  We study a quantum fermion field on a background non-extremal Kerr black
hole. We discuss the definition of the standard black hole quantum states
(Boulware, Unruh and Hartle-Hawking), focussing particularly on the differences
between fermionic and bosonic quantum field theory. Since all fermion modes
(both particle and anti-particle) have positive norm, there is much greater
flexibility in how quantum states are defined compared with the bosonic case.
In particular, we are able to define a candidate `Boulware'-like state, empty
at both past and future null infinity; and a candidate `Hartle-Hawking'-like
equilibrium state, representing a thermal bath of fermions surrounding the
black hole. Neither of these states have analogues for bosons on a non-extremal
Kerr black hole and both have physically attractive regularity properties. We
also define a number of other quantum states, numerically compute differences
in expectation values of the fermion current and stress-energy tensor between
two states, and discuss their physical properties.


  We use local numerical simulations to investigate the strength and nature of
magnetohydrodynamic (MHD) turbulence in the outer regions of protoplanetary
disks, where ambipolar diffusion is the dominant non-ideal MHD effect. The
simulations include vertical stratification and assume zero net vertical
magnetic flux. We employ a super time-stepping technique to ameliorate the
Courant restriction on the diffusive time step. We find that in idealized
stratified simulations, with a spatially constant ambipolar Elsasser number Am,
turbulence driven by the magnetorotational instability (MRI) behaves in a
similar manner as in prior unstratified calculations. Turbulence dies away for
Am < 1, and becomes progressively more vigorous as ambipolar diffusion is
decreased. Near-ideal MHD behavior is recovered for Am > 1000. In the
intermediate regime (10 < Am < 1000) ambipolar diffusion leads to substantial
increases in both the period of the MRI dynamo cycle and the characteristic
scales of magnetic field structures. To quantify the impact of ambipolar
physics on disk accretion, we run simulations at 30 AU and 100 AU that include
a vertical Am profile based upon far ultraviolet (FUV) ionized disk models.
These models develop a vertically layered structure analogous to the Ohmic dead
zone that is present at smaller radii. We find that, although the levels of
surface turbulence can be strong (and consistent with constraints on turbulent
line widths at these radii), the inferred accretion rates are at least an order
of magnitude smaller than those observed in T Tauri stars. This discrepancy is
very likely due to the assumption of zero vertical magnetic field in our
simulations and suggests that vertical magnetic fields are essential for
MRI-driven accretion in the outer regions of protoplanetary disks.


  **PLEASE FIND THE FULL EXTENDED ARTICLE "From OTFS to AFDM: A Comparative
Study of Next-Generation Waveforms for ISAC in Doubly-Dispersive Channels"
(Accepted for publication at the IEEE Signal Processing Magazine - Special
Issue on Signal Processing for the Integrated Sensing and Communications
Revolution)**
  This white paper aims to briefly describe a proposed article that will
provide a thorough comparative study of waveforms designed to exploit the
features of doubly-dispersive channels arising in heterogeneous high-mobility
scenarios as expected in the beyond fifth generation (B5G) and sixth generation
(6G), in relation to their suitability to integrated sensing and communications
(ISAC) systems. In particular, the full article will compare the
well-established delay-Doppler domain-based orthognal time frequency space
(OTFS) and the recently proposed chirp domain-based affine frequency division
multiplexing (AFDM) waveforms. Both these waveforms are designed based on a
full delay- Doppler representation of the time variant (TV) multipath channel,
yielding not only robustness and orthogonality of information symbols in
high-mobility scenarios, but also a beneficial implication for environment
target detection through the inherent capability of estimating the path delay
and Doppler shifts, which are standard radar parameters. These modulation
schemes are distinct candidates for ISAC in B5G/6G systems, such that a
thorough study of their advantages, shortcomings, implications to signal
processing, and performance of communication and sensing functions are well in
order. In light of the above, a sample of the intended contribution (Special
Issue paper) is provided below.


  Mixed-initiative Procedural Content Generation (PCG) refers to tools or
systems in which a human designer works with an algorithm to produce game
content. This area of research remains relatively under-explored, with the
majority of mixed-initiative PCG level design systems using a common set of
search-based PCG algorithms. In this paper, we introduce a mixed-initiative
tool employing Exhaustive PCG (EPCG) for puzzle level design to further explore
mixed-initiative PCG. We run an online human subject study in which individuals
use the tool with an EPCG component turned on or off. Our analysis of the
results demonstrates that, although a majority of users did not prefer the
tool, it made the level design process significantly easier, and that the tool
impacted the subjects' design process. This paper describes the study results
and draws lessons for mixed-initiative PCG tool design.


  We show that if Teichm\"uller geodesics spend enough time in the thick part
of moduli space, they display CAT(-1)-type properties. In particular, they
exponentially contract along strongly stable leaves. As an application we prove
two closing lemmas.


  We introduce a model for a sandpile, with N sites, critical height N and each
site connected to every other site. It is thus a mean-field model in the
spin-glass sense. We find an exact solution for the steady state probability
distribution of avalanche sizes, and discuss its asymptotics for large N.


  Three-dimensional hydrodynamic simulations, covering the spatial domain from
hundreds of Schwarzschild radii to $2\ \mathrm{pc}$ around the central
supermassive black hole of mass $10^8 M_\odot$, with detailed radiative cooling
processes, are performed. Generically found is the existence of a significant
amount of shock heated, high temperature ($\geq 10^8\ \mathrm{K}$) coronal gas
in the inner ($\leq 10^4 r_\mathrm{sch}$) region. It is shown that the
composite bremsstrahlung emission spectrum due to coronal gas of various
temperatures are in reasonable agreement with the overall ensemble spectrum of
AGNs and hard X-ray background. Taking into account inverse Compton processes,
in the context of the simulation-produced coronal gas, our model can readily
account for the wide variety of AGN spectral shape, which can now be understood
physically. The distinguishing feature of our model is that X-ray coronal gas
is, for the first time, an integral part of the inflow gas and its observable
characteristics are physically coupled to the concomitant inflow gas. One
natural prediction of our model is the anti-correlation between accretion disk
luminosity and spectral hardness: as the luminosity of SMBH accretion disk
decreases, the hard X-ray luminosity increases relative to the UV/optical
luminosity.


  We propose a transformer architecture for time series forecasting with a
focus on time series tokenisation and apply it to a real-world prediction
problem from the pricing domain. Our architecture aims to learn effective
representations at many scales across all available data simultaneously. The
model contains a number of novel modules: a differentiated form of time series
patching which employs multiple resolutions, a multiple-resolution module for
time-varying known variables, a mixer-based module for capturing cross-series
information, and a novel output head with favourable scaling to account for the
increased number of tokens. We present an application of this model to a real
world prediction problem faced by the markdown team at a very large retailer.
On the experiments conducted our model outperforms in-house models and the
selected existing deep learning architectures.


  We study the steady-state current in a minimal model for a quantum dot
dominated by charge fluctuations and analytically describe the time evolution
into this state. The current is driven by a finite bias voltage V across the
dot, and two different renormalization group methods are used to treat small to
intermediate local Coulomb interactions. The corresponding flow equations can
be solved analytically which allows to identify all microscopic cutoff scales.
Exploring the entire parameter space we find rich non-equilibrium physics which
cannot be understood by simply considering the bias voltage as an infrared
cutoff. For the experimentally relevant case of left-right asymmetric
couplings, the current generically shows a power-law suppression for large V.
The relaxation dynamics towards the steady state features characteristic
oscillations as well as an interplay of exponential and power-law decay.


  We present a framework based on conditional entropy and the Dirichlet
distribution for classifying chromosomes based on the degree to which they use
synonymous codons uniformly or preferentially, that is, whether or not codons
that code for an amino acid appear with the same relative frequency. Applying
the approach to a large collection of annotated bacterial chromosomes reveals
three distinct groups of bacteria.


  A recently proposed phenomenological model, which includes non perturbative
effects from dimension two gluon condensates, is applied to analyze the
available lattice data for the heavy quark free energy in the deconfined phase
of quenched QCD. For large $q\bar{q}$ separations we recover previous results
for the Polyakov loop, exhibiting unequivocal condensate contributions. For the
$q\bar{q}$ potential at finite temperature and finite separation we find that a
good overall description of the lattice data can be achieved once the
condensate is properly accounted for. In addition, the model predicts a duality
between the zero temperature potential as a function of the $q\bar{q}$
separation, on the one hand, and the quark selfenergy as a function of the
temperature, on the other, which turns out to be satisfied to a high degree by
the lattice data.


  The emission of nuclear clusters is investigated within the framework of
isospin dependent lattice gas model and classical molecular dynamics model. It
is found that the emission of individual cluster which is heavier than proton
is almost Poissonian except near the transition temperature at which the system
is leaving the liquid-vapor phase coexistence and the thermal scaling is
observed by the linear Arrhenius plots which is made from the average
multiplicity of each cluster versus the inverse of temperature in the liquid
vapor phase coexistence. The slopes of the Arrhenius plots, {\it i.e.} the
"emission barriers", are extracted as a function of the mass or charge number
and fitted by the formula embodied with the contributions of the surface energy
and Coulomb interaction. The good agreements are obtained in comparison with
the data for low energy conditional barriers. In addition, the possible
influences of the source size, Coulomb interaction and "freeze-out" density and
related physical implications are discussed.


  Using the CFBDSIR wide field survey for brown dwarfs, we identified
CFBDSIRJ214947.2-040308.9, a late T dwarf with atypically red J-Ks colour. We
obtained an X-Shooter spectra, with signal detectable from 0.8 to 2.3 micron,
which confirmed a T7 spectral type with an enhanced Ks-band flux indicative of
a potentially low-gravity, young, object. The comparison of our near infrared
spectrum with atmosphere models, for solar metallicity, shows that
CFBDSIRJ214947.2-040308.9 is probably a 650-750 K, log g=3.75-4.0 substellar
object. Using evolution models, this translates into a planetary mass object,
with an age in the 20-200 Myr range. An independent Bayesian analysis from
proper motion measurements results in a 87% probability that this free-floating
planet is a member of the 50-120 Myr old AB Doradus moving group, which
strengthens the spectroscopic youth diagnosis. By combining our atmospheric
characterisation with the age and metallicity constraints arising from the
probable membership to the AB Doradus moving group, we find that
CFBDSIRJ214947.2-040308.9 is probably a 4-7 Jupiter masses free-floating planet
with an effective temperature of ~700K and a log g of ~4.0, typical of the late
T-type exoplanets that are targeted by direct imaging. We stress that this
object could be used as a benchmark for understanding the physics of the
similar T-type exoplanets that will be discovered by the upcoming high contrast
imagers.


  We present a precise measurement of the deuteron longitudinal spin asymmetry
A_1^d and of the deuteron spin-dependent structure function g_1^d at Q^2 < 1
GeV^2 and 4*10^-5 < x < 2.5*10^-2 based on the data collected by the COMPASS
experiment at CERN during the years 2002 and 2003. The statistical precision is
tenfold better than that of the previous measurement in this region. The
measured A_1^d and g_1^d are found to be consistent with zero in the whole
range of x.


  We evaluate the ability of Convolutional Neural Networks (CNNs) to predict
galaxy cluster masses in the BAHAMAS hydrodynamical simulations. We train four
separate single-channel networks using: stellar mass, soft X-ray flux,
bolometric X-ray flux, and the Compton $y$ parameter as observational tracers,
respectively. Our training set consists of $\sim$4800 synthetic cluster images
generated from the simulation, while an additional $\sim$3200 images form a
validation set and a test set, each with 1600 images. In order to mimic real
observation, these images also contain uncorrelated structures located within
50 Mpc in front and behind clusters and seen in projection, as well as
instrumental systematics including noise and smoothing. In addition to CNNs for
all the four observables, we also train a `multi-channel' CNN by combining the
four observational tracers. The learning curves of all the five CNNs converge
within 1000 epochs. The resulting predictions are especially precise for halo
masses in the range $10^{13.25}M_{\odot}<M<10^{14.5}M_{\odot}$, where all five
networks produce mean mass biases of order $\approx$1\% with a scatter of
$\lesssim$20\%. The network trained with Compton $y$ parameter maps yields the
most precise predictions. We interpret the network's behaviour using two
diagnostic tests to determine which features are used to predict cluster mass.
The CNN trained with stellar mass images detect galaxies (not surprisingly),
while CNNs trained with gas-based tracers utilise the shape of the signal to
estimate cluster mass.


  The influence of decoherence on the fidelity of quantum memories for photonic
qubits based on dark-state polaritons in atomic ensembles is discussed. It is
shown that despite the large entanglement of the collective storage states
corresponding to single photons or nonclassical states of light the sensitivity
to decoherence does not scale with the number of atoms. This is due to the
existence of equivalence classes of storage states corresponding to states with
the same number of dark-state polariton excitations but arbitrary excitations
in other polariton modes. Several decoherence processes are discussed in
detail: single-atom spin-flips and dephasing, atom loss and motion of atoms.


  Recently an inflationary model with a vector field coupled to the inflaton
was proposed and the phenomenology studied for the Bianchi type I spacetime. It
was found that the model demonstrates a counter-example to the cosmic no-hair
theorem since there exists a stable anisotropically inflationary fix-point. One
of the great triumphs of inflation, however, is that it explains the observed
flatness and isotropy of the universe today without requiring special initial
conditions. Any acceptable model for inflation should thus explain these
observations in a satisfactory way. To check whether the model meets this
requirement, we introduce curvature to the background geometry and consider
axisymmetric spacetimes of Bianchi type II,III and the Kantowski-Sachs metric.
We show that the anisotropic Bianchi type I fix-point is an attractor for the
entire family of such spacetimes. The model is predictive in the sense that the
universe gets close to this fix-point after a few e-folds for a wide range of
initial conditions. If inflation lasts for N e-folds, the curvature at the end
of inflation is typically of order exp(-2N). The anisotropy in the expansion
rate at the end of inflation, on the other hand, while being small on the
one-percent level, is highly significant. We show that after the end of
inflation there will be a period of isotropization lasting for about 2N/3
e-folds. After that the shear scales as the curvature and becomes dominant
around N e-folds after the end of inflation. For plausible bounds on the reheat
temperature the minimum number of e-folds during inflation, required for
consistency with the isotropy of the supernova Ia data, lays in the interval
(21,48). Thus the results obtained for our restricted class of spacetimes
indicates that inflation with anisotropic hair is cosmologically viable.


  In this paper we use tools from differential topology to give a geometric
description of cohomology for Hilbert manifolds. Our model is Quillen's
geometric description of cobordism groups for finite dimensional smooth
manifolds \cite{Q}. Quillen stresses the fact that this construction allows the
definition of Gysin maps for "oriented" proper maps. For finite dimensional
manifolds one has a Gysin map in singular cohomology which is based on
Poincar\'e duality, hence it is not clear how to extend it to infinite
dimensional manifolds. But perhaps one can overcome this difficulty by giving a
Quillen type description of singular cohomology for Hilbert manifolds. This is
what we do in this paper. Besides constructing a general Gysin map, one of our
motivations was a geometric construction of equivariant cohomology, which even
for a point is the cohomology of the infinite dimensional space $BG$, which has
a Hilbert manifold model. Besides that, we demonstrate the use of such a
geometric description of cohomology by several other applications. We give a
quick description of characteristic classes of a finite dimensional vector
bundle and apply it to a generalized Steenrod representation problem for
Hilbert manifolds and define a notion of a degree of proper oriented Fredholm
maps of index $0$.


  We study 2D solids with weak substrate disorder, using Coulomb gas
renormalisation. The melting transition is found to be replaced by a sharp
crossover between a high $T$ liquid with thermally induced dislocations, and a
low $T$ glassy regime with disorder induced dislocations at scales larger than
$\xi_{d}$ which we compute ($\xi_{d}\gg R_{c}\sim R_{a}$, the Larkin and
translational correlation lengths). We discuss experimental consequences,
reminiscent of melting, such as size effects in vortex flow and AC response in
superconducting films.


  The paper aims at emphasizing that, even relaxed, the hypothesis of
compositionality has to face many problems when used for interpreting natural
language texts. Rather than fixing these problems within the compositional
framework, we believe that a more radical change is necessary, and propose
another approach.


  We obtain, using a reformulation of the tunneling mechanism, the Hawking
black body spectrum with the appropriate temperature for a black hole. This is
a new result in the tunneling formalism of discussing Hawking effect. Our
results are given for a spherically symmetric geometry that is asymptotically
flat.


  We present new estimators for the statistical analysis of the dependence of
the mean gap time length between consecutive recurrent events, on a set of
explanatory random variables and in the presence of right censoring. The
dependence is expressed through regression-like and overdispersion parameters,
estimated via conditional estimating equations. The mean and variance of the
length of each gap time, conditioned on the observed history of prior events
and other covariates, are known functions of parameters and covariates. Under
certain conditions on censoring, we construct normalized estimating functions
that are asymptotically unbiased and contain only observed data. We discuss the
existence, consistency and asymptotic normality of a sequence of estimators of
the parameters, which are roots of these estimating equations. Simulations
suggest that our estimators could be used successfully with a relatively small
sample size in a study of short duration.


  Atom-based quantum simulators have had tremendous success in tackling
challenging quantum many-body problems, owing to the precise and dynamical
control that they provide over the systems' parameters. They are, however,
often optimized to address a specific type of problems. Here, we present the
design and implementation of a $^6$Li-based quantum gas platform that provides
wide-ranging capabilities and is able to address a variety of quantum many-body
problems. Our two-chamber architecture relies on a robust and easy-to-implement
combination of gray molasses and optical transport from a laser-cooling chamber
to a glass cell with excellent optical access. There, we first create unitary
Fermi superfluids in a three-dimensional axially symmetric harmonic trap and
characterize them using in situ thermometry, reaching temperatures below 20 nK.
This allows us to enter the deep superfluid regime with samples of extreme
diluteness, where the interparticle spacing is sufficiently large for direct
single-atom imaging. Secondly, we generate optical lattice potentials with
triangular and honeycomb geometry in which we study diffraction of molecular
Bose-Einstein condensates, and show how going beyond the Kapitza-Dirac regime
allows us to unambiguously distinguish between the two geometries. With the
ability to probe quantum many-body physics in both discrete and continuous
space, and its suitability for bulk and single-atom imaging, our setup
represents an important step towards achieving a wide-scope quantum simulator.


  We extend the centering model for the resolution of intra-sentential anaphora
and specify how to handle complex sentences. An empirical evaluation indicates
that the functional information structure guides the search for an antecedent
within the sentence.


  We address questions posed by Lorenzini about relations between Jacobians,
Tutte polynomials, and the Brill-Noether theory of finite graphs, as encoded in
his two-variable zeta functions. In particular, we give examples showing that
none of these invariants is determined by the other two.


  Deep neural networks (DNNs) have become the essential components for various
commercialized machine learning services, such as Machine Learning as a Service
(MLaaS). Recent studies show that machine learning services face severe privacy
threats - well-trained DNNs owned by MLaaS providers can be stolen through
public APIs, namely model stealing attacks. However, most existing works
undervalued the impact of such attacks, where a successful attack has to
acquire confidential training data or auxiliary data regarding the victim DNN.
In this paper, we propose ES Attack, a novel model stealing attack without any
data hurdles. By using heuristically generated synthetic data, ES Attack
iteratively trains a substitute model and eventually achieves a functionally
equivalent copy of the victim DNN. The experimental results reveal the severity
of ES Attack: i) ES Attack successfully steals the victim model without data
hurdles, and ES Attack even outperforms most existing model stealing attacks
using auxiliary data in terms of model accuracy; ii) most countermeasures are
ineffective in defending ES Attack; iii) ES Attack facilitates further attacks
relying on the stolen model.


  Unforeseen particle accelerator interruptions, also known as interlocks, lead
to abrupt operational changes despite being necessary safety measures. These
may result in substantial loss of beam time and perhaps even equipment damage.
We propose a simple yet powerful binary classification model aiming to forecast
such interruptions, in the case of the High Intensity Proton Accelerator
complex at the Paul Scherrer Institut. The model is formulated as logistic
regression penalized by least absolute shrinkage and selection operator, based
on a statistical two sample test to distinguish between unstable and stable
states of the accelerator.
  The primary objective for receiving alarms prior to interlocks is to allow
for countermeasures and reduce beam time loss. Hence, a continuous evaluation
metric is developed to measure the saved beam time in any period, given the
assumption that interlocks could be circumvented by reducing the beam current.
The best-performing interlock-to-stable classifier can potentially increase the
beam time by around 5 min in a day. Possible instrumentation for fast
adjustment of the beam current is also listed and discussed.


  We present the complete set of {\it Hubble Space Telescope} imaging of the
binary neutron star merger GW170817 and its optical counterpart AT 2017gfo.
Including deep template imaging in F814W, F110W, F140W, and F160W at 3.4 years
post-merger, we re-analyze the full light curve of AT 2017gfo across 12 bands
from 5--1273 rest-frame days after merger. We obtain four new detections of the
short $\gamma$-ray burst (GRB) 170817A afterglow from 109--170 rest-frame days
post-merger. These detections are consistent with the previously observed
$\beta=-0.6$ spectral index in the afterglow light curve with no evidence for
spectral evolution. We also analyze our limits in the case of novel late-time
optical and IR emission signatures, such as a kilonova afterglow or infrared
dust echo, but find our limits are not constraining in these contexts. We use
the new data to construct deep optical and infrared stacks, reaching limits of
$M=-6.3$ to $-4.6$ mag, to analyze the local environment around AT 2017gfo and
low surface brightness features in its host galaxy NGC 4993. We rule out the
presence of any globular cluster at the position of AT 2017gfo to $2.3 \times
10^{4} L_{\odot}$, including those with the reddest $V-H$ colors. Finally, we
analyze the substructure of NGC 4993 in deep residual imaging, and find shell
features which extend up to 71.8\arcsec\ (14.2 kpc) from the center of the
galaxy. We find that the shells have a cumulative stellar mass of
$6.3\times10^{8} M_{\odot}$, roughly 2% the total stellar mass of NGC 4993, and
mass-weighted ages of $>$3 Gyr. We conclude that it was unlikely the GW170817
progenitor system formed in the galaxy merger, which based on dynamical
signatures and the stellar population in the shells mostly likely occurred
220--685 Myr ago.


  Different representations to describe noise processes and finding connections
or equivalence between them have been part of active research for decades, in
particular for linear time-invariant case. In this paper the linear
parameter-varying (LPV) setting is addressed; starting with the connection
between an LPV state-space (SS) representation with a general noise structure
and the LPV-SS model in an innovation structure, i.e., the Kalman filter. More
specifically, the considered LPV-SS representation with general noise structure
has static, affine dependence on the scheduling signal; however, we show that
its companion innovation structure has a dynamic, rational dependency
structure. Following, we would like to highlight the consequences of
approximating this Kalman gain by a static, affine dependency structure. To
this end, firstly, we use the "fading memory" effect of the Kalman filter to
reason how the Kalman gain can be approximated to depend only on a partial
trajectory of the scheduling signal. This effect is shown by proving an
asymptotically decreasing error upper bound on the covariance matrix associated
to the innovation structure in case the covariance matrix is subjected to an
incorrect initialization or disturbance. Secondly, we show by an example that
an LPV-SS representation that has dynamical, rational dependency on the
scheduling signal can be transformed into static, affinely dependent
representation by introducing additional states. Therefore, an approximated
Kalman gain can, in some cases, be represented by a static, affine Kalman gain
at the cost of additional states.


  The vertex-nullity interlace polynomial of a graph, described by Arratia,
Bollob\'as and Sorkin as evolving from questions of DNA sequencing, and
extended to a two-variable interlace polynomial by the same authors, evokes
many open questions. These include relations between the interlace polynomial
and the Tutte polynomial and the computational complexity of the vertex-nullity
interlace polynomial. Here, we prove that the one-variable vertex-nullity
interlace polynomial is in general #P-hard to compute. We also show a relation
between the two-variable interlace polynomial and the topological Tutte
polynomial of Bollob\'as and Riordan. We define the \gamma invariant as the
coefficient of x^1 in the vertex-nullity interlace polynomial, analogously to
the \beta invariant, which is the coefficient of x^1 in the Tutte polynomial.
We then turn to distance hereditary graphs, and show that graphs in this class
have \gamma invariant of 2^{n+1} when n true twins are added in their
construction. We furthermore show that bipartite distance hereditary graphs are
exactly the class of graphs with \gamma invariant 2, just as the
series-parallel graphs are exactly the class of graphs with \beta invariant 1.
In addition, we show that a bipartite distance hereditary graph arises
precisely as the circle graph of any Euler circuit in the oriented medial graph
of a series-parallel graph. From this we conclude that the vertex-nullity
interlace polynomial is polynomial time to compute for bipartite distance
hereditry graphs, just as the Tutte polynomial is polynomial time to compute
for series-parallel graphs.


  A new observation of D in a primordial gas cloud, made using the high
resolution spectrograph at the Keck telescope, indicates an abundance $ D/H
=(1.9-2.5) \times 10^{-4}$ \cite{SCHR}. Since deuterium is destroyed by stars,
and the predicted Big Bang Nucleosynthesis (BBN) abundance falls monotonically
with increasing baryon density, deuterium places a reliable upper limit on the
baryon density of the universe. Because the new measurement is substationally
larger than previous, galactic estimates, it would force a reassessment of BBN
predictions--- if it is confirmed. Using a new BBN Monte Carlo code and
analysis technique \cite{KK} we derive constraints implied by a lower limit of
$D/H =1.9 \times 10^{-4}$. We find $\Omega_B \le .0068h^{-2}$, which is
definitively incompatible with baryonic halo dark matter. We also explore
implications of combining the D measurement with other light element
abundances. $^7Li$ provides a lower bound, $\Omega_B \ge .004h^{-2}$. Also, the
initial $^4He$ mass fraction ($Y_p$) would have to be less than $23.5\%$,
assuming 3 light neutrino species---in good agreement with present best fits.
Finally, observational upper limits of $Y_p \le 24 \%$ and $^7Li/H \le 2.3
\times 10^{-10}$ would allow the number of neutrinos to be as big as 3.9.


  We demonstrate, by giving a specific example, that supersymmetry can be left
unbroken without running into conflict with observation. The key idea is to
employ a discrete form of supersymmetry. Amongst other interesting features,
this construction goes roughly half the way in removing the hierarchy between
the observed cosmological constant and the vacuum energies expected from field
theory.


  Comment on Conjugate heat transfer of mixed convection for viscoelastic fluid
past a stretching sheet, by Kai-Long Hsiao and Guan-Bang Chen, Mathematical
Problems in Engineering, Volume 2007, article 17058, 21 pages


  We present static and dynamic properties of molecular correlation functions
S_{lmn,l'm'n'}(q,t) in a simulated supercooled liquid of water molecules, as a
preliminary effort in the direction of solving the molecular mode coupling
theory (MMCT) equations for supercooled molecular liquids. The temperature and
time dependence of various molecular correlation functions, calculated from 250
ns long molecular dynamics simulations, show the characteristic patterns
predicted by MMCT and shed light on the driving mechanism responsible for the
slowing down of the molecular dynamics. We also discuss the symmetry properties
of the molecular correlation functions which can be predicted on the basis of
the C_{2v}-symmetry of the molecule. The analysis of the MD--results for the
static correlators S_{lmn,l'm'n'}(q) reveals that additional relationships
between correlators with different signs of n and n' exist. We prove that for
molecules with C_{rv}-symmetry this unexpected result becomes exact at least
for high temperatures.


  We report new data for the topological susceptibility computed on 2+1 flavor
dynamical configurations with lattice spacing 0.06 fm, generated with the
asqtad action. The topological susceptibility is computed by HYP smearing and
compared with rooted staggered chiral perturbation theory as the pion mass goes
to zero. At 0.06 fm, the raw data is already quite close to the continuum
extrapolated values obtained from coarser lattices. These results provide a
further test of the asqtad action with rooted staggered flavors.


  Let $A$ be a matrix with its pseudo-matrix $A^{\dagger}$ and set
$S=I-A^{\dagger}A$. We prove that, after re-ordering the columns of $A$, the
matrix $S$ has a block-diagonal form where each block corresponds to a set of
linearly dependent columns. This allows us to identify redundant columns in
$A$. We explore some applications in supervised and unsupervised learning,
specially feature selection, clustering, and sensitivity of solutions of least
squares solutions.


  The influence of a temporal forcing on the pattern formation in
Langmuir-Blodgett transfer is studied employing a generalized Cahn-Hilliard
model. The occurring frequency locking effects allow for controlling the
pattern formation process. In the case of one-dimensional (i.e., stripe)
patterns one finds various synchronization phenomena such as entrainment
between the distance of deposited stripes and the forcing frequency. In two
dimensions, the temporal forcing gives rise to the formation of intricate
complex patterns such as vertical stripes, oblique stripes and lattice
structures. Remarkably, it is possible to influence the system in the spatial
direction perpendicular to the forcing direction leading to synchronization in
two spatial dimensions.


  A loop-algebraic presentation is given for toroidal Lie superalgebras of
classical types. Based on the loop superalgebra presentation free field
realizations of toroidal Lie superalgebras are constructed for types $A(m,n)$,
$B(m,n)$, C(n) and $D(m,n)$.


  In a well-aimed search based on the recently published Gaia~DR2 radial
velocity data and performed with the robotic TIGRE telescope, we have
discovered 19 bright ($m_V<7.66$~mag) spectroscopic binary stars. We present
the radial velocity curves of five of these stars (HD~20656, HD~27259,
HD~98812, HD~150600, HD~193082) with periods of $P<365$~days. We determined
their orbital parameters using the toolkit RadVel. The complete set of basic
stellar parameters (effective temperature, surface gravity, metallicity, mass
and age) of the primary stars are determined using the observed intermediate
resolution spectra ($R\approx20,000$) with iSpec and a comparison of the
position in the HRD with stellar evolution tracks. All five primary stars have
already evolved to the giant phase and possess low-mass secondaries, which do
not noticeably affect the spectra. Finally, we determined the minimum masses of
the secondary stars of the binary systems and present conclusions about the
properties of the binary systems.


  While policy optimization algorithms have played an important role in recent
empirical success of Reinforcement Learning (RL), the existing theoretical
understanding of policy optimization remains rather limited -- they are either
restricted to tabular MDPs or suffer from highly suboptimal sample complexity,
especial in online RL where exploration is necessary. This paper proposes a
simple efficient policy optimization framework -- Optimistic NPG for online RL.
Optimistic NPG can be viewed as a simple combination of the classic natural
policy gradient (NPG) algorithm [Kakade, 2001] with optimistic policy
evaluation subroutines to encourage exploration. For $d$-dimensional linear
MDPs, Optimistic NPG is computationally efficient, and learns an
$\varepsilon$-optimal policy within $\tilde{O}(d^2/\varepsilon^3)$ samples,
which is the first computationally efficient algorithm whose sample complexity
has the optimal dimension dependence $\tilde{\Theta}(d^2)$. It also improves
over state-of-the-art results of policy optimization algorithms [Zanette et
al., 2021] by a factor of $d$. In the realm of general function approximation,
which subsumes linear MDPs, Optimistic NPG, to our best knowledge, stands as
the first policy optimization algorithm that achieves polynomial sample
complexity for learning near-optimal policies.


  Standard quantum state reconstruction techniques indicate that a detection
efficiency of $0.5$ is an absolute threshold below which quantum interferences
cannot be measured. However, alternative statistical techniques suggest that
this threshold can be overcome at the price of increasing the statistics used
for the reconstruction. In the following we present numerical experiments
proving that quantum interferences can be measured even with a detection
efficiency smaller than $0.5$. At the same time we provide a guideline for
handling the tomographic reconstruction of quantum states based on homodyne
data collected by low efficiency detectors.


  We describe an infinite-dimensional Kac-Moody-Virasoro algebra of new hidden
symmetries for the self-dual Yang-Mills equations related to conformal
transformations of the 4-dimensional base space.


  We report early results on galaxies at z~6, selected from Hubble Space
Telescope imaging for the Great Observatories Origins Deep Survey. Spectroscopy
of one object with the Advanced Camera for Surveys grism and from the Keck and
VLT observatories a shows a strong continuum break and asymmetric line
emission, identified as Lya at z=5.83. We detect only five spatially extended,
z~6 candidates with signal-to-noise ratios > 10, two of which have
spectroscopic confirmation. This is many fewer than would be expected if
galaxies at z=6 had the same luminosity function as those at z=3. There are
many fainter candidates, but we expect substantial contamination from
foreground interlopers and spurious detections. Our best estimates favor a z=6
galaxy population with fainter luminosities, higher space density, and similar
co-moving ultraviolet emissivity to that at z=3, but this depends critically on
counts at fluxes fainter than those reliably probed by the current data.


  In the paper we show that the monoid $\mathbf{I}\mathbb{N}_{\infty}$ of all
partial cofinite isometries of positive integers does not embed isomorphically
into the monoid $\mathbf{ID}_{\infty}$ of all partial cofinite isometries of
integers. Moreover, every non-annihilating homomorphism $\mathfrak{h}\colon
\mathbf{I}\mathbb{N}_{\infty}\to\mathbf{ID}_{\infty}$ has the following
property: the image $(\mathbf{I}\mathbb{N}_{\infty})\mathfrak{h}$ is isomorphic
either to the two-element cyclic group $\mathbb{Z}_2$ or to the additive group
of integers $\mathbb{Z}(+)$. Also we prove that the monoid
$\mathbf{I}\mathbb{N}_{\infty}$ is not finitely generated, and, moreover,
monoid $\mathbf{I}\mathbb{N}_{\infty}$ does not contain a minimal generating
set.


  The extragalactic gamma-ray sky is dominated by the emission arising from
blazars, one of the most peculiar classes of radio-loud active galaxies. Since
the launch of Fermi several methods were developed to search for blazars as
potential counterparts of unidentified gamma-ray sources (UGSs). To confirm the
nature of the selected candidates, optical spectroscopic observations are
necessary. In 2013 we started a spectroscopic campaign to investigate gamma-ray
blazar candidates selected according to different procedures. The main goals of
our campaign are: 1) to confirm the nature of these candidates, and 2) whenever
possible determine their redshifts. Optical spectroscopic observations will
also permit us to verify the robustness of the proposed associations and check
for the presence of possible source class contaminants to our counterpart
selection. This paper reports the results of observations carried out in 2014
in the Northern hemisphere with Kitt Peak National Observatory (KPNO) and in
the Southern hemisphere with the Southern Astrophysical Research (SOAR)
telescopes. We also report three sources observed with the Magellan and Palomar
telescopes. Our selection of blazar-like sources that could be potential
counterparts of UGSs is based on their peculiar IR colors and on their
combination with radio observations both at high and low frequencies (i.e.,
above and below ~ 1 GHz) in publicly available large radio surveys. We present
the optical spectra of 27 objects. [....] We also report the case for WISE
J173052.85-035247.2, candidate counterpart of the source 2FGL J1730.6-0353,
which has no radio counterpart in the major radio surveys. We confirm that our
selection of gamma-ray blazars candidates can successfully indentify low-energy
counterparts to Fermi unassociated sources and allow us to discover new
blazars.


  Sampling-based motion planners rely on incremental densification to discover
progressively shorter paths. After computing feasible path $\xi$ between start
$x_s$ and goal $x_t$, the Informed Set (IS) prunes the configuration space
$\mathcal{C}$ by conservatively eliminating points that cannot yield shorter
paths. Densification via sampling from this Informed Set retains asymptotic
optimality of sampling from the entire configuration space. For path length
$c(\xi)$ and Euclidean heuristic $h$, $IS = \{ x | x \in \mathcal{C}, h(x_s, x)
+ h(x, x_t) \leq c(\xi) \}$.
  Relying on the heuristic can render the IS especially conservative in high
dimensions or complex environments. Furthermore, the IS only shrinks when
shorter paths are discovered. Thus, the computational effort from each
iteration of densification and planning is wasted if it fails to yield a
shorter path, despite improving the cost-to-come for vertices in the search
tree. Our key insight is that even in such a failure, shorter paths to vertices
in the search tree (rather than just the goal) can immediately improve the
planner's sampling strategy. Guided Incremental Local Densification (GuILD)
leverages this information to sample from Local Subsets of the IS. We show that
GuILD significantly outperforms uniform sampling of the Informed Set in
simulated $\mathbb{R}^2$, $SE(2)$ environments and manipulation tasks in
$\mathbb{R}^7$.


  The analysis and verification of hybrid automata (HA) models against rich
formal properties can be a challenging task. Existing methods and tools can
mainly reason whether a given property is satisfied or violated. However, such
qualitative answers might not provide sufficient information about the model
behaviors. This paper presents the ForFET$^{SMT}$ tool which can be used to
reason quantitatively about such properties. It employs feature automata and
can evaluate quantitative property corners of HA. ForFET$^{SMT}$ uses two
third-party formal verification tools as its backbone: the SpaceEx reachability
tool and the SMT solver dReach/dReal. Herein, we describe the design and
implementation of ForFET$^{SMT}$ and present its functionalities and modules.
To improve the usability of the tool for non-expert users, we also provide a
list of quantitative property templates.


  This paper targets the problem of multi-task dense prediction which aims to
achieve simultaneous learning and inference on a bunch of multiple dense
prediction tasks in a single framework. A core objective in design is how to
effectively model cross-task interactions to achieve a comprehensive
improvement on different tasks based on their inherent complementarity and
consistency. Existing works typically design extra expensive distillation
modules to perform explicit interaction computations among different
task-specific features in both training and inference, bringing difficulty in
adaptation for different task sets, and reducing efficiency due to clearly
increased size of multi-task models. In contrast, we introduce feature-wise
contrastive consistency into modeling the cross-task interactions for
multi-task dense prediction. We propose a novel multi-task contrastive
regularization method based on the consistency to effectively boost the
representation learning of the different sub-tasks, which can also be easily
generalized to different multi-task dense prediction frameworks, and costs no
additional computation in the inference. Extensive experiments on two
challenging datasets (i.e. NYUD-v2 and Pascal-Context) clearly demonstrate the
superiority of the proposed multi-task contrastive learning approach for dense
predictions, establishing new state-of-the-art performances.


  Some hydrogen-poor supernovae (SNe) are found to undergo interaction with
dense circumstellar matter (CSM) that may originate from mass eruption(s) just
prior to core-collapse. We model the interaction between the remaining star and
the bound part of the erupted CSM that eventually fall back to the star. We
find that while fallback initially results in a continuous CSM down to the
star, feedback processes from the star can push the CSM to large radii of
$\gtrsim 10^{15}$ cm from several years after the eruption. In the latter case,
a tenuous bubble surrounded by a dense and detached CSM extending to $\gtrsim
10^{16}$ cm is expected. Our model offers a natural unifying explanation for
the diverse CSM structures seen in hydrogen-poor SNe, such as Type Ibn/Icn SNe
that show CSM signatures soon after explosion, and the recently discovered Type
Ic SNe 2021ocs and 2022xxf ("the Bactrian") with CSM signatures seen only at
late times.


  This work explores the use of a forward-backward martingale method together
with a decoupling argument and entropic estimates between the conditional and
averaged measures to prove a strong averaging principle for stochastic
differential equations with order of convergence 1/2. We obtain explicit
expressions for all the constants involved. At the price of some extra
assumptions on the time marginals and an exponential bound in time, we loosen
the usual boundedness and Lipschitz assumptions. We conclude with an
application of our result to Temperature-Accelerated Molecular Dynamics.


This paper has been withdrawn.


  Reinforcement learning (RL) is capable of managing wireless,
energy-harvesting IoT nodes by solving the problem of autonomous management in
non-stationary, resource-constrained settings. We show that the
state-of-the-art policy-gradient approaches to RL are appropriate for the IoT
domain and that they outperform previous approaches. Due to the ability to
model continuous observation and action spaces, as well as improved function
approximation capability, the new approaches are able to solve harder problems,
permitting reward functions that are better aligned with the actual application
goals. We show such a reward function and use policy-gradient approaches to
learn capable policies, leading to behavior more appropriate for IoT nodes with
less manual design effort, increasing the level of autonomy in IoT.


  Quantum battery is one of the most prominent micro-devices in the
rapid-developing quantum thermodynamics. We propose a quantum charging protocol
in which both battery and charger are consisted of a many-spin system. The
battery and charger are connected via the Kittle mode of the magnon system,
that serves as a charging wire and then enables a long-range charging protocol.
Counter-intuitively, we find that the spin-spin couplings insider both battery
and charger can be used to promote the charging performance in comparison to
the noninteracting condition. And a remarkable promotion is realized at a
"sweet" spot in terms of the average coupling strength. Moreover, we apply the
quantum-state-diffusion equation to test the robustness of our magnon-mediated
charging protocol to the systematic errors about the magnon frequency.


  We study hypersurfaces in a nearly $\mathrm{G}_2$ manifold. We define various
quantities associated to such a hypersurface using the $\mathrm{G}_2$ structure
of the ambient manifold and prove several relationships between them. In
particular, we give a necessary and sufficient condition for a hypersurface
with an almost complex structure induced from the $\mathrm{G}_2$ structure of
the ambient manifold, to be nearly K$\ddot{\text{a}}$hler. Then using the
nearly $\mathrm{G}_2$ structure on the round sphere $S^7$, we prove that for a
compact minimal hypersurface $M^6$ of constant scalar curvature in $S^7$ with
the shape operator $A$ satisfying $|A|^2>6$, there exists an eigenvalue
$\lambda >12$ of the Laplace operator on $M$ such that $|A|^2=\lambda - 6$,
thus giving the next discrete value of $|A|^2$ greater than $0$ and $6$, thus
generalizing an earlier result about nearly K$\ddot{\text{a}}$hler $S^6$.


  The problem of finding an ancestral acyclic directed mixed graph (ADMG) that
represents the causal relationships between a set of variables is an important
area of research on causal inference. Most existing score-based structure
learning methods focus on learning directed acyclic graph (DAG) models without
latent variables. A number of score-based methods have recently been proposed
for the ADMG learning, yet they are heuristic in nature and do not guarantee an
optimal solution. We propose a novel exact score-based method that solves an
integer programming (IP) formulation and returns a score-maximizing ancestral
ADMG for a set of continuous variables that follow a multivariate Gaussian
distribution. We generalize the state-of-the-art IP model for DAG learning
problems and derive new classes of valid inequalities to formulate an IP model
for ADMG learning. Empirically, our model can be solved efficiently for
medium-sized problems and achieves better accuracy than state-of-the-art
score-based methods as well as benchmark constraint-based methods.


  Conventional hyperparameter optimization methods are computationally
intensive and hard to generalize to scenarios that require dynamically adapting
hyperparameters, such as life-long learning. Here, we propose an online
hyperparameter optimization algorithm that is asymptotically exact and
computationally tractable, both theoretically and practically. Our framework
takes advantage of the analogy between hyperparameter optimization and
parameter learning in recurrent neural networks (RNNs). It adapts a
well-studied family of online learning algorithms for RNNs to tune
hyperparameters and network parameters simultaneously, without repeatedly
rolling out iterative optimization. This procedure yields systematically better
generalization performance compared to standard methods, at a fraction of
wallclock time.


  We present optical photometry and spectra of the peculiar Type Ib supernova
(SN) 2006jc. Strong and relatively narrow He I emission lines indicate the
progenitor star exploded inside a dense circumstellar medium (CSM) rich in He.
An exceptionally blue apparent continuum persists from our first spectrum
obtained 15 days after discovery through our last spectrum ~1 month later.
Based on the presence of isolated Fe II emission lines, we interpret the blue
"continuum" as blended, perhaps fluorescent, Fe emission. One or two of the
reddest He I line profiles in our spectra are double peaked, suggesting that
the CSM has an aspherical geometry. The He I lines that are superposed on the
blue continuum show P-Cygni profiles, while the redder He I lines do not,
implying that the blue continuum also originates from an asymmetric mass
distribution. The He-rich CSM, aspherical geometry, and line velocities
indicate that the progenitor star was a WNE Wolf-Rayet (WR) star. A recent (2
years before the SN), coincident, luminous outburst similar to those seen in
luminous blue variables (LBVs) is the leading candidate for the dense CSM. Such
an eruption associated with a WR star has not been seen before, indicating that
the progenitor star may have recently transitioned from the LBV phase. We also
present unpublished spectral and photometric data on SN 2002ao which, along
with SN 1999cq, is very similar to SN 2006jc. We propose that these three
objects may represent a new and distinct class of SNe arising from WR
progenitors surrounded by a dense CSM.


  We show that a method based on logistic regression, using all the data,
solves the inverse Ising problem far better than mean-field calculations
relying only on sample pairwise correlation functions, while still
computationally feasible for hundreds of nodes. The largest improvement in
reconstruction occurs for strong interactions. Using two examples, a diluted
Sherrington-Kirkpatrick model and a two-dimensional lattice, we also show that
interaction topologies can be recovered from few samples with good accuracy and
that the use of $l_1$-regularization is beneficial in this process, pushing
inference abilities further into low-temperature regimes.


  Narrowband Internet of Things (NB-IoT) is a new Low Power Wide Area Network
(LPWAN) technology released by 3GPP. The primary goals of NB-IoT are improved
coverage, massive capacity, low cost, and long battery life. In order to
improve coverage, NB-IoT has promising solutions, such as increasing
transmission repetitions, decreasing bandwidth, and adapting the Modulation and
Coding Scheme (MCS). In this paper, we present an implementation of coverage
enhancement features of NB-IoT in NS-3, an end-to-end network simulator. The
resource allocation and link adaptation in NS-3 are modified to comply with the
new features of NB-IoT. Using the developed simulation framework, the influence
of the new features on network reliability and latency is evaluated.
Furthermore, an optimal hybrid link adaptation strategy based on all three
features is proposed. To achieve this, we formulate an optimization problem
that has an objective function based on latency, and constraint based on the
Signal to Noise Ratio (SNR). Then, we propose several algorithms to minimize
latency and compare them with respect to accuracy and speed. The best hybrid
solution is chosen and implemented in the NS-3 simulator by which the latency
formulation is verified. The numerical results show that the proposed
optimization algorithm for hybrid link adaptation is eight times faster than
the exhaustive search approach and yields similar latency.


  We employ the Boltzmann transport model to study the sequential suppression
pattern of $\Upsilon(1S,2S,3S)$ states in both small (p-Pb) and the large
(Pb-Pb) collision systems at $\sqrt{s_{NN}}=5.02$ TeV. The cold nuclear matter
effects happen before the formation of bottomonium, which is the same for
different bottomonium states $\Upsilon(1S,2S,3S)$. The sequential suppression
pattern of bottomonium states is treated as a signal of the hot medium effects,
where bottomonium states suffer different magnitudes of the color screening and
parton inelastic scatterings due to their different geometry sizes and binding
energies. Bottomonium excited states are more easily dissociated with smaller
binding energy via the final-state interactions. Including both cold and hot
medium effects, transport model consistently explains the experimental data
about bottomonium in both small and large collision systems.


  We consider the classical problem of determining the stationary distribution
of the semimartingale reflected Brownian motion (SRBM) in a two-dimensional
wedge. Under standard assumptions on the parameters of the model (opening of
the wedge, angles of the reflections, drift), we study the algebraic and
differential nature of the Laplace transform of this stationary distribution.
We derive necessary and sufficient conditions for this Laplace transform to be
rational, algebraic, differentially finite or more generally differentially
algebraic. These conditions are explicit linear dependencies between the angles
of the model.
  A complicated integral expression for this Laplace transform has recently
been obtained by two authors of this paper. In the differentially algebraic
case, we provide a simple, explicit integral-free expression in terms of a
hypergeometric function. It specializes to earlier expressions in several
classical cases: the skew-symmetric case, the orthogonal reflections case and
the sum-of-exponential densities case (corresponding to the so-called
Dieker-Moriarty conditions on the parameters). This paper thus closes, in a
sense, the quest of all ``simple'' cases.
  To prove these results, we start from a functional equation that the Laplace
transform satisfies, to which we apply tools from diverse horizons. To
establish differential algebraicity, a key ingredient is Tutte's invariant
approach, which originates in enumerative combinatorics. It allows us to
express the Laplace transform (or its square) as a rational function of a
certain canonical invariant, a hypergeometric function in our context. To
establish differential transcendence, we turn the functional equation into a
difference equation and apply Galoisian results on the nature of the solutions
to such equations.


  The one-dimensional configuration coordinate model (1D-CCM) is widely used
for the analysis of photoluminescence in molecules and doped solids, and relies
on a linear combination of the equilibrium nuclear configurations of ground and
excited states. It delivers an estimation of the energy barrier at which ground
and excited state curves cross, semi-classically linked to non-radiative
transition rate and thermal quenching. To assess its predictive power for the
latter properties, we propose a new \textit{optimized configuration path (OCP)
method in which} the ground-state and excited-state forces are mixed instead of
their configurations. We also define another one-parameter model thanks a
double energy parabola hypothesis (DEPH). We compare the OCP method and the
DEPH reference with the 1D-CCM for three paradigmatic 4f-5d phosphors
Y$_3$Al$_5$O$_{12}$:Ce, Lu$_2$SiO$_5$:Ce, and YAlO$_3$:Ce. We find that the OCP
and DEPH methods yield similar results with geometries that have significantly
lower ground-state energies than the 1D-CCM for the same 4f-5d energy
difference. However, the OCP method suffers from the appearance of multiple
local minima, rendering the clear determination of the optimal geometry very
difficult in practice. Still the OCP method allows one to quantify the
deviations from the 1D-CCM, therefore increasing confidence in the lower bound
obtained from the DEPH for the 4f-5d crossing barrier, and its comparison with
the energy of the auto-ionization thermal quenching mechanism. We expect the
OCP approach to be applicable to other luminescent materials or molecules.


  Real time seizure detection is a fundamental problem in computational
neuroscience towards diagnosis and treatment's improvement of epileptic
disease. We propose a real-time computational method for tracking and detection
of epileptic seizures from raw neurophysiological recordings. Our mechanism is
based on the topological analysis of the sliding-window embedding of the time
series derived from simultaneously recorded channels. We extract topological
biomarkers from the signals via the computation of the persistent homology of
time-evolving topological spaces. Remarkably, the proposed biomarkers robustly
captures the change in the brain dynamics during the ictal state. We apply our
methods in different types of signals including scalp and intracranial
electroencephalograms and magnetoencephalograms, in patients during interictal
and ictal states, showing high accuracy in a range of clinical situations.


  Let $M$ be a Lorentz surface and $F:M\rightarrow N$ a time-like and conformal
immersion of $M$ into a 4-dimensional neutral space form $N$ with zero mean
curvature vector. We see that the curvature $K$ of the induced metric on $M$ by
$F$ is identically equal to the constant sectional curvature $L_0$ of $N$ if
and only if the covariant derivatives of both of the time-like twistor lifts
are zero or light-like. If $K\equiv L_0$, then the normal connection
$\nabla^{\perp}$ of $F$ is flat, while the converse is not necessarily true. We
see that a holomorphic paracomplex quartic differential $Q$ on $M$ defined by
$F$ is zero or null if and only if the covariant derivative of at least one of
the time-like twistor lifts is zero or light-like. In addition, we see that $K$
is identically equal to $L_0$ if and only if not only $\nabla^{\perp}$ is flat
but also $Q$ is zero or null.


  A hybrid framework of spin hydrodynamics is proposed that combines the
results of kinetic theory for particles with spin 1/2 with the Israel-Stewart
method of introducing non-equilibrium dynamics. The framework of kinetic theory
is used to define the perfect-fluid description that conserves baryon number,
energy, linear momentum and spin part of angular momentum. This leads to the
entropy conservation although, in the presence of spin degrees of freedom, the
perfect-fluid formalism includes extra terms whose structure is usually
attributed to dissipation. The genuine dissipative terms appear from the
condition of positive entropy production in non-equilibrium processes. They are
responsible for the transfer between the spin and orbital parts of angular
momentum, with the total angular momentum being conserved.


  The N-tree approximation scheme, introduced in the context of random directed
polymers, is here applied to the computation of the maximum Lyapunov exponent
in a coupled map lattice. We discuss both an exact implementation for small
tree-depth $n$ and a numerical implementation for larger $n$s. We find that the
phase-transition predicted by the mean field approach shifts towards larger
values of the coupling parameter when the depth $n$ is increased. We conjecture
that the transition eventually disappears.


  We construct a complex analytic version of an equivariant cohomology theory
which appeared in a recent paper of Rezk, and which is roughly modeled on the
Borel-equivariant cohomology of the double free loop space. The construction is
defined on finite, torus-equivariant CW complexes and takes values in coherent
holomorphic sheaves over the moduli stack of complex elliptic curves. Our
methods involve an inverse limit construction over all finite dimensional
subcomplexes of the double free loop space, following an analogous construction
of Kitchloo for single free loop spaces. We show that, for any given complex
elliptic curve $\mathcal{C}$, the fiber of our construction over $\mathcal{C}$
is isomorphic to Grojnowski's equivariant elliptic cohomology theory associated
to $\mathcal{C}$.


  The effectiveness and efficiency of modeling complex spectral-spatial
relations are both crucial for Hyperspectral image (HSI) classification. Most
existing methods based on CNNs and transformers still suffer from heavy
computational burdens and have room for improvement in capturing the
global-local spectral-spatial feature representation. To this end, we propose a
novel lightweight parallel design called lightweight dual-stream
Mamba-convolution network (DualMamba) for HSI classification. Specifically, a
parallel lightweight Mamba and CNN block are first developed to extract global
and local spectral-spatial features. First, the cross-attention
spectral-spatial Mamba module is proposed to leverage the global modeling of
Mamba at linear complexity. Within this module, dynamic positional embedding is
designed to enhance the spatial location information of visual sequences. The
lightweight spectral/spatial Mamba blocks comprise an efficient scanning
strategy and a lightweight Mamba design to efficiently extract global
spectral-spatial features. And the cross-attention spectral-spatial fusion is
designed to learn cross-correlation and fuse spectral-spatial features. Second,
the lightweight spectral-spatial residual convolution module is proposed with
lightweight spectral and spatial branches to extract local spectral-spatial
features through residual learning. Finally, the adaptive global-local fusion
is proposed to dynamically combine global Mamba features and local convolution
features for a global-local spectral-spatial representation. Compared with
state-of-the-art HSI classification methods, experimental results demonstrate
that DualMamba achieves significant classification accuracy on three public HSI
datasets and a superior reduction in model parameters and floating point
operations (FLOPs).


  Dopant-based quantum computing implementations often require the dopants to
be situated close to an interface to facilitate qubit manipulation with local
gates. Interfaces not only modify the energies of the bound states but also
affect their symmetry. Making use of the successful effective mass theory we
study the energy spectra of acceptors in Si or Ge taking into account the
quantum confinement, the dielectric mismatch and the central cell effects. The
presence of an interface puts constraints to the allowed symmetries and lead to
the splitting of the ground state in two Kramers doublets [J. Mol et al, App.
Phys. Lett. 106, 203110 (2015)]. Inversion symmetry breaking also implies
parity mixing which affects the allowed optical transitions. Consequences for
acceptor qubits are discussed.


  A mathematical notion of interaction is introduced for noncommutative
dynamical systems, i.e., for one parameter groups of *-automorphisms of $\Cal
B(H)$ endowed with a certain causal structure. With any interaction there is a
well-defined "state of the past" and a well-defined "state of the future". We
describe the construction of many interactions involving cocycle perturbations
of the CAR/CCR flows and show that they are nontrivial. The proof of
nontriviality is based on a new inequality, relating the eigenvalue lists of
the "past" and "future" states to the norm of a linear functional on a certain
C^*-algebra.


  We investigate how the Next-Two-Higgs Doublet Model extension (N2HDM) should
look if we are to address the naturalness problem using dimensional
regularization. In such a model, new Higgs states are predicted, namely: three
CP-even $h_{1,2,3}$, one CP-odd $A$, and a pair of charged Higgs boson $H^\pm$.
Our calculations of the overall quadratic divergences have been performed with
full consistency with the latest data from the Large Hadron Collider (LHC)
concerning the observed 125 GeV Higgs boson, alongside precision electroweak
data tests and lower mass limits on charged Higgs boson. It is shown that the
quadratically divergent quantum corrections $\delta_i$ (i=1,2,3) for the three
CP-even Higgs bosons are controllably small, though hidden fine-tuning might
still be required. This reveals a significant impact on the model parameter
space, Higgs spectrum mass, and notably the singlet-doublet admixture.


  We compute the matching coefficients between QCD and non-relativistic QCD for
external vector, axial-vector, scalar and pseudo-scalar currents up to
three-loop order. We concentrate on the non-singlet contributions and present
precise numerical results with an accuracy of about ten digits. For the vector
current the results from arXiv:1401.3004 are confirmed, increasing the accuracy
by several orders of magnitude.


  We study the non-equilibrium noise in mesoscopic diffusive wires hosting
magnetic impurities. We find that the shot-noise to current ratio develops a
peak at intermediate source-drain biases of the order of the Kondo temperature.
The enhanced impurity contribution at intermediate biases is also manifested in
the effective distribution. The predicted peak represents increased inelastic
scattering rate at the non-equilibrium Kondo crossover.


  We prove the existence and stability of smooth solutions to the steady
Navier-Stokes equations near plane Poiseuille-Couette flow. Consequently, we
also provide the zero viscosity limit of the 2D steady Navier-Stokes equations
to the steady Euler equations. First, in the absence of any external force, we
prove that there exist smooth solutions to the steady Navier-Stokes equations
which are stable under infinitesimal perturbations of plane Poiseuille-Couette
flow. In particular, if the basic flow is the Couette flow, then we can prove
that the flow is stable for any finite perturbation small enough. Moreover, we
also show that, if we put a proper external force to control the flow, then we
can also obtain a large class of smooth solution of the steady Navier-Stokes
equations which is stable for infinitesimal perturbation of the external force.
Finally, based on the same linear estimates, we establish the zero viscosity
limit of all the solutions obtained above to the solutions of the Euler
equations.


  We present the marginal unbiased score expansion (MUSE) method, an algorithm
for generic high-dimensional hierarchical Bayesian inference. MUSE performs
approximate marginalization over arbitrary non-Gaussian latent parameter
spaces, yielding Gaussianized asymptotically unbiased and near-optimal
constraints on global parameters of interest. It is computationally much
cheaper than exact alternatives like Hamiltonian Monte Carlo (HMC), excelling
on funnel problems which challenge HMC, and does not require any
problem-specific user supervision like other approximate methods such as
Variational Inference or many Simulation-Based Inference methods. MUSE makes
possible the first joint Bayesian estimation of the delensed Cosmic Microwave
Background (CMB) power spectrum and gravitational lensing potential power
spectrum, demonstrated here on a simulated data set as large as the upcoming
South Pole Telescope 3G 1500 deg$^2$ survey, corresponding to a latent
dimensionality of ${\sim}\,6$ million and of order 100 global bandpower
parameters. On a subset of the problem where an exact but more expensive HMC
solution is feasible, we verify that MUSE yields nearly optimal results. We
also demonstrate that existing spectrum-based forecasting tools which ignore
pixel-masking underestimate predicted error bars by only ${\sim}\,10\%$. This
method is a promising path forward for fast lensing and delensing analyses
which will be necessary for future CMB experiments such as SPT-3G, Simons
Observatory, or CMB-S4, and can complement or supersede existing HMC
approaches. The success of MUSE on this challenging problem strengthens its
case as a generic procedure for a broad class of high-dimensional inference
problems.


  In this paper we propose a very effective method for constructing matrix
commuting differential operators of rank 2 and vector rank (2,2). We find new
matrix commuting differential operators L, M of orders 2 and 2g respectively.


  In a recent work we have argued that nosy energy momentum diffusion due to
space-time discreteness at the Planck scale (naturally expected to arise from
quantum gravity) can be responsible for the generation of a cosmological
constant during the electro-weak phase transition era of the cosmic evolution.
Simple dimensional analysis and an effectively Brownian description of the
propagation of fundamental particles on a granular background yields a
cosmological constant of the order of magnitude of the observed value, without
fine tuning. While the energy diffusion is negligible for matter in standard
astrophysical configurations (from ordinary stars to neutron stars) here we
argue that a similar diffusion mechanism could, nonetheless be important for
black holes. If such effects are taken into account two observational puzzles
might be solved by a single mechanism: the `$H_0$ tension' and the relatively
low rotational spin of the black holes detected via gravitational wave
astronomy.


  The set of segments, each of the next is n times bigger than the first one is
a simple geometric interpretation of the set $\mathbb{N}$ of natural numbers.
In this paper we investigate the opposite situation. We give algebraic
structure to the set of similar triangles with parallel sides. We present
geometric construction of adding of triangles and use it to dissection of
triangles into 15 triangles of different sides.


  We report an automated morphological classification of galaxies into S-wise
spirals, Z-wise spirals, and non-spirals using big image data taken from
Subaru/Hyper Suprime-Cam (HSC) Survey and a convolutional neural
network(CNN)-based deep learning technique. The HSC i-band images are about 25
times deeper than those from the Sloan Digital Sky Survey (SDSS) and have a two
times higher spatial resolution, allowing us to identify substructures such as
spiral arms and bars in galaxies at z>0.1. We train CNN classifiers by using
HSC images of 1447 S-spirals, 1382 Z-spirals, and 51,650 non-spirals. As the
number of images in each class is unbalanced, we augment the data of spiral
galaxies by horizontal flipping, rotation, and rescaling of images to make the
numbers of three classes similar. The trained CNN models correctly classify
97.5% of the validation data, which is not used for training. We apply the CNNs
to HSC images of a half million galaxies with an i-band magnitude of i<20 over
an area of 320 deg^2. 37,917 S-spirals and 38,718 Z-spirals are identified,
indicating no significant difference between the numbers of two classes. Among
a total of 76,635 spiral galaxies, 48,576 are located at z>0.2, where we are
hardly able to identify spiral arms in the SDSS images. Our attempt
demonstrates that a combination of the HSC big data and CNNs has a large
potential to classify various types of morphology such as bars, mergers and
strongly-lensed objects.


  Language identification of social media text still remains a challenging task
due to properties like code-mixing and inconsistent phonetic transliterations.
In this paper, we present a supervised learning approach for language
identification at the word level of low resource Bengali-English code-mixed
data taken from social media. We employ two methods of word encoding, namely
character based and root phone based to train our deep LSTM models. Utilizing
these two models we created two ensemble models using stacking and threshold
technique which gave 91.78% and 92.35% accuracies respectively on our testing
data.


  We use recurrent-events survival analysis techniques and methods to analyze
the duration of Olympic records. The Kaplan-Meier estimator is used to perform
preliminary tests and recurrent event survivor function estimators proposed by
Wang & Chang (1999) and Pena et al. (2001) are used to estimate survival
curves. Extensions of the Cox Proportional Hazards model are employed as well
as a discrete-time logistic model for repeated events to estimate models and
quantify parameter significance. The logistic model was the best fit to the
data according to the Akaike Information Criterion (AIC). We discuss, in
detail, covariate significance for this model and make predictions of how many
records will be set at the 2012 Olympic Games in London.
  Keywords: survival analysis, recurrent events, Kaplan-Meier estimator, Cox
proportional hazards model, Olympics.


  We give an upper bound for the essential dimension of a smooth unipotent
algebraic group over an arbitrary field. We also show that over a field $k$
which is finitely generated over a perfect field, a smooth unipotent algebraic
$k$-group is of essential dimension 0 if and only if it is $k$-split.


  We prove the following conjecture of Shkredov and Solymosi: every subset $A
\subset \mathbf{Z}^2$ such that $\sum_{a\in A\setminus\{0\}}
1/\left\|a\right\|^{2} = +\infty$ contains the three vertices of an isosceles
right triangle. To do this, we adapt the proof of the recent breakthrough by
Bloom and Sisask on sets without three-term arithmetic progressions, to handle
more general equations of the form $T_1a_1+T_2a_2+T_3a_3 = 0$ in a finite
abelian group $G$, where the $T_i$'s are automorphisms of $G$.


  Plasmas screen electromagnetic waves of frequency less than the plasma
frequency, omega_p, with a skin depth, delta, specified by the electrical
conductivity, sigma. Current estimates of the transport properties of the QCD
plasma indicates that photons with energy less than 250--500 MeV would be
screened with a skin depth of 2--4 fm. In a hadron gas, a little below T_c,
screening occurs for much softer photons, of energy less than approximately 50
MeV, albeit with similar values of delta. If the QCD plasma is indeed strongly
interacting, then it can be proven by merely looking at the brightness of a
fireball from different angles.


  The singlet-triplet relaxation in nanowire-based quantum dots induced by
confined phonons is investigated theoretically. Due to the
quasi-one-dimensional nature of the confined phonons, the singlet-triplet
relaxation rates exhibit multi-peaks as function of magnetic field and the
relaxation rate between the singlet and the spin up triplet state is found to
be enhanced at the vicinity of the singlet-triplet anti-crossing. We compare
the effect of the deformation-potential coupling and the piezoelectric coupling
and find that the deformation-potential coupling dominates the relaxation rates
in most cases.


  A procedure for demonstrating quantum coherence and measuring decoherence
times between different fluxoid states of a SQUID by using ``adiabatic
inversion'' is discussed. One fluxoid state is smoothly transferred into the
other, like a spin reversing direction by following a slowly moving magnetic
field. This is accomplished by sweeping an external applied flux, and depends
on a well-defined quantum phase between the two macroscopic states. Varying the
speed of the sweep relative to the decoherence time permits one to move from
the quantum regime, where such a well-defined phase exists, to the classical
regime where it is lost and the inversion is inhibited. Thus observing whether
inversion has taken place or not as a function of sweep speed offers the
possibility of measuring the decoherence time. Estimates with some typical
SQUID parameters are presented and it appears that such a procedure should be
experimentally possible.
  The main requirement for the feasibility of the scheme appears to be that the
low temperature relaxation time among the quantum levels of the SQUID be long
compared to other time scales of the problem, including the readout time.
Applications to the ``quantum computer'', with the level system of the SQUID
playing the role of the qbit, are briefly examined.


  We present a topological approach to the input-output relations of photonic
driven-dissipative lattices acting as directional amplifiers. Our theory relies
on a mapping from the optical non-Hermitian coupling matrix to an effective
topological insulator Hamiltonian. This mapping is based on the singular value
decomposition of non-Hermitian coupling matrices, whose inverse matrix
determines the linear input-output response of the system. In topologically
non-trivial regimes, the input-output response of the lattice is dominated by
singular vectors with zero singular values that are the equivalent of
zero-energy states in topological insulators, leading to directional
amplification of a coherent input signal. In such topological amplification
regime, our theoretical framework allows us to fully characterize the
amplification properties of the quantum device such as gain, bandwidth, added
noise, and noise-to-signal ratio. We exemplify our ideas in a one-dimensional
non-reciprocal photonic lattice, for which we derive fully analytical
predictions. We show that the directional amplification is near quantum-limited
with a gain growing exponentially with system size, $N$, while the
noise-to-signal ratio is suppressed as $1/\sqrt{N}$. This points out to
interesting applications of our theory for quantum signal amplification and
single-photon detection.


  We calculate the contained and upward muon and shower fluxes due to neutrinos
produced via dark matter annihilation or decay in the Galactic center. We
consider dark matter models in which the dark matter particle is a gravitino, a
Kaluza-Klein particle and a particle in leptophilic models. The
Navarro-Frenk-White profile for the dark matter density distribution in the
Galaxy is used. We incorporate neutrino oscillations by assuming maximal mixing
and parametrize our results for muon and shower distributions. The muon and
shower event rates and the minimum observation times in order to reach
2$\sigma$ detection significance are evaluated. We illustrate how observation
times vary with the cone half angle chosen about the Galactic center, with the
result that the optimum angles are about 10$^\circ$ and 50$^\circ$ for the muon
events and shower events, respectively. We find that for the annihilating dark
matter models such as the leptophilic and Kaluza-Klein models, upward and
contained muon as well as showers are promising signals for dark matter
detection in just a few years of observation, whereas for decaying dark matter
models, the same observation times can only be reached with showers. We also
illustrate for each model the parameter space probed with the 2$\sigma$ signal
detection in five years. We discuss how the shape of the parameter space probed
change with significance and the observation time.


  In this paper, we study the relation between Fr\'echet derivatives and Dupire
derivatives, in which the latter are recently introduced by Dupire [4]. After
introducing the definition of Fr\'echet derivatives for non-anticipative
functionals, we prove that the Dupire derivatives and the extended Fr\'echet
derivatives are coherent on continuous pathes.


  We demonstrate a Fock-state filter which is capable of preferentially
blocking single photons over photon pairs. The large conditional nonlinearities
are based on higher-order quantum interference, using linear optics, an ancilla
photon, and measurement. We demonstrate that the filter acts coherently by
using it to convert unentangled photon pairs to a path-entangled state. We
quantify the degree of entanglement by transforming the path information to
polarisation information, applying quantum state tomography we measure a tangle
of T=(20+/-9)%.


  We study the gravitational description of conformal half-BPS domain wall
operators in N=4 SYM, which are described by defect CFT's. These defect CFT's
arise in the low energy limit of a Hanany-Witten like brane setup and are
described in a probe brane approximation by a Karch-Randall brane
configuration. The gravitational backreaction takes the five-branes in
AdS_5xS^5 through a geometric transition and turns them into appropriate fluxes
which are supported on non-trivial three-spheres.


  Neural network compression methods have enabled deploying large models on
emerging edge devices with little cost, by adapting already-trained models to
the constraints of these devices. The rapid development of AI-capable edge
devices with limited computation and storage requires streamlined methodologies
that can efficiently satisfy the constraints of different devices. In contrast,
existing methods often rely on heuristic and manual adjustments to maintain
accuracy, support only coarse compression policies, or target specific device
constraints that limit their applicability. We address these limitations by
proposing the TOlerance-based COmpression (TOCO) framework. TOCO uses an
in-depth analysis of the model, to maintain the accuracy, in an active learning
system. The results of the analysis are tolerances that can be used to perform
compression in a fine-grained manner. Finally, by decoupling compression from
the tolerance analysis, TOCO allows flexibility to changes in the hardware.


  Accurate characterisation of visual attributes such as spiculation,
lobulation, and calcification of lung nodules is critical in cancer management.
The characterisation of these attributes is often subjective, which may lead to
high inter- and intra-observer variability. Furthermore, lung nodules are often
heterogeneous in the cross-sectional image slices of a 3D volume. Current
state-of-the-art methods that score multiple attributes rely on deep
learning-based multi-task learning (MTL) schemes. These methods, however,
extract shared visual features across attributes and then examine each
attribute without explicitly leveraging their inherent intercorrelations.
Furthermore, current methods either treat each slice with equal importance
without considering their relevance or heterogeneity, which limits performance.
In this study, we address these challenges with a new convolutional neural
network (CNN)-based MTL model that incorporates multiple attention-based
learning modules to simultaneously score 9 visual attributes of lung nodules in
computed tomography (CT) image volumes. Our model processes entire nodule
volumes of arbitrary depth and uses a slice attention module to filter out
irrelevant slices. We also introduce cross-attribute and attribute
specialisation attention modules that learn an optimal amalgamation of
meaningful representations to leverage relationships between attributes. We
demonstrate that our model outperforms previous state-of-the-art methods at
scoring attributes using the well-known public LIDC-IDRI dataset of pulmonary
nodules from over 1,000 patients. Our model also performs competitively when
repurposed for benign-malignant classification. Our attention modules also
provide easy-to-interpret weights that offer insights into the predictions of
the model.


  Reaction dynamics involving subdiffusive species is an interesting topic with
only few known results, especially when the motion of different species is
characterized by different anomalous diffusion exponents. Here we study the
reaction dynamics of a (sub)diffusive particle surrounded by a sea of
(sub)diffusive traps in one dimension. Under some reasonable assumptions we
find rigorous results for the asymptotic survival probability of the particle
in most cases, but have not succeeded in doing so for a particle that diffuses
normally while the anomalous diffusion exponent of the traps is smaller than
2/3.


  We measure and quantify properties of galactic outflows and diffuse gas at $z
\geq 1$ in cosmological hydrodynamical simulations. Our novel sub-resolution
model, MUPPI, implements supernova feedback using fully local gas properties,
where the wind velocity and mass loading are not given as input. We find the
following trends at $z = 2$ by analysing central galaxies having a stellar mass
higher than $10^{9} M_{\odot}$. The outflow velocity and mass outflow rate
($\dot{M}_{\rm out}$) exhibit positive correlations with galaxy mass and with
the star formation rate (SFR). However, most of the relations present a large
scatter. The outflow mass loading factor ($\eta$) is between $0.2 - 10$. The
comparison Effective model generates a constant outflow velocity, and a
negative correlation of $\eta$ with halo mass. The number fraction of galaxies
where outflow is detected decreases at lower redshifts, but remains more than
$80 \%$ over $z = 1 - 5$. High SF activity at $z \sim 2 - 4$ drives strong
outflows, causing the positive and steep correlations of velocity and
$\dot{M}_{\rm out}$ with SFR. The outflow velocity correlation with SFR becomes
flatter at $z = 1$, and $\eta$ displays a negative correlation with halo mass
in massive galaxies. Our study demonstrates that both the MUPPI and Effective
models produce significant outflows at $\sim 1 / 10$ of the virial radius; at
the same time shows that the properties of outflows generated can be different
from the input speed and mass loading in the Effective model. Our MUPPI model,
using local properties of gas in the sub-resolution recipe, is able to develop
galactic outflows whose properties correlate with global galaxy properties, and
consistent with observations.


  The Cherenkov Telescope Array (CTA) is a large collaborative effort aimed at
the design and operation of an observatory dedicated to the VHE gamma-ray
astrophysics in the energy range 30 GeV-100 TeV, which will improve by about
one order of magnitude the sensitivity with respect to the current major arrays
(H.E.S.S., MAGIC, and VERITAS). In order to achieve such improved performance,
for both the northern and southern CTA sites, four units of 23m diameter Large
Size Telescopes (LSTs) will be deployed close to the centre of the array with
telescopes separated by about 100m. A larger number (about 25 units) of 12m
Medium Size Telescopes (MSTs, separated by about 150m), will cover a larger
area. The southern site will also include up to 24 Schwarzschild-Couder
dual-mirror medium-size Telescopes (SCTs) with the primary mirror diameter of
9.5m. Above a few TeV, the Cherenkov light intensity is such that showers can
be detected even well outside the light pool by telescopes significantly
smaller than the MSTs. To achieve the required sensitivity at high energies, a
huge area on the ground needs to be covered by Small Size Telescopes (SSTs)
with a FOV of about 10 deg and an angular resolution of about 0.2 deg, making
the dual-mirror configuration very effective. The SST sub-array will be
composed of 50-70 telescopes with a mirror area of about 5-10 square meters and
about 300m spacing, distributed across an area of about 10 square kilometers.
We will focus on the innovative solution for the optical design of the medium
and small size telescopes based on a dual-mirror configuration. This layout
will allow us to reduce the dimension and the weight of the camera at the focal
plane of the telescope, to adopt SiPMs as light detectors thanks to the reduced
plate-scale, and to have an optimal imaging resolution on a wide FOV.


  We describe a classification of pairs $M, N$ of lattices isometric to
$EE_8:=\sqrt 2 E_8$ such that the lattice $M + N$ is integral and rootless and
such that the dihedral group associated to them has order at most 12. It turns
out that most of these pairs may be embedded in the Leech lattice. Complete
proofs will appear in another article. This theory of integral lattices has
connections to vertex operator algebra theory and moonshine.


  Branching fractions were measured for electric dipole transitions from the 5p
upper levels to the 5s levels in neutral krypton atoms. The measurements were
made with a wall-stabilized electric arc and a 2m monochromator for the
spectral lines in the visible, and with a hollow cathode lamp and the NIST
2m-Fourier transform spectrometer for the lines in the near infrared. A
semi-empirical calculation, based on accurately known lifetimes for six upper
levels, was used to calculate lifetimes for which accurate measurements do not
exist. This resulted in a complete set of lifetimes for all 5p levels.
Branching fractions and lifetimes were used to calculate transition rates for
the 5p-5s transitions. The relative uncertainties of the transition rates range
from less than 1% for the strongest lines to about 10% for the weakest lines.
Our data also reveal that most of the previous measurements appear to have been
affected by opacity effects in the light sources.


  Commitment devices are powerful tools that can influence and incentivise
certain behaviours by linking them to rewards or punishments. These devices are
particularly useful in decision-making, as they can steer individuals towards
specific choices. In the field of game theory, commitment devices can alter a
player's payoff matrix, ultimately changing the game's Nash equilibria.
Interestingly, agents, whom we term game manipulators and who can be external
to the original game, can leverage such devices to extract fees from players by
making them contingent offers that modify the payoffs of their actions. This
can result in a different Nash equilibrium with potentially lower payoffs for
the players compared to the original game. For this scheme to work, it is
required that all commitments be binding, meaning that once an offer is made,
it cannot be revoked. Consequently, we analyse binding contracts as the
commitment mechanism that enables game manipulation scenarios. The main focus
of this study is to formulate the logic of this setting, expand its scope to
encompass more intricate schemes, and analyse the behaviour of
regret-minimizing agents in scenarios involving game manipulation.


  Galaxies are observed to host magnetic fields with a typical total strength
of around 15microgauss. A coherent large-scale field constitutes up to a few
microgauss of the total, while the rest is built from strong magnetic
fluctuations over a wide range of spatial scales. This represents sufficient
magnetic energy for it to be dynamically significant. Several questions
immediately arise: What is the physical mechanism that gives rise to such
magnetic fields? How do these magnetic fields affect the formation and
evolution of galaxies? In which physical processes do magnetic fields play a
role, and how can that role be characterized? Numerical modelling of magnetized
flows in galaxies is playing an ever-increasing role in finding those answers.
We review major techniques used for these models. Current results strongly
support the conclusion that field growth occurs during the formation of the
first galaxies on timescales shorter than their accretion timescales due to
small-scale turbulent dynamos. The saturated small-scale dynamo maintains field
strengths at a few percent of equipartition with turbulence. The subsequent
action of large-scale dynamos in differentially rotating discs produces
observed modern field strengths in equipartition with the turbulence and having
power at large scales. The field structure resulting appears consistent with
observations including Faraday rotation and polarisation from synchrotron and
dust thermal emission. Major remaining challenges include scaling numerical
models toward realistic scale separations and Prandtl and Reynolds numbers.


  Comment on the paper P. E. Jonsson, H. Yoshino, and P. Nordblad, Phys. Rev.
Lett. 89, 097201 (2002), also cond-mat/0203444.


  We find a new class of metastable de Sitter solutions in compactifications of
six-dimensional supergravity motivated by type IIB or heterotic string vacua.
Two Fayet-Iliopoulos terms of a local U(1) symmetry are generated by magnetic
flux and by the Green-Schwarz term canceling the gauge anomalies, respectively.
The interplay between the induced D-term, the moduli dependence of the
effective gauge coupling, and a nonperturbative superpotential stabilizes the
moduli and determines the size of the extra dimensions.


  This paper is concerned with numerical algorithms for Biot model. By
introducing an intermediate variable, the classical 2-field Biot model is
written into a 3-field formulation. Based on such a 3-field formulation, we
propose a coupled algorithm, some time-extrapolation based decoupled
algorithms, and an iterative decoupled algorithm. Our focus is the analysis of
the iterative decoupled algorithm. It is shown that the convergence of the
iterative decoupled algorithm requires no extra assumptions on physical
parameters or stabilization parameters. Numerical experiments are provided to
demonstrate the accuracy and efficiency of the proposed method.


  According to the May-Wigner stability theorem, increasing the complexity of a
network inevitably leads to its destabilization, such that a small perturbation
will be able to disrupt the entire system. One of the principal arguments
against this observation is that it is valid only for random networks, and
therefore does not apply to real-world networks, which presumably are
structured. Here we examine how the introduction of small-world topological
structure into networks affect their stability. Our results indicate that, in
structured networks, the parameter values at which the stability-instability
transition occurs with increasing complexity is identical to that predicted by
the May-Wigner criteria. However, the nature of the transition, as measured by
the finite-size scaling exponent, appears to change as the network topology
transforms from regular to random, with the small-world regime as the
cross-over region. This behavior is related to the localization of the largest
eigenvalues along the real axis in the eigenvalue plain with increasing
regularity in the network.


  We study the synchronization transition (ST) of a modified Kuramoto model on
two different types of modular complex networks. It is found that the ST
depends on the type of inter-modular connections. For the network with
decentralized (centralized) inter-modular connections, the ST occurs at finite
coupling constant (behaves abnormally). Such distinct features are found in the
yeast protein interaction network and the Internet, respectively. Moreover, by
applying the finite-size scaling analysis to an artificial network with
decentralized inter-modular connections, we obtain the exponent associated with
the order parameter of the ST to be $\beta \approx 1$ different from
$\beta_{\rm MF} \approx 1/2$ obtained from the scale-free network with the same
degree distribution but the absence of modular structure, corresponding to the
mean field value.