  We prove exponential correlation decay in dispersing billiard flows on the
2-torus assuming finite horizon and lack of corner points. With applications
aimed at describing heat conduction, the highly singular initial measures are
concentrated here on 1-dimensional submanifolds (given by standard pairs) and
the observables are supposed to satisfy a generalized H\"older continuity
property. The result is based on the exponential correlation decay bound of
Baladi, Demers and Liverani obtained recentlyfor H\"older continuous
observables in these billiards. The model dependence of the bounds is also
discussed.


  In massive primordial galaxies, the gas may directly collapse and form a
single central massive object if cooling is suppressed. Line cooling by
molecular hydrogen can be suppressed in the presence of a strong
soft-ultraviolet radiation field, but the role played by other cooling
mechanisms is less clear. In optically thin gas, Lyman-Alpha cooling can be
very effective, maintaining the gas temperature below 10^4 K over many orders
of magnitude in density. However, the large neutral hydrogen column densities
present in primordial galaxies render them highly optically thick to
Lyman-Alpha photons. In this letter, we examine in detail the effects of the
trapping of these Lyman-Alpha photons on the thermal and chemical evolution of
the gas. We show that despite the high optical depth in the Lyman series lines,
cooling is not strongly suppressed, and proceeds via other atomic hydrogen
transitions, in particular the 2s-1s and the 3-2 transitions. At densities
larger than 10^9 cm^{-3}, collisional dissociation of molecular hydrogen
becomes the dominant cooling process and decreases the gas temperature to about
5000 K. The gas temperature evolves with density as $T \propto
\rho^{\gamma_{\rm eff} - 1}$, with $\gamma_{\rm eff} = 0.97-0.98$. The
evolution is thus very close to isothermal, and so fragmentation is possible,
but unlikely to occur during the initial collapse. However, after the formation
of a massive central object, we expect that later-infalling, higher angular
momentum material will form an accretion disk that may be unstable to
fragmentation, which may give rise to star formation with a top-heavy IMF.


  Considerable attention has been devoted to the wormhole physics in the past
30 years by exploring the possibilities of finding traversable wormholes
without the need of exotic matter. In particular the thin-shell wormhole
formalism has been widely investigated by exploiting the cut-and-paste
technique to merge two space-time regions and, to research the stability of
these wormholes developed by Visser. This method helps us to minimize the
amount of the exotic matter. In this paper we construct a four dimensional,
spherically symmetric, dyonic thin-shell wormhole with electric charge $Q$,
magnetic charge $P$, and dilaton charge $\Sigma$, in the context of
Einstein-Maxwell-dilaton theory. We have applied Darmois-Israel formalism and
the cut-and-paste method by joining together two identical spacetime solutions.
We carry out the dyonic thin-shell wormhole stability analyses by using a
linear barotropic gas, Chaplygin gas, and logarithmic gas for the exotic
matter. It is shown that by choosing suitable parameter values as well as
equation of state parameter, under specific conditions we obtain a stable
dyonic thin-shell wormhole solution. Finally we argue that, the stability
domain of the dyonic thin-shell wormhole can be increased in terms of electric
charge, magnetic charge, and dilaton charge.


  In accreting neutron star (NS) low-mass X-ray binary (LMXB) systems, NS
accretes material from its low-mass companion via a Keplerian disk. In a
viscous accretion disk, inflows orbit the NS and spiral in due to dissipative
processes, such as the viscous process and collisions of elements. The dynamics
of accretion flows in the inner region of an accretion disk is significantly
affected by the rotation of NS. The rotation makes NS, thus the space-time
metric, deviate from the originally spherical symmetry, and leads to
gravitational quadrupole, on one hand. On the other hand, a rotating NS drags
the local inertial frame in its vicinity, which is known as the rotational
frame-dragging effect. In this paper, {\bf we investigate the orbital motion of
accretion flows of accreting NS/LMXBs and demonstrate that the rotational
effects of NS result in a band of quasi-quantized structure in the inner region
of the accretion disk, which is different, in nature, from the scenario in the
strong gravity of black hole arising from the resonance for frequencies related
to epicyclic and orbital motions. We also demonstrate that such a disk
structure may account for frequencies seen in X-ray variability, such as
quasi-periodic oscillations, and can be a potential promising tool of
investigation for photon polarization.


  In this article, the strong coupling constants of vertices $BB\Upsilon$,
$BB^{*}\Upsilon$, $B^{*}B^{*}\Upsilon$, $BB^{*}\eta_{b}$ and
$B^{*}B^{*}\eta_{b}$ are analyzed in the framework of QCD sum rules. In this
work, all possible off-shell cases and the contributions of vacuum condensate
terms including $\langle\overline{q}q\rangle$, $\langle\overline{q}g_{s}\sigma
Gq\rangle$, $\langle g_{s}^{2}G^{2}\rangle$, $\langle f^{3}G^{3}\rangle$ and
$\langle\overline{q}q\rangle\langle g_{s}^{2}G^{2}\rangle$ are considered. The
momentum dependent strong coupling constants are first calculated and then are
fitted into analytical functions $g(Q^{2})$ which are used to extrapolate into
time-like regions to obtain the final values of strong coupling constants. The
final results are $g_{BB\Upsilon}=40.67^{+7.55}_{-4.20}$,
$g_{BB^{*}\Upsilon}=11.58^{+2.19}_{-1.09}$ GeV$^{-1}$,
$g_{B^{*}B^{*}\Upsilon}=57.02^{+5.32}_{-5.31}$,
$g_{BB^{*}\eta_{b}}=23.39^{+4.74}_{-2.30}$ and
$g_{B^{*}B^{*}\eta_{b}}=12.49^{+2.12}_{-1.35}$ GeV$^{-1}$. These strong
coupling constants are important input parameters which reflect the dynamic
properties of the interactions among the mesons and quarkonia.


  Quasistatics is introduced so that it fits smoothly into the standard
textbook presentation of electrodynamics. The usual path from statics to
general electrodynamics is rather short and surprisingly simple. A closer look
reveals however that it is not without confusing issues as has been illustrated
by many contributions to this Journal. Quasistatic theory is conceptually
useful by providing an intermediate level in between statics and the full set
of Maxwell's equations. Quasistatics is easier than general electrodynamics and
in some ways more similar to statics. It is however, in terms of interesting
physics and important applications, far richer than statics. Quasistatics is
much used in electromagnetic modeling, an activity that today is possible on a
PC and which also has great pedagogical potential. The use of electromagnetic
simulations in teaching gives additional support for the importance of
quasistatics. This activity may also motivate some change of focus in the
presentation of basic electrodynamics.


  This is the fourth in a series of articles devoted to showing that a typical
covering map of large degree to a fixed, regular graph has its new adjacency
eigenvalues within the bound conjectured by Alon for random regular graphs.
  In this paper we prove a {\em Sidestepping Theorem} that is more general and
easier to use than earlier theorems of this kind. Such theorems concerns a
family probability spaces $\{{\mathcal{M}}_n\}$ of $n\times n$ matrices, where
$n$ varies over some infinite set, $N$, of natural numbers. Many trace methods
use simple "Markov bounds" to bound the expected spectral radius of elements of
${\mathcal{M}}_n$: this consists of choosing one value, $k=k(n)$, for each
$n\in N$, and proving expected spectral radius bounds based on the expected
value of the trace of the $k=k(n)$-power of elements of ${\mathcal{M}}_n$. {\em
Sidestepping} refers to bypassing such simple Markov bounds, obtaining improved
results using a number of values of $k$ for each fixed $n\in N$.
  In more detail, if the $M\in {\mathcal{M}}_n$ expected value of ${\rm
Trace}(M^k)$ has an asymptotic expansion in powers of $1/n$, whose coefficients
are "well behaved" functions of $k$, then one can get improved bounds on the
spectral radius of elements of ${\mathcal{M}}_n$ that hold with high
probability. Such asymptotic expansions are shown to exist in the third article
in this series for the families of matrices that interest us; in the fifth and
sixth article in this series we will apply the Sidestepping Theorem in this
article to prove the main results in this series of articles.
  This article is independent of all other articles in this series; it can be
viewed as a theorem purely in probability theory, concerning random matrices
or, equivalently, the $n$ random variables that are the eigenvalues of the
elements of ${\mathcal{M}}_n$.


  We have observed 12CO J = 2-1 and J = 1-0, and 13CO J = 1-0 emission in two
regions of HI Self-Absorption (HISA) in Perseus: a small, isolated HISA feature
called the globule and a more extended HISA cloud called the complex. Using
both Large Velocity Gradient and Monte Carlo radiative transfer codes we found
that, in the globule, N(12CO) < 6.0x10^15 cm-2 which, using PDR models, implies
that N(H_2) < 9.9x10^20 cm-2. In the complex we found that the H_2 column
densities ranged from 1.2 - 2.2 x 10^21 cm-2. By comparing the HISA and CO
observations we are able to constrain the physical conditions and atomic gas
fraction (f). In the globule, 8 K < T_spin < 22 K and 0.02 < f < 0.2 depending
on whether the (unknown) gas density is 10^2, 10^3, or 10^4 cm-3. In the
complex, 12 K < T_spin < 24 K, 0.02 < f < 0.05, and we were also able to
constrain the gas density (100 < n < 1200 cm-3). These results imply that the
gas in the HISA clouds is colder and denser than that usually associated with
the atomic ISM and, indeed, is similar to that seen in molecular clouds. The
small atomic gas fractions also imply that there is a significant molecular
component in these HISA clouds, even when little or no 12CO is detected. The
level of 12CO detected and the visual extinction due to dust is consistent with
the idea that these HISA clouds are undergoing a transition from the atomic to
molecular phase.


  By now, Internet word-of-mouth marketing has established its importance for
almost every kind of products. When word-of-mouth spread of a marketing
campaign goes viral, the success of its strategy has been proved to be
path-breaking for the firms. The current study is designed to extend knowledge
about customer response to these campaigns, and is focused on the motivations
that lead them to different responses. Primary goal of this study is to
investigate the reasons that drive this dynamics and to generate a justified
theoretical framework of diffusion dynamics of viral marketing campaigns. A
rigorous analysis of data obtained through a questionnaire-based survey helped
us to understand how people, who are from different age group, sex and
locations, define and evaluate the encounter with a referral campaign online,
in similar and unique logical ways. The key finding was a conceptual framework
of customer motivation which in turn dictates the dynamics of the campaign. We
have identified important themes related to customer motives, like significance
of: incentives and ease, inherent forgetting, reminders from peers compared to
company's remarketing mails, trust and brand value etc. Drawing an analogy with
differential equation-based models of infectious disease spread, this paper
further provides some initial evidence that participation in viral marketing
campaigns has several consumer related dynamical factors which can be
incorporated in an epidemiological model for mathematical treatment, to
indicate key operational factors in ensuring an effective spread of the
marketing campaign.


  Present neutrino data are consistent with neutrino masses arising from a
common seed at some ``neutrino unification'' scale $M_X$. Such a simple
theoretical ansatz naturally leads to quasi-degenerate neutrinos that could lie
in the electron-volt range with neutrino mass splittings induced by
renormalization effects associated with supersymmetric thresholds. In such a
scheme the leptonic analogue of the Cabibbo angle $\theta_{\odot}$ describing
solar neutrino oscillations is nearly maximal. Its exact value is correlated
with the smallness of $\theta_{reactor}$. These features agree both with latest
data on the solar neutrino spectra and with the reactor neutrino data. The two
leading mass-eigenstate neutrinos present in \ne form a pseudo-Dirac neutrino,
avoiding conflict with neutrinoless double beta decay.


  We derive a hydrodynamic model for a liquid of arbitrarily curved flux-lines
and vortex loops using the mapping of the vortex liquid onto a liquid of
relativistic charged quantum bosons in 2+1 dimensions recently suggested by
Tesanovic and by Sudbo and collaborators. The loops in the flux-line system
correspond to particle-antiparticle fluctuations in the bosons. We explicitly
incorporate the externally applied magnetic field which in the boson model
corresponds to a chemical potential associated with the conserved charge
density of the bosons. We propose this model as a convenient and physically
appealing starting point for studying the properties of the vortex liquid.


  Non-classical non-linear waves exist in dense gases for large specific heats
at pressures and temperatures of the order of critical point values. These
waves behave precisely opposite to the classical non-linear waves, with
inverted classical waves like the expansion shocks which do not violate entropy
conditions. More complex equation of state (EOS) other than the ideal or
perfect EOS is typically used in describing dense gases. Algorithm development
with non-ideal/real gas EOS and application to dense gasses is gaining
importance from a numerical perspective. Extending the algorithms designed for
perfect gas EOS to dense gas flows with arbitrary real gas EOS is non-trivial.
Most of the algorithms designed for prefect gas EOS are modified significantly
when applied to real gas EOS. These algorithms can become complicated and some
times impossible based on the EOS under consideration. The objective of the
present work is to develop central solvers with smart diffusion capabilities
independent of the eigenstructure and extendable to any arbitrary EOS. Euler
equations with van der Waals EOS along with two newly developed algorithms,
Method of Optimal Viscosity for Enhanced resolution of shocks (MOVERS+) and
Riemann Invariants based Contact capturing Algorithm (RICCA), are used to
simulate dense gasses over simple geometries. Various One Dimensional (1D) and
Two Dimensional (2D) benchmark test cases are validated using these algorithms,
and the results are compared with the those obtained from the literature.


  In the recent article [Jentzen, A., M\"uller-Gronbach, T., and Yaroslavtseva,
L., Commun. Math. Sci., 14(6), 1477--1500, 2016] it has been established that
for every arbitrarily slow convergence speed and every natural number $d \in
\{4,5,\ldots\}$ there exist $d$-dimensional stochastic differential equations
(SDEs) with infinitely often differentiable and globally bounded coefficients
such that no approximation method based on finitely many observations of the
driving Brownian motion can converge in absolute mean to the solution faster
than the given speed of convergence. In this paper we strengthen the above
result by proving that this slow convergence phenomena also arises in two
($d=2$) and three ($d=3$) space dimensions.


  In this paper the structural, magnetic, and dielectric properties of
langasite compound Pb$_3$TeMn$_3$P$_2$O$_{14}$ have been investigated as a
candidate of short-range magnetic correlations driven development of dielectric
anomaly above N$\acute{e}$el temperature of ($T_N$=) 7 K. Presence of
dielectric anomaly, structural phase transition and a short range magnetic
correlation at the same temperature (at around 100 K) as well as magnetic field
dependent capacitance clearly indicate that this compound shows
magnetodielectric coupling at around 100 K. In addition, unusual behaviour is
observed in two polarization loop at room temperature and liquid nitrogen
temperature, where coercive field at liquid nitrogen temperature is larger than
room temperature. Further, $P$-$E$ loop at liquid nitrogen temperature with
different frequencies also affirm that the coercive field and remnant
polarization are firstly reduced (but very small value) but when frequency is
further increased to 15 Hz and 100 Hz, both of them are enhanced. Therefore, a
transition is observed at around 15 Hz in frequency dependent $P_r$ and $E_C$
curve, which may be usually attributed to the generalized pinning and depinning
of the dislocation arrays to polarization.


  Potential Energy Surface for the ($\mathrm{\bar{p}} - \mathrm{He}^{2+} -
\mathrm{He}$) system is calculated in the framework of the restricted (singlet
spin state) Hartree-Fock method with subsequent account of the electronic
correlations within the second order perturbation method (MP2). The geometry of
heavy particles is described in variables of distance $r$ from nucleus $a$ to
antiproton, distance $R$ from the center of mass of the $(\bar{p} - a)$ pair to
$\mathrm{He}$ atom containing nucleus $b$, and an angle $\theta$ between
$\mathbf{r}$ and $\mathbf{R}$. The potential $V(R,r,\cos\theta)$ of the
interaction between $\mathrm{He}$ atom and a $\bar{p} - a$ subsystem involves a
total energy of two electrons in the field of three heavy particles and Coulomb
interactions of the $b$ nucleus with antiproton and the $a$ nucleus. The
expansion of this potential in terms of Legendre polynomials
$\mathrm{P}_k(\cos\theta)$ is obtained. Matrices of the multipole terms
$V^k(r,R)$ ($k=0,1,2$) are obtained in the basis of antiprotonic helium ion
states. The results are compared with the model potential that was used earlier
in the calculations of collisional Stark transitions of
$(\mathrm{\bar{p}He}^{2+})$ ion.Total cross sections of collisional Stark
transitions obtained with the PES potentials exceed the model results by 15 -
20\% at $E \leq 12$ K.


  Generative adversarial networks (GANs) exhibit training pathologies that can
lead to convergence-related degenerative behaviors, whereas
spatially-distributed, coevolutionary algorithms (CEAs) for GAN training, e.g.
Lipizzaner, are empirically robust to them. The robustness arises from
diversity that occurs by training populations of generators and discriminators
in each cell of a toroidal grid. Communication, where signals in the form of
parameters of the best GAN in a cell propagate in four directions: North,
South, West, and East, also plays a role, by communicating adaptations that are
both new and fit. We propose Lipi-Ring, a distributed CEA like Lipizzaner,
except that it uses a different spatial topology, i.e. a ring. Our central
question is whether the different directionality of signal propagation
(effectively migration to one or more neighbors on each side of a cell) meets
or exceeds the performance quality and training efficiency of Lipizzaner
Experimental analysis on different datasets (i.e, MNIST, CelebA, and COVID-19
chest X-ray images) shows that there are no significant differences between the
performances of the trained generative models by both methods. However,
Lipi-Ring significantly reduces the computational time (14.2%. . . 41.2%).
Thus, Lipi-Ring offers an alternative to Lipizzaner when the computational cost
of training matters.


  Generalized-ensemble simulations enable the study of complex adsorption
scenarios of a coarse-grained model polymer near an attractive nanostring,
representing an ultrathin nanowire. We perform canonical and microcanonical
statistical analyses to investigate structural transitions of the polymer and
discuss their dependence on the temperature and on model parameters such as
effective wire thickness and attraction strength. The result is a complete
hyperphase diagram of the polymer phases, whose locations and stability are
influenced by the effective material properties of the nanowire and the
strength of the thermal fluctuations. Major structural polymer phases in the
adsorbed state include compact droplets attached to or wrapping around the
wire, and tubelike conformations with triangular pattern that resemble ideal
boron nanotubes. The classification of the transitions is performed by
microcanonical inflection-point analysis.


  In the rapidly growing literature on explanation algorithms, it often remains
unclear what precisely these algorithms are for and how they should be used. We
argue that this is because explanation algorithms are often mathematically
complex but don't admit a clear interpretation. Unfortunately, complex
statistical methods that don't have a clear interpretation are bound to lead to
errors in interpretation, a fact that has become increasingly apparent in the
literature. In order to move forward, papers on explanation algorithms should
make clear how precisely the output of the algorithms should be interpreted.
They should also clarify what questions about the function can and cannot be
answered given the explanations. Our argument is based on the distinction
between statistics and their interpretation. It also relies on parallels
between explainable machine learning and applied statistics.


  In this paper, the $\eta$-Nash equilibrium ($\eta$-NE) region of the two-user
Gaussian interference channel (IC) with perfect output feedback is approximated
to within $1$ bit/s/Hz and $\eta$ arbitrarily close to $1$ bit/s/Hz. The
relevance of the $\eta$-NE region is that it provides the set of rate-pairs
that are achievable and stable in the IC when both transmitter-receiver pairs
autonomously tune their own transmit-receive configurations seeking an
$\eta$-optimal individual transmission rate. Therefore, any rate tuple outside
the $\eta$-NE region is not stable as there always exists one link able to
increase by at least $\eta$ bits/s/Hz its own transmission rate by updating its
own transmit-receive configuration. The main insights that arise from this work
are: $(i)$ The $\eta$-NE region achieved with feedback is larger than or equal
to the $\eta$-NE region without feedback. More importantly, for each rate pair
achievable at an $\eta$-NE without feedback, there exists at least one rate
pair achievable at an $\eta$-NE with feedback that is weakly Pareto superior.
$(ii)$ There always exists an $\eta$-NE transmit-receive configuration that
achieves a rate pair that is at most $1$ bit/s/Hz per user away from the outer
bound of the capacity region.


  We theoretically study a spin-spin correlation enhanced by non-magnetic
impurities in a frustrated two-leg spin ladder. The frustration is introduced
by the next-nearest-neighbor antiferromagnetic interaction in the leg direction
in the antiferromagnetic two-leg spin ladder. The spin-spin correlation
function around impurity site is calculated by the density-matrix
renormalization-group method. We find that the spin-spin correlation is
enhanced around impurity site with the wavenumber reflecting the frustration.
As increasing the frustration, the wavenumber is shifted from commensurate to
incommensurate. We discuss several experimental results on BiCu2PO6 in the
light of our theory.


  We investigate the dynamics of a tagged particle embedded in a strongly
interacting confined liquid enclosed between two opposing flat walls. Using the
Zwanzig-Mori projection operator formalism we obtain an equation of motion for
the incoherent scattering function suitably generalized to account for the lack
of translational symmetry. We close the equations of motion by a
self-consistent mode-coupling ansatz. The interaction of the tracer with the
surrounding liquid is encoded in generalized direct correlation functions. We
extract the in-plane dynamics and provide a microscopic expression for the
diffusion coefficient parallel to the walls. The solute particle may differ in
size or interaction from the surrounding host-liquid constituents offering the
possibility of a systematic analysis of dynamic effects on the tagged-particle
motion in confinement.


  The CUPID-0 experiment searches for neutrinoless double beta decay
($0\nu\beta\beta$) using the first array of enriched Zn$^{82}$Se scintillating
bolometers with double (heat and light) read-out. To further enhance the
CUPID-0 detector performances, the heat-light correlation has been exploited to
improve the energy resolution. Different decorrelation algorithms have been
studied and the best result is the average reduction of the full width at half
maximum (FWHM) energy resolution to $(90.5\pm0.6)~\%$ of its original value ,
corresponding to a change from $\text{FWHM}=(20.7\pm0.5)~\text{keV}$ to
$\text{FWHM}=(18.7\pm0.5)~\text{keV}$ at the 2615 keV $\gamma$ line.


  Previous models have shown that stochastic scattering of stars in a
two-dimensional galaxy disc can generate a time-independent surface density
distribution that is an exponential divided by radius when a constant inward
scattering bias is present. Here we show, using a Markov chain model, that
similar profiles result from an outward scattering bias, although the disc
surface density decreases slowly with time because of a net stellar outflow.
The trend towards a near-exponential surface profile is robust, as it exists
even if the scattering intensity has moderate radial and time dependences,
subject to some limitations on the scattering rates discussed in the text. The
exponential scale length of the pseudo-equilibrium disc depends on the
scattering bias, the scattering length, and the size of the disc where
scattering is important.


  LLMs have recently made impressive inroads on tasks whose output is
structured, such as coding, robotic planning and querying databases. The vision
of creating AI-powered personal assistants also involves creating structured
outputs, such as a plan for one's day, or for an overseas trip. Here, since the
plan is executed by a human, the output doesn't have to satisfy strict
syntactic constraints. A useful assistant should also be able to incorporate
vague constraints specified by the user in natural language. This makes LLMs an
attractive option for planning.
  We consider the problem of planning one's day. We develop an LLM-based
planner (LLMPlan) extended with the ability to self-reflect on its output and a
symbolic planner (SymPlan) with the ability to translate text constraints into
a symbolic representation. Despite no formal specification of constraints, we
find that LLMPlan performs explicit constraint satisfaction akin to the
traditional symbolic planners on average (2% performance difference), while
retaining the reasoning of implicit requirements. Consequently, LLM-based
planners outperform their symbolic counterparts in user satisfaction (70.5% vs.
40.4%) during interactive evaluation with 40 users.


  Coherence, which represents the superposition of orthogonal states, is a
fundamental concept in quantum mechanics and can also be precisely defined
within quantum resource theory. Thus exploring quantum coherence in neutrino
oscillations can not only help in examining the intrinsic quantum nature but
can also explore their potential applications in quantum information
technologies. Previous studies on quantum coherence have focused on neutrino
flavor oscillations (FO). However, FO imply that neutrinos have mass and this
can lead to the generation of a tiny but finite magnetic dipole moment of
neutrinos through quantum loop diagrams at higher orders of perturbative
expansion of the interaction. This electromagnetic property of neutrinos can
induce spin flavor oscillations (SFO) in the presence of an external magnetic
field and hence is expected to enrich the study of coherence. In this work, we
investigate quantum coherence in neutrino SFO with three flavor mixing within
the interstellar as well as intergalactic magnetic fields, quantified by the
$l_1$ norm and the relative entropy of coherence, and express these measures in
terms of neutrino SFO probabilities. For FO, coherence measures can sustain
higher values (say, within 50% of the maximum) over distances of several
kilometers, which are relevant for terrestrial experiments like reactor and
accelerator neutrinos. However, for SFO, we find that the coherence scale can
extend to astrophysical distances, spanning from kiloparsecs to gigaparsecs.


  Understanding whether self-supervised learning methods can scale with
unlimited data is crucial for training large-scale models. In this work, we
conduct an empirical study on the scaling capability of masked image modeling
(MIM) methods (e.g., MAE) for visual recognition. Unlike most previous works
that depend on the widely-used ImageNet dataset, which is manually curated and
object-centric, we take a step further and propose to investigate this problem
in a more practical setting. Specifically, we utilize the web-collected
Coyo-700M dataset. We randomly sample varying numbers of training images from
the Coyo dataset and construct a series of sub-datasets, containing 0.5M, 1M,
5M, 10M, and 100M images, for pre-training. Our goal is to investigate how the
performance changes on downstream tasks when scaling with different sizes of
data and models. The study reveals that: 1) MIM can be viewed as an effective
method to improve the model capacity when the scale of the training data is
relatively small; 2) Strong reconstruction targets can endow the models with
increased capacities on downstream tasks; 3) MIM pre-training is data-agnostic
under most scenarios, which means that the strategy of sampling pre-training
data is non-critical. We hope these observations could provide valuable
insights for future research on MIM.


  This chapter provides a summary of the activities and results of the European
Network For Gender Balance in Informatics (EUGAIN, EU COST Action CA19122). The
main aim and objective of the network is to improve gender balance in
informatics at all levels, from undergraduate and graduate studies to
participation and leadership both in academia and industry, through the
creation of a European network of colleagues working at the forefront of the
efforts for gender balance in informatics in their countries and research
communities.


  We propose a new point of view regarding the problem of time in quantum
mechanics, based on the idea of replacing the usual time operator $\mathbf{T}$
with a suitable real-valued function $T$ on the space of physical states. The
proper characterization of the function $T$ relies on a particular relation
with the dynamical evolution of the system rather than with the infinitesimal
generator of the dynamics (Hamiltonian). We first consider the case of
classical Hamiltonian mechanics, where observables are functions on phase space
and the tools of differential geometry can be applied. The idea is then
extended to the case of the unitary evolution of pure states of finite-level
quantum systems by means of the geometric formulation of quantum mechanics. It
is found that $T$ is a function on the space of pure states which is not
associated to any self-adjoint operator. The link between $T$ and the dynamical
evolution is interpreted as defining a simultaneity relation for the states of
the system with respect to the dynamical evolution itself. It turns out that
different dynamical evolutions lead to different notions of simultaneity, i.e.,
the notion of simultaneity is a dynamical notion.


  In an earlier paper by Kazhdan and the author, a map from the set of
unipotent classes in a reductive connected group over C to the conjugacy
classes in the Weyl group was defined. Here we present some experimental
evidence for a possibly alternative definition of that map in terms of the
pieces in certain partitions of the flag manifold.


  Engineered microbubbles can be acoustically driven to cavitate against a
substrate to produce localized erosion and fragmentation. This mechanical
action has therapeutic applications in the treatment of biomineralizations,
such as in urinary stone disease. However, current methods for quantifying the
mechanical action of cavitation on a substrate are slow or imprecise. In this
paper, we describe the design of a device that applies calibrated pressures to
microbubbles engineered to target a calcium-containing hydroxyapatite substrate
under physiological conditions and quantifies the result via an automated
submerged mass measurement with high precision and low drift. Measurements of
microbubble-mediated mass loss were observed to be linear with time, with
variance that was comparable to the resolution of the instrument. The rate of
mass loss with microbubbles present was 5.5-fold greater than in the absence of
microbubbles. This research instrument captures the essential mechanical and
physiological features of in vivo microbubble-mediated erosion and
fragmentation of urinary stones and has been used to optimize the parameters of
this treatment in a clinical trial for a promising new approach to the
treatment of nephrolithiasis. In addition to clinically relevant therapeutic
applications, this approach will contribute to broader understanding of
acoustic cavitation against a substrate.


  New equiatomic nickel germanides MgNiGe, SrNiGe, and BaNiGe have been
synthesized from the elements in sealed tantalum tubes using a high-frequency
furnace. The compounds were investigated by X-ray diffraction both on powders
and single crystals. MgNiGe crystallizes with TiNiSi structure type, space
group Pnma, Z = 4, a = 6.4742(2) A, b = 4.0716(1) A, c = 6.9426(2) A, wR2 =
0.033, 305 F2 values, 20 variable parameters. SrNiGe and BaNiGe are isotypic
and crystallize with anti-SnFCl structure type (Z = 4, Pnma) with a = 5.727(1)
A, b = 4.174(1) A, c = 11.400(3) A, wR2 = 0.078, 354 F2 values, 20 variable
parameters for SrNiGe and a = 5.969(4) A, b = 4.195(1) A, c = 11.993(5) A, wR2
= 0.048, 393 F2 values, 20 variable parameters for BaNiGe. The increase of the
cation size leads to a reduction of the dimensionality of [NiGe] polyanions. In
the MgNiGe structure the nickel and germanium atoms build a three-dimensional
3D-[NiGe] network with magnesium atoms in the channels. In contrast, in
Sr(Ba)NiGe one-dimensional 1D-[NiGe] ribbons, running parallel to the b-axis,
are separated by strontium/barium atoms. The crystal chemistry and chemical
bonding in equiatomic alkaline-earth nickel germanides AeNiGe (Ae = Mg, Ca, Sr,
Ba) is discussed. The experimental results are reconciled with electronic
structure calculations performed using the tight-binding linear muffin-tin
orbital (TB-LMTO-ASA) method.


  Exploiting a Lagrangian strategy we present a numerical study for both
perturbative and nonperturbative regions of the Kraichnan advection model. The
major result is the numerical assessment of the first-order $1/d$-expansion by
M. Chertkov, G. Falkovich, I. Kolokolov and V. Lebedev ({\it Phys. Rev. E},
{\bf 52}, 4924 (1995)) for the fourth-order scalar structure function in the
limit of high dimensions $d$'s. %Two values of the velocity scaling exponent
$\xi$ have been considered: %$\xi=0.8$ and $\xi=0.6$. In the first case, the
perturbative regime %takes place at $d\sim 30$, while in the second at $d\sim
25$, %in agreement with the fact that the relevant small parameter %of the
theory is $\propto 1/(d (2-\xi))$. In addition to the perturbative results, the
behavior of the anomaly for the sixth-order structure functions {\it vs} the
velocity scaling exponent, $\xi$, is investigated and the resulting behavior
discussed.


  We have observed Bose-Einstein condensation (BEC) of trapped atomic hydrogen,
and studied it by two-photon spectroscopy of the 1S-2S transition. In these
lecture notes we briefly review the history of spin-polarized atomic hydrogen
and describe the final steps to BEC. Laser spectroscopy, which probes the
difference in mean field energy of the 1S and 2S states, is used to study the
condensate, which has a peak density of 4.8e15 cm^-3 and population of 10^9.


  We take an axiomatic approach to study redistribution problems when agents
report income and needs. We formalize axioms reflecting ethical and operational
principles such as additivity, impartiality and individual rationality.
Different combinations of those axioms characterize three focal rules (laissez
faire, full redistribution, and need-adjusted full redistribution) as well as
compromises among them. We also uncover the structure of those compromises
exploring the Lorenz dominance criterion as well as majority voting. Our
analysis provides an axiomatic justification for a linear income tax system. We
conclude our analysis resorting to Eurostat's Household Budget Survey from
where we illustrate the different redistribution patterns accounting for needs
across European countries.


  Is there a general theorem that tells us when we can hope for exponential
speedups from quantum algorithms, and when we cannot? In this paper, we make
two advances toward such a theorem, in the black-box model where most quantum
algorithms operate.
  First, we show that for any problem that is invariant under permuting inputs
and outputs (like the collision or the element distinctness problems), the
quantum query complexity is at least the 7th root of the classical randomized
query complexity. (An earlier version of this paper gave the 9th root.) This
resolves a conjecture of Watrous from 2002.
  Second, inspired by recent work of O'Donnell et al. (2005) and Dinur et al.
(2006), we conjecture that every bounded low-degree polynomial has a "highly
influential" variable. Assuming this conjecture, we show that every T-query
quantum algorithm can be simulated on most inputs by a poly(T)-query classical
algorithm, and that one essentially cannot hope to prove P!=BQP relative to a
random oracle.


  Soft supersymmetry breaking terms were recently derived for type IIB string
flux compactifications with all moduli stabilised. Depending on the choice of
the discrete input parameters of the compactification such as fluxes and ranks
of hidden gauge groups, the string scale was found to have any value between
the TeV and GUT scales. We study the phenomenological implications of these
compactifications at low energy. Three realistic scenarios can be identified
depending on whether the Standard Model lies on D3 or D7 branes and on the
value of the string scale. For the MSSM on D7 branes and the string scale
between 10^12 GeV and 10^17 GeV we find that the LSP is a neutralino, while for
lower scales it is the stop. At the GUT scale the results of the fluxed MSSM
are reproduced, but now with all moduli stabilised. For the MSSM on D3 branes
we identify two realistic scenarios. The first one corresponds to an
intermediate string scale version of split supersymmetry. The second is a
stringy mSUGRA scenario. This requires tuning of the flux parameters to obtain
the GUT scale. Phenomenological constraints from dark matter, (g-2)_mu and
BR(b->s gamma) are considered for the three scenarios. We provide benchmark
points with the MSSM spectrum, making the models suitable for a detailed
phenomenological analysis.


  We study the non-Markovian decoherence and disentanglement dynamics of
dissipative quantum systems with special emphasis on non-Gaussian continuous
variable systems. The dynamics are described by the Hu-Paz-Zhang master
equation of quantum Brownian motion. The time evolution of the decoherence
function of a single-mode superposition is compared to the concurrence of a
two-mode entangled state. It is verified that moderate non-Markovian influences
slow down the decay of interference fringes and quantum correlations, while
strong non-Markovian effects resulting from an out-of-resonance bath can even
accelerate the loss of coherence, compared to predictions of Markovian
approximations. Qualitatively different scenarios including exponential,
Gaussian or algebraic decay of the decoherence function are analyzed. It is
shown that partial revivals of coherence can occur in case of non-Lindblad-type
dynamics.


  We find numerical solutions of Einstein equations and scalar field equation
for a global defect in higher dimensional spacetimes ($\geq 6$). We examine in
detail the relation among the expansion rate $H$ and the symmetry-breaking
scale $\eta$ and the number of extra dimensions $n$ for these solutions. We
find that even if the extra dimensions do not have a cigar geometry, the
expansion rate $H$ grows as $\eta$ increases, which is opposite to what is
needed for the recently proposed mechanism for solving the cosmological
constant problem. We also find that the expansion rate $H$ decreases as $n$
increases.


  Tensor models can be interpreted as theory of dynamical fuzzy spaces. In this
paper, I study numerically the fluctuation spectra around a Gaussian classical
solution of a tensor model, which represents a fuzzy flat space in arbitrary
dimensions. It is found that the momentum distribution of the low-lying
low-momentum spectra is in agreement with that of the metric tensor modulo the
general coordinate transformation in the general relativity at least in the
dimensions studied numerically, i.e. one to four dimensions. This result
suggests that the effective field theory around the solution is described in a
similar manner as the general relativity.


  We discuss the relationship between Hopf superalgebras and Hopf algebras. We
list the braided vector spaces of diagonal type with generalized root system of
super type and give the defining relations of the corresponding Nichols
algebras.


  We define the Bohr-Sommerfeld quantization via $T$-modules for a
$b$-symplectic toric manifold and show that it coincides with the formal
geometric quantization of [GMW18b]. In particular, we prove that its dimension
is given by a signed count of the integral points in the moment polytope of the
toric action on the manifold.


  Meeting eye contact is the essential prerequisite skill of a human to
initiate any conversation with others. However, it is not an easy task for a
robot to meet eye contact with a human if they are not facing each other
initially or the human is intensely engaged his or her task. If the robot would
like to start communication with a particular person, it should turn its gaze
to that person first. However, only such a turning action alone is not always
enough to set up eye contact. Sometimes, the robot should perform some strong
actions so that it can capture the human's attention toward it. In this paper,
we proposed a computational model for robots that can proactively capture human
attention and makes eye contact with him or her. Evaluation experiments by
using a robotic head reveal the effectiveness of the proposed model in
different viewing situations.


  The discovery of the microquasar LS 5039 well within the 95% conficence
contour of the Unidentified EGRET Source (UES) 3EG J1824-1514 was a major step
towards the possible association between microquasars (MQs) and UESs. The
recent discovery of precessing relativistic radio jets in LS I +61 303, a
source associated for long time with 2CG 135+01 and with the UES 3EG
J0241+6103, has given further support to this idea. Finally, the very recently
proposed association between the microquasar candidate AX J1639.0-4642 and the
UES 3EG J1639-4702 points towards a population of High Mass X-ray Binary
(HMXB)/Neutron Star (NS) microquasars as counterparts of low-latitude
unidentified EGRET sources.


  Ordinary approach to quantum algorithm is based on quantum Turing machine or
quantum circuits. It is known that this approach is not powerful enough to
solve NP-complete problems. In this paper we study a new approach to quantum
algorithm which is a combination of the ordinary quantum algorithm with a
chaotic dynamical system. We consider the satisfiability problem as an example
of NP-complete problems and argue that the problem, in principle, can be solved
in polynomial time by using our new quantum algorithm.


  Internet of Things (IoT) have widely penetrated in different aspects of
modern life and many intelligent IoT services and applications are emerging.
Recently, federated learning is proposed to train a globally shared model by
exploiting a massive amount of user-generated data samples on IoT devices while
preventing data leakage. However, the device, statistical and model
heterogeneities inherent in the complex IoT environments pose great challenges
to traditional federated learning, making it unsuitable to be directly
deployed. In this article we advocate a personalized federated learning
framework in a cloud-edge architecture for intelligent IoT applications. To
cope with the heterogeneity issues in IoT environments, we investigate emerging
personalized federated learning methods which are able to mitigate the negative
effects caused by heterogeneity in different aspects. With the power of edge
computing, the requirements for fast-processing capacity and low latency in
intelligent IoT applications can also be achieved. We finally provide a case
study of IoT based human activity recognition to demonstrate the effectiveness
of personalized federated learning for intelligent IoT applications.


  In the present work, we study random walks on complex networks subject to
stochastic resetting when the resetting probability is node-dependent. Using a
renewal approach, we derive the exact expressions of the stationary occupation
probabilities of the walker on each node and the mean first passage time
between arbitrary two nodes. Finally, we demonstrate our theoretical results on
three networks with two different resetting protocols, validated by numerical
simulations as well. We find that under a delicate setting it is advantageous
to optimize the efficiency of a global search on such networks by the
node-dependent resetting probability.


  We report on field-induced variations of the microwave surface resistance at
9.6 GHz of Ba(0.6)K(0.4)BiO(3) crystals. Energy losses have been investigated
as a function of the static magnetic field in the range of temperatures 4.2 K -
Tc. By analyzing the experimental results in the framework of the Coffey and
Clem model we determine the temperature dependence of the first-penetration
field, upper critical field and depinning frequency. The results show that the
pinning energy of this bismuthate superconductor is weaker than those of
cuprates.


  We explore the structure of the $\lambda$-deformed $\sigma$-model action by
setting up a perturbative expansion around the free field point corresponding
to the identity group element. We include all field interaction terms up to
sixth order. We compute the two- and three-point functions of current and
primary filed operators, their anomalous dimensions as well as the
$\beta$-function for the $\lambda$-parameter. Our results are in complete
agreement with those obtained previously using gravitational and/or CFT
perturbative methods in conjunction with the non-perturbative symmetry, as well
as with those obtained using methods exploiting the geometry defined in the
space of couplings. The advantage of this approach is that all deformation
effects are already encoded in the couplings of the interaction vertices and in
the $\lambda$-dressed operators.


  The q-rational numbers and the q-irrational numbers are introduced by S.
Morier-Genoud and V. Ovsienko. In this paper, we focus on q-real quadratic
irrational numbers, especially q-metallic numbers and q-rational sequences
which converge to q-metallic numbers, and consider the radiuses of convergence
of them when we assume that q is a complex number. We construct two sequences
given by recurrence formula as a generalization of the q-deformation of
Fibonacci numbers and Pell numbers which are introduced by S. Morier-Genoud and
V. Ovsienko. We give an estimation of radiuses of convergence of them, and we
solve on conjecture of the lower bound expected which is introduced by L.
Leclere, S. Morier-Genoud, V. Ovsienko, and A. Veselov for the metallic numbers
and its convergence rational sequence. In addition, we obtain a relationship
between the radius of convergence of the [n,n,...,n,...]_q and [n,n,...,n]_q in
the case of n=3 and n=4.


  The recently established formalism of a worldline quantum field theory, which
describes the classical scattering of massive bodies in Einstein gravity, is
generalized up to quadratic order in spin -- for a pair of Kerr black holes
revealing a hidden ${\mathcal N}=2$ supersymmetry. The far-field time-domain
waveform of the gravitational waves produced in such a spinning encounter is
computed at leading order in the post-Minkowskian (weak field, but generic
velocity) expansion, and exhibits this supersymmetry. From the waveform we
extract the leading-order total radiated angular momentum in a generic
reference frame, and the total radiated energy in the center-of-mass frame to
leading order in a low-velocity approximation.


  Inferring cluster structure in microarray datasets is a fundamental task for
the -omic sciences. A fundamental question in Statistics, Data Analysis and
Classification, is the prediction of the number of clusters in a dataset,
usually established via internal validation measures. Despite the wealth of
internal measures available in the literature, new ones have been recently
proposed, some of them specifically for microarray data. In this dissertation,
a study of internal validation measures is given, paying particular attention
to the stability based ones. Indeed, this class of measures is particularly
prominent and promising in order to have a reliable estimate the number of
clusters in a dataset. For those measures, a new general algorithmic paradigm
is proposed here that highlights the richness of measures in this class and
accounts for the ones already available in the literature. Moreover, some of
the most representative validation measures are also considered. Experiments on
12 benchmark datasets are performed in order to assess both the intrinsic
ability of a measure to predict the correct number of clusters in a dataset and
its merit relative to the other measures. The main result is a hierarchy of
internal validation measures in terms of precision and speed, highlighting some
of their merits and limitations not reported before in the literature. This
hierarchy shows that the faster the measure, the less accurate it is. In order
to reduce the time performance gap between the fastest and the most precise
measures, the technique of designing fast approximation algorithms is
systematically applied. The end result is a speed-up of many of the measures
studied here that brings the gap between the fastest and the most precise
within one order of magnitude in time, with no degradation in their prediction
power. Prior to this work, the time gap was at least two orders of magnitude.


  The simplest building blocks for quantum computations are the qbit-qbit
quantum channels. In this paper we analyse the structure of these channels via
their Choi representation. The restriction of a quantum channel to the space of
classical states (i.e. probability distributions) is called the underlying
classical channel. The structure of quantum channels over a fixed classical
channel is studied, the volume of general and unital qubit channels over real
and complex state spaces with respect to the Lebesgue measure is computed and
explicit formulas are presented for the distribution of the volume of quantum
channels over given classical channels. Moreover an algorithm is presented to
generate uniformly distributed channels with respect to the Lebesgue measure,
which enables further studies. With this algorithm the distribution of
trace-distance contraction coefficient (Dobrushin) is investigated numerically
by Monte-Carlo simulations, which leads to some conjectures and points out the
strange behaviour of the real state space.


  We analyze the anti-de Sitter (AdS) superparticle and superstring systems
described in terms of supermatrix valued coordinates proposed by Roiban and
Siegel. This approach gives simple symmetry transformations and equations of
motion. We examine their kappa-transformations, infinite reducibility and
kappa-gauge fixing conditions. A closed first class constraint set for the AdS
superparticle is GL(4|4) covariant and keeping superconformal symmetry
manifestly. For the AdS superstring $\sigma$-dependence breaks the GL(4|4)
covariance, where supercovariant derivatives and currents satisfy the
inhomogeneous GL(4|4). A closed first class constraint set for the AdS
superstring turns out to be the same as the one for a superstring in flat
space, namely ABCD constraints.


  This paper deals with the \emph{integral} version of the Dirichlet
homogeneous fractional Laplace equation. For this problem weighted and
fractional Sobolev a priori estimates are provided in terms of the H\"older
regularity of the data. By relying on these results, optimal order of
convergence for the standard linear finite element method is proved for
quasi-uniform as well as graded meshes. Some numerical examples are given
showing results in agreement with the theoretical predictions.


  The elastic energy of a planar convex body is defined by $E(\Om)=\frac
12\,\int\_{\partial\Om} k^2(s)\,ds$where $k(s)$ is the curvature of the
boundary. In this paper we are interested in the minimization problemof
$E(\Om)$ with a constraint on the inradius of $\Om$. By contrast with all the
other minimization problemsinvolving this elastic energy (with a perimeter,
area, diameter or circumradius constraints) for which thesolution is always the
disk, we prove here that the solution of this minimization problem is not the
disk and we completely characterizeit in terms of elementary functions.


  Over the past decade, the existence of a substantial population of optically
invisible, massive galaxies at $z\gtrsim3$ has been implied from mid-infrared
to millimeter observations. With the unprecedented sensitivity of the JWST,
such extremely massive galaxy candidates have immediately been identified even
at $z>7$, in much larger numbers than expected. These discoveries raised a hot
debate. If confirmed, early, high-mass galaxies challenge the current models of
galaxy formation. However, the lack of spectroscopic confirmations leads to
uncertain stellar mass ($M_{\star}$) estimates, and the possible presence of
active galactic nuclei (AGN) adds further uncertainty. Here, we present the
first sample of 36 dust-obscured galaxies with robust spectroscopic redshifts
at $z_{\rm spec}=5-9$ from the JWST FRESCO survey. The three most extreme
sources at $z\sim5-6$ ($\sim$1 billion years after the Big Bang) are so massive
(log$M_{\star}/M_{\odot}$ $\gtrsim11.0$) that they would require, on average,
about 50% of the baryons in their halos to be converted into stars -- two to
three times higher than even the most efficient galaxies at later times. The
extended emission of these galaxies suggests limited contribution by AGN. This
population of ultra-massive galaxies accounts for 20% of the total cosmic star
formation rate density at $z\sim5-6$, suggesting a substantial proportion of
extremely efficient star formation in the early Universe.


  In this work, we propose a two-stage approach to strengthen piecewise
McCormick relaxations for mixed-integer nonlinear programs (MINLP) with
multi-linear terms. In the first stage, we exploit Constraint Programing (CP)
techniques to contract the variable bounds. In the second stage we partition
the variables domains using a dynamic multivariate partitioning scheme. Instead
of equally partitioning the domains of variables appearing in multi-linear
terms, we construct sparser partitions yet tighter relax- ations by iteratively
partitioning the variable domains in regions of interest. This approach
decouples the number of partitions from the size of the variable domains, leads
to a significant reduction in computation time, and limits the number of binary
variables that are introduced by the partitioning. We demonstrate the
performance of our algorithm on well-known benchmark problems from MINLPLIB and
discuss the computational benefits of CP-based bound tightening procedures.


  We present an analysis of CMDs of three intermediate-age LMC clusters, namely
NGC 2173, SL 556 and NGC 2155. The main goal of our project is to investigate
the amount of convective core overshoot necessary to reproduce the CMDs of
relatively metal-poor, intermediate age stellar populations. We conclude that a
moderate amount of overshoot and some fraction of binary stars are essential
for reproducing the observed shapes around the turnoff in the CMD's of all
three clusters: unresolved binary stars fill in the expected core contraction
gap, and make a unique sequence near the gap, which cannot be reproduced by
single stars alone, even with a larger amount of overshoot. From our overall
analysis such as, shape of isochrones, star counts, color distribution, and
synthetic CMD comparisons, we conclude that overshoot ~ 20% of the local
pressure scale height best reproduces the CMD properties of all three clusters.
The best age estimates are 1.5, 2.1 and 2.9 Gyr for NGC 2173, SL 556 and NGC
2155, respectively.


  Transistors have provided the basis of modern electronics. Being relatively
intricate devices, and often exhibiting intense parameter variation, this type
of electronic devices has motivated much research interest especially regarding
their characterization and modeling. In this work, we apply a recently reported
modeling methodology, based on the Early effect, for characterizing new old
stock NPN and PNP small signal germanium junction transistors and comparing
them to more modern silicon bipolar junction devices. The Early approach is
special in the sense that its two parameters, namely the Early voltage $V_a$
and a proportionality parameter $s$, are fixed and independent of transistor
operation. Remarkable results are obtained, including the fact that the four
considered groups, namely NPN and PNP germanium and silicon devices, occupy
mostly non-overlapping regions in the Early parameter space, with PNP devices
presenting larger parameter variability in both cases. Surprisingly, the
considered germanium devices exhibited smaller parameter variability than
observed for the silicon counterparts. When mapped into the more traditional
space defined by the current gain and output resistance parameters, the four
transistor groups exhibited much larger overlaps and yielded a clustering
structure much less organized than allowed by the Early mapping. This result
suggest that the Early representation of transistors is more compatible and
inherently related to the structure of amplifying devices such as those
considered in this work. In addition, it was verified that the center of mass
of each of the NPN-PNP pairs of germanium and silicon devices are crossed by
respective $\beta$-isolines for gains of 130 and 250, respectively. Germanium
devices were also characterized as having smaller output resistance and smaller
magnitudes of Early voltage.


  Layered topological insulators, for example, Bi$_2$Se$_3$ are optically
hyperbolic materials in a range of THz frequencies. Such materials possess
deeply subdiffractional, highly directional collective modes: hyperbolic
phonon-polaritons. In thin crystals the dispersion of such modes is split into
discrete subbands and is strongly influenced by electron surface states. If the
surface states are doped, then hybrid collective modes result from coupling of
the phonon-polaritons with surface plasmons. The strength of the hybridization
can be controlled by an external gate that varies the chemical potential of the
surface states. Momentum-dependence of the plasmon-phonon coupling leads to a
polaritonic analog of the Goos-H\"anchen effect. Directionality of the
polaritonic rays and their tunable Goos-H\"anchen shift are observable via THz
nanoimaging.


  We have newly observed the Class 0/I protostar L1527 IRS using the Atacama
Large Millimeter/submillimeter Array (ALMA) during its Cycle 1 in 220 GHz dust
continuum and C18O (J=2-1) line emissions with a ~2 times higher angular
resolution (~0.5") and ~4 times better sensitivity than our ALMA Cycle 0
observations. Continuum emission shows elongation perpendicular to the
associated outflow, with a deconvolved size of 0.53"x0.15". C18O emission shows
similar elongation, indicating that both emissions trace the disk and the
flattened envelope surrounding the protostar. The velocity gradient of the C18O
emission along the elongation due to rotation of the disk/envelope system is
re-analyzed, identifying Keplerian rotation proportional to r^-0.5 more clearly
than the Cycle 0 observations. The Keplerian-disk radius and the dynamical
stellar mass are kinematically estimated to be ~74 AU and ~0.45 Mo,
respectively. The continuum visibility is fitted by models without any annulus
averaging, revealing that the disk is in hydrostatic equilibrium. The best-fit
model also suggests a density jump by a factor of ~5 between the disk and the
envelope, suggesting that disks around protostars can be geometrically
distinguishable from the envelope from a viewpoint of density contrast.
Importantly, the disk radius geometrically identified with the density jump is
consistent with the radius kinematically estimated. Possible origin of the
density jump due to the mass accretion from the envelope to the disk is
discussed. C18O observations can be reproduced by the same geometrical
structures derived from the dust observations, with possible C18O freeze-out
and localized C18O desorption.


  Metamorphic Testing is a software testing paradigm which aims at using
necessary properties of a system-under-test, called metamorphic relations, to
either check its expected outputs, or to generate new test cases. Metamorphic
Testing has been successful to test programs for which a full oracle is not
available or to test programs for which there are uncertainties on expected
outputs such as learning systems. In this article, we propose Adaptive
Metamorphic Testing as a generalization of a simple yet powerful reinforcement
learning technique, namely contextual bandits, to select one of the multiple
metamorphic relations available for a program. By using contextual bandits,
Adaptive Metamorphic Testing learns which metamorphic relations are likely to
transform a source test case, such that it has higher chance to discover
faults. We present experimental results over two major case studies in machine
learning, namely image classification and object detection, and identify
weaknesses and robustness boundaries. Adaptive Metamorphic Testing efficiently
identifies weaknesses of the tested systems in context of the source test case.


  A canonical feature of the constraint satisfaction problems in NP is
approximation hardness, where in the worst case, finding sufficient-quality
approximate solutions is exponentially hard for all known methods.
Fundamentally, the lack of any guided local minimum escape method ensures both
exact and approximate classical approximation hardness, but the equivalent
mechanism(s) for quantum algorithms are poorly understood. For algorithms based
on Hamiltonian time evolution, we explore this question through the
prototypically hard MAX-3-XORSAT problem class. We conclude that the mechanisms
for quantum exact and approximation hardness are fundamentally distinct. We
review known results from the literature, and identify mechanisms that make
conventional quantum methods (such as Adiabatic Quantum Computing) weak
approximation algorithms in the worst case. We construct a family of spectrally
filtered quantum algorithms that escape these issues, and develop analytical
theories for their performance. We show that, for random hypergraphs in the
approximation-hard regime, if we define the energy to be $E =
N_{\mathrm{unsat}}-N_{\mathrm{sat}}$, spectrally filtered quantum optimization
will return states with $E \leq q_m E_{\mathrm{GS}}$ (where $E_{\rm GS}$ is the
ground state energy) in sub-quadratic time, where conservatively, $q_m \simeq
0.59$. This is in contrast to $q_m \to 0$ for the hardest instances with
classical searches. We test all of these claims with extensive numerical
simulations. We do not claim that this approximation guarantee holds for all
possible hypergraphs, though our algorithm's mechanism can likely generalize
widely. These results suggest that quantum computers are more powerful for
approximate optimization than had been previously assumed.


  In this work we elaborate on two recently discovered invariance principles,
according to which transport coefficients are, to a large extent, independent
of the microscopic definition of the densities and currents of the conserved
quantities being transported (energy, momentum, mass, charge). The first such
principle, gauge invariance, allows one to define a quantum adiabatic energy
current from density-functional theory, from which the heat conductivity can be
uniquely defined and computed using equilibrium ab initio molecular dynamics.
When combined with a novel topological definition of atomic oxidation states,
gauge invariance also sheds new light onto the mechanisms of charge transport
in ionic conductors. The second principle, convective invariance, allows one to
extend the analysis to multi-component systems. These invariance principles can
be combined with new spectral analysis methods for the current time series to
be fed into the Green-Kubo formula to obtain accurate estimates of transport
coefficients from relatively short molecular dynamics simulations.


  The recently discovered cuprate superconductor Ba$_2$CuO$_{3+\delta}$
exhibits a high $T_c\simeq73$K at $\delta\simeq0.2$. The polycrystal grown
under high pressure has a structure similar to La$_2$CuO$_4$, but with
dramatically different lattice parameters due to the CuO$_6$ octahedron
compression. The crystal field in the compressed Ba$_2$CuO$_4$ leads to an
inverted Cu $3d$ $e_g$ complex with the $d_{x^2-y^2}$ orbital sitting below the
$d_{3z^2-r^2}$ and an electronic structure highly unusual compared to the
conventional cuprates. We construct a two-orbital Hubbard model for the Cu
$d^9$ state at hole doping $x=2\delta$ and study the orbital-dependent strong
correlation and superconductivity. For the undoped case at $x=0$, we found that
strong correlation drives an orbital-polarized Mott insulating state with the
spin-$1/2$ moment of the localized $d_{3z^2-r^2}$ orbital. In contrast to the
single-band cuprates where superconductivity is suppressed in the overdoped
regime, hole doping the two-orbital Mott insulator leads to orbital-dependent
correlations and the robust spin and orbital exchange interactions produce a
high-$T_c$ antiphase $d$-wave superconductor even in the heavily doped regime
at $x=0.4$. We conjecture that Ba$_2$CuO$_{3+\delta}$ realizes mixtures of such
heavily hole-doped superconducting Ba$_2$CuO$_4$ and disordered Ba$_2$CuO$_{3}$
chains in a single-layer or predominately separated bilayer structure. Our
findings suggest that unconventional cuprates with liberated orbitals as doped
two-band Mott insulators can be a direction for realizing high-T$_c$
superconductivity with enhanced transition temperature $T_c$.


  The superconducting-state heat capacity of
Na$_{0.3}$CoO$_{2}$$\cdot$1.3H$_{2}$O shows unusual, marked deviations from BCS
theory, at all temperatures. At low temperatures the heat capacity has the
$T^2$ dependence characteristic of line nodes in the energy gap, rather than
the exponential temperature dependence of a fully gapped, conventional
superconductor. At temperatures of the order of one fifth of the critical
temperature and above, the deviations are strikingly similar to those of
MgB$_2$, which are known to be a consequence of the existence of substantially
different energy gaps on different sheets of the Fermi surface. A two-gap fit
to the Na$_{0.3}$CoO$_{2}$$\cdot$1.3H${_2}$O data gives gap amplitudes of 45%
and 125% of the BCS value, on parts of the Fermi surface that contribute,
respectively, 45% and 55% to the normal-state density of states. The
temperature of the onset of the transition to the vortex state is independent
of magnetic field, which shows the presence of unusually strong fluctuations.


  Establishing that a set of population-splitting events occurred at the same
time can be a potentially persuasive argument that a common process affected
the populations. Oaks et al. (2013) assessed the ability of an
approximate-Bayesian method (msBayes) to estimate such a pattern of
simultaneous divergence across taxa, to which Hickerson et al. (2014)
responded. Both papers agree the method is sensitive to prior assumptions and
often erroneously supports shared divergences; the papers differ about the
explanation and solution. Oaks et al. (2013) suggested the method's behavior is
caused by the strong weight of uniform priors on divergence times leading to
smaller marginal likelihoods of models with more divergence-time parameters
(Hypothesis 1); they proposed alternative priors to avoid strongly weighted
posteriors. Hickerson et al. (2014) suggested numerical approximation error
causes msBayes analyses to be biased toward models of clustered divergences
(Hypothesis 2); they proposed using narrow, empirical uniform priors. Here, we
demonstrate that the approach of Hickerson et al. (2014) does not mitigate the
method's tendency to erroneously support models of clustered divergences, and
often excludes the true parameter values. Our results also show that the
tendency of msBayes analyses to support models of shared divergences is
primarily due to Hypothesis 1. This series of papers demonstrate that if our
prior assumptions place too much weight in unlikely regions of parameter space
such that the exact posterior supports the wrong model of evolutionary history,
no amount of computation can rescue our inference. Fortunately, more flexible
distributions that accommodate prior uncertainty about parameters without
placing excessive weight in vast regions of parameter space with low likelihood
increase the method's robustness and power to detect temporal variation in
divergences.


  We have performed large-scale shell-model calculations of the half-lives and
neutron-branching probabilities of the r-process waiting point nuclei at the
magic neutron numbers N=50, 82, and 126. The calculations include contributions
from allowed Gamow-Teller and first-forbidden transitions. We find good
agreement with the measured half-lives for the N=50 nuclei with charge numbers
Z=28-32 and for the N=82 nuclei 129Ag and 130Cd. The contribution of forbidden
transitions reduce the half-lives of the N=126 waiting point nuclei
significantly, while they have only a small effect on the half-lives of the
N=50 and 82 r-process nuclei.


  Graph convolutional networks (GCNs) are a powerful deep learning approach for
graph-structured data. Recently, GCNs and subsequent variants have shown
superior performance in various application areas on real-world datasets.
Despite their success, most of the current GCN models are shallow, due to the
{\em over-smoothing} problem. In this paper, we study the problem of designing
and analyzing deep graph convolutional networks. We propose the GCNII, an
extension of the vanilla GCN model with two simple yet effective techniques:
{\em Initial residual} and {\em Identity mapping}. We provide theoretical and
empirical evidence that the two techniques effectively relieves the problem of
over-smoothing. Our experiments show that the deep GCNII model outperforms the
state-of-the-art methods on various semi- and full-supervised tasks. Code is
available at https://github.com/chennnM/GCNII .


  Deep learning models can enable accurate and efficient disease diagnosis, but
have thus far been hampered by the data scarcity present in the medical world.
Automated diagnosis studies have been constrained by underpowered single-center
datasets, and although some results have shown promise, their generalizability
to other institutions remains questionable as the data heterogeneity between
institutions is not taken into account. By allowing models to be trained in a
distributed manner that preserves patients' privacy, federated learning
promises to alleviate these issues, by enabling diligent multi-center studies.
We present the first federated learning study on the modality of cardiovascular
magnetic resonance (CMR) and use four centers derived from subsets of the M\&M
and ACDC datasets, focusing on the diagnosis of hypertrophic cardiomyopathy
(HCM). We adapt a 3D-CNN network pretrained on action recognition and explore
two different ways of incorporating shape prior information to the model, and
four different data augmentation set-ups, systematically analyzing their impact
on the different collaborative learning choices. We show that despite the small
size of data (180 subjects derived from four centers), the privacy preserving
federated learning achieves promising results that are competitive with
traditional centralized learning. We further find that federatively trained
models exhibit increased robustness and are more sensitive to domain shift
effects.


  There is an increasing need for more automated system-log analysis tools for
large scale online system in a timely manner. However, conventional way to
monitor and classify the log output based on keyword list does not scale well
for complex system in which codes contributed by a large group of developers,
with diverse ways of encoding the error messages, often with misleading pre-set
labels. In this paper, we propose that the design of a large scale online log
analysis should follow the "Least Prior Knowledge Principle", in which
unsupervised or semi-supervised solution with the minimal prior knowledge of
the log should be encoded directly. Thereby, we report our experience in
designing a two-stage machine learning based method, in which the system logs
are regarded as the output of a quasi-natural language, pre-filtered by a
perplexity score threshold, and then undergo a fine-grained classification
procedure. Tests on empirical data show that our method has obvious advantage
regarding to the processing speed and classification accuracy.


  We study homologically maximizing timelike geodesics in conformally flat
tori. A causal geodesic $\gamma$ in such a torus is said to be homologically
maximizing if one (hence every) lift of $\gamma$ to the universal cover is
arclength maximizing. First we prove a compactness result for homologically
maximizing timelike geodesics. This yields the Lipschitz continuity of the time
separation of the universal cover on strict sub-cones of the cone of future
pointing vectors. Then we introduce the stable time separation $\mathfrak{l}$.
As an application we prove relations between the concavity properties of
$\mathfrak{l}$ and the qualitative behavior of homologically maximizing
geodesics.


  Deep neural networks (DNNs) are increasingly being deployed in
safety-critical systems such as personal healthcare devices and self-driving
cars. In such DNN-based systems, error resilience is a top priority since
faults in DNN inference could lead to mispredictions and safety hazards. For
latency-critical DNN inference on resource-constrained edge devices, it is
nontrivial to apply conventional redundancy-based fault tolerance techniques.
In this paper, we propose FitAct, a low-cost approach to enhance the error
resilience of DNNs by deploying fine-grained post-trainable activation
functions. The main idea is to precisely bound the activation value of each
individual neuron via neuron-wise bounded activation functions so that it could
prevent fault propagation in the network. To avoid complex DNN model
re-training, we propose to decouple the accuracy training and resilience
training and develop a lightweight post-training phase to learn these
activation functions with precise bound values. Experimental results on widely
used DNN models such as AlexNet, VGG16, and ResNet50 demonstrate that FitAct
outperforms state-of-the-art studies such as Clip-Act and Ranger in enhancing
the DNN error resilience for a wide range of fault rates while adding
manageable runtime and memory space overheads.


  We address the following conjecture about the existence of common zeros for
commuting vector fields in dimension three: if $X,Y$ are two $C^1$ commuting
vector fields on a $3$-manifold $M$, and $U$ is a relatively compact open such
that $X$ does not vanish on the boundary of $U$ and has a non vanishing
Poincar\'e-Hopf index in $U$, then $X$ and $Y$ have a common zero inside $U$.
We prove this conjecture when $X$ and $Y$ are of class $C^3$ and every periodic
orbit of $Y$ along which $X$ and $Y$ are collinear is partially hyperbolic. We
also prove the conjecture, still in the $C^3$ setting, assuming that the flow
$Y$ leaves invariant a transverse plane field. These results shed new light on
the $C^3$ case of the conjecture.


  It is difficult to design a visually appealing layout for common users, which
takes time even for professional designers. In this paper, we present an
interactive layout design system with shadow guidance and layout retrieval to
help users obtain satisfactory design results. This study focuses in particular
on the design of academic presentation slides. The user may refer to the shadow
guidance as a heat map, which is the layout distribution of our gathered data
set, using the suggested shadow guidance. The suggested system is data-driven,
allowing users to analyze the design data naturally. The layout may then be
edited by the user to finalize the layout design. We validated the suggested
interface in our user study by comparing it with common design interfaces. The
findings show that the suggested interface may achieve high retrieval accuracy
while simultaneously offering a pleasant user experience.


  For hybrid systems, such as molecules grafted onto solid surfaces, the
calculation of linear response in time dependent density functional theory is
slowed down by the need to calculate, in N^4 operations, the susceptibility of
N non interacting Kohn-Sham reference electrons. We show how this
susceptibility can be calculated N times faster within finite precision. By
itself or in combination with previous methods, this should facilitate the
calculation of TDDFT response and optical spectra of hybrid systems.


  Adhesion processes of biological membranes that enclose cells and cellular
organelles are essential for immune responses, tissue formation, and signaling.
These processes depend sensitively on the binding constant K2D of the
membrane-anchored receptor and ligand proteins that mediate adhesion, which is
difficult to measure in the 'two-dimensional' (2D) membrane environment of the
proteins. An important problem therefore is to relate K2D} to the binding
constant K3D} of soluble variants of the receptors and ligands that lack the
membrane anchors and are free to diffuse in three dimensions (3D). In this
article, we present a general theory for the binding constants K2D and K3D of
rather stiff proteins whose main degrees of freedom are translation and
rotation, along membranes and around anchor points 'in 2D', or unconstrained
'in 3D'. The theory generalizes previous results by describing how K2D depends
both on the average separation and thermal nanoscale roughness of the apposing
membranes, and on the length and anchoring flexibility of the receptors and
ligands. Our theoretical results for the ratio K2D/K3D of the binding constants
agree with detailed results from Monte Carlo simulations without any data
fitting, which indicates that the theory captures the essential features of the
'dimensionality reduction' due to membrane anchoring. In our Monte Carlo
simulations, we consider a novel coarse-grained model of biomembrane adhesion
in which the membranes are represented as discretized elastic surfaces, and the
receptors and ligands as anchored molecules that diffuse continuously along the
membranes and rotate at their anchor points.


  The detection of change points is a pivotal task in statistical analysis. In
the quantum realm, it is a new primitive where one aims at identifying the
point where a source that supposedly prepares a sequence of particles in
identical quantum states starts preparing a mutated one. We obtain the optimal
procedure to identify the change point with certainty---naturally at the price
of having a certain probability of getting an inconclusive answer. We obtain
the analytical form of the optimal probability of successful identification for
any length of the particle sequence. We show that the conditional success
probabilities of identifying each possible change point show an unexpected
oscillatory behaviour. We also discuss local (online) protocols and compare
them with the optimal procedure.


  Alternating Direction Method of Multipliers (ADMM) has become a widely used
optimization method for convex problems, particularly in the context of data
mining in which large optimization problems are often encountered. ADMM has
several desirable properties, including the ability to decompose large problems
into smaller tractable sub-problems and ease of parallelization, that are
essential in these scenarios. The most common form of ADMM is the two-block, in
which two sets of primal variables are updated alternatingly. Recent years have
seen advances in multi-block ADMM, which update more than two blocks of primal
variables sequentially. In this paper, we study the empirical question: {\em Is
two-block ADMM always comparable with sequential multi-block ADMM solving an
equivalent problem?} In the context of optimization problems arising in
multi-task learning, through a comprehensive set of experiments we surprisingly
show that multi-block ADMM consistently outperformed two-block ADMM on
optimization performance, and as a consequence on prediction performance,
across all datasets and for the entire range of dual step sizes. Our results
have an important practical implication: rather than simply using the popular
two-block ADMM, one may considerably benefit from experimenting with
multi-block ADMM applied to an equivalent problem.


  Abrikosov Vortices have long been considered as means to encode classical
information in low-temperature logic circuits (1) and memory devices (2-4).
Although it is possible to control individual vortices using local probes
(5-11), scalability towards the control of of multiple vortices remains
challenging. Vortex logic devices require means to shuttle selected vortices
reliably over long distances between engineered pinning potentials.
Concomitantly, all other vortices should remains fixed to their precise
locations. Here we demonstrate such capabilities using Nb loops patterned below
a NbSe$_2$ layer. SQUID-on-Tip (SOT) microscopy reveals that the loops can
position vortices in sites designated to a precision better than 100 nm; they
can realize "push" and "pull" operations of vortices as far as 3$\mu$m.
Successive application of such operations shuttles a vortex between adjacent
loops. Our results may be used as means to integrate vortices in future quantum
circuitry. Strikingly, we are able to demonstrate a winding operation. Such
winding, if realized in topological superconductors, is considered an essential
part of future topological quantum information processing (12-17).


  It is inevitable that the $L_{g}(s)$ association scheme with $g\geq 3, s\geq
g+2$ is a pseudo-$L_{g}(s)$ association scheme. On the contrary, although $s^2$
treatments of the pseudo-$L_{g}(s)$ association scheme can form one $L_{g}(s)$
association scheme, it is not always an $L_{g}(s)$ association scheme. Mainly
because the set of cardinality $s$, which contains two first-associates
treatments of the pseudo-$L_{g}(s)$ association scheme, is non-unique. Whether
the order $s$ of a Latin square $\mathbf{L}$ is a prime power or not, the paper
proposes two new conditions in order to extend a $POL(s,w)$ containing
$\mathbf{L}$. It has been known that a $POL(s,w)$ can be extended to a
$POL(s,s-1)$ so long as Bruck's \cite{brh} condition $s\geq
\frac{(s-1-w)^4-2(s-1-w)^3+2(s-1-w)^2+(s-1-w)}{2}$ is satisfied, Bruck's
condition will be completely improved through utilizing six properties of the
$L_{w+2}(s)$ association scheme in this paper. Several examples are given to
elucidate the application of our results.


  We use a conditional deep convolutional generative adversarial network to
predict the geopotential height of the 500 hPa pressure level, the two-meter
temperature and the total precipitation for the next 24 hours over Europe. The
proposed models are trained on 4 years of ERA5 reanalysis data from 2015-2018
with the goal to predict the associated meteorological fields in 2019. The
forecasts show a good qualitative and quantitative agreement with the true
reanalysis data for the geopotential height and two-meter temperature, while
failing for total precipitation, thus indicating that weather forecasts based
on data alone may be possible for specific meteorological parameters. We
further use Monte-Carlo dropout to develop an ensemble weather prediction
system based purely on deep learning strategies, which is computationally cheap
and further improves the skill of the forecasting model, by allowing to
quantify the uncertainty in the current weather forecast as learned by the
model.


  We consider an infinite asexual population with a mutator allele which can
elevate mutation rates. With probability $f$, a transition from nonmutator to
mutator state occurs but the reverse transition is forbidden. We find that at
$f=0$, the population is in the state with minimum mutation rate and at
$f=f_c$, a phase transition occurs between a mixed phase with both nonmutators
and mutators and a pure mutator phase. We calculate the critical probability
$f_c$ and the total mutator fraction $Q$ in the mixed phase exactly. Our
predictions for $Q$ are in agreement with those seen in microbial populations
in static environments.


  We give an introduction to the concept of Kan extensions, and study its
relation with the notions of coend and adjoint functors. We state and prove in
detail a well known formula to compute Kan extensions by using coends: a
certain colimit related to the concept of copower. Finally, we study the tensor
product of functors, and its relation with Kan extensions, in order to
represent the tensor product of $\mathcal{C}$-modules as a particular case.


  Federated Learning is introduced to protect privacy by distributing training
data into multiple parties. Each party trains its own model and a meta-model is
constructed from the sub models. In this way the details of the data are not
disclosed in between each party. In this paper we investigate the model
interpretation methods for Federated Learning, specifically on the measurement
of feature importance of vertical Federated Learning where feature space of the
data is divided into two parties, namely host and guest. For host party to
interpret a single prediction of vertical Federated Learning model, the
interpretation results, namely the feature importance, are very likely to
reveal the protected data from guest party. We propose a method to balance the
model interpretability and data privacy in vertical Federated Learning by using
Shapley values to reveal detailed feature importance for host features and a
unified importance value for federated guest features. Our experiments indicate
robust and informative results for interpreting Federated Learning models.


  We present a time-dependent, one-dimensional magnetically driven disk-wind
model based on magnetohydrodynamical (MHD) equations in the context of tidal
disruption events (TDEs). We assume that the disk is geometrically thin and
gas-pressure dominant and explicitly take into account magnetic braking as well
as turbulent viscosity by an extended alpha viscosity prescription. We find a
particular wind solution for a set of basic equations that satisfies the
necessary and sufficient conditions for vertically unbound MHD flows. The
solution demonstrates that the disk evolves with losing mass due to wind and
accretion from the initial Gaussian density distribution. We confirm that the
mass accretion rate follows the power law of time $t^{-19/16}$ at late times if
the wind is absent, which corresponds to the classical solution of Cannizzo et
al. (1990). We find that the mass accretion rate is steeper than the
$t^{-19/16}$ curve if the disk wind is present. This is because the wind
removes a significant fraction of the mass and angular momentum. Mass accretion
is further induced by magnetic braking, known as a wind-driven accretion
mechanism, resulting in more rapid decay with time in both the mass accretion
and loss rates. The ultraviolet (UV) luminosity is the highest among the
optical, UV, and X-ray luminosities from early to late evolutionary phases,
suggesting optical and X-ray emissions from the disk are observationally
insignificant due to magnetically driven winds in TDEs. Our model predicts that
late-time bolometric light curves steeper than $t^{-19/16}$ in UV-bright TDEs
potentially serve as compelling indicators for magnetically driven winds.


  Based on the four-level double-$\Delta$ model composed of two degenerated
(left- and right-handed) chiral ground states and two achiral excited states,
we propose a purely optical method for enantio-conversion of chiral mixture. By
choosing appropriate parameters, the original four-level model will be
simplified to two effective two-level sub-systems with each of them involving
one chiral ground state. Then, with the help of well-designed optical
operations, the initial unwanted and wanted chiral ground states are converted,
respectively, to the wanted chiral ground state and an auxiliary chiral excited
state with the wanted chirality, i.e., achieving the enantioconversion of the
chiral mixture. Comparing with the original work of enantioconversion based on
the four-level double-$\Delta$ model with the requirement of the time-consuming
relaxation step and repeated operations, our method can be three orders of
magnitude faster since we use only purely optical operations. Thus, it offers a
promising candidate for fast enantioconversion when the total operation time is
limited due to the racemization and/or experimental conditions.


  We produce an explicit family of totally real cyclic quartic polynomials that
are monogenic in many cases, and conjecturally are monogenic and generating
distinct quartic fields infinitely often.


  Spiking Neural Networks (SNN) are known to be very effective for neuromorphic
processor implementations, achieving orders of magnitude improvements in energy
efficiency and computational latency over traditional deep learning approaches.
Comparable algorithmic performance was recently made possible as well with the
adaptation of supervised training algorithms to the context of SNN. However,
information including audio, video, and other sensor-derived data are typically
encoded as real-valued signals that are not well-suited to SNN, preventing the
network from leveraging spike timing information. Efficient encoding from
real-valued signals to spikes is therefore critical and significantly impacts
the performance of the overall system. To efficiently encode signals into
spikes, both the preservation of information relevant to the task at hand as
well as the density of the encoded spikes must be considered. In this paper, we
study four spike encoding methods in the context of a speaker independent digit
classification system: Send on Delta, Time to First Spike, Leaky Integrate and
Fire Neuron and Bens Spiker Algorithm. We first show that all encoding methods
yield higher classification accuracy using significantly fewer spikes when
encoding a bio-inspired cochleagram as opposed to a traditional short-time
Fourier transform. We then show that two Send On Delta variants result in
classification results comparable with a state of the art deep convolutional
neural network baseline, while simultaneously reducing the encoded bit rate.
Finally, we show that several encoding methods result in improved performance
over the conventional deep learning baseline in certain cases, further
demonstrating the power of spike encoding algorithms in the encoding of
real-valued signals and that neuromorphic implementation has the potential to
outperform state of the art techniques.


  We consider an exponential model within the so-called f(T) theory of gravity,
where $T$ denotes the torsion scalar. We focus our work on a cosmological
feature of such a model, checking whether it may describe the de Sitter stage
of the current universe through the analysis of the redshift z. Our results
shows that the model reproduces the de Sitter stage only for low-redshifts,
where the perturbation function goes toward zero as the low values of the
redshift are reached, whereas the effective parameter of equation of state goes
to -1, which is the expected behavior for any model able to reproduce de Sitter
stage.


  Planetary nebulae (PNe) and their central stars (CSs) are ideal tools to test
evolutionary theory: photospheric properties of their exciting stars give
stringent constraints for theoretical predictions of stellar evolution. The
nebular abundances display the star's photosphere at the time of the nebula's
ejection which allows to look back into the history of stellar evolution - but,
more importantly, they even provide a possibility to investigate on the
chemical evolution of our Galaxy because most of the nuclear processed material
goes back into the interstellar medium via PNe.
  The recent developments in observation techniques and a new three-dimensional
photoionization code MOCASSIN enable us to analyze PNe properties precisely by
the construction of consistent models of PNe and CSs. In addition to PNe
imaging and spectroscopy, detailed information about the velocity field within
the PNe is a pre-requisite to employ de-projection techniques in modeling the
physical structureof the PNe.


  Metriplectic dynamics couple a Poisson bracket of the Hamiltonian description
with a kind of metric bracket, for describing systems with both Hamiltonian and
dissipative components. The construction builds in asymptotic convergence to a
preselected equilibrium state. Phenomena such as friction, electric
resistivity, thermal conductivity and collisions in kinetic theories all fit
within this framework. In this paper an application of metriplectic dynamics is
presented that is of interest for the theory of control: a suitably chosen
torque, expressed through a metriplectic extension of its "natural" Poisson
algebra, an algebra obtained by reduction of a canonical Hamiltonian system, is
applied to a free rigid body. On a practical ground, the effect is to drive the
body to align its angular velocity to rotation about a stable principal axis of
inertia, while conserving its kinetic energy in the process. On theoretical
grounds, this example provides a class of non-Hamiltonian torques that can be
added to the canonical Hamiltonian description of the free rigid body and
reduce to metriplectic dissipation. In the canonical description these torques
provide convergence to a higher dimensional attractor. The method of
construction of such torques can be extended to other dynamical systems
describing "machines" with non-Hamiltonian motion having attractors.


  Reinforcement Learning (RL) is a widely employed technique in decision-making
problems, encompassing two fundamental operations -- policy evaluation and
policy improvement. Enhancing learning efficiency remains a key challenge in
RL, with many efforts focused on using ensemble critics to boost policy
evaluation efficiency. However, when using multiple critics, the actor in the
policy improvement process can obtain different gradients. Previous studies
have combined these gradients without considering their disagreements.
Therefore, optimizing the policy improvement process is crucial to enhance
learning efficiency. This study focuses on investigating the impact of gradient
disagreements caused by ensemble critics on policy improvement. We introduce
the concept of uncertainty of gradient directions as a means to measure the
disagreement among gradients utilized in the policy improvement process.
Through measuring the disagreement among gradients, we find that transitions
with lower uncertainty of gradient directions are more reliable in the policy
improvement process. Building on this analysis, we propose a method called von
Mises-Fisher Experience Resampling (vMFER), which optimizes the policy
improvement process by resampling transitions and assigning higher confidence
to transitions with lower uncertainty of gradient directions. Our experiments
demonstrate that vMFER significantly outperforms the benchmark and is
particularly well-suited for ensemble structures in RL.


  The phase and amplitude responses of a narrow-band optical filter are
measured simultaneously using a microwave network analyzer. The measurement is
based on an interferometric arrangement to split light into two paths and then
combine them. In one of the two paths, a Mach-Zehnder modulator generates two
tones without carrier and the narrow-band optical filter just passes through
one of the tones. The temperature and environmental variations are removed by
separated phase and amplitude averaging. The amplitude and phase responses of
the optical filter are measured to the resolution and accuracy of the network
analyzer.


  A striking prediction from the random matrix theory in mesoscopic physics is
the existence of "open channels": waves that can use multipath interference to
achieve perfect transmission across an opaque disordered medium even in the
multiple-scattering regime. Realization of such open channels requires a
coherent control of the complete incident wavefront. To date, the open channels
have only been demonstrated in scalar two-dimensional (2D) structures, both
experimentally and with numerical studies. Here, we utilize a recently proposed
"augmented partial factorization" full-wave simulation method to compute the
scattering matrix from 3D vectorial Maxwell's equations and demonstrate the
existence of open channels in 3D disordered media. We examine the spatial
profile of such open channels, demonstrate the existence of a bimodal
transmission eigenvalue distribution with full control, and study the effects
of incomplete polarization control and of a finite illumination area. This
study confirms the validity of the random matrix theory in vectorial systems.
The simulation framework provides full access to the complex multi-channel wave
transport in 3D disordered systems, filling the gap left by experimental
capabilities.