  Stalagmites are an extraordinarily powerful resource for the reconstruction
of climatological palaeoseasonality. Here, we provide a comprehensive review of
different types of seasonality preserved by stalagmites and methods for
extracting this information. A new drip classification scheme is introduced,
which facilitates the identification of stalagmites fed by seasonally
responsive drips and which highlights the wide variability in drip types
feeding stalagmites. This hydrological variability, combined with seasonality
in Earth atmospheric processes, meteoric precipitation, biological processes
within the soil, and cave atmosphere composition means that every stalagmite
retains a different and distinct (but correct) record of environmental
conditions. Replication of a record is extremely useful but should not be
expected unless comparing stalagmites affected by the same processes in the
same proportion. A short overview of common microanalytical techniques is
presented, and suggested best practice discussed. In addition to geochemical
methods, a new modelling technique for extracting meteoric precipitation and
temperature palaeoseasonality from stalagmite d18O data is discussed and tested
with both synthetic and real-world datasets. Finally, world maps of
temperature, meteoric precipitation amount, and meteoric precipitation oxygen
isotope ratio seasonality are presented and discussed, with an aim of helping
to identify regions most sensitive to shifts in seasonality.


  We study the non-thermal jet emission of the BL Lac object B3 2247+381 during
a high optical state. The MAGIC telescopes observed the source during 13 nights
between September 30th and October 30th 2010, collecting a total of 14.2 hours
of good quality very high energy (VHE) $\gamma$-ray data. Simultaneous
multiwavelength data was obtained with X-ray observations by the Swift
satellite and optical R-band observations at the KVA-telescope. We also use
high energy $\gamma$-ray (HE, 0.1 GeV-100 GeV) data from the Fermi satellite.
The BL Lac object B3 2247+381 (z=0.119) was detected, for the first time, at
VHE $\gamma$-rays at a statistical significance of 5.6 $\sigma$. A soft VHE
spectrum with a photon index of -3.2 $\pm$ 0.6 was determined. No significant
short term flux variations were found. We model the spectral energy
distribution using a one-zone SSC-model, which can successfully describe our
data.


  Recent theoretical results together with established theory have identified
the displacement thickness and the velocity at the boundary layer edge as
similarity scaling parameter candidates for the wall-bounded turbulent boundary
layer. In the work described herein, we examine these scaling parameters along
with the Prandtl Plus scaling's and the Zagarola and Smits scaling's to search
for similarity in the outer region of experimental turbulent boundary layer
velocity profile datasets. A new integral area method combined with the
traditional chi-by-eye method is used to search for similar velocity profiles.
The results indicate that strict whole profile similarity is not evident in any
of the datasets we searched. However, ten datasets are found that display
"similar-like" behavior using the ratio of the inner to outer thickness ratio
as a search criterion. In alignment with theory, the preferred similarity
scaling parameters for the similar-like behavior case are the displacement
thickness and the velocity at the boundary layer edge. It was found that there
are a few datasets for which the Prandtl Plus scaling and the Zagarola and
Smits scaling also work.


  Long-term stability studies of nonlinear Hamiltonian systems require
symplectic integration algorithms which are both fast and accurate. In this
paper, we study a symplectic integration method wherein the symplectic map
representing the Hamiltonian system is refactorized using polynomial symplectic
maps. This method is analyzed in detail for the three degree of freedom case.
We obtain explicit formulas for the action of the constituent polynomial maps
on phase space variables.


  The field of exo-atmospheric characterisation is progressing at an
extraordinary pace. Atmospheric observations are now available for tens of
exoplanets, mainly hot and warm inflated gas giants, and new molecular species
continue to be detected revealing a richer atmospheric composition than
previously expected. Thanks to its warm equilibrium temperature (963$\pm$18~K)
and low-density (0.219$\pm$0.031~g cm$^{-3}$), the close-in gas giant WASP-69b
represents a golden target for atmospheric characterization. With the aim of
searching for molecules in the atmosphere of WASP-69b and investigating its
properties, we performed high-resolution transmission spectroscopy with the
GIANO-B near-infrared spectrograph at the Telescopio Nazionale Galileo. We
observed three transit events of WASP-69b. During a transit, the planetary
lines are Doppler-shifted due to the large change in the planet's radial
velocity, allowing us to separate the planetary signal from the
quasi-stationary telluric and stellar spectrum. Considering the three nights
together, we report the detection of CH$_4$, NH$_3$, CO, C$_2$H$_2$, and
H$_2$O, at more than $3.3\sigma$ level. We did not identify the presence of HCN
and CO$_2$ with confidence level higher than 3$\sigma$. This is the first time
that five molecules are simultaneously detected in the atmosphere of a warm
giant planet. These results suggest that the atmosphere of WASP-69b is possibly
carbon-rich and characterised by the presence of disequilibrium chemistry.


  A novel technique for deep learning of image classifiers is presented. The
learned CNN models offer better separation of deep features (also known as
embedded vectors) measured by Euclidean proximity and also no deterioration of
the classification results by class membership probability. The latter feature
can be used for enhancing image classifiers having the classes at the model's
exploiting stage different from from classes during the training stage. While
the Shannon information of SoftMax probability for target class is extended for
mini-batch by the intra-class variance, the trained network itself is extended
by the Hadamard layer with the parameters representing the class centers.
Contrary to the existing solutions, this extra neural layer enables interfacing
of the training algorithm to the standard stochastic gradient optimizers, e.g.
AdaM algorithm. Moreover, this approach makes the computed centroids
immediately adapting to the updating embedded vectors and finally getting the
comparable accuracy in less epochs.


  Elements of a new model of strong interactions are described. The model is
based on an extension of electrodynamics that is derived from a regular
Lagrangian density. Here the electric and magnetic fields of Maxwell equations
play a symmetric role. It is shown that results are in accordance with general
properties of nuclear and nucleon systems.


  We consider two-dimensional (2D) "artificial atoms" confined by an axially
symmetric potential $V(\rho)$. Such configurations arise in circular quantum
dots and other systems effectively restricted to a 2D layer. Using the
semiclassical method, we present the first fully self-consistent and analytic
solution yielding equations describing the density distribution, energy, and
other quantities for any form of $V(\rho)$ and an arbitrary number of confined
particles. An essential and nontrivial aspect of the problem is that the 2D
density of states must be properly combined with 3D electrostatics. The
solution turns out to have a universal form, with scaling parameters
$\rho^2/R^2$ and $R/a_B^*$ ($R$ is the dot radius and $a_B^*$ is the effective
Bohr radius).


  Currently time-domain astronomy can scan the entire sky on a daily basis,
discovering thousands of interesting transients every night. Classifying the
ever-increasing number of new transients is one of the main challenges for the
astronomical community. One solution that addresses this issue is the
robotically controlled Spectral Energy Distribution Machine (SEDM) which
supports the Zwicky Transient Facility (ZTF). SEDM with its pipeline PYSEDM
demonstrates that real-time robotic spectroscopic classification is feasible.
In an effort to improve the quality of the current SEDM data, we present here
two new modules, BYECR and CONTSEP. The first removes contamination from cosmic
rays, and the second removes contamination from non-target light. These new
modules are part of the automated PYSEDM pipeline and fully integrated with the
whole process. Employing BYECR and CONTSEP modules together automatically
extracts more spectra than the current PYSEDM pipeline. Using SNID
classification results, the new modules show an improvement in the
classification rate and accuracy of 2.8% and 1.7%, respectively, while the
strength of the cross-correlation remains the same. Improvements to the SEDM
astrometry would further boost the improvement of the CONTSEP module. This kind
of robotic follow-up with a fully automated pipeline has the potential to
provide the spectroscopic classifications for the transients discovered by ZTF
and also by the Rubin Observatory's Legacy Survey of Space and Time.


  The talk contains an overview of recent experimental results obtained by the
LHC experiments. The measured inclusive event properties, as well as various
correlation phenomena, are compared with predictions of phenomenological
models.


  There are nonstandard models of normal open induction ($NOI$) for which
$\mathbb{Z}$ is a direct summand of their additive group. We show that this is
impossible for nonstandard models of $IE_2$.


  Based on the truth of Feichtinger conjecture by Marcus, Spielman and
Srivastava \textit{[Ann. of Math. (2), 2015]} and from the localized version by
Gr\"{o}chenig \textit{[Adv. Comput. Math., 2003]}, we introduce the notion of
localization of approximate Schauder frames (ASFs) and approximate Riesz
sequences (ARSs). We show that localized bounded below ASFs are finite unions
of ARSs.


  The perturbation method in supersymmetric quantum mechanics (SUSYQM) is used
to study whether the spheroidal equations have the shape-invariance property.
Expanding the super-potential term by term in the parameter alpha and solving
it, we find that the superpotential loses its shape-invariance property upon to
the second term. This first means that we could not solve the spheroidal
problems by the SUSQM; further it is not unreasonable to say they are
non-solvable in some sense.


  We use high-speed imaging to study the effect of thixotropic aging in drop
impact of yield-stress fluids on pre-coated substrates. Our results reveal that
drop splashing is suppressed for "aged" compared to "unaged" samples,
indicating that thixotropic breakdown timescales during impact are long enough
to affect the dynamics. We propose and test several hypotheses for modifying
the dimensionless group ${\rm IF}(D/t)$ [1,2] to account for thixotropic aging.
The main challenge is that the steady flow properties (Herschel-Bulkley model
parameters) used in the current dimensionless group cannot be defined or
measured for thixotropically aged samples, because any deformation inherently
rejuvenates and breaks down the microstructure. We find the most suitable
hypothesis is to only increase the yield stress ($\sigma_{\rm y}$, plastic
component) based on the storage modulus of aged samples, while keeping the
viscous parameters ($K$ or $\eta_{\infty}$) constant. The work reveals
fundamental insight into rarely studied short-timescale flow conditions with
thixotropic effects. These results are important for applications such as fire
suppression or spray coating that involve complex fluids of varying degrees of
thixotropic aging.


  Pimple is one of the most common skin diseases for humans. The mechanical
modeling of pimple growth is very limited. A finite element model is developed
to quantify the deformation field with the expansion of follicle, and then the
mechanical stimulus is related to the sensation of pain during the development
of pimple. Through these models, parametric studies show the dependence of
mechanical stimulus and pain level on the pimple-surrounded structures,
follicle depth and mechanical properties of the epidermis. The findings in this
paper may provide useful insights on prevention or pain relief of pimples, as
well as those related to cosmetics and other tissue growth.


  We present an efficient and simple modification of the standard transport
algorithm used in explicit eulerian fixed polar grid codes, aimed at getting
rid of the average azimuthal velocity when applying the Courant condition. This
results in a much larger timestep then the usual procedure, and it is
particularly well-suited to the description of a Keplerian disk where one is
traditionally limited by the very demanding Courant condition on the fast
orbital motion at the inner boundary. In this modified algorithm, the timestep
is limited by the perturbed velocity and by the shear arising from the
differential rotation. FARGO stands for ``Fast Advection in Rotating Gaseous
Objects''. The speed-up resulting from the use of the FARGO algorithm is
problem dependent. In the example presented here, which shows the evolution of
a Jupiter sized protoplanet embedded in a minimum mass protoplanetary nebula,
the FARGO algorithm is about an order of magnitude faster than a traditional
transport scheme, with a much smaller numerical diffusivity.


  Let $(M,g)$ and $(M',g')$ be non-orientable Riemannian surfaces with fixed
boundary $\Gamma$ and fixed Euler characterictic $m$, and $\Lambda$ and
$\Lambda'$ be their Dirichlet-to-Neumann maps, respectively. We prove that the
closeness of $\Lambda'$ to $\Lambda$ in the operator norm implies the existence
of of the near-conformal diffeomorphism $\beta$ between $(M,g)$ and $(M',g')$
which does not move the points of $\Gamma$. Hence we establish the continuity
of the determination $\Lambda\mapsto [(M,g)]$, where $[(M,g)]$ is the conformal
class of $(M,g)$ and the set of such conformal classes is endowed with the
natural Teichm\"uller-type metric $d_T$. In both orientable and non-orientable
case we provide quantitative estimates of $d_T([(M,g)],[(M',g')])$ via the
operator norm of the difference $\Lambda'-\Lambda$. We also obtain
generalizations of the results above to the case in which the
Dirichlet-to-Neumann map is given only on a segment of the boundary.


  The axially symmetric non-local solution in the Heisenberg equation is found.
It is regular in the whole space and has the finite energy on the unit of
length according to this we may consider the solution as a string. Taking the
non-local spherically symmetric solution, which was found by Finkelstein et.
al., and our solution in account we suggest to consider the Heisenberg equation
as a quantum equation for non-local objects (strings, flux tubes, membranes and
so on). The received solution is used for the obtaining the meson model as a
rotating string with the quark on its ends.


  We describe work being done at Baylor University investigating the
possibility of new states of mesonic matter containing two or more
quark-antiquark pairs. To put things in context, we begin by describing the
lattice approach to hadronic physics. We point out there is a need for a quark
model which can give an overall view of the quark interaction landscape. A new
application of the Thomas-Fermi (TF) statistical quark model is described,
similar to a previous application to baryons. The main usefulness of this model
will be to detect systematic energy trends in the composition of the various
particles. It could be a key to identifying {\it families} of bound states,
rather than individual cases. Numerical results based upon a set of parameters
derived from a phenomenological model of tetraquarks are given.


  Recent theories of Sr$_2$RuO$_4$ based on the interplay of strong
interactions, spin-orbit coupling and multi-band anisotropy predict chiral or
helical ground states with strong anisotropy of the pairing states, with deep
minima in the excitation gap, as well as strong phase anisotropy for the chiral
ground state. We develop time-dependent mean field theory to calculate the
Bosonic spectrum for the class of 2D chiral superconductors spanning $^3$He-A
to chiral superconductors with strong anisotropy. Chiral superconductors
support a pair of massive Bosonic excitations of the time-reversed pairs
labeled by their parity under charge conjugation. These modes are degenerate
for 2D $^3$He-A. Crystal field anisotropy lifts the degeneracy. Strong
anisotropy also leads to low-lying Fermions, and thus to channels for the decay
of the Bosonic modes. Selection rules and phase space considerations lead to
large asymmetries in the lifetimes and hybridization of the Bosonic modes with
the continuum of un-bound Fermion pairs. We also highlight results for the
excitation of the Bosonic modes by microwave radiation that provide clear
signatures of the Bosonic modes of an anisotropic chiral ground state.


  Electronic medical records (EMRs) are stored in relational databases. It can
be challenging to access the required information if the user is unfamiliar
with the database schema or general database fundamentals. Hence, researchers
have explored text-to-SQL generation methods that provide healthcare
professionals direct access to EMR data without needing a database expert.
However, currently available datasets have been essentially "solved" with
state-of-the-art models achieving accuracy greater than or near 90%. In this
paper, we show that there is still a long way to go before solving text-to-SQL
generation in the medical domain. To show this, we create new splits of the
existing medical text-to-SQL dataset MIMICSQL that better measure the
generalizability of the resulting models. We evaluate state-of-the-art language
models on our new split showing substantial drops in performance with accuracy
dropping from up to 92% to 28%, thus showing substantial room for improvement.
Moreover, we introduce a novel data augmentation approach to improve the
generalizability of the language models. Overall, this paper is the first step
towards developing more robust text-to-SQL models in the medical
domain.\footnote{The dataset and code will be released upon acceptance.


  We study the totally asymmetric simple exclusion process (TASEP) on complex
networks, as a paradigmatic model for transport subject to excluded volume
interactions. Building on TASEP phenomenology on a single segment and borrowing
ideas from random networks we investigate the effect of connectivity on
transport. In particular, we argue that the presence of disorder in the
topology of vertices crucially modifies the transport features of a network:
irregular networks involve homogeneous segments and have a bimodal distribution
of edge densities, whereas regular networks are dominated by shocks leading to
a unimodal density distribution. The proposed numerical approach of solving for
mean-field transport on networks provides a general framework for studying
TASEP on large networks, and is expected to generalize to other transport
processes.


  Using the spinning worldline quantum field theory formalism we calculate the
quadratic-in-spin momentum impulse $\Delta p_i^\mu$ and spin kick $\Delta
a_i^\mu$ from a scattering of two arbitrarily oriented spinning massive bodies
(black holes or neutron stars) in a weak gravitational background up to third
post-Minkowskian (PM) order ($G^3$). Two-loop Feynman integrals are performed
in the potential region, yielding conservative results. For spins aligned to
the orbital angular momentum we find a conservative scattering angle that is
fully consistent with state-of-the-art post-Newtonian results. Using the 2PM
radiated angular momentum previously obtained by Plefka, Steinhoff and the
present authors we generalize the angle to include radiation-reaction effects,
in which case it avoids divergences in the high-energy limit.


  Due to the dispersion of optical phonons, long range electron-phonon
correlations renormalize downwards the coupling strength in the Holstein model.
We evaluate the size of this effect both in a linear chain and in a square
lattice for a time averaged {\it e-ph} potential, where the time variable is
introduced according to the Matsubara formalism. Mapping the Holstein
Hamiltonian onto the time scale we derive the perturbing source current which
appears to be non time retarded. This property permits to disentangle phonon
and electron coordinates in the general path integral for an electron coupled
to dispersive phonons. While the phonon paths can be integrated out
analytically, the electron path integrations have to be done numerically. The
equilibrium thermodynamic properties of the model are thus obtained as a
function of the electron hopping value and of the phonon spectrum parameters.
We derive the {\it e-ph} corrections to the phonon free energy and show that
its temperature derivatives do not depend on the {\it e-ph} effective coupling
hence, the Holstein phonon heat capacity is strictly harmonic. A significant
upturn in the low temperature total heat capacity over $T$ ratio is attributed
to the electron hopping which largely contributes to the action.


  The conditions are established for nullity of quantum discord for mixed
states of a general two-qubit system whose dynamical behaviour is given by an
X-state form density matrix. It is found that quantum discord can vanish at a
finite time even in the presence of correlations between qubits. The degeneracy
of the populations of the combined states of the qubits and the multi-qubit
quantum coherences are shown to be responsible for vanishing of the discord. We
illustrate our considerations by examining the coherent-state Tavis-Cummings
model in which two qubits interact dispersively with the cavity field. The
disappearance of the discord is shown to be dependent on the initial atomic
conditions that it may occur at a discrete instance or periodically at discrete
instances redistributed over whole range of the evolution time. The nullity of
the discord is interpreted as resulting from equal preparation of the spin
anti-correlated and spin correlated product states in superposition~states.


  We propose a new model of cluster growth according to which the probability
that a new unit is placed in a point at a distance $r$ from the city center is
a Gaussian with mean equal to the cluster radius and variance proportional to
the mean, modulated by the local density $\rho(r)$. The model is analytically
solvable in $d=2$ dimensions, where the density profile varies as a
complementary error function. The model reproduces experimental observations
relative to the morphology of cities, determined via an original analysis of
digital maps with a very high spatial resolution, and helps understanding the
emergence of vehicular traffic.


  We investigate, at the fundamental level, the questions of `why', `when' and
`how' one could or should reach out to poor and vulnerable people to support
them in the absence of governmental institutions. We provide a simple and new
approach that is rooted in linear algebra and basic graph theory to capture the
dynamics of income circulation among economic agents. A new linear algebraic
model for income circulation is introduced, based on which we are able to
categorize societies as fragmented or cohesive. We show that, in the case of
fragmented societies, convincing wealthy agents at the top of the social
hierarchy to support the poor and vulnerable will be very difficult. We also
highlight how linear-algebraic and simple graph-theoretic methods help explain,
from a fundamental point of view, some of the mechanics of class struggle in
fragmented societies. Then, we explain intuitively and prove mathematically
why, in cohesive societies, wealthy agents at the top of the social hierarchy
tend to benefit by supporting the vulnerable in their society. A number of new
concepts emerge naturally from our mathematical analysis to describe the level
of cohesiveness of the society, the number of degrees of separation in business
(as opposed to social) networks, and the level of generosity of the overall
economy, which all tend to affect the rate at which the top wealthy class
recovers its support money back. In the discussion on future perspectives, the
connections between the proposed matrix model and statistical physics concepts
are highlighted.


  Cyber-Physical Systems (CPS) are formed through interconnected components
capable of computation, communication, sensing and changing the physical world.
The development of these systems poses a significant challenge since they have
to be designed in a way to ensure cyber-security without impacting their
performance. This article presents the Security Cost Modelling Framework (SCMF)
and shows supported by an experimental study how it can be used to measure,
normalise and aggregate the overall performance of a CPS. Unlike previous
studies, our approach uses different metrics to measure the overall performance
of a CPS and provides a methodology for normalising the measurement results of
different units to a common Cost Unit. Moreover, we show how the Security Costs
can be extracted from the overall performance measurements which allows to
quantify the overhead imposed by performing security-related tasks.
Furthermore, we describe the architecture of our experimental testbed and
demonstrate the applicability of SCMF in an experimental study. Our results
show that measuring the overall performance and extracting the security costs
using SCMF can serve as basis to redesign interactions to achieve the same
overall goal at less costs.


  Latest results from the PHENIX experiment at RHIC on these topics will be
presented. Results will be shown for Au+Au compared to p-p collisions as well
as compared to results from fully reconstructed jets at LHC.


  We introduce Dynamic Tiling, a model-agnostic, adaptive, and scalable
approach for small object detection, anchored in our inference-data-centric
philosophy. Dynamic Tiling starts with non-overlapping tiles for initial
detections and utilizes dynamic overlapping rates along with a tile minimizer.
This dual approach effectively resolves fragmented objects, improves detection
accuracy, and minimizes computational overhead by reducing the number of
forward passes through the object detection model. Adaptable to a variety of
operational environments, our method negates the need for laborious
recalibration. Additionally, our large-small filtering mechanism boosts the
detection quality across a range of object sizes. Overall, Dynamic Tiling
outperforms existing model-agnostic uniform cropping methods, setting new
benchmarks for efficiency and accuracy.


  A strong confluence result for Q*, a quantum lambda-calculus with
measurements, is proved. More precisely, confluence is shown to hold both for
finite and infinite computations. The technique used in the confluence proof is
syntactical but innovative. This makes Q* different from similar quantum lambda
calculi, which are either measurement-free or provided with a reduction
strategy.


  The paper is devoted to recent advances in stochastic modeling of anomalous
kinetic processes observed in dielectric materials which are prominent examples
of disordered (complex) systems. Theoretical studies of dynamical properties of
those "structures with variations" [Goldenfield and Kadanoff 1999 Science 284
87--9] require application of such mathematical tools by means of which their
random nature can be analyzed and, independently of the details differing
various systems (dipolar materials, glasses, semiconductors, liquid crystals,
polymers, etc.), the empirical universal kinetic patterns can be derived. We
begin with a brief survey of the historical background of the dielectric
relaxation study. After a short outline of the theoretical ideas providing the
random tools applicable to modeling of relaxation phenomena, we present
probabilistic implications for the study of the relaxation-rate distribution
models. In the framework of the probability distribution of relaxation rates we
consider description of complex systems, in which relaxing entities form random
clusters interacting with each other and single entities. Then we focus on
stochastic mechanisms of the relaxation phenomenon. We discuss the diffusion
approach and its usefulness for understanding of anomalous dynamics of the
relaxing systems. We also discuss extensions of the diffusive approach to the
systems under tempered random processes. Useful relationships among different
stochastic approaches to the anomalous dynamics of complex systems allow us to
get a fresh look on this subject. The paper closes with a final discussion on
achievements of stochastic tools describing the anomalous time evolution of the
complex systems.


  The Milky Way's dark matter halo is thought to contain large numbers of
smaller subhalos. These objects can contain very high densities of dark matter,
and produce potentially observable fluxes of gamma rays. In this article, we
study the gamma ray sources in the Fermi Gamma Ray Space Telescope's recently
published First Source Catalog, and attempt to determine whether this catalog
might contain a population of dark matter subhalos. We find that, while
approximately 20-60 of the catalog's unidentified sources could plausibly be
dark matter subhalos, such a population cannot be clearly identified as such at
this time. From the properties of the sources in the First Source Catalog, we
derive limits on the dark matter's annihilation cross section that are
comparably stringent to those derived from recent observations of dwarf
spheroidal galaxies.


  We propose a number of variational regularisation methods for the estimation
and decomposition of motion fields on the $2$-sphere. While motion estimation
is based on the optical flow equation, the presented decomposition models are
motivated by recent trends in image analysis. In particular we treat $u+v$
decomposition as well as hierarchical decomposition. Helmholtz decomposition of
motion fields is obtained as a natural by-product of the chosen numerical
method based on vector spherical harmonics. All models are tested on time-lapse
microscopy data depicting fluorescently labelled endodermal cells of a
zebrafish embryo.


  By utilizing the effective-field theory based on the Glauber-type stochastic
dynamics, the dynamic behaviors of the hexagonal Ising nanowire (HIN) system in
the presence of a time dependent magnetic field are obtained. The time
variations of average order parameters and the thermal behavior of the dynamic
order parameters are studied to analyze the nature of transitions and to obtain
the dynamic phase transition points. The dynamic phase diagrams are introduced
in the plane of the reduced temperature versus magnetic field amplitude. The
dynamic phase diagrams exhibit coexistence phase region, several ordered phases
and critical point as well as a reentrant behavior.


  We explore the masses of neutral and charged $\rho$ mesons in strong abelian
magnetic field in $SU(3)$ gluodynamics. The behaviour of the ground state
energy of these particles in the external magnetic field depends on its spin
projection $s_z$ on the axis of external magnetic field. The masses of $\rho^0$
meson with $s_z=\pm 1$ increase with the field. The masses of $\rho^{\pm}$
mesons with zero spin also grow with the magnetic field. The ground state
energies of $\rho^{-}$ meson with $s_z=-1$ and $\rho^{+}$ meson with $s_z=+1$
diminish as a function of the field, while the energies of $\rho^{+}$ meson
with $s_z=-1$ and $\rho^{-}$ meson with $s_z=+1$ rise with the field value.


  We characterize the mechanical recovery of compliant silicone gels following
adhesive contact failure. We establish broad, stable adhesive contacts between
rigid microspheres and soft gels, then stretch the gels to large deformations
by pulling quasi-statically on the contact. Eventually, the adhesive contact
begins to fail, and ultimately slides to a final contact point on the bottom of
the sphere. Immediately after detachment, the gel recoils quickly with a
self-similar surface profile that evolves as a power law in time, suggesting
that the adhesive detachment point is singular. The singular dynamics we
observe are consistent with a relaxation process driven by surface stress and
slowed by viscous flow through the porous, elastic network of the gel. Our
results emphasize the importance of accounting for both the liquid and solid
phases of gels in understanding their mechanics, especially under extreme
deformation.


  The repartition of the separation between energy levels of various isotropic
S=1/2 antiferromagnetic chains is studied numerically with the aim of
investigating the transition from integrable to non-integrable systems. We
begin by displaying the level separation distribution of the integrable Bethe
chain. Then two non-integrable systems, two coupled chains and a
next-nearest-neighbor coupled chain, are studied as a function of the coupling.
We examine how the level spacing evolves from the Poisson distribution to the
GOE distribution. Finally we consider the Haldane-Shastry $1/r^{2}$ model. A
number of conclusions regarding the behaviour and relevance of the level
spacing distribution in these spin systems is drawn.


  We construct a new compactification of the moduli space of maps from pointed
nonsingular projective stable curves to a nonsingular projective variety with
prescribed ramification indices at the points. It is shown to be a proper
Deligne-Mumford stack equipped with a natural virtual fundamental class.


  To explore the sub-MeV/MeV gamma-ray window for astronomy, we have developed
the Electron-Tracking Compton Camera (ETCC), and carried out the first
performance test at room condition using several gamma-ray sources in the
sub-MeV energy band. Using a simple track analysis for a quick first test of
the performance, the gamma-ray imaging capability was demonstrated by clear
images and 5.3 degrees of angular resolution measure (ARM) measured at 662 keV.
As the greatest impact of this work, a gamma-ray detection efficiency on the
order of $10^{-4}$ was achieved at the sub-MeV gamma-ray band, which is one
order of magnitude higher than our previous experiment. This angular resolution
and detection efficiency enables us to detect the Crab Nebula at the 5 sigma
level with several hours observation at balloon altitude in middle latitude.
Furthermore, good consistency of efficiencies between this performance test and
simulation including only physical processes has a large importance; it means
we achieve nearly 100% detection of Compton recoil electrons. Thus, our
estimation of enhancements by upgrades of the detector is more dependable. We
are planning to confirm the imaging capability of the ETCC by observation of
celestial objects in the SMILE-II (Sub-MeV gamma ray Imaging Loaded-on-balloon
Experiment II). The SMILE-II and following SMILE-III project will be an
important key of sub-MeV/MeV gamma-ray astronomy.


  In a fixed time horizon, appropriately executing a large amount of a
particular asset -- meaning a considerable portion of the volume traded within
this frame -- is challenging. Especially for illiquid or even highly liquid but
also highly volatile ones, the role of "market quality" is quite relevant in
properly designing execution strategies. Here, we model it by considering
uncertain volatility and liquidity; hence, moments of high or low price impact
and risk vary randomly throughout the trading period. We work under the central
assumption: although there are these uncertain variations, we assume they occur
in a fast mean-reverting fashion. We thus employ singular perturbation
arguments to study approximations to the optimal strategies in this framework.
By using high-frequency data, we provide estimation methods for our model in
face of microstructure noise, as well as numerically assess all of our results.


  We theoretically study the topography of a biphasic surfactant monolayer in
the vicinity of domain boundaries. The differing elastic properties of the two
phases generally lead to a nonflat topography of ``mesas'', where domains of
one phase are elevated with respect to the other phase. The mesas are steep but
low, having heights of up to 10 nm. As the monolayer is laterally compressed,
the mesas develop overhangs and eventually become unstable at a surface tension
of about K(dc)^2 (dc being the difference in spontaneous curvature and K a
bending modulus). In addition, the boundary is found to undergo a
topography-induced rippling instability upon compression, if its line tension
is smaller than about K(dc). The effect of diffuse boundaries on these features
and the topographic behavior near a critical point are also examined. We
discuss the relevance of our findings to several experimental observations
related to surfactant monolayers: (i) small topographic features recently found
near domain boundaries; (ii) folding behavior observed in mixed phospholipid
monolayers and model lung surfactants; (iii) roughening of domain boundaries
seen under lateral compression; (iv) the absence of biphasic structures in
tensionless surfactant films.


  Lorentz [of the Lorentz transforms and Lorentz contractions fame] contended
against Einstein that there had to be a medium in which electro-magnetic waves
exist and propagate, and that that would of necessity be an absolute frame of
reference for the universe. Einstein won that dispute contending that
electro-magnetic waves needed no medium and that there was no absolute frame of
reference. But, that victory was in a conflict of the Lorentz opinion opposed
to the Einstein opinion combined with the substantial other successes and
reputation or Einstein. It was not a victory of solid reasoning nor
demonstrated factual evidence. Now solid reasoning and new data not available
to Einstein and Lorentz show that Lorentz was correct and that Einstein's
Theory of Relativity should correctly be termed Einstein's Principle of
Invariance. It is shown that Einstein's comprehensive relativity and denial of
an absolute frame of reference for the universe are incorrect and that the
universe has an absolute universal prime frame of reference. The significance
of this correction in its relation to the interaction of science and society is
then presented.


  The family of admissible positions in a transaction costs model is a random
closed set, which is convex in case of proportional transaction costs. However,
the convexity fails, e.g. in case of fixed transaction costs or when only a
finite number of transfers are possible. The paper presents an approach to
measure risks of such positions based on the idea of considering all selections
of the portfolio and checking if one of them is acceptable. Properties and
basic examples of risk measures of non-convex portfolios are presented.


  Starting from the QCD Schroedinger functional (SF), we define a family of
renormalization schemes for two four-quark operators, which are, in the chiral
limit, protected against mixing with other operators. With the appropriate
flavour assignments these operators can be interpreted as part of either the
$\Delta F=1$ or $\Delta F=2$ effective weak Hamiltonians. In view of lattice
QCD with Wilson-type quarks, we focus on the parity odd components of the
operators, since these are multiplicatively renormalized both on the lattice
and in continuum schemes. We consider 9 different SF schemes and relate them to
commonly used continuum schemes at one-loop order of perturbation theory. In
this way the two-loop anomalous dimensions in the SF schemes can be inferred.
As a by-product of our calculation we also obtain the one-loop cutoff effects
in the step-scaling functions of the respective renormalization constants, for
both O(a) improved and unimproved Wilson quarks. Our results will be needed in
a separate study of the non-perturbative scale evolution of these operators.


  We study the impact of warm dark matter mass on the internal properties of
individual galaxies using a large suite of 1,024 state-of-the-art cosmological
hydrodynamic simulations from the DREAMS project. We take individual galaxies'
properties from the simulations, which have different cosmologies,
astrophysics, and warm dark matter masses, and train normalizing flows to learn
the posterior of the parameters. We find that our models cannot infer the value
of the warm dark matter mass, even when the values of the cosmological and
astrophysical parameters are given explicitly. This result holds for galaxies
with stellar mass larger than $2\times10^8 M_\odot/h$ at both low and high
redshifts. We calculate the mutual information and find no significant
dependence between the WDM mass and galaxy properties. On the other hand, our
models can infer the value of $\Omega_{\rm m}$ with a $\sim10\%$ accuracy from
the properties of individual galaxies while marginalizing astrophysics and warm
dark matter masses.


  We prove an inequality related to questions in Approximation Theory,
Probability Theory, and to Irregularities of Distribution.
  Let $h_R$ denote an $L ^{\infty}$ normalized Haar function adapted to a
dyadic rectangle $R\subset [0,1] ^{3}$. We show that there is a postive $\eta$
so that for all integers $n$, and coefficients $ \alpha (R)$ we have
  2 ^{-n} \sum_{\abs{R}=2 ^{-n}} \abs{\alpha(R)} {}\lesssim{} n ^{1 - \eta}
\NOrm \sum_{\abs{R}=2 ^{-n}} \alpha(R) h_R >.\infty .
  This is an improvement over the `trivial' estimate by an amount of $n ^{-
\eta}$, and the optimal value of $\eta$ (which we do not prove) would be $ \eta
=\frac12$. There is a corresponding lower bound on the $L ^{\infty}$ norm of
the Discrepancy function of an arbitary distribution of a finite number of
points in the unit cube in three dimensions. The prior result, in dimension 3,
is that of J{\'o}zsef Beck \cite{MR1032337}, in which the improvement over the
trivial estimate was logarithmic in $n$. We find several simplifications and
extensions of Beck's argument to prove the result above.


  In this paper, a new reinforcement learning (RL) method known as the method
of temporal differential is introduced. Compared to the traditional
temporal-difference learning method, it plays a crucial role in developing
novel RL techniques for continuous environments. In particular, the
continuous-time least squares policy evaluation (CT-LSPE) and the
continuous-time temporal-differential (CT-TD) learning methods are developed.
Both theoretical and empirical evidences are provided to demonstrate the
effectiveness of the proposed temporal-differential learning methodology.


  The concept of free extreme value distributions as universal limit laws for
the spectral maximum of free noncommutative real random variables was
discovered by Ben Arous and Voiculescu in 2006. This paper contributes to study
the convergence of densities towards free extreme value distributions under the
von Mises condition for sample distributions.


  Define a $(n^4+n^2)/2\times (n^4+n^2)/2$ symmetric $B$. $(ij)(kl)$ is an
index where $i,j,k,l\in [n]$, $(ab)$ is an unordered pair and $(kl)$ is an
ordered pair when $i\neq j$, otherwise it is also an unordered pair.
$B((ij)(kl),(ab)(xy))$ is equal to the number of permutations of S_n in which
$\min\{i,j\}$ maps to $k$, $\max\{i,j\}$ maps to $l$, $\min\{a,b\}$ maps to $x$
and $\max\{a,b\}$ maps to $y$. We will show that $B$ has four distinct
eigenvalues: $(3/2)n!$, $n(n-3)!$, $(n-1)!/(n-3)$, $2n(n-2)!$ and the
corresponding eigenspace dimensions are 1, ${{n-1}\choose{2}}^2$,
$({{n-1}\choose{2}}-1)^2$, $(n-1)^2$ respectively.


  ASD (Abstract Stone Duality) is a re-axiomatisation of general topology in
which the topology on a space is treated, not as an infinitary lattice, but as
an exponential object of the same category as the original space, with an
associated lambda-calculus. In this paper, this is shown to be equivalent to a
notion of computable basis for locally compact sober spaces or locales,
involving a family of open subspaces and accompanying family of compact ones.
This generalises Smyth's effectively given domains and Jung's strong proximity
lattices. Part of the data for a basis is the inclusion relation of compact
subspaces within open ones, which is formulated in locale theory as the
way-below relation on a continuous lattice. The finitary properties of this
relation are characterised here, including the Wilker condition for the cover
of a compact space by two open ones. The real line is used as a running
example, being closely related to Scott's domain of intervals. ASD does not use
the category of sets, but the full subcategory of overt discrete objects plays
this role; it is an arithmetic universe (pretopos with lists). In particular,
we use this subcategory to translate computable bases for classical spaces into
objects in the ASD calculus.


  Double Field Theory (DFT) is a proposal to incorporate T-duality, a
distinctive symmetry of string theory, as a symmetry of a field theory defined
on a double configuration space. The aim of this review is to provide a
pedagogical presentation of DFT and its applications. We first introduce some
basic ideas on T-duality and supergravity in order to proceed to the
construction of generalized diffeomorphisms and an invariant action on the
double space. Steps towards the construction of a geometry on the double space
are discussed. We then address generalized Scherk-Schwarz compactifications of
DFT and their connection to gauged supergravity and flux compactifications. We
also discuss U-duality extensions, and present a brief parcours on world-sheet
approaches to DFT. Finally, we provide a summary of other developments and
applications that are not discussed in detail in the review.


  We explore practical tradeoffs in blockchain-based biometric template
storage. We first discuss opportunities and challenges in the integration of
blockchain and biometrics, with emphasis in biometric template storage and
protection, a key problem in biometrics still largely unsolved. Blockchain
technologies provide excellent architectures and practical tools for securing
and managing the sensitive and private data stored in biometric templates, but
at a cost. We explore experimentally the key tradeoffs involved in that
integration, namely: latency, processing time, economic cost, and biometric
performance. We experimentally study those factors by implementing a smart
contract on Ethereum for biometric template storage, whose cost-performance is
evaluated by varying the complexity of state-of-the-art schemes for face and
handwritten signature biometrics. We report our experiments using popular
benchmarks in biometrics research, including deep learning approaches and
databases captured in the wild. As a result, we experimentally show that
straightforward schemes for data storage in blockchain (i.e., direct and
hash-based) may be prohibitive for biometric template storage using
state-of-the-art biometric methods. A good cost-performance tradeoff is shown
by using a blockchain approach based on Merkle trees.


  We present the discovery of a triplet of quasars at z~1.51. The whole system
is well accommodated within 25 arcsec (i.e., 200 kpc in projected distance).
The velocity differences among the three objects (as measured through the broad
MgII emission line) are less than 1000 km/s, suggesting that the quasars belong
to the same physical structure. Broad band NIR images of the field do not
reveal evidence of galaxies or galaxy clusters that could act as a
gravitational lens, ruling out the possibility that two or all the three
quasars are multiple images of a single, strongly lensed source. QQQ J1519+0627
is the second triplet of quasars known up to date. We estimate that these
systems are extremely rare in terms of simple accidental superposition. The
lack of strong galaxy overdensity suggests that this peculiar system is
harboured in the seeds of a yet-to-be-formed massive structure.
  Based on observations collected at the La Silla Observatory with the New
Technology Telescope (NTT) of the European Southern Observatory (ESO) and at
the Calar Alto Observatory with the 3.5m telescope of the Centro Astron\'omico
Hispano Alem\'an (CAHA).


  We present first results for diquark correlation functions calculated in
Landau gauge on the lattice. Masses have been extracted from the long distance
behaviour of these correlation functions. We find that the ordering of diquark
masses with spin 0 and 1 states in colour anti-triplet and sextet channels is
in accordance with instanton motivated interaction models. Although we find
evidence for an attractive interaction in colour anti-triplet states with a
splitting between spin 0 and spin 1 diquarks that can account for the mass
splitting between the nucleon and the delta, there is no evidence for a deeply
bound diquark state.


  Deep learning models are often trained on distributed, web-scale datasets
crawled from the internet. In this paper, we introduce two new dataset
poisoning attacks that intentionally introduce malicious examples to a model's
performance. Our attacks are immediately practical and could, today, poison 10
popular datasets. Our first attack, split-view poisoning, exploits the mutable
nature of internet content to ensure a dataset annotator's initial view of the
dataset differs from the view downloaded by subsequent clients. By exploiting
specific invalid trust assumptions, we show how we could have poisoned 0.01% of
the LAION-400M or COYO-700M datasets for just $60 USD. Our second attack,
frontrunning poisoning, targets web-scale datasets that periodically snapshot
crowd-sourced content -- such as Wikipedia -- where an attacker only needs a
time-limited window to inject malicious examples. In light of both attacks, we
notify the maintainers of each affected dataset and recommended several
low-overhead defenses.


  Discovering governing equations from data, in particular high dimensional
data, is challenging in various fields of science and engineering, and it has
potential to revolutionise the science and technology in this big data era.
This paper combines sparse identification and deep learning with non-linear
fluid dynamics, in particular the turbulent flows, to discover governing
equations of nonlinear fluid dynamics in the lower nonlinear manifold space.
The autoencoder deep neural network is used to project the high dimensional
space into a lower dimensional nonlinear manifold space. The Proper Orthogonal
Decomposition (POD) is then used to stabilise the nonlinear manifold space in
order to guarantee a stable manifold space for pattern or equations discovery
for the highly nonlinear problems such as turbulent flows. Sparse regression is
then used to discover the lower dimensional governing equations of fluid
dynamics in the lower dimensional nonlinear manifold space. What distinguishes
this approach is its ability to discover a lower dimensional governing
equations of fluid dynamics in the nonlinear manifold space. We demonstrate
this method on a number of high-dimensional fluid dynamic systems such as lock
exchange, flow past one and two cylinders. The results demonstrate that the
resulting method is capable of discovering lower dimensional governing
equations that took researchers in this community many decades years to
resolve. In addition, this model discovers dynamics in a lower dimensional
manifold space, thus leading to great computational efficiency, model
complexity and avoiding overfitting. It also provides a new insight for our
understanding of sciences such as turbulent flows.


  In this paper, for a Seifert loop (i.e., a knot in a Seifert three-manifold),
first we give a family of an explicit function $\Phi(q; N)$ whose special
values at roots of unity are identified with the Witten-Reshetikhin-Turaev
invariants of the Seifert loop for the integral homology sphere. Second, we
show that the function $\Phi(q; N)$ satisfies a $q$-difference equation whose
classical limit coincides with a component of the character varieties of the
Seifert loop. Third, we give an interpretation of the function $\Phi(q; N)$
from the view point of the resurgent analysis.


  In this short note we study the existence and number of solutions in the set
of integers ($Z$) and in the set of natural numbers ($N$) of Diopahntine
Equations of second degree with two variables of the general form
$ax^2-by^2=c$.


  Sparse convex clustering is to cluster observations and conduct variable
selection simultaneously in the framework of convex clustering. Although a
weighted $L_1$ norm is usually employed for the regularization term in sparse
convex clustering, its use increases the dependence on the data and reduces the
estimation accuracy if the sample size is not sufficient. To tackle these
problems, this paper proposes a Bayesian sparse convex clustering method based
on the ideas of Bayesian lasso and global-local shrinkage priors. We introduce
Gibbs sampling algorithms for our method using scale mixtures of normal
distributions. The effectiveness of the proposed methods is shown in simulation
studies and a real data analysis.


  We propose a projection-free conditional gradient-type algorithm for smooth
stochastic multi-level composition optimization, where the objective function
is a nested composition of $T$ functions and the constraint set is a closed
convex set. Our algorithm assumes access to noisy evaluations of the functions
and their gradients, through a stochastic first-order oracle satisfying certain
standard unbiasedness and second moment assumptions. We show that the number of
calls to the stochastic first-order oracle and the linear-minimization oracle
required by the proposed algorithm, to obtain an $\epsilon$-stationary
solution, are of order $\mathcal{O}_T(\epsilon^{-2})$ and
$\mathcal{O}_T(\epsilon^{-3})$ respectively, where $\mathcal{O}_T$ hides
constants in $T$. Notably, the dependence of these complexity bounds on
$\epsilon$ and $T$ are separate in the sense that changing one does not impact
the dependence of the bounds on the other. Moreover, our algorithm is
parameter-free and does not require any (increasing) order of mini-batches to
converge unlike the common practice in the analysis of stochastic conditional
gradient-type algorithms.


  The input of almost every machine learning algorithm targeting the properties
of matter at the atomic scale involves a transformation of the list of
Cartesian atomic coordinates into a more symmetric representation. Many of the
most popular representations can be seen as an expansion of the symmetrized
correlations of the atom density, and differ mainly by the choice of basis.
Considerable effort has been dedicated to the optimization of the basis set,
typically driven by heuristic considerations on the behavior of the regression
target. Here we take a different, unsupervised viewpoint, aiming to determine
the basis that encodes in the most compact way possible the structural
information that is relevant for the dataset at hand. For each training dataset
and number of basis functions, one can determine a unique basis that is optimal
in this sense, and can be computed at no additional cost with respect to the
primitive basis by approximating it with splines. We demonstrate that this
construction yields representations that are accurate and computationally
efficient, particularly when constructing representations that correspond to
high-body order correlations. We present examples that involve both molecular
and condensed-phase machine-learning models.


  We addressed the question of optimality of private quantum channels. We have
shown that the Shannon entropy of the classical key necessary to securely
transfer the quantum information is lower bounded by the entropy exchange of
the private quantum channel $\cal E$ and von Neumann entropy of the ciphertext
state $\varrho^{(0)}$. Based on these bounds we have shown that decomposition
of private quantum channels into orthogonal unitaries (if exists) is optimizing
the entropy. For non-ancillary single qubit PQC we have derived the optimal
entropy for arbitrary set of plaintexts. In particular, we have shown that
except when the (closure of the) set of plaintexts contains all states, one bit
key is sufficient. We characterized and analyzed all the possible single qubit
private quantum channels for arbitrary set of plaintexts. For the set of
plaintexts consisting of all qubit states we have characterized all possible
approximate private quantum channels and we have derived the relation between
the security parameter and the corresponding minimal entropy.


  Motivated by a recent experiment on the superfluid 3He A-phase with a chiral
p-wave pairing confined in a thin slab, we propose designing a concrete
experimental setup for observing the Majorana edge modes that appear around the
circumference edge region. We solve the quasi-classical Eilenberger equation,
which is quantitatively reliable, to evaluate several observables. To derive
the property inherent to the Majorana edge state, the full quantum mechanical
Bogoliubov-de Gennes equation is solved in this setting. On the basis of the
results obtained, we perform decisive experiments to check the Majorana nature.


  Blazars emit a highly-variable non-thermal spectrum. It is usually assumed
that the same non-thermal electrons are responsible for the IR-optical-UV
emission (via synchrotron) and the gamma-ray emission (via inverse Compton).
Hence, the light curves in the two bands should be correlated. Orphan gamma-ray
flares (i.e., lacking a luminous low-frequency counterpart) challenge our
theoretical understanding of blazars. By means of large-scale two-dimensional
radiative particle-in-cell simulations, we show that orphan gamma-ray flares
may be a self-consistent by-product of particle energization in turbulent
magnetically-dominated pair plasmas. The energized particles produce the
gamma-ray flare by inverse Compton scattering an external radiation field,
while the synchrotron luminosity is heavily suppressed since the particles are
accelerated nearly along the direction of the local magnetic field. The ratio
of inverse Compton to synchrotron luminosity is sensitive to the initial
strength of turbulent fluctuations (a larger degree of turbulent fluctuations
weakens the anisotropy of the energized particles, thus increasing the
synchrotron luminosity). Our results show that the anisotropy of the
non-thermal particle population is key to modeling the blazar emission.


  Based on forward curves modelled as Hilbert-space valued processes, we
analyse the pricing of various options relevant in energy markets. In
particular, we connect empirical evidence about energy forward prices known
from the literature to propose stochastic models. Forward prices can be
represented as linear functions on a Hilbert space, and options can thus be
viewed as derivatives on the whole curve. The value of these options are
computed under various specifications, in addition to their deltas. In a second
part, cross-commodity models are investigated, leading to a study of square
integrable random variables with values in a "two-dimensional" Hilbert space.
We analyse the covariance operator and representations of such variables, as
well as presenting applications to pricing of spread and energy quanto options.


  We argue that one of the following statements must be true: (a) extensive
violations of quantum information theory's additivity conjectures exist or (b)
there exists a set of `disentangled' black hole microstates that can account
for the entire Bekenstein-Hawking entropy, up to at most a subleading $O(1)$
correction. Possibility (a) would be a significant result in quantum
communication theory, demonstrating that entanglement can enhance the ability
to transmit information much more than has currently been established. Option
(b) would provide new insight into the microphysics of black holes. In
particular, the disentangled microstates would have to have nontrivial
structure at or outside the black hole horizon, assuming the validity of the
quantum extremal surface prescription for calculating entanglement entropy in
AdS/CFT.


  We study the statistical properties of the combined emission of a population
of discrete sources (e.g. X-ray emission of a galaxy due to its X-ray binaries
population). Namely, we consider the dependence of their total luminosity
L_tot=SUM(L_k) and of fractional rms_tot of their variability on the number of
sources N or, equivalently, on the normalization of the luminosity function. We
show that due to small number statistics a regime exists, in which L_tot grows
non-linearly with N, in an apparent contradiction with the seemingly obvious
prediction <L_tot>=integral(dN/dL*L*dL) ~ N. In this non-linear regime, the
rms_tot decreases with N significantly more slowly than expected from the rms ~
1/sqrt(N) averaging law. For example, for a power law luminosity function with
a slope of a=3/2, in the non-linear regime, L_tot ~ N^2 and the rms_tot does
not depend at all on the number of sources N. Only in the limit of N>>1 do
these quantities behave as intuitively expected, L_tot ~ N and rms_tot ~
1/sqrt(N). We give exact solutions and derive convenient analytical
approximations for L_tot and rms_tot.
  Using the total X-ray luminosity of a galaxy due to its X-ray binary
population as an example, we show that the Lx-SFR and Lx-M* relations predicted
from the respective ``universal'' luminosity functions of high and low mass
X-ray binaries are in a good agreement with observations. Although caused by
small number statistics the non-linear regime in these examples extends as far
as SFR<4-5 Msun/yr and log(M*/Msun)<10.0-10.5, respectively.


  Accurate prediction of chemical and material properties from first principles
quantum chemistry is a challenging task on traditional computers. Recent
developments in quantum computation offer a route towards highly accurate
solutions with polynomial cost, however this solution still carries a large
overhead. In this perspective, we aim to bring together known results about the
locality of physical interactions from quantum chemistry with ideas from
quantum computation. We show that the utilization of spatial locality combined
with the Bravyi-Kitaev transformation offers an improvement in the scaling of
known quantum algorithms for quantum chemistry and provide numerical examples
to help illustrate this point. We combine these developments to improve the
outlook for the future of quantum chemistry on quantum computers.


  We present the discovery and asteroseismic analysis of a new g-mode hot B
subdwarf (sdB) pulsator, EC 21494-7018 (TIC 278659026), monitored in TESS first
sector using 120-second cadence. The light curve analysis reveals that EC
21494-7018 is a sdB pulsator counting up to 20 frequencies associated with
independent g-modes. The seismic analysis singles out an optimal model solution
in full agreement with independent measurements provided by spectroscopy
(atmospheric parameters derived from model atmospheres) and astrometry
(distance evaluated from Gaia DR2 trigonometric parallax). Several key
parameters of the star are derived. Its mass (0.391 +/- 0.009 Msun) is
significantly lower than the typical mass of sdB stars, and suggests that its
progenitor has not undergone the He-core flash, and therefore could originate
from a massive (>2 Msun) red giant, an alternative channel for the formation of
hot B subdwarfs. Other derived parameters include the H-rich envelope mass
(0.0037 +/- 0.0010 Msun), radius (0.1694 +/- 0.0081 Rsun), and luminosity
(8.2+/-1.1 Lsun). The optimal model fit has a double-layered He+H composition
profile, which we interpret as an incomplete but ongoing process of
gravitational settling of helium at the bottom of a thick H-rich envelope.
Moreover, the derived properties of the core indicate that EC 21494-7018 has
burnt ~43% (in mass) of its central helium and possesses a relatively large
mixed core (Mcore = 0.198 +/- 0.010 Msun), in line with trends already
uncovered from other g-mode sdB pulsators analysed with asteroseismology.
Finally, we obtain for the first time an estimate of the amount of oxygen (in
mass; X(O)core = 0.16 -0.05 +0.13) produced at this stage of evolution by an
helium-burning core. This result, along with the core-size estimate, is an
interesting constraint that may help to narrow down the still uncertain
C12(alpha,gamma)O16 nuclear reaction rate.


  Supercapacitors (aka electrostatic double-layer capacitors -- EDLCs) offer
excellent power storage capacity and kinetics, but suffer under rapid
self-discharge. We introduced a zeolite framework into the active capacitor
electrode, with the goal to tailor the free desorption energy and thus the
self-discharge rate of supercapacitors. Low-cost carbonaceous materials and
benchtop production methods were used to create supercapacitor electrodes with
a measured specific capacitance of 17.25 F g$^{-1}$, a coulombic efficiency of
100%, and charge retention of over 25% over 24 hours determined by
galvanostatic charge/discharge curve measurements. This charge retention was an
enhancement of $\sim$350% compared with electrodes without zeolite coating.


  We prove that transport in the phase space of the "most strongly chaotic"
dynamical systems has three different stages. Consider a finite Markov
partition (coarse graining) $\xi$ of the phase space of such a system. In the
first short times interval there is a hierarchy with respect to the values of
the first passage probabilities for the elements of $\xi$ and therefore finite
time predictions can be made about which element of the Markov partition
trajectories will be most likely to hit first at a given moment. In the third
long times interval, which goes to infinity, there is an opposite hierarchy of
the first passage probabilities for the elements of $\xi$ and therefore again
finite time predictions can be made. In the second intermediate times interval
there is no hierarchy in the set of all elements of the Markov partition. We
also obtain estimates on the length of the short times interval and show that
its length is growing with refinement of the Markov partition which shows that
practically only this interval should be taken into account in many cases.
These results demonstrate that finite time predictions for the evolution of
strongly chaotic dynamical systems are possible. In particular, one can predict
that an orbit is more likely to first enter one subset of phase space than
another at a given moment in time. Moreover, these results suggest an algorithm
which accelerates the process of escape through "holes" in the phase space of
dynamical systems with strongly chaotic behavior.


  Many secure communication libraries used by distributed systems, such as SSL,
TLS, and Kerberos, fail to make a clear distinction between the authentication,
session, and communication layers. In this paper we introduce CEDAR, the secure
communication library used by the Condor High Throughput Computing software,
and present the advantages to a distributed computing system resulting from
CEDAR's separation of these layers. Regardless of the authentication method
used, CEDAR establishes a secure session key, which has the flexibility to be
used for multiple capabilities. We demonstrate how a layered approach to
security sessions can avoid round-trips and latency inherent in network
authentication. The creation of a distinct session management layer allows for
optimizations to improve scalability by way of delegating sessions to other
components in the system. This session delegation creates a chain of trust that
reduces the overhead of establishing secure connections and enables centralized
enforcement of system-wide security policies. Additionally, secure channels
based upon UDP datagrams are often overlooked by existing libraries; we show
how CEDAR's structure accommodates this as well. As an example of the utility
of this work, we show how the use of delegated security sessions and other
techniques inherent in CEDAR's architecture enables US CMS to meet their
scalability requirements in deploying Condor over large-scale, wide-area grid
systems.


  The vacuum expectation value of the Wilson loop in the dual representation is
calculated in the dual Higgs model with dual Dirac strings. It is shown that
the averaged value of the Wilson loop in the dual representation obeys the
area-law falloff. Quantum fluctuations of the dual-vector and the Higgs field
around Abrikosov flux lines induced by dual Dirac strings in a dual
superconducting vacuum and string shape fluctuations are taken into account.


  This paper uses the peridynamic theory, which is well-suited to crack
studies, to predict the crack patterns in a moving disk and classify them
according to the modes and finally perform regression analysis. In that way,
the crack patterns are obtained according to each mode by Molecular Dynamic
(MD) simulation using the peridynamics. Image classification and regression
studies are conducted through Convolutional Neural Networks (CNNs) and the
neural processes. First, we increased the amount and quality of the data using
peridynamics, which can theoretically compensate for the problems of the finite
element method (FEM) in generating crack pattern images. Second, we did the
case study for the PMB, LPS, and VES models that were obtained using the
peridynamic theory. Case studies were performed to classify the images using
CNNs and determine the PMB, LBS, and VES models' suitability. Finally, we
performed the regression analysis for the images of the crack patterns with
neural processes to predict the crack patterns. In the regression problem, by
representing the results of the variance according to the epochs, it can be
confirmed that the result of the variance is decreased by increasing the epoch
numbers through the neural processes. The most critical point of this study is
that the neural processes make an accurate prediction even if there are missing
or insufficient training data.


  The widely spread CoronaVirus Disease (COVID)-19 is one of the worst
infectious disease outbreaks in history and has become an emergency of primary
international concern. As the pandemic evolves, academic communities have been
actively involved in various capacities, including accurate epidemic
estimation, fast clinical diagnosis, policy effectiveness evaluation and
development of contract tracing technologies. There are more than 23,000
academic papers on the COVID-19 outbreak, and this number is doubling every 20
days while the pandemic is still on-going [1]. The literature, however, at its
early stage, lacks a comprehensive survey from a data analytics perspective. In
this paper, we review the latest models for analyzing COVID19 related data,
conduct post-publication model evaluations and cross-model comparisons, and
collect data sources from different projects.


  We present an efficient algorithm to solve semirandom planted instances of
any Boolean constraint satisfaction problem (CSP). The semirandom model is a
hybrid between worst-case and average-case input models, where the input is
generated by (1) choosing an arbitrary planted assignment $x^*$, (2) choosing
an arbitrary clause structure, and (3) choosing literal negations for each
clause from an arbitrary distribution "shifted by $x^*$" so that $x^*$
satisfies each constraint. For an $n$ variable semirandom planted instance of a
$k$-arity CSP, our algorithm runs in polynomial time and outputs an assignment
that satisfies all but a $o(1)$-fraction of constraints, provided that the
instance has at least $\tilde{O}(n^{k/2})$ constraints. This matches, up to
$polylog(n)$ factors, the clause threshold for algorithms that solve fully
random planted CSPs [FPV15], as well as algorithms that refute random and
semirandom CSPs [AOW15, AGK21]. Our result shows that despite having worst-case
clause structure, the randomness in the literal patterns makes semirandom
planted CSPs significantly easier than worst-case, where analogous results
require $O(n^k)$ constraints [AKK95, FLP16].
  Perhaps surprisingly, our algorithm follows a significantly different
conceptual framework when compared to the recent resolution of semirandom CSP
refutation. This turns out to be inherent and, at a technical level, can be
attributed to the need for relative spectral approximation of certain random
matrices - reminiscent of the classical spectral sparsification - which ensures
that an SDP can certify the uniqueness of the planted assignment. In contrast,
in the refutation setting, it suffices to obtain a weaker guarantee of absolute
upper bounds on the spectral norm of related matrices.


  In this work some aspects of the detection of certain general relativistic
effects in the weak gravitational field of the Earth via laser-ranged data to
some existing or proposed geodetic satellites are examined. The focus is on the
Lense-Thirring drag of the orbit of a test body, the gravitomagnetic clock
effect and the gravitoelectric perigee shift. The impact of some sources of
systematic errors is investigated. An experiment whose goal is the measurement
of the PPN parameters beta and gamma in the terrestrial field with LAGEOS
satellites at a level of 10^(-3)is presented. A modified version of the
proposed LARES mission is examined.


  Gao, Huo, Liu and Ma (2019) proved a result on the existence of paths
connecting specified two vertices whose lengths differ by one or two. By using
this result, they settled two famous conjectures due to Thomassen (1983). In
this paper, we improve their result, and obtain a generalization of a result of
Bondy and Vince (1998).


  Using X-ray free electron laser (XFEL) radiation to conduct an X-ray pump
X-ray probe experiment, we studied strongly ionized water as part of our
ongoing work on radiation damage. After irradiance with a pump pulse with a
nominal fluence of ~$5 \times 10^5$ J/cm$^2$, we observed for pump-probe delays
of 75 fs and longer an unexpected structural rearrangement, exhibiting a
characteristic length scale of ~9 \r{A}. Simulations suggest that the
experiment probes a superposition of ionized water in two distinct regimes. In
the first, fluences expected at the X-ray focus create nearly completely
ionized water, which as a result becomes effectively transparent to the probe.
In the second regime, out of focus pump radiation produces O$^{1+}$ and
O$^{2+}$ ions, which rearrange due to Coulombic repulsion over 10s of fs.
Importantly, structural changes in the low fluence regime have implications for
the design of two-pulse X-ray experiments that aim to study unperturbed liquid
samples. Our simulations account for two key observations in the experimental
data: the decrease in ambient water signal and an increase in low-angle X-ray
scattering. They cannot, however, account for the experimentally observed 9
\r{A} feature. A satisfactory account of this feature presents a new challenge
for theory.


  The two-channel photoproductions of $\gamma p \to K^{*+} \Sigma^{0}$ and
$\gamma p \to K^{*0} \Sigma^{+}$ are investigated based on an effective
Lagrangian approach at the tree-level Born approximation. In addition to the
$t$-channel $K$, $\kappa$, $K^*$ exchanges, the $s$-channel nucleon ($N$) and
$\Delta$ exchanges, the $u$-channel $\Lambda$, $\Sigma$, $\Sigma^*$ exchanges,
and the generalized contact term, we try to take into account the minimum
number of baryon resonances in constructing the reaction amplitudes to describe
the experimental data. It is found that by including the $\Delta(1905)5/2^+$
resonance with its mass, width, and helicity amplitudes taken from the Review
of Particle Physics [Particle Data Group, C. Patrignani {\it et al.}, Chin.
Phys. C {\bf 40}, 100001 (2016)], the calculated differential and total cross
sections for these two reactions are in good agreement with the experimental
data. An analysis of the reaction mechanisms shows that the cross sections of
$\gamma p \to K^{*+}\Sigma^{0}$ are dominated by the $s$-channel
$\Delta(1905)5/2^+$ exchange at low energies and $t$-channel $K^*$ exchange at
high energies, with the $s$-channel $\Delta$ exchange providing significant
contributions in the near-threshold region. For $\gamma p \to
K^{*0}\Sigma^{+}$, the angular dependences are dominated by the $t$-channel $K$
exchange at forward angles and the $u$-channel $\Sigma^*$ exchange at backward
angles, with the $s$-channel $\Delta$ and $\Delta(1905)5/2^+$ exchanges making
considerable contributions at low energies. Predictions are given for the beam,
target, and recoil asymmetries for both reactions.


  We study the problem of similarity learning and its application to image
retrieval with large-scale data. The similarity between pairs of images can be
measured by the distances between their high dimensional representations, and
the problem of learning the appropriate similarity is often addressed by
distance metric learning. However, distance metric learning requires the
learned metric to be a PSD matrix, which is computational expensive and not
necessary for retrieval ranking problem. On the other hand, the bilinear model
is shown to be more flexible for large-scale image retrieval task, hence, we
adopt it to learn a matrix for estimating pairwise similarities under the
regression framework. By adaptively updating the target matrix in regression,
we can mimic the hinge loss, which is more appropriate for similarity learning
problem. Although the regression problem can have the closed-form solution, the
computational cost can be very expensive. The computational challenges come
from two aspects: the number of images can be very large and image features
have high dimensionality. We address the first challenge by compressing the
data by a randomized algorithm with the theoretical guarantee. For the high
dimensional issue, we address it by taking low rank assumption and applying
alternating method to obtain the partial matrix, which has a global optimal
solution. Empirical studies on real world image datasets (i.e., Caltech and
ImageNet) demonstrate the effectiveness and efficiency of the proposed method.


  Our Galaxy resides in the center of a vast "Halo" of Dark Matter (DM). This
concentration produces, in many viable particle physics models, an indirect
Weakly Interacting Massive Particle (WIMP) annihilation signal that peaks in
the Fermi-LAT's energy range. Our knowledge of the diffuse background is
essential to placing reasonable limits on the DM mass and cross-section. We
incorporate a systematic variation of the GALPROP galactic diffuse background
model, constrained by current cosmic-ray measurements, into a profile
likelihood analysis and present preliminary upper limits on the DM annihilation
cross-section using the Fermi-LAT data.


  We prove a certain 'fat hyperplane section' Weak Lefschetz-type theorem for
etale cohomology of non-projective varieties, similar to a result of Goresky
and MacPherson (over complex numbers). This statement easily yields certain
(vast) generalizations of the 'ordinary' Weak Lefschetz and Barth's theorems in
arbitrary characteristic (that do not require any stratified Morse theory for
their proof).


  This paper describes the system developed by the NPU team for the 2020
personalized voice trigger challenge. Our submitted system consists of two
independently trained subsystems: a small footprint keyword spotting (KWS)
system and a speaker verification (SV) system. For the KWS system, a
multi-scale dilated temporal convolutional (MDTC) network is proposed to detect
wake-up word (WuW). For SV system, Write something here. The KWS predicts
posterior probabilities of whether an audio utterance contains WuW and
estimates the location of WuW at the same time. When the posterior probability
ofWuW reaches a predefined threshold, the identity information of triggered
segment is determined by the SV system. On evaluation dataset, our submitted
system obtains detection costs of 0.081and 0.091 in close talking and far-field
tasks, respectively.


  If a mathematical theory contains incompatible postulates then it is likely
that the theory will produce theorems or results that are contradictory. It
will be shown that this is the case with Dirac field theory. An example of such
a contradiction is the problem asociated with evaluating the Schwinger term. It
is generally known that different ways of evaluating this quantity yield
different results. It will be shown that the reason for this is that Dirac
field theory is mathematically inconsistent, i.e., it contains incompatible
assumptions or postulates. The generally accepted definition of the vacuum
state must be modified in order to create a consistent theory.


  We consider the behaviour at a boundary point of an open subset
$U\subset\mathbb{C}$ of distributions that are holomorphic on $U$ and belong to
what are called negative Lipschitz classes. The result explains the
significance for holomorphic functions of series of Wiener type involving
Hausdorff contents of dimension between $0$ and $1$. We begin with a survey
about function spaces and capacities that sets the problem in context and
reviews the relevant general theory. The techniques used include the
construction of a special partition of the identity that may be of independent
interest.


  We extend previous analyses of soliton solutions in (4+1) gravity to new
ranges of their defining parameters. The geometry, as studied using invariants,
has the topology of wormholes found in (3+1) gravity. In the induced-matter
picture, the fluid does not satisfy the strong energy conditions, but its
gravitational mass is positive. We infer the possible existance of (4+1)
wormholes which, compared to their (3+1) counterparts, are less exotic.


  Developing software for scientific applications that require the integration
of diverse types of computing, instruments, and data present challenges that
are distinct from commercial software. These applications require scale, and
the need to integrate various programming and computational models with
evolving and heterogeneous infrastructure. Pervasive and effective abstractions
for distributed infrastructures are thus critical; however, the process of
developing abstractions for scientific applications and infrastructures is not
well understood. While theory-based approaches for system development are
suited for well-defined, closed environments, they have severe limitations for
designing abstractions for scientific systems and applications. The design
science research (DSR) method provides the basis for designing practical
systems that can handle real-world complexities at all levels. In contrast to
theory-centric approaches, DSR emphasizes both practical relevance and
knowledge creation by building and rigorously evaluating all artifacts. We show
how DSR provides a well-defined framework for developing abstractions and
middleware systems for distributed systems. Specifically, we address the
critical problem of distributed resource management on heterogeneous
infrastructure over a dynamic range of scales, a challenge that currently
limits many scientific applications. We use the pilot-abstraction, a widely
used resource management abstraction for high-performance, high throughput, big
data, and streaming applications, as a case study for evaluating the DSR
activities. For this purpose, we analyze the research process and artifacts
produced during the design and evaluation of the pilot-abstraction. We find DSR
provides a concise framework for iteratively designing and evaluating systems.
Finally, we capture our experiences and formulate different lessons learned.


  Let $G$ be a finite group of order divisible by a prime $p$. The number of
$p$-regular and $p'$-regular conjugacy classes of $G$ is at least
$2\sqrt{p-1}$. Also, the number of $p$-rational and $p'$-rational irreducible
characters of $G$ is at least $2\sqrt{p-1}$. Along the way we prove a uniform
lower bound for the number of $p$-regular classes in a finite simple group of
Lie type in terms of its rank and size of the underlying field.


  We show that the multivariate additive higher Chow groups of a smooth affine
$k$-scheme $\Spec (R)$ essentially of finite type over a perfect field $k$ of
characteristic $\not = 2$ form a differential graded module over the big de
Rham-Witt complex $\W_m\Omega^{\bullet}_{R}$. In the univariate case, we show
that additive higher Chow groups of $\Spec (R)$ form a Witt-complex over $R$.
We use these structures to prove an \'etale descent for multivariate additive
higher Chow groups.


  We propose an algorithm for finding zero divisors in quaternion algebras over
quadratic number fields, or equivalently, solving homogeneous quadratic
equations in three variables over $\mathbb{Q}(\sqrt{d})$ where $d$ is a
square-free integer. The algorithm is randomized and runs in polynomial time if
one is allowed to call oracles for factoring integers.


  Randomization procedures are used in legal and statistical applications,
aiming to shield important decisions from spurious influences. This article
gives an intuitive introduction to randomization and examines some intended
consequences of its use related to truthful statistical inference and fair
legal judgment. This article also presents an open-code Java implementation for
a cryptographically secure, statistically reliable, transparent, traceable, and
fully auditable randomization tool.


  We studied phase coherent phenomena in mesoscopic Permalloy samples by
exploring low temperature transport. Both, differential conductance as a
function of bias voltage and magnetoconductance of individual wires display
conductance fluctuations. Analysis of these fluctuations yields a phase
coherence length of $\sim250$ nm at 25 mK as well as a $1/\sqrt{T}$ temperature
dependence. To suppress conductance fluctuations by ensemble averaging we
investigated low temperature transport in wire arrays and extended Permalloy
films. In these samples we have measured conductance corrections which stem
from electron-electron interaction (EEI) but attempts to detect signatures of
weak localization were without success.


  We study the Fourier transform for compactly supported distributional
sections of complex homogeneous vector bundles on symmetric spaces of
non-compact type $X = G/K$. We prove a characterisation of their range. In
fact, from Delorme's Paley-Wiener theorem for compactly supported smooth
functions on a real reductive group of Harish-Chandra class, we deduce
topological Paley-Wiener and Paley-Wiener-Schwartz theorems for sections.


  Recently, the philosophy of visual saliency and attention has started to gain
popularity in the robotics community. Therefore, this paper aims to mimic this
mechanism in SLAM framework by using saliency prediction model. Comparing with
traditional SLAM that treated all feature points as equal important in
optimization process, we think that the salient feature points should play more
important role in optimization process. Therefore, we proposed a saliency model
to predict the saliency map, which can capture both scene semantic and
geometric information. Then, we proposed Salient Bundle Adjustment by using the
value of saliency map as the weight of the feature points in traditional Bundle
Adjustment approach. Exhaustive experiments conducted with the state-of-the-art
algorithm in KITTI and EuRoc datasets show that our proposed algorithm
outperforms existing algorithms in both indoor and outdoor environments.
Finally, we will make our saliency dataset and relevant source code open-source
for enabling future research.


  The Baumslag group had been a candidate for a group with an extremely
difficult word problem until Myasnikov, Ushakov, and Won succeeded to show that
its word problem can be solved in polynomial time. Their result used the newly
developed data structure of power circuits allowing for a non-elementary
compression of integers. Later this was extended in two directions: Laun showed
that the same applies to the Baumslag groups $G_{1, q}$ for $q \geq 2$ and we
established that the word problem of the Baumslag group $G_{1, 2}$ can be
solved in $\mathsf{TC}^2$.
  In this work we refine the operations on reduced power circuits to further
improve upon both results. We show that the word problem of the Baumslag groups
$G_{p, pq}$ with $|p|,|q| \geq 1$ can be solved in $\mathsf{uTC}^1$. Moreover,
we prove that the conjugacy problem in $G_{p, pq}$ is strongly generically in
$\mathsf{uTC}^1$ (meaning that for "most" inputs it is in $\mathsf{uTC}^1$).
Finally, for every fixed $g \in G_{1, q}$ (case $p=1$) conjugacy to $g$ can be
decided in $\mathsf{uTC}^1$ for all inputs.
  We further show that the word problem of the Baumslag-Solitar groups $BS_{p,
pq}$ is in $\mathsf{uAC}^0(F_2)$ if the input word is given in a quite
compressed form and so give a complexity result for a special case of the power
word problem for these groups.


  A highly effective approach to the search for hypothetical new interactions
through isotope shift spectroscopy of hydrogen-like ions is presented. A
weighted difference of the g factor and ground-state energy is shown to assist
in the suppression of detrimental uncertainties from nuclear structure, while
preserving the hypothetical contributions from new interactions. Experimental
data from only a single isotope pair is required. Account is taken of the
small, subleading nuclear corrections, allowing to show that, provided feasible
experimental progress is achieved in UV/X-ray spectroscopy, the presented
approach can yield competitive bounds on New Physics electron coupling
parameters improved by more than an order of magnitude compared to leading
bounds from atomic physics.


  By using quark Yukawa matrices only, we can construct renormalization
invariants that are exact at the one-loop level in the standard model. One of
them $I^q$ is accidentally consistent with unity, even though quark masses are
strongly hierarchical. We calculate a lepton version of the invariant $I^l$ for
Dirac and Majorana neutrino cases and find that $I^l$ can also be close to
unity. For the Dirac neutrino and inverted hierarchy case, if the lightest
neutrino mass is 3.0 meV to 8.8 meV, an equality $I^q=I^l$ can be satisfied.
These invariants are not changed even if new particles couple to the standard
model particles, as long as those couplings are generation independent.


  We investigate the Cherenkov radiation by a charge uniformly moving inside a
dielectric cylindrical channel in a homogeneous medium. The expressions for the
Fourier components of the electric and magnetic fields are derived by using the
electromagnetic field Green tensor. The spectral distribution of the Cherenkov
radiation intensity in the exterior medium is studied for the general case of
frequency dispersion of the interior and exterior dielectric functions. It is
shown that, under certain conditions on the dielectric permittivities, strong
narrow peaks appear in the spectral distribution. The spectral locations of
those peaks are specified and their heights and widths are estimated
analytically on the base of the dispersion equation for the electromagnetic
eigenmodes of the cylinder.


  Single-shot positron annihilation lifetime spectroscopy (SSPALS) is an
extremely useful tool for experiments involving the positronium atom (Ps). I
examine some of the methods that are typically employed to analyze lifetime
spectra, and use a Monte-Carlo simulation to explore the advantages and
limitations these have in laser spectroscopy experiments, such as
resonance-enhanced multiphoton ionization (REMPI) or the production of Rydberg
Ps.


  I study the behavior and the performance of the long-term forecasts issued by
financial analysts with respect to the Extrapolation Hypothesis. That
hypothesis states that investors, extrapolating from the firms' recent
performances, are too optimistic about growth and large firms and too
pessimistic about value and small firms. I find that the forecasting errors are
higher for the growth firms and large firms, thus providing support for the
Extrapolation Hypothesis. However, in addition to the rosy picture of the
growth and large firms, the forecasts of the value and small firms are not so
gloomy in many cases. My analysis also reveals that expectations move together
for all categories of book-to-market and all sizes of firms. I proceed by
investigating some common factors that may influence analysts' long-term
forecasts, including co-movement and excessive optimism. I find that macro
factors beyond a firm's recent performance may influence the formation of
expectations.


  It is shown that the temperature may be quantized in some spacetime due to
the periodically topological structure of the Euclidean section. The quanta of
the temperature is the Hawking-Unruh temperature.


  In this paper, we present an Intersection-over-Union (IoU) guided two-stage
3D object detector with a voxel-to-point decoder. To preserve the necessary
information from all raw points and maintain the high box recall in voxel based
Region Proposal Network (RPN), we propose a residual voxel-to-point decoder to
extract the point features in addition to the map-view features from the voxel
based RPN. We use a 3D Region of Interest (RoI) alignment to crop and align the
features with the proposal boxes for accurately perceiving the object position.
The RoI-Aligned features are finally aggregated with the corner geometry
embeddings that can provide the potentially missing corner information in the
box refinement stage. We propose a simple and efficient method to align the
estimated IoUs to the refined proposal boxes as a more relevant localization
confidence. The comprehensive experiments on KITTI and Waymo Open Dataset
demonstrate that our method achieves significant improvements with novel
architectures against the existing methods. The code is available on Github
URL\footnote{\url{https://github.com/jialeli1/From-Voxel-to-Point}}.


  Bernstein blocks of complex representations of p-adic reductive groups have
been computed in a large amount of examples, in part thanks to the theory of
types a la Bushnell and Kutzko. The output of these purely
representation-theoretic computations is that many of these blocks are
equivalent. The motto of this paper is that most of these coincidences are
explained, and many more can be predicted, by a functoriality principle
involving dual groups. We prove a precise statement for groups related to GL n
, and then state conjectural generalizations in two directions : more general
reductive groups and/or integral l-adic representations.


  In this paper we define and study the global Hilbert-Kunz multiplicity and
the global F-signature of prime characteristic rings which are not necessarily
local. Our techniques are made meaningful by extending many known theorems
about Hilbert-Kunz multiplicity and F-signature to the non-local case.


  We probe star formation in the environments of massive
$\sim10^{13}\,M_{\odot}$ dark matter halos at redshifts of $z$$\sim$$1$. This
star formation is linked to a sub-millimetre clustering signal which we detect
in maps of the Planck High Frequency Instrument that are stacked at the
positions of a sample of high-redshift ($z$$>$$2$) strongly-lensed dusty
star-forming galaxies (DSFGs) selected from the South Pole Telescope (SPT) 2500
deg$^2$ survey. The clustering signal has sub-millimetre colours which are
consistent with the mean redshift of the foreground lensing halos
($z$$\sim$$1$). We report a mean excess of star formation rate (SFR) compared
to the field, of $(2700\pm700)\,M_{\odot}\,{yr}^{-1}$ from all galaxies
contributing to this clustering signal within a radius of 3.5' from the SPT
DSFGs. The magnitude of the Planck excess is in broad agreement with
predictions of a current model of the cosmic infrared background. The model
predicts that 80$\%$ of the excess emission measured by Planck originates from
galaxies lying in the neighbouring halos of the lensing halo. Using Herschel
maps of the same fields, we find a clear excess, relative to the field, of
individual sources which contribute to the Planck excess. The mean excess SFR
compared to the field is measured to be ($370\pm40)$$\,M_{\odot}\,{yr}^{-1}$
per resolved, clustered source. Our findings suggest that the environments
around these massive $z$$\sim$$1$ lensing halos host intense star formation out
to about $2\,$Mpc. The flux enhancement due to clustering should also be
considered when measuring flux densities of galaxies in Planck data.


  As children grow older, they develop an intuitive understanding of the
physical processes around them. Their physical understanding develops in
stages, moving along developmental trajectories which have been mapped out
extensively in previous empirical research. Here, we investigate how the
learning trajectories of deep generative neural networks compare to children's
developmental trajectories using physical understanding as a testbed. We
outline an approach that allows us to examine two distinct hypotheses of human
development - stochastic optimization and complexity increase. We find that
while our models are able to accurately predict a number of physical processes,
their learning trajectories under both hypotheses do not follow the
developmental trajectories of children.


  Weird, unusual, and uncanny images pique the curiosity of observers because
they challenge commonsense. For example, an image released during the 2022
world cup depicts the famous soccer stars Lionel Messi and Cristiano Ronaldo
playing chess, which playfully violates our expectation that their competition
should occur on the football field. Humans can easily recognize and interpret
these unconventional images, but can AI models do the same? We introduce
WHOOPS!, a new dataset and benchmark for visual commonsense. The dataset is
comprised of purposefully commonsense-defying images created by designers using
publicly-available image generation tools like Midjourney. We consider several
tasks posed over the dataset. In addition to image captioning, cross-modal
matching, and visual question answering, we introduce a difficult explanation
generation task, where models must identify and explain why a given image is
unusual. Our results show that state-of-the-art models such as GPT3 and BLIP2
still lag behind human performance on WHOOPS!. We hope our dataset will inspire
the development of AI models with stronger visual commonsense reasoning
abilities. Data, models and code are available at the project website:
whoops-benchmark.github.io