  We revisit the problem of secular resonance sweeping during the dissipation
of a protoplanetary disk and its possible role in exciting the orbits of
primordial asteroids, in the light of recent models of solar system evolution.
We develop an integrator that incorporates the gravitational effect of a
uniformly (or not) depleting, axisymmetric disk with arbitrary surface density
profile; its performance is verified by analytical calculations. The secular
response of fictitious asteroids, under perturbations from Jupiter, Saturn and
a decaying disk, is thoroughly studied. Note that the existence of a symmetry
plane induced by the disk, lifts the inherent degeneracy of the two-planet
system, such that the '$s_5$' nodal frequency can also play a major role. We
examine different resonant configurations for the planets (2:1, 3:2, 5:3), disk
models and depletion scenarios. For every case we compute the corresponding
time paths of secular resonances, which show when and where resonance crossing
occurs. The excitation of asteroids, particularly in inclination, is studied in
the various models and compared to analytical estimates. We find that
inclination excitation in excess of $\sim 10^{\circ}$ is possible in the
asteroid belt, but the nearly uniform spread of $\Delta i\sim 20^{\circ}$
observed calls for the combined action of secular resonance sweeping with other
mechanisms (e.g. scattering by Mars-sized embryos) that would be operating
during terrestrial planet formation. Our results are also applicable to
extrasolar planetary systems.


  In this paper, we propose a novel design of image deblurring in the form of
one-shot convolution filtering that can directly convolve with naturally
blurred images for restoration. The problem of optical blurring is a common
disadvantage to many imaging applications that suffer from optical
imperfections. Despite numerous deconvolution methods that blindly estimate
blurring in either inclusive or exclusive forms, they are practically
challenging due to high computational cost and low image reconstruction
quality. Both conditions of high accuracy and high speed are prerequisites for
high-throughput imaging platforms in digital archiving. In such platforms,
deblurring is required after image acquisition before being stored, previewed,
or processed for high-level interpretation. Therefore, on-the-fly correction of
such images is important to avoid possible time delays, mitigate computational
expenses, and increase image perception quality. We bridge this gap by
synthesizing a deconvolution kernel as a linear combination of Finite Impulse
Response (FIR) even-derivative filters that can be directly convolved with
blurry input images to boost the frequency fall-off of the Point Spread
Function (PSF) associated with the optical blur. We employ a Gaussian low-pass
filter to decouple the image denoising problem for image edge deblurring.
Furthermore, we propose a blind approach to estimate the PSF statistics for two
Gaussian and Laplacian models that are common in many imaging pipelines.
Thorough experiments are designed to test and validate the efficiency of the
proposed method using 2054 naturally blurred images across six imaging
applications and seven state-of-the-art deconvolution methods.


  Early-type galaxies (ETGs) are supposed to follow the virial relation $M =
k_e \sigma_*^2 R_e / G$, with $M$ being the mass, $\sigma_*$ being the stellar
velocity dispersion, $R_e$ being the effective radius, $G$ being Newton's
constant, and $k_e$ being the virial factor, a geometry factor of order unity.
Applying this relation to (a) the ATLAS3D sample of Cappellari et al. (2013)
and (b) the sample of Saglia et al. (2016) gives ensemble-averaged factors
$\langle k_e\rangle =5.15\pm0.09$ and $\langle k_e\rangle =4.01\pm0.18$,
respectively, with the difference arising from different definitions of
effective velocity dispersions. The two datasets reveal a statistically
significant tilt of the empirical relation relative to the theoretical virial
relation such that $M\propto(\sigma_*^2R_e)^{0.92}$. This tilt disappears when
replacing $R_e$ with the semi-major axis of the projected half-light ellipse,
$a$. All best-fit scaling relations show zero intrinsic scatter, implying that
the mass plane of ETGs is fully determined by the virial relation. Whenever a
comparison is possible, my results are consistent with, and confirm, the
results by Cappellari et al. (2013). The difference between the relations using
either $a$ or $R_e$ arises from a known lack of highly elliptical high-mass
galaxies; this leads to a scaling $(1-\epsilon) \propto M^{0.12}$, with
$\epsilon$ being the ellipticity and $R_e = a\sqrt{1-\epsilon}$. Accordingly,
$a$, not $R_e$, is the correct proxy for the scale radius of ETGs. By geometry,
this implies that early-type galaxies are axisymmetric and oblate in general,
in agreement with published results from modeling based on kinematics and light
distributions.


  Financial sector and especially the insurance industry collect vast volumes
of text on a daily basis and through multiple channels (their agents, customer
care centers, emails, social networks, and web in general). The information
collected includes policies, expert and health reports, claims and complaints,
results of surveys, and relevant social media posts. It is difficult to
effectively extract label, classify, and interpret the essential information
from such varied and unstructured material. Therefore, the Insurance Industry
is among the ones that can benefit from applying technologies for the
intelligent analysis of free text through Natural Language Processing (NLP).
  In this paper, CRL+, a novel text classification model combining Contrastive
Representation Learning (CRL) and Active Learning is proposed to handle the
challenge of using semi-supervised learning for text classification. In this
method, supervised (CRL) is used to train a RoBERTa transformer model to encode
the textual data into a contrastive representation space and then classify
using a classification layer. This (CRL)-based transformer model is used as the
base model in the proposed Active Learning mechanism to classify all the data
in an iterative manner. The proposed model is evaluated using unstructured
obituary data with objective to determine the cause of the death from the data.
This model is compared with the CRL model and an Active Learning model with the
RoBERTa base model. The experiment shows that the proposed method can
outperform both methods for this specific task.


  We generalize Franz' independence in tensor categories with inclusions from
two morphisms (which represent generalized random variables) to arbitrary
ordered families of morphisms. We will see that this only works consistently if
the unit object is an initial object, in which case the inclusions can be
defined starting from the tensor category alone. The obtained independence for
morphisms is called categorial independence. We define categorial L\'evy
processes on every tensor category with initial unit object and present a
construction generalizing the reconstruction of a L\'evy process from its
convolution semigroup via the Daniell-Kolmogorov theorem. Finally, we discuss
examples showing that many known independences from algebra as well as from
(noncommutative) probability are special cases of categorial independence.


  Nearby galactic nuclei are observed to be very much dimmer than active
galactic nuclei in distant galaxies. The Chandra X-ray Observatory has provided
a definitive explanation for why this is so. With its excellent angular
resolution, Chandra has imaged hot X-ray-emitting gas close to the
gravitational capture radius of a handful of supermassive black holes,
including Sgr A* in the nucleus of our own Galaxy. These observations provide
direct and reliable estimates of the Bondi mass accretion rate in these nuclei.
It is found that the Bondi accretion rate is significantly below the Eddington
mass accretion rate, but this alone does not explain the dimness of the
accretion flows. In all the systems observed so far, the accretion luminosity
is orders of magnitude less than Mdot_{Bondi} times c^2, which means that the
accretion must occur via a radiatively inefficient mode. This conclusion, which
was strongly suspected for many years, is now inescapable. Furthermore, if the
accretion in these nuclei occurs via either a Bondi flow or an
advection-dominated accretion flow, the accreting plasma must be
two-temperature at small radii, and the central mass must have an event
horizon. Convection, winds and jets may play a role, but observations do not
yet permit definite conclusions.


  The interaction between vortex density waves and high-frequency second sound
in counterflow superfluid turbulence is examined, incorporating diffusive and
elastic contributions of the vortex tangle. The analysis is based on a set of
evolution equations for the energy density, the heat flux, the vortex line
density, and the vortex flux, the latter being considered here as an
independent variable, in contrast to previous works. The latter feature is
crucial in the transition from diffusive to propagative behavior of vortex
density perturbations, which is necessary to interpret the details of
high-frequency second sound.


  We discuss the partitioning of a quantum system by subsystem separation
through unitary block-diagonalization (SSUB) applied to a Fock operator. Our
separation can be formulated in a very general way. It can be applied to very
different partitionings ranging from those driven by features in the molecular
structure (such as a solute surrounded by solvent molecules) to those that aim
at an orbital separation (such as core-valence separation). Our framework
embraces recent developments of Manby and Miller as well as older ones of
Huzinaga and Cantu. Projector-based embedding is simplified and accelerated by
SSUB. Moreover, it directly relates to decoupling approaches for relativistic
four-component many-electron theory. For a Fock operator based on the Dirac
one-electron Hamiltonian, one would like to separate the so-called positronic
(negative-energy) states from the electronic bound and continuum states. The
exact two-component (X2C) approach developed for this purpose becomes a special
case of the general SSUB framework and may therefore be viewed as a
system-environment decoupling approach. Moreover, for SSUB there exists no
restriction with respect to the number of subsystems that are generated - in
the limit, decoupling of all single-particle states is recovered, which
represents exact diagonalization of the problem. The fact that a Fock operator
depends on its eigenvectors poses challenges to all system-environment
decoupling approaches and is discussed in terms of the SSUB framework. Apart
from improved conceptual understanding, these relations bring about technical
advances as developments in different fields can immediately cross-fertilize
one another. As an important example we discuss the atomic decomposition of the
unitary block-diagonalization matrix in X2C-type approaches that can inspire
approaches for the efficient partitioning of large total systems based on SSUB.


  UAV collective motion has become a hot research topic in recent years. The
realization of UAV collective motion, however, relied heavily on centralized
control method and suffered from instability. Inspired by bird flocking theory,
a control strategy for UAV collective motion with distributed measure and
control methods was proposed in this study. In order to appropriately adjust
the inter-agent distance suitable for realization, the control law based on
bird flocking theory was optimized, and the convergence of velocities and
collision avoidance properties were presented through simulation results.
Furthermore, the stable collective motion of two UAVs using visual relative
information only with proposed strategy in both indoor and outdoor GPS-denied
environments were realized.


  Current Hardware Trojan (HT) detection techniques are mostly developed based
on a limited set of HT benchmarks. Existing HT benchmark circuits are generated
with multiple shortcomings, i.e., i) they are heavily biased by the designers'
mindset when created, and ii) they are created through a one-dimensional lens,
mainly the signal activity of nets. We introduce the first automated
Reinforcement Learning (RL) HT insertion and detection framework to address
these shortcomings. In the HT insertion phase, an RL agent explores the
circuits and finds locations best for keeping inserted HTs hidden. On the
defense side, we introduce a multi-criteria RL-based HT detector that generates
test vectors to discover the existence of HTs. Using the proposed framework,
one can explore the HT insertion and detection design spaces to break the
limitations of human mindset and benchmark issues, ultimately leading toward
the next generation of innovative detectors. We demonstrate the efficacy of our
framework on ISCAS-85 benchmarks, provide the attack and detection success
rates, and define a methodology for comparing our techniques.


  The goal of combining beamforming and space-time coding in this work is to
obtain full-diversity order and to provide additional received power (array
gain) compared to conventional space-time codes. In our system, we consider a
quasi-static fading environment and we incorporate both high-rate and low-rate
feedback channels with possible feedback errors. To utilize feedback
information, a class of code constellations is proposed, inspired from
orthogonal designs and precoded space-time block codes, which is called
generalized partly orthogonal designs or generalized PODs. Furthermore, to
model feedback errors, we assume that the feedback bits go through binary
symmetric channels (BSCs). Two cases are studied: first, when the BSC bit error
probability is known a priori to the transmission ends and second, when it is
not known exactly. In the first case, we derive a minimum pairwise error
probability (PEP) design criterion for generalized PODs. Then we design the
quantizer for the erroneous feedback channel and the precoder codebook of PODs
based on this criterion. The quantization scheme in our system is a channel
optimized vector quantizer (COVQ). In the second case, the design of the
quantizer and the precoder codebook is based on similar approaches, however
with a worst-case design strategy. The attractive property of our combining
scheme is that it converges to conventional space-time coding with low-rate and
erroneous feedback and to directional beamforming with high-rate and error-free
feedback. This scheme shows desirable robustness against feedback channel
modeling mismatch.


  Development of autonomous cyber system defense strategies and action
recommendations in the real-world is challenging, and includes characterizing
system state uncertainties and attack-defense dynamics. We propose a
data-driven deep reinforcement learning (DRL) framework to learn proactive,
context-aware, defense countermeasures that dynamically adapt to evolving
adversarial behaviors while minimizing loss of cyber system operations. A
dynamic defense optimization problem is formulated with multiple protective
postures against different types of adversaries with varying levels of skill
and persistence. A custom simulation environment was developed and experiments
were devised to systematically evaluate the performance of four model-free DRL
algorithms against realistic, multi-stage attack sequences. Our results suggest
the efficacy of DRL algorithms for proactive cyber defense under multi-stage
attack profiles and system uncertainties.


  The Ashkin-Teller (AT) model is a generalization of Ising 2-d to a four
states spin model; it can be written in the form of two Ising layers (in
general with different couplings) interacting via a four-spin interaction. It
was conjectured long ago (by Kadanoff and Wegner, Wu and Lin, Baxter and
others) that AT has in general two critical points, and that universality
holds, in the sense that the critical exponents are the same as in the Ising
model, except when the couplings of the two Ising layers are equal (isotropic
case). We obtain an explicit expression for the specific heat from which we
prove this conjecture in the weakly interacting case and we locate precisely
the critical points. We find the somewhat unexpected feature that, despite
universality holds for the specific heat, nevertheless nonuniversal critical
indexes appear: for instance the distance between the critical points rescales
with an anomalous exponent as we let the couplings of the two Ising layers
coincide (isotropic limit); and so does the constant in front of the logarithm
in the specific heat. Our result also explains how the crossover from universal
to nonuniversal behaviour is realized.


  Dressing quantum states of matter with virtual photons can create exotic
effects ranging from vacuum-field modified transport to polaritonic chemistry,
and may drive strong squeezing or entanglement of light and matter modes. The
established paradigm of cavity quantum electrodynamics focuses on resonant
light-matter interaction to maximize the coupling strength
$\Omega_\mathrm{R}/\omega_\mathrm{c}$, defined as the ratio of the vacuum Rabi
frequency and the carrier frequency of light. Yet, the finite oscillator
strength of a single electronic excitation sets a natural limit to
$\Omega_\mathrm{R}/\omega_\mathrm{c}$. Here, we demonstrate a new regime of
record-strong light-matter interaction which exploits the cooperative dipole
moments of multiple, highly non-resonant magnetoplasmon modes specifically
tailored by our metasurface. This multi-mode coupling creates an ultrabroadband
spectrum of over 20 polaritons spanning 6 optical octaves, vacuum ground state
populations exceeding 1 virtual excitation quantum for electronic and optical
modes, and record coupling strengths equivalent to
$\Omega_\mathrm{R}/\omega_\mathrm{c}=3.19$. The extreme interaction drives
strongly subcycle exchange of vacuum energy between multiple bosonic modes akin
to high-order nonlinearities otherwise reserved to strong-field physics, and
entangles previously orthogonal electronic excitations solely via vacuum
fluctuations of the common cavity mode. This offers avenues towards tailoring
phase transitions by coupling otherwise non-interacting modes, merely by
shaping the dielectric environment.


  We report the first measurements of transverse single-spin asymmetries for
inclusive jet and jet + $\pi^{\pm}$ production at midrapidity from transversely
polarized proton-proton collisions at $\sqrt{s} = 500$ GeV. The data were
collected in 2011 with the STAR detector sampled from 23 pb$^{-1}$ integrated
luminosity with an average beam polarization of 53%. Asymmetries are reported
for jets with transverse momenta $6 < p_{T, jet} < 55$ GeV/c and pseudorapidity
$|\eta| < 1$. Presented are measurements of the inclusive-jet azimuthal
transverse single-spin asymmetry, sensitive to twist-3 initial-state
quark-gluon correlators; the Collins asymmetry, sensitive to quark transversity
coupled to the polarized Collins fragmentation function; and the first
measurement of the "Collins-like" asymmetry, sensitive to linearly polarized
gluons. Within the present statistical precision, inclusive-jet and
Collins-like asymmetries are small, with the latter allowing the first
experimental constraints on gluon linear polarization in a polarized proton. At
higher values of jet transverse momenta, we observe the first non-zero Collins
asymmetries in polarized-proton collisions, with a statistical significance of
greater than $5\sigma$. The results span a range of x similar to results from
SIDIS but at much higher $Q^{2}$. The Collins results enable tests of
universality and factorization-breaking in the transverse momentum-dependent
formulation of perturbative quantum chromodynamics.


  We demonstrate a novel microcavity-based photon up-conversion using second
harmonic generation (SHG) from a polar nematic fluid media doped with a laser
dye. The present idea is based on coherent light generation via simultaneous
frequency doubling and stimulated emission (lasing) inside a microcavity. The
polar nematic fluid equips very high even-order optical nonlinearity due to the
polar symmetry and large dipole moment along the molecular long axis. At the
same time, its inherent fluidic nature allows us to easily functionalize the
media just by doping, in the present case, with an emissive laser dye. Our
demonstrated system exhibits a giant nonlinear optical response to input light,
while enabling spectral narrowing and multiple-signal output of up-converted
light, that is not attainable though the simple SH-conversion of input light.
Furthermore, susceptibility of the liquid crystal offers dynamic modulation
capabilities under external stimulus, such as signal switching with electric
field application or wavelength tuning through temperature variation. Such a
brand-new type of simple coherent flexible up-conversion system must be
promising as a new principle for easy-accessible and down-scalable wavelength
conversion devices.


  With the first observation of a binary neutron star merger through
gravitational waves and light GW170817, compact binary mergers have now taken
the center stage in nuclear astrophysics. They are thought to be one of the
main astrophysical sites of production of r-process elements, and merger
observations have become a fundamental tool to constrain the properties of
matter. Here, we review our current understanding of the dynamics of neutron
star mergers, in general, and of GW170817 in particular. We discuss the
physical processes governing the inspiral, merger, and postmerger evolution,
and we highlight the connections between these processes, the dynamics, and the
multimessenger observables. Finally, we discuss open questions and issues in
the field and the need to address them through a combination of better
theoretical models and new observations.


  In this note, we establish the convergence in distribution of the maxima of
i.i.d. random variables to the Gumbel distribution with the associated
normalizing sequences for several examples that are related to the normal
distribution. Motivated by tests for jumps in high-frequency data, our main
interest is in the half-normal distribution and the sum or difference of two
independent half-normally distributed random variables. Since the half-normal
distribution is neither stable nor symmetric, these examples are non-obvious
generalizations. It is shown that the sum and difference of two independent
half-normally distributed random variables and other examples yield
distributions with tail behaviours that relate to the normal case. It turns out
that the Gumbel convergence for all such distributions can be proved following
similar steps. We illustrate the results in Monte Carlo simulations.


  Due to its exceptional electronic and thermal properties, graphene is a key
material for bolometry, calorimetry, and photon detection. However, despite
graphene's relatively simple electronic structure, the physical processes
responsible for the transport of heat from the electrons to the lattice are
experimentally still elusive. Here, we measure the thermal response of
low-disorder graphene encapsulated in hexagonal boron nitride (hBN) by
integrating it within a multi-terminal superconducting device coupled to a
microwave resonator. This technique allows us to simultaneously apply Joule
heat power to the graphene flake while performing calibrated readout of the
electron temperature. We probe the thermalization rates of both electrons and
holes with high precision and observe a thermalization scaling exponent
consistent with cooling dominated by resonant electron-phonon coupling
processes occurring at the interface between graphene and superconducting
leads. The technique utilized here is applicable for wide range of
semiconducting-superconducting interface heterostructures and provides new
insights into the thermalization pathways essential for the next-generation
thermal detectors.


  For minimally $k$-connected graphs on $n$ vertices, Mader proved a tight
lower bound for the number $|V_k|$ of vertices of degree $k$ in dependence on
$n$ and $k$. Oxley observed 1981 that in many cases a considerably better bound
can be given if $m := |E|$ is used as additional parameter, i.e. in dependence
on $m$, $n$ and $k$. It was left open to determine whether Oxley's bound is
best possible.
  We show that this is not the case, but propose a closely related bound that
deviates from Oxley's long-standing one only for small values of $m$. We prove
that this new bound is best possible. The bound contains Mader's bound as
special case.


  We investigate how the possible presence of unit roots and cointegration
affects forecasting with Big Data. As most macroeoconomic time series are very
persistent and may contain unit roots, a proper handling of unit roots and
cointegration is of paramount importance for macroeconomic forecasting. The
high-dimensional nature of Big Data complicates the analysis of unit roots and
cointegration in two ways. First, transformations to stationarity require
performing many unit root tests, increasing room for errors in the
classification. Second, modelling unit roots and cointegration directly is more
difficult, as standard high-dimensional techniques such as factor models and
penalized regression are not directly applicable to (co)integrated data and
need to be adapted. We provide an overview of both issues and review methods
proposed to address these issues. These methods are also illustrated with two
empirical applications.


  We introduce a new critical value $c_\infty(L)$ for Tonelli Lagrangians $L$
on the tangent bundle of the 2-sphere without minimizing measures supported on
a point. We show that $c_\infty(L)$ is strictly larger than the Ma\~n\'e
critical value $c(L)$, and on every energy level $e\in(c(L),c_\infty(L))$ there
exist infinitely many periodic orbits of the Lagrangian system of $L$, one of
which is a local minimizer of the free-period action functional. This has
applications to Finsler metrics of Randers type on the 2-sphere. We show that,
under a suitable criticality assumption on a given Randers metric, after
rescaling its magnetic part with a sufficiently large multiplicative constant,
the new metric admits infinitely many closed geodesics, one of which is a
waist. Examples of critical Randers metrics include the celebrated Katok
metric.


  We describe the architecture and algorithms of the Adaptive Charging Network
(ACN), which was first deployed on the Caltech campus in early 2016 and is
currently operating at over 100 other sites in the United States. The
architecture enables real-time monitoring and control and supports electric
vehicle (EV) charging at scale. The ACN adopts a flexible Adaptive Scheduling
Algorithm based on convex optimization and model predictive control and allows
for significant over-subscription of electrical infrastructure. We describe
some of the practical challenges in real-world charging systems, including
unbalanced three-phase infrastructure, non-ideal battery charging behavior, and
quantized control signals. We demonstrate how the Adaptive Scheduling Algorithm
handles these challenges, and compare its performance against baseline
algorithms from the deadline scheduling literature using real workloads
recorded from the Caltech ACN and accurate system models. We find that in these
realistic settings, our scheduling algorithm can improve operator profit by 3.4
times over uncontrolled charging and consistently outperforms baseline
algorithms when delivering energy in highly congested systems.


  Physically informed neural networks (PINNs) are a promising emerging method
for solving differential equations. As in many other deep learning approaches,
the choice of PINN design and training protocol requires careful craftsmanship.
Here, we suggest a comprehensive theoretical framework that sheds light on this
important problem. Leveraging an equivalence between infinitely
over-parameterized neural networks and Gaussian process regression (GPR), we
derive an integro-differential equation that governs PINN prediction in the
large data-set limit -- the neurally-informed equation. This equation augments
the original one by a kernel term reflecting architecture choices and allows
quantifying implicit bias induced by the network via a spectral decomposition
of the source term in the original differential equation.


  We consider smooth linear statistics of determinantal point processes on the
complex plane, and their large scale asymptotics. We prove asymptotic normality
in the finite variance case, where Soshnikov's theorem is not applicable. The
setting is similar to that of Rider and Vir\'ag [Electron. J. Probab., 12, no.
45, 1238--1257, (2007)] for the complex plane, but replaces analyticity
conditions by the assumption that the correlation kernel is reproducing. Our
proof is a streamlined version of that of Ameur, Hedenmalm and Makarov [Duke
Math J., 159, 31--81, (2011)] for eigenvalues of normal random matrices. In our
case, the reproducing property is brought to bear to compensate for the lack of
analyticity and radial symmetries.


  We present a simple-to-apply criterion for recognizing topological groups
that are (locally) homeomorphic to LF-spaces.


  A galaxy-sized halo may contain a large number of intermediate mass (10^{2-4}
solar mass) compact objects (IMCOs), which can be intermediate mass black holes
(IMBHs) or the CDM subhalos. We propose to directly detect the IMBHs by
observing multiply imaged QSO-galaxy lens systems with a high angular
resolution (~ 0.03mas), which would be achieved by the next-VLBI space
missions. The silhouette of the IMBHs would appear as an either monopole-like
or dipole-like variation at the scale of the Einstein radius against the QSO
jets. As a byproduct, we can also directly detect the 10^{4-5} solar mass CDM
subhalos. From a measurement of the local distortion in the surface brightness
of the QSO jet, we can make a distinction between a point mass (corresponding
to an IMBH) and an extended structure (corresponding to a CDM subhalo). It
would be a unique probe of the IMCOs whose nature has been under the veil of
mistery.


  In this paper we discuss the coupling between a quantum mechanical
superconducting quantum interference device (SQUID) and an applied static
magnetic field. We demonstrate that the backreaction of a SQUID on the applied
field can interfere with the ability to bias the SQUID at values of the static
(DC) magnetic flux at, or near to, transitions in the quantum mechanical SQUID.


  Noisy distance estimates associated with photometric rather than
spectroscopic redshifts lead to a mis-estimate of the luminosities, and produce
a correlated mis-estimate of the sizes. We consider a sample of early-type
galaxies from the SDSS DR6 and apply the generalization of the V_max method to
correct for these biases. We show that our technique recovers the true
redshift, magnitude and size distributions, as well as the true size-luminosity
relation. Regardless the specific application outlined here, our method impacts
a broader range of studies, when at least one distance-dependent quantity is
involved.


  The Earth's Moon is thought to have formed from a circumterrestrial disk
generated by a giant impact between the proto-Earth and an impactor
approximately 4.5 billion years ago. Since the impact was energetic, the disk
would have been hot and partially vaporized. This formation process is thought
to be responsible for the geochemical observation that the Moon is depleted in
volatiles. This model predicts that the Moon should be significantly depleted
in water as well, but this appears to contradict some of the recently measured
lunar water abundances and D/H ratios that suggest that the Moon is more
water-rich than previously thought. Alternatively, the Moon could have retained
its water if the upper of the disk were dominated by heavier species because
hydrogen would have had to diffuse out from the heavy-element rich disk, and
therefore the escape rate would have been limited by this slow diffusion
process (diffusion-limited escape). To identify which escape the disk would
have experienced and to quantify volatiles loss from the disk, we compute the
thermal structure of the Moon-forming disk considering various bulk water
abundances and mid-plane disk temperatures. Our calculations show that the
upper parts of the Moon-forming disk are dominated by heavy atoms or molecules
and hydrogen is a minor species. This indicates that hydrogen escape would have
been diffusion-limited, and therefore the amount of lost water and hydrogen
would have been small compared to the initial abundance assumed. This result
indicates that the giant impact hypothesis can be consistent with the
water-rich Moon. Furthermore, since the hydrogen wind would have been weak, the
other volatiles would not have escaped either. Thus, the observed volatile
depletion of the Moon requires another mechanism.


  We study the fluid-like dynamics of eigenvalues of the Wilson operator in the
context of the order-disorder (Durhuus-Olesen) transition in large $N_c$
Yang-Mills theory. We link the universal behavior at the closure of the gap
found by Narayanan and Neuberger to the phenomenon of spectral shock waves in
the complex Burgers equation, where the role of viscosity is played by $1/N_c$.
Next, we explain the relation between the universal behavior of eigenvalues and
certain class of random matrix models. Finally, we conlude the discussion of
universality by recalling exact analogies between Yang-Mills theories at large
$N_c$ and the so-called diffraction catastrophes.


  We examine the time-dependent behavior of a nonlinear system driven by a
two-frequency forcing. By using a non-perturbative approach, we are able to
derive an asymptotic expression, valid in the long-time limit, for the time
average of the output variable which describes the response of the system. We
identify several universal features of the asymptotic response of the system,
which are independent of the details of the model. In particular, we determine
an asymptotic expression for the width of the resonance observed by keeping one
frequency fixed, and varying the other one. We show that this width is smaller
than the usually assumed Fourier width by a factor determined by the two
driving frequencies, and independent of the model system parameters. Additional
general features can also be identified depending on the specific symmetry
properties of the system. Our results find direct application in the study of
sub-Fourier signal processing with nonlinear systems.


  We present a new approach to the modelling of stress propagation in static
granular media, focussing on the conical sandpile constructed from a point
source. We view the medium as consisting of cohesionless hard particles held up
by static frictional forces; these are subject to microscopic indeterminacy
which corresponds macroscopically to the fact that the equations of stress
continuity are incomplete -- no strain variable can be defined. We propose that
in general the continuity equations should be closed by means of a constitutive
relation (or relations) between different components of the (mesoscopically
averaged) stress tensor. The primary constitutive relation relates radial and
vertical shear and normal stresses (in two dimensions, this is all one needs).
We argue that the constitutive relation(s) should be local, and should encode
the construction history of the pile: this history determines the organization
of the grains at a mesoscopic scale, and thereby the local relationship between
stresses. To the accuracy of published experiments, the pattern of stresses
beneath a pile shows a scaling between piles of different heights (RSF scaling)
which severely limits the form the constitutive relation can take ...


  We demonstrate the miniaturization of perpendicularly magnetized
superparamagnetic tunnel junctions (SMTJs) down to 50 nm in diameter. We
experimentally show stochastic reversals in those junctions, with tunable mean
dwell times down to a few nanoseconds through applied magnetic field and
voltage. The mean dwell times measured at negligible bias voltage agree with
our simulations based on Langer's theory. We shed light on an Arrhenius
prefactor $\tau_0$ of a few femtoseconds, implying that the rates of
thermally-activated magnetic transitions exceed the GHz-to-THz limitation of
macrospin models, whereby $\tau_0\sim1$ ns. We explain the small prefactor
values by a Meyer-Neldel compensation phenomenon, where the prefactor exhibits
a large entropic contribution with an exponential dependence on the activation
energy. These findings pave the way towards the development of ultrafast,
low-power unconventional computing schemes operating by leveraging thermal
noise in perpendicular SMTJs, which are scalable below 20 nm.


  Nuclear magnetic resonance spectroscopy is a prominent analytical tool, with
applications throughout chemistry, medicine and solid-state physics. While
conventional NMR spectrometers require large magnetic fields to interrogate a
sample, recent advances in atomic magnetometry have enabled this spectroscopy
far below geomagnetic field strengths. This zero-to-ultralow (ZULF) field
regime can be advantageous since it mitigates relaxation and reveals spin
couplings that are otherwise obscured, all while using compact and
lower-overhead instrumentation. The resulting spectra are nonetheless difficult
to interpret without computation, which can be taxing due to the presence of
vector couplings and long-range spin networks. Following recent proposals, we
demonstrate how fault-tolerant quantum computation could be used to simulate
these spectra. Our analysis spans from input selection to the construction of
explicit circuits based on qubitized quantum dynamics. By maintaining parity
with experimental requirements, we demonstrate how NMR spectral prediction
might be an early application for fault-tolerant quantum computers.


  On the basis of an exact vacuum solution of Einstein's equations, {\it vis}.
the pencil-of-light field, we study the light-like motion of test and non-test
objects. We also consider the quantum theoretical interaction of massless
scalar particles through virtual gravitons. The dragging phenomenon is
manifested and its agreement with astronomical observations established.
  This paper submitted to {\bf arXiv} is a somewhat reedited copy of my article
dedicated to Dr. Ivar Piir in a volume published on the occasion of his 60th
birthday in 1989 in Tartu by the Estonian Academy of Sciences.


  Machine learning models are vulnerable to adversarial examples. For the
black-box setting, current substitute attacks need pre-trained models to
generate adversarial examples. However, pre-trained models are hard to obtain
in real-world tasks. In this paper, we propose a data-free substitute training
method (DaST) to obtain substitute models for adversarial black-box attacks
without the requirement of any real data. To achieve this, DaST utilizes
specially designed generative adversarial networks (GANs) to train the
substitute models. In particular, we design a multi-branch architecture and
label-control loss for the generative model to deal with the uneven
distribution of synthetic samples. The substitute model is then trained by the
synthetic samples generated by the generative model, which are labeled by the
attacked model subsequently. The experiments demonstrate the substitute models
produced by DaST can achieve competitive performance compared with the baseline
models which are trained by the same train set with attacked models.
Additionally, to evaluate the practicability of the proposed method on the
real-world task, we attack an online machine learning model on the Microsoft
Azure platform. The remote model misclassifies 98.35% of the adversarial
examples crafted by our method. To the best of our knowledge, we are the first
to train a substitute model for adversarial attacks without any real data.


  Recent works on quantum resource theories of non-Gaussianity, which are based
upon the type of tools available in contemporary experimental settings, put
Gaussian states and their convex combinations on equal footing. Motivated by
this, in this article, we derive a new model of dissipative time evolution
based on unitary Lindblad operators which, while does not preserve the set of
Gaussian states, preserves the set of their convex combinations, i.e. so-called
quantum Gaussian states. As we demonstrate, the considered evolution proves
useful both as a description for random scattering and as a tool in dissipator
engineering.


  Basing on self-consistent solution of non-linear GL-equations, the phase
boundary is found, which divides the regions of I- and II-order phase
transitions of a superconducting cylinder in magnetic field to normal state.
This boundary is a complicated function of the parameters (m,R,kappa) (m is the
vorticity, R is the cylinder radius, kappa is the GL-parameter), which does not
coincide with the simple phase boundary kappa=1/\sqrt{2}, dividing the regions
of I- and II-order phase transitions in infinite (open) superconducting
systems.


  We present extraction of tree structures, such as airways, from image data as
a graph refinement task. To this end, we propose a graph auto-encoder model
that uses an encoder based on graph neural networks (GNNs) to learn embeddings
from input node features and a decoder to predict connections between nodes.
Performance of the GNN model is compared with mean-field networks in their
ability to extract airways from 3D chest CT scans.


  Competition among exchange interactions is able to induce novel spin
correlations on a bipartite lattice without geometrical frustration. A
prototype example is the spiral spin liquid, which is a correlated paramagnetic
state characterized by sub-dimensional degenerate propagation vectors. Here,
using spectral graph theory, we show that spiral spin liquids on a bipartite
lattice can be approximated by a further-neighbor model on the corresponding
line-graph lattice that is non-bipartite, thus broadening the space of
candidate materials that may support the spiral spin liquid phases. As
illustrations, we examine neutron scattering experiments performed on two
spinel compounds, ZnCr$_2$Se$_4$ and CuInCr$_4$Se$_8$, to demonstrate the
feasibility of this new approach and expose its possible limitations in
experimental realizations.


  The heaviness of the top quark makes its 2-body decay mode $t \to bW^+$ to be
dominant, at such level that hardly any other decay mode reaches a detectable
branching ratio (BR) within the SM. Here we study the decay $t \to
bW^+\ell^-\ell^+$ ($\ell=e, \mu, \tau$), which diverges for massless leptons,
and it can reach a BR $\sim \mathcal{O}(10^{-5}\sim 10^{-6})$ for reasonable
values of the low energy cut in the lepton-pair invariant mass. This rate
surpasses almost any other rare decays such as $t \to cX$ ($X=\gamma,Z,g,H,
W^+W^-$), and thus offers the possibility of being detectable. Furthermore, the
estimate of this channel is relevant because it can mimic the signal arising
from the lepton number violating decay $t\to bW^-\ell^+\ell^+$, when the $W$
boson decays into lepton channels.


  As COVID-19 pandemic progresses, severe flu seasons may happen alongside an
increase in cases in cases and death of COVID-19, causing severe burdens on
health care resources and public safety. A consequence of a twindemic may be a
mixture of two different infections in the same person at the same time,
"flurona". Admist the raising trend of "flurona", forecasting both influenza
outbreaks and COVID-19 waves in a timely manner is more urgent than ever, as
accurate joint real-time tracking of the twindemic aids health organizations
and policymakers in adequate preparation and decision making. Under the current
pandemic, state-of-art influenza and COVID-19 forecasting models carry valuable
domain information but face shortcomings under current complex disease
dynamics, such as similarities in symptoms and public healthcare seeking
patterns of the two diseases. Inspired by the inner-connection between
influenza and COVID-19 activities, we propose ARGOX-Joint-Ensemble which allows
us to combine historical influenza and COVID-19 disease forecasting models to a
new ensemble framework that handles scenarios where flu and COVID co-exist. Our
framework is able to emphasize learning from COVID-related or influenza
signals, through a winner-takes-all ensemble fashion. Moreover, our experiments
demonstrate that our approach is successful in adapting past influenza
forecasting models to the current pandemic, while improving upon previous
COVID-19 forecasting models, by steadily outperforming alternative benchmark
methods, and remaining competitive with publicly available models.


  This paper is devoted to check the validity of laws of thermodynamics for
Kaluza-Klein universe in the state of thermal equilibrium, composed of dark
matter and dark energy. The generalized holographic dark energy and generalized
Ricci dark energy models are considered here. It is proved that the first and
generalized second law of thermodynamics are valid on the apparent horizon for
both of these models. Further, we take a horizon of radius $L$ with modified
holographic or Ricci dark energy. We conclude that these models do not obey the
first and generalized second law of thermodynamics on the horizon of fixed
radius $L$ for a specific range of model parameters.


  The Vapnik-Chervonenkis dimension of a set K in R^n is the maximal dimension
of the coordinate cube of a given size, which can be found in coordinate
projections of K. We show that the VC dimension of a convex body governs its
entropy. This has a number of consequences, including the optimal Elton's
theorem and a uniform central limit theorem in the real valued case.


  The absolute frequency of the $^{87}{\rm Sr}$ clock transition measured in
2015 was reevaluated using an improved frequency link to the SI second. The
scale interval of International Atomic Time (TAI) that we used as the reference
was calibrated for an evaluation interval of five days instead of the
conventional interval of one month which is regularly employed in Circular T.
The calibration on a five-day basis removed the uncertainty in assimilating the
TAI scale of the five-day mean to that of the one-month mean. The reevaluation
resulted in the total uncertainty of $10^{-16}$ level for the first time
without local cesium fountains. Since there are presumably no correlations
among systematic shifts of cesium fountains worldwide, the measurement is not
limited by the systematic uncertainty of a specific primary frequency standard.


  We review the current understanding of the uniform two-dimensional (2D) Fermi
gas with short-range interactions. We first outline the basics of two-body
scattering in 2D, including a discussion of how such a 2D system may be
realized in practice using an anisotropic confining potential. We then discuss
the thermodynamic and dynamical properties of 2D Fermi gases, which cold-atom
experiments have only just begun to explore. Of particular interest are the
different pairing regimes as the interparticle attraction is varied; the
superfluid transition and associated finite-temperature phenomenology; few-body
properties and their impact on the many-body system; the Fermi polaron problem;
and the symmetries underlying the collective modes. Where possible, we include
the contributions from 2D experiment. An underlying theme throughout is the
effect of the quasi-2D geometry, which we view as an added richness to the
problem rather than an unwanted complication.


  We provide an overview of the observational properties of the four major
Taurid showers, namely the Northern and Southern Taurids (#017 NTA and #002
STA), the Beta Taurids (#173 BTA) and the Zeta Perseids (#172 ZPE). Analysing
more than two decades of meteor observations from visual, optical and radar
measurements we present the Taurids average activity, annual variations in
strength, radiant drift and orbital variations as a function of solar longitude
and particle size. The Taurid showers are detected over several weeks in the
spring and autumn, but their annual activity level is generally low (less than
15 visual meteors per hour). We find the STA to be predominant in autumn, while
its twin the ZPE dominates over the BTA in spring. Due to their long duration,
the position of each shower's radiant and orbital elements are variable with
time. Optical measurements have previously recorded enhanced STA activity and
increased fireball rates caused by the return of a swarm of meteoroids trapped
in the 7:2 mean motion resonance with Jupiter. However, we find no presence of
the swarm in radar data, suggesting that small meteoroids are removed from the
resonance faster than fireball-producing meteoroids. We also find the STA to be
enriched in smaller particles early in their activity period. The differences
we identify in our analysis between the showers at different particle sizes
provide strong observational constraints to future dynamical modelling of the
Taurid Meteoroid Complex.


  Twisted bilayers of transition metal dichalcogenide semiconductors have
enabled the discovery of superconductivity, ferromagnetism, correlated
insulators and a series of new topological phases of matter. However, the
connection between these electronic phases and the underlying band structure
singularities in these materials has remained largely unexplored. Here,
combining the magnetic circular dichroism and electronic compressibility
measurements, we investigate the influence of a van Hove singularity on the
correlated phases in bilayer WSe2 with twist angle between 2-3 degrees. We
demonstrate stabilizing the Stoner ferromagnetism below moir\'e lattice filling
one and Chern insulators at filling one by tuning the van Hove singularity
cross the Fermi level using the electric and magnetic fields. The experimental
observations are supported by the continuum model band structure calculations.
Our results highlight the prospect of engineering the electronic phases by
tunable van Hove singularities.


  The mathematical model of periodically amplitude and phase modulated
sinusoidal oscillation is studied, and its Fourier spectrum is given
analytically. The Fourier spectrum of the model explains the main features of
the frequency spectrum of RR Lyrae stars showing light curve modulation called
the Blazhko effect: among others the appearance of multiplets, the rapid
decrease of their amplitudes in increasing orders, the asymmetry of the
amplitudes of the side frequency pairs, and the possibility of the occurrence
of frequency doublets instead of triplets in the spectrum. The good agreement
of the results of this mathematical model with observational facts favours
those physical models of the Blazhko effect which explain the phenomenon as a
modulation of the oscillation with the modulation frequency, f_m.


  The quantity and distribution of land which is eligible for renewable energy
sources is fundamental to the role these technologies will play in future
energy systems. As it stands, however, the current state of land eligibility
investigation is found to be insufficient to meet the demands of the future
energy modelling community. Three key areas are identified as the predominate
causes of this; inconsistent criteria definitions, inconsistent or unclear
methodologies, and inconsistent dataset usage. To combat these issues, a land
eligibility framework is developed and described in detail. The validity of
this framework is then shown via the recreation of land eligibility results
found in the literature, showing strong agreement in the majority of cases.
Following this, the framework is used to perform an evaluation of land
eligibility criteria within the European context whereby the relative
importance of commonly considered criteria are compared.


  We propose a modification of the non-perturbative renormalization-group
(NPRG) which applies to lattice models. Contrary to the usual NPRG approach
where the initial condition of the RG flow is the mean-field solution, the
lattice NPRG uses the (local) limit of decoupled sites as the (initial)
reference system. In the long-distance limit, it is equivalent to the usual
NPRG formulation and therefore yields identical results for the critical
properties. We discuss both a lattice field theory defined on a $d$-dimensional
hypercubic lattice and classical spin systems. The simplest approximation, the
local potential approximation, is sufficient to obtain the critical temperature
and the magnetization of the 3D Ising, XY and Heisenberg models to an accuracy
of the order of one percent. We show how the local potential approximation can
be improved to include a non-zero anomalous dimension $\eta$ and discuss the
Berezinskii-Kosterlitz-Thouless transition of the 2D XY model on a square
lattice.


  Previous work has demonstrated that words are hyperarticulated on dimensions
of speech that differentiate them from a minimal pair competitor. This
phenomenon has been termed contrastive hyperarticulation (CH). We present a
dynamic neural field (DNF) model of voice onset time (VOT) planning that
derives CH from an inhibitory influence of the minimal pair competitor during
planning. We test some predictions of the model with a novel experiment
investigating CH of voiceless stop consonant VOT in pseudowords. The results
demonstrate a CH effect in pseudowords, consistent with a basis for the effect
in the real-time planning and production of speech. The scope and magnitude of
CH in pseudowords was reduced compared to CH in real words, consistent with a
role for interactive activation between lexical and phonological levels of
planning. We discuss the potential of our model to unify an apparently
disparate set of phenomena, from CH to phonological neighborhood effects to
phonetic trace effects in speech errors.


  Giving or recommending appropriate content based on the quality of experience
is the most important and challenging issue in recommender systems. As
collaborative filtering (CF) is one of the most prominent and popular
techniques used for recommender systems, we propose a new clustering-based CF
(CBCF) method using an incentivized/penalized user (IPU) model only with
ratings given by users, which is thus easy to implement. We aim to design such
a simple clustering-based approach with no further prior information while
improving the recommendation accuracy. To be precise, the purpose of CBCF with
the IPU model is to improve recommendation performance such as precision,
recall, and $F_1$ score by carefully exploiting different preferences among
users. Specifically, we formulate a constrained optimization problem, in which
we aim to maximize the recall (or equivalently $F_1$ score) for a given
precision. To this end, users are divided into several clusters based on the
actual rating data and Pearson correlation coefficient. Afterwards, we give
each item an incentive/penalty according to the preference tendency by users
within the same cluster. Our experimental results show a significant
performance improvement over the baseline CF scheme without clustering in terms
of recall or $F_1$ score for a given precision.


  Context. Binary systems of supermassive black holes are expected to be strong
sources of long gravitational waves prior to merging. These systems are good
candidates to be observed with forthcoming space-borne detectors. Only a few of
these systems, however, have been firmly identified to date.
  Aims. We aim at providing a criterion for the identification of some
supermassive black hole binaries based on the characteristics of the
high-energy emission of a putative relativistic jet launched from the most
massive of the two black holes.
  Methods. We study supermassive black hole binaries where the less massive
black hole has carved an annular gap in the circumbinary disk, but nevertheless
there is a steady mass flow across its orbit. Such a perturbed disk is hotter
and more luminous than a standard thin disk in some regions. Assuming that the
jet contains relativistic electrons, we calculate its broadband spectral energy
distribution focusing on the inverse Compton up-scattering of the disk photons.
We also compute the opacity to the gamma rays produced in the jet by photon
annihilation with the disk radiation and take into account the effects of the
anisotropy of the target photon field as seen from the jet.
  Results. We find that the excess of low-energy photons radiated by the
perturbed disk causes an increment in the external Compton emission from the
jet in the X-ray band, and a deep absorption feature at energies of tens of
TeVs for some sets of parameters. According to our results, observations with
Cherenkov telescopes might help in the identification of supermassive black
hole binaries, especially those black hole binaries that host primaries from
tens to hundreds of million of solar masses.


  A comparison among different constructions of the quaternionic $4$-form
$\Phi_{Sp(2)Sp(1)}$ and of the Cayley calibration $\Phi_{Spin(7)}$ shows that
one can start for them from the same collections of "K\"ahler 2-forms",
entering in dimension 8 both in quaternion K\"ahler and in $Spin(7)$ geometry.
This comparison relates with the notions of even Clifford structure and of
Clifford system. Going to dimension $16$, similar constructions allow to write
explicit formulas for the canonical $4$-forms $\Phi_{Spin(8)}$ and
$\Phi_{Spin(7)U(1)}$, associated with Clifford systems related with the
subgroups $Spin(8)$ and $Spin(7)U(1)$ of $SO(16)$. We characterize the
calibrated $4$-planes of the $4$-forms $\Phi_{Spin(8)}$ and
$\Phi_{Spin(7)U(1)}$, extending in two different ways the notion of Cayley
$4$-plane to dimension $16$.


  We show that a neural network, trained on the entanglement spectra of a
nearest neighbor Heisenberg chain in a random transverse magnetic field, can be
used to efficiently study the ergodic/many-body localized properties of a
number of other quantum systems, without further re-training. We benchmark our
computational architecture against a $J_{1}\!\!-\!\!J_{2}$ - model, which
extends the Heisenberg chain to include next-to-nearest neighbor interactions,
with excellent agreement with known results. When applied to Hamiltonians that
differ from the training model by the topology of the underlying graph, the
neural network is able to predict the critical disorders and, more generally,
shapes of the mobility edge that agree with our heuristic expectation. We take
this as proof of principle that machine learning algorithms furnish a powerful
new set of computational tools to explore the many-body physics frontier.


  Starting from the Fisher-Kolmogorov-Petrovskii-Piskunov equation (FKPP) we
model the dynamic of a diffusive system with two mutually communicating
identical patches and isolated of the remaining matrix. For this system we find
the minimal size of each fragment in the explicit form and compare with the
explicit results for similar problems found in the literature. From this
comparison emerges an unexpected result that for a same set of the parameters,
the isolated system studied in this work with size L, can be better or worst
than the non isolated systems with the same size L, uniquely depending on the
parameter $a_{0}$ (internal conditions of the patches). Due to the fact that
this result is unexpected we propose a experimental verification.


  The main goal of this paper is to characterize the Hilbert functions of all
(artinian) codimension 4 Gorenstein algebras that have at least two independent
relations of degree four. This includes all codimension 4 Gorenstein algebras
whose initial relation is of degree at most 3. Our result shows that those
Hilbert functions are exactly the so-called {\em SI-sequences} starting with
(1,4,h_2,h_3,...), where $h_4 \leq 33$. In particular, these Hilbert functions
are all unimodal.
  We also establish a more general unimodality result, which relies on the
values of the Hilbert function not being too big, but is independent of the
initial degree.


  We present a detailed study of the gaussian laser propagation through a
dielectric system composed by two right angle prisms. We investigate the
transition between the spatial coherence limit, which exhibits wave-like
properties and for which the resonance phenomenon can be seen, and the
decoherence limit, which exhibits particle-like properties and for which the
multiple diffusion occurs. We also analyze the tunneling at critical angles. In
our numerical analysis, we shall use BK7 and Fused Silica prisms and a gaussian
He-Ne laser with a wavelength of 632.8 nm and beam waists of 2 mm and 200
micron.


  In this paper we prove that for every $s\geq 2$ and every graph $H$ the
following holds. Let $G$ be a graph with average degree $\Omega_H(s^{C|H|^2})$,
for some absolute constant $C>0$, then $G$ either contains a $K_{s,s}$ or an
induced subdivision of $H$. This is essentially tight and confirms a conjecture
of Bonamy, Bousquet, Pilipczuk, Rz\k{a}\.zewski, Thomass\'e, and Walczak. A
slightly weaker form of this has been independently proved by Bourneuf,
Buci\'c, Cook, and Davies.
  We actually prove a much more general result which implies the above (with
worse dependence on $|H|$). We show that for every $ k\geq 2$ there is $C_k>0$
such that any graph $G$ with average degree $s^{C_k}$ either contains a
$K_{s,s}$ or an induced subgraph $G'\subseteq G$ without $C_4$'s and with
average degree at least $k$.
  Finally, using similar methods we can prove the following. For every $k,t\geq
2$ every graph $G$ with average degree at least $C_tk^{\Omega(t)}$ must contain
either a $K_k$, an induced $K_{t,t}$ or an induced subdivision of $K_k$. This
is again essentially tight up to the implied constants and answers in a strong
form a question of Davies.


  Mobile agents represent a new model for network computing. Many different
languages have been used to implement mobile agents. The characteristics that
make a language useful for writing mobile agents are: (1) their support of
agent migration, (2) their support for agent-to-agent communication, (3) how
they allow agents to interact with local resources, (4) security mechanisms,
(5) execution efficiency, (6) language implementation across multiple
platforms, and (7) the language's ease of programming of the tasks mobile
agents perform.


  Classifying streaming data requires the development of methods which are
computationally efficient and able to cope with changes in the underlying
distribution of the stream, a phenomenon known in the literature as concept
drift. We propose a new method for detecting concept drift which uses an
Exponentially Weighted Moving Average (EWMA) chart to monitor the
misclassification rate of an streaming classifier. Our approach is modular and
can hence be run in parallel with any underlying classifier to provide an
additional layer of concept drift detection. Moreover our method is
computationally efficient with overhead O(1) and works in a fully online manner
with no need to store data points in memory. Unlike many existing approaches to
concept drift detection, our method allows the rate of false positive
detections to be controlled and kept constant over time.


  An increasing number of people use wearables and other smart devices to
quantify various health conditions, ranging from sleep patterns, to body
weight, to heart rates. Of these Quantified Selfs many choose to openly share
their data via online social networks such as Twitter and Facebook. In this
study, we use data for users who have chosen to connect their smart scales to
Twitter, providing both a reliable time series of their body weight, as well as
insights into their social surroundings and general online behavior.
Concretely, we look at which social media features are predictive of physical
status, such as body weight at the individual level, and activity patterns at
the population level. We show that it is possible to predict an individual's
weight using their online social behaviors, such as their self-description and
tweets. Weekly and monthly patterns of quantified-self behaviors are also
discovered. These findings could contribute to building models to monitor
public health and to have more customized personal training interventions.
  While there are many studies using either quantified self or social media
data in isolation, this is one of the few that combines the two data sources
and, to the best of our knowledge, the only one that uses public data.


  We prove the global-in-time well-posedness for a broad class of mean field
game problems, which is beyond the special linear-quadratic setting, as long as
the mean field sensitivity is not too large. Through the stochastic maximum
principle, we adopt the FBSDE approach to investigate the unique existence of
the corresponding equilibrium strategies. The corresponding FBSDEs are first
solved locally in time, then by controlling the sensitivity of the backward
solutions with respect to the initial condition via some suitable apriori
estimates for the corresponding Jacobian flows, the global-in-time solution is
warranted. Further analysis on these Jacobian flows will be discussed to
establish the regularities, such as linear functional differentiability, of the
respective value functions that leads to the ultimate classical well-posedness
of the master equation on $\mathbb{R}^d$. To the best of our knowledge, it is
the first article to deal with the mean field game problem, as well as its
associated master equation, with general cost functionals having quadratic
growth under the small mean field effect. In this current approach, we directly
impose the structural conditions on the cost functionals, rather than
conditions on the Hamiltonian. The advantages of this are threefold: (i)
compared with imposing conditions on Hamiltonian, the structural conditions
imposed in this work are easily verified, and less demanding on the regularity
requirements of the cost functionals while solving the master equation; (ii)
the displacement monotonicity is basically just a direct consequence of small
mean field effect in the structural conditions; and (iii) when the mean field
effect is not that small, we can still provide an accurate lifespan for the
local existence. The method in this work can be readily extended to the case
with nonlinear drift and non-separable cost functionals.


  The mass-loss mechanism in normal K--M giant stars with small variability
amplitudes is not yet understood, although they are the majority among red
giant stars. We present high-spatial and high-spectral resolution observations
of the 2.3 micron CO lines in the M7 giant BK Vir with a spatial resolution of
9.8 mas and a spectral resolution of 12000, using AMBER at the Very Large
Telescope Interferometer (VLTI). The angular diameters observed in the CO lines
are 12--31% larger than those measured in the continuum. We also detected
asymmetry in the CO line-forming region. The data taken 1.5 months apart show
possible time variation on a spatial scale of 30 mas (corresponding to 3 x
stellar diameter) at the CO band head. Comparison of the observed data with the
MARCS photospheric model shows that whereas the observed CO line spectrum can
be well reproduced by the model, the angular sizes observed in the CO lines are
much larger than predicted by the model. Our model with two additional CO
layers above the MARCS photosphere reproduces the observed spectrum and
interferometric data in the CO lines simultaneously. This model suggests that
the inner CO layer at ~1.2 stellar radii is very dense and warm with a CO
column density of ~10^{22} cm^{-2} and temperatures of 1900--2100K, while the
outer CO layer at 2.5--3.0 stellar radii is characterized by column densities
of 10^{19}--10^{20} cm^{-2} and temperatures of 1500--2100K. Our AMBER
observations of BK Vir have spatially resolved the extended molecular outer
atmosphere of a normal M giant in the individual CO lines for the first time.
The temperatures derived for the CO layers are higher than or equal to the
uppermost layer of the MARCS photospheric model, implying the operation of some
heating mechanism in the outer atmosphere.


  Since its launch in 1990, the Hubble Space Telescope (HST) has served as a
platform with unique capabilities for remote observations of comets in the
far-ultraviolet region of the spectrum. Successive generations of imagers and
spectrographs have seen large advances in sensitivity and spectral resolution
enabling observations of the diverse properties of a representative number of
comets during the past 25 years. To date, four comets have been observed in the
far-ultraviolet by the Cosmic Origins Spectrograph (COS), the last spectrograph
to be installed in HST, in 2009: 103P/Hartley 2, C/2009 P1 (Garradd), C/2012 S1
(ISON), and C/2014 Q2 (Lovejoy). COS has unprecedented sensitivity, but limited
spatial information in its 2.5 arcsec diameter circular aperture, and our
objective was to determine the CO production rates from measurements of the CO
Fourth Positive system in the spectral range of 1400 to 1700 A. In the two
brightest comets, nineteen bands of this system were clearly identified. The
water production rates were derived from nearly concurrent observations of the
OH (0,0) band at 3085 A by the Space Telescope Imaging Spectrograph (STIS). The
derived CO/H2O production rate ratio ranged from ~0.3% for Hartley 2 to ~22%
for Garradd. In addition, strong partially resolved emission features due to
multiplets of S I, centered at 1429 A and 1479 A, and of C I at 1561 A and 1657
A, were observed in all four comets. Weak emission from several lines of the H2
Lyman band system, excited by solar Lyman-alpha and Lyman-beta pumped
fluorescence, were detected in comet Lovejoy.


  Connected and autonomous vehicles (CAVs) are often discussed as a solution to
pressing issues of the current transport systems, including congestion, safety,
social inclusion and ecological sustainability. Scientifically, there is
agreement that CAVs may solve, but can also aggravate these issues, depending
on the specific CAV solution. In the current paper, we investigate the visions
and worst-case scenarios of various stakeholders, including representatives of
public administrations, automotive original equipment manufacturers, insurance
companies, public transportation service providers, mobility experts and
politicians. A qualitative analysis of 17 semi-structured interviews is
presented. It reveals experts' ambivalence towards the introduction of CAVs,
reflecting high levels of uncertainty about CAV consequences, including issues
of efficiency, comfort and sustainability, and concerns about road co-users
such as pedestrians and cyclists. Implications of the sluggishness of
policymakers to set boundary conditions and for the labor market are discussed.
An open debate between policymakers, citizens and other stakeholders on how to
introduce CAVs seems timely.


  We present the full results of our decade-long astrometric monitoring
programs targeting 31 ultracool binaries with component spectral types M7-T5.
Joint analysis of resolved imaging from Keck Observatory and Hubble Space
Telescope and unresolved astrometry from CFHT/WIRCam yields parallactic
distances for all systems, robust orbit determinations for 23 systems, and
photocenter orbits for 19 systems. As a result, we measure 38 precise
individual masses spanning 30-115 $M_{\rm Jup}$. We determine a
model-independent substellar boundary that is $\approx$70 $M_{\rm Jup}$ in mass
($\approx$L4 in spectral type), and we validate Baraffe et al. (2015)
evolutionary model predictions for the lithium-depletion boundary (60 $M_{\rm
Jup}$ at field ages). Assuming each binary is coeval, we test models of the
substellar mass-luminosity relation and find that in the L/T transition, only
the Saumon & Marley (2008) "hybrid" models accounting for cloud clearing match
our data. We derive a precise, mass-calibrated spectral type-effective
temperature relation covering 1100-2800 K. Our masses enable a novel direct
determination of the age distribution of field brown dwarfs spanning L4-T5 and
30-70 $M_{\rm Jup}$. We determine a median age of 1.3 Gyr, and our population
synthesis modeling indicates our sample is consistent with a constant star
formation history modulated by dynamical heating in the Galactic disk. We
discover two triple-brown-dwarf systems, the first with directly measured
masses and eccentricities. We examine the eccentricity distribution, carefully
considering biases and completeness, and find that low-eccentricity orbits are
significantly more common among ultracool binaries than solar-type binaries,
possibly indicating the early influence of long-lived dissipative gas disks.
Overall, this work represents a major advance in the empirical view of very
low-mass stars and brown dwarfs.


  We study gravitational waves emitted in the late inspiral stage of binary
neutron stars by analyzing the waveform obtained in numerical-relativity
simulations. For deriving the physical gravitational waveforms from the
numerical results, the resolution extrapolation plays an essential role for our
simulations. The extrapolated gravitational-wave phases are compared with those
calculated in the post-Newtonian (PN) and effective-one-body (EOB) formalisms
including corrections of tidal effects. We show that the extrapolated
gravitational-wave phases in numerical relativity agree well with those by the
PN and EOB calculations for most of the inspiral stage except for a
tidally-dominated, final inspiral stage, in which the PN and EOB results
underestimate the tidal effects. Nevertheless, the accumulated phase difference
between our extrapolated results and the results by the PN/EOB calculations is
at most 1--3 radian in the last 15 cycles.


  We introduce the nonlinear spin coherent state via its ladder operator
formalism and propose a type of nonlinear spin coherent state by the nonlinear
time evolution of spin coherent states. By a new version of spectroscopic
squeezing criteria we study the spin squeezing in both the spin coherent state
and nonlinear spin coherent state. The results show that the spin coherent
state is not squeezed in the x, y, and z directions, and the nonlinear spin
coherent state may be squeezed in the x and y directions.


  In this paper, we adopt a Bayesian point of view for predicting real
continuous-time processes. We give two equivalent definitions of a Bayesian
predictor and study some properties: admissibility, prediction sufficiency,
non-unbiasedness, comparison with efficient predictors. Prediction of Poisson
process and prediction of Ornstein-Uhlenbeck process in the continuous and
sampled situations are considered. Various simulations illustrate comparison
with non-Bayesian predictors.


  This paper introduces the concept of travel behavior embeddings, a method for
re-representing discrete variables that are typically used in travel demand
modeling, such as mode, trip purpose, education level, family type or
occupation. This re-representation process essentially maps those variables
into a latent space called the \emph{embedding space}. The benefit of this is
that such spaces allow for richer nuances than the typical transformations used
in categorical variables (e.g. dummy encoding, contrasted encoding, principal
components analysis). While the usage of latent variable representations is not
new per se in travel demand modeling, the idea presented here brings several
innovations: it is an entirely data driven algorithm; it is informative and
consistent, since the latent space can be visualized and interpreted based on
distances between different categories; it preserves interpretability of
coefficients, despite being based on Neural Network principles; and it is
transferrable, in that embeddings learned from one dataset can be reused for
other ones, as long as travel behavior keeps consistent between the datasets.
  The idea is strongly inspired on natural language processing techniques,
namely the word2vec algorithm. Such algorithm is behind recent developments
such as in automatic translation or next word prediction. Our method is
demonstrated using a model choice model, and shows improvements of up to 60\%
with respect to initial likelihood, and up to 20% with respect to likelihood of
the corresponding traditional model (i.e. using dummy variables) in
out-of-sample evaluation. We provide a new Python package, called PyTre (PYthon
TRavel Embeddings), that others can straightforwardly use to replicate our
results or improve their own models. Our experiments are themselves based on an
open dataset (swissmetro).


  Radio-frequency interference is a growing concern as wireless technology
advances, with potentially life-threatening consequences like interference
between radar altimeters and 5G cellular networks. Mobile transceivers mix
signals with varying ratios over time, posing challenges for conventional
digital signal processing (DSP) due to its high latency. These challenges will
worsen as future wireless technologies adopt higher carrier frequencies and
data rates. However, conventional DSPs, already on the brink of their clock
frequency limit, are expected to offer only marginal speed advancements. This
paper introduces a photonic processor to address dynamic interference through
blind source separation (BSS). Our system-on-chip processor employs a fully
integrated photonic signal pathway in the analogue domain, enabling rapid
demixing of received mixtures and recovering the signal-of-interest in under 15
picoseconds. This reduction in latency surpasses electronic counterparts by
more than three orders of magnitude. To complement the photonic processor,
electronic peripherals based on field-programmable gate array (FPGA) assess the
effectiveness of demixing and continuously update demixing weights at a rate of
up to 305 Hz. This compact setup features precise dithering weight control,
impedance-controlled circuit board and optical fibre packaging, suitable for
handheld and mobile scenarios. We experimentally demonstrate the processor's
ability to suppress transmission errors and maintain signal-to-noise ratios in
two scenarios, radar altimeters and mobile communications. This work pioneers
the real-time adaptability of integrated silicon photonics, enabling online
learning and weight adjustments, and showcasing practical operational
applications for photonic processing.


  One familiar with the Euler zeta function, which established the remarkable
relationship between the prime and composite numbers, might naturally ponder
the results of the application of this special function in cases where there is
no known way to sieve composite numbers out of the product term in this famous
equation. Such would be case when an infinite series of numbers to be analyzed
are calculated by a polynomial expression that yields successively increasing
positive integer values and which has as its input domain the positive integers
themselves. In such cases there may not be an intuitive way to eliminate the
composite terms from the product term on the right-hand side of the Euler zeta
function equation by either scaling a previous prime number calculation or by
employing predictable values of the domain of the function which would make
future outputs of the polynomial prime. So the best one may be able to hope for
in this case is to determine some value to be added or subtracted from unity in
the numerator above the product term to make both sides of the equation equal
in the hope that that value can be used to predict the number of prime numbers
that exist as outputs of the polynomial function for some limit less than or
equal to x of the input domain.


  The proximity-induced couplings in graphene due to the vicinity of a
ferromagnetic insulator are analyzed. We combine general symmetry principles
and simple tight-binding descriptions to consider different orientations of the
magnetization. We find that, in addition to a simple exchange field, a number
of other terms arise. Some of these terms act as magnetic orbital couplings,
and others are proximity-induced spin-orbit interactions. The couplings are of
similar order of magnitude, and depend on the orientation of the magnetization.
A variety of phases, and anomalous Hall effect regimes, are possible.


  We present the results of spectroscopic-- and orbit--sampled photometric
observations of the faint UV-excess object PG 2200+085. The optical CCD
photometry observations of this object were performed by the Russian-Turkish
1.5-meter telescope RTT150 at the TUBITAK National Observatory (Turkey). The
long-slit optical spectroscopy observations with 2.6 A resolution were carried
out by 6-meter telescope BTA at the Special Astrophysical Observatory (Russia).
The photometric variations over two nights are almost sinusoidal with an
amplitude \Delta m_V = 0.04 m and a period of P = 0.3186 d. Such a light curve
is typical of a detached close binary with an illumination effect or the
ellipsoidal deformation of a secondary star. The observed spectrum clearly
displays a featureless blue continuum of a hot component and a rich
absorption--line and molecular band K--star spectrum. The CaII line profiles
with strong emission cores are remarkably similar to those of V471 Tau. We
tentatively classify PG 2200+085 as a pre-cataclysmic binary of the V471 Tau
type.


  The directed bond percolation process is studied in the presence of com-
pressible velocity fluctuations with long-range correlations. We discuss a
construction of a field theoretic action and a way of obtaining its large scale
properties using the perturbative renormalization group. The most interest- ing
results for the frozen velocity limit are given.


  Heteroskedastic errors can lead to inaccurate statistical conclusions if they
are not properly handled. We introduce a test for heteroskedasticity for the
nonparametric regression model with multiple covariates. It is based on a
suitable residual-based empirical distribution function. The residuals are
constructed using local polynomial smoothing. Our test statistic involves a
detection function that can verify heteroskedasticity by exploiting just the
independence-dependence structure between the detection function and model
errors, i.e. we do not require a specific model of the variance function. The
procedure is asymptotically distribution free: inferences made from it do not
depend on unknown parameters. It is consistent at the parametric (root-n) rate
of convergence. Our results are extended to the case of missing responses and
illustrated with simulations.


  Virtual Reality (VR) technology has been subject to a rapid democratization
in recent years, driven in large by the entertainment industry, and epitomized
by the emergence of consumer-grade, plug-and-play, room-scale VR devices. To
explore the scientific potential of this technology for the field of
observational astrophysics, we have created an experimental VR application:
E0102-VR. The specific scientific goal of this application is to facilitate the
characterization of the 3D structure of the oxygen-rich ejecta in the young
supernova remnant 1E 0102.2-7219 in the Small Magellanic Cloud. Using E0102-VR,
we measure the physical size of two large cavities in the system, including a
(7.0$\pm$0.5) pc-long funnel structure on the far-side of the remnant. The
E0102-VR application, albeit experimental, demonstrates the benefits of using
human depth perception for a rapid and accurate characterization of complex 3D
structures. Given the implementation costs (time-wise) of a dedicated VR
application like E0102-VR, we conclude that the future of VR for scientific
purposes in astrophysics most likely resides in the development of a robust,
generic application dedicated to the exploration and visualization of 3D
observational datasets, akin to a ``ds9-VR''.


  Global light duty electric vehicle (EV) sales exceeded 10.5 million units in
2022, with a year-on-year growth of 55%, but these trends differ regionally.
Despite the robust growth, upfront purchase price remains a challenge for
consumers in different regions, and thus, OEMs make technology choices to
respond to market needs. This paper examines the electrification portfolio
choices of three major automotive manufacturers (OEMs) in different regions of
the world, including Europe, Americas, Asia Pacific, and Africa/Middle-East.
The analysis focuses on trends in dominant segments for Battery Electric
Vehicles (BEV) and Plug-in Hybrid Electric Vehicles (PHEV), as well as battery
chemistry choices. Regional differences show a trend towards SUVs for both BEVs
and PHEVs. Tesla's dominance in the BEV market influences battery chemistry
choices. Average battery sizes for BEVs remain similar in Europe and Americas,
but lower in Asia Pacific and Africa/Middle East.


  We revisit the one- and two-stage detector distillation tasks and present a
simple and efficient semantic-aware framework to fill the gap between them. We
address the pixel-level imbalance problem by designing the category anchor to
produce a representative pattern for each category and regularize the
topological distance between pixels and category anchors to further tighten
their semantic bonds. We name our method SEA (SEmantic-aware Alignment)
distillation given the nature of abstracting dense fine-grained information by
semantic reliance to well facilitate distillation efficacy. SEA is well adapted
to either detection pipeline and achieves new state-of-the-art results on the
challenging COCO object detection task on both one- and two-stage detectors.
Its superior performance on instance segmentation further manifests the
generalization ability. Both 2x-distilled RetinaNet and FCOS with ResNet50-FPN
outperform their corresponding 3x ResNet101-FPN teacher, arriving 40.64 and
43.06 AP, respectively. Code will be made publicly available.


  We give a detailed description of the adjoint representation of Drinfeld's
twist element, as well as of its coproduct, for $su_{q}(2)$. We also discuss,
as applications, the computation of the universal R-matrix in this
representation and the problem of symmetrization of identical-particle states
with quantum $su(2)$ symmetry.


  We propose an in-silico experiment to introduce classical density functional
theory (cDFT). Den- sity functional theories, whether quantum or classical,
rely on abstract concepts that are non- intuitive. However, they are at the
heart of powerful tools and active fields of research in both physics and
chemistry. They led to the 1998 Nobel Prize in chemistry. DFT is illustrated
here in its most simple and yet physically relevant form: the classical density
functional theory of an ideal fluid of classical particles. For illustration
purpose, it is applied to the prediction of the molecular structure of liquid
neon. The numerical experiment proposed therein is built around the writing of
a cDFT code by students in Mathematica. Students thus have to deal with (i) the
cDFT theory, (ii) some basic concepts of statistical mechanics of simple
fluids, (iii) functional minimization, and (iv) a useful functional programming
language. This computational experiment is proposed during a molecular
simulation class, but may also be of interest in a quantum chemistry class to
illustrate electronic DFT, if one highlights the analogies between the quantum
and classical DFTs.


  We consider the large number of particles limit of a periodic Toda lattice
for a family of initial data close to the equilibrium state. We show that each
of the two edges of the spectra of the corresponding Jacobi matrices is up to
an error, determined by the spectra of two Hill operators, associated to this
family. We then show that the spectra of the Jacobi matrices remain almost
constant when the matrices evolve along the two limiting KdV equations. Finally
we prove that the Toda actions, when appropriately renormalized, converge to
the ones of KdV .


  Real social interactions occur on networks in which each individual is
connected to some, but not all, of others. In social dilemma games with a fixed
population size, heterogeneity in the number of contacts per player is known to
promote evolution of cooperation. Under a common assumption of positively
biased payoff structure, well-connected players earn much by playing
frequently, and cooperation once adopted by well-connected players is
unbeatable and spreads to others. However, maintaining a social contact can be
costly, which would prevent local payoffs from being positively biased. In
replicator-type evolutionary dynamics, it is shown that even a relatively small
participation cost extinguishes the merit of heterogeneous networks in terms of
cooperation. In this situation, more connected players earn less so that they
are no longer spreaders of cooperation. Instead, those with fewer contacts win
and guide the evolution. The participation cost, or the baseline payoff, is
irrelevant in homogeneous populations but is essential for evolutionary games
on heterogeneous networks.


  Using an exact solution for the pair interaction potential, we show that
long, rigid, chiral molecules with helical surface charge patterns have a
preferential interaxial angle ~((RH)^1/2)/L, where L is the length of the
molecules, R is the closest distance between their axes, and H is the helical
pitch. Estimates based on this formula suggest a solution for the puzzle of
small interaxial angles in a-helix bundles and in cholesteric phases of DNA.


  We consider pairs of finite-length individual sequences that are realizations
of unknown, finite alphabet, stationary sources in a clas M of sources with
vanishing memory (e.g. stationary Markov sources).
  The task of a universal classifier is to decide whether the two sequences are
emerging from the same source or are emerging from two distinct sources in M,
and it has to carry this task without any prior knowledge of the two underlying
probability measures.
  Given a fidelity function and a fidelity criterion, the probability of
classification error for a given universal classifier is defined.
  Two universal classifiers are defined for pairs of $N$ -sequence: A
"classical" fixed-length (FL) universal classifier and an alternative
variable-length (VL) universal classifier.
  Following Wyner and Ziv (1996) it is demonstrated that if the length of the
individual sequences N is smaller than a cut-off value that is determined by
the properties of the class M, any universal classifier will fail with high
probability .
  It is demonstrated that for values of N larger than the cut-off rate, the
classification error relative to either one of the two classifiers tends to
zero as the length of the sequences tends to infinity.
  However, the probability of classification error that is associated with the
variable-length universal classifier is uniformly smaller (or equal) to the one
that is associated with the "classical" fixed-length universal classifier, for
any finite length.


  Divergence-free Lie algebras (also known as the special Lie algebras of
Cartan type) are Lie algebras of volume-preserving transformation groups. They
are simple in generic case. Dokovic and Zhao found a certain graded
generalization of them. In this paper, we classify all the irreducible and
indecomposable multiplicity-free modules of the simple generalized
divergence-free Lie algebras.


  Quantum indistinguishability by path identity generates a new way of optical
coherence, called ``induced coherence". The phenomenon, originally uncovered by
Zou, Wang, and Mandel's experiment, is an emerging notion in modern quantum
experiments with a wide range of implications. However, there has been
controversy over its true quantum nature and whether the result can be emulated
with classical light. We design a suitable contextuality test that can
determine the conditions under which the setting produces distinguishing
quantum predictions that cannot be described classically, namely, via the
noncontextual hidden variable model.


  This research aims to demonstrate that AI can function not only as a tool for
learning, but also as an intelligent agent with which humans can engage in
collaborative learning (CL) to change epistemic practices in science
classrooms. We adopted a design and development research approach, following
the Analysis, Design, Development, Implementation and Evaluation (ADDIE) model,
to prototype a tangible instructional system called Collaborative Learning with
AI Speakers (CLAIS). The CLAIS system is designed to have 3-4 human learners
join an AI speaker to form a small group, where humans and AI are considered as
peers participating in the Jigsaw learning process. The development was carried
out using the NUGU AI speaker platform. The CLAIS system was successfully
implemented in a Science Education course session with 15 pre-service
elementary science teachers. The participants evaluated the CLAIS system
through mixed methods surveys as teachers, learners, peers, and users.
Quantitative data showed that the participants' Intelligent-Technological,
Pedagogical, And Content Knowledge was significantly increased after the CLAIS
session, the perception of the CLAIS learning experience was positive, the peer
assessment on AI speakers and human peers was different, and the user
experience was ambivalent. Qualitative data showed that the participants
anticipated future changes in the epistemic process in science classrooms,
while acknowledging technical issues such as speech recognition performance and
response latency. This study highlights the potential of Human-AI Collaboration
for knowledge co-construction in authentic classroom settings and exemplify how
AI could shape the future landscape of epistemic practices in the classroom.


  We study the problem of estimating the best B term Fourier representation for
a given frequency-sparse signal (i.e., vector) $\textbf{A}$ of length $N \gg
B$. More explicitly, we investigate how to deterministically identify B of the
largest magnitude frequencies of $\hat{\textbf{A}}$, and estimate their
coefficients, in polynomial$(B,\log N)$ time. Randomized sub-linear time
algorithms which have a small (controllable) probability of failure for each
processed signal exist for solving this problem. However, for failure
intolerant applications such as those involving mission-critical hardware
designed to process many signals over a long lifetime, deterministic algorithms
with no probability of failure are highly desirable. In this paper we build on
the deterministic Compressed Sensing results of Cormode and Muthukrishnan (CM)
\cite{CMDetCS3,CMDetCS1,CMDetCS2} in order to develop the first known
deterministic sub-linear time sparse Fourier Transform algorithm suitable for
failure intolerant applications. Furthermore, in the process of developing our
new Fourier algorithm, we present a simplified deterministic Compressed Sensing
algorithm which improves on CM's algebraic compressibility results while
simultaneously maintaining their results concerning exponential decay.


  We study finite dimensional representations over some Noetherian algebras
over a field of characteristic zero. More precisely, we give necessary and
sufficient conditions for the category of locally finite dimensional
representations to be closed under taking injective hulls and extend results
known for group rings and enveloping algebras to Ore extensions, Hopf crossed
products and affine Hopf algebras of low Gelfand-Kirillov dimension.


  We present our new model for the description of the very high energy Galactic
gamma-ray emission based on a discrete injection of cosmic rays by individual
sources. We investigate the morphology of the very high energy gamma-ray sky,
the detectability of cosmic-ray sources and the clumpiness of the diffuse
gamma-ray flux, assuming two different scenarios for cosmic-ray propagation.
Namely, a standard isotropic and homogeneous diffusion process and an isotropic
and inhomogeneous diffusion process. We notably formulate a possible
explanation to the small number of hadronic PeVatrons recently detected by
LHAASO. In the case of the inhomogeneous diffusion process, we constrain the
number of hadronic PeVatrons to be small. Finally, we give an argument that may
explain the discrepancy between the interstellar gas density distribution and
the very high energy diffuse gamma-ray flux.


  One possible way to produce ultracold, high-phase-space-density quantum gases
of molecules in the rovibronic ground state is given by molecule association
from quantum-degenerate atomic gases on a Feshbach resonance and subsequent
coherent optical multi-photon transfer into the rovibronic ground state. In
ultracold samples of Cs_2 molecules, we observe two-photon dark resonances that
connect the intermediate rovibrational level |v=73,J=2> with the rovibrational
ground state |v=0,J=0> of the singlet $X^1\Sigma_g^+$ ground state potential.
For precise dark resonance spectroscopy we exploit the fact that it is possible
to efficiently populate the level |v=73,J=2> by two-photon transfer from the
dissociation threshold with the stimulated Raman adiabatic passage (STIRAP)
technique. We find that at least one of the two-photon resonances is
sufficiently strong to allow future implementation of coherent STIRAP transfer
of a molecular quantum gas to the rovibrational ground state |v=0,J=0>.


  After the justification of the maximum entropy approach for equilibrium
thermodynamic system, and of a maximum path entropy algorithm for
nonequilibrium thermodynamic systems by virtue of the principle of virtual
work, we present in this paper another application of the principle to
thermodynamic systems out of equilibrium. Unlike the justification of maximum
path entropy for the motion trajectories during a period of time, this work is
on the maximum of the entropy defined as a measure of the momentary dynamical
uncertainty as a function of the probability distribution over the microstates
of the system at any given moment.


  This work proposes a stringent concept of matter mass generation and Theta
vacuum in the context of local gauge theory for the strong force under the
constraint of the flat universe. The matter mass is generated as the
consequence of dynamical spontaneous symmetry breaking (DSSB) of gauge symmetry
and discrete symmetries, which is motivated by the parameter Theta representing
the surface term. Matter mass generation introduces the typical features of
constituent particle mass, dual Meissner effect, and hyperfine structure. The
Theta term plays important roles on the DSSB of the gauge group and on the
quantization of the matter and vacuum space. The Theta vacuum exhibits the
intrinsic principal number and intrinsic angular momentum for intrinsic space
quantization in analogy with the extrinsic principal number and extrinsic
angular momentum for extrinsic space quantization.