  Due to the nature of applications such as critical infrastructure and the
Internet of Things etc. side channel analysis attacks are becoming a serious
threat. Side channel analysis attacks take advantage from the fact that the
behavior of crypto implementations can be observed and provides hints that
simplify revealing keys. A new type of SCA are the so called horizontal SCAs.
Well known randomization based countermeasures are effective means against
vertical DPA attacks but they are not effective against horizontal DPA attacks.
In this paper we investigate how the formula used to implement the
multiplication of $GF(2^n)$-elements influences the results of horizontal DPA
attacks against a Montgomery kP implementation. We implemented 5 designs with
different partial multipliers, i.e. based on different multiplication formulae.
We used two different technologies, i.e. a 130 and a 250 nm technology, to
simulate power traces for our analysis. We show that the implemented
multiplication formula influences the success of horizontal attacks
significantly, but we also learned that its impact differs from technology to
technology. Our analysis also reveals that the use of different multiplication
formulae as the single countermeasure is not sufficient to protect
cryptographic designs against horizontal DPA attacks.


  The introduction of the Rossby number (R$_0$), which incorporates the
convective turnover time ($\tau$), in 1984 was a pioneering idea for
understanding the correlation between stellar rotation and activity. The
convective turnover time, which cannot be measured directly, is often inferred
using existing $\tau$-mass or $\tau$-color relations, typically established
based on an ensemble of different types of stars by assuming that $\tau$ is a
function of mass. In this work, we use {\it Gaia} Early Data Release 3 to
demonstrate that the masses used to establish one of the most cited $\tau$-mass
relations are overestimated for G type dwarfs and significantly underestimated
for late M dwarfs, offsets that affect studies using this $\tau$-mass relation
to draw conclusions. We discuss the challenges of creating such relations then
and now. In the era of {\it Gaia} and other large datasets, stars used to
establish these relations require characterization in a multi-dimensional
space, rather than via the single-characteristic relations of the past. We
propose that new multi-dimensional relations should be established based on
updated theoretical models and all available stellar parameters for different
interior structures from a set of carefully vetted single stars, so that the
convective turnover time can be estimated more accurately.


  Fast and accurate reconstruction of magnetic resonance (MR) images from
under-sampled data is important in many clinical applications. In recent years,
deep learning-based methods have been shown to produce superior performance on
MR image reconstruction. However, these methods require large amounts of data
which is difficult to collect and share due to the high cost of acquisition and
medical data privacy regulations. In order to overcome this challenge, we
propose a federated learning (FL) based solution in which we take advantage of
the MR data available at different institutions while preserving patients'
privacy. However, the generalizability of models trained with the FL setting
can still be suboptimal due to domain shift, which results from the data
collected at multiple institutions with different sensors, disease types, and
acquisition protocols, etc. With the motivation of circumventing this
challenge, we propose a cross-site modeling for MR image reconstruction in
which the learned intermediate latent features among different source sites are
aligned with the distribution of the latent features at the target site.
Extensive experiments are conducted to provide various insights about FL for MR
image reconstruction. Experimental results demonstrate that the proposed
framework is a promising direction to utilize multi-institutional data without
compromising patients' privacy for achieving improved MR image reconstruction.
Our code will be available at https://github.com/guopengf/FLMRCM.


  Large Vision Model (LVM) has recently demonstrated great potential for
medical imaging tasks, potentially enabling image enhancement for sparse-view
Cone-Beam Computed Tomography (CBCT), despite requiring a substantial amount of
data for training. Meanwhile, Deep Image Prior (DIP) effectively guides an
untrained neural network to generate high-quality CBCT images without any
training data. However, the original DIP method relies on a well-defined
forward model and a large-capacity backbone network, which is notoriously
difficult to converge. In this paper, we propose a stable optimization method
for the forward-model-free, LVM-based DIP model for sparse-view CBCT. Our
approach consists of two main characteristics: (1) multi-scale perceptual loss
(MSPL) which measures the similarity of perceptual features between the
reference and output images at multiple resolutions without the need for any
forward model, and (2) a reweighting mechanism that stabilizes the iteration
trajectory of MSPL. One shot optimization is used to simultaneously and stably
reweight MSPL and optimize LVM. We evaluate our approach on two publicly
available datasets: SPARE and Walnut. The results show significant improvements
in both image quality metrics and visualization that demonstrates reduced
streak artifacts. The source code is available upon request.


  We compute f-mode travel-time sensitivity kernels for flows. Using a
two-dimensional model, we show that it is important to account for several
systematic effects, such as the foreshortening and the projection of the
velocity vector onto the line of sight. Correcting for these effects is
necessary before any data inversion is attempted away from the center of the
solar disk.


  Recent scientific and technological advances have enabled the detection of
gravitational waves, autonomous driving, and the proposal of a communications
network on the Moon (Lunar Internet or LunaNet). These efforts are based on the
measurement of minute displacements and correspondingly the forces or fields
transduction, which translate to acceleration, velocity, and position
determination for navigation. State-of-the-art accelerometers use capacitive or
piezo resistive techniques, and micro-electromechanical systems (MEMS) via
integrated circuit (IC) technologies in order to drive the transducer and
convert its output for electric readout. In recent years, laser optomechanical
transduction and readout have enabled highly sensitive detection of motional
displacement. Here we further examine the theoretical framework for the novel
mechanical frequency readout technique of optomechanical transduction when the
sensor is driven into oscillation mode [8]. We demonstrate theoretical and
physical agreement and characterize the most relevant performance parameters
with a device with 1.5mg/Hz acceleration sensitivity, a 2.5 fm/Hz1/2
displacement resolution corresponding to a 17.02 ug/Hz1/2 force-equivalent
acceleration, and a 5.91 Hz/nW power sensitivity, at the thermodynamical
limits. In addition, we present a novel technique for dynamic range extension
while maintaining the precision sensing sensitivity. Our inertial accelerometer
is integrated on-chip, and enabled for packaging, with a laser-detuning-enabled
approach.


  In partial answer to a question posed by Arnie Miller
(http://www.math.wisc.edu/~miller/res/problem.pdf) and X. Caicedo, we obtain
sufficient conditions for an L_{omega_1,omega} theory to have an independent
axiomatization. As a consequence we obtain two corollaries: The first, assuming
Vaught's Conjecture, every L_{omega_1,omega} theory in a countable language has
an independent axiomatization. The second, this time outright in ZFC, every
intersection of a family of Borel sets can be formed as the intersection of a
family of independent Borel sets.


  We present an update of the D meson decay constant f_D using the Highly
Improved Staggered Quark (HISQ) action for valence charm and light quarks on
MILC N_f = 2+1 lattices. The new determination incorporates HPQCD's improved
scale r_1^{N_f = 2+1} = 0.3133(23) fm, accurately retuned bare charm quark
masses and data from an ensemble that is more chiral than in our previous
calculations. We find f_D = 208.3(3.4) MeV. Combining the new f_D with D -> \mu
\nu\ branching fraction data from CLEO-c, we extract the CKM matrix element
|V_cd| = 0.223(10)_{exp.}(4)_{lat.}. This value is in excellent agreement with
|V_cd| from D semileptonic decays and from neutrino scattering experiments and
has comparable total errors. We determine the ratio between semileptonic form
factor and decay constant and find [f^{D -> \pi}_+(0) / f_D ]_{lat.} = 3.20(15)
GeV^{-1} to be compared with the experimental value of [f^{D -> \pi}_+(0) / f_D
]_{exp.} = 3.19(18) GeV^{-1}. Finally, we mention recent preliminary but
already more accurate D -> \mu \nu\ branching fraction measurements from BES
III and discuss their impact on precision |V_cd| determinations in the future.


  Twisted rapid passage is a type of non-adiabatic rapid passage that generates
controllable quantum interference effects that were first observed
experimentally in 2003. It is shown that twisted rapid passage sweeps can be
used to implement a universal set of quantum gates that operate with
high-fidelity. The gate set consists of the Hadamard and NOT gates, together
with variants of the phase, pi/8, and controlled-phase gates. For each gate g
in the universal set, sweep parameter values are provided which numerical
simulations indicate will produce a unitary operation that approximates g with
error probability less than 10**(-4). Note that all gates in the universal set
are implemented using a single family of control-field, and the error
probability for each gate falls below the rough-and-ready estimate for the
accuracy threshold of 10**(-4).


  We study the fluctuations of systems modeled by Markov jump processes with
periodic generators. We focus on observables defined through time-periodic
functions of the system's states or transitions. Using large deviation theory,
canonical biasing and generalized Doob transform, we characterize the
asymptotic fluctuations of such observables after a large number of periods by
obtaining the Markov process that produces them. We show that this process,
called driven process, is the minimum under constraint of the large deviation
function for occupation and jumps.


  The topic of this paper are (multi-window) Gabor frames for signals over
finite Abelian groups, generated by an arbitrary lattice within the finite
time-frequency plane. Our generic approach covers simultaneously
multi-dimensional signals as well as non-separable lattices. The main results
reduce to well-known fundamental facts about Gabor expansions of finite signals
for the case of product lattices, as they have been given by Qiu, Wexler-Raz or
Tolimieri-Orr, Bastiaans and Van-Leest, among others. In our presentation a
central role is given to spreading function of linear operators between
finite-dimensional Hilbert spaces. Another relevant tool is a symplectic
version of Poisson's summation formula over the finite time-frequency plane. It
provides the Fundamental Identity of Gabor Analysis.In addition we highlight
projective representations of the time-frequency plane and its subgroups and
explain the natural connection to twisted group algebras. In the
finite-dimensional setting these twisted group algebras are just matrix
algebras and their structure provides the algebraic framework for the study of
the deeper properties of finite-dimensional Gabor frames.


  The aim of our analysis is twofold. On the one hand we are interested in
addressing whether a sample of ETGs morphologically selected differs from a
sample of passive galaxies in terms of galaxy statistics. On the other hand we
study how the relative abundance of galaxies, the number density and the
stellar mass density for different morphological types change over the redshift
range 0.6<z<2.5. From the 1302 galaxies brighter than Ks=22 selected from the
GOODS-MUSIC catalogue, we classified the ETGs on the basis of their morphology
and the passive galaxies on the basis of their sSFR. We proved how the
definition of passive galaxy depends on the IMF adopted in the models and on
the assumed sSFR threshold. We find that ETGs cannot be distinguished from the
other morphological classes on the basis of their low sSFR, irrespective of the
IMF adopted in the models. Using the sample of 1302 galaxies morphologically
classified into spheroidal galaxies (ETGs) and not spheroidal galaxies (LTGs),
we find that their fractions are constant over the redshift range 0.6<z<2.5
(20-30% ETGs vs 70-80% LTGs). However, at z<1 these fractions change among the
population of the most massive (M*>=10^(11) M_sol) galaxies, with the fraction
of massive ETGs rising up to 40% and the fraction of massive LTGs decreasing
down to 60%. Moreover, we find that the number density and the stellar mass
density of the whole population of massive galaxies increase almost by a factor
of ~10 between 0.6<z<2.5, with a faster increase of these densities for the
ETGs than for the LTGs. Finally, we find that the number density of the
highest-mass galaxies (M*>3-4x10^(11) M_sol) both ETGs and LTGs do not increase
since z~2.5, contrary to the lower mass galaxies. This suggests that the
population of the most massive galaxies formed at z>2.5-3 and that the assembly
of such high-mass galaxies is not effective at lower redshift.


  In many extentions of standard model, dimension-5 scalar di-photon
($g_{\gamma\gamma\phi}\phi$ $F^{\mu\nu}F_{\mu\nu}$ ) or pseudoscalar di-photon
($g_{\gamma\gamma a}a\tilde{F}^{\mu\nu} F_{\mu\nu} $, ) interaction
materializes due to scale symmetry breaking or $U_A(1)$ symmetry breaking. In a
magnetized vacuum (i.e., in an external background field $\bar{F}_{\mu\nu} $)
the transverse degrees of freedom of the photons-- for such systems-- can be
described in terms of the form factors constructed out of the background field
strength tensor ($\bar{F}_{\mu\nu} $) and the same for dynamical photon
($f^{\mu\nu}$); they happen to be $\bar{F}_{\mu\nu}f^{\mu\nu}$ and
${\tilde{\bar{F}}_{\mu\nu}f^{\mu\nu}}$. These form factors transform
differently under CP transformation. While $\bar{F}_{\mu\nu}f^{\mu\nu}$
(describing polarization orthogonal to B ($|\gamma_{||} >$)) is CP even,
  the other one, ${\tilde{\bar{F}}_{\mu\nu}f^{\mu\nu}}$( describing
polarization along B ($|\gamma_\bot >$), is CP odd. In the interaction
Lagrangian, if the scalar is interchanged with the pseudoscalar, the role of
the two form factors just gets interchanged. Thus for nearly degenerate
strengths of the coupling constants ( $g_{\gamma\gamma\phi}$ and
$g_{\gamma\gamma _a}$ ) and masses ($m_\phi$ and $m_a$ ) of the respective
candidates, proper identification of one from the other may become very
difficult in laboratory or astrophysics based experiments. The basic motivation
of this investigation is to reduce this uncertainty through incorporation of
parity violating ({\it originating through magnetized medium effects }) part of
the photon self-energy in the effective Lagrangian. This step, in turn affects
the (Pseudo) Scalar Photon mixing dynamics drastically and brings out a
significant change in the spectrum of the electromagnetic beam undergoing such
interaction.


  The non-perturbative (D-instanton corrected) low-energy effective action of
the universal hypermultiplet in the type-IIA string theory compactified on a
Calabi-Yau threefold is calculated in 4d, N=2 supergravity. The action is given
by the quaternionic non-linear sigma-model whose metric is governed by the
Eisenstein series E_{3/2}. The U(1)xU(1) isometry and SL(2,Z) modular
invariance play the key role in our construction.


  The paper deals with the existence of solutions for quasilinear elliptic
systems involving singular and convection terms with variable exponents. Our
approach combines the sub-supersolutions method and Schauder's fixed point
theorem.


  Applying braided Yang-Baxter equation quantum integrable and Bethe ansatz
solvable 1D anyonic lattice and field models are constructed. Along with known
models we discover novel lattice anyonic and $q$-anyonic models as well as
nonlinear Schr\"odinger equation (NLS) and the derivative NLS quantum field
models involving anyonic operators, $N$-particle sectors of which yield the
well known anyon gases, interacting through $\delta$ and derivative
$\delta$-function potentials.


  Untangling is a process in which some vertices of a planar graph are moved to
obtain a straight-line plane drawing. The aim is to move as few vertices as
possible. We present an algorithm that untangles the cycle graph C_n while
keeping at least \Omega(n^{2/3}) vertices fixed. For any graph G, we also
present an upper bound on the number of fixed vertices in the worst case. The
bound is a function of the number of vertices, maximum degree and diameter of
G. One of its consequences is the upper bound O((n log n)^{2/3}) for all
3-vertex-connected planar graphs.


  To construct models of large, multivariate complex systems, such as those in
biology, one needs to constrain which variables are allowed to interact. This
can be viewed as detecting "local" structures among the variables. In the
context of a simple toy model of 2D natural and synthetic images, we show that
pairwise correlations between the variables -- even when severely undersampled
-- provide enough information to recover local relations, including the
dimensionality of the data, and to reconstruct arrangement of pixels in fully
scrambled images. This proves to be successful even though higher order
interaction structures are present in our data. We build intuition behind the
success, which we hope might contribute to modeling complex, multivariate
systems and to explaining the success of modern attention-based machine
learning approaches.


  We address the problem of estimating human pose and body shape from 3D scans
over time. Reliable estimation of 3D body shape is necessary for many
applications including virtual try-on, health monitoring, and avatar creation
for virtual reality. Scanning bodies in minimal clothing, however, presents a
practical barrier to these applications. We address this problem by estimating
body shape under clothing from a sequence of 3D scans. Previous methods that
have exploited body models produce smooth shapes lacking personalized details.
We contribute a new approach to recover a personalized shape of the person. The
estimated shape deviates from a parametric model to fit the 3D scans. We
demonstrate the method using high quality 4D data as well as sequences of
visual hulls extracted from multi-view images. We also make available BUFF, a
new 4D dataset that enables quantitative evaluation
(http://buff.is.tue.mpg.de). Our method outperforms the state of the art in
both pose estimation and shape estimation, qualitatively and quantitatively.


  A simple theorem is proved: When a gauge-invariant local field theory is
written in terms of matter fields alone, a composite gauge boson or bosons must
be formed dynamically. The theorem results from the fact that the Noether
current vanishes in such theories. The proof is carried out by use of the
charge-field algebra at equal time in the Heisenberg picture together with the
well-established analyticity of the form factor of the current. While there is
no need of diagram calculation for the proof, we demonstrate in the leading 1/N
expansion of the existing models what the theorem means in diagrams and how the
composite gauge boson emerges.


  We construct exceptional collections of line bundles of maximal length 4 on
$S=(C \times D)/G$ which is a surface isogenous to a higher product with
$p_g=q=0$ where $G=G(32,27)$ is a finite group of order 32 having number 27 in
the list of Magma library. From these exceptional collections, we obtain new
examples of quasiphantom categories as their orthogonal complements.


  As digital virtual assistants become ubiquitous, it becomes increasingly
important to understand the situated behaviour of users as they interact with
these assistants. To this end, we introduce SIMMC, an extension to ParlAI for
multi-modal conversational data collection and system evaluation. SIMMC
simulates an immersive setup, where crowd workers are able to interact with
environments constructed in AI Habitat or Unity while engaging in a
conversation. The assistant in SIMMC can be a crowd worker or Artificial
Intelligent (AI) agent. This enables both (i) a multi-player / Wizard of Oz
setting for data collection, or (ii) a single player mode for model / system
evaluation. We plan to open-source a situated conversational data-set collected
on this platform for the Conversational AI research community.


  Deep learning models generalize well to in-distribution data but struggle to
generalize compositionally, i.e., to combine a set of learned primitives to
solve more complex tasks. In sequence-to-sequence (seq2seq) learning,
transformers are often unable to predict correct outputs for longer examples
than those seen at training. This paper introduces iterative decoding, an
alternative to seq2seq that (i) improves transformer compositional
generalization in the PCFG and Cartesian product datasets and (ii) evidences
that, in these datasets, seq2seq transformers do not learn iterations that are
not unrolled. In iterative decoding, training examples are broken down into a
sequence of intermediate steps that the transformer learns iteratively. At
inference time, the intermediate outputs are fed back to the transformer as
intermediate inputs until an end-of-iteration token is predicted. We conclude
by illustrating some limitations of iterative decoding in the CFQ dataset.


  Recently, it has been argued by Kuklov et al., that unusual features
associated with the superflow-through-solid effect observed in solid He4 can be
explained by unique properties of dilute distribution of superfluid edge
dislocations. We demonstrate that stability of supercurrents controlled by
quantum phase slips (instantons), and other exotic infrared properties of the
superfluid dislocations readily follow from a one-dimensional quantum liquid
distinguished by an effectively infinite compressibility (in the absence of
Peierls potential) associated with the edge dislocation's ability to climb.
This establishes a new class of quasi-one-dimensional superfluid states that
remain stable and long-range ordered despite their low dimensionality. We
propose an experiment to test our mass-current--pressure characteristic
prediction.


  There are several indications that brain is organized not on a basis of
individual unreliable neurons, but on a micro-circuital scale providing Lego
blocks employed to create complex architectures. At such an intermediate scale,
the firing activity in the microcircuits is governed by collective effects
emerging by the background noise soliciting spontaneous firing, the degree of
mutual connections between the neurons, and the topology of the connections. We
compare spontaneous firing activity of small populations of neurons adhering to
an engineered scaffold with simulations of biologically plausible CMOS
artificial neuron populations whose spontaneous activity is ignited by tailored
background noise. We provide a full set of flexible and low-power consuming
silicon blocks including neurons, excitatory and inhibitory synapses, and both
white and pink noise generators for spontaneous firing activation. We achieve a
comparable degree of correlation of the firing activity of the biological
neurons by controlling the kind and the number of connection among the silicon
neurons. The correlation between groups of neurons, organized as a ring of four
distinct populations connected by the equivalent of interneurons, is triggered
more effectively by adding multiple synapses to the connections than increasing
the number of independent point-to-point connections. The comparison between
the biological and the artificial systems suggests that a considerable number
of synapses is active also in biological populations adhering to engineered
scaffolds.


  It has been argued for years that the accretion mode changes from bright
active galactic nuclei (AGNs) to low-luminosity AGNs (LLAGNs) at a rough
dividing point of bolometric Eddington ratio $\lambda \sim 10^{-2}$. In this
work, we strengthen this scenario through investigation of the relationship
between the radio loudness $R_{\rm UV}$ and the optical-to-X-ray spectral index
$\alpha_{\rm ox}$ in LLAGNs with $10^{-6} \lesssim \lambda \lesssim 10^{-3}$.
We compile from literature a sample of 32 LLAGNs, consisting 18 LINERs and 14
low Eddington ratio Seyfert galaxies, and observe a strong negative $R_{\rm
UV}$--$\alpha_{\rm ox}$ relationship, with large scatter in both $R_{\rm UV}$
and $\alpha_{\rm ox}$. We further demonstrate that this negative correlation,
and the additional two negative relationships reported in literature ($R_{\rm
UV}$--$\lambda$ and $\alpha_{\rm ox}$--$\lambda$ correlations), can be
understood consistently and comprehensively under the truncated accretion--jet
model, the model that has been applied successfully applied to LLAGNs. We argue
that the scatter in the observations are (mainly) due to the spread in the
viscosity parameter $\alpha$ of a hot accretion flow, a parameter that
potentially can serve as a diagnose of the strength and/or configuration of
magnetic fields in accretion flows.


  We calculate the ultra-relativistic Bose-Einstein condensation temperature of
a complex scalar field with weak lambda Phi^4 interaction. We show that at high
temperature and finite density we can use dimensional reduction to produce an
effective three-dimensional theory which then requires non-perturbative
analysis. For simplicity and ease of implementation we illustrate this process
with the linear delta expansion.


  We report on unusual regimes of operation of a laser with a gain medium with
a large Raman scattering cross-section, which is often inherent in new types of
gain media such as colloidal and epitaxial quantum dots and perovskite
materials. These media are characterized by a strong electron-phonon coupling.
Using the Fr\"ohlich Hamiltonian to describe the electron-phonon coupling in
such media, we analyze the operation of the system above the lasing threshold.
We show that below a critical value of the Fr\"ohlich constant, the laser can
only operate in the conventional regime: namely, there are coherent cavity
photons but no coherent phonons. Above the critical value, a new pump rate
threshold appears. Above this threshold, either joint self-oscillations of
coherent phonons in the gain medium and photons in a cavity or a chaotic regime
are established. We also find a range of the values of the Fr\"ohlich constant,
the pump rate, and the resonator eigenfrequency, in which more than one
dynamical regime of the system is stable. In this case the laser dynamics is
determined by the initial values of the resonator field, the active medium
polarization, the population inversion, and phonon amplitude.


  Social structures emerge as a result of individuals managing a variety of
different of social relationships. Societies can be represented as highly
structured dynamic multiplex networks. Here we study the dynamical origins of
the specific community structures of a large-scale social multiplex network of
a human society that interacts in a virtual world of a massive multiplayer
online game. There we find substantial differences in the community structures
of different social actions, represented by the various network layers in the
multiplex. Community size distributions are either similar to a power-law or
appear to be centered around a size of 50 individuals. To understand these
observations we propose a voter model that is built around the principle of
triadic closure. It explicitly models the co-evolution of node- and
link-dynamics across different layers of the multiplex. Depending on link- and
node fluctuation rates, the model exhibits an anomalous shattered fragmentation
transition, where one layer fragments from one large component into many small
components. The observed community size distributions are in good agreement
with the predicted fragmentation in the model. We show that the empirical
pairwise similarities of network layers, in terms of link overlap and degree
correlations, practically coincide with the model. This suggests that several
detailed features of the fragmentation in societies can be traced back to the
triadic closure processes.


  We perform a NLO QCD analysis of the combined SLAC-BCDMS-NMC-E665-H1-ZEUS
data on inclusive deep inelastic cross section. Particular attention was paid
to the extraction of strong coupling constant $\alpha_s$ and high twist (HT)
contribution to the structure functions $F_2$ and $F_L$. It was shown that at
small and moderate x there is a visible dependence of the extracted values of
HT contribution to $F_2$ on the QCD renormalization scale, which indicates that
in this region extracted HT can absorb NNLO QCD corrections. At larger x the
dependence of HT on the renormalization scale is negligible and the influence
of NNLO correction on their values should be less significant. The value of
$\alpha_s(M_Z)=0.1159\pm0.0031$ (total) is obtained, where the error includes
statistical, systematical and theoretical uncertainties.


  In Part 1, we showed how to develop a conceptual model of a problem involving
variables of multiple dimensions, like Products, Regions, Sectors and Months.
The conceptual model is presented as a Formula Diagram, giving a global view of
the interaction between all the variables, and a Formula List, giving a precise
view of the interaction between the variables. In this paper, we present
precise steps to implement a multi-dimensional problem in a way that will
produce a spreadsheet that is easy to maintain


  We address the problem of algorithmic fairness: ensuring that sensitive
variables do not unfairly influence the outcome of a classifier. We present an
approach based on empirical risk minimization, which incorporates a fairness
constraint into the learning problem. It encourages the conditional risk of the
learned classifier to be approximately constant with respect to the sensitive
variable. We derive both risk and fairness bounds that support the statistical
consistency of our approach. We specify our approach to kernel methods and
observe that the fairness requirement implies an orthogonality constraint which
can be easily added to these methods. We further observe that for linear models
the constraint translates into a simple data preprocessing step. Experiments
indicate that the method is empirically effective and performs favorably
against state-of-the-art approaches.


  The paper studies the continuous-time dynamics of VIX with stochastic
volatility and jumps in VIX and volatility. Built on the general parametric
affine model with stochastic volatility and jump in logarithm of VIX, we derive
a linear relation between the stochastic volatility factor and VVIX index. We
detect the existence of co-jump of VIX and VVIX and put forward a double-jump
stochastic volatility model for VIX through its joint property with VVIX. With
VVIX index as a proxy for the stochastic volatility, we use MCMC method to
estimate the dynamics of VIX. Comparing nested models on VIX, we show the jump
in VIX and the volatility factor is statistically significant. The jump
intensity is also statedependent. We analyze the impact of jump factor on the
VIX dynamics.


  Let C = {x_1,...,x_N} \subset {0,1}^n be an [n,N] binary error correcting
code (not necessarily linear). Let e \in {0,1}^n be an error vector. A codeword
x in C is said to be "disturbed" by the error e if the closest codeword to x +
e is no longer x. Let A_e be the subset of codewords in C that are disturbed by
e. In this work we study the size of A_e in random codes C (i.e. codes in which
each codeword x_i is chosen uniformly and independently at random from
{0,1}^n). Using recent results of Vu [Random Structures and Algorithms 20(3)]
on the concentration of non-Lipschitz functions, we show that |A_e| is strongly
concentrated for a wide range of values of N and ||e||.
  We apply this result in the study of communication channels we refer to as
"oblivious". Roughly speaking, a channel W(y|x) is said to be oblivious if the
error distribution imposed by the channel is independent of the transmitted
codeword x. For example, the well studied Binary Symmetric Channel is an
oblivious channel.
  In this work, we define oblivious and partially oblivious channels and
present lower bounds on their capacity. The oblivious channels we define have
connections to Arbitrarily Varying Channels with state constraints.


  Multiferroics represent a class of advanced materials for promising
applications and stand at the forefront of modern science for the special
feature possessing both charge polar and magnetic order. Previous studies
indicate that the family of RECrO3 (RE = rare earth) compounds is likely
another rare candidate system holding both ferroelectricity and magnetism.
However, many issues remain unsolved, casting hot disputes about whether RECrO3
is multiferroic or not. For example, an incompatibility exists between reported
structural models and observed ferroelectric behaviors, and it is not easy to
determine the spin canting degree. To address these questions, one key step is
to grow single crystals because they can provide more reliable information than
other forms of matter do. In this review, the parent and doped ferroelectric
YCrO3 compounds are comprehensively reviewed based on scientific and patent
literatures from 1954 to 2022. The materials syntheses with different methods,
including poly-, nano-, and single-crystalline samples and thin films, are
summarized. The structural, magnetic, ferroelectric and dielectric, optical,
and chemical-pressure (on Y and Cr sites by doping) dependent chemical and
physical properties and the corresponding phase diagrams, are discussed.
Diverse (potential) applications, including anti-corrosion, magnetohydrodynamic
electrode, catalyst, negative-temperature-coefficient thermistor, magnetic
refrigeration, protective coating, and solid oxide fuel cell, are present. To
conclude, we summarize general results, reached consensuses, and existing
controversies of the past nearly 69 years of intensive studies and highlight
future research opportunities and emerging challenges to address existing
issues.


  The detection of face-screen distance on smartphone (i.e., the distance
between the user face and the smartphone screen) is of paramount importance for
many mobile applications, including dynamic adjustment of screen on-off, screen
resolution, screen luminance, font size, with the purposes of power saving,
protection of human eyesight, etc. Existing detection techniques for
face-screen distance depend on external or internal hardware, e.g., an
accessory plug-in sensor (e.g., infrared or ultrasonic sensors) to measure the
face-screen distance, a built-in proximity sensor that usually outputs a
coarse-grained, two-valued, proximity index (for the purpose of powering on/off
the screen), etc. In this paper, we present a fine-grained detection method,
called "Look Into My Eyes (LIME)", that utilizes the front camera and inertial
accelerometer of the smartphone to estimate the facescreen distance.
Specifically, LIME captures the photo of the user's face only when the
accelerometer detects certain motion patterns of mobile phones, and then
estimates the face-screen distance by looking at the distance between the
user's eyes. Besides, LIME is able to take care of the user experience when
multiple users are facing the phone screen. The experimental results show that
LIME can achieve a mean squared error smaller than 2.4 cm in all of
experimented scenarios, and it incurs a small cost on battery life when
integrated into an SMS application for enabling dynamic font size by detecting
the face-screen distance.


  We study the long time asymptotics of a modified compressible Navier-Stokes
system (mcNS) inspired by the previous work of Hoff and Zumbrun. We introduce a
new decomposition of the momentum field into its irrotational and
incompressible parts, and a new method for approximating solutions of the heat
equation in terms of Hermite functions in which $n^{th}$ order approximations
can be computed for solutions with $n^{th}$ order moments. We then obtain
existence of solutions to the mcNS system and show that the approximation in
terms of Hermite functions gives the leading order terms in the long-time
asymptotics, and under certain assumptions can be evaluated explicitly.


  The purpose of the paper, of which this is part II, is to review, clarify,
and critically analyse modern mathematical cosmology. The emphasis is upon
mathematical objects and structures, rather than numerical computations. Part
II provides a critical analysis of inflationary cosmology and quantum
cosmology, with particular attention to the claims made that these theories can
explain the creation of the universe.


  Estimating magnetic susceptibility using MRI depends on inverting a forward
relationship between the susceptibility and measured Larmor frequency. However,
an often-overlooked constraint in susceptibility fitting is that the Larmor
frequency is only measured inside the sample, and after background field
removal, susceptibility sources should only reside inside the same sample. Here
we test the impact of accounting for such effects in susceptibility fitting and
demonstrate that such effects should not be ignored.


  A race is held between ten species of organic gas molecules on a graphene
substrate driven by thermal gradients via molecular dynamics. Fast conduction
of the molecules is observed with selectivity for aromatic compounds. This
selectivity stems from the fact that the planar structure of the aromatic
molecule helps keep a shorter distance to the substrate, which is the key to
the driving force at the gas-solid interface. The drift velocity monotonically
increases with decreasing molecule density, with no ballistic transport
observable even for a single molecule. A non-linear regime is discovered for
the conduction of benzene molecules under large thermal gradients. At low
temperature, molecules formed aggregation and move collectively along specific
path in the graphene substrate.


  A substantial magnetodielectric effect is often an indication of coupled
magnetic and elastic order, such as is found in the multiferroics. However, it
has recently been shown that magnetism is not necessary to produce either a
magnetoresistance or a magnetocapacitance when the material is inhomogeneous
[M. M. Parish and P. B. Littlewood, Phys. Rev. Lett. 101, 166602 (2008)]. Here,
we will investigate the characteristic magnetic-field-dependent dielectric
response of such an inhomogeneous system using exact calculations and numerical
simulations of conductor-dielectric composites. In particular, we will show
that even simple conductor-dielectric layers exhibit a magnetocapacitance, and
thus random bulk inhomogeneities are not a requirement for this effect. Indeed,
this work essentially provides a natural generalisation of the Maxwell-Wagner
effect to finite magnetic field. We will also discuss how this phenomenon has
already been observed experimentally in some materials.


  Solar oscillation frequencies change with the level of magnetic activity.
Localizing subsurface magnetic field concentrations in the Sun with
helioseismology will help us to understand the solar dynamo. Because the
magnetic fields are not considered in standard solar models, adding them to the
basic equations of stellar structure changes the eigenfunctions and
eigenfrequencies. We use quasi-degenerate perturbation theory to calculate the
effect of toroidal magnetic fields on solar oscillation mean multiplet
frequencies for six field configurations. In our calculations, we consider both
the direct effect of the magnetic field, which describes the coupling of modes,
and the indirect effect, which accounts for changes in stellar structure due to
the magnetic field. We limit our calculations to self-coupling of modes. We
find that the magnetic field affects the multiplet frequencies in a way that
depends on the location and the geometry of the field inside the Sun. Comparing
our theoretical results with observed shifts, we find that strong tachocline
fields cannot be responsible for the observed frequency shifts of p modes over
the solar cycle. We also find that part of the surface effect in helioseismic
oscillation frequencies might be attributed to magnetic fields in the outer
layers of the Sun. The theory presented here is also applicable to models of
solar-like stars and their oscillation frequencies.


  We explore the potential of Time-Of-Flight Medium Energy Ion Scattering
(TOFMEIS) for thin film analysis and analyze possible difficulties in
evaluation of experimental spectra. Issues regarding different combinations of
composition and stopping power as well as the influence of channeling are
discussed. As a model system high-k material stacks made from ultra-thin films
of HfO2 grown on a p-type Si (100) substrate with a 0.5 nm SiO2 interface layer
have been investigated. By comparison of experimental spectra and computer
simulations TOF-MEIS was employed to establish a depth profile of the films and
thus obtaining information on stoichiometry and film quality. Nominal film
thicknesses were in the range from 1.8 to 12.2 nm. As a supporting method
Rutherford-Backscattering spectrometry (RBS) was employed to obtain the areal
density of Hf atoms in the films.


  We have discovered an optically thick, edge-on circumstellar disk around a
Herbig Ae star in the binary system PDS 144, providing the first
intermediate-mass analog of HK Tau and similar T Tauris. This system consists
of a V ~ 13 mag. primary and a fainter companion, with spectra of both stars
showing evidence for circumstellar disks and accretion; both stars were
classified as Herbig Aes by the Pico dos Dias survey. In Lick adaptive optics
polarimetry, we resolved extended polarized light scattered from dust around
the northern star. Followup Keck adaptive optics and mid-infrared observations
show that this star is entirely hidden by an optically thick disk at all
wavelengths from 1.2 to 11.7 microns. The disk major axis subtends ~ 0.8" on
the sky, corresponding to ~ 800 AU at a distance of 1000 pc. Bright "wings"
extend 0.3" above and below the disk ansae, due most likely to scattering from
the edges of an outflow cavity in a circumstellar envelope. We discuss the
morphology of the disk and the spectral energy distributions of the two PDS 144
stars, present preliminary disk models, and identify a number of open questions
regarding this fascinating system.


  We study equivalence relations that arise from translation actions
$\Gamma\curvearrowright G$ which are associated to dense embeddings $\Gamma<G$
of countable groups into second countable locally compact groups. Assuming that
$G$ is simply connected and the action $\Gamma\curvearrowright G$ is strongly
ergodic, we prove that $\Gamma\curvearrowright G$ is orbit equivalent to
another such translation action $\Lambda\curvearrowright H$ if and only if
there exists an isomorphism $\delta:G\rightarrow H$ such that
$\delta(\Gamma)=\Lambda$. If $G$ is moreover a real algebraic group, then we
establish analogous rigidity results for the translation actions of $\Gamma$ on
homogeneous spaces of the form $G/\Sigma$, where $\Sigma<G$ is either a
discrete or an algebraic subgroup. We also prove that if $G$ is simply
connected and the action $\Gamma\curvearrowright G$ has property (T), then any
cocycle $w:\Gamma\times G\rightarrow\Lambda$ with values into a countable group
$\Lambda$ is cohomologous to a homomorphism $\delta:\Gamma\rightarrow\Lambda$.
As a consequence, we deduce that the action $\Gamma\curvearrowright G$ is orbit
equivalent superrigid: any free nonsingular action $\Lambda\curvearrowright Y$
which is orbit equivalent to $\Gamma\curvearrowright G$, is necessarily
conjugate to an induction of $\Gamma\curvearrowright G$.


  In this paper, we consider a discontinuous Dirac operator with eigenparameter
dependent both boundary and two transmission conditions. We introduce a
suitable Hilbert space formulation and get some properties of eigenvalues and
eigenfunctions. Then, we investigate Green's function, resolvent operator and
some uniqueness theorems by using Weyl function and some spectral data.


  We construct a continuously differentiable curve in the plane that can be
covered by a collection of lines such that every line intersects the curve at a
single point and the union of the lines has Hausdorff dimension 1. We show that
for twice differentiable curves this is impossible. In that case, the union of
the lines must have Hausdorff dimension 2. If we use only tangent lines then
the differentiability of the curve already implies that the union of the lines
must have Hausdorff dimension 2, unless the curve is a line. We also construct
a continuous curve, which is in fact the graph of a strictly convex function,
such that the union of (one sided) tangent lines has Hausdorff dimension 1.


  Ensembles, where multiple neural networks are trained individually and their
predictions are averaged, have been shown to be widely successful for improving
both the accuracy and predictive uncertainty of single neural networks.
However, an ensemble's cost for both training and testing increases linearly
with the number of networks, which quickly becomes untenable.
  In this paper, we propose BatchEnsemble, an ensemble method whose
computational and memory costs are significantly lower than typical ensembles.
BatchEnsemble achieves this by defining each weight matrix to be the Hadamard
product of a shared weight among all ensemble members and a rank-one matrix per
member. Unlike ensembles, BatchEnsemble is not only parallelizable across
devices, where one device trains one member, but also parallelizable within a
device, where multiple ensemble members are updated simultaneously for a given
mini-batch. Across CIFAR-10, CIFAR-100, WMT14 EN-DE/EN-FR translation, and
out-of-distribution tasks, BatchEnsemble yields competitive accuracy and
uncertainties as typical ensembles; the speedup at test time is 3X and memory
reduction is 3X at an ensemble of size 4. We also apply BatchEnsemble to
lifelong learning, where on Split-CIFAR-100, BatchEnsemble yields comparable
performance to progressive neural networks while having a much lower
computational and memory costs. We further show that BatchEnsemble can easily
scale up to lifelong learning on Split-ImageNet which involves 100 sequential
learning tasks.


  We prove that every variety with log-terminal singularities admits a crepant
resolution by a smooth Artin stack. We additionally prove new McKay
correspondences for resolutions by Artin stacks, expressing stringy invariants
of $\mathbb{Q}$-Gorenstein varieties in terms of motivic integrals on arc
spaces of smooth stacks. In the crepant case, these McKay correspondences are
particularly simple, demonstrating one example of the utility of crepant
resolutions by Artin stacks.


  Observations of black hole-neutron star (BH-NS) mergers via gravitational
waves (GWs) are of great interest for their electromagnetic counterparts, such
as short gamma-ray bursts, and could provide crucial information on the nature
of BHs and the NS crust and magnetosphere. While no event has been confirmed, a
recent possible detection of a BH-NS merger event by the LIGO-Virgo
collaboration has attracted a lot of attention to these sources. In this second
paper of the series, we follow-up our study of the dynamical evolution of
triples comprised of an inner BH-NS binary. In particular, we examine how the
progenitor metallicity affects the characteristics of the BH-NS mergers in
triples. We determine the distributions of masses, orbital parameters and
merger times, as a function of the progenitor metallicity and initial triple
orbital distributions, and show that the typical eccentricity in the LIGO band
is $\sim 10^{-2}-10^{-1}$. We derive a merger rate range of
$\Gamma_\mathrm{BH-NS}=1.9\times 10^{-4}-22 \ \mathrm{Gpc}^{-3}\
\mathrm{yr}^{-1}$, consistent the LIGO-Virgo upper limit. Finally, we study the
expected spin-orbit misalignments of merging BH-NS binaries from this channel,
and find that typically the effective spin distribution is peaked at $\chi_{\rm
eff}\sim 0$ with significant tails.


  We present single-epoch spectropolarimetry of the Type II supernovae (SNe II)
1997ds, 1998A, and 1999gi. SN 1997ds and SN 1998A were both observed during the
early photospheric phase, while spectropolarimetry of SN 1999gi was obtained
near the start of the transition to the nebular phase. Uncorrected for
interstellar polarization (ISP), SN 1997ds is characterized by p_V = 0.85 +/-
0.02%, SN 1998A has p_V = 0.24 +/- 0.05%, and SN 1999gi is polarized at p_V =
5.72 +/- 0.01%. The low continuum polarization inferred for SN 1997ds and SN
1998A and the amplitude of polarization modulations across strong line features
are consistent with those measured at similar epochs for SN 1987A and the Type
II-plateau SN 1999em, and supports the growing consensus that core-collapse
events with hydrogen envelopes substantially intact at the time of explosion
are not significantly aspherical during the early photospheric phase.
  The spectral shape of the high continuum polarization of SN 1999gi closely
resembles a ``Serkowski'' ISP curve, and is inconsistent with the
wavelength-independent nature of electron scattering expected for an aspherical
SN atmosphere. Since Galactic reddening is minimal along this line-of-sight,
the majority of the observed polarization in SN 1999gi is believed to be due to
ISP of the host galaxy, although significant (up to p = 2%) intrinsic
polarization cannot be ruled out. The potential power of SN spectropolarimetry
to study the properties of interstellar dust in external galaxies is described
and applied to the SN 1999gi data, where it is shown that if the polarization
is indeed predominantly interstellar in origin, then R_V = 3.0 +/- 0.2 for the
dust along this line-of-sight in NGC 3184.


  We build a holographic description of non-relativistic system for
superconductivity in strongly interacting condensed matter via gauge/gravity
duality. We focus on the phase transition and give an example to show that a
simple gravitational theory can provide a non-relativistic holographical dual
description of a superconductor. There is also a critical temperature like the
relativistic case, below which a charged condensation field appears by a second
order phase transition and the (DC) conductivity becomes infinite. We also
calculated the frequency dependent conductivity.


  We consider the gravitational collapse of self-gravitating spherical dust
cloud in the Hamiltonian formalism. We address both homogeneous and
inhomogeneous cases. Our novel derivation of the Hamiltonian of the system is
based on the improved variational principle that was proposed in \cite{KMM}.
The present derivation differs from usual treatments due to the presence of an
extra boundary term added to the Hilbert action. As expected, the standard
equations of motion are retrieved. However, differently from other treatments,
the total Hamiltonian obtained with our procedure in the Schwarzschild
time-gauge is identical to the total mass of the system as measured from
infinity, as it would be expected. Implications for the quantization of the
system are suggested.


  Motivated by recent progress in development of cryogenic memory compatible
with single flux quantum (SFQ) circuits we have performed a theoretical study
of magnetic SIsFS Josephson junctions, where 'S' is a bulk superconductor, 's'
is a thin superconducting film, 'F' is a metallic ferromagnet and 'I' is an
insulator. We calculate the Josephson current as a function of s and F layers
thickness, temperature and exchange energy of F film. We outline several modes
of operation of these junctions and demonstrate their unique ability to have
large product of a critical current $I_{C}$ and a normal-state resistance
$R_{N}$ in the $\pi$ state, comparable to that in SIS tunnel junctions commonly
used in SFQ circuits. We develop a model describing switching of the Josephson
critical current in these devices by external magnetic field. The results are
in good agreement with the experimental data for
Nb-Al/AlO${_x}$-Nb-Pd$_{0.99}$Fe$_{0.01}$-Nb junctions.


  We analyze line-of-sight velocity and proper motion data of stars in the
Galactic globular cluster M15 using a new method to fit dynamical models to
discrete kinematic data. Our fitting method maximizes the likelihood for
individual stars and, as such, does not suffer the same loss of spatial and
velocity information incurred when spatially binning data or measuring velocity
moments. In this paper, we show that the radial variation in M15 of the
mass-to-light ratio is consistent with previous estimates and theoretical
predictions, which verifies our method. Our best-fitting axisymmetric Jeans
models do include a central dark mass of $\sim2 \pm 1\cdot 10^3M_\odot$, which
can be explained by a high concentration of stellar remnants at the cluster
center. This paper shows that, from a technical point of view and with current
computing power, spatial binning of data is no longer necessary. This not only
leads to more accurate fits, but also avoids biased mass estimates due to the
loss of resolution. Furthermore, we find that the mass concentration in M15 is
significantly higher than previously measured, and is in close agreement with
theoretical predictions for core-collapsed globular clusters without a central
intermediate-mass black hole.


  The image quality of X-ray imaging relies heavily on the X-ray output number
which is dependent on the X-ray tube current and the exposure time. Hybrid
X-ray imaging modalities like X-ray luminescence CT (XLCT) and X-ray
fluorescence CT (XFCT) rely on the intensity of the X-ray tube to provide an
accurate image reconstruction of the nanoprobe distribution in the imaging
sample. A limiting factor of good image quality is the radiation dose that will
be delivered to the imaging object. To accurately estimate the absorbed dose in
an imaging protocol, it is better to simulate the X-ray imaging with a Monte
Carlo platform such as GATE (Geant4 Application for Tomographic Emission).
However, the input of GATE is a photon number of the simulated X-ray tube. So
far, there is no good way to setup the photon number for a desired X-ray tube
current. In this work, the accumulated radiation dose of a micro-CT X-ray tube
at different current exposure times was recorded with a general-purpose ion
chamber. GATE was used to model the total absorbed dose (cGy) in the sensitive
volume of the ion chamber with different X-ray output numbers. A linear
regression model was generated between the X-ray photon number in the GATE
simulations and the tube current exposure time (mAs). The findings of this work
provide an approach to correlate the X-ray tube current exposure time (mAs) to
the X-ray photon number in GATE simulation of the X-ray tube.


  The next generation of Far-infrared and X-ray space observatories will
require detector arrays with thousands of transition edge sensor (TES) pixel.
It is extremely important to have a tool that is able to characterize all the
pixels and that can give a clear picture of the performance of the devices. In
particular, we refer to those aspects that can affect the global energy
resolution of the array: logarithmic resistance sensitivity with respect to
temperature and current ($\alpha$ and $\beta$ parameters, respectively),
uniformity of the TESs and the correct understanding of the detector thermal
model. Complex impedance measurement of a TES is the only technique that can
give all this information at once, but it has been established only for a
single pixel under DC bias. We have developed a complex impedance measurement
method for TESs that are AC biased since we are using a MHz frequency domain
multiplexing (FDM) system to readout an array. We perform a complete set of AC
impedance measurements for different X-ray TES microcalorimeters based on
superconducting TiAu bilayers with or without normal metal Au bar structures.
We discuss the statistical analysis of the residual between impedance data and
fitting model to determine the proper calorimeter thermal model for our
detectors. Extracted parameters are used to improve our understanding of the
differences and capabilities among the detectors and additionally the quality
of the array. Moreover, we use the results to compare the calculated noise
spectra with the measured data.


  We reconsider the generalized flare-out conditions in static wormhole throats
given by Hochberg and Visser. We show that due to the presence of matter
sources on the throat, these conditions are not applicable to the thin-shell
wormholes.


  In this paper we use ternary representation of numbers for compressing text
data. We use a binary map for ternary digits and introduce a way to use the
binary 11-pair, which has never been use for coding data before, and we futher
use 4-Digits ternary representation of alphabet with lowercase and uppercase
with some extra symbols that are most commonly used in day to day life. We find
a way to minimize the length of the bits string, which is only possible in
ternary representation thus drastically reducing the length of the code. We
also find some connection between this technique of coding dat and Fibonacci
numbers.


  The sister of Eisenstein-Picard modular group was described in [8]. In this
paper we give a similar definition of the sister of the Euclidean-Picard
modular group and find its generators by using a geometric method.


  Kinetic inductance bolometer (KIB) technology is a candidate for passive
sub-millimeter wave and terahertz imaging systems. Its benefits include
scalability into large 2D arrays and operation with intermediate cryogenics in
the temperature range of 5 -- 10 K. We have previously demonstrated the
scalability in terms of device fabrication, optics integration, and cryogenics.
In this article, we address the last missing ingredient, the readout. The
concept, serial addressed frequency excitation (SAFE), is an alternative to
full frequency-division multiplexing at microwave frequencies conventionally
used to read out kinetic inductance detectors. We introduce the concept, and
analyze the criteria of the multiplexed readout avoiding the degradation of the
signal-to-noise ratio in the presence of a thermal anti-alias filter inherent
to thermal detectors. We present a practical scalable realization of a readout
system integrated into a prototype imager with 8712 detectors. This is used for
demonstrating the noise properties of the readout. Furthermore, we present
practical detection experiments with a stand-off laboratory-scale imager.


  We examine extended bound states in the dilute limit of the extended Hubbard
model with next-nearest-neighbour (nnn) hopping and nnn interactions on the two
dimensional square lattice. By the exact solution of a two-body problem we show
that d-wave pairing is strongly favored by the nnn hopping. We determine the
binding energies, mobilities and dispersion curves across Brillouin zone (B.z.)
for bound states of various symmetries. The numerical analysis shows that the
deep bound states can exist in the whole B.z. and we have also found s*-d
mixing of the bound states.


  We have quantified the galaxy environments around a sample of 0.5 < z < 0.8
radio-quiet quasars using the amplitude of the spatial galaxy--quasar
correlation function, B_gq. The quasars exist in a wide variety of
environments, some sources are located in clusters as rich as Abell class 1--2
clusters, whereas others exist in environments comparable to the field. We find
that on average, the quasars prefer poorer clusters of approximately Abell
class 0, which suggests that quasars are biased tracers of mass compared to
galaxies.
  The mean B_gq for the sample is found to be indistinguishable from the mean
amplitude for a sample of radio-loud quasars matched in redshift and optical
luminosity. These observations are consistent with recent studies of the hosts
of radio-quiet quasars at low to intermediate redshifts, and suggest that the
mechanism for the production of powerful radio jets in radio-loud quasars is
controlled by processes deep within the active galactic nucleus itself, and is
unrelated to the nature of the hosts or their environments.


  The cosmic energy density in the form of radiation before and during Big Bang
Nucleosynthesis (BBN) is typically parameterized in terms of the effective
number of neutrinos N_eff. This quantity, in case of no extra degrees of
freedom, depends upon the chemical potential and the temperature characterizing
the three active neutrino distributions, as well as by their possible
non-thermal features. In the present analysis we determine the upper bounds
that BBN places on N_eff from primordial neutrino--antineutrino asymmetries,
with a careful treatment of the dynamics of neutrino oscillations. We consider
quite a wide range for the total lepton number in the neutrino sector, eta_nu=
eta_{nu_e}+eta_{nu_mu}+eta_{nu_tau} and the initial electron neutrino asymmetry
eta_{nu_e}^in, solving the corresponding kinetic equations which rule the
dynamics of neutrino (antineutrino) distributions in phase space due to
collisions, pair processes and flavor oscillations. New bounds on both the
total lepton number in the neutrino sector and the nu_e -bar{nu}_e asymmetry at
the onset of BBN are obtained fully exploiting the time evolution of neutrino
distributions, as well as the most recent determinations of primordial 2H/H
density ratio and 4He mass fraction. Note that taking the baryon fraction as
measured by WMAP, the 2H/H abundance plays a relevant role in constraining the
allowed regions in the eta_nu -eta_{nu_e}^in plane. These bounds fix the
maximum contribution of neutrinos with primordial asymmetries to N_eff as a
function of the mixing parameter theta_13, and point out the upper bound N_eff
< 3.4. Comparing these results with the forthcoming measurement of N_eff by the
Planck satellite will likely provide insight on the nature of the radiation
content of the universe.


  We find the general solution of the 6-dimensional Einstein-Gauss-Bonnet
equations in a large class of space and time-dependent warped geometries.
Several distinct families of solutions are found, some of which include black
string metrics, space and time-dependent solutions and black holes with exotic
horizons. Among these, some are shown to verify a Birkhoff type staticity
theorem, although here, the usual assumption of maximal symmetry on the horizon
is relaxed, allowing exotic horizon geometries. We provide explicit examples of
such static exotic black holes, including ones whose horizon geometry is that
of a Bergman space. We find that the situation is very different from
higher-dimensional general relativity, where Einstein spaces are admissible
black hole horizons and the associated black hole potential is not even
affected. In Einstein-Gauss-Bonnet theory, on the contrary, the non-trivial
Weyl tensor of such exotic horizons is exposed to the bulk dynamics through the
higher order Gauss-Bonnet term, severely constraining the allowed horizon
geometries and adding a novel charge-like parameter to the black hole
potential. The latter is related to the Euler characteristic of the
four-dimensional horizon and provides, in some cases, additional black hole
horizons.


  Graph convolutional networks (GCNs) have received considerable research
attention recently. Most GCNs learn the node representations in Euclidean
geometry, but that could have a high distortion in the case of embedding graphs
with scale-free or hierarchical structure. Recently, some GCNs are proposed to
deal with this problem in non-Euclidean geometry, e.g., hyperbolic geometry.
Although hyperbolic GCNs achieve promising performance, existing hyperbolic
graph operations actually cannot rigorously follow the hyperbolic geometry,
which may limit the ability of hyperbolic geometry and thus hurt the
performance of hyperbolic GCNs. In this paper, we propose a novel hyperbolic
GCN named Lorentzian graph convolutional network (LGCN), which rigorously
guarantees the learned node features follow the hyperbolic geometry.
Specifically, we rebuild the graph operations of hyperbolic GCNs with
Lorentzian version, e.g., the feature transformation and non-linear activation.
Also, an elegant neighborhood aggregation method is designed based on the
centroid of Lorentzian distance. Moreover, we prove some proposed graph
operations are equivalent in different types of hyperbolic geometry, which
fundamentally indicates their correctness. Experiments on six datasets show
that LGCN performs better than the state-of-the-art methods. LGCN has lower
distortion to learn the representation of tree-likeness graphs compared with
existing hyperbolic GCNs. We also find that the performance of some hyperbolic
GCNs can be improved by simply replacing the graph operations with those we
defined in this paper.


  Fleets of autonomous vehicles can mitigate traffic congestion through simple
actions, thus improving many socioeconomic factors such as commute time and gas
costs. However, these approaches are limited in practice as they assume precise
control over autonomous vehicle fleets, incur extensive installation costs for
a centralized sensor ecosystem, and also fail to account for uncertainty in
driver behavior. To this end, we develop a class of learned residual policies
that can be used in cooperative advisory systems and only require the use of a
single vehicle with a human driver. Our policies advise drivers to behave in
ways that mitigate traffic congestion while accounting for diverse driver
behaviors, particularly drivers' reactions to instructions, to provide an
improved user experience. To realize such policies, we introduce an improved
reward function that explicitly addresses congestion mitigation and driver
attitudes to advice. We show that our residual policies can be personalized by
conditioning them on an inferred driver trait that is learned in an
unsupervised manner with a variational autoencoder. Our policies are trained in
simulation with our novel instruction adherence driver model, and evaluated in
simulation and through a user study (N=16) to capture the sentiments of human
drivers. Our results show that our approaches successfully mitigate congestion
while adapting to different driver behaviors, with up to 20% and 40%
improvement as measured by a combination metric of speed and deviations in
speed across time over baselines in our simulation tests and user study,
respectively. Our user study further shows that our policies are
human-compatible and personalize to drivers.


  We propose a Gaussian variational inference framework for the motion planning
problem. In this framework, motion planning is formulated as an optimization
over the distribution of the trajectories to approximate the desired trajectory
distribution by a tractable Gaussian distribution. Equivalently, the proposed
framework can be viewed as a standard motion planning with an entropy
regularization. Thus, the solution obtained is a transition from an optimal
deterministic solution to a stochastic one, and the proposed framework can
recover the deterministic solution by controlling the level of stochasticity.
To solve this optimization, we adopt the natural gradient descent scheme. The
sparsity structure of the proposed formulation induced by factorized objective
functions is further leveraged to improve the scalability of the algorithm. We
evaluate our method on several robot systems in simulated environments, and
show that it achieves collision avoidance with smooth trajectories, and
meanwhile brings robustness to the deterministic baseline results, especially
in challenging environments and tasks.


  In order to explain rapid light curve variability without invoking a variable
source, several authors have proposed "minijets" that move relativistically
relative to the main flow of the jet. Here we consider the possibility that
these minijets, instead of being isotropically distributed in the comoving
frame of the jet, form primarily perpendicular to the direction of the flow, as
the jet dissipates its energy at a large emission radius. This yields two
robust features. First, the emission is significantly delayed compared with the
isotropic case. This delay allows for the peak of the afterglow emission to
appear while the source is still active, in contrast to the simplest isotropic
model. Secondly, the flux decline after the source turns off is steeper than
the isotropic case. We find that these two features are realized in gamma-ray
bursts (GRBs): 1. The peak of most GeV light curves (ascribed to the external
shock) appears during the prompt emission phase. 2. Many X-ray light curves
exhibit a period of steep decay, which is faster than that predicted by the
standard isotropic case. The gamma-ray generation mechanism in GRBs, and
possibly in other relativistic flows, may therefore be anisotropic.


  The Loops that Matter method (Schoenberg et. al, 2019) for understanding
model behavior provides metrics showing the contribution of the feedback loops
in a model to behavior at each point in time. To provide these metrics, it is
necessary find the set of loops on which to compute them. We show in this paper
the necessity of including loops that are important at different points in the
simulation. These important loops may not be independent of one another and
cannot be determined from static analysis of the model structure. We then
describe an algorithm that can be used to discover the most important loops in
models that are too feedback rich for exhaustive loop discovery. We demonstrate
the use of this algorithm in terms of its ability to find the most explanatory
loops, and its computational performance for large models. By using this
approach, the Loops that Matter method can be applied to models of any size or
complexity.


  We propose a mechanism in which two molecular knots pass through each other
and swap positions along a polymer strand. Associated free energy barriers in
our simulations only amount to a few $k_{B}T$, which may enable the interchange
of knots on a single DNA strand.


  DP-coloring (also known as correspondence coloring) of a simple graph is a
generalization of list coloring. It is known that planar graphs without
4-cycles adjacent to triangles are 4-choosable, and planar graphs without
4-cycles are DP-4-colorable. In this paper, we show that planar graphs without
4-cycles adjacent to triangles are DP-4-colorable, which is an extension of the
two results above.


  We present a computational study of smooth curves of degree six in the real
projective plane. In the Rokhlin-Nikulin classification, there are 56
topological types, refined into 64 rigid isotopy classes. We developed software
that determines the topological type of a given sextic and used it to compute
empirical probability distributions on the various types. We list 64 explicit
representatives with integer coefficients, and we perturb these to draw many
samples from each class. This allows us to explore how many of the bitangents,
inflection points and tensor eigenvectors are real. We also study the real
tensor rank, the construction of quartic surfaces with prescribed topology, and
the avoidance locus, which is the locus of all real lines that do not meet a
given sextic. This is a union of up to 46 convex regions, bounded by the dual
curve.


  We study the classical and quantum models of a flat
Friedmann-Robertson-Walker (FRW) space-time, coupled to a perfect fluid, in the
context of the consensus and a gauge-fixed Lagrangian frameworks. It is shown
that, either in the usual or in the gauge-fixed actions, the evolution of the
universe based on the classical cosmology represents a late time power law
expansion, coming from a big-bang singularity in which the scale factor goes to
zero for the standard matter, and tending towards a big-rip singularity in
which the scale factor diverges for the phantom fluid. We then employ the
familiar canonical quantization procedure in the given cosmological setting to
find the cosmological wave functions in the corresponding minisuperspace. Using
a gauge-fixed (reduced) Lagrangian, we show that, it may lead to a
Schr\"{o}dinger equation for the quantum-mechanical description of the model
under consideration, the eigenfunctions of which can be used to construct the
time dependent wave function of the universe. We use the resulting wave
function in order to investigate the possibility of the avoidance of classical
singularities due to quantum effects by means of the many-worlds and
ontological interpretation of quantum cosmology.


  Thin film processing by means of sputter deposition inherently depends on the
interaction of energetic particles with a target surface and the subsequent
particle transport. The length and time scales of the underlying physical
phenomena span orders of magnitudes. A theoretical description which bridges
all time and length scales is not practically possible. Advantage can be taken
particularly from the well-separated time scales of the fundamental surface and
plasma processes. Initially, surface properties may be calculated from a
surface model and stored for a number of representative cases. Subsequently,
the surface data may be provided to gas-phase transport simulations via
appropriate model interfaces (e.g., analytic expressions or look-up tables) and
utilized to define insertion boundary conditions. During run-time evaluation,
however, the maintained surface data may prove to be not sufficient. In this
case, missing data may be obtained by interpolation (common), extrapolation
(inaccurate), or be supplied on-demand by the surface model (computationally
inefficient). In this work, a potential alternative is established based on
machine learning techniques using artificial neural networks. As a proof of
concept, a multilayer perceptron network is trained and verified with sputtered
particle distributions obtained from transport of ions in matter based
simulations for Ar projectiles bombarding a Ti-Al composite. It is demonstrated
that the trained network is able to predict the sputtered particle
distributions for unknown, arbitrarily shaped incident ion energy
distributions. It is consequently argued that the trained network may be
readily used as a machine learning based model interface (e.g., by
quasi-continuously sampling the desired sputtered particle distributions from
the network), which is sufficiently accurate also in scenarios which have not
been previously trained.


  The Gaia DR3 Catalogue contains for the first time about eight hundred
thousand solutions with either orbital elements or trend parameters for
astrometric, spectroscopic and eclipsing binaries, and combinations of them.
This paper aims to illustrate the huge potential of this large non-single star
catalogue. Using the orbital solutions together with models of the binaries, a
catalogue of tens of thousands of stellar masses, or lower limits, partly
together with consistent flux ratios, has been built. Properties concerning the
completeness of the binary catalogues are discussed, statistical features of
the orbital elements are explained and a comparison with other catalogues is
performed. Illustrative applications are proposed for binaries across the H-R
diagram. The binarity is studied in the RGB/AGB and a search for genuine SB1
among long-period variables is performed. The discovery of new EL CVn systems
illustrates the potential of combining variability and binarity catalogues.
Potential compact object companions are presented, mainly white dwarf
companions or double degenerates, but one candidate neutron star is also
presented. Towards the bottom of the main sequence, the orbits of
previously-suspected binary ultracool dwarfs are determined and new candidate
binaries are discovered. The long awaited contribution of Gaia to the analysis
of the substellar regime shows the brown dwarf desert around solar-type stars
using true, rather than minimum, masses, and provides new important constraints
on the occurrence rates of substellar companions to M dwarfs. Several dozen new
exoplanets are proposed, including two with validated orbital solutions and one
super-Jupiter orbiting a white dwarf, all being candidates requiring
confirmation. Beside binarity, higher order multiple systems are also found.


  By undertaking additional analyses postponed in a previous paper, we complete
our construction of a manifestly supersymmetric gauge-covariant regularization
of supersymmetric chiral gauge theories. We present the following: An
evaluation of the covariant gauge anomaly; a proof of the integrability of the
covariant gauge current in anomaly-free cases; a calculation of a one-loop
superconformal anomaly in the gauge supermultiplet sector. On the last point,
we find that the ghost-anti-ghost supermultiplet and the Nakanishi-Lautrup
supermultiplet give rise to BRST exact contributions which, due to
``tree-level'' Slavnov-Taylor identities in our regularization scheme, can
safely be neglected, at least at the one-loop level.


  Wearable devices like smart glasses are approaching the compute capability to
seamlessly generate real-time closed captions for live conversations. We build
on our recently introduced directional Automatic Speech Recognition (ASR) for
smart glasses that have microphone arrays, which fuses multi-channel ASR with
serialized output training, for wearer/conversation-partner disambiguation as
well as suppression of cross-talk speech from non-target directions and noise.
  When ASR work is part of a broader system-development process, one may be
faced with changes to microphone geometries as system development progresses.
  This paper aims to make multi-channel ASR insensitive to limited variations
of microphone-array geometry. We show that a model trained on multiple similar
geometries is largely agnostic and generalizes well to new geometries, as long
as they are not too different. Furthermore, training the model this way
improves accuracy for seen geometries by 15 to 28\% relative. Lastly, we refine
the beamforming by a novel Non-Linearly Constrained Minimum Variance criterion.


  Necessary and sufficient conditions for the existence of an entropy structure
for certain classes of cross-diffusion systems with diffusion matrix $A(u)$ are
derived, based on results from matrix factorization. The entropy structure is
important in the analysis for such equations since $A(u)$ is typically neither
symmetric nor positive definite. In particular, the normal ellipticity of
$A(u)$ for all $u$ and the symmetry of the Onsager matrix implies its positive
definiteness and hence an entropy structure. If $A$ is constant or nearly
constant in a certain sense, the existence of an entropy structure is
equivalent to the normal ellipticity of $A$. Several applications and examples
are presented, including the $n$-species population model of Shigesada,
Kawasaki, and Teramoto, a volume-filling model, and a fluid mixture model with
partial pressure gradients. Furthermore, the normal elipticity of these models
is investigated and some extensions are discussed.


  How do the quark and gluon share the nucleon momentum? How does the nucleon
spin distribute among its constituents? What means the quark and gluon
momentum, spin and orbital angular momentum? These problems are analyzed and a
solution is proposed based on {\it gauge invariance principle, canonical
quantization rule and Poincar$\acute{e}$ covariance}.


  We report on a study of (001) oriented fcc Fe-Ni alloy surfaces which
combines first-principles calculations and low-temperature STM experiments.
Density functional theory calculations show that Fe-Ni alloy surfaces are
buckled with the Fe atoms slightly shifted outwards and the Ni atoms inwards.
This is consistent with the observation that the atoms in the surface layer can
be chemically distinguished in the STM image: brighter spots (corrugation
maxima with increased apparent height) indicate iron atoms, darker ones nickel
atoms. This chemical contrast reveals a c2x2 chemical order (50% Fe) with
frequent Fe-rich defects on Invar alloy surface. The calculations also indicate
that subsurface composition fluctuations may additionally modulate the apparent
height of the surface atoms. The STM images show that this effect is pronounced
compared to the surfaces of other disordered alloys, which suggests that some
chemical order and corresponding concentration fluctuations exist also in the
subsurface layers of Invar alloy. In addition, detailed electronic structure
calculations allow us to identify the nature of a distinct peak below the Fermi
level observed in the tunneling spectra. This peak corresponds to a surface
resonance band which is particularly pronounced in iron-rich surface regions
and provides a second type of chemical contrast with less spatial resolution
but one that is essentially independent of the subsurface composition.


  Since the discovery of two-dimensional electron gas (2DEG) at the oxide
interface of LaAlO3/SrTiO3, improving carrier mobility has become an important
issue for device applications. In this paper, by using an alternate polar
perovskite insulator (La0.3Sr0.7)(Al0.65Ta0.35)O3 (LSAT) for reducing lattice
mismatch from 3.0% to 1.0%, the low-temperature carrier mobility has been
increased 30 fold to 35,000 cm2V-1s-1. Moreover, two critical thicknesses for
the LSAT/SrTiO3 (001) interface are found: one at 5 unit cell for appearance of
the 2DEG, the other at 12 unit cell for a peak in the carrier mobility. By
contrast, the conducting (110) and (111) LSAT/STO interfaces only show a single
critical thickness of 8 unit cells. This can be explained in terms of polar
fluctuation arising from LSAT chemical composition. In addition to lattice
mismatch and crystal symmetry at the interface, polar fluctuation arising from
composition has been identified as an important variable to be tailored at the
oxide interfaces to optimise the 2DEG transport.


  Modeling in Computer Vision has evolved to MLPs. Vision MLPs naturally lack
local modeling capability, to which the simplest treatment is combined with
convolutional layers. Convolution, famous for its sliding window scheme, also
suffers from this scheme of redundancy and low computational efficiency. In
this paper, we seek to dispense with the windowing scheme and introduce a more
elaborate and effective approach to exploiting locality. To this end, we
propose a new MLP module, namely Shifted-Pillars-Concatenation (SPC), that
consists of two steps of processes: (1) Pillars-Shift, which generates four
neighboring maps by shifting the input image along four directions, and (2)
Pillars-Concatenation, which applies linear transformations and concatenation
on the maps to aggregate local features. SPC module offers superior local
modeling power and performance gains, making it a promising alternative to the
convolutional layer. Then, we build a pure-MLP architecture called Caterpillar
by replacing the convolutional layer with the SPC module in a hybrid model of
sMLPNet. Extensive experiments show Caterpillar's excellent performance and
scalability on both ImageNet-1K and small-scale classification benchmarks.


  This work considers supervised contrastive learning for semantic
segmentation. We apply contrastive learning to enhance the discriminative power
of the multi-scale features extracted by semantic segmentation networks. Our
key methodological insight is to leverage samples from the feature spaces
emanating from multiple stages of a model's encoder itself requiring neither
data augmentation nor online memory banks to obtain a diverse set of samples.
To allow for such an extension we introduce an efficient and effective sampling
process, that enables applying contrastive losses over the encoder's features
at multiple scales. Furthermore, by first mapping the encoder's multi-scale
representations to a common feature space, we instantiate a novel form of
supervised local-global constraint by introducing cross-scale contrastive
learning linking high-resolution local features to low-resolution global
features. Combined, our multi-scale and cross-scale contrastive losses boost
performance of various models (DeepLabV3, HRNet, OCRNet, UPerNet) with both CNN
and Transformer backbones, when evaluated on 4 diverse datasets from natural
(Cityscapes, PascalContext, ADE20K) but also surgical (CaDIS) domains. Our code
is available at https://github.com/RViMLab/MS_CS_ContrSeg. datasets from
natural (Cityscapes, PascalContext, ADE20K) but also surgical (CaDIS) domains.


  A general discussion is given for first-kind (FK) and quantum non-demolition
(QND) measurements. The general conditions for these measurements are derived,
including the most general one (called the weak condition), an intermediate
one, and the strongest one. The weak condition indicates that we can realize a
FK or QND measuring apparatus of wide classes of observables by allowing the
apparatus to have a finite response range. A recently-proposed QND
photodetector using an electron interferometer is an example of such apparatus.


  This paper considers the parameter estimation problem in Mixed Membership
Stochastic Block Model (MMSB), which is a quite general instance of random
graph model allowing for overlapping community structure. We present the new
algorithm successive projection overlapping clustering (SPOC) which combines
the ideas of spectral clustering and geometric approach for separable
non-negative matrix factorization. The proposed algorithm is provably
consistent under MMSB with general conditions on the parameters of the model.
SPOC is also shown to perform well experimentally in comparison to other
algorithms.


  We give criteria for an invariant of lens space links to bound the maximal
self-linking number in certain tight contact lens spaces. As a corollary we
extend the Franks-Williams-Morton inequality to the setting of lens spaces.


  The Magnetoelectric Spin-Orbit (MESO) technology aims to bring logic into
memory by combining a ferromagnet with a magnetoelectric (ME) element for
information writing, and a spin-orbit (SO) element for information read-out
through spin-charge conversion. Among candidate SO materials to achieve a large
MESO output signal, oxide Rashba two-dimensional electron gases (2DEGs) have
shown very large spin-charge conversion efficiencies, albeit mostly in
spin-pumping experiments. Here, we report all-electrical spin-injection and
spin-charge conversion experiments in nanoscale devices harnessing the inverse
Edelstein effect of SrTiO3 2DEGs. We have designed, patterned and fabricated
nanodevices in which a spin current injected from a cobalt layer into the 2DEG
is converted into a charge current. We optimized the spin-charge conversion
signal by applying back-gate voltages, and studied its temperature evolution.
We further disentangled the inverse Edelstein contribution from spurious
effects such as the planar Hall effect, the anomalous Hall effect or the
anisotropic magnetoresistance. The combination of non-volatility and high
energy efficiency of these devices could potentially lead to new technology
paradigms for beyond-CMOS computing architectures.


  We analyze photo-refractive incoherent soliton beams and their interactions
in Kerr-like nonlinear media. The field in each of M incoherently interacting
components is calculated using an integrable set of coupled nonlinear
Schrodinger equations. In particular, we obtain a general N-soliton solution,
describing propagation of multi-soliton complexes and their collisions. The
analysis shows that the evolution of such higher-order soliton beams is
determined by coherent and incoherent contributions from fundamental solitons.
Common features and differences between these internal interactions are
revealed and illustrated by numerical examples.


  We report the realization of a Bose-Einstein condensate (BEC) in the
hydrodynamic regime. The hydrodynamic regime is reached by evaporative cooling
at a relative low density suppressing the effect of avalanches. With the
suppression of avalanches a BEC containing 120.10^6 atoms is produced. The
collisional opacity can be tuned from the collisionless regime to a collisional
opacity of more than 3 by compressing the trap after condensation. In the
collisional opaque regime a significant heating of the cloud at time scales
shorter than half of the radial trap period is measured. This is direct proof
that the BEC is hydrodynamic.


  Human-machine systems required a deep understanding of human behaviors. Most
existing research on action recognition has focused on discriminating between
different actions, however, the quality of executing an action has received
little attention thus far. In this paper, we study the quality assessment of
driving behaviors and present WiQ, a system to assess the quality of actions
based on radio signals. This system includes three key components, a deep
neural network based learning engine to extract the quality information from
the changes of signal strength, a gradient based method to detect the signal
boundary for an individual action, and an activitybased fusion policy to
improve the recognition performance in a noisy environment. By using the
quality information, WiQ can differentiate a triple body status with an
accuracy of 97%, while for identification among 15 drivers, the average
accuracy is 88%. Our results show that, via dedicated analysis of radio
signals, a fine-grained action characterization can be achieved, which can
facilitate a large variety of applications, such as smart driving assistants.


  Applying general symmetry properties of electromagnetic interaction,
information from electron proton elastic scattering data can be related to
charge asymmetry in the annihilation channels $e^++e^-\leftrightarrow \bar p +
p$ and to the ratio of the cross section of elastic electron and positron
scattering on the proton. A compared analysis of the existing data allows to
draw conclusions on the reaction mechanism.


  The recent emergence of deepfakes has brought manipulated and generated
content to the forefront of machine learning research. Automatic detection of
deepfakes has seen many new machine learning techniques, however, human
detection capabilities are far less explored. In this paper, we present results
from comparing the abilities of humans and machines for detecting audio
deepfakes used to imitate someone's voice. For this, we use a web-based
application framework formulated as a game. Participants were asked to
distinguish between real and fake audio samples. In our experiment, 472 unique
users competed against a state-of-the-art AI deepfake detection algorithm for
14912 total of rounds of the game. We find that humans and deepfake detection
algorithms share similar strengths and weaknesses, both struggling to detect
certain types of attacks. This is in contrast to the superhuman performance of
AI in many application areas such as object detection or face recognition.
Concerning human success factors, we find that IT professionals have no
advantage over non-professionals but native speakers have an advantage over
non-native speakers. Additionally, we find that older participants tend to be
more susceptible than younger ones. These insights may be helpful when
designing future cybersecurity training for humans as well as developing better
detection algorithms.


  There has been a significant amount of debate around gender differences in
intellectual functioning, however, most of this research concerns typically
developing populations and lacks research into atypically developing
populations and those with specific learning disabilities (SLD). To address
this, we examined performance on the WISC-IV in children with SLDs (N=1238, N
female= 539, Age range = 7-16 years). We further divided the sample into those
with specific deficits in reading, mathematics, and those with mixed disorder.
Results indicate that gender predicts significant differences in the working
memory index and processing speed index only, indicating a small but
significant female superiority. Results also show different profiles for the
different disorders investigated, with some gender differences emerging. The
most prominent gender difference appears to be in the coding subtest indicating
a female advantage, particularly in those with SLDs with mathematical
difficulties. We discuss the theoretical and clinical implications of the
findings.


  Null shells are a useful geometric construction to study the propagation of
infinitesimally thin concentrations of massless particles or impulsive waves.
In this paper, we determine and study the necessary and sufficient conditions
for the matching of two spacetimes with respective null embedded hypersurfaces
as boundaries. Whenever the matching is possible, it is shown to depend on a
diffeomorphism between the set of null generators in each boundary and a scalar
function, called step function, that determines a shift of points along the
null generators. Generically there exists at most one possible matching but in
some circumstances this is not so. When the null boundaries are totally
geodesic, the point-to-point identification between them introduces a freedom
whose nature and consequences are analyzed in detail. The expression for the
energy-momentum tensor of a general null shell is also derived. Finally, we
find the most general shell (with non-zero energy, energy flux and pressure)
that can be generated by matching two Minkowski regions across a null
hyperplane. This allows us to show how the original Penrose's cut-and-paste
construction fits and connects with the standard matching formalism.


  In this work we explore the information processing inside neural networks
using logistic regression probes \cite{probes} and the saturation metric
\cite{featurespace_saturation}. We show that problem difficulty and neural
network capacity affect the predictive performance in an antagonistic manner,
opening the possibility of detecting over- and under-parameterization of neural
networks for a given task. We further show that the observed effects are
independent from previously reported pathological patterns like the ``tail
pattern'' described in \cite{featurespace_saturation}. Finally we are able to
show that saturation patterns converge early during training, allowing for a
quicker cycle time during analysis


  In this note we study systems with a closed algebra of second class
constraints. We describe a construction of the reduced theory that resembles
the conventional treatment of first class constraints. It suggests, in
particular, to compute the symplectic form on the reduced space by a fiber
integral of the symplectic form on the original space. This approach is then
applied to a class of systems with loop group symmetry. The chiral anomaly of
the loop group action spoils the first class character of the constraints but
not their closure. Proceeding along the general lines described above, we
obtain a 2-form from a fiber (path)integral. This form is not closed as a
relict of the anomaly. Examples of such reduced spaces are provided by D-branes
on group manifolds with WZW action.


  We present a detailed study of the dynamics associated with the ghost sector
of quenched QCD in the Landau gauge, where the relevant dynamical equations are
supplemented with key inputs originating from large-volume lattice simulations.
In particular, we solve the coupled system of Schwinger-Dyson equations that
governs the evolution of the ghost dressing function and the ghost-gluon
vertex, using as input for the gluon propagator lattice data that have been
cured from volume and discretization artifacts. In addition, we explore the
soft gluon limit of the same system, employing recent lattice data for the
three-gluon vertex that enters in one of the diagrams defining the
Schwinger-Dyson equation of the ghost-gluon vertex. The results obtained from
the numerical treatment of these equations are in excellent agreement with
lattice data for the ghost dressing function, once the latter have undergone
the appropriate scale-setting and artifact elimination refinements. Moreover,
the coincidence observed between the ghost-gluon vertex in general kinematics
and in the soft gluon limit reveals an outstanding consistency of physical
concepts and computational schemes.


  Upcoming and existing large-scale surveys of galaxies require accurate
theoretical predictions of the dark matter clustering statistics for thousands
of mock galaxy catalogs. We demonstrate that this goal can be achieve with our
new Parallel Particle-Mesh (PM) Nbody code (PPM-GLAM) at a very low
computational cost. We run about 15,000 simulations with ~2 billion particles
that provide ~1% accuracy of the dark matter power spectra P(k) for
wave-numbers up to k~ 1h/Mpc. Using this large data-set we study the power
spectrum covariance matrix, the stepping stone for producing mock catalogs. In
contrast to many previous analytical and numerical results, we find that the
covariance matrix normalised to the power spectrum C(k,k')/P(k)P(k') has a
complex structure of non-diagonal components. It has an upturn at small k,
followed by a minimum at k=0.1-0.2h/Mpc. It also has a maximum at
k=0.5-0.6h/Mpc. The normalised covariance matrix strongly evolves with
redshift: C(k,k')~delta(t)^alpha P(k)P(k'), where delta is the linear growth
factor and alpha ~ 1-1.25, which indicates that the covariance matrix depends
on cosmological parameters. We also show that waves longer than 1Gpc have very
little impact on the power spectrum and covariance matrix. This significantly
reduces the computational costs and complexity of theoretical predictions:
relatively small volume ~ (1Gpc)^3 simulations capture the necessary properties
of dark matter clustering statistics. All the power spectra obtained from many
thousands of our simulations are publicly available.


  We study analytically and numerically the properties of one-dimensional chain
of cold ions placed in a periodic potential of optical lattice and global
harmonic potential of a trap. In close similarity with the Frenkel-Kontorova
model, a transition from sliding to pinned phase takes place with the increase
of the optical lattice potential for the density of ions incommensurate with
the lattice period. Quantum fluctuations lead to a quantum phase transition and
melting of pinned instanton glass phase at large values of dimensional Planck
constant. The obtained results are also relevant for a Wigner crystal placed in
a periodic potential.