  The evolution of galaxies and the history of star formation in the Universe
are among the most important topics in today's astrophysics. Especially, the
role of small, irregular galaxies in the star-formation history of the Universe
is not yet clear. Using the data from the AKARI IRC survey of the Large
Magellanic Cloud at 3.2, 7, 11, 15, and 24 {\mu}m wavelengths, i.e., at the
mid- and near-infrared, we have constructed a multiwavelength catalog
containing data from a cross-correlation with a number of other databases at
different wavelengths. We present the separation of different classes of stars
in the LMC in color-color, and color-magnitude, diagrams, and analyze their
contribution to the total LMC flux, related to point sources at different
infrared wavelengths.


  Driven by the need to accelerate numerical simulations, the use of machine
learning techniques is rapidly growing in the field of computational solid
mechanics. Their application is especially advantageous in concurrent
multiscale finite element analysis (FE$^2$) due to the exceedingly high
computational costs often associated with it and the high number of similar
micromechanical analyses involved. To tackle the issue, using surrogate models
to approximate the microscopic behavior and accelerate the simulations is a
promising and increasingly popular strategy. However, several challenges
related to their data-driven nature compromise the reliability of surrogate
models in material modeling. The alternative explored in this work is to
reintroduce some of the physics-based knowledge of classical constitutive
modeling into a neural network by employing the actual material models used in
the full-order micromodel to introduce non-linearity. Thus, path-dependency
arises naturally since every material model in the layer keeps track of its own
internal variables. For the numerical examples, a composite Representative
Volume Element with elastic fibers and elasto-plastic matrix material is used
as the microscopic model. The network is tested in a series of challenging
scenarios and its performance is compared to that of a state-of-the-art
Recurrent Neural Network (RNN). A remarkable outcome of the novel framework is
the ability to naturally predict unloading/reloading behavior without ever
seeing it during training, a stark contrast with popular but data-hungry models
such as RNNs. Finally, the proposed network is applied to FE$^2$ examples to
assess its robustness for application in nonlinear finite element analysis.


  With the tremendous growth of videos over the Internet, video thumbnails,
providing video content previews, are becoming increasingly crucial to
influencing users' online searching experiences. Conventional video thumbnails
are generated once purely based on the visual characteristics of videos, and
then displayed as requested. Hence, such video thumbnails, without considering
the users' searching intentions, cannot provide a meaningful snapshot of the
video contents that users concern. In this paper, we define a distinctively new
task, namely sentence specified dynamic video thumbnail generation, where the
generated thumbnails not only provide a concise preview of the original video
contents but also dynamically relate to the users' searching intentions with
semantic correspondences to the users' query sentences. To tackle such a
challenging task, we propose a novel graph convolved video thumbnail pointer
(GTP). Specifically, GTP leverages a sentence specified video graph
convolutional network to model both the sentence-video semantic interaction and
the internal video relationships incorporated with the sentence information,
based on which a temporal conditioned pointer network is then introduced to
sequentially generate the sentence specified video thumbnails. Moreover, we
annotate a new dataset based on ActivityNet Captions for the proposed new task,
which consists of 10,000+ video-sentence pairs with each accompanied by an
annotated sentence specified video thumbnail. We demonstrate that our proposed
GTP outperforms several baseline methods on the created dataset, and thus
believe that our initial results along with the release of the new dataset will
inspire further research on sentence specified dynamic video thumbnail
generation. Dataset and code are available at https://github.com/yytzsy/GTP.


  The time analysis of the signal induced by the drift of charge carriers in
high purity germanium detectors provides information on the event topology.
Millions of charge carriers are produced in a typical event. Their initial
distribution, stochastic diffusion and Coulomb self-repulsion affect the time
structure of the signal. We present a comprehensive study of these effects and
evaluate their impact on the event discrimination capabilities for the three
geometries which will be used in the Legend experiment for neutrinoless
double-beta decay.


  We present new high resolution imaging of a light-scattering dust ring and
halo around the young star HD 35841. Using spectroscopic and polarimetric data
from the Gemini Planet Imager in H-band (1.6 microns), we detect the highly
inclined (i=85 deg) ring of debris down to a projected separation of ~12 au
(~0.12") for the first time. Optical imaging from HST/STIS shows a smooth dust
halo extending outward from the ring to >140 au (>1.4"). We measure the ring's
scattering phase function and polarization fraction over scattering angles of
22-125 deg, showing a preference for forward scattering and a polarization
fraction that peaks at ~30% near the ansae. Modeling of the scattered-light
disk indicates that the ring spans radii of ~60-220 au, has a vertical
thickness similar to that of other resolved dust rings, and contains grains as
small as 1.5 microns in diameter. These models also suggest the grains have a
low porosity, are more likely to consist of carbon than astrosilicates, and
contain significant water ice. The halo has a surface brightness profile
consistent with that expected from grains pushed by radiation pressure from the
main ring onto highly eccentric but still bound orbits. We also briefly
investigate arrangements of a possible inner disk component implied by our
spectral energy distribution models, and speculate about the limitations of Mie
theory for doing detailed analyses of debris disk dust populations.


  Inelastic neutron scattering (INS), electron spin (ESR) and nuclear magnetic
resonance (NMR) measurements were employed to establish the origin of the
strong magnetic signal in lightly hole-doped La_{1-x}Sr_xCoO_3, x=0.002. Both,
INS and ESR low temperature spectra show intense excitations with large
effective g-factors ~10-18. NMR data indicate the creation of extended magnetic
clusters. From the Q-dependence of the INS magnetic intensity we conclude that
the observed anomalies are caused by the formation of octahedrally shaped
spin-state polarons comprising seven Co ions.


  We prove two Liouville type theorems for the stationary Navier-Stokes
equations in $\mathbb{R}^3$ under some assumptions on 1) the growth of the
$L^s$ mean oscillation of a potential function of the velocity field, or 2) the
relative decay of the head pressure and the square of the velocity field at
infinity. The main idea is to use Saint-Venant type estimates to characterize
the growth of Dirichlet energy of nontrivial solutions. These assumptions are
weaker than those previously known of a similar nature.


  One of the key drivers of complexity in the classical (stochastic)
multi-armed bandit (MAB) problem is the difference between mean rewards in the
top two arms, also known as the instance gap. The celebrated Upper Confidence
Bound (UCB) policy is among the simplest optimism-based MAB algorithms that
naturally adapts to this gap: for a horizon of play n, it achieves optimal
O(log n) regret in instances with "large" gaps, and a near-optimal O(\sqrt{n
log n}) minimax regret when the gap can be arbitrarily "small." This paper
provides new results on the arm-sampling behavior of UCB, leading to several
important insights. Among these, it is shown that arm-sampling rates under UCB
are asymptotically deterministic, regardless of the problem complexity. This
discovery facilitates new sharp asymptotics and a novel alternative proof for
the O(\sqrt{n log n}) minimax regret of UCB. Furthermore, the paper also
provides the first complete process-level characterization of the MAB problem
under UCB in the conventional diffusion scaling. Among other things, the
"small" gap worst-case lens adopted in this paper also reveals profound
distinctions between the behavior of UCB and Thompson Sampling, such as an
"incomplete learning" phenomenon characteristic of the latter.


  We study random graphs, both $G(n,p)$ and $G(n,m)$, with random orientations
on the edges. For three fixed distinct vertices s,a,b we study the correlation,
in the combined probability space, of the events a -> s and s -> b.
  For G(n,p), we prove that there is a p_c=1/2 such that for a fixed p<p_c the
correlation is negative for large enough n and for p>p_c the correlation is
positive for large enough n. We conjecture that for a fixed n\ge 27 the
correlation changes sign three times for three critical values of p.
  For G(n,m) it is similarly proved that, with $p=m/\binom{n}{2}$, there is a
critical p_c that is the solution to a certain equation and approximately equal
to 0.7993. A lemma, which computes the probability of non existence of any k
directed edges in G(n,m), is thought to be of independent interest.
  We present exact recursions to compute P(a -> s)$ and P(a -> s, s -> b)$. We
also briefly discuss the corresponding question in the quenched version of the
problem.


  Tractable contextual bandit algorithms often rely on the realizability
assumption - i.e., that the true expected reward model belongs to a known
class, such as linear functions. In this work, we present a tractable bandit
algorithm that is not sensitive to the realizability assumption and
computationally reduces to solving a constrained regression problem in every
epoch. When realizability does not hold, our algorithm ensures the same
guarantees on regret achieved by realizability-based algorithms under
realizability, up to an additive term that accounts for the misspecification
error. This extra term is proportional to T times a function of the mean
squared error between the best model in the class and the true model, where T
is the total number of time-steps. Our work sheds light on the bias-variance
trade-off for tractable contextual bandits. This trade-off is not captured by
algorithms that assume realizability, since under this assumption there exists
an estimator in the class that attains zero bias.


  We discuss and prove a theorem which asserts that any n-dimensional
semi-Riemannian manifold can be locally embedded in a (n+1)-dimensional space
with a non-degenerate Ricci tensor which is equal, up to a local analytic
diffeomorphism, to the Ricci tensor of an arbitrary specified space. This may
be regarded as a further extension of the Campbell-Magaard theorem. We
highlight the significance of embedding theorems of increasing degrees of
generality in the context of higher dimensional spacetimes theories and
illustrate the new theorem by establishing the embedding of a general class of
Ricci-flat spacetimes.


  We propose a universal elastic energy for proteins, which depends only on the
radius of gyration $R_{g}$ and the residue number $N$. It is constructed using
physical arguments based on the hydrophobic effect and hydrogen bonding.
Adjustable parameters are fitted to data from the computer simulation of the
folding of a set of proteins using the CSAW (conditioned self-avoiding walk)
model. The elastic energy gives rise to scaling relations of the form
$R_{g}\sim N^{\nu}$ in different regions. It shows three folding stages
characterized by the progression with exponents $\nu = 3/5, 3/7, 2/5$, which we
identify as the unfolded stage, pre-globule, and molten globule, respectively.
The pre-globule goes over to the molten globule via a break in behavior akin to
a first-order phase transition, which is initiated by a sudden acceleration of
hydrogen bonding.


  In 3D single particle imaging with X-ray free-electron lasers, particle
orientation is not recorded during measurement but is instead recovered as a
necessary step in the reconstruction of a 3D image from the diffraction data.
Here we use harmonic analysis on the sphere to cleanly separate the angu- lar
and radial degrees of freedom of this problem, providing new opportunities to
efficiently use data and computational resources. We develop the
Expansion-Maximization-Compression algorithm into a shell-by-shell approach and
implement an angular bandwidth limit that can be gradually raised during the
reconstruction. We study the minimum number of patterns and minimum rotation
sampling required for a desired angular and radial resolution. These extensions
provide new av- enues to improve computational efficiency and speed of
convergence, which are critically important considering the very large datasets
expected from experiment.


  We study the localization properties of weakly interacting Bose gas in a
quasiperiodic potential commonly known as Aubry-Andr\'e model. Effect of
interaction on localization is investigated by computing the `superfluid
fraction' and `inverse participation ratio'. For interacting Bosons the inverse
participation ratio increases very slowly after the localization transition due
to `multisite localization' of the wave function. We also study the
localization in Aubry-Andr\'e model using an alternative approach of classical
dynamical map, where the localization is manifested by chaotic classical
dynamics. For weakly interacting Bose gas, Bogoliubov quasiparticle spectrum
and condensate fraction are calculated in order to study the loss of coherence
with increasing disorder strength. Finally we discuss the effect of trapping
potential on localization of matter wave.


  Quantum vacuum fluctuations give rise to effective non-linear interactions
between electromagnetic fields. A prominent signature of quantum vacuum
nonlinearities driven by macroscopic fields are signal photons differing in
characteristic properties such as frequency, propagation direction and
polarization from the driving fields. We devise a strategy for the efficient
tracing of the various vacuum-fluctuation-mediated interaction processes in
order to identify the most prospective signal photon channels. As an example,
we study the collision of up to four optical laser pulses and pay attention to
sum and difference frequency generation. We demonstrate how this information
can be used to enhance the signal photon yield in laser pulse collisions for a
given total laser energy.


  CP violating interactions are required to realize the matter abundance of our
Universe. It is however known that the standard model of particle physics does
not contain sufficient CP violation. The electric dipole moment (EDM) of atomic
systems is a very sensitive experimental probe of CP violation beyond the
standard model, and it is very actively studied in experiments. The atomic EDM
has a significant sensitivity to hadronic CP violation, but its quantification
has for long been obstructed by the nonperturbative physics of quantum
chromodynamics. Quite recently, the contribution of the Weinberg operator (CP
violating three-gluon interaction) to the atomic EDM has been analyzed, and we
are almost attaining the quantification era of the CP violating hadronic
interaction in the leading order of standard model effective field theory. In
this proceedings contribution, we summarize the current attempt to quantify the
hadronic CP violation contribution to the EDM of atoms.


  In this paper, some preliminaries about signal flow graph, linear
time-invariant system on F(z) and computational complexity are first introduced
in detail. In order to synthesize the necessary and sufficient condition on
F(z) for a general 2-path problem, the sufficient condition on F(z) or R and
necessary conditions on F(z) for a general 2-path problem are secondly analyzed
respectively. Moreover, an equivalent sufficient and necessary condition on R
whether there exists a general 2-path is deduced in detail. Finally, the
computational complexity of the algorithm for this equivalent sufficient and
necessary condition is introduced so that it means that the general 2-path
problem is a P problem.


  The formation of textured patterns has been predicted to occur in two stages.
The first is an early time, domain-forming stage with dynamics characterized by
a disorder function $\bar\delta (\beta) \sim t^{-\sigma_{E}}$, with $\sigma_{E}
= {1/2}\beta$; this decay is universal. Coarsening of domains occurs in the
second stage, in which $\bar\delta (\beta) \sim t^{-\sigma_{L}}$, where
$\sigma_{L}$ is a nonlinear function of $\beta$ whose form is system and model
dependent. Our experiments on a vertically oscillated granular layer are in
accord with theory, yielding $\sigma_{E}\approx 0.5\beta$, and $\sigma_{L}$ a
nonlinear function of $\beta$.


  The Manhattan Project was one of the largest scientific collaborations ever
undertaken. It operated thanks to a complex social network of extraordinary
minds and it became undoubtedly one of the most remarkable intellectual efforts
of human history. It also had devastating consequences during and after the
atomic bombings of Hiroshima and Nagasaki. Despite the loss of hundreds of
thousands of human lives during the bombing and the subsequent events, the
scientific journey itself stands as a testament to human achievement, as
highlighted in Christopher Nolan's film portrayal of Oppenheimer.


  In this paper, we study modular transformation properties of a certain class
of functions with indefinite quadratic forms.


  We present a fitting function to describe the statistics of flux modulations
caused by interstellar scintillation. The function models a very general
quantity: the cross-correlation of the flux observed from a compact radio
source of finite angular size observed at two frequencies and at two positions
or times. The formula will be useful for fitting data from sources such as
intra-day variables and gamma-ray burst afterglows. These sources are often
observed at relatively high frequencies (several gigahertz) where interstellar
scattering is neither very strong nor very weak, so that asymptotic formulae
are inapplicable.


  Discourse involves two perspectives: a person's intention in making an
utterance and others' perception of that utterance. The misalignment between
these perspectives can lead to undesirable outcomes, such as misunderstandings,
low productivity and even overt strife. In this work, we present a
computational framework for exploring and comparing both perspectives in online
public discussions.
  We combine logged data about public comments on Facebook with a survey of
over 16,000 people about their intentions in writing these comments or about
their perceptions of comments that others had written. Unlike previous studies
of online discussions that have largely relied on third-party labels to
quantify properties such as sentiment and subjectivity, our approach also
directly captures what the speakers actually intended when writing their
comments. In particular, our analysis focuses on judgments of whether a comment
is stating a fact or an opinion, since these concepts were shown to be often
confused.
  We show that intentions and perceptions diverge in consequential ways. People
are more likely to perceive opinions than to intend them, and linguistic cues
that signal how an utterance is intended can differ from those that signal how
it will be perceived. Further, this misalignment between intentions and
perceptions can be linked to the future health of a conversation: when a
comment whose author intended to share a fact is misperceived as sharing an
opinion, the subsequent conversation is more likely to derail into uncivil
behavior than when the comment is perceived as intended. Altogether, these
findings may inform the design of discussion platforms that better promote
positive interactions.


  In this Supporting Information, we provide background information on our
model of the in vitro Kai system and the calculations that we have performed.
We will closely follow the outline of the main text.


  In the studies of the Bose-Einstein condensation of ideal gases in finite
systems, the divergence problem usually arises in the equation of state. In
this paper, we present a technique based on the heat kernel expansion and the
zeta-function regularization to solve the divergence problem, and obtain the
analytical expression of the Bose-Einstein condensation temperature for general
finite systems. The result is represented by the heat kernel coefficients, in
which the asymptotic energy spectrum of the system is used. Besides the general
case, for the systems with exact spectra, e.g., ideal gases in an infinite slab
or in a three-sphere, the sums of the spectra can be performed exactly and the
calculation of the corrections to the critical temperatures is more direct. For
the system confined in a bounded potential, the form of the heat kernel is
different from the usual heat kernel expansion. We show that, as long as the
asymptotic form of the global heat kernel can be found out, our method also
works. For Bose gases confined in three- and two-dimensional isotropic harmonic
potentials, we obtain the higher-order corrections to the usual results of the
critical temperatures. Our method also can be applied to the problem of the
generalized condensation, and we give the correction of the boundary on the
second critical temperature in a highly anisotropic slab.


  We construct central elements in a completion of the quantum affine algebra
at the critical level c=-g from the universal R-matrix (g being the dual
Coxeter number of the corresponding simple Lie algebra), using the method of
Reshetikhin and Semenov-Tian-Shansky. This construction defines an action of
the Grothendieck algebra of the category of finite-dimensional representations
of the quantum affine algebra on any module over this algebra from category O
with c=-g. We explain the connection between the central elements and transfer
matrices in statistical mechanics. In the quasiclassical approximation this
connection was explained by Feigin, E.Frenkel, and Reshetikhin in hep-th
9402022, and it was mentioned that one could generalize it to the quantum case
to get Bethe vectors for transfer matrices. Using this connection, we prove
that the central elements
  (for all finite dimensional representations) applied to the highest weight
vector of a generic Verma module at the critical level generate the whole space
of singular vectors in this module. We also compute the first term of the
quasiclassical expansion of the central elements near q=1, and show that it
always gives the Sugawara current with a certain coefficient.


  Constrained by the Nielsen-Ninomiya no-go theorem, in all so-far
experimentally determined Weyl semimetals (WSMs) the Weyl points (WPs) always
appear in pairs in the momentum space with no exception. As a consequence,
Fermi arcs occur on surfaces which connect the projections of the WPs with
opposite chiral charges. However, this situation can be circumvented in the
case of unpaired WP, without relevant surface Fermi arc connecting its surface
projection, appearing singularly, while its Berry curvature field is absorbed
by nontrivial charged nodal walls. Here, combining angle-resolved photoemission
spectroscopy with density functional theory calculations, we show
experimentally that a singular Weyl point emerges in PtGa at the center of the
Brillouin zone (BZ), which is surrounded by closed Weyl nodal walls located at
the BZ boundaries and there is no Fermi arc connecting its surface projection.
Our results reveal that nontrivial band crossings of different dimensionalities
can emerge concomitantly in condensed matter, while their coexistence ensures
the net topological charge of different dimensional topological objects to be
zero. Our observation extends the applicable range of the original
Nielsen-Ninomiya no-go theorem which was derived from zero dimensional paired
WPs with opposite chirality.


  We consider a modification of the well studied Hamiltonian Mean-Field model
by introducing a hard-core point-like repulsive interaction and propose a
numerical integration scheme to integrate numerically its dynamics. Our results
show that the outcome of the initial violent relaxation is altered, and also
that the phase-diagram is modified with a critical temperature at a higher
value than in the non-collisional counterpart.


  Search for double $\beta$ decay of $^{136}$Ce and $^{138}$Ce was realized
with 732 g of deeply purified cerium oxide sample measured over 1900 h with the
help of an ultra-low background HPGe $\gamma$ detector with a volume of 465
cm$^3$ at the STELLA facility of the Gran Sasso National Laboratories of the
INFN (Italy). New improved half-life limits on double beta processes in the
cerium isotopes were set at the level of $\lim T_{1/2}\sim 10^{17}-10^{18}$~yr;
many of them are even two orders of magnitude larger than the best previous
results.


  In the Standard theory of neutrino oscillations is used scheme of mass
mixings, i.e., oscillation parameters are expressed via terms of mass matrix.
In this work neutrino oscillations generated by charge (the weak interaction
couple constants) mixings are considered. Expressions for angle mixings and
lengths of oscillations are obtained. The expressions of the probability for
three neutrino oscillations are given. Neutrino oscillations in this scheme
(mechanism) are virtual if neutrino masses are not equal and real if neutrino
masses are equal.


  The Einstein-Podolsky-Rosen (EPR) paradox that argues for the incompleteness
of quantum mechanics as a description of physical reality has been put to rest
by John Bell's famous theorem, which inspired numerous experimental tests and
brought about further affirmations of quantum reality. Nevertheless, in his
writings and public presentations, Richard Feynman never acknowledged the
significance of Bell's contribution to the resolution of the EPR paradox. In
this paper, we discuss several variants of the Bell inequalities (including one
that was specifically espoused by Feynman), and explore the ways in which they
demolish the arguments in favor of local hidden-variable theories. We also
examine the roots of Feynman's attitude toward Bell's theorem in the context of
Feynman's special perspective on quantum mechanics.


  We present a model calculation of the spectral function of an electron in a
superconducting resonating valence bond (RVB) state. The RVB state, described
by the phase-string mean field theory is characterized by three important
features: (i) spin-charge separation, (ii) short range antiferromagnetic
correlations, and (iii) holon condensation. The results of our calculation are
in good agreement with data obtained from Angle Resolved Photoemission
Spectroscopy (ARPES) in superconducting Bi 2212 at optimal doping
concentration.


  The exclusive omega electroproduction off the proton was studied in a large
kinematical domain above the nucleon resonance region and for the highest
possible photon virtuality (Q2) with the 5.75 GeV beam at CEBAF and the CLAS
spectrometer. Cross sections were measured up to large values of the
four-momentum transfer (-t < 2.7 GeV2) to the proton. The contributions of the
interference terms sigma_TT and sigma_TL to the cross sections, as well as an
analysis of the omega spin density matrix, indicate that helicity is not
conserved in this process. The t-channel pi0 exchange, or more generally the
exchange of the associated Regge trajectory, seems to dominate the reaction
gamma* p -> omega p, even for Q2 as large as 5 GeV2. Contributions of handbag
diagrams, related to Generalized Parton Distributions in the nucleon, are
therefore difficult to extract for this process. Remarkably, the high-t
behaviour of the cross sections is nearly Q2-independent, which may be
interpreted as a coupling of the photon to a point-like object in this
kinematical limit.


  We show that radio-frequency spectroscopy on weakly-bound molecules is a
powerful and sensitive tool to probe molecular energy structure as well as
atomic scattering properties. An analytic expression of the rf excitation
lineshape is derived, which in general contains a bound-free component and a
bound-bound component. In particular, we show that the bound-free process
strongly depends on the sign of the scattering length in the outgoing channel
and acquires a Fano-type profile near a Feshbach resonance. The derived
lineshapes provide an excellent fit to both the numerical calculation and the
experimental measurements.


  Electron-positron pair creation in supercritical electric fields limits the
net charge of any static, spherical object, such as superheavy nuclei,
strangelets, and Q-balls, or compact stars like neutron stars, quark stars, and
black holes. For radii between $4\times10^2$ fm and $10^4$ fm the upper bound
on the net charge is given by the universal relation $Z=0.71R_{fm}$, and for
larger radii (measured in fm or km) $Z = 7 \times 10^{-5} R_{fm}^2 = 7 \times
10^{31} R_{km}^2$. For objects with nuclear density the relation corresponds to
$Z \approx 0.7 A^{1/3}$ ($10^{8} < A < 10^{12}$) and $Z \approx 7\times10^{-5}
A^{2/3}$ ($A > 10^{12}$), where $A$ is the baryon number. For some systems this
universal upper bound improves existing charge limits in the literature.


  We study magnetic properties of NdFe$_{11}$Ti$X$, where $X$=B, C, N, O, and
F, by using the first-principles calculation based on the density functional
theory. Its parent compound NdFe$_{11}$Ti has the ThMn$_{12}$ structure, which
has the symmetry of space group $I4/mmm$, No. 139. The magnetization increases
by doping B, C, N, O, and F at the $2b$ site of the ThMn$_{12}$ structure. The
amount of the increase is larger for $X$=N, O, F than for $X$=B, C. On the
other hand, the crystal field parameter $\langle r^{2} \rangle A_{0}^{2}$,
which controls the axial magnetic anisotropy of the Nd $4f$ magnetic moment,
depends differently on the dopant. With increase of the atomic number from
$X$=B, $\langle r^{2} \rangle A_{0}^{2}$ increases, takes a maximum value for
$X$=N, and then turns to decrease. This suggests that in NdFe$_{11}$Ti$X$,
nitrogen is the most appropriate dopant among B, C, N, O, and F for permanent
magnets in terms of magnetization and anisotropy. The above calculated
properties are explained based on the detailed analysis of the electronic
structures of NdFe$_{11}$Ti$X$.


  We investigate broad-band emission from supernova ejecta powered by a
relativistic wind from a central compact object. A recent two-dimensional
hydrodynamic simulation studying the dynamical evolution of supernova ejecta
with a central energy source has revealed that outermost layers of the ejecta
are accelerated to mildly relativistic velocities because of the breakout of a
hot bubble driven by the energy injection. The outermost layers decelerate as
they sweep a circumstellar medium surrounding the ejecta, leading to the
formation of the forward and reverse shocks propagating in the circumstellar
medium and the ejecta. While the ejecta continue to release the internal energy
as thermal emission from the photosphere, the energy dissipation at the forward
and reverse shock fronts gives rise to non-thermal emission. We calculate light
curves and spectral energy distributions of thermal and non-thermal emission
from central engine powered supernova ejecta embedded in a steady stellar wind
with typical mass loss rates for massive stars. The light curves are compared
with currently available radio and X-ray observations of hydrogen-poor
superluminous supernovae, as well as the two well-studied broad-lined Ic
supernovae, 1998bw and 2009bb, which exhibit bright radio emission indicating
central engine activities. We point out that upper limits on radio luminosities
of nearby superluminous supernovae may indicate the injected energy is mainly
converted to thermal radiation rather than creating mildly relativistic flows
owing to photon diffusion time scales comparable to the injection time scale.


  Charged Lepton Flavour Violating (cLFV) decays constrain the relationship
between the neutrino and the scalar sectors of the Grimus-Neufeld model (GNM),
an appealing minimal model of neutrino masses. It turns out, that in the
scenario, where the seesaw scale is lower than the electroweak one, cLFV is
completely defined by the new Yukawa interactions between the additional single
heavy Majorana neutrino, the second Higgs doublet and the lepton doublets.
Therefore, we derive a useful parameterization for the Yukawa couplings which
reproduces by construction the correct PMNS matrix and the correct neutrino
masses for both Normal and Inverted ordering at one-loop level. We embed this
scenario in the $\texttt{FlexibleSUSY}$ spectrum-generator generator to perform
parameter scans. Focusing on the tiny seesaw scale, we show that current
$\mu\to e\gamma$ limits provide significant constraints on the scalar sector,
and we evaluate the impact of future cLFV $\tau$-decay searches for the cases
of discovery or non-discovery. The tiny seesaw scale makes the neutrino sector
and the cLFV processes in the GNM similar to the scotogenic and the
scoto-seesaw models, so we provide constraints for these models as well.


  The influence of an applied shear field on the dynamics of an aging colloidal
suspension has been investigated by the dynamic light scattering determination
of the density autocorrelation function. Though a stationary state is never
observed, the slow dynamics crosses between two different non-equilibrium
regimes as soon as the structural relaxation time approaches the inverse shear
rate. In the shear dominated regime (at high shear rate values) the structural
relaxation time is found to be strongly sensitive to shear rate while aging
proceeds at a very slow rate. The effect of shear on the detailed shape of the
density autocorrelation function is quantitatively described assuming that the
structural relaxation process arises from the heterogeneous superposition of
many relaxing units each one independently coupled to shear with a parallel
composition rule for timescales.


  Summarizing text-rich documents has been long studied in the literature, but
most of the existing efforts have been made to summarize a static and
predefined multi-document set. With the rapid development of online platforms
for generating and distributing text-rich documents, there arises an urgent
need for continuously summarizing dynamically evolving multi-document sets
where the composition of documents and sets is changing over time. This is
especially challenging as the summarization should be not only effective in
incorporating relevant, novel, and distinctive information from each concurrent
multi-document set, but also efficient in serving online applications. In this
work, we propose a new summarization problem, Evolving Multi-Document sets
stream Summarization (EMDS), and introduce a novel unsupervised algorithm PDSum
with the idea of prototype-driven continuous summarization. PDSum builds a
lightweight prototype of each multi-document set and exploits it to adapt to
new documents while preserving accumulated knowledge from previous documents.
To update new summaries, the most representative sentences for each
multi-document set are extracted by measuring their similarities to the
prototypes. A thorough evaluation with real multi-document sets streams
demonstrates that PDSum outperforms state-of-the-art unsupervised
multi-document summarization algorithms in EMDS in terms of relevance, novelty,
and distinctiveness and is also robust to various evaluation settings.


  In the causal set approach, spacetime is a continuous approximation of a
faithful embedded causal set. But the faithful embedded causal set describes
the empty spacetime and cannot describe matter. Only faithful embedding of
coarse grained causal set is necessary for the emergence of spacetime. The
initial causal set can be unfaithfully embedded. The following properties of
the universe can be the consequences of the unfaithful embedding:
antiparticles, fields of interactions, the inhomogeneous distribution of the
matter.


  In the present paper we construct deformations of the Poincar\'e algebra as
representations on a noncommutative spacetime with canonical commutation
relations. These deformations are obtained by solving a set of conditions by an
appropriate ansatz for the deformed Lorentz generator. They turn out to be Hopf
algebras of quantum universal enveloping algebra type with nontrivial
antipodes. In order to present a notion of $\theta$-deformed Minkowski space
$\mathcal{M}_\theta$, we introduce Casimir operators and spacetime invariants
for all deformations obtained.


  Cool astrophysical objects, such as (exo)planets, brown dwarfs, or asymptotic
giant branch stars, can be strongly affected by condensation. Condensation does
not only directly affect the chemical composition of the gas phase by removing
elements but the condensed material also influences other chemical and physical
processes in these objects. This includes, for example, the formation of clouds
in planetary atmospheres and brown dwarfs or the dust-driven winds of evolved
stars. In this study we introduce FastChem Cond, a new version of the FastChem
equilibrium chemistry code that adds a treatment of equilibrium condensation.
Determining the equilibrium composition under the impact of condensation is
complicated by the fact that the number of condensates that can exist in
equilibrium with the gas phase is limited by a phase rule. However, this phase
rule does not directly provide information on which condensates are stable. As
a major advantage of FastChem Cond is able to automatically select the set
stable condensates satisfying the phase rule. Besides the normal equilibrium
condensation, FastChem Cond can also be used with the rainout approximation
that is commonly employed in atmospheres of brown dwarfs or (exo)planets.
FastChem Cond is available as open-source code, released under the GPLv3
licence. In addition to the C++ code, FastChem Cond also offers a Python
interface. Together with the code update we also add about 290 liquid and solid
condensate species to FastChem.


  We investigate the formation of spiral crack patterns during the desiccation
of thin layers of precipitates in contact with a substrate. This
symmetry-breaking fracturing mode is found to arise naturally not from torsion
forces, but from a propagating stress front induced by the fold-up of the
fragments. We model their formation mechanism using a coarse-grain model for
fragmentation and successfully reproduce the spiral cracks. Fittings of
experimental and simulation data show that the spirals are logarithmic,
corresponding to constant deviation from a circular crack path. Theoretical
aspects of the logarithmic spirals are discussed. In particular we show that
this occurs generally when the crack speed is proportional to the propagating
speed of stress front.


  As a consequence of the increasing influence of machine learning on our
lives, everyone needs competencies to understand corresponding phenomena, but
also to get involved in shaping our world and making informed decisions
regarding the influences on our society. Therefore, in K-12 education, students
need to learn about core ideas and principles of machine learning. However, for
this target group, achieving all of the aforementioned goals presents an
enormous challenge. To this end, we present a teaching concept that combines a
playful and accessible unplugged approach focusing on conceptual understanding
with empowering students to actively apply machine learning methods and reflect
their influence on society, building upon decision tree learning.


  In this work, the possible dynamics associated with leptophilic Z_l boson at
CLIC (Compact Linear Collider) have been investigated by using artificial
neural networks (ANNs). These hypotetic massive boson Z_l have been shown
through the process e+e- -> M+M-. Furthermore, the invariant mass distributions
for final muons have been consistently predicted by using ANN. For these highly
non-linear data, we have constructed consistent empirical physical formulas
(EPFs) by appropriate feed- forward ANN. These ANN-EPFs can be used to derive
further physical functions which could be relevant to studying Z_l.


  The HELP project focuses on the data from ESA's Herschel mission, which
covered over 1300$deg^2$ and is preparing to publish a multi-wavelength
catalogue of millions of objects. Our main goal is to find the best approach to
simultaneously fitting SEDs of millions of galaxies across a wide redshift
range to obtain homogeneous estimates of the main physical parameters of
detected IR galaxies. We perform SED fitting on the UV/NIR to FIR emission of
42 047 galaxies from the pilot HELP field: ELAIS N1. We use the latest release
of CIGALE, a galaxy SED fitting code relying on energy balance, to deliver the
main physical parameters such as $M_{star}$, SFR, and $L_{dust}$. We implement
additional quality criteria to the fits by calculating $\chi^2$ values for the
stellar and dust part of the spectra independently. These criteria allow us to
identify the best fits and to identify peculiar galaxies. We perform the SED
fitting by assuming three different dust attenuation laws separately allowing
us to test the impact of the assumed law on estimated physical parameters. We
implemented two additional quality value checks for the SED fitting method
based on $M_{star}$ estimation and energy budget. This method allows us to
identify possible objects with incorrect matching in the catalogue and peculiar
galaxies; we found 351 possible candidates of lensed galaxies using two
complementary $\chi^2$s criteria (stellar and IR) and $z_{phot}$ calculated for
the IR part of the spectrum only. We find that the attenuation law has an
important impact on the $M_{star}$ estimate (on average leading to disparities
of a factor of two). We derive the relation between $M_{star}$ estimates
obtained by different attenuation laws and we find the best recipe for our
sample. We also make independent estimates of the total $L_{dust}$ parameter
from stellar emission by fitting the galaxies with and without IR data
separately.


  The paper proposes a possible mechanism of interaction of microwaves with
superfluid helium that results in an experimentally observed narrow peak of
microwave absorption on the frequencies by the order of the roton frequency.
The obtained microwave photon absorption coefficient depends on the local
equilibrium distribution function which is established due to fast roton-roton
and roton-phonon interactions. With the availability of superfluid flows, the
local equilibrium distribution function depends on their velocity. The critical
velocity of the flows, at which the absorption of microwaves is replaced by
their radiation, is found.


  Vast investments have recently been made worldwide in developing the
Cyber-Physical System (CPS) technology with the expectations of improving
economical and societal structures. However, great care must be paid to the
CPS' complexity, the impact of emerging IoT (Internet of Things) protocols on
the CPS infrastructure as well as the impact of information dissemination by
these protocols on the safety of these infrastructures. This paper addresses
the issue of CPS safety by proposing and evaluating the performance of a CPS
management framework and the analysis of the dynamics of the underlining IoT
network in the cyber-space. The main contributions of this paper are in
threefold. Firstly, a new CPS framework is proposed; that: 1) builds around a
layered architecture to compartmentalise the CPS functionalities into different
modules for efficiency and scalability and 2) uses an inner feedback loop for
the efficient management of CPS infrastructure. Secondly, building upon this
framework, a novel diffusion model that uses the epidemic (interference) sets
to produce accurate diffusion patterns across the CPS IoT subsystem is
proposed. Finally, the proposed diffusion model is numerically analysed to show
how it can be used to achieve efficient CPS surveillance in order to trigger
reconfiguration to re-optimise the CPS when it is under stress. in IoT
settings. The numerical analysis of the diffusion model shows that interference
propagates in pairwise disjoint sets, with IoT nodes migrating from
"susceptible" to "attacked" statuses and finally reaching the "removed" state
at a predictable time. Deployment considerations on some of the current social
and public networks are also onsidered


  We compute the dilatation generator in the su(2) sector of planar N=4 super
Yang-Mills theory at four-loops. We use the known world-sheet scattering matrix
to constrain the structure of the generator. The remaining few coefficients can
be computed directly from Feynman diagrams. This allows us to confirm previous
conjectures for the leading contribution to the dressing phase which is
proportional to zeta(3).


  With the extension of the spectral exploitation of optical fibers beyond the
C-band, accurate modeling and simulation of nonlinear interference (NLI)
generation is of the utmost performance. Models and numerical simulation tools
rely on the widely used Manakov equation (ME): however, this approach when
considering also the effect of polarization mode dispersion (PMD) is formally
valid only over a narrow optical bandwidth. In order to analyze the range of
validity of the ME and its applicability to future wide-band systems, we
present numerical simulations, showing the interplay between NLI generation and
PMD over long dispersion-uncompensated optical links, using coherent
polarization division multiplexing (PDM) quadrature amplitude modulation (QAM)
formats. Using a Monte-Carlo analysis of different PMD realizations based on
the coupled nonlinear Schr\"{o}dinger equations, we show that PMD has a
negligible effect on NLI generation, independently from the total system
bandwidth. Based on this, we give strong numerical evidence that the ME can be
safely used to estimate NLI generation well beyond its bandwidth of validity
that is limited to the PMD coherence bandwidth.


  Information transfer which reveals the state variation of variables usually
plays a vital role in big data analytics and processing. In fact, the measures
for information transfer could reflect the system change by use of the variable
distributions, similar to KL divergence and Renyi divergence. Furthermore, in
terms of the information transfer in big data, small probability events usually
dominate the importance of the total message to some degree. Therefore, it is
significant to design an information transfer measure based on the message
importance which emphasizes the small probability events. In this paper, we
propose a message importance transfer measure (MITM) and investigate its
characteristics and applications on three aspects. First, the message
importance transfer capacity based on MITM is presented to offer an upper bound
for the information transfer process with disturbance. Then, we extend the MITM
to the continuous case and discuss the robustness by using it to measuring
information distance. Finally, we utilize the MITM to guide the queue length
selection in the caching operation of mobile edge computing.


  Photonic crystals, material structures in which the dielectric function
varies periodically in one, two, or three dimensions, can provide exquisite
control over the propagation and confinement of light. By tailoring their band
structure, exceptional optical effects can be achieved, such as slow light
propagation or, through the creation of photonic bandgaps, optical cavities
with both a high quality factor and a small mode volume. Photonic crystal
cavities have been used to realize compact nano-lasers and achieve strong
coupling to quantum emitters, such as semiconductor quantum dots, color
centers, or cold atoms. A useful attribute of photonic crystals is the ability
to create chirped mirrors. Chirping has underpinned advances in ultra-fast
lasers based on bulk mirrors, but has yet to be fully exploited in integrated
photonics, where it could provide a means to engineer otherwise unattainable
dispersion profiles for a range of nonlinear optical applications, including
soliton frequency comb generation. The vast majority of integrated resonators
for frequency combs make use of microring geometries, where only waveguide
width and height are varied to engineer dispersion. Generation of frequency
combs has been demonstrated with one-dimensional photonic crystal cavities made
of silicon nitride, but the low index contrast prevents formation of broad
soliton combs. We overcome these challenges by using a photonic-crystal
Fabry-P\'erot resonator made of gallium phosphide, a material with a high
refractive index and a Kerr nonlinearity 200 times larger than that of silicon
nitride. We employ chirped photonic crystal mirrors to provide anomalous
dispersion. With subharmonic pulsed pumping at an average power of 23.6 mW, we
are able to access stable dissipative Kerr frequency combs. We demonstrate
soliton formation with a 3-dB bandwidth of 3.0 THz, corresponding to a pulse
duration of 60 fs.


  The increased importance of mobile photography created a need for fast and
performant RAW image processing pipelines capable of producing good visual
results in spite of the mobile camera sensor limitations. While deep
learning-based approaches can efficiently solve this problem, their
computational requirements usually remain too large for high-resolution
on-device image processing. To address this limitation, we propose a novel
PyNET-V2 Mobile CNN architecture designed specifically for edge devices, being
able to process RAW 12MP photos directly on mobile phones under 1.5 second and
producing high perceptual photo quality. To train and to evaluate the
performance of the proposed solution, we use the real-world Fujifilm UltraISP
dataset consisting on thousands of RAW-RGB image pairs captured with a
professional medium-format 102MP Fujifilm camera and a popular Sony mobile
camera sensor. The results demonstrate that the PyNET-V2 Mobile model can
substantially surpass the quality of tradition ISP pipelines, while
outperforming the previously introduced neural network-based solutions designed
for fast image processing. Furthermore, we show that the proposed architecture
is also compatible with the latest mobile AI accelerators such as NPUs or APUs
that can be used to further reduce the latency of the model to as little as 0.5
second. The dataset, code and pre-trained models used in this paper are
available on the project website: https://github.com/gmalivenko/PyNET-v2


  NiS, exhibiting a text-book example of a first-order transition with many
unusual properties at low temperatures, has been variously described in terms
of conflicting descriptions of its ground state during the past several
decades. We calculate these physical properties within first-principle
approaches based on the density functional theory and conclusively establish
that all experimental data can be understood in terms of a rather unusual
ground state of NiS that is best described as a self-doped, nearly compensated,
antiferromagnetic metal, resolving the age-old controversy. We trace the origin
of this novel ground state to the specific details of the crystal structure,
band dispersions and a sizable Coulomb interaction strength that is still
sub-critical to drive the system in to an insulating state. We also show how
the specific antiferromagnetic structure is a consequence of the less-discussed
90 degree and less than 90 degree superexchange interactions built in to such
crystal structures.


  The direct detection of MeV-scale dark matter (DM) particles hinges on
achieving an exceptionally low energy detection threshold. Germanium (Ge)
detectors, meticulously tailored with precise impurity compositions, hold the
potential to enhance sensitivity to energy levels below the sub-electronvolt
(sub-eV) range. This study explores the behavior of residual impurities
inherent to Ge detectors at helium temperatures, unveiling a captivating
freeze-out phenomenon leading to the formation of excited localized states
known as dipole states. Using compelling evidence from relative capacitance
measurements obtained from two detectors, we elucidate the transition of
impurity atoms from free charge states to these dipole states as the
temperature drops from 11 K to 6.5 K. Our investigation comprehensively covers
the intricate formation of these dipole states in both n-type and p-type
impurities. Furthermore, we shed light on the electric field generated by these
dipole states, revealing their ability to trap charges and facilitate the
creation of cluster dipole states. Confirming findings from previous
measurements, we establish that these excited dipole states exhibit a binding
energy of less than 10 meV, offering an exceptionally low detection threshold
for MeV-scale DM. Building upon this concept, we propose the development of a
1-kg Ge detector with internal charge amplification, an innovative approach
poised to surpass electrical noise and enable the detection of MeV-scale DM
with unprecedented sensitivity.


  We investigate the initial value problem for some defocusing coupled
nonlinear fourth-order Schrodinger equations. Global well-posedness and
scattering in the energy space are obtained.


  We review black holes with second-order phase transition in string theory
(R-charged black holes and holographic superconductors) and review their static
and dynamic critical phenomena. Holographic superconductors have conventional
mean-field values for static critical exponents, but R-charged black holes have
unconventional ones. For dynamic universality class, holographic
superconductors belong to model A, and R-charged black holes belong to model B
in the classification of Hohenberg and Halperin. The analysis suggests that
some black holes do obey the theory of critical phenomena in condensed matter
physics.


  Bistability is a key property of many systems arising in the nonlinear
sciences. For example, it appears in many partial differential equations
(PDEs). For scalar bistable reaction-diffusions PDEs, the bistable case even
has take on different names within communities such as Allee, Allen-Cahn,
Chafee-Infante, Nagumo, Ginzburg-Landau, $\Phi_4$, Schl\"ogl, Stommel, just to
name a few structurally similar bistable model names. One key mechanism, how
bistability arises under parameter variation is a pitchfork bifurcation. In
particular, taking the pitchfork bifurcation normal form for reaction-diffusion
PDEs is yet another variant within the family of PDEs mentioned above. More
generally, the study of this PDE class considering steady states and stability,
related to bifurcations due to a parameter is well-understood for the
deterministic case. For the stochastic PDE (SPDE) case, the situation is less
well-understood and has been studied recently. In this paper we generalize and
unify several recent results for SPDE bifurcations. Our generalisation is
motivated directly by applications as we introduce in the equation a spatially
heterogeneous term and relax the assumptions on the covariance operator that
defines the noise. For this spatially heterogeneous SPDE, we prove a
finite-time Lyapunov exponent bifurcation result. Furthermore, we extend the
theory of early warning signs in our context and we explain, the role of
universal exponents between covariance operator warning signs and the lack of
finite-time Lyapunov uniformity. Our results are accompanied and
cross-validated by numerical simulations.


  Rerandomization utilizes modern computing ability to search for covariate
balance improved experimental design while adhering to the randomization
principle originally advocated by RA Fisher. Conditionally affinely invariant
rerandomization has the ``Equal Percent Variance Reducing'' property on subsets
of conditionally ellipsoidally symmetric covariates. It is suitable to deal
with covariates of varying importance or mixed types and usually produces
multiple balance scores. ``Unified'' and `` intersection'' methods are common
ways of deciding on multiple scores. In general, `` intersection'' methods are
computationally more efficient but asymptotically inadmissible. As
computational cost is not a major concern in experimental design, we recommend
``unified'' methods to build admissible criteria for rerandomization


  Many-body dipolar effects in Fermi gases are quite subtle as they
energetically compete with the large kinetic energy at and below the Fermi
surface (FS). Recently it was experimentally observed that the FS is deformed
from a sphere to an ellipsoid due to the presence of the anisotropic and
long-range dipole-dipole interaction. Moreover, it was suggested that, when the
dipoles are rotated by means of an external field, the FS follows their
rotation, thereby keeping the major axis of the momentum-space ellipsoid
parallel to the dipoles. Here we generalise a previous Hartree-Fock mean-field
theory to systems confined in an elongated triaxial trap with an arbitrary
orientation of the dipoles relative to the trap. With this we study for the
first time the effects of the dipoles' arbitrary orientation on the
ground-state properties of the system. Furthermore, taking into account the
geometry of the system, we show how the ellipsoidal FS deformation can be
reconstructed, assuming ballistic expansion, from the experimentally measurable
real-space aspect ratio after a free expansion. We compare our theoretical
results with new experimental data measured with an erbium Fermi gas for
various trap parameters and dipole orientations. The observed remarkable
agreement demonstrates the ability of our model to capture the full angular
dependence of the FS deformation. Moreover, for systems with even higher dipole
moment, our theory predicts an additional unexpected effect: the FS does not
simply follow rigidly the orientation of the dipoles, but softens showing a
change in the aspect ratio depending on the dipoles' orientation relative to
the trap geometry, as well as on the trap anisotropy itself. Our theory
provides the basis for understanding and interpreting phenomena in which the
investigated physics depends on the underlying structure of the FS, such as
fermionic pairing and superfluidity.


  This paper proposes the simulation of structured behaviors in a crowd of
virtual agents by extending the BioCrowds simulation model.
  Three behaviors were simulated and evaluated, a queue as a generic case and
two specific behaviors observed at rock concerts. The extended model
incorporates new parameters and modifications to replicate these behaviors
accurately. Experiments were conducted to analyze the impact of parameters on
simulation results, and computational performance was considered.
  The results demonstrate the model's effectiveness in simulating structured
behaviors and its potential for replicating complex social phenomena in diverse
scenarios.


  The Goldreich-Levin algorithm was originally proposed for a cryptographic
purpose and then applied to learning. The algorithm is to find some larger
Walsh coefficients of an $n$ variable Boolean function. Roughly speaking, it
takes a $poly(n,\frac{1}{\epsilon}\log\frac{1}{\delta})$ time to output the
vectors $w$ with Walsh coefficients $S(w)\geq\epsilon$ with probability at
least $1-\delta$. However, in this paper, a quantum algorithm for this problem
is given with query complexity $O(\frac{\log\frac{1}{\delta}}{\epsilon^4})$,
which is independent of $n$. Furthermore, the quantum algorithm is generalized
to apply for an $n$ variable $m$ output Boolean function $F$ with query
complexity $O(2^m\frac{\log\frac{1}{\delta}}{\epsilon^4})$.


  We study the existence and stability of stationary solutions of
Poisson-Nernst- Planck equations with steric effects (PNP-steric equations)
with two counter-charged species. These equations describe steady current
through open ionic channels quite well. The current levels in open ionic
channels are known to switch between `open' or `closed' states in a spontaneous
stochastic process called gating, suggesting that their governing equations
should give rise to multiple stationary solutions that enable such multi-stable
behavior. We show that within a range of parameters, steric effects give rise
to multiple stationary solutions that are smooth. These solutions, however, are
all unstable under PNP-steric dynamics. Following these findings, we introduce
a novel PNP-Cahn-Hilliard model, and show that it admits multiple stationary
solutions that are smooth and stable. The various branches of stationary
solutions and their stability are mapped utilizing bifurcation analysis and
numerical continuation methods.


  In this work, we analyze the probabilistic cooperation of a full-duplex relay
in a multiuser random-access network. The relay is equipped with on/off modes
for the receiver and the transmitter independently. These modes are modeled as
probabilities by which the receiver and the transmitter are activated. We
provide analytical expressions for the performance of the relay queue, such as
arrival and service rates, stability conditions, and the average queue size. We
optimize the relay's operation setup to maximize the network-wide throughput
while, simultaneously, we keep the relay's queue stable and minimize the
consumed energy. Furthermore, we study the effect of the SINR threshold and the
self-interference (SI) coefficient on the per-user and network-wide throughput.
For low SINR threshold, we show under which circumstances it is beneficial to
switch off the relay completely, or switch off the relay's receiver only.


  We fabricated GaN based hollow nanowires standing upright on a sapphire
substrate by the sublimation method and found that they exhibit laser
oscillation at room temperature. These very long, hollow, nano-sized structures
cannot be fabricated by other means. Furthermore, we determined the condition
under which the fundamental mode is azimuthally polarized by investigating the
dispersion of the hollow structure. Examination of the measured emission
properties indicates that the hollow nanowire operates as a topological,
vector-beam, light source.


  We use a holographic description of technicolor dynamics to study gauge
theories that only break chiral symmetry when aided by a strong four fermion
interaction. These Nambu-Jona-Lasinio (NJL) assisted technicolor models provide
examples of different dynamics from walking technicolor which can, by tuning,
generate a light higgs like $\sigma$ meson. We compute the vector meson
($\rho$) and axial vector meson (A) spectrum for a variety of models with
techni-quarks in the fundamental representation, enlarging the available
parameter space over a previous analysis of walking theories. These predictions
determine the parameter space of a low energy effective description where LHC
constraints from dilepton channels have already been applied. Many of the
models with low numbers of electroweak doublets still lie beyond current
constraints and motivate exploration of new signatures beyond dilepton for LHC
and a 100 TeV proton collider.


  This paper is a report from a recent meeting on "the Future of
high-resolution imaging in the visible and infrared", reviewing the
astronomical drivers for development and the technological advances that might
boost performance. Each of the authors listed contributed a section themselves.


  Relying on an effective-string approach in which glueballs --- bound states
of pure Yang-Mills theory --- are modelled by closed strings, we give arguments
suggesting that anyonic glueballs, \textit{i.e.} glueballs with arbitrary spin,
may exist in $(2+1)$-$\,$dimensional Yang-Mills theory. We then focus on the
large$\,$-$N_c$ limit of $SU$($N_c$) Yang-Mills theory and show that our model
leads to a mass spectrum in good agreement with lattice data in the scalar
sector, while it predicts the masses and spins of anyonic glueball states.


  We show somewhat unexpectedly that whenever a general Bernstein-type maximal
inequality holds for partial sums of a sequence of random variables, a maximal
form of the inequality is also valid.


  Many two-dimensional classical field theories have hidden symmetries that
form an infinite-dimensional algebra. For those examples that correspond to
effective descriptions of compactified superstring theories, the duality group
is expected to be a large discrete subgroup of the hidden symmetry group. With
this motivation, we explore the hidden symmetries of principal chiral models
and symmetric space models.


  This essay investigates the question of how the naive Bayes classifier and
the support vector machine compare in their ability to forecast the Stock
Exchange of Thailand. The theory behind the SVM and the naive Bayes classifier
is explored. The algorithms are trained using data from the month of January
2010, extracted from the MarketWatch.com website. Input features are selected
based on previous studies of the SET100 Index. The Weka 3 software is used to
create models from the labeled training data. Mean squared error and proportion
of correctly classified instances, and a number of other error measurements are
the used to compare the two algorithms. This essay shows that these two
algorithms are currently not advanced enough to accurately model the stock
exchange. Nevertheless, the naive Bayes is better than the support vector
machine at predicting the Stock Exchange of Thailand.


  The decoupling-style concept begins to ignite in the speech enhancement area,
which decouples the original complex spectrum estimation task into multiple
easier sub-tasks i.e., magnitude-only recovery and the residual complex
spectrum estimation)}, resulting in better performance and easier
interpretability. In this paper, we propose a dual-branch federative magnitude
and phase estimation framework, dubbed DBT-Net, for monaural speech
enhancement, aiming at recovering the coarse- and fine-grained regions of the
overall spectrum in parallel. From the complementary perspective, the magnitude
estimation branch is designed to filter out dominant noise components in the
magnitude domain, while the complex spectrum purification branch is elaborately
designed to inpaint the missing spectral details and implicitly estimate the
phase information in the complex-valued spectral domain. To facilitate the
information flow between each branch, interaction modules are introduced to
leverage features learned from one branch, so as to suppress the undesired
parts and recover the missing components of the other branch. Instead of
adopting the conventional RNNs and temporal convolutional networks for sequence
modeling, we employ a novel attention-in-attention transformer-based network
within each branch for better feature learning. More specially, it is composed
of several adaptive spectro-temporal attention transformer-based modules and an
adaptive hierarchical attention module, aiming to capture long-term
time-frequency dependencies and further aggregate intermediate hierarchical
contextual information. Comprehensive evaluations on the WSJ0-SI84 +
DNS-Challenge and VoiceBank + DEMAND dataset demonstrate that the proposed
approach consistently outperforms previous advanced systems and yields
state-of-the-art performance in terms of speech quality and intelligibility.


  The clathrate-like or caged compounds have attracted continuing interest due
to their structural flexibility as well as fertile physical properties. Here we
report a pressure-induced reemergence of superconductivity in BaIr2Ge7 and
Ba3Ir4Ge16, two new caged superconductors with two-dimensional building blocks
of cage structures. After suppressing the ambient-pressure superconducting
(SC-I) state, a new superconducting (SC-II) state emerges unexpectedly, with Tc
increased to a maximum of 4.4 K and 4.0 K for BaIr2Ge7 and Ba3Ir4Ge16,
respectively. Combined with high-pressure synchrotron x-ray diffraction and
Raman measurements, we propose that the reemergence of superconductivity in the
caged superconductors can be ascribed to a pressure-induced phonon softening
linked to the cage shrink.


  In this work we introduce the randomness which is truly quantum mechanical in
nature arising as an act of measurement. For a composite classical system we
have the joint entropy to quantify the randomness present in the total system
and that happens to be equal to the sum of the entropy of one subsystem and the
conditional entropy of the other subsystem given we know the first system. The
same analogy caries over to the quantum setting by replacing the Shannon
entropy by the Von Neumann entropy. However, if we replace the conditional von
Neumann entropy by the average conditional entropy due to measurement, we find
that it is different from the joint entropy of the system. We call this
difference Measurement Induced Randomness (MIR) and argue that this is unique
of quantum mechanical systems and there is no classical counterpart to this. In
other words the joint Von Neumann entropy gives only the total randomness that
arises because of the heterogeneity of the mixture and we show that it is not
the total randomness that can be generated in the composite system. We
generalize this quantity for N-qubit systems and show that it reduces to
quantum discord for two qubit systems. Further, we show that it is exactly
equal to the change in the cost of quantum state merging that arises because of
the measurement. We argue that for a quantum information processing tasks like
state merging the change in the cost as a result of discarding prior
information can also be viewed as a rise of randomness due to measurement.


  In this paper, we revamp the forgotten classical Semi-Supervised Distance
Metric Learning (SSDML) problem from a Riemannian geometric lens, to leverage
stochastic optimization within a end-to-end deep framework. The motivation
comes from the fact that apart from a few classical SSDML approaches learning a
linear Mahalanobis metric, deep SSDML has not been studied. We first extend
existing SSDML methods to their deep counterparts and then propose a new method
to overcome their limitations. Due to the nature of constraints on our metric
parameters, we leverage Riemannian optimization. Our deep SSDML method with a
novel affinity propagation based triplet mining strategy outperforms its
competitors.


  Here, we analyze in a non-contact fashion charge carrier mobility as a
function of injection level and temperature in silicon by time resolved THz
spectroscopy (TRTS) and parametrize our data by the classical semi-empirical
models of Klaassen and Dorkel & Leturcq. Our experimental results are in very
good agreement with the pioneering works of Krausse and D\"annhauser analyzing
this phenomena by employing contact-based methods. This agreement, that
validates our methodology, can only be achieved by considering charge carrier
diffusion effects following above bandgap near-surface pump photo-excitation of
the sample. From our results, obtained over a large range of injection levels,
we conclude that the model of Klaassen is the best on describing the collected
data at room temperature. Furthermore, we analyze by TRTS the dependence of
charge carrier mobility with temperature for a fixed injection level. Once
more, the parametrization made by the classical model of Klaassen describe our
data appropriately even without the necessity of applying any fitting
parameters (just with the charge carrier density as an input). In this respect,
our work supports the validity of the model and parametrization proposed by
Klaassen, and also illustrate how TRTS can be reliably employed for the
quantitative determination of mobility in semiconductors as a function of key
parameters as injection level and temperature.


  An alternative to the effective field theory approach to treat ghosts in
higher derivative theories is to attempt to integrate them out via the
Euclidean path integral formalism. It has been suggested that this method could
provide a consistent framework within which we might tolerate the ghost degrees
of freedom that plague, among other theories, the higher derivative gravity
models that have been proposed to explain cosmic acceleration. We consider the
extension of this idea to treating a class of terms with order six derivatives,
and find that for a general term the Euclidean path integral approach works in
the most trivial background, Minkowski. Moreover we see that even in de Sitter
background, despite some difficulties, it is possible to define a probability
distribution for tensorial perturbations of the metric.


  I introduce a Markov chain Monte Carlo (MCMC) scheme in which sampling from a
distribution with density pi(x) is done using updates operating on an
"ensemble" of states. The current state x is first stochastically mapped to an
ensemble, x^{(1)},...,x^{(K)}. This ensemble is then updated using MCMC updates
that leave invariant a suitable ensemble density, rho(x^{(1)},...,x^{(K)}),
defined in terms of pi(x^{(i)}) for i=1,...,K. Finally a single state is
stochastically selected from the ensemble after these updates. Such ensemble
MCMC updates can be useful when characteristics of pi and the ensemble permit
pi(x^{(i)}) for all i in {1,...,K}, to be computed in less than K times the
amount of computation time needed to compute pi(x) for a single x. One common
situation of this type is when changes to some "fast" variables allow for quick
re-computation of the density, whereas changes to other "slow" variables do
not. Gaussian process regression models are an example of this sort of problem,
with an overall scaling factor for covariances and the noise variance being
fast variables. I show that ensemble MCMC for Gaussian process regression
models can indeed substantially improve sampling performance. Finally, I
discuss other possible applications of ensemble MCMC, and its relationship to
the "multiple-try Metropolis" method of Liu, Liang, and Wong and the "multiset
sampler" of Leman, Chen, and Lavine.


  We discuss the problem of ultra high energy particles propagation in
astrophysical backgrounds. We present two different computational schemes based
on both kinetic and Monte Carlo approaches. The kinetic approach is an
analytical computation scheme based on the hypothesis of continuos energy
losses while the Monte Carlo scheme takes into account also the stochastic
nature of particle interactions. These schemes, that give quite reliable
results, enable the computation of fluxes keeping track of the different
primary and secondary components, providing a fast and useful workbench to
study Ultra High Energy Cosmic Rays.


  We consider a random walk in a random potential on a square lattice of
arbitrary dimension. The potential is a function of an ergodic environment and
some steps of the walk. The potential can be unbounded, but it is subject to a
moment assumption whose strictness is tied to the mixing of the environment,
the best case being the i.i.d. environment. We prove that the infinite volume
quenched point-to-point free energy exists and has a variational formula in
terms of an entropy. We establish regularity properties of the point-to-point
free energy, as a function of the potential and as a function on the convex
hull of the admissible steps of the walk, and link it to the infinite volume
free energy and quenched large deviations of the endpoint of the walk. One
corollary is a quenched large deviation principle for random walk in an ergodic
random environment, with a continuous rate function.


  Learning heterogeneous treatment effects (HTEs) is an important problem
across many fields. Most existing methods consider the setting with a single
treatment arm and a single outcome metric. However, in many real world domains,
experiments are run consistently - for example, in internet companies, A/B
tests are run every day to measure the impacts of potential changes across many
different metrics of interest. We show that even if an analyst cares only about
the HTEs in one experiment for one metric, precision can be improved greatly by
analyzing all of the data together to take advantage of cross-experiment and
cross-outcome metric correlations. We formalize this idea in a tensor
factorization framework and propose a simple and scalable model which we refer
to as the low rank or LR-learner. Experiments in both synthetic and real data
suggest that the LR-learner can be much more precise than independent HTE
estimation.


  We study the spectrum of fermionic modes on cosmic string loops. We find no
fermionic zero modes nor massive bound states - this implies that vortons
stabilized by fermionic currents do not exist. We have also studied
kink-(anti)kink and vortex-(anti)vortex systems and find that all systems that
have vanishing net topological charge do not support fermionic bound modes.


  As a key task of question answering, question retrieval has attracted much
attention from the communities of academia and industry. Previous solutions
mainly focus on the translation model, topic model, and deep learning
techniques. Distinct from the previous solutions, we propose to construct
fine-grained semantic representations of a question by a learned importance
score assigned to each keyword, so that we can achieve a fine-grained question
matching solution with these semantic representations of different lengths.
Accordingly, we propose a multi-view semantic matching model by reusing the
important keywords in multiple semantic representations.
  As a key of constructing fine-grained semantic representations, we are the
first to use a cross-task weakly supervised extraction model that applies
question-question labelled signals to supervise the keyword extraction process
(i.e. to learn the keyword importance). The extraction model integrates the
deep semantic representation and lexical matching information with statistical
features to estimate the importance of keywords. We conduct extensive
experiments on three public datasets and the experimental results show that our
proposed model significantly outperforms the state-of-the-art solutions.


  It has long been proposed that doping a chiral spin liquid (CSL) or
fractional quantum Hall state can give rise to topological superconductivity.
Despite of intensive effort, definitive evidences still remain lacking. We
address this problem by studying the $t$-$J$ model supplemented by
time-reversal symmetry breaking chiral interaction $J_\chi$ on the triangular
lattice using density-matrix renormalization group with a finite concentration
$\delta$ of doped holes. It has been established that the undoped, i.e.,
$\delta$=0, system has a CSL ground state in the parameter region $0.32\le
J_\chi/J \le 0.56$. Upon light doping, we find that the ground state of the
system is consistent with a Luther-Emery liquid with power-law superconducting
and charge-density-wave correlations but short-range spin-spin correlations. In
particular, the superconducting correlations, whose pairing symmetry is
consistent with $d\pm id$-wave, are dominant at all hole doping concentrations.
Our results provide direct evidences that doping the CSL on the triangular
lattice can naturally give rise to topological superconductivity.


  This paper addresses the understanding and characterization of residual
networks (ResNet), which are among the state-of-the-art deep learning
architectures for a variety of supervised learning problems. We focus on the
mapping component of ResNets, which map the embedding space towards a new
unknown space where the prediction or classification can be stated according to
linear criteria. We show that this mapping component can be regarded as the
numerical implementation of continuous flows of diffeomorphisms governed by
ordinary differential equations. Especially, ResNets with shared weights are
fully characterized as numerical approximation of exponential diffeomorphic
operators. We stress both theoretically and numerically the relevance of the
enforcement of diffeormorphic properties and the importance of numerical issues
to make consistent the continuous formulation and the discretized ResNet
implementation. We further discuss the resulting theoretical and computational
insights on ResNet architectures.


  We introduce a novel coarse-grained bead-spring model for flexible polymers
to systematically examine the effects of an adjusted bonded potential on the
formation and stability of structural macrostates in a thermal environment. The
density of states obtained in advanced replica-exchange Monte Carlo simulations
is analyzed by employing the recently developed generalized microcanonical
inflection-point analysis method, which enables the identification of diverse
structural phases and the construction of a suitably parameterized hyperphase
diagram. It reveals that icosahedral phases dominate for polymers with
asymmetric and narrow bond potentials, whereas polymers with symmetric and more
elastic bonds tend to form amorphous structures with non-icosahedral cores. We
also observe a hierarchy in the freezing transition behavior associated with
the formation of the surface layer after nucleation.


  For every conserved quantity written as a sum of local terms, there exists a
corresponding current operator that satisfies the continuity equation. The
expectation values of current operators at equilibrium define the persistent
currents that characterize spontaneous flows in the system. In this work, we
consider quantum many-body systems on a finite one-dimensional lattice and
discuss the scaling of the persistent currents as a function of the system
size. We show that, when the conserved quantities are given as the Noether
charges associated with internal symmetries or the Hamiltonian itself, the
corresponding persistent currents can be bounded by a correlation function of
two operators at a distance proportional to the system size, implying that they
decay at least algebraically as the system size increases. In contrast, the
persistent currents of accidentally conserved quantities can be nonzero even in
the thermodynamic limit and even in the presence of the time-reversal symmetry.
We discuss `the current of energy current' in $S=1/2$ XXZ spin chain as an
example and obtain an analytic expression of the persistent current.


  Developing protocols for preserving information in quantum systems is a
central quest for implementing realistic quantum computation. In this regard,
the quantum Zeno effect has emerged as a widely utilized technique to safeguard
classical information stored in quantum systems. However, existing results
pertaining to this method often assume operations performed infinitely fast on
the system of interest, which only serves as an approximation to real-world
scenarios where the temporal resolution of any experimental apparatus is
inherently finite. In this study, we go beyond this conventional assumption and
derive the effective Zeno dynamics for any time interval between operations.
Our analysis considers a qubit undergoing thermalization, as described by a
generalized amplitude damping channel, while the operations performed consist
of projections onto an orthonormal basis that may or may not coincide with the
pointer basis to which the system is thermalizing. By obtaining the probability
of successfully storing a bit of information after a given time, we investigate
the performance of the protocol in two important scenarios: the limit of many
interventions, with a first-order correction to the Zeno limit, and the limit
of very few interventions. In doing so, we provide valuable insights into the
protocol's performance by establishing bounds on its efficacy. These findings
enhance our understanding of the practical applicability of the quantum Zeno
effect in preserving classical information stored in quantum systems, allowing
for better design and optimization of quantum information processing protocols.


  Partial label learning deals with the problem where each training instance is
assigned a set of candidate labels, only one of which is correct. This paper
provides the first attempt to leverage the idea of self-training for dealing
with partially labeled examples. Specifically, we propose a unified formulation
with proper constraints to train the desired model and perform pseudo-labeling
jointly. For pseudo-labeling, unlike traditional self-training that manually
differentiates the ground-truth label with enough high confidence, we introduce
the maximum infinity norm regularization on the modeling outputs to
automatically achieve this consideratum, which results in a convex-concave
optimization problem. We show that optimizing this convex-concave problem is
equivalent to solving a set of quadratic programming (QP) problems. By
proposing an upper-bound surrogate objective function, we turn to solving only
one QP problem for improving the optimization efficiency. Extensive experiments
on synthesized and real-world datasets demonstrate that the proposed approach
significantly outperforms the state-of-the-art partial label learning
approaches.


  Advances in deep learning have led to substantial increases in prediction
accuracy but have been accompanied by increases in the cost of rendering
predictions. We conjecture that fora majority of real-world inputs, the recent
advances in deep learning have created models that effectively "overthink" on
simple inputs. In this paper, we revisit the classic question of building model
cascades that primarily leverage class asymmetry to reduce cost. We introduce
the "I Don't Know"(IDK) prediction cascades framework, a general framework to
systematically compose a set of pre-trained models to accelerate inference
without a loss in prediction accuracy. We propose two search based methods for
constructing cascades as well as a new cost-aware objective within this
framework. The proposed IDK cascade framework can be easily adopted in the
existing model serving systems without additional model re-training. We
evaluate the proposed techniques on a range of benchmarks to demonstrate the
effectiveness of the proposed framework.


  Given two positive definite matrices $A$ and $B$, a well known result by
Gelfand, Naimark and Lidskii establishes a relationship between the eigenvalues
of $A$ and $B$ and those of $AB$ by means of majorization inequalities. In this
work we make a local study focused in the spectrum of the matrices that achieve
the equality in those inequalities. As an application, we complete some
previous results concerning Procustes problems for unitarily invariant norms in
the manifold of positive definite matrices.


  The general/finite PCTL satisfiability problem asks whether a given PCTL
formula has a general/finite model. We show that the finite PCTL satisfiability
problem is undecidable, and the general PCTL satisfiability problem is even
highly undecidable (beyond the arithmetical hierarchy). Consequently, there are
no sound deductive systems proving all generally/finitely valid PCTL formulae.


  This review is split into two parts: one on chromospheric line formation in
answer to the frequent question "where is my line formed", and one presenting
state-of-the-art imagery of the chromosphere. In the first part I specifically
treat the formation of the Na D lines, Ca II H & K, and Halpha. In the second I
show DOT, IBIS, VAULT, and TRACE images as evidence that the chromosphere
consists of fibrils of intrinsically different types. The straight-up ones are
hottest. The slanted ones are filled by shocks and likely possess thin
transition sheaths to coronal plasma. The ones hovering horizontally over
"clapotispheric" cell interiors outline magnetic canopies and are buffeted by
shocks, most violently in the quietest regions. In the absence of
integral-field ultraviolet spectrometry, H$\alpha$ remains the principal
chromosphere diagnostic. The required fast-cadence profile-sampling imaging is
an important quest for new telescope technology.


  In this paper, we describe families of those bounded linear operators on a
separable Hilbert space that are simultaneously unitarily equivalent to
integral operators on $L_2(R)$ with bounded and arbitrarily smooth Carleman
kernels. The main result is a qualitative sharpening of an earlier result of
[7].


  In this paper we study a model of s-wave marginal Fermi liquid superconductor
at two dimensions. Besides the usual Bardeen-Cooper-Schrieffer(BCS) point
attractive interaction, the fermions in our system also interact {\em via} a
long range repulsive interaction $v(q)=g_{D}/q^2$. We find that although
superconductivity is not destroyed by the long-ranged repulsion interaction
(demonstrated by the existence of Meissner effect), nevertheless the BCS
pairing function $\Delta_{BCS}$ is strongly suppressed resulting in power-law
correlation $<\Delta_{BCS}(\vec{x})\Delta_{BCS}(0)>\sim|\vec{x}|^{-2\gamma}$.
This observation is explained within a general picture of marginal Fermi
liquid.


  We introduce the notion of a bisector field, which is a maximal collection of
pairs of lines such that for each line in each pair, the midpoint of the points
where the line crosses every pair is the same, regardless of choice of pair. We
use this to study asymptotic properties of pencils of affine conics over fields
and show that pairs of lines in the plane that occur as the asymptotes of
hyperbolas from a pencil of affine conics belong to a bisector field. By
including also pairs of parallel lines arising from degenerate parabolas in the
pencil, we obtain a full characterization: Every bisector field arises from a
pencil of affine conics, and vice versa, every nontrivial pencil of affine
conics is asymptotically a bisector field. Our main results are valid over any
field of characteristic other than $2$ and hence hold in the classical
Euclidean setting as well as in Galois geometries.


  We prove here that the Poincar\'e exponent of a geometrically finite group od
isometries of the 3-dimensionnal hyperbolic space coincides with the Hausdorff
dimension of its limit set. We also compare the natural measures supported by
this set: the Patterson measure and the Hausdorff and packing measures
corresponding to the standart jauge.


  The nuclear breathing-mode giant monopole resonance is studied within an
improved relativistic Boltzmann-Uehling-Uhlenbeck (BUU) transport approach. As
a new feature, the numerical treatment of ground state nuclei and their
phase-space evolution is realized with the same semiclassical energy density
functional. With this new method a very good stability of ground state nuclei
in BUU simulations is achieved. This is important in extracting clear
breathing-mode signals for the excitation energy and, in particular, for the
lifetime from transport theoretical studies including mean-field and
collisional effects.


  This work is devoted to deriving the Onsager-Machlup action functional for a
class of stochastic differential equations with (non-Gaussian) L\'{e}vy process
as well as Brownian motion in high dimensions. This is achieved by applying the
Girsanov transformation for probability measures and then by a path
representation. The Poincar\'{e} lemma is essential to handle such path
representation problem in high dimensions. We provide a sufficient condition on
the vector field such that this path representation holds in high dimensions.
Moreover, this Onsager-Machlup action functional may be considered as the
integral of a Lagrangian. Finally, by a variational principle, we investigate
the most probable transition pathways analytically and numerically.


  We review the different frameworks in which Galileon scalar fields have been
seen to emerge such an in DGP, New Massive Gravity and Ghost-free massive
Gravity and emphasize their relation with the Lovelock invariant in braneworld
models. The existence of a non-renormalization theorem for Galileon scalar
fields makes them especially attractive candidates for inflation as well as for
late-time acceleration. In particular we review the self-accelerating and
degravitating branches of solutions present in Galileon models when arising
from Massive Gravity and discuss their phenomenology.


  We study two types of discrete operations on Coulomb branches of $3d$
$\mathcal{N}=4$ quiver gauge theories using both abelianisation and the
monopole formula. We generalise previous work on discrete quotients of Coulomb
branches and introduce novel wreathed quiver theories. We further study quiver
folding which produces Coulomb branches of non-simply laced quivers.


  We present a tomographic imaging technique, termed Deep Prior Diffraction
Tomography (DP-DT), to reconstruct the 3D refractive index (RI) of thick
biological samples at high resolution from a sequence of low-resolution images
collected under angularly varying illumination. DP-DT processes the multi-angle
data using a phase retrieval algorithm that is extended by a deep image prior
(DIP), which reparameterizes the 3D sample reconstruction with an untrained,
deep generative 3D convolutional neural network (CNN). We show that DP-DT
effectively addresses the missing cone problem, which otherwise degrades the
resolution and quality of standard 3D reconstruction algorithms. As DP-DT does
not require pre-captured data or pre-training, it is not biased towards any
particular dataset. Hence, it is a general technique that can be applied to a
wide variety of 3D samples, including scenarios in which large datasets for
supervised training would be infeasible or expensive. We applied DP-DT to
obtain 3D RI maps of bead phantoms and complex biological specimens, both in
simulation and experiment, and show that DP-DT produces higher-quality results
than standard regularization techniques. We further demonstrate the generality
of DP-DT, using two different scattering models, the first Born and multi-slice
models. Our results point to the potential benefits of DP-DT for other 3D
imaging modalities, including X-ray computed tomography, magnetic resonance
imaging, and electron microscopy.


  Unsupervised domain adaptation is critical in various computer vision tasks,
such as object detection, instance segmentation, and semantic segmentation,
which aims to alleviate performance degradation caused by domain-shift. Most of
previous methods rely on a single-mode distribution of source and target
domains to align them with adversarial learning, leading to inferior results in
various scenarios. To that end, in this paper, we design a new spatial
attention pyramid network for unsupervised domain adaptation. Specifically, we
first build the spatial pyramid representation to capture context information
of objects at different scales. Guided by the task-specific information, we
combine the dense global structure representation and local texture patterns at
each spatial location effectively using the spatial attention mechanism. In
this way, the network is enforced to focus on the discriminative regions with
context information for domain adaption. We conduct extensive experiments on
various challenging datasets for unsupervised domain adaptation on object
detection, instance segmentation, and semantic segmentation, which demonstrates
that our method performs favorably against the state-of-the-art methods by a
large margin. Our source code is available at
https://isrc.iscas.ac.cn/gitlab/research/domain-adaption.


  The $\Z_{p^s}$-additive codes of length $n$ are subgroups of $\Z_{p^s}^n$,
and can be seen as a generalization of linear codes over $\Z_2$, $\Z_4$, or
$\Z_{2^s}$ in general. A $\Z_{p^s}$-linear generalized Hadamard (GH) code is a
GH code over $\Z_p$ which is the image of a $\Z_{p^s}$-additive code by a
generalized Gray map. A partial classification of these codes by using the
dimension of the kernel is known. In this paper, we establish that some
$\Z_{p^s}$-linear GH codes of length $p^t$ are equivalent, once $t$ is fixed.
This allows us to improve the known upper bounds for the number of such
nonequivalent codes. Moreover, up to $t=10$, this new upper bound coincides
with a known lower bound (based on the rank and dimension of the kernel).


  In recent years, distinct machine learning (ML) models have been separately
used for feature extraction and noise reduction from energy-momentum dispersion
intensity maps obtained from raw angle-resolved photoemission spectroscopy
(ARPES) data. In this work, we employ a shallow variational auto-encoder (VAE)
neural network to demonstrate the prospect of using ML for both denoising of as
well as feature extraction from ARPES dispersion maps.


  Gauge-gravity duality provides a robust mathematical framework for studying
the behavior of strongly coupled non-abelian plasmas both near and far away
from thermodynamic equilibrium. In particular, their near-equilibrium transport
coefficients such as viscosity, conductivity, diffusion constants, etc. can be
determined from poles of the retarded Green's function which are the
dissipative eigenmodes i.e., the quasinormal modes (QNMs) of the dual
gravitational field equations. The AdS5/CFT4 correspondence admits the
description of a strongly coupled $\mathcal{N}$= 4 Supersymmetric Yang Mills
(SYM) plasma at non-zero temperature as a dual AdS5 black brane geometry. We
demonstrate the application of pseudospectral methods to solving the dual
Einstein field equations using the example of homogenous isotropization in
$\mathcal{N}$= 4 SYM plasma far from equilibrium. Using this framework, we also
compute the quasinormal modes of electrically (Reissner-Nordstrom) and
magnetically charged AdS5 black branes for the case of vanishing spatial
momenta. The near-extremal behavior of these QNMs is analyzed for both types of
black branes.


  We compute the expectation value of the 1/2 BPS circular Wilson loop in ABJM
theory at two loops in perturbation theory. The result shows perfect agreement
with the prediction from localization and the proposed framing factor.


  In this short note we study a class of multi-player, turn-based games with
deterministic state transitions and reachability / safety objectives (this
class contains as special cases "classic" two-player reachability and safety
games as well as multi-player "stay--in-a-set" and "reach-a-set" games).
Quantitative and qualitative versions of the objectives are presented and for
both cases we prove the existence of a deterministic and memoryless Nash
equilibrium; the proof is short and simple, using only Fink's classic result
about the existence of Nash equilibria for multi-player discounted stochastic
games.