data, and the discriminator learns to
distinguish fake data created by the generator from real data samples. Given
the rapid growth of GANs over the last few years and their application in
various fields, it is necessary to investigate these networks accurately. In
this paper, after introducing the main concepts and the theory of GAN, two new
deep generative models are compared, the evaluation metrics utilized in the
literature and challenges of GANs are also explained. Moreover, the most
remarkable GAN architectures are categorized and discussed. Finally, the
essential applications in computer vision are examined.


  In Offline Model Learning for Planning and in Offline Reinforcement Learning,
the limited data set hinders the estimate of the Value function of the relative
Markov Decision Process (MDP). Consequently, the performance of the obtained
policy in the real world is bounded and possibly risky, especially when the
deployment of a wrong policy can lead to catastrophic consequences. For this
reason, several pathways are being followed with the scope of reducing the
model error (or the distributional shift between the learned model and the true
one) and, more broadly, obtaining risk-aware solutions with respect to model
uncertainty. But when it comes to the final application which baseline should a
practitioner choose? In an offline context where computational time is not an
issue and robustness is the priority we propose Exploitation vs Caution (EvC),
a paradigm that (1) elegantly incorporates model uncertainty abiding by the
Bayesian formalism, and (2) selects the policy that maximizes a risk-aware
objective over the Bayesian posterior between a fixed set of candidate policies
provided, for instance, by the current baselines. We validate EvC with
state-of-the-art approaches in different discrete, yet simple, environments
offering a fair variety of MDP classes. In the tested scenarios EvC manages to
select robust policies and hence stands out as a useful tool for practitioners
that aim to apply offline planning and reinforcement learning solvers in the
real world.


  We present contemporary infrared and optical spectroscopic observations of
the type IIn SN 1998S for the period between 3 and 127 days after discovery. In
the first week the spectra are characterised by prominent broad emission lines
with narrow peaks superimposed on a very blue continuum(T~24000K). In the
following two weeks broad, blueshifted absorption components appeared in the
spectra and the temperature dropped. By day 44, broad emission components in H
and He reappeared in the spectra. These persisted to 100-130d, becoming
increasingly asymmetric. We agree with Leonard et al. (2000) that the broad
emission lines indicate interaction between the ejecta and circumstellar
material (CSM) and deduce that progenitor of SN 1998S appears to have gone
through at least two phases of mass loss, giving rise to two CSM zones.
Examination of the spectra indicates that the inner zone extended to <90AU,
while the outer CSM extended from 185AU to over 1800AU. Analysis of high
resolution spectra shows that the outer CSM had a velocity of 40-50 km/s.
Assuming a constant velocity, we can infer that the outer CSM wind commenced
more than 170 years ago, and ceased about 20 years ago, while the inner CSM
wind may have commenced less than 9 years ago. During the era of the outer CSM
wind the outflow was high, >2x10^{-5}M_{\odot}/yr corresponding to a mass loss
of at least 0.003M_{\odot} and suggesting a massive progenitor. We also model
the CO emission observed in SN 1998S. We deduce a CO mass of ~10^{-3} M_{\odot}
moving at ~2200km/s, and infer a mixed metal/He core of ~4M_{\odot}, again
indicating a massive progenitor.


  We investigate the asymptotic symmetry group of a scalar field
minimally-coupled to an abelian gauge field using the Hamiltonian formulation.
This extends previous work by Henneaux and Troessaert on the pure
electromagnetic case. We deal with minimally coupled massive and massless
scalar fields and find that they behave differently insofar as the latter do
not allow for canonically implemented asymptotic boost symmetries. We also
consider the abelian Higgs model and show that its asymptotic canonical
symmetries reduce to the Poincar\'e group in an unproblematic fashion.


  In this proceeding, we give an overview of the latest progress in lattice-QCD
calculations of the Roper resonance, and Roper-nucleon electromagnetic and
axial transition form factors using dynamical gauge ensembles.


  We propose a way to recover Lorentz invariance of the perturbative S matrix
in the Discrete Light-Cone Quantization (DLCQ) in the continuum limit without
spoiling the trivial vacuum.


  The implementation of Goods and Services Tax(GST) is often attributed as the
main cause of the rising prices of goods and services. The main objective of
this study is to estimate the extent of GST implementation impact on the costs
of production, which in turn have implication on households living costs.


  Ultrasound (US) images usually contain identifying information outside the
ultrasound fan area and manual annotations placed by the sonographers during
exams. For those images to be exploitable in a Deep Learning framework, one
needs to first delineate the border of the fan which delimits the ultrasound
fan area and then remove other annotations inside. We propose a parametric
probabilistic approach for the first task. We make use of this method to
generate a training data set with segmentation masks of the region of interest
(ROI) and train a U-Net to perform the same task in a supervised way, thus
considerably reducing computational time of the method, one hundred and sixty
times faster. These images are then processed with existing inpainting methods
to remove annotations present inside the fan area. To the best of our
knowledge, this is the first parametric approach to quickly detect the fan in
an ultrasound image without any other information than the image itself.


  In the article, Besov-Orlicz regularity of sample paths of stochastic
processes that are represented by multiple integrals of order $n\in\mathbb{N}$
is treated. We give sufficient conditions for the considered processes to have
paths in the exponential Besov-Orlicz space
$$B_{\varPhi_{2/n},\infty}^\alpha(0,T)\qquad \mbox{with}\qquad
\varPhi_{2/n}(x)=\mathrm{e}^{x^{2/n}}-1.$$ These results provide an extension
of what is known for scalar Gaussian stochastic processes to stochastic
processes in an arbitrary finite Wiener chaos. As an application, the
Besov-Orlicz path regularity of fractionally filtered Hermite processes is
studied. But while the main focus is on the non-Gaussian case, some new path
properties are obtained even for fractional Brownian motions.


  Besides using the laser beam, it is very tempting to directly testify the
Bell inequality at high energy experiments where the spin correlation is
exactly what the original Bell inequality investigates. In this work, we follow
the proposal raised in literature and use the successive decays
$J/\psi\to\gamma\eta_c\to \Lambda\bar\Lambda\to p\pi^-\bar p\pi^+$ to testify
the Bell inequality. Our goal is twofold, namely, we first make a Monte-Carlo
simulation of the processes based on the quantum field theory (QFT). Since the
underlying theory is QFT, it implies that we pre-admit the validity of quantum
picture. Even though the QFT is true, we need to find how big the database
should be, so that we can clearly show deviations of the correlation from the
Bell inequality determined by the local hidden variable theory. There have been
some critiques on the proposed method, so in the second part, we suggest some
improvements which may help to remedy the ambiguities indicated by the
critiques. It may be realized at an updated facility of high energy physics,
such as BES III.


  A liquid scintillator (LS) is developed for the Taishan Antineutrino
Observatory (TAO), a ton-level neutrino detector to measure the reactor
antineutrino spectrum with sub-percent energy resolution by adopting Silicon
Photomultipliers (SiPMs) as photosensor. To reduce the dark noise of SiPMs to
an acceptable level, the LS has to work at -50 degree or lower. A customized
apparatus based on a charge-coupled device (CCD) is developed to study the
transparency of the liquid samples in a cryostat. We find that the water
content in LS results in transparency degradation at low temperature, which can
be cured by bubbling dry nitrogen to remove water. Adding 0.05% ethanol as
co-solvent cures the solubility decrease problem of the fluors PPO and bis-MSB
at low temperature. Finally, a Gadoliniumdoped liquid scintillator (GdLS), with
0.1% Gd by weight, 2 g/L PPO, 1 mg/L bis-MSB, and 0.05% ethanol by weight in
the solvent LAB, shows good transparency at -50 degree and also good light
yield.


  We construct a genus zero PALF structure on each of plugs introduced by
Akbulut and Yasui and describe the monodromy as a positive factorization in the
mapping class group of a fiber. We also examine the monodromies of PALFs on a
certain pair of compact Stein surfaces such that one is obtained by applying a
plug twist to the other.


  Modern autonomous systems often rely on LiDAR scanners, in particular for
autonomous driving scenarios. In this context, reliable scene understanding is
indispensable. Current learning-based methods typically try to achieve maximum
performance for this task, while neglecting a proper estimation of the
associated uncertainties. In this work, we introduce a novel approach for
solving the task of uncertainty-aware panoptic segmentation using LiDAR point
clouds. Our proposed EvLPSNet network is the first to solve this task
efficiently in a sampling-free manner. It aims to predict per-point semantic
and instance segmentations, together with per-point uncertainty estimates.
Moreover, it incorporates methods for improving the performance by employing
the predicted uncertainties. We provide several strong baselines combining
state-of-the-art panoptic segmentation networks with sampling-free uncertainty
estimation techniques. Extensive evaluations show that we achieve the best
performance on uncertainty-aware panoptic segmentation quality and calibration
compared to these baselines. We make our code available at:
https://github.com/kshitij3112/EvLPSNet


  We give improvements of estimates of invariant metrics in the normal
direction on strictly pseudoconvex domains. Specifically we will give the
second term in the expansion of the metrics. This depends on an improved
localisation result and estimates in the one variable case. Finally we will
give a new characterisation of the unit disk in $\mathbb C$ in terms of the
asymptotic behaviour of quotients of invariant metrics.


  We present here a recopilation of recent results about the possibility of
detecting solar electron antineutrinos produced by solar core and convective
magnetic fields. These antineutrinos are predicted by spin-flavor oscillations
at a significant rate even if this mechanism is not the leading solution to the
SNP. Using the recent Kamland results and assuming a concrete model for
antineutrino production by spin-flavor precession in the convective zone based
on chaotic magnetic fields,we obtain bounds on the flux of solar antineutrinos,
on the average conversion neutrino-antineutrino probability and on intrinsic
neutrino magnetic moment. In the most conservative case, $\mu\lsim 2.5\times
10^{-11} \mu_B$ (95% CL). When studying the effects of a core magnetic field,
we find in the weak limit a scaling of the antineutrino probability with
respect to the magnetic field profile in the sense that the same probability
function can be reproduced by any profile with a suitable peak field value. In
this way the solar electron antineutrino spectrum can be unambiguosly
predicted. We use this scaling and the negative results indicated by the
KamLAND experiment to obtain upper bounds on the solar electron antineutrino
flux. We find that, for a wide family of magnetic field profiles in the sun
interior, the antineutrino appearance probability is largely determined by the
magnetic field intensity but not by its shape. Explicit limits on neutrino
transition moments are also obtained consistent with the convective case. These
limits are therefore largerly independent of the detailed structure of the
magnetic field in the solar interior.


  Coordination of distributed agents is required for problems arising in many
areas, including multi-robot systems, networking and e-commerce. As a formal
framework for such problems, we use the decentralized partially observable
Markov decision process (DEC-POMDP). Though much work has been done on optimal
dynamic programming algorithms for the single-agent version of the problem,
optimal algorithms for the multiagent case have been elusive. The main
contribution of this paper is an optimal policy iteration algorithm for solving
DEC-POMDPs. The algorithm uses stochastic finite-state controllers to represent
policies. The solution can include a correlation device, which allows agents to
correlate their actions without communicating. This approach alternates between
expanding the controller and performing value-preserving transformations, which
modify the controller without sacrificing value. We present two efficient
value-preserving transformations: one can reduce the size of the controller and
the other can improve its value while keeping the size fixed. Empirical results
demonstrate the usefulness of value-preserving transformations in increasing
value while keeping controller size to a minimum. To broaden the applicability
of the approach, we also present a heuristic version of the policy iteration
algorithm, which sacrifices convergence to optimality. This algorithm further
reduces the size of the controllers at each step by assuming that probability
distributions over the other agents actions are known. While this assumption
may not hold in general, it helps produce higher quality solutions in our test
problems.


  We study a security threat to reinforcement learning where an attacker
poisons the learning environment to force the agent into executing a target
policy chosen by the attacker. As a victim, we consider RL agents whose
objective is to find a policy that maximizes reward in infinite-horizon problem
settings. The attacker can manipulate the rewards and the transition dynamics
in the learning environment at training-time, and is interested in doing so in
a stealthy manner. We propose an optimization framework for finding an optimal
stealthy attack for different measures of attack cost. We provide lower/upper
bounds on the attack cost, and instantiate our attacks in two settings: (i) an
offline setting where the agent is doing planning in the poisoned environment,
and (ii) an online setting where the agent is learning a policy with poisoned
feedback. Our results show that the attacker can easily succeed in teaching any
target policy to the victim under mild conditions and highlight a significant
security threat to reinforcement learning agents in practice.


  A fundamental task for the heterotic superstring theory is the determination
of the effective action describing the physics of massless string excitations
at low energies. This is necessary for the phenomenological applications of
string theory, in particular for the unification of gauge interactions and for
the gaugino condensation mechanism of supersymmetry breaking. In this talk, I
report on the recent progress in computing the effective supergravity action
from superstring scattering amplitudes, at the tree level and beyond. I discuss
moduli-dependent string loop corrections to gauge and Yukawa couplings. Talk
presented at the 7th Meeting of the American Physical Society Division of
Particles and Fields, Fermilab, November 10-14, 1992


  In resent years ANN is widely reported for modeling in different areas of
science including electro chemistry. This includes modeling of different
technological batteries such as lead acid battery, Nickel cadmium batteries
etc. Lithium ion batteries are advance battery technology which satisfy most of
the space mission requirements. Low earth orbit (LEO)space craft batteries
undergo large number of charge discharge cycles (about 25000 cycles)compared to
other ground level or space applications. This study is indented to develop ANN
model for about 25000 cycles, cycled under various temperature, Depth Of
Discharge (DOD) settings with constant charge voltage limit to predict the
retained capacity and End of Discharge Voltage (EODV). To extract firm
conclusion and distinguish the capability of ANN method, the predicted values
are compared with experimental result by statistical method and Bland Altman
plot.


  Gravitational wave science is a pioneering field with rapidly evolving data
analysis methodology currently assimilating and inventing deep learning
techniques. The bulk of the sophisticated flagship searches of the field rely
on the time-tested matched filtering principle within their core. In this
paper, we make a key observation on the relationship between the emerging deep
learning and the traditional techniques: matched filtering is formally
equivalent to a particular neural network. This means that a neural network can
be constructed analytically to exactly implement matched filtering, and can be
further trained on data or boosted with additional complexity for improved
performance. Moreover, we show that the proposed neural network architecture
can outperform matched filtering, both with or without knowledge of a prior on
the parameter distribution. When a prior is given, the proposed neural network
can approach the statistically optimal performance. We also propose and
investigate two different neural network architectures MNet-Shallow and
MNet-Deep, both of which implement matched filtering at initialization and can
be trained on data. MNet-Shallow has simpler structure, while MNet-Deep is more
flexible and can deal with a wider range of distributions. Our theoretical
findings are corroborated by experiments using real LIGO data and synthetic
injections, where our proposed methods significantly outperform matched
filtering at false positive rates above $5\times 10^{-3}\%$. The fundamental
equivalence between matched filtering and neural networks allows us to define a
"complexity standard candle" to characterize the relative complexity of the
different approaches to gravitational wave signal searches in a common
framework. Finally, our results suggest new perspectives on the role of deep
learning in gravitational wave detection.


  We have observed the Galactic Center 50km/s molecular cloud (50MC) with ALMA
to search for filamentary structures. In the CS J=2-1 emission line channel
maps, we succeeded in identifying 27 molecular cloud filaments using the
DisPerSE algorithm. This is the first attempt of "filament-finding" in the
Galactic Center Region. These molecular cloud filaments strongly suggest that
the molecular cloud filaments are also ubiquitous in the molecular clouds of
the Galactic Center Region.


  A Bayesian approach is used to estimate the covariance matrix of Gaussian
data. Ideas from Gaussian graphical models and model selection are used to
construct a prior for the covariance matrix that is a mixture over all
decomposable graphs. For this prior the probability of each graph size is
specified by the user and graphs of equal size are assigned equal probability.
Most previous approaches assume that all graphs are equally probable. We show
empirically that the prior that assigns equal probability over graph sizes
outperforms the prior that assigns equal probability over all graphs, both in
identifying the correct decomposable graph and in more efficiently estimating
the covariance matrix.


  We study a percolation model with restrictions on the opening of sites on the
square lattice. In this model, each site $s \in \mathbb{Z}^{2}$ starts closed
and an attempt to open it occurs at time $t=t_s$, where $(t_s)_{s \in
\mathbb{Z}^2}$ is a sequence of independent random variables uniformly
distributed on the interval $[0,1]$. The site will open if the volume
difference between the two largest clusters adjacent to it is greater than or
equal to a constant $r$ or if it has at most one adjacent cluster. Through
numerical analysis, we determine the critical threshold $t_c(r)$ for various
values of $r$, verifying that $t_c(r)$ is non-decreasing in $r$ and that there
exists a critical value $r_c=5$ beyond which percolation does not occur.
Additionally, when $t=1$ and $1 \leq r \leq 9$, we estimate the averages of the
density of open sites, the number of distinct cluster volumes, and the volume
of the largest cluster.


  Absolute proper motions for $\sim$ 7.7 million objects were derived based on
data from the South Galactic Cap u-band Sky Survey (SCUSS) and astrometric data
derived from uncompressed Digitized Sky Surveys that the Space Telescope
Science Institute (STScI) created from the Palomar and UK Schmidt survey
plates. We put a great deal of effort into correcting the position-,
magnitude-, and color-dependent systematic errors in the derived absolute
proper motions. The spectroscopically confirmed quasars were used to test the
internal systematic and random error of the proper motions. The systematic
errors of the overall proper motions in the SCUSS catalog are estimated as
-0.08 and -0.06 mas/yr for {\mu}{\alpha} cos {\delta} and {\mu}{\delta},
respectively. The random errors of the proper motions in the SCUSS catalog are
estimated independently as 4.2 and 4.4 mas/yr for {\mu}{\alpha} cos {\delta}
and {\mu}{\delta}. There are no obvious position-, magnitude-, and
color-dependent systematic errors of the SCUSS proper motions. The random error
of the proper motions goes up with the magnitude from about 3 mas/yr at u <
18.0 mag to about 7 mas/yr at u = 22.0 mag. The proper motions of stars in
SCUSS catalog are compared with those in the SDSS catalog, and they are highly
consistent.


  We measure the spatial distribution of all components of the color fields
surrounding a static quark antiquark pair in QCD with (2+1) HISQ flavors. We
isolate the nonperturbative component of the longitudinal chromoelectric color
field responsible for the linear term in the confining potential.


  We propose a new approach in Lagrangian formalism for studying the fluid
dynamics on noncommutative space. Starting with the Poisson bracket for single
particle, a map from canonical Lagrangian variables to Eulerian variables is
constructed for taking into account of the noncommutative effects. The
advantage of this approach is that the kinematic and potential energies in the
Lagrangian formalism continuously change in the infinite limit to the ones in
Eulerian formalism, and hence make sure that both the kinematical and potential
energies are taken into account correctly. Furthermore, in our approach, the
equations of motion of the mass density and current density are naturally
expressed into conservative form. Based on this approach, the noncommutative
Poisson bracket is introduced, and the noncommutative algebra among Eulerian
variables, as well as the noncommutative corrections on the equations of motion
are obtained. We find that the noncommutative corrections generally depend on
the derivatives of potential under consideration. Furthermore, we find that the
noncommutative algebra does modify the usual Friedmann equation, and the
noncommutative corrections measure the symmetry properties of the density
function $\rho(\vec{z})$ under rotation around the direction $\vec{\theta}$.
This characterization results in vanishing corrections for spherically
symmetric mass density distribution and potential.


  We present $\mathcal{N}=1$ supergravity models of mutated hilltop inflation
(MHI) for both large and small field sectors. Models with canonical kinetic
terms are developed based on a shift symmetric K\"ahler potential in inflaton
superfield, and with a superpotential linear in Goldstino superfield. We also
construct models with non-canonical kinetic terms for MHI by generalizing the
shift symmetry. We found that a good fraction of the models can address the
entire branch of MHI in a single framework.


  We present detailed analysis of the non-Poissonian population of excitons
produced by MEG effect in quantum dots on the base of statistic theory of MEG
and synergetic approach for chemical reactions. From the analysis we can
conclude that a non-Poissonian distribution of exciton population is evidence
of non-linear and non-equilibrium character of the process of multiple
generation of excitons in quantum dots at a single photon absorption


  We study soliton solutions to a generalized Korteweg - de Vries (KdV)
equation with a saturated nonlinearity, following the line of inquiry of the
authors for the nonlinear Schr\"odinger equation (NLS). KdV with such a
nonlinearity is known to possess a minimal-mass soliton. We consider a small
perturbation of a minimal-mass soliton and identify a system of ODEs, which
models the behavior of the perturbation for short times. This connects nicely
to a work of Comech, Cuccagna & Pelinovsky (2007). These ODEs form a simple
dynamical system with a single unstable hyperbolic fixed point with two
possible dynamical outcomes. A particular feature of the dynamics are that they
are non-oscillatory. This distinguishes the KdV problem from the analogous NLS
one.


  The van der Waals ferromagnet Fe5GeTe2 has a Curie temperature TC of about
270 K, which can be raised above room temperature by tuning the Fe deficiency
content. To achieve insights into its ferromagnetic exchange, we have studied
the critical behavior by measuring the magnetization in bulk Fe5GeTe2 crystal
around the ferromagnetic to paramagnetic phase transition. The analysis of the
magnetization by employing various techniques including the modified Arrott
plot, Kouvel-Fisher plot and critical isotherm analysis achieved a set of
reliable critical exponents with TC = 273.7 K, beta = 0.3457, gamma = 1.40617,
and delta = 5.021, suggesting a three-dimensional magnetic exchange with the
distance decaying as J(r) ~ (r)$^-4.916, which is close to that of a
three-dimensional Heisenberg model with long-range magnetic coupling.


  Observations with the Space Telescope Imaging Spectrograph aboard the Hubble
Space Telescope have been used to constrain the atomic sulfur column density in
Io's atmosphere. The SI 1479 dipole allowed and forbidden transition multiplets
have been resolved for the first time at Io, enabling the study of both
optically thick and thin transitions from a single atomic species. The allowed
transitions contribute 62 +/- 8% and the forbidden transitions 38 +/- 8%, on
average, to the total signal of the SI 1479 multiplets. Using the optically
thick and thin transitions of SI 1479 observed near the limbs of Io, we derive
a tangential atmospheric sulfur column abundance of 3.6*10^12 cm^-2 < N_s <
1.7*10^13 cm^-2, which is independent of electron temperature and density. A
low density SO_2 atmosphere, N_so2 ~ 5-10*10^15 cm^-2, consistent with that
inferred from other recent observations, is most consistent with these bounds.


  I present the theory and analysis behind the experiment by Fomalont and
Kopeikin involving Jupiter and quasar J0842+1845 that purported to measure the
speed of gravity. The computation of the v_J/c correction to the gravitational
time delay difference relevant to the experiment is derived, where v_J is the
speed of Jupiter as measured from Earth. Since the v_J/c corrections are too
small to have been measured in the Jupiter/quasar experiment, it is impossible
that the speed of gravity was extracted from the data, and I explain what when
wrong with the data analysis. Finally, mistakes are shown in papers by Fomalont
and Kopeikin intended to rebut my work and the work of others.


  We consider a class of models predicting new heavy neutral fermionic states,
whose mixing with the light neutrinos can be naturally significant and produce
observable effects below the threshold for their production. We update the
indirect limits on the flavour non-diagonal mixing parameters that can be
derived from unitarity, and show that significant rates are in general expected
for one-loop-induced rare processes due to the exchange of virtual heavy
neutrinos, involving the violation of the muon and electron lepton numbers. In
particular, the amplitudes for $\mu$--$e$ conversion in nuclei and for $\mu\to
ee^+e^-$ show a non-decoupling quadratic dependence on the heavy neutrino mass
$M$, while $\mu\to e\gamma$ is almost independent of the heavy scale above the
electroweak scale. These three processes are then used to set stringent
constraints on the flavour-violating mixing angles. In all the cases
considered, we point out explicitly that the non-decoupling behaviour is
strictly related to the spontaneous breaking of the SU(2) symmetry.


  We investigate the existence of multiple bound states of prescribed mass for
the nonlinear Schr\"odinger equation on a noncompact metric graph. The main
feature is that the nonlinearity is localized only in a compact part of the
graph. Our main result states that for every integer k, the equation possesses
at least k solutions of prescribed mass, provided that the mass is large
enough. These solutions arise as constrained critical points of the NLS energy
functional. Estimates for the energy of the solutions are also established.


  We propose to use the properties of the Lie algebra of the angular momentum
to build symplectic integrators dedicated to the Hamiltonian of the free rigid
body. By introducing a dependence of the coefficients of integrators on the
moments of inertia of the integrated body, we can construct symplectic
dedicated integrators with fewer stages than in the general case for a
splitting in three parts of the Hamiltonian. We perform numerical tests to
compare the developed dedicated 4th-order integrators to the existing reference
integrators for the water molecule. We also estimate analytically the accuracy
of these new integrators for the set of the rigid bodies and conclude that they
are more accurate than the existing ones only for very asymmetric bodies.


  We discuss milestones on the tour towards DPLL(MAPF), a multi-agent path
finding (MAPF) solver fully integrated with the Davis-Putnam-Logemann-Loveland
(DPLL) propositional satisfiability testing algorithm through satisfiability
modulo theories (SMT). The task in MAPF is to navigate agents in an undirected
graph in a non-colliding way so that each agent eventually reaches its unique
goal vertex. At most one agent can reside in a vertex at a time. Agents can
move instantaneously by traversing edges provided the movement does not result
in a collision. Recently attempts to solve MAPF optimally w.r.t. the
sum-of-costs or the makespan based on the reduction of MAPF to propositional
satisfiability (SAT) have appeared. The most successful methods rely on
building the propositional encoding for the given MAPF instance lazily by a
process inspired in the SMT paradigm. The integration of satisfiability testing
by the SAT solver and the high-level construction of the encoding is however
relatively loose in existing methods. Therefore the ultimate goal of research
in this direction is to build the DPLL(MAPF) algorithm, a MAPF solver where the
construction of the encoding is fully integrated with the underlying SAT
solver. We discuss the current state-of-the-art in MAPF solving and what steps
need to be done to get DPLL(MAPF). The advantages of DPLL(MAPF) in terms of its
potential to be alternatively parametrized with MAPF$^R$, a theory of
continuous MAPF with geometric agents, are also discussed.


  A machine learning (ML) method aided by domain knowledge was proposed to
predict saturated magnetization (Bs) and critical diameter (Dmax) of soft
magnetic metallic glass (MGs). Two datasets were established based on published
experimental works about soft magnetic MGs. A general feature space was
proposed and proved to be adaptive for ML model training for different
prediction tasks. It was found that the predictive performance of ML models was
better than traditional physical knowledge-based estimation methods. In
addition, domain knowledge aided feature selection can greatly reduce the
number of features without significantly reducing the prediction accuracy.
Finally, binary classification of the critical size of soft magnetic metallic
glass was studied.


  Hydrogen bonding is a phenomenon of paramount importance in room-temperature
ionic liquids. The presence or absence of the hydrogen bond drastically
alternates self-diffusion, shear viscosity, phase transition points, and other
key properties of a pure substance. For certain applications, the presence of
cation-anion hydrogen bonding is undesirable. In the present paper, we
investigate perspectives of removing the hydrogen...fluorine interionic
attraction in the imidazolium borates, the strongest non-covalent interaction
in this type of system. Chemical modification of the tetrafluoroborate anion
not only eliminates hydrogen bonding but also changes the most
thermodynamically preferable orientation of the cation in the vicinity of the
anion. Although the most acidic hydrogen atom of the imidazole ring remains the
paramount electrophilic center of the cation, it does not engender a strong
electrostatically driven coordination pattern with the properly modified
anions. The reported new physical insights help compose more robust ionic
liquids and tune solvation properties of the imidazolium-based RTILs.


  We formulate a spectral graph-partitioning algorithm that uses the two
leading eigenvectors of the matrix corresponding to a selected quality function
to split a network into three communities in a single step. In so doing, we
extend the recursive bipartitioning methods developed by Newman [Proc. Nat.
Acad. Sci. 103, 8577 (2006); Phys. Rev. E 74, 036104 (2006)] to allow one to
consider the best available two-way and three-way divisions at each recursive
step. We illustrate the method using simple "bucket brigade" examples and then
apply the algorithm to examine the community structures of the coauthorship
graph of network scientists and of U. S. Congressional networks inferred from
roll-call voting similarities.


  This work focuses on the analysis of the Casimir effect for pistons subject
to transmittal boundary conditions. In particular we consider, as piston
configuration, a direct product manifold of the type $I\times N$ where $I$ is a
closed interval of the real line and $N$ is a smooth compact Riemannian
manifold. By utilizing the spectral zeta function regularization technique, we
compute the Casimir energy of the system and the Casimir force acting on the
piston. Explicit results for the force are provided when the manifold $N$ is a
$d$-dimensional ball.


  This paper has been withdrawn by the authors due to the crucial wrong
assumption following Eq. 9.


  The systematics of Statistical Model parameters extracted from heavy-ion
collisions at lower energies are exploited to extrapolate in the LHC regime.
Predictions of various particle ratios are presented and particle production in
central Pb-Pb collisions at LHC is discussed in the context of the Statistical
Model. The sensitivity of several ratios on the temperature and the baryon
chemical potential is studied in detail, and some of them, which are
particularly appropriate to determine the chemical freeze-out point
experimentally, are indicated. The impact of feed-down contributions from
resonances, especially to light hadrons, is illustrated.


  We give a new and complete proof of Hamilton's injectivity radius estimate
for sequences with bounded and almost nonnegative curvature operators,
unbounded diameters, and bump-like origins. Such sequences arise in particular
from dilations about a singularity of the Ricci flow on a 3-manifold.


  Fairness aware data mining (FADM) aims to prevent algorithms from
discriminating against protected groups. The literature has come to an impasse
as to what constitutes explainable variability as opposed to discrimination.
This distinction hinges on a rigorous understanding of the role of proxy
variables; i.e., those variables which are associated both the protected
feature and the outcome of interest. We demonstrate that fairness is achieved
by ensuring impartiality with respect to sensitive characteristics and provide
a framework for impartiality by accounting for different perspectives on the
data generating process. In particular, fairness can only be precisely defined
in a full-data scenario in which all covariates are observed. We then analyze
how these models may be conservatively estimated via regression in partial-data
settings. Decomposing the regression estimates provides insights into
previously unexplored distinctions between explainable variability and
discrimination that illuminate the use of proxy variables in fairness aware
data mining.


  In this paper we study the problem of recovering a low-rank matrix from
linear measurements. Our algorithm, which we call Procrustes Flow, starts from
an initial estimate obtained by a thresholding scheme followed by gradient
descent on a non-convex objective. We show that as long as the measurements
obey a standard restricted isometry property, our algorithm converges to the
unknown matrix at a geometric rate. In the case of Gaussian measurements, such
convergence occurs for a $n_1 \times n_2$ matrix of rank $r$ when the number of
measurements exceeds a constant times $(n_1+n_2)r$.


  Background: Novel coronavirus disease has spread rapidly worldwide. As recent
radiological literatures on Covid-19 related pneumonia is primarily focused on
CT findings, the American College of Radiology (ACR) recommends using portable
chest X-radiograph (CXR). A tool to assist for detection and monitoring of
Covid-19 cases from CXR is highly required. Purpose: To develop a fully
automatic framework to detect Covid-19 related pneumonia using CXR images and
evaluate its performance. Materials and Methods: In this study, a novel deep
learning model, named CovIDNet (Covid-19 Indonesia Neural-Network), was
developed to extract visual features from chest x-ray images for the detection
of Covid-19 related pneumonia. The model was trained and validated by chest
x-rays datasets collected from several open source provided by GitHub and
Kaggle. Results and Discussion: In the validation stage using open-source data,
the accuracy to recognize Covid-19 and others classes reaches 98.44%, that is,
100% Covid-19 precision and 97% others precision. Discussion: The use of the
model to classify Covid-19 and other pathologies might slightly decrease the
accuracy. Although SoftMax was used to handle classification bias, this
indicates the benefit of additional training upon the introduction of new set
of data. Conclusion: The model has been tested and get 98.4% accuracy for open
source datasets, the sensitivity and specificity are 100% and 96.97%,
respectively.


  Clustering bifurcations are investigated by considering models of globally
coupled map lattices. Typical classes of clustering bifurcations are revealed.
The clustering bifurcation thresholds of the coupled system are closely related
to the bifurcation structures of single map. In particular, cluster-doubling
bifurcation induced period-doubling bifurcations and clustering induced chaos
are found. At the onset of multiple-cluster states,
equal-site-occupation-partition, and consequently, equal-phase-shift states
(the so-called antiphase states reported previously, Phys. Rev. Lett. 65, 1749
(1990)) are always identified numerically.


  We present the current and planned beam physics research program and
accelerator education program at Advanced Superconducting Test Accelerator
(ASTA) at Fermilab.


  Fluctuation Theorems are central in stochastic thermodynamics, as they allow
for quantifying the irreversibility of single trajectories. Although they have
been experimentally checked in the classical regime, a practical demonstration
in the framework of quantum open systems is still to come. Here we propose a
realistic platform to probe fluctuation theorems in the quantum regime. It is
based on an effective two-level system coupled to an engineered reservoir, that
enables the detection of the photons emitted and absorbed by the system. When
the system is coherently driven, a measurable quantum component in the entropy
production is evidenced. We quantify the error due to photon detection
inefficiency, and show that the missing information can be efficiently
corrected, based solely on the detected events. Our findings provide new
insights into how the quantum character of a physical system impacts its
thermodynamic evolution.


  The reconstruction of the unknown acoustic source is studied using the noisy
multiple frequency data on a remote closed surface. Assume that the unknown
source is coded in a spatial dependent piecewise constant function, whose
support set is the target to be determined. In this setting, the unknown source
can be formalized by a level set function. The function is explored with
Bayesian level set approach. To reduce the infinite dimensional problem to
finite dimension, we parameterize the level set function by the radial basis
expansion. The well-posedness of the posterior distribution is proven. The
posterior samples are generated according to the Metropolis-Hastings algorithm
and the sample mean is used to approximate the unknown. Several shapes are
tested to verify the effectiveness of the proposed algorithm. These numerical
results show that the proposed algorithm is feasible and competitive with the
Mat\'ern random field for the acoustic source problem.


  In a recent letter, Buhot proposes that entropy driven phase-separation in
hard-core binary mixtures is directly related to a bond-percolation transition.
However, at least for binary hard-sphere mixtures, calculations based on an
accurate approximation to g_{ll}(r) demonstrate that n_b varies widely along
the phase-boundaries calculated directly by simulations, implying that
bond-percolation is unrelated to the phase-separation in these systems.


  \This paper presents a novel decentralized autonomous organization (DAO)
model leveraging non-fungible tokens (NFTs) for advanced access control and
privacy-preserving interactions within a Punishment not Reward (PnR) blockchain
framework. The proposed model introduces a dual NFT architecture: Membership
NFTs (\(NFT_{auth}\)) for authentication and access control, and Interaction
NFTs (\(NFT_{priv}\)) for enabling private, encrypted interactions among
participants. Governance is enforced through smart contracts that manage
reputation and administer punitive measures, such as conditional identity
disclosure. By prioritizing privacy, security, and deterrence over financial
rewards, this model addresses key challenges in existing blockchain incentive
structures, paving the way for more sustainable and decentralized governance
frameworks.


  By taking into account the nonlinear effect of the cause, the inner noise
effect, and the measurement distortion effect in the observed variables, the
post-nonlinear (PNL) causal model has demonstrated its excellent performance in
distinguishing the cause from effect. However, its identifiability has not been
properly addressed, and how to apply it in the case of more than two variables
is also a problem. In this paper, we conduct a systematic investigation on its
identifiability in the two-variable case. We show that this model is
identifiable in most cases; by enumerating all possible situations in which the
model is not identifiable, we provide sufficient conditions for its
identifiability. Simulations are given to support the theoretical results.
Moreover, in the case of more than two variables, we show that the whole causal
structure can be found by applying the PNL causal model to each structure in
the Markov equivalent class and testing if the disturbance is independent of
the direct causes for each variable. In this way the exhaustive search over all
possible causal structures is avoided.


  Structural matrix-variate observations routinely arise in diverse fields such
as multi-layer network analysis and brain image clustering. While data of this
type have been extensively investigated with fruitful outcomes being delivered,
the fundamental questions like its statistical optimality and computational
limit are largely under-explored. In this paper, we propose a low-rank Gaussian
mixture model (LrMM) assuming each matrix-valued observation has a planted
low-rank structure. Minimax lower bounds for estimating the underlying low-rank
matrix are established allowing a whole range of sample sizes and signal
strength. Under a minimal condition on signal strength, referred to as the
information-theoretical limit or statistical limit, we prove the minimax
optimality of a maximum likelihood estimator which, in general, is
computationally infeasible. If the signal is stronger than a certain threshold,
called the computational limit, we design a computationally fast estimator
based on spectral aggregation and demonstrate its minimax optimality. Moreover,
when the signal strength is smaller than the computational limit, we provide
evidences based on the low-degree likelihood ratio framework to claim that no
polynomial-time algorithm can consistently recover the underlying low-rank
matrix. Our results reveal multiple phase transitions in the minimax error
rates and the statistical-to-computational gap. Numerical experiments confirm
our theoretical findings. We further showcase the merit of our spectral
aggregation method on the worldwide food trading dataset.


  We study the Automatic Relevance Determination procedure applied to deep
neural networks. We show that ARD applied to Bayesian DNNs with Gaussian
approximate posterior distributions leads to a variational bound similar to
that of variational dropout, and in the case of a fixed dropout rate,
objectives are exactly the same. Experimental results show that the two
approaches yield comparable results in practice even when the dropout rates are
trained. This leads to an alternative Bayesian interpretation of dropout and
mitigates some of the theoretical issues that arise with the use of improper
priors in the variational dropout model. Additionally, we explore the use of
the hierarchical priors in ARD and show that it helps achieve higher sparsity
for the same accuracy.


  Meeting growing demand for automotive battery resources is predicted to be
costly from both economic and environmental perspectives. To minimize these
costs, battery resources should be deployed as efficiently as possible. A
potential source of inefficiency in battery deployment is the fact that the
batteries of personal vehicles are typically much larger than needed to meet
most daily mobility needs. In this paper, we consider whether battery resources
can be used more efficiently in a setting where drivers, in addition to having
personal vehicle batteries, have access to a shared battery resource. More
precisely, we consider the problem of minimizing aggregate battery capacity in
settings with and without a shared resource subject to the requirement that
driver commuting needs are met with high reliability. To assess the potential
for reductions in deployed battery capacity with the addition of a shared
resource, we quantify the difference in deployed battery capacity with and
without a shared resource in case study using real-world longitudinal mobility
data from Puget Sound, Washington. We find that giving drivers access to a
shared battery resource can substantially reduces deployed battery capacity.
Furthermore, relative reductions in battery capacity increase with number of
drivers and the level of reliability desired.


  We investigate the general relativistic phase of an electromagnetic wave as
it propagates in the gravitational field of the Earth, which is modeled as an
isolated, weakly aspherical gravitating body. We introduce coordinate systems
to describe light propagation in the Earth's vicinity along with the relevant
coordinate transformations, and discuss the transformations between proper and
coordinate times. We represent the Earth's gravitational field using Cartesian
symmetric trace-free (STF) mass multipole moments. The light propagation
equation is solvable along the trajectory of a light ray to all STF orders
$\ell$. Although we focus primarily on the quadrupole ($\ell=2$), octupole
($\ell=3$), and hexadecapole ($\ell=4$) cases, our approach is valid to all
orders. We express the STF moments via spherical harmonic coefficients of
various degree and order, $C_{\ell k}, S_{\ell k}$. The result is the
gravitational phase shift expressed in terms of the spherical harmonics. These
results are new. We also consider contributions due to tides and the Earth's
rotation. We estimate the characteristic magnitudes of each term of the
resulting overall gravitational phase shift. The terms assessed are either
large enough to impact current-generation clocks or will become significant as
future-generation clocks offer greater sensitivity. Our formulation is useful
for many practical and scientific applications, including space-based time and
frequency transfers, relativistic geodesy and navigation, as well as quantum
communication links and space-based tests of fundamental physics.


  This paper has been withdrawn by the author due to an error in the
computation of E(n,x) on page 6 which appears to be essential for the result.
The author is currently trying to correct this proof


  An image is not just a collection of objects, but rather a graph where each
object is related to other objects through spatial and semantic relations.
Using relational reasoning modules, such as the non-local module
\cite{wang2017non}, can therefore improve object detection. Current schemes
apply such dedicated modules either to a specific layer of the bottom-up
stream, or between already-detected objects. We show that the relational
process can be better modeled in a coarse-to-fine manner and present a novel
framework, applying a non-local module sequentially to increasing resolution
feature maps along the top-down stream. In this way, information can naturally
passed from larger objects to smaller related ones. Applying the module to fine
feature maps further allows the information to pass between the small objects
themselves, exploiting repetitions of instances of the same class. In practice,
due to the expensive memory utilization of the non-local module, it is
infeasible to apply the module as currently used to high-resolution feature
maps. We redesigned the non local module, improved it in terms of memory and
number of operations, allowing it to be placed anywhere along the network. We
further incorporated relative spatial information into the module, in a manner
that can be incorporated into our efficient implementation. We show the
effectiveness of our scheme by improving the results of detecting small objects
on COCO by 1-2 AP points over Faster and Mask RCNN and by 1 AP over using
non-local module on the bottom-up stream.


  The theoretical prospects for quantum electrodynamics with Lorentz-violating
operators of mass dimensions up to six are revisited in this work. The dominant
effects due to Lorentz and CPT violation are studied in measurements of
magnetic moments of particles confined in Penning traps. Using recently
reported experimental results, new coefficients for Lorentz violation are
constrained and existing bounds of various coefficients are improved.


  Core-collapse supernovae are powerful neutrino sources. The observation of a
future (extra-)galactic supernova explosion or of the relic supernova neutrinos
might provide important information on the supernova dynamics, on the supernova
formation rate and on neutrino properties. One might learn more about unknown
neutrino properties either from indirect effects in the supernova (e.g. on the
explosion or on in the r-process) or from modifications of the neutrino time or
energy distributions in a detector on Earth. Here we will discuss in particular
possible effects of CP violation in the lepton sector. We will also mention the
interest of future neutrino-nucleus interaction measurements for the precise
knowledge of supernova neutrino detector response to electron neutrinos.


  This paper addresses the problem of computational terminology evaluation not
per se but in a specific application context. This paper describes the
evaluation procedure that has been used to assess the validity of our overall
indexing approach and the quality of the IndDoc indexing tool. Even if
user-oriented extended evaluation is irreplaceable, we argue that early
evaluations are possible and they are useful for development guidance.