  We derive estimates of the Hessian of two smooth functions defined on
Grassmannian manifold. Based on it, we can derive curvature estimates for
minimal submanifolds in Euclidean space via Gauss map. In this way, the result
for Bernstein type theorem done by Jost and the first author could be improved.


  We consider all degenerate scalar-tensor theories that depend quadratically
on second order derivatives of a scalar field, which we have identified in a
previous work. These theories, whose degeneracy in general ensures the absence
of Ostrogradski instability, include the quartic Horndenski Lagrangian as well
as its quartic extension beyond Horndeski, but also other families of
Lagrangians. We study how all these theories transform under general
conformal-disformal transformations and find that they can be separated into
three main classes that are stable under these transformations. This leads to a
complete classification modulo conformal-disformal transformations. Finally, we
show that these higher order theories include mimetic gravity and some
particular khronometric theories. They also contain theories that do not
correspond, to our knowledge, to already studied theories, even up to field
redefinitions.


  When machine-learning algorithms are used in high-stakes decisions, we want
to ensure that their deployment leads to fair and equitable outcomes. This
concern has motivated a fast-growing literature that focuses on diagnosing and
addressing disparities in machine predictions. However, many machine
predictions are deployed to assist in decisions where a human decision-maker
retains the ultimate decision authority. In this article, we therefore consider
in a formal model and in a lab experiment how properties of machine predictions
affect the resulting human decisions. In our formal model of statistical
decision-making, we show that the inclusion of a biased human decision-maker
can revert common relationships between the structure of the algorithm and the
qualities of resulting decisions. Specifically, we document that excluding
information about protected groups from the prediction may fail to reduce, and
may even increase, ultimate disparities. In the lab experiment, we demonstrate
how predictions informed by gender-specific information can reduce average
gender disparities in decisions. While our concrete theoretical results rely on
specific assumptions about the data, algorithm, and decision-maker, and the
experiment focuses on a particular prediction task, our findings show more
broadly that any study of critical properties of complex decision systems, such
as the fairness of machine-assisted human decisions, should go beyond focusing
on the underlying algorithmic predictions in isolation.


  Projecting fields between different meshes commonly arises in computational
physics. This operation requires a supermesh construction and its computational
cost is proportional to the number of cells of the supermesh $n$. Given any two
quasi-uniform meshes of $n_A$ and $n_B$ cells respectively, we show under
standard assumptions that n is proportional to $n_A + n_B$. This result
substantially improves on the best currently available upper bound on $n$ and
is fundamental for the analysis of algorithms that use supermeshes.


  This paper considers the joint antenna selection (AS) problem for a classical
two-user non-orthogonal multiple access (NOMA) network where both the base
station and users are equipped with multiple antennas. Since the
exhaustive-search-based optimal AS scheme is computationally prohibitive when
the number of antennas is large, two computationally efficient joint AS
algorithms, namely max-min-max AS (AIA-AS) and max-max-max AS (A$^3$-AS), are
proposed to maximize the system sum-rate. The asymptotic closed-form
expressions for the average sum-rates for both AIA-AS and A$^3$-AS are derived
in the high signal-to-noise ratio (SNR) regime, respectively. Numerical results
demonstrate that both AIA-AS and A$^3$-AS can yield significant performance
gains over comparable schemes. Furthermore, AIA-AS can provide better user
fairness, while the A$^3$-AS scheme can achieve the near-optimal sum-rate
performance.


  Machine learning models have been shown vulnerable to adversarial attacks
launched by adversarial examples which are carefully crafted by attacker to
defeat classifiers. Deep learning models cannot escape the attack either. Most
of adversarial attack methods are focused on success rate or perturbations
size, while we are more interested in the relationship between adversarial
perturbation and the image itself. In this paper, we put forward a novel
adversarial attack based on contour, named FineFool. Finefool not only has
better attack performance compared with other state-of-art white-box attacks in
aspect of higher attack success rate and smaller perturbation, but also capable
of visualization the optimal adversarial perturbation via attention on object
contour. To the best of our knowledge, Finefool is for the first time combines
the critical feature of the original clean image with the optimal perturbations
in a visible manner. Inspired by the correlations between adversarial
perturbations and object contour, slighter perturbations is produced via
focusing on object contour features, which is more imperceptible and difficult
to be defended, especially network add-on defense methods with the trade-off
between perturbations filtering and contour feature loss. Compared with
existing state-of-art attacks, extensive experiments are conducted to show that
Finefool is capable of efficient attack against defensive deep models.


  We performed simulations of the resolution function of the 4SEASONS
spectrometer at J-PARC by using the Monte Carlo simulation package McStas. The
simulations showed reasonably good agreement with analytical calculations of
energy and momentum resolutions by using a simplified description. We
implemented new functionalities in Utsusemi, the standard data analysis tool
used in 4SEASONS, to enable visualization of the simulated resolution function
and predict its shape for specific experimental configurations.


  We introduce a data-driven approach to aid the repairing and conservation of
archaeological objects: ORGAN, an object reconstruction generative adversarial
network (GAN). By using an encoder-decoder 3D deep neural network on a GAN
architecture, and combining two loss objectives: a completion loss and an
Improved Wasserstein GAN loss, we can train a network to effectively predict
the missing geometry of damaged objects. As archaeological objects can greatly
differ between them, the network is conditioned on a variable, which can be a
culture, a region or any metadata of the object. In our results, we show that
our method can recover most of the information from damaged objects, even in
cases where more than half of the voxels are missing, without producing many
errors.


  We consider (for the first time) the ratios of doubly heavy baryon masses
(spin 3/2 over spin 1/2 and SU(3) mass-splittings) using double ratios of sum
rules (DRSR), which are more accurate than the usual simple ratios often used
in the literature for getting the hadron masses. In general, our results agree
and compete in precision with potential model predictions. In our approach, the
alpha_s corrections induced by the anomalous dimensions of the correlators are
the main sources of the Xi^*_{QQ}- Xi_{QQ} mass-splittings, which seem to
indicate a 1/M_Q behaviour and can only allow the electromagnetic decay
Xi^*_{QQ} to Xi_{QQ}+ gamma but not to Xi_{QQ}+ pi. Our results also show that
the SU(3) mass-splittings are (almost) independent of the spin of the baryons
and behave approximately like 1/M_Q, which could be understood from the QCD
expressions of the corresponding two-point correlator. Our results can improved
by including radiative corrections to the SU(3) breaking terms and can be
tested, in the near future, at Tevatron and LHCb.


  In this paper we study random walks on the hypergroup of conics in finite
fields. We investigate the behavior of random walks on this hypergroup, the
equilibrium distribution and the mixing times. We use the coupling method to
show that the mixing time of random walks on hypergroup of conics is only
linear.


  This work is the final version of my master thesis. Many, but not all of its
key results are already available as a preprint with Chiara Esposito and Stefan
Waldmann on arxiv.org under the title "Convergence of the Gutt Star Product",
which has been submitted to a journal. The main goal of this work is to
understand a certain deformation of the commutative product on the symmetric
tensor algebra over a given (locally convex) Lie algebra in a topological
setting. Therefore, the tensor algebra over the Lie algebra is topologized in a
locally convex manner. This finally provides a new example of a strict
deformation quantization, i.e. a continuous star product. The related formal
star product was found by Simone Gutt and is therefore called the Gutt star
product. Together with this star product, the symmetric tensor algebra is
isomorphic to the universal enveloping algebra of the Lie algebra. Hence, this
work also presents an explicit locally convex topology on the universal
enveloping algebra of certain locally convex Lie algebras. Since this
construction has good functorial properties, it is also an interesting object
of study not only in quantization and quantum group theory, but also in Lie
theory.


  Lightcurves of many classical novae deviate from the canonical "fast rise -
smooth decline" pattern and display complex variability behavior. We present
the first TESS-space-photometry-based investigation of this phenomenon. We use
TESS Sector 41 full-frame images to extract a lightcurve of the slow Galactic
nova V606 Vul that erupted nine days prior to the start of the TESS
observations. The lightcurve covers the first of two major peaks of V606 Vul
that was reached 19 days after the start of the eruption. The nova reached its
brightest visual magnitude V=9.9 in its second peak 64 days after the eruption
onset, following the completion of Sector 41 observations. To increase the
confidence level of the extracted lightcurve, we performed the analysis using
four different codes implementing the aperture photometry (Lightkurve, VaST)
and image subtraction (TESSreduce, tequila_shots) and find good agreement
between them. We performed ground-based photometric and spectroscopic
monitoring to complement the TESS data. The TESS lightcurve reveals two
features: periodic variations (0.12771d, 0.01mag average peak-to-peak
amplitude) that disappeared when the source was within 1mag of peak optical
brightness and a series of isolated mini-flares (with peak-to-peak amplitudes
of up to 0.5mag) appearing at seemingly random times. We interpret the periodic
variations as the result of azimuthal asymmetry of the photosphere engulfing
the nova-hosting binary that was distorted by and rotating with the binary.
Whereas we use spectra to associate the two major peaks in the nova lightcurve
with distinct episodes of mass ejection, the origin of mini-flares remains
elusive.


  We generalize the dynamical phase diagram of a Bardeen-Cooper-Schrieffer
condensate, considering attractive to repulsive, i.e., critical quenches (CQ)
and a non-constant density of states (DOS). We show that different synchronized
Higgs dynamical phases can be stabilized, associated with singularities in the
density of states (DOS) and different quench protocols. In particular, the CQ
can stabilize an overlooked high-frequency Higgs dynamical phase related to the
upper edge of the fermionic band. For a compensated Dirac system we find a
Dirac-Higgs mode associated with the cusp singularity at the Fermi level, and
we show that synchronized phases become more pervasive across the phase
diagram. The relevance of these remarkable phenomena and their realization in
ensembles of fermionic cold atoms confined in optical lattices is also
discussed.


  We have monitored four fields containing nine previously identified members
of the MBM 12 association to search for photometric variability and periodicity
in these pre-main sequence stars. Seven of the nine are found to be variable
and definite periodicity (of 1.2, 2.6 and 6.2 days) is found for three of them,
including the classical T Tauri star LkH-alpha 264. Two other members are
possibly periodic but each requires confirmation. In addition, a "field" star
that is associated with the X-ray source RX J0255.9+2005 was discovered to be a
variable with a period of 4.2 days. Our results indicate that the photometric
variability characteristics of the known MBM 12 association members are typical
of what is found in ~few My old stellar groups such as IC 348, supporting
arguments for a similar age. In particular, there is a mix of periodic and
non-periodic variables with typical amplitudes (in Cousins I) of 0.1-0.5 mag,
in addition to a small number of larger amplitude variables. The periods, as a
group, are somewhat shorter than in IC 348 but when allowance is made for the
known dependence of period on mass in pre-main sequence stars the difference
may not be significant. Our data confirm and illustrate the value of
photometric monitoring as a tool for identifying likely association members and
for studying rotation in extremely young stellar groups.


  Fully Programmable Valve Array (FPVA) has emerged as a new architecture for
the next-generation flow-based microfluidic biochips. This 2D-array consists of
regularly-arranged valves, which can be dynamically configured by users to
realize microfluidic devices of different shapes and sizes as well as
interconnections. Additionally, the regularity of the underlying structure
renders FPVAs easier to integrate on a tiny chip. However, these arrays may
suffer from various manufacturing defects such as blockage and leakage in
control and flow channels. Unfortunately, no efficient method is yet known for
testing such a general-purpose architecture. In this paper, we present a novel
formulation using the concept of flow paths and cut-sets, and describe an
ILP-based hierarchical strategy for generating compact test sets that can
detect multiple faults in FPVAs. Simulation results demonstrate the efficacy of
the proposed method in detecting manufacturing faults with only a small number
of test vectors.


  The pentagram map takes a planar polygon $P$ to a polygon $P'$ whose vertices
are the intersection points of consecutive shortest diagonals of $P$. The orbit
of a convex polygon under this map is a sequence of polygons which converges
exponentially to a point. Furthermore, as recently proved by Glick, coordinates
of that limit point can be computed as an eigenvector of a certain operator
associated with the polygon. In the present paper we show that Glick's operator
can be interpreted as the infinitesimal monodromy of the polygon. Namely, there
exists a certain natural infinitesimal perturbation of a polygon, which is
again a polygon but in general not closed; what Glick's operator measures is
the extent to which this perturbed polygon does not close up.


  Quantum criticality provides an important route to revealing universal
non-equilibrium behaviour. A canonical example of a quantum critical point is
the Bose-Hubbard model, which we study under the application of an electric
field. A Boltzmann transport formalism and $\epsilon$-expansion are used to
obtain the non-equilibrium conductivity and current noise. This approach allows
us to explicitly identify how a universal non-equilibrium steady state is
maintained, by identifying the rate-limiting step in balancing Joule heating
and dissipation to a heat bath. It also reveals that the non-equilibrium
distribution function is very far from a thermal distribution.


  We study the spin-dependent transport of spin-1/2 electrons through an
interferometer made of two elongated quantum dots or quantum nanowires, which
are subject to both an Aharonov-Bohm flux and (Rashba and Dresselhaus)
spin-orbit interactions. Similar to the diamond interferometer proposed in our
previous papers [Phys. Rev. B {\bf 84}, 035323 (2011); Phys. Rev. B {\bf 87},
205438 (2013)], we show that the double-dot interferometer can serve as a
perfect spin filter due to a spin interference effect. By appropriately tuning
the external electric and magnetic fields which determine the Aharonov-Casher
and Aharonov-Bohm phases, and with some relations between the various hopping
amplitudes and site energies, the interferometer blocks electrons with a
specific spin polarization, independent of their energy. The blocked
polarization and the polarization of the outgoing electrons is controlled
solely by the external electric and magnetic fields and do not depend on the
energy of the electrons. Furthermore, the spin filtering conditions become
simpler in the linear-response regime, in which the electrons have a fixed
energy. Unlike the diamond interferometer, spin filtering in the double-dot
interferometer does not require high symmetry between the hopping amplitudes
and site energies of the two branches of the interferometer and thus may be
more appealing from an experimental point of view.


  This paper develops a Tikhonov regularization theory for nonlinear ill-posed
operator equations in Banach spaces. As the main challenge, we consider the
so-called oversmoothing state in the sense that the Tikhonov penalization is
not able to capture the true solution regularity and leads to the infinite
penalty value in the solution. We establish a vast extension of the Hilbertian
convergence theory through the use of invertible sectorial operators from the
holomorphic functional calculus and the prominent theory of interpolation
scales in Banach spaces. Applications of the proposed theory involving
$\ell^1$, Bessel potential spaces, and Besov spaces are discussed.


  We study the equilibrium properties of an Ising model on a disordered random
network where the disorder can be quenched or annealed. The network consists of
four-fold coordinated sites connected via variable length one-dimensional
chains. Our emphasis is on nonuniversal properties and we consider the
transition temperature and other equilibrium thermodynamic properties,
including those associated with one dimensional fluctuations arising from the
chains. We use analytic methods in the annealed case, and a Monte Carlo
simulation for the quenched disorder. Our objective is to study the difference
between quenched and annealed results with a broad random distribution of
interaction parameters. The former represents a situation where the time scale
associated with the randomness is very long and the corresponding degrees of
freedom can be viewed as frozen, while the annealed case models the situation
where this is not so. We find that the transition temperature and the entropy
associated with one dimensional fluctuations are always higher for quenched
disorder than in the annealed case. These differences increase with the
strength of the disorder up to a saturating value. We discuss our results in
connection to physical systems where a broad distribution of interaction
strengths is present.


  The postulational basis of classical thermodynamics has been expanded to
incorporate equilibrium fluctuations. The main additional elements of the
proposed thermodynamic theory are the concept of quasi-equilibrium states, a
definition of non-equilibrium entropy, a fundamental equation of state in the
entropy representation, and a fluctuation postulate describing the probability
distribution of macroscopic parameters of an isolated system. Although these
elements introduce a statistical component that does not exist in classical
thermodynamics, the logical structure of the theory is different from that of
statistical mechanics and represents an expanded version of thermodynamics.
Based on this theory, we present a regular procedure for calculations of
equilibrium fluctuations of extensive parameters, intensive parameters and
densities in systems with any number of fluctuating parameters. The proposed
fluctuation formalism is demonstrated by four applications: (1) derivation of
the complete set of fluctuation relations for a simple fluid in three different
ensembles; (2) fluctuations in finite-reservoir systems interpolating between
the canonical and micro-canonical ensembles; (3) derivation of fluctuation
relations for excess properties of grain boundaries in binary solid solutions,
and (4) derivation of the grain boundary width distribution for pre-melted
grain boundaries in alloys. The last two applications offer an efficient
fluctuation-based approach to calculations of interface excess properties and
extraction of the disjoining potential in pre-melted grain boundaries. Possible
future extensions of the theory are outlined.


  We study a spin-5/2 antiferromagnetic trimerized chain substance SrMn3P4O14
using neutron powder diffraction experiments. The coplanar spiral magnetic
structure appears below T_N1 = 2.2(1) K. Values of several magnetic structure
parameters change rapidly at T_N2 = 1.75(5) K, indicating another phase
transition, although the magnetic structures above and below T_N2 are the
qualitatively same. The spiral magnetic structure can be explained by
frustration between nearest-neighbor and next-nearest-neighbor exchange
interactions in the trimerized chains.


  Electroweak interactions need three Nambu-Goldstone bosons to provide a mass
to the W and the Z gauge bosons but they also need an ultra-violet moderator or
new physics to unitarize the gauge boson scattering amplitudes. In this talk, I
will present various recent models of physics at the Fermi scale: several
deformations of the Minimal Supersymmetric Standard Model, Little Higgs models,
holographic composite Higgs models, 5D Higgsless models.


  This report describes a 2nd place solution of the detection challenge which
is held within CVPR 2020 Retail-Vision workshop. Instead of going further
considering previous results this work mainly aims to verify previously
observed takeaways by re-experimenting. The reliability and reproducibility of
the results are reached by incorporating a popular object detection toolbox -
MMDetection. In this report, I firstly represent the results received for
Faster-RCNN and RetinaNet models, which were taken for comparison in the
original work. Then I describe the experiment results with more advanced
models. The final section reviews two simple tricks for Faster-RCNN model that
were used for my final submission: changing default anchor scale parameter and
train-time image tiling. The source code is available at
https://github.com/tyomj/product_detection.


  Latent factor GARCH models are difficult to estimate using Bayesian methods
because standard Markov chain Monte Carlo samplers produce slowly mixing and
inefficient draws from the posterior distributions of the model parameters.
This paper describes how to apply the particle Gibbs algorithm to estimate
factor GARCH models efficiently. The method has two advantages over previous
approaches. First, it generalises in a straightfoward way to models with
multiple factors and to various members of the GARCH family. Second, it scales
up well as the dimension of the o, bservation vector increases.


  Several quantum key distribution (QKD) protocols employ iterative sifting.
After each quantum transmission round, Alice and Bob disclose part of their
setting information (including their basis choices) for the detected signals.
The quantum phase of the protocol then ends when the numbers of detected
signals per basis exceed certain pre-agreed threshold values. Recently,
however, Pfister et al. [New J. Phys. 18 053001 (2016)] showed that iterative
sifting makes QKD insecure, especially in the finite key regime, if the
parameter estimation for privacy amplification uses the random sampling theory.
This implies that a number of existing finite key security proofs could be
flawed and cannot guarantee security. Here, we solve this serious problem by
showing that the use of Azuma's inequality for parameter estimation makes QKD
with iterative sifting secure again. This means that the existing protocols
whose security proof employs this inequality remain secure even if they employ
iterative sifting. Also, our results highlight a fundamental difference between
the random sampling theorem and Azuma's inequality in proving security.


  Let R be the set of isomorphism classes of ideals in the Weyl algebra
$A=A_{1}$, and let C be the set of isomorphism classes of triples (V; X, Y),
where V is a finite-dimensional (complex) vector space, and X, Y are
endomorphisms of V such that [X,Y]+I has rank 1. Following a suggestion of L.
Le Bruyn, we define a map $\theta: R \to C$ by appropriately extending an ideal
of A to a sheaf over a quantum projective plane, and then using standard
methods of homological algebra. We prove that $\theta$ is inverse to a
bijection $\omega: C \to R$ constructed in \cite{BW} by a completely different
method. The main step in the proof is to show that $\theta$ is equivariant with
respect to natural actions of the group G=Aut(A) on R and C: for that we have
to study also the extensions of an ideal to certain weighted quantum projective
planes. Along the way, we find an elementary description of \theta.


  We survey the design of elections that are resilient to attempted
interference by third parties. For example, suppose votes have been cast in an
election between two candidates, and then each vote is randomly changed with a
small probability, independently of the other votes. It is desirable to keep
the outcome of the election the same, regardless of the changes to the votes.
It is well known that the US electoral college system is about 5 times more
likely to have a changed outcome due to vote corruption, when compared to a
majority vote. In fact, Mossel, O'Donnell and Oleszkiewicz proved in 2005 that
the majority voting method is most stable to this random vote corruption, among
voting methods where each person has a small influence on the election. We
discuss some recent progress on the analogous result for elections between more
than two candidates. In this case, plurality should be most stable to
corruption in votes. We also survey results on adversarial election
manipulation (where an adversary can select particular votes to change, perhaps
in a non-random way), and we briefly discuss ranked choice voting methods
(where a vote is a ranked list of candidates).


  A singly heavy baryon can be viewed as $N_c-1$ ($N_c$ as the number of
colors) light valence quarks bound by the pion mean fields that are created by
the presence of the $N_c-1$ valence quarks self-consistently, while the heavy
quark inside a singly heavy baryon is regarded as a static color source. We
investigate how the pion mean fields are created by the presence of $N_c$,
$N_c-1$, and $N_c-2$ light valence quarks, which correspond to the systems of
light baryons, singly heavy baryons, and doubly heavy baryons. As the number of
color decreases from $N_c$ to $N_c-1$, the pion mean fields undergo changes. As
a result, the valence-quark contributions to the moments of inertia of the
soliton become larger than the case of the $N_c$ valence quarks, whereas the
sea-quark contributions decrease systematically. On the other hand, the
presence of the $N_c-2$ valence quarks is not enough to produce the strong pion
mean fields, which leads to the fact that the classical soliton can not be
formed. It indicates that the pion mean-field approach is not suitable to
describe doubly heavy baryons. We show that the mass spectra of the singly
heavy baryons are better described by the improved pion mean fields, compared
with the previous work in which the pion mean fields are assumed to be intact
with $N_c$ varied.


  The mechanism underlying magnetoreception has long eluded explanation. A
popular hypothesis attributes this sense to the quantum coherent spin dynamics
of spin-selective recombination reactions of radical pairs in the protein
cryptochrome. However, concerns about the validity of the hypothesis have been
raised as unavoidable inter-radical interactions, such as strong
electron-electron dipolar coupling, appear to suppress its sensitivity. We
demonstrate that this can be overcome by driving the spin system through a
modulation of the inter-radical distance. It is shown that this dynamical
process markedly enhances geomagnetic field sensitivity in strongly coupled
radical pairs via a Landau-Zener type transition between singlet and triplet
states. These findings suggest that a "live" harmonically driven
magnetoreceptor can be more sensitive than its "dead" static counterpart.


  In this paper we present a formula for Macdonald's polynomials for the root
system A(n-1) which arises from the representation theory of quantum sl(n).
This formula expresses Macdonald's polynomials via (weighted) traces of
intertwining operators between certain modules over quantum sl(n). We also
describe the commutative system of Macdonald's difference operators using the
generators of the center of the quantum universal enveloping algebra, and use
this description to prove a trace formula for generic eigenfunctions of these
operators. These functions are generalized q-hypergeometric functions which are
related to solutions of the quantum Knizhnik-Zamolodchikov equations.


  Mixture of autoregressions (MoAR) models provide a model-based approach to
the clustering of time series data. The maximum likelihood (ML) estimation of
MoAR models requires the evaluation of products of large numbers of densities
of normal random variables. In practical scenarios, these products converge to
zero as the length of the time series increases, and thus the ML estimation of
MoAR models becomes infeasible without the use of numerical tricks. We propose
a maximum pseudolikelihood (MPL) estimation approach as an alternative to the
use of numerical tricks. The MPL estimator is proved to be consistent and can
be computed via an EM (expectation--maximization) algorithm. Simulations are
used to assess the performance of the MPL estimator against that of the ML
estimator in cases where the latter was able to be calculated. An application
to the clustering of time series data arising from a resting-state fMRI
experiment is presented as a demonstration of the methodology.


  Realizing the theoretical promise of quantum computers will require
overcoming decoherence. Here we demonstrate numerically that high fidelity
quantum gates are possible within a framework of quantum dynamical decoupling.
Orders of magnitude improvement in the fidelities of a universal set of quantum
gates, relative to unprotected evolution, is achieved over a broad range of
system-environment coupling strengths, using recursively constructed
(concatenated) dynamical decoupling pulse sequences.


  We show gapped critical environment could remarkably prevent an enhanced
decay of decoherence factor and quantum correlations at the critical point,
which is nontrivially different from the ones in a gapless critical environment
(Quan, et.al Phys. Rev. Lett. \textbf{96}, 140604 (2006)). The quantum
correlations display very fast decaying to their local minimum at the critical
point while maximum decaying occurs away from this point. In particular, our
results imply that collapse of decoherence factor is not indicator of a quantum
phase transition of environment as opposed to what happens in a gapless
criticallity. In the week coupling regime, the relaxation time, at which the
quantum correlations touch rapidly local minima, shows a power-law singularity
as a function of gap. Furthermore, quantum correlations decay exponentially
with second power of relaxation time. Our results are important for a better
understanding and characterisation of gap critical environment and its ability
as entanglers in open quantum systems.


  This study establishes that SMC, LMC and Milky Way extinction curves obey the
same extinction law which depends on the 2200A bump size and one parameter, and
generalizes the Cardelli, Clayton and Mathis (1989) relationship. This suggests
that extinction in all three galaxies is of the same nature. The role of linear
reddening laws over all the visible/UV wavelength range, particularly important
in the SMC but also present in the LMC and in the Milky Way, is also
highlighted and discussed.


  Real-world graph applications, such as advertisements and product
recommendations make profits based on accurately classify the label of the
nodes. However, in such scenarios, there are high incentives for the
adversaries to attack such graph to reduce the node classification performance.
Previous work on graph adversarial attacks focus on modifying existing graph
structures, which is infeasible in most real-world applications. In contrast,
it is more practical to inject adversarial nodes into existing graphs, which
can also potentially reduce the performance of the classifier. In this paper,
we study the novel node injection poisoning attacks problem which aims to
poison the graph. We describe a reinforcement learning based method, namely
NIPA, to sequentially modify the adversarial information of the injected nodes.
We report the results of experiments using several benchmark data sets that
show the superior performance of the proposed method NIPA, relative to the
existing state-of-the-art methods.


  The gravitational Chern-Simons term coupled to an evolving axion is known to
generate lepton number through the gravitational anomaly. We examine this
leptogenesis scenario in the presence of the Gauss-Bonnet term over and above
the gravitational Chern-Simons term. We find that the lepton production can be
exponentially enhanced. The Gauss-Bonnet term creates CP-violating instability
of gravitational waves that may appear transiently after inflation, and during
the period of instability elliptically polarized gravitational waves are
exponentially amplified at sub-horizon scales. This instability does not affect
the spectrum of the cosmic microwave background as it occurs at much shorter
length scales. In a typical scenario based on natural inflation, the observed
baryon asymmetry of the Universe corresponds to the UV cutoff scale at
$10^{14-16}$ GeV.


  Nonuniformities in the imaging characteristics of modern image sensors are a
primary factor in the push to develop a pixel-level generalization of the
photon transfer characterization method. In this paper, we seek to develop a
body of theoretical results leading toward a comprehensive approach for
tackling the biggest obstacle in the way of this goal: a means of pixel-level
conversion gain estimation. This is accomplished by developing an estimator for
the reciprocal-difference of normal variances and then using this to construct
a novel estimator of the conversion gain. The first two moments of this
estimator are derived and used to construct exact and approximate confidence
intervals for its absolute relative bias and absolute coefficient of variation,
respectively. A means of approximating and computing optimal sample sizes are
also discussed and used to demonstrate the process of pixel-level conversion
gain estimation for a real image sensor.


  Vaccine hesitancy is considered as one main cause of the stagnant uptake
ratio of COVID-19 vaccines in Europe and the US where vaccines are sufficiently
supplied. Fast and accurate grasp of public attitudes toward vaccination is
critical to address vaccine hesitancy, and social media platforms have proved
to be an effective source of public opinions. In this paper, we describe the
collection and release of a dataset of tweets related to COVID-19 vaccines.
This dataset consists of the IDs of 2,198,090 tweets collected from Western
Europe, 17,934 of which are annotated with the originators' vaccination
stances. Our annotation will facilitate using and developing data-driven models
to extract vaccination attitudes from social media posts and thus further
confirm the power of social media in public health surveillance. To lay the
groundwork for future research, we not only perform statistical analysis and
visualisation of our dataset, but also evaluate and compare the performance of
established text-based benchmarks in vaccination stance extraction. We
demonstrate one potential use of our data in practice in tracking the temporal
changes of public COVID-19 vaccination attitudes.


  Partial domain adaptation aims to adapt knowledge from a larger and more
diverse source domain to a smaller target domain with less number of classes,
which has attracted appealing attention. Recent practice on domain adaptation
manages to extract effective features by incorporating the pseudo labels for
the target domain to better fight off the cross-domain distribution
divergences. However, it is essential to align target data with only a small
set of source data. In this paper, we develop a novel Discriminative
Cross-Domain Feature Learning (DCDF) framework to iteratively optimize target
labels with a cross-domain graph in a weighted scheme. Specifically, a weighted
cross-domain center loss and weighted cross-domain graph propagation are
proposed to couple unlabeled target data to related source samples for
discriminative cross-domain feature learning, where irrelevant source centers
will be ignored, to alleviate the marginal and conditional disparities
simultaneously. Experimental evaluations on several popular benchmarks
demonstrate the effectiveness of our proposed approach on facilitating the
recognition for the unlabeled target domain, through comparing it to the
state-of-the-art partial domain adaptation approaches.


  We consider dimensionality reduction methods for face recognition in a
supervised setting, using an image-as-matrix representation. A common procedure
is to project image matrices into a smaller space in which the recognition is
performed. These methods are often called "two-dimensional" in the literature
and there exist counterparts that use an image-as-vector representation. When
two face images are close to each other in the input space they may remain
close after projection - but this is not desirable in the situation when these
two images are from different classes, and this often affects the recognition
performance. We extend a previously developed `repulsion Laplacean' technique
based on adding terms to the objective function with the goal or creation a
repulsion energy between such images in the projected space. This scheme, which
relies on a repulsion graph, is generic and can be incorporated into various
two-dimensional methods. It can be regarded as a multilinear generalization of
the repulsion strategy by Kokiopoulou and Saad [Pattern Recog., 42 (2009), pp.
2392--2402]. Experimental results demonstrate that the proposed methodology
offers significant recognition improvement relative to the underlying
two-dimensional methods.


  In this paper, a grid of the binary evolution models are calculated for the
study of blue straggler (BS) population in intermediate age ($\log
Age$=7.85-8.95) star clusters. The BS formation via mass transfer and merging
is studied systematically using our models. Both Case A and B close binary
evolutionary tracks are calculated in a large range of parameters. The results
show that BSs formed via Case B are generally bluer and even more luminous than
those produced by Case A. Furthermore, the larger range in orbital separations
of Case B models provide a probability of producing more BSs than Case A. Based
on the grid of models, several Monte-Carlo simulations of BS populations in the
clusters in the age range are carried out. The results show that BSs formed via
different channels populate different areas in color magnitude diagram(CMD).
The locations of BSs in CMD for a number of clusters are compared to our
simulations as well. In order to investigate the influence of mass transfer
efficiency in the models and simulations, a set of models are also calculated
by implementing a constant mass transfer efficiency, $\beta$=0.5 during Roche
lobe overflow (Case A binary evolution excluded). The result shows BSs can be
formed via mass transfer at any given age in both cases. However, the
distributions of the BS populations on CMD are different.


  Reconfigurable interaction induces another dimension of nondeterminism in
concurrent systems which makes it hard to reason about the different choices of
the system from a global perspective. Namely, (1) choices that correspond to
concurrent execution of independent events; and (2) forced interleaving (or
scheduling) due to reconfiguration. Unlike linear order semantics of
computations, partial order semantics recovers information about the
interdependence among the different events for fixed interaction, but still is
unable to handle reconfiguration. We introduce glued partial orders as a way to
capture reconfiguration. Much like partial orders capture all possible choices
for fixed systems, glued partial orders capture all possible choices alongside
reconfiguration. We show that a glued partial order is sufficient to correctly
capture all partial order computations that differ in forced interleaving due
to reconfiguration. Furthermore, we show that computations belonging to
different glued partial orders are only different due to non-determinism.


  Conversational dense retrieval has shown to be effective in conversational
search. However, a major limitation of conversational dense retrieval is their
lack of interpretability, hindering intuitive understanding of model behaviors
for targeted improvements. This paper presents CONVINV, a simple yet effective
approach to shed light on interpretable conversational dense retrieval models.
CONVINV transforms opaque conversational session embeddings into explicitly
interpretable text while faithfully maintaining their original retrieval
performance as much as possible. Such transformation is achieved by training a
recently proposed Vec2Text model based on the ad-hoc query encoder, leveraging
the fact that the session and query embeddings share the same space in existing
conversational dense retrieval. To further enhance interpretability, we propose
to incorporate external interpretable query rewrites into the transformation
process. Extensive evaluations on three conversational search benchmarks
demonstrate that CONVINV can yield more interpretable text and faithfully
preserve original retrieval performance than baselines. Our work connects
opaque session embeddings with transparent query rewriting, paving the way
toward trustworthy conversational search.


  We prove non-hyperbolicity of primitive symplectic varieties with $b_2 \geq
5$ that satisfy the rational SYZ conjecture. If in addition $b_2 \geq 7$, we
establish that the Kobayashi pseudometric vanishes identically. This in
particular applies to all currently known examples of irreducible symplectic
manifolds and thereby completes the results by Kamenova--Lu--Verbitsky. The key
new contribution is that a projective primitive symplectic variety with a
Lagrangian fibration has vanishing Kobayashi pseudometric. The proof uses
ergodicity, birational contractions, and cycle spaces.


  Cell fate determination, the process through which cells commit to
differentiated states is commonly mediated by gene regulatory motifs with
mutually exclusive expression states. The classical deterministic picture for
cell fate determination includes bistability and hysteresis, which enables the
persistence of the acquired cellular state after withdrawal of the stimulus,
ensuring a robust cellular response. However, the stochasticity inherent to
gene expression dynamics is not compatible with hysteresis, since the
stationary solution of the governing Chemical Master Equation does not depend
on the initial conditions. In this work, we provide a quantitative description
of a transient hysteresis phenomenon that reconciles experimental evidence of
hysteretic behaviour in gene regulatory networks with their inherent
stochasticity. Under sufficiently slow dynamics, the dependency of the
non-stationary solutions on the initial state of the cells can lead to what we
denote here as transient hysteresis. To quantify this phenomenon, we provide an
estimate of the convergence rate to the equilibrium. We also introduce the
equation of a natural landscape capturing the evolution of the system that,
unlike traditional cell fate potential landscapes, is compatible with the
notion of coexistence at the microscopic level.


  Traditional anti-jamming techniques like spread spectrum, adaptive power/rate
control, and cognitive radio, have demonstrated effectiveness in mitigating
jamming attacks. However, their robustness against the growing complexity of
internet-of-thing (IoT) networks and diverse jamming attacks is still limited.
To address these challenges, machine learning (ML)-based techniques have
emerged as promising solutions. By offering adaptive and intelligent
anti-jamming capabilities, ML-based approaches can effectively adapt to dynamic
attack scenarios and overcome the limitations of traditional methods. In this
paper, we propose a deep reinforcement learning (DRL)-based approach that
utilizes state input from realistic wireless network interface cards. We train
five different variants of deep Q-network (DQN) agents to mitigate the effects
of jamming with the aim of identifying the most sample-efficient, lightweight,
robust, and least complex agent that is tailored for power-constrained devices.
The simulation results demonstrate the effectiveness of the proposed DRL-based
anti-jamming approach against proactive jammers, regardless of their jamming
strategy which eliminates the need for a pattern recognition or jamming
strategy detection step. Our findings present a promising solution for securing
IoT networks against jamming attacks and highlights substantial opportunities
for continued investigation and advancement within this field.


  Transport and magnetic studies of Ca3Ru2O7 for temperatures ranging from 0.4
K to 56 K and magnetic fields, B, up to 45 T leads to strikingly different
behavior when the field is applied along the different crystal axes. A
ferromagnetic (FM) state with full spin polarization is achieved for B||a-axis,
but colossal magnetoresistance is realized only for B||b-axis. For B||c-axis,
Shubnikov-de Haas oscillations are observed and followed by a less resistive
state than for B||a. Hence, in contrast to standard colossal magnetoresistive
materials, the FM phase is the least favorable for electron hopping. These
properties together with highly unusual spin-charge-lattice coupling near the
Mott transition (48 K) are driven by the orbital degrees of freedom.


  A microscopic density functional theory is used to investigate the adsorption
of short chains on attractive solid surfaces. We analyze the structure of the
adsorbed fluid and investigate how the wetting transition changes with the
change of the chain length and with relative strength of the fluid-solid
interaction. End segments adsorb preferentially in the first adsorbed layer
whereas the concentration of the middle segments is enhanced in the second
layer. We observe that the wetting temperature rescaled by the bulk critical
temperature decreases with an increase of the chain length. For longer chains
this temperature reaches a plateau. For the surface critical temperature an
inverse effect is observed, i.e. the surface critical temperature increases
with the chain length and then attains a plateau. These findings may serve as a
quick estimate of the wetting and surface critical temperatures for fluids of
longer chain lengths.


  In this paper, we deal with some specific domains of applications to game
theory. This is one of the major class of models in the new approaches of
modelling in the economic domain. For that, we use genetic automata which allow
to buid adaptive strategies for the players. We explain how the automata-based
formalism proposed - matrix representation of automata with multiplicities -
allows to define a semi-distance between the strategy behaviors. With that
tools, we are able to generate an automatic processus to compute emergent
systems of entities whose behaviors are represented by these genetic automata.


  Periodically kicked Floquet systems such as the kicked rotor are a
paradigmatic and illustrative simple model of chaos. For non-integrable quantum
dynamics there are several diagnostic measures of the presence of (or the
transition to) chaotic behaviour including the Loschmidt echo, Wigner function,
spectral function and OTOC. We analytically compute these measures in terms of
the eigensystem of the unitary Floquet operator of driven quantum systems. We
also determine equivalent alternative forms of the eigen-equation of the
Floquet operator for the quantum kicked rotor. For a simpler integrable variant
of the kicked rotor, we give a representation theoretic derivation of its
dynamics.


  The automatic projection filter is a recently developed numerical method for
projection filtering that leverages sparse-grid integration and automatic
differentiation. However, its accuracy is highly sensitive to the accuracy of
the cumulant-generating function computed via the sparse-grid integration,
which in turn is also sensitive to the choice of the bijection from the
canonical hypercube to the state space. In this paper, we propose two new
adaptive parametric bijections for the automatic projection filter. The first
bijection relies on the minimization of Kullback--Leibler divergence, whereas
the second method employs the sparse-grid Gauss--Hermite quadrature. The two
new bijections allow the sparse-grid nodes to adaptively move within the
high-density region of the state space, resulting in a substantially improved
approximation while using only a small number of quadrature nodes. The
practical applicability of the methodology is illustrated in three simulated
nonlinear filtering problems.


  This work presents a novel numerical investigation of the dynamics of
free-boundary flows of viscoelastic liquid membranes. The governing equation
describes the balance of linear momentum, in which the stresses include the
viscoelastic response to deformations of Maxwell type. A penalty method is
utilized to enforce near incompressibility of the viscoelastic media, in which
the penalty constant is proportional to the viscosity of the fluid. A finite
element method is used, in which the slender geometry representing the liquid
membrane, is discretized by linear three-node triangular elements under plane
stress conditions. Two applications of interest are considered for the
numerical framework provided: shear flow, and extensional flow in drawing
processes.


  Imaging the propagation of light in time and space is crucial in optics,
notably for the study of complex media. We here demonstrate the passive
measurement of time-dependent Green's functions between every point at the
surface of a strongly scattering medium by means of low coherence
interferometry. The experimental access to this Green's matrix is essential
since it contains all the information about the complex trajectories of light
within the medium. In particular, the spatio-temporal spreading of the
diffusive halo and the coherent backscattering effect can be locally
investigated in the vicinity of each point acting as a virtual source. On the
one hand, this approach allows a quantitative imaging of the diffusion constant
in the scattering medium with a spatial resolution of the order of a few
transport mean free paths. On the other hand, our approach is able to reveal
and quantify the anisotropy of light diffusion, which can be of great interest
for optical characterization purposes. This study opens important perspectives
both in optical diffuse tomography with potential applications to biomedical
imaging and in fundamental physics for the experimental investigation of
Anderson localization.


  We present a new expression for the partition function of refined
Chern-Simons theory on $S^3$ with arbitrary gauge group, which is explicitly
equal to $1$, when the coupling constant is zero. Using this form of partition
function we show that the previously known Krefl-Schwarz representation of
partition function of refined Chern-Simons theory on $S^3$ can be generalized
to all simply-laced algebras. For all non-simply-laced gauge algebras, we
derive similar representations of that partition function, which makes it
possible to transform it into a product of multiple sine functions aiming at
the further establishment of duality with refined topological strings.


See hep-ph/0304045


  Several issues concerning the self-dual solutions of the Chern-Simons-Higgs
model are addressed. The topology of the configuration space of the model is
analysed when the space manifold is either the plane or an infinite cylinder.
We study the local structure of the moduli space of self-dual solitons in the
second case by means of an index computation. It is shown how to manage the
non-integer contribution to the heat-kernel supertrace due to the
non-compactness of the base space. A physical picture of the local coordinates
parametrizing the non-topological soliton moduli space arises .


  We consider a multi-server queueing system under the power-of-two policy with
Poisson job arrivals, heterogeneous servers and a general job requirement
distribution; each server operates under the first-come first-serve policy and
there are no buffer constraints. We analyze the performance of this system in
light traffic by evaluating the first two light traffic derivatives of the
average job response time. These expressions point to several interesting
structural features associated with server heterogeneity in light traffic: For
unequal capacities, the average job response time is seen to decrease for small
values of the arrival rate, and the more diverse the server speeds, the greater
the gain in performance. These theoretical findings are assessed through
limited simulations.


  Light (anti-)nuclei in relativistic heavy-ion collisions are considered to be
formed by the coalescence mechanism of (anti-)nucleons in the present work.
Using a dynamical phase-space coalescence model coupled with a multi-phase
transport (AMPT) model, we explore the formation of light clusters such as
deuteron, triton and their anti-particles in different centralities for
$^{197}$Au + $^{197}$Au collisions at $\sqrt{s_{NN}} = 39$ GeV. The calculated
transverse momentum spectra of protons, deuterons, and tritons are comparable
to those of experimental data from the RHIC-STAR collaboration. Both
coalescence parameters $B_{2}$ for (anti-)deuteron and $B_{3}$ for triton
increase with the transverse momentum as well as the collision centrality, and
they are comparable with the measured values in experiments. The effect of
system size on the production of light nuclei is also investigated by $^{10}$B
+ $^{10}$B, $^{16}$O + $^{16}$O, $^{40}$Ca + $^{40}$Ca, and $^{197}$Au +
$^{197}$Au systems in central collisions. The results show that yields of light
nuclei increase with system size, while the values of coalescence parameters
present an opposite trend. It is interesting to see that the system size, as
well as the centrality dependence of $B_A$ ($A$ = 2, 3), falls into the same
group, which further demonstrates production probability of light nuclei is
proportional to the size of the fireball. Furthermore, we compare our
coalescence results with other models, such as the thermal model and analytic
coalescence model, it seems that the description of light nuclei production is
consistent with each other.


  We prove an asymptotic formula for the second moment of automorphic
L-functions of even weight and prime power level. The error term is estimated
uniformly in all parameters: level, weight, shift and twist.


  We show that any $q$-query locally decodable code (LDC) gives a copy of
$\ell_1^k$ with small distortion in the Banach space of $q$-linear forms on
$\ell_{p_1}^N\times\cdots\times\ell_{p_q}^N$, provided $1/p_1 + \cdots + 1/p_q
\leq 1$ and where $k$, $N$, and the distortion are simple functions of the code
parameters. We exhibit the copy of $\ell_1^k$ by constructing a basis for it
directly from "smooth" LDC decoders. Based on this, we give alternative proofs
for known lower bounds on the length of 2-query LDCs. Using similar techniques,
we reprove known lower bounds for larger $q$. We also discuss the relation with
an alternative proof, due to Pisier, of a result of Naor, Regev, and the author
on cotype properties of projective tensor products of $\ell_p$ spaces.


  The exact perturbation approach is used to derive the (seven) elementary
correlation lengths and related mass gaps of the two-dimensional dilute A$_4$
lattice model in regime 2- from the Bethe ansatz solution. This model provides
a realisation of the integrable $\phi(1,2)$ perturbation of the c=7/10
conformal field theory, which is known to describe the off-critical thermal
behaviour of the tricritical Ising model. The E$_7$ masses predicted from
purely elastic scattering theory follow in the approach to criticality.
Universal amplitudes for the tricritical Ising model are calculated.


  Existing continual learning methods use Batch Normalization (BN) to
facilitate training and improve generalization across tasks. However, the
non-i.i.d and non-stationary nature of continual learning data, especially in
the online setting, amplify the discrepancy between training and testing in BN
and hinder the performance of older tasks. In this work, we study the
cross-task normalization effect of BN in online continual learning where BN
normalizes the testing data using moments biased towards the current task,
resulting in higher catastrophic forgetting. This limitation motivates us to
propose a simple yet effective method that we call Continual Normalization (CN)
to facilitate training similar to BN while mitigating its negative effect.
Extensive experiments on different continual learning algorithms and online
scenarios show that CN is a direct replacement for BN and can provide
substantial performance improvements. Our implementation is available at
\url{https://github.com/phquang/Continual-Normalization}.


  We show that the time dependence of mean value of a physical quantity is
related with the transition energies of a quantum system. In the case when the
operator of a physical quantity anticommutes with the Hamiltonian of a system,
studies of the evolution of its mean value allow determining the energy levels
of the system. On the basis of the result, we propose a method for determining
energy levels of physical systems on a quantum computer. The method opens a
possibility to achieve quantum supremacy in solving the problem of finding
minimal or maximal energy of Ising model with spatially anisotropic interaction
using multi-qubit quantum computers. We apply the method for spin systems (spin
in magnetic field, spin chain, Ising model on squared lattice) and realize it
on IBM's quantum computers.


  Linearly polarized emission is described, in general, in terms of the Stokes
parameters $Q$ and $U$, from which the polarization intensity and polarization
angle can be determined. Although the polarization intensity and polarization
angle provide an intuitive description of the polarization, they are affected
by the limitations of interferometric data, such as missing single-dish data in
the u-v plane, from which radio frequency interferometric data is visualized.
To negate the effects of these artefacts, it is desirable for polarization
diagnostics to be rotationally and translationally invariant in the $Q$-$U$
plane. One rotationally and translationally invariant quantity, the
polarization gradient, has been shown to provide a unique view of spatial
variations in the turbulent interstellar medium when applied to diffuse radio
frequency synchrotron emission. In this paper we develop a formalism to derive
additional rotationally and translationally invariant quantities. We present
new diagnostics that can be applied to diffuse or point-like polarized emission
in any waveband, including a generalization of the polarization gradient, the
polarization directional curvature, polarization wavelength derivative, and
polarization wavelength curvature. In Paper II we will apply these diagnostics
to observed and simulated images of diffuse radio frequency synchrotron
emission.


  For the first time, in March 2024, the transient Galactic black hole
candidate Swift J151857.0-572147 experienced an outburst. Using publicly
available archived {\it Insight}-HXMT data, we analyze the timing and spectral
features of this source. Through model fitting of the power density spectrum,
we were able to extract the properties of quasi-periodic oscillations, and
based on those properties, we have determined that the QPOs are of type C. We
also conclude that the shock instabilities in the transonic advective accretion
processes surrounding black holes may be the source of the QPOs. This shock
instability could produce variabilities of flux up to 48 keV, as we checked
from the QPO energy dependence. High-frequency QPO is not observed during this
period. In the broad energy band of $2-100$ keV, simultaneous data from the
three on-board instruments of \textit{Insight}-HXMT were used to perform the
spectral analysis. A combination of models, including broken power-law,
multi-color disk-blackbody continuum, interstellar absorption, and reflection
in both neutral and ionized medium were needed for spectral fitting to obtain
the best fit. We discovered that at the beginning of the analysis period, the
source was in an intermediate state and was transitioning toward the softer
states based on the spectral features. It has a hydrogen column density of
$(4.3-6.9) \times 10^{22}$ cm$^{-2}$.


  The self-energy of the photo-hole in 2H-TaSe2 is measured by angle-resolved
photoemission spectroscopy (ARPES) as a function of binding energy and
temperature. In the charge-density wave (CDW) state, a structure in the self-
energy is detected at ~ 65 meV that can not be explained by electron-phonon
scattering. A reduction in the scattering rates below this energy indicates the
collapse of a major scattering channel with the formation of the CDW state,
accompanying the appearance of a bosonic "mode" in the excitation spectrum of
the system.


  The $Gaia$ mission has detected many white dwarfs (WDs) in binary and triple
configurations, and while observations suggest that triple stellar systems are
common in our Galaxy, not much attention was devoted to WDs in triples. For
stability reasons, these triples must have hierarchical configurations, i.e.,
two stars are on a tight orbit (the inner binary), with the third companion on
a wider orbit about the inner binary. In such a system, the two orbits torque
each other via the eccentric Kozai-Lidov mechanism (EKL), which can alter the
orbital configuration of the inner binary. We simulate thousands of triple
stellar systems for over $10$ Gyr, tracking gravitational interactions, tides,
general relativity, and stellar evolution up to their WD fate. As demonstrated
here, three-body dynamics coupled with stellar evolution is a critical channel
to form tight WD binaries or merge a WD binary. Amongst these triples, we
explore their manifestations as cataclysmic variables, Type Ia supernovae, and
gravitational-wave events. The simulated systems are then compared to a sample
of WD triples selected from the $Gaia$ catalog. We find that including the
effect of mass loss-induced kicks is crucial for producing a distribution of
the inner binary-tertiary separations that is consistent with $Gaia$
observations. Lastly, we leverage this consistency to estimate that, at
minimum, $30\%$ of solar-type stars in the local $200$ parsecs were born in
triples.


  Fluctuations in the large-scale structure of the Universe contain significant
information about cosmological physics, but are modulated in survey datasets by
various observational effects. Building on existing literature, we provide a
general treatment of how fluctuation power spectra are modified by a
position-dependent selection function, noise, weighting, smoothing,
pixelization and discretization. Our work has relevance for the spatial power
spectrum analysis of galaxy surveys with spectroscopic or accurate photometric
redshifts, and radio intensity-mapping surveys of the sky brightness
temperature including generic noise, telescope beams and pixelization. We
consider the auto-power spectrum of a field, the cross-power spectrum between
two fields and the multipoles of these power spectra with respect to a curved
sky, deriving the corresponding power spectrum models, estimators, errors and
optimal weights. We note that "FKP weights" for individual tracers do not in
general provide the optimal weights when measuring the cross-power spectrum. We
validate our models using mock datasets drawn from N-body simulations, and
provide the python code we use for these tests at
https://github.com/cblakeastro/intensitypower. Our treatment should be useful
for modelling and studying cosmological fluctuation fields in observed and
simulated datasets.


  Quantum Ising model in one dimension is an exactly solvable example of a
quantum phase transition. We investigate its behavior during a quench from a
paramagnetic to ferromagnetic phase caused by a gradual turning off of the
transverse field at a fixed rate characterized by the quench time $\tau_Q$. In
agreement with Kibble-Zurek mechanism, quantum state of the system after the
transition exhibits a characteristic correlation length $\hat\xi$ proportional
to the square root of the quench time $\tau_Q$. The inverse of this correlation
length determines average density of defects after the transition. In this
paper, we show that $\hat\xi$ also controls the entropy of entanglement of a
block of $L$ spins with the rest of the system. For large $L$, this entropy
saturates at $\frac16\log_2\hat\xi$, as might have been expected from the
Kibble-Zurek mechanism. Close to the critical point, the entropy saturates when
the block size $L\approx\hat\xi$, but -- in the subsequent evolution in the
ferromagnetic phase -- a somewhat larger length scale
$l=\sqrt{\tau_Q}\ln\tau_Q$ develops as a result of quantum dephasing, and the
entropy saturates when $L\approx l$. We also study the spin-spin correlation.
We find that close to the critical point ferromagnetic correlations decay
exponentially with the dynamical correlation length $\hat\xi$, but in the
following evolution this correlation function becomes oscillatory at distances
less than this scale. However, both the wavelength and the correlation length
of these oscillations are still determined by $\hat\xi$. We also derive
probability distribution for the number of kinks in a finite spin chain after
the transition.


  Parameter sharing has proven to be a parameter-efficient approach. Previous
work on Transformers has focused on sharing parameters in different layers,
which can improve the performance of models with limited parameters by
increasing model depth. In this paper, we study why this approach works from
two perspectives. First, increasing model depth makes the model more complex,
and we hypothesize that the reason is related to model complexity (referring to
FLOPs). Secondly, since each shared parameter will participate in the network
computation several times in forward propagation, its corresponding gradient
will have a different range of values from the original model, which will
affect the model convergence. Based on this, we hypothesize that training
convergence may also be one of the reasons. Through further analysis, we show
that the success of this approach can be largely attributed to better
convergence, with only a small part due to the increased model complexity.
Inspired by this, we tune the training hyperparameters related to model
convergence in a targeted manner. Experiments on 8 machine translation tasks
show that our model achieves competitive performance with only half the model
complexity of parameter sharing models.


  We prove that every rational angled hyperbolic triangle has transcendental
side lengths and that every rational angled hyperbolic quadrilateral has at
least one transcendental side length. Thus, there does not exist a rational
angled hyperbolic triangle or quadrilateral with algebraic side lengths. We
conjecture that there does not exist a rational angled hyperbolic polygon with
algebraic side lengths.


  We study stochastic dynamical systems in settings where only partial
statistical information about the noise is available, e.g., in the form of a
limited number of noise realizations. Such systems are particularly challenging
to analyze and control, primarily due to an absence of a distributional
uncertainty model which: (1) is expressive enough to capture practically
relevant scenarios; (2) can be easily propagated through system maps; (3) is
invariant under propagation; and (4) allows for computationally tractable
control actions. In this paper, we propose to model distributional uncertainty
via Optimal Transport ambiguity sets and show that such modeling choice
satisfies all of the above requirements. We then specialize our results to
stochastic LTI systems, and start by showing that the distributional
uncertainty can be efficiently captured, with high probability, within an
Optimal Transport ambiguity set on the space of noise trajectories. Then, we
show that such ambiguity sets propagate exactly through the system dynamics,
giving rise to stochastic tubes that contain, with high probability, all
trajectories of the stochastic system. Finally, we show that the control task
is very interpretable, unveiling an interesting decomposition between the roles
of the feedforward and the feedback control terms. Our results are actionable
and successfully applied in stochastic reachability analysis and in trajectory
planning under distributional uncertainty.


  Our main aim in this thesis is to address the results and prospects of
boundary logarithmic conformal field theories: theories with boundaries that
contain the above Jordan cell structure. We have investigated c_{p,q} boundary
theory in search of logarithmic theories and have found logarithmic solutions
of two-point functions in the context of the Coulomb gas picture. Other
two-point functions have also been studied in the free boson construction of
BCFT with SU(2)_k symmetry. In addition, we have analyzed and obtained the
boundary Ishibashi state for a rank-2 Jordan cell structure [hep-th/0103064].
We have also examined the (generalised) Ishibashi state construction and the
symplectic fermion construction at c=-2 for boundary states in the context of
the c=-2 triplet model. The differences between two constructions are
interpreted, resolved and extended beyond each case.


  Studies have been made on the phase transition phenomena of an oscillator
network model based on a standard Hebb learning rule like the Hopfield model.
The relative phase informations---the in-phase and anti-phase, can be embedded
in the network. By self-consistent signal-to-noise analysis (SCSNA), it was
found that the storage capacity is given by $\alpha_c = 0.042$, which is better
than that of Cook's model. However, the retrieval quality is worse. In
addition, an investigation was made into an acceleration effect caused by
asymmetry of the phase dynamics. Finally, it was numerically shown that the
storage capacity can be improved by modifying the shape of the coupling
function.


  The Zak-OTFS input/output (I/O) relation is predictable and non-fading when
the delay and Doppler periods are greater than the effective channel delay and
Doppler spreads, a condition which we refer to as the crystallization
condition. The filter taps can simply be read off from the response to a single
Zak-OTFS pilot pulsone, and the I/O relation can be reconstructed for a sampled
system that operates under finite duration and bandwidth constraints. In
previous work we had measured BER performance of a baseline system where we
used separate Zak-OTFS subframes for sensing and data transmission. In this
Letter we demonstrate how to use turbo signal processing to match BER
performance of this baseline system when we integrate sensing and communication
within the same Zak-OTFS subframe. The turbo decoder alternates between channel
sensing using a noise-like waveform (spread pulsone) and recovery of data
transmitted using point pulsones.


  In this paper the notion of an abstract square function (estimate) is
introduced as an operator X to gamma (H; Y), where X, Y are Banach spaces, H is
a Hilbert space, and gamma(H; Y) is the space of gamma-radonifying operators.
By the seminal work of Kalton and Weis, this definition is a coherent
generalisation of the classical notion of square function appearing in the
theory of singular integrals. Given an abstract functional calculus (E, F, Phi)
on a Banach space X, where F (O) is an algebra of scalar-valued functions on a
set O, we define a square function Phi_gamma(f) for certain H-valued functions
f on O. The assignment f to Phi_gamma(f) then becomes a vectorial functional
calculus, and a "square function estimate" for f simply means the boundedness
of Phi_gamma(f). In this view, all results linking square function estimates
with the boundedness of a certain (usually the H-infinity) functional calculus
simply assert that certain square function estimates imply other square
function estimates. In the present paper several results of this type are
proved in an abstract setting, based on the principles of subordination,
integral representation, and a new boundedness concept for subsets of Hilbert
spaces, the so-called ell-1 -frame-boundedness. These abstract results are then
applied to the H-infinity calculus for sectorial and strip type operators. For
example, it is proved that any strip type operator with bounded scalar
H-infinity calculus on a strip over a Banach space with finite cotype has a
bounded vectorial H-infinity calculus on every larger strip.


  We study the structure of classical groups of equivalences for smooth
multigerms $f \colon (N,S) \to (P,y)$, and extend several known results for
monogerm equivalences to the case of mulitgerms. In particular, we study the
group $\A$ of source- and target diffeomorphism germs, and its stabilizer
$\A_f$. For monogerms $f$ it is well-known that if $f$ is finitely
$\A$-determined, then $\A_f$ has a maximal compact subgroup $MC(\A_f)$, unique
up to conjugacy, and $\A_f/MC(\A_f)$ is contractible. We prove the same result
for finitely $\A$-determined multigerms $f$. Moreover, we show that for a
ministable multigerm $f$, the maximal compact subgroup $MC(\A_f)$ decomposes as
a product of maximal compact subgroups $MC(\A_{g_i})$ for suitable
representatives $g_i$ of the monogerm components of $f$. We study a product
decomposition of $MC(\A_f)$ in terms of $MC(\mathscr{R}_f)$ and a group of
target diffeomorphisms, and conjecture a decomposition theorem. Finally, we
show that for a large class of maps, maximal compact subgroups are small and
easy to compute.


  (Abridged) We review recent observations that suggest the star formation rate
density is declining over 3$<z<$7 and illustrate the challenges that this poses
in explaining the assembled stellar mass in z$\sim$6 galaxies deduced from
Spitzer data. A plausible conclusion is a vigorous period of earlier star
formation. Prior to JWST and TMT, strong lensing offers a unique probe of the
extent of this earlier activity. We discuss the first results of a blind
spectroscopic survey of lensing clusters for 8.5$<z<$10 Ly$\alpha$ emitters
using NIRSPEC which is achieving a limiting star formation rate of 0.1
$M_{\odot}$ yr$^{-1}$. A companion HST/IRAC survey is targeting lensed $z$ and
$J$-band dropouts and probes a $\simeq$1 arcmin$^2$ region 1 magnitude deeper
than the UDF/NICMOS observations. Both surveys will constrain the contribution
of early, low luminosity, sources to cosmic reionization.


  The article uses two examples to explore the statement that, contrary to the
common wisdom, the properties of singular cardinals are actually more intuitive
than those of the regular ones.


  In 1993, Savage, Squire, and West described an inductive construction for
generating every acyclic orientation of a chordal graph exactly once, flipping
one arc at a time. We provide two generalizations of this result. Firstly, we
describe Gray codes for acyclic orientations of hypergraphs that satisfy a
simple ordering condition, which generalizes the notion of perfect elimination
order of graphs. This unifies the Savage-Squire-West construction with a recent
algorithm for generating elimination trees of chordal graphs. Secondly, we
consider quotients of lattices of acyclic orientations of chordal graphs, and
we provide a Gray code for them, addressing a question raised by Pilaud. This
also generalizes a recent algorithm for generating lattice congruences of the
weak order on the symmetric group. Our algorithms are derived from the
Hartung-Hoang-M\"utze-Williams combinatorial generation framework, and they
yield simple algorithms for computing Hamilton paths and cycles on large
classes of polytopes, including chordal nestohedra and quotientopes. In
particular, we derive an efficient implementation of the Savage-Squire-West
construction. Along the way, we give an overview of old and recent results
about the polyhedral and order-theoretic aspects of acyclic orientations of
graphs and hypergraphs.


  Sighted and blind and low vision (BLV) creators alike use videos to
communicate with broad audiences. Yet, video editing remains inaccessible to
BLV creators. Our formative study revealed that current video editing tools
make it difficult to access the visual content, assess the visual quality, and
efficiently navigate the timeline. We present AVscript, an accessible
text-based video editor. AVscript enables BLV creators to edit their video
using a script that embeds the video's visual content, visual errors (e.g.,
dark or blurred footage), and speech. BLV creators can use AVscript to
efficiently navigate between scenes and visual errors or to locate objects in
the frame or spoken words of interest. A comparison study (N=12) showed that
AVscript significantly lowered BLV creators' mental demands while increasing
confidence and independence in video editing. We further demonstrate the
potential of AVscript through an exploratory study (N=3) where BLV creators
edited their own footage.


  Two-dimensional (2D) semiconductors with direct and modest bandgap and
ultrahigh carrier mobility are highly desired functional materials for
nanoelectronic applications. Herein, we predict that monolayer CaP3 is a new 2D
functional material that possesses not only a direct bandgap of 1.15 eV (based
on HSE06 computation), and also a very high electron mobility up to 19930 cm2
V-1 s-1, comparable to that of monolayer phosphorene. More remarkably, contrary
to the bilayer phosphorene which possesses dramatically reduced carrier
mobility compared to its monolayer counterpart, CaP3 bilayer possesses even
higher electron mobility (22380 cm2 V-1 s-1) than its monolayer counterpart.
The bandgap of 2D CaP3 can be tuned over a wide range from 1.15 to 0.37 eV
(HSE06 values) through controlling the number of stacked CaP3 layers. Besides
novel electronic properties, 2D CaP3 also exhibits optical absorption over the
entire visible-light range. The combined novel electronic, charge mobility, and
optical properties render 2D CaP3 an exciting functional material for future
nanoelectronic and optoelectronic applications.


  The idea of obtaining a pilot-wave quantum theory on a lattice with discrete
time is presented. The motion of quantum particles is described by a
$|\Psi|^2$-distributed Markov chain. Stochastic matrices of the process are
found by the discrete version of the least-action principle. Probability
currents are the consequence of Hamilton's principle and the stochasticity of
the Markov process is minimized. As an example, stochastic motion of single
particles in a double-slit experiment is examined.


  Wire chambers are routinely operated as tracking detectors in magnetic
spectrometers at high-intensity continuous electron beams. Especially in
experiments studying reactions with small cross-sections the reaction yield is
limited by the background rate in the chambers. One way to determine the track
of a charged particle through a multi-wire proportional chamber (MWPC) is the
measurement of the charge distribution induced on its cathodes. In practical
applications of this read-out method, the algorithm to relate the measured
charge distribution to the avalanche position is an important factor for the
achievable position resolution and for the track reconstruction efficiency. An
algorithm was developed for operating two large-sized MWPCs in a strong
background environment with multiple-particle tracks. Resulting efficiencies
were determined as a function of the electron beam current and on the signal
amplitudes. Because of the different energy-losses of pions, kaons, and protons
in the momentum range of the spectrometer the efficiencies depend also on the
particle species.


  Within this work, the kinetics of the growing stage of porous silicon (PS)
during the etching process was studied using the photoacoustic technique. A
p-type Si with low resistivity was used as a substrate. An extension of
Rosencwaig and Gersho model is proposed in order to analyze the temporary
changes that take place in the amplitude of the photoacoustic signal during the
PS growth. The solution of the heat equation takes into account the modulated
laser beam, the changes in the reflectance of the PS-backing heterostructure,
the electrochemical reaction, and the Joule effect as thermal sources. The
model includes the time-dependence of the sample thickness during the
electrochemical etching of PS. The changes in the reflectance are identified as
the laser reflections in the internal layers of the system. The reflectance is
modeled by an additional sinusoidal-monochromatic light source and its
modulated frequency is related to the velocity of the PS growth. The chemical
reaction and the DC components of the heat sources are taken as an average
value from the experimental data. The theoretical results are in agreement with
the experimental data and hence provided a method to determine variables of the
PS growth, such as the etching velocity and the thickness of the porous layer
during the growing process.


  We construct a mean-field elastoplastic description of the dynamics of
amorphous solids under arbitrary time-dependent perturbations, building on the
work of Lin and Wyart [Phys. Rev. X 6, 011005 (2016)] for steady shear. Local
stresses are driven by power-law distributed mechanical noise from yield events
throughout the material, in contrast to the well-studied H\'{e}braud-Lequeux
model where the noise is Gaussian. We first use a mapping to a mean first
passage time problem to study the phase diagram in the absence of shear, which
shows a transition between an arrested and a fluid state. We then introduce a
boundary layer scaling technique for low yield rate regimes, which we first
apply to study the scaling of the steady state yield rate on approaching the
arrest transition. These scalings are further developed to study the aging
behaviour in the glassy regime, for different values of the exponent $\mu$
characterizing the mechanical noise spectrum. We find that the yield rate
decays as a power-law for $1<\mu<2$, a stretched exponential for $\mu=1$ and an
exponential for $\mu<1$, reflecting the relative importance of far-field and
near-field events as the range of the stress propagator is varied. Comparison
of the mean-field predictions with aging simulations of a lattice elastoplastic
model shows excellent quantitative agreement, up to a simple rescaling of time.


  Recently, deep learning approaches for MR motion artifact correction have
been extensively studied. Although these approaches have shown high performance
and reduced computational complexity compared to classical methods, most of
them require supervised training using paired artifact-free and
artifact-corrupted images, which may prohibit its use in many important
clinical applications. For example, transient severe motion (TSM) due to acute
transient dyspnea in Gd-EOB-DTPA-enhanced MR is difficult to control and model
for paired data generation. To address this issue, here we propose a novel
unsupervised deep learning scheme through outlier-rejecting bootstrap
subsampling and aggregation. This is inspired by the observation that motions
usually cause sparse k-space outliers in the phase encoding direction, so
k-space subsampling along the phase encoding direction can remove some outliers
and the aggregation step can further improve the results from the
reconstruction network. Our method does not require any paired data because the
training step only requires artifact-free images. Furthermore, to address the
smoothing from potential bias to the artifact-free images, the network is
trained in an unsupervised manner using optimal transport driven cycleGAN. We
verify that our method can be applied for artifact correction from simulated
motion as well as real motion from TSM successfully, outperforming existing
state-of-the-art deep learning methods.


  This paper proposes an approach to mitigate epidemic spread in a population
of strategic agents by encouraging safer behaviors through carefully designed
rewards. These rewards, which vary according to the state of the epidemic, are
ascribed by a dynamic payoff mechanism we seek to design. We use a modified
SIRS model to track how the epidemic progresses in response to the population's
agents strategic choices. By employing perturbed best response evolutionary
dynamics to model the population's strategic behavior, we extend previous
related work so as to allow for noise in the agents' perceptions of the rewards
and intrinsic costs of the available strategies. Central to our approach is the
use of system-theoretic methods and passivity concepts to obtain a Lyapunov
function, ensuring the global asymptotic stability of an endemic equilibrium
with minimized infection prevalence, under budget constraints. We use the
Lyapunov function to construct anytime upper bounds for the size of the
population's infectious fraction. For a class of one-parameter perturbed best
response models, we propose a method to learn the model's parameter from data.


  We present new ways of producing a channel chart [1] employing model-based
approaches. We estimate the angle of arrival theta and the distance rho between
the base station and the user equipment by employing our algorithms, inverse of
the root sum squares of channel coefficients (ISQ) algorithm, linear regression
(LR) algorithm, and MUSIC/MUSIC (MM) algorithm. We compare these methods with
the channel charting algorithms principal component analysis (PCA), Sammon's
method (SM), and autoencoder (AE) [1]. We show that ISQ, LR, and MM surpass
PCA, SM, and AE in performance. We also compare our algorithm MM with an
algorithm from the literature that uses the MUSIC algorithm jointly on theta
and rho. We call this algorithm the JM algorithm. JM performs very slightly
better than MM but at a substantial increase in complexity. Finally, we
introduce the rotate-and-sum (RS) algorithm which has about the same
performance as the MM and JM algorithms. Unlike MUSIC, RS does not employ
eigenvalue and eigenvector analysis. Thus, it is more suitable for direct
register transfer logic (RTL) implementation.


  We construct a multi-band Hubbard model on the lattice obtained by
"decorating" a closely packed $d$-dimensional lattice $\mathcal{M}$ (such as
the triangular lattice) where $d\ge2$. We take the limits in which the Coulomb
interaction and the band gap become infinitely large. Then there remains only a
single band with finite energy, on which electrons are supported. Let the
electron number be $N_\mathrm{e}=|\mathcal{M}|-N_\mathrm{h}$, where
$|\mathcal{M}|$ corresponds to the electron number which makes the lowest
(finite energy) band half-filled, and $N_\mathrm{h}$ is the number of "holes".
It is expected that the model exhibits metallic ferromagnetism if
$N_\mathrm{h}/|\mathcal{M}|$ is nonvanishing but sufficiently small. We prove
that the ground states exhibit saturated ferromagnetism if
$N_\mathrm{h}\le(\text{const.})|\mathcal{M}|^{2/(d+2)}$, and exhibit (not
necessarily saturated) ferromagnetism if
$N_\mathrm{h}\le(\mathrm{const.})|\mathcal{M}|^{(d+1)/(d+2)}$. This may be
regarded as a rigorous example of metallic ferromagnetism provided that the
system size $|\mathcal{M}|$ is not too large.


  We consider the question of cocompleting partially presentable parametrized
$\infty$-categories in the sense of arXiv:2307.11001. As our main result we
show that in certain cases one may compute such relative cocompletions via a
very explicit formula given in terms of partially lax limits. We then apply
this to equivariant homotopy theory, building on the work of op. cit. and
arXiv:2301.08240, to conclude that the global $\infty$-category of globally
equivariant spectra is the relative cocompletion of the global
$\infty$-category of equivariant spectra. Evaluating at a group $G$ we obtain a
description of the $\infty$-category of $G$-global spectra as a partially lax
limit, extending the main result of arXiv:2206.01556 for finite groups to
$G$-global homotopy theory. Finally we investigate the question of stabilizing
global $\infty$-categories by inverting the action of representation spheres,
and deduce a second universal property for the global $\infty$-category of
globally equivariant spectra, similar to that of arXiv:2302.06207.


  The fundamental issues associated with the magnetoplasmon excitations are
investigated in a quantum wire characterized by a confining harmonic potential
and subjected to a perpendicular magnetic field. We embark on the
charge-density excitations in a two-subband model within the framework of
Bohm-Pines' random-phase approximation. Essentially, the focus of our study is
the intersubband (magnetoroton) collective excitation which changes the sign of
its group velocity twice before merging with the respective single-particle
continuum. The computation of the gain coefficient suggests an interesting and
important application: the electronic device based on such magnetoroton modes
can act as an {\it active} laser medium.


  Interactions of the uniform mode with higher order modes due to
incommensurately modulated equilibrium state change the usual relaxation
behaviour to a more complex one. A multirelaxation character should be present
even in the single-plane-wave limit. Our model enables one to describe dynamic
dielectric response of the incommensurable modulated phases in order-disorder
systems.


  Researchers have increasingly turned to crowdfunding platforms to gain
insights into entrepreneurial activity and dynamics. While previous studies
have explored various factors influencing crowdfunding success, such as
technology, communication, and marketing strategies, the role of visual
elements that can be automatically extracted from images has received less
attention. This is surprising, considering that crowdfunding platforms
emphasize the importance of attention-grabbing and high-resolution images, and
previous research has shown that image characteristics can significantly impact
product evaluations. Indeed, a comprehensive review of empirical articles (n =
202) that utilized Kickstarter data, focusing on the incorporation of visual
information in their analyses. Our findings reveal that only 29.70% controlled
for the number of images, and less than 12% considered any image details. In
this manuscript, we review the literature on image processing and its relevance
to the business domain, highlighting two types of visual variables: visual
counts (number of pictures and number of videos) and image details. Building
upon previous work that discussed the role of color, composition and
figure-ground relationships, we introduce visual scene elements that have not
yet been explored in crowdfunding, including the number of faces, the number of
concepts depicted, and the ease of identifying those concepts. To demonstrate
the predictive value of visual counts and image details, we analyze Kickstarter
data. Our results highlight that visual count features are two of the top three
predictors of success. Our results also show that simple image detail features
such as color matter a lot, and our proposed measures of visual scene elements
can also be useful. We supplement our article with R and Python codes that help
authors extract image details (https://osf.io/ujnzp/).


  Ultrafast time-resolved photoluminescence spectroscopy following one- and
two-photon excitation of ZnO powder is used to gain unprecedented insight into
the surprisingly high external quantum efficiency of its "green" defect
emission band. The role of exciton diffusion, the effects of reabsorption, and
the spatial distributions of radiative and nonradiative traps are comparatively
elucidated for the ultraviolet excitonic and "green" defect emission bands in
both unannealed, nanometer-sized ZnO powders and annealed, micrometer-sized
ZnO:Zn powders. We find that the primary mechanism limiting quantum efficiency
is surface recombination because of the high density of nonradiative surface
traps in these powders. It is found that unannealed ZnO has a high density of
bulk nonradiative traps as well, but the annealing process reduces the density
of these bulk traps while simultaneously creating a high density of
green-emitting defects near the particle surface. The data are discussed in the
context of a simple rate equation model that accounts for the quantum
efficiencies of both emission bands. The results indicate how defect
engineering could improve the efficiency of ultraviolet-excited ZnO:Zn-based
white light phosphors.


  We propose a new method for Point Spread Function (PSF) correction in weak
gravitational lensing shear analysis using an artificial image with the same
ellipticity as the lensed image. This avoids the systematic error associated
with the approximation in PSF correction used in previous approaches. We test
the new method with simulated objects which have Gaussian or Cersic profiles
smeared by a Gaussian PSF, and confirm that there is no systematic error.


  In this paper, we determine minimal generating sets for several well-known
monoids of matrices over semirings. In particular, we find minimal generating
sets for the monoids consisting of: all $n\times n$ boolean matrices when
$n\leq 8$; the $n\times n$ boolean matrices containing the identity matrix (the
reflexive boolean matrices) when $n\leq 7$; the $n\times n$ boolean matrices
containing a permutation (the Hall matrices) when $n \leq 8$; the upper, and
lower, triangular boolean matrices of every dimension; the $2 \times 2$
matrices over the semiring $\mathbb{N} \cup \{-\infty\}$ with addition $\oplus$
defined by $x\oplus y = \max(x, y)$ and multiplication $\otimes$ given by
$x\otimes y = x + y$ (the max-plus semiring); the $2\times 2$ matrices over any
quotient of the max-plus semiring by the congruence generated by $t = t + 1$
where $t\in \mathbb{N}$; the $2\times 2$ matrices over the min-plus semiring
and its finite quotients by the congruences generated by $t = t + 1$ for all
$t\in \mathbb{N}$; and the $n \times n$ matrices over $\mathbb{Z} /
n\mathbb{Z}$ relative to their group of units.


  In real-world applications, an object detector often encounters object
instances from new classes and needs to accommodate them effectively. Previous
work formulated this critical problem as incremental object detection (IOD),
which assumes the object instances of new classes to be fully annotated in
incremental data. However, as supervisory signals are usually rare and
expensive, the supervised IOD may not be practical for implementation. In this
work, we consider a more realistic setting named semi-supervised IOD (SSIOD),
where the object detector needs to learn new classes incrementally from a few
labelled data and massive unlabelled data without catastrophic forgetting of
old classes. A commonly-used strategy for supervised IOD is to encourage the
current model (as a student) to mimic the behavior of the old model (as a
teacher), but it generally fails in SSIOD because a dominant number of object
instances from old and new classes are coexisting and unlabelled, with the
teacher only recognizing a fraction of them. Observing that learning only the
classes of interest tends to preclude detection of other classes, we propose to
bridge the coexistence of unlabelled classes by constructing two teacher models
respectively for old and new classes, and using the concatenation of their
predictions to instruct the student. This approach is referred to as
DualTeacher, which can serve as a strong baseline for SSIOD with limited
resource overhead and no extra hyperparameters. We build various benchmarks for
SSIOD and perform extensive experiments to demonstrate the superiority of our
approach (e.g., the performance lead is up to 18.28 AP on MS-COCO). Our code is
available at \url{https://github.com/chuxiuhong/DualTeacher}.


  This note is a brief survey of some results of the recent collaboration of
neurobiologists and mathematicians dedicated to stimulus reconstruction from
neuronal spiking activity. This collaboration, in particular, led to the
consideration of binary codes used by brain for encoding a stimuli domain such
as a rodent's territory through the combinatorics of its covering by local
neighborhoods.
  The survey is addressed to mathematicians (cf. [DeSch01]) and focuses on the
idea that stimuli spaces are represented by the relevant neural codes as
simplicial sets and thus encode say, the homotopy type of space if local
neighborhoods are convex (see [CuIt08], [CuItVCYo13], [Yo14], [SiGh07]).}


  In this paper we prove a Cauchy-Kowalevski theorem for the functions
satisfying the system DfD=0 (called inframonogenic functions).