  We present the first fully relativistic numerical calculations of
differentially rotating Strange Quark Stars models for broad ranges of the
maximum density and of the degree of differential rotation. Our simulations are
performed with the very accurate and stable multi-domain spectral code FlatStar
and use the MIT Bag model for describing strange quark matter. Our calculations
based on a thorough exploration of the solution space show that the maximum
mass of strange stars depends on both the degree of differential rotation and a
type of solution, similarly to neutron stars described by a polytropic
equations of state. The highest increase of the maximum mass (compared to the
value for a non-rotating star) is obtained for models with a low degree of
differential rotation. This highest mass is over four times larger than that of
the equivalent non rotating configuration. Comparing our results with
calculations done for realistic models of neutron stars, we conclude that with
the help of differential rotation, strange stars can sustain masses much larger
than stars made from nuclear matter for low degree of differential rotation
which reinforces the hope of demonstrating, or of ruling out, the existence of
strange matter through the observation by gravitational waves, by gamma rays or
by neutrinos of the massive material object born from the merger of a compact
binary system or during some supernova events.


  The statistical property of a growing scale-free network is studied based on
an earlier model proposed by Krapivsky, Rodgers, and Redner [Phys. Rev. Lett.
86, 5401 (2001)], with the additional constraints of forbidden of
self-connection and multiple links of the same direction between any two nodes.
Scaling exponents in the range of 1-2 are obtained through Monte Carlo
simulations and various clustering coefficients are calculated, one of which,
$C_{\rm out}$, is of order $10^{-1}$, indicating the network resembles a
small-world. The out-degree distribution has an exponential cut-off for large
out-degree.


  We investigate orbital alignment dynamics within the valence shell of atoms
in coherently excited $j=3/2,1/2$ fine-structure manifolds generated by
short-pulse photodetachment of F$^-$, Cl$^-$ and Br$^-$ anions. Using
Keldysh-type theory, we calculate the density matrix of the residual atoms
generated by few-cycle pulses, whose elements determine the populations and
coherence among the electronic states. Our calculations demonstrate that the
degree of atomic coherence can be represented by a near universal function of
the ratio between the pulse duration $\tilde{\tau}_p$ and the beat period
$\tau_{j'j}$ of the atomic system, which allows one to characterize the
coherence generated in atomic states.


  The purpose of this communication is to suggest that the mechanism for
generating the geomagnetic field and the energy source for powering it are one
and the same, a nuclear georeactor at the center of the Earth. Toward this end,
I: i) Present evidence that the nuclear georeactor fission-product sub-shell is
fluid; ii)Suggest that the geomagnetic field is generated within the georeactor
sub-shell, rather than within Earth's iron-alloy fluid core; iii) Describe why
convection appears more feasible within the georeactor sub-shell than within
the iron-alloy core; iv) Disclose additional relative physical advantages for
georeactor sub-shell dynamo operation; and, v) Outline briefly the research
that should be conducted to advance the state of knowledge of
georeactor-geomagnetic field generation. The concept of geomagnetic field
production by the nuclear georeactor is presented specifically for the Earth.
The concepts and principles, however, are generally applicable to planetary
magnetic field production.


  A new era in particle physics is being spurred on by new data from the Large
Hadron Collider. Non-vanishing neutrino masses represent firm observational
evidence of new physics beyond the Standard Model. An extension of the latter,
based on a SU(3)_C x SU(2)_L x U(1)_Y x U(1)_{B-L} symmetry, incorporating an
established Baryon minus Lepton number invariance, is proposed as a viable and
testable solution to the neutrino mass problem. We argue that LHC data will
probe all the new content of this model: heavy neutrinos, an extra gauge boson
emerging from spontaneous breaking of the additional gauge group at the TeV
scale, onset by a new heavier Higgs boson, also visible at the CERN
proton-proton collider. An even more exciting version of this model is the one
exploiting Supersymmetry: firstly, it incorporates all its well known benefits;
secondly, it alleviates the flaws of its more minimal realisations. Finally,
this model provides a credible cold Dark Matter candidate, the lightest
sneutrino, detectable in both underground and collider experiments.


  Distributed data storage services tailored to specific applications have
grown popular in the high-performance computing (HPC) community as a way to
address I/O and storage challenges. These services offer a variety of specific
interfaces, semantics, and data representations. They also expose many tuning
parameters, making it difficult for their users to find the best configuration
for a given workload and platform.
  To address this issue, we develop a novel variational-autoencoder-guided
asynchronous Bayesian optimization method to tune HPC storage service
parameters. Our approach uses transfer learning to leverage prior tuning
results and use a dynamically updated surrogate model to explore the large
parameter search space in a systematic way.
  We implement our approach within the DeepHyper open-source framework, and
apply it to the autotuning of a high-energy physics workflow on Argonne's Theta
supercomputer. We show that our transfer-learning approach enables a more than
$40\times$ search speedup over random search, compared with a $2.5\times$ to
$10\times$ speedup when not using transfer learning. Additionally, we show that
our approach is on par with state-of-the-art autotuning frameworks in speed and
outperforms them in resource utilization and parallelization capabilities.


  Designing civil structures such as bridges, dams or buildings is a complex
task requiring many synergies from several experts. Each is responsible for
different parts of the process. This is often done in a sequential manner, e.g.
the structural engineer makes a design under the assumption of certain material
properties (e.g. the strength class of the concrete), and then the material
engineer optimizes the material with these restrictions. This paper proposes a
holistic optimization procedure, which combines the concrete mixture design and
structural simulations in a joint, forward workflow that we ultimately seek to
invert. In this manner, new mixtures beyond standard ranges can be considered.
Any design effort should account for the presence of uncertainties which can be
aleatoric or epistemic as when data is used to calibrate physical models or
identify models that fill missing links in the workflow. Inverting the causal
relations established poses several challenges especially when these involve
physics-based models which most often than not do not provide
derivatives/sensitivities or when design constraints are present. To this end,
we advocate Variational Optimization, with proposed extensions and
appropriately chosen heuristics to overcome the aforementioned challenges. The
proposed methodology is illustrated using the design of a precast concrete beam
with the objective to minimize the global warming potential while satisfying a
number of constraints associated with its load-bearing capacity after 28days
according to the Eurocode, the demoulding time as computed by a complex
nonlinear Finite Element model, and the maximum temperature during the
hydration.


  Our aim is to investigate the tidal interaction in High-mass X-ray Binaries
and Symbiotic stars in order to determine in which objects the rotation of the
mass donors is synchronized or pseudosynchronized with the orbital motion of
the compact companion. We find that the Be/X-ray binaries are not synchronized
and the orbital periods of the systems are greater than the rotational periods
of the mass donors. The giant and supergiant High-mass X-ray binaries and
symbiotic stars are close to synchronization. We compare the rotation of mass
donors in symbiotics with the projected rotational velocities of field giants
and find that the M giants in S-type symbiotics rotate on average 1.5 times
faster than the field M giants. We find that the projected rotational velocity
of the red giant in symbiotic star MWC 560 is v sin i = 8.2 +/- 1.5 km/s, and
estimate its rotational period to be Prot = 144 - 306 days. Using the
theoretical predictions of tidal interaction and pseudosynchronization, we
estimate the orbital eccentricity e = 0.68 - 0.82.


  Target Speech Extraction (TSE) is a crucial task in speech processing that
focuses on isolating the clean speech of a specific speaker from complex
mixtures. While discriminative methods are commonly used for TSE, they can
introduce distortion in terms of speech perception quality. On the other hand,
generative approaches, particularly diffusion-based methods, can enhance speech
quality perceptually but suffer from slower inference speed. We propose an
efficient generative approach named Diffusion Conditional Expectation Model
(DCEM) for TSE. It can handle multi- and single-speaker scenarios in both noisy
and clean conditions. Additionally, we introduce Regenerate-DCEM (R-DCEM) that
can regenerate and optimize speech quality based on pre-processed speech from a
discriminative model. Our method outperforms conventional methods in terms of
both intrusive and non-intrusive metrics and demonstrates notable strengths in
inference efficiency and robustness to unseen tasks. Audio examples are
available online (https://vivian556123.github.io/dcem).


  Over the last few years a theoretical treatment for the weak decays of
heavy-flavour hadrons has been developed that is genuinely based on QCD. Its
methodology is described as it applies to total lifetimes, and the underlying
theoretical issues are discussed. Theoretical expectations are compared with
present data. One discrepancy emerges: the beauty baryon lifetime appears to be
significantly shorter than predicted. The ramifications of those findings are
analyzed in detail, and future refinements are described.


  We construct an inflationary model in 6D supergravity that is based on
explicit time-dependent solutions to the full higher-dimensional field
equations, back-reacting to the presence of a 4D inflaton rolling on a
space-filling codimension-2 source brane. Fluxes in the bulk stabilize all
moduli except the `breathing' modulus (that is generically present in
higher-dimensional supergravities). Back-reaction to the inflaton roll causes
the 4D Einstein-frame on-brane geometry to expand, a(t) ~ t^p, as well as
exciting the breathing mode and causing the two off-brane dimensions to expand,
r(t) ~ t^q. The model evades the general no-go theorems precluding 4D de Sitter
solutions, since adjustments to the brane-localized inflaton potential allow
the power p to be dialed to be arbitrarily large, with the 4D geometry becoming
de Sitter in the limit p -> infinity (in which case q = 0). Slow-roll solutions
give accelerated expansion with p large but finite, and q = 1/2. Because the
extra dimensions expand during inflation, the present-day 6D gravity scale can
be much smaller than it was when primordial fluctuations were generated -
potentially allowing TeV gravity now to be consistent with the much higher
gravity scale required at horizon-exit for observable primordial gravity waves.
Because p >> q, the 4 on-brane dimensions expand more quickly than the 2
off-brane ones, providing a framework for understanding why the observed four
dimensions are presently so much larger than the internal two. If uplifted to a
10D framework with 4 dimensions stabilized, the 6D evolution described here
could describe how two of the six extra dimensions evolve to become much larger
than the others, as a consequence of the enormous expansion of the 4 large
dimensions we can see.


  Zhou and Smith (2017) investigate different multi-species harvesting
scenarios using a simple Holling-Tanner model. Among these scenarios are two
methods for implementing balanced harvesting, where fishing is distributed
across trophic levels in accordance with their productivity. This note examines
the effects of a different quantitative implementation of balanced harvesting,
where the fishing mortality rate is proportional to the total production rate
of each trophic level. The results show that setting fishing mortality rate to
be proportional to total production rate, rather than to productivity per unit
biomass, better preserves trophic structure and provides a crucial safeguard
for rare and threatened ecological groups. This is a key ingredient of balanced
harvesting if it is to meet its objective of preserving biodiversity.


  The growth of an interface formed by the hierarchical deposition of particles
of unequal size is studied in the framework of a dynamical network generated by
a horizontal visibility algorithm. For a deterministic model of the deposition
process, the resulting network is scale-free with dominant degree exponent
$\gamma_e = \ln{3}/\ln{2}$ and transient exponent $\gamma_o = 1$. An exact
calculation of the network diameter and clustering coefficient reveals that the
network is scale invariant and inherits the modular hierarchical nature of the
deposition process. For the random process, the network remains scale free,
where the degree exponent asymptotically converges to $\gamma =3$, independent
of the system parameters. This result shows that the model is in the class of
fractional Gaussian noise (fGn) through the relation between the degree
exponent and the series' Hurst exponent $H$. Finally, we show through the
degree-dependent clustering coefficient $C(k)$ that the modularity remains
present in the system.


  Topological materials provide a platform that utilizes the geometric
characteristics of structured materials to control the flow of waves, enabling
unidirectional and protected transmission that is immune to defects or
impurities. The topologically designed photonic materials can carry quantum
states and electromagnetic energy, benefiting nanolasers or quantum photonic
systems. This article reviews recent advances in the topological applications
of photonic materials for radiative heat transfer, especially in the near
field. When the separation distance between media is considerably smaller than
the thermal wavelength, the heat transfer exhibits super-Planckian behavior
that surpasses Planck's blackbody predictions. Near-field thermal radiation in
subwavelength systems supporting surface modes has various applications,
including nanoscale thermal management and energy conversion. Photonic
materials and structures that support topological surface states show immense
potential for enhancing or suppressing near-field thermal radiation. We present
various topological effects, such as periodic and quasi-periodic nanoparticle
arrays, Dirac and Weyl semimetal-based materials, structures with broken global
symmetries, and other topological insulators, on near-field heat transfer.
Also, the possibility of realizing near-field thermal radiation in such
topological materials for alternative thermal management and heat flux guiding
in nano-scale systems is discussed based on the existing technology.


  We show that the conditional displacement operator $\hat{U}_{CD}=\exp
[\hat{b}^{\dagger}\hat{b}(\beta \hat{a}^{\dagger}-\beta ^{\ast}\hat{a})]$
acting upon an arbitrary state of traveling waves can be well approximated by
the action of a Kerr medium placed between two beam splitters whose respective
second ports are fed by highly excited coherent states. Applications to the
generation of nonclassical states and measurement of Wigner function of
arbitrary states are also considered.


  X-ray radiation damage provides a serious bottle neck for investigating
{\mu}s to s dynamics on nanometer length scales employing X-ray photon
correlation spectroscopy. This limitation hinders the investigation of real
time dynamics in most soft matter and biological materials which can tolerate
only X-ray doses of kGy and below. Here, we show that this bottleneck can be
overcome by low dose X-ray speckle visibility spectroscopy. Employing X-ray
doses of 22 kGy to 438 kGy and analyzing the sparse speckle pattern of count
rates as low as 6.7x10-3 per pixel we follow the slow nanoscale dynamics of an
ionic liquid (IL) at the glass transition. At the pre-peak of nanoscale order
in the IL we observe complex dynamics upon approaching the glass transition
temperature TG with a freezing in of the alpha relaxation and a multitude of
milli-second local relaxations existing well below TG. We identify this fast
relaxation as being responsible for the increasing development of nanoscale
order observed in ILs at temperatures below TG.


  In order to mitigate the spread of COVID-19, Wuhan was the first city to
implement strict lockdown policy in 2020. Even though numerous researches have
discussed the travel restriction between cities and provinces, few studies
focus on the effect of transportation control inside the city due to the lack
of the measurement and available data in Wuhan. Since the public transports
have been shut down in the beginning of city lockdown, the change of traffic
density is a good indicator to reflect the intracity population flow.
Therefore, in this paper, we collected time-series high-resolution remote
sensing images with the resolution of 1m acquired before, during and after
Wuhan lockdown by GF-2 satellite. Vehicles on the road were extracted and
counted for the statistics of traffic density to reflect the changes of human
transmissions in the whole period of Wuhan lockdown. Open Street Map was used
to obtain observation road surfaces, and a vehicle detection method combing
morphology filter and deep learning was utilized to extract vehicles with the
accuracy of 62.56%. According to the experimental results, the traffic density
of Wuhan dropped with the percentage higher than 80%, and even higher than 90%
on main roads during city lockdown; after lockdown lift, the traffic density
recovered to the normal rate. Traffic density distributions also show the
obvious reduction and increase throughout the whole study area. The significant
reduction and recovery of traffic density indicates that the lockdown policy in
Wuhan show effectiveness in controlling human transmission inside the city, and
the city returned to normal after lockdown lift.


  This work mainly focuses on the spatial decay properties of solutions to the
Zakharov-Kuznetsov equation. In earlier studies for the two- and
three-dimensional cases, it was established that if the initial condition $u_0$
verifies $\langle \sigma\cdot x\rangle^{r}u_{0}\in L^{2}(\left\{\sigma\cdot
x\geq \kappa\right\}),$ for some $r\in\mathbb{N}$, $\kappa \in\mathbb{R}$,
being $\sigma$ be a suitable non-null vector in the Euclidean space, then the
corresponding solution $u(t)$ generated from this initial condition verifies
$\langle \sigma\cdot x\rangle ^{r}u(t)\in L^2\left(\left\{\sigma\cdot
x>\kappa-\nu t\right\}\right)$, for any $\nu >0$. In this regard, we first
extend such results to arbitrary dimensions, decay power $r>0$ not necessarily
an integer, and we give a detailed description of the gain of regularity
propagated by solutions in terms of the magnitude of the weight $r$. The
deduction of our results depends on a new class of pseudo-differential
operators, which is useful to quantify decay and smoothness properties on a
fractional scale. Secondly, we show that if the initial data $u_{0}$ has a
decay of exponential type on a particular half space, that is, $e^{b\,
\sigma\cdot x}u_{0}\in L^{2}(\left\{\sigma\cdot x\geq \kappa\right\}),$ then
the corresponding solution satisfies $e^{b\, \sigma\cdot x} u(t)\in
H^{p}\left(\left\{\sigma\cdot x>\kappa-\nu t\right\}\right),$ for all
$p\in\mathbb{N}$, and time $t\geq \delta,$ where $\delta>0$. To our knowledge,
this is the first study of such property. As a further consequence, we also
obtain well-posedness results in anisotropic weighted Sobolev spaces in
arbitrary dimensions.
  Finally, as a by-product of the techniques considered here, we show that our
results are also valid for solutions of the Korteweg-de Vries equation.


  We study the effects of Kohn anomalies on the superconducting properties in
electron- and hole-doped cases of monolayer blue phosphorene, considering both
adiabatic and non-adiabatic phonon dispersions using first-principles
calculations. We show that the topology of the Fermi surface is crucial for the
formation of Kohn anomalies of doped blue phosphorene. By using the anisotropic
Eliashberg formalism, we further carefully consider the temperature dependence
of the non-adiabatic phonon dispersions. In cases of low hole densities, strong
electron-phonon coupling leads to a maximum critical temperature of $T_c=97$ K
for superconductivity. In electron-doped regimes, on the other hand, a maximum
superconducting critical temperature of $T_c=38$ K is reached at a doping level
that includes a Lifshitz transition point. Furthermore, our results indicate
that the most prominent component of electron-phonon coupling arises from the
coupling between an in-plane (out-of-plane) deformation and in-plane
(out-of-plane) electronic states of the electron (hole) type doping.


  In this work, we address two coupled tasks of gaze prediction and action
recognition in egocentric videos by exploring their mutual context. Our
assumption is that in the procedure of performing a manipulation task, what a
person is doing determines where the person is looking at, and the gaze point
reveals gaze and non-gaze regions which contain important and complementary
information about the undergoing action. We propose a novel mutual context
network (MCN) that jointly learns action-dependent gaze prediction and
gaze-guided action recognition in an end-to-end manner. Experiments on public
egocentric video datasets demonstrate that our MCN achieves state-of-the-art
performance of both gaze prediction and action recognition.


  Boosting algorithms are frequently used in applied data science and in
research. To date, the distinction between boosting with either gradient
descent or second-order Newton updates is often not made in both applied and
methodological research, and it is thus implicitly assumed that the difference
is irrelevant. The goal of this article is to clarify this situation. In
particular, we present gradient and Newton boosting, as well as a hybrid
variant of the two, in a unified framework. We compare these boosting
algorithms with trees as base learners using various datasets and loss
functions. Our experiments show that Newton boosting outperforms gradient and
hybrid gradient-Newton boosting in terms of predictive accuracy on the majority
of datasets. We also present evidence that the reason for this is not faster
convergence of Newton boosting. In addition, we introduce a novel tuning
parameter for tree-based Newton boosting which is interpretable and important
for predictive accuracy.


  We consider different online algorithms for a generalized scheduling problem
for parallel machines, described in details in the first section. This problem
is the generalization of the classical parallel machine scheduling problem,
when the make-span is minimized; in that case each job contains only one task.
On the other hand, the problem in consideration is still a special version of
the workflow scheduling problem. We present several heuristic algorithms and
compare them by computer tests.


  We show that the geometry of a Riemannian manifold (M,g) is sensitive to the
apparently purely homotopy-theoretic invariant of M known as the
Lusternik-Schnirelmann category, denoted cat_{LS}(M). Here we introduce a
Riemannian analogue of cat_{LS}(M), called the systolic category of M. It is
denoted cat_{sys}(M), and defined in terms of the existence of systolic
inequalities satisfied by every metric g, as initiated by C. Loewner and later
developed by M. Gromov. We compare the two categories. In all our examples, the
inequality cat_{sys}(M) \le cat_{LS}(M) is satisfied, which typically turns out
to be an equality, e.g. in dimension 3. We show that a number of existing
systolic inequalities can be reinterpreted as special cases of such equality,
and that both categories are sensitive to Massey products. The comparison with
the value of cat(M) leads us to prove or conjecture new systolic inequalities
on M.


  It is well known that the Zee model induces small neutrino masses by
radiative corrections, where the bi-maximal flavor mixing is possible. We
analyze the cosmological condition in order for the baryon asymmetry generated
in the early universe not to be washed out in the
  Zee model. Since the lepton number is violated explicitly in the Zee model,
the baryon asymmetry might be washed out through the sphaleron processes
together with the lepton-number violating interactions. In this letter, we will
show that the baryon asymmetry is {\it not} washed out, although it has been
said that the Zee model cannot preserve the baryon asymmetry generated in the
early universe. This can be seen by considering an approximately conserved
number, $L' \equiv
  L_e-L_\mu-L_\tau$.


  We study the chiral gauge model (11112) of four left-movers and one
right-mover with strong interactions in the 1+1 dimensional lattice. Exact
computations of relevant $S$-matrix elements demonstrate a loophole that so
constructed model and its dynamics can possibly evade the ``no-go'' theorem of
Nielsen and Ninomiya.


  A connection of a variety of tight-binding models of noninteracting electrons
on a rectangular lattice in a magnetic field with theta functions is
established. A new spectrum generating symmetry is discovered which essentialy
reduces the problem of diagonalization of these models. Provided that one knows
one eigenvector at one point in the parameter space of the corresponding Harper
equation one knows an eigenfunction of the corresponding model in the whole
range of momentum singlet out by the Landau gauge.


  We provide a rigorous proof of the CPT theorem within the framework of
'Lagrangian' quantum field theory. This is in contrast to the usual rigorous
proofs in purely axiomatic frameworks, and non-rigorous proof-sketches within
the Lagrangian framework.


  Nonlinear electrodynamics model in hypercomplex form is considered. Its
linearization around a solution is obtained. The appropriate problem for linear
waves around static dyon solution (SDS) of Born-Infeld electrodynamics is
investigated. Two types of wave scattering on SDS are considered: dissipative
(with momentum transmission from plane wave to SDS) and non-dissipative (for
SDS imbedded to an equilibrium wave background). Resonance phenomenon in the
problem is discovered and some resonance frequencies are obtained by using a
numerical method. The form of resonance wave modes are discussed. The sum of a
plane wave (as the elementary component of the wave background) with one
resonance mode is considered. The appropriate energy density is investigated at
infinity. The averaged energy density is demonstrated to have the term
proportional to inverse radius. This fact allow to consider such field
configurations as the cause of gravitational interaction, taking into account
the effective Riemann space effect discovered in my previous works. A behavior
of the linearized solution at origin of coordinates and the problem beyond the
linearization are discussed.


  Nash equilibrium is employed to find a game theoretic robust security bound
on quantum bit error rate (QBER) for DL04 protocol which is a scheme for
quantum secure direct communication that has been experimentally realized
recently. The receiver, sender and eavesdropper (Eve) are considered to be
quantum players (players having the capability to perform quantum operations).
Specifically, Eve is considered to have the capability of performing quantum
attacks (e.g., W\'ojcik's original attack, W\'ojcik's symmetrized attack and
Pavi\v{c}i\'c attack) and classical intercept and resend attack. Game theoretic
analysis of the security of DL04 protocol in the above scenario is performed by
considering several game scenarios. The analysis revealed the absence of a
Pareto optimal Nash equilibrium point within these game scenarios.
Consequently, mixed strategy Nash equilibrium points are identified and
employed to establish both upper and lower bounds for QBER. Further, the
vulnerability of the DL04 protocol to Pavi\v{c}i\'c attack in the message mode
is established. In addition, it is observed that the quantum attacks performed
by Eve are more powerful than the classical attack, as the QBER value and the
probability of detecting Eve's presence are found to be lower in quantum
attacks compared to classical ones.


  We show that for one dimensional systems with momentum conservation, the
thermal conductivity $\kappa$ generically diverges with system size as
$L^{1/3}.$


  We investigate the dynamics of the Josephson vortex lattice in layered
high-T$_{c}$ superconductors at high magnetic fields. Starting from coupled
equations for superconducting phases and magnetic field we derive equations for
the relative displacements [phase shifts] between the planar Josephson arrays
in the layers. These equations reveal two families of steady-state solutions:
lattices with constant phase shifts between neighboring layers, starting from
zero for a rectangular configuration to $\pi$ for a triangular configuration,
and double-periodic lattices. We find that the excess Josephson current is
resonantly enhanced when the Josephson frequency matches the frequency of the
plasma mode at the wave vector selected by the lattice structure. The regular
lattices exhibit several kinds of instabilities. We find stability regions of
the moving lattice in the plane lattice structure - Josephson frequency. A
specific lattice structure at given velocity is selected uniquely by boundary
conditions, which are determined by the reflection properties of
electromagnetic waves generated by the moving lattice. With increase of
velocity the moving configuration experiences several qualitative
transformations. At small velocities the regular lattice is stable and the
phase shift between neighboring layers smoothly decreases with increase of
velocity, starting from $\pi$ for a static lattice. At the critical velocity
the lattice becomes unstable. At even higher velocity a regular lattice is
restored again with the phase shift smaller than $\pi/2$. With increase of
velocity, the structure evolves towards a rectangular configuration.


  The past decade has witnessed a dramatically growing interest in collective
intelligence - the phenomenon of groups having an ability to make more accurate
decisions than isolated individuals. However, the vast majority of studies to
date have focused, either explicitly or implicitly, on spatial decisions (e.g.,
potential nest sites, food patches, or migration directions). We highlight the
equally important, but severely understudied, realm of temporal collective
decision-making, i.e., decisions about when to perform an action. We argue that
temporal collective decision making is likely to differ from spatial decision
making in several crucial ways and probably involves different mechanisms,
model predictions, and experimental outcomes. We anticipate that research
focused on temporal decisions should lead to a radically expanded understanding
of the adaptiveness and constraints of living in groups.


  Missing data poses significant challenges while learning representations of
video sequences. We present Disentangled Imputed Video autoEncoder (DIVE), a
deep generative model that imputes and predicts future video frames in the
presence of missing data. Specifically, DIVE introduces a missingness latent
variable, disentangles the hidden video representations into static and dynamic
appearance, pose, and missingness factors for each object. DIVE imputes each
object's trajectory where data is missing. On a moving MNIST dataset with
various missing scenarios, DIVE outperforms the state of the art baselines by a
substantial margin. We also present comparisons for real-world MOTSChallenge
pedestrian dataset, which demonstrates the practical value of our method in a
more realistic setting. Our code and data can be found at
https://github.com/Rose-STL-Lab/DIVE.


  Nonzero and relatively large of $\theta_{13}$ mixing angle has some
phenomenological consequences on neutrino physics beyond the standard model.
One of the consequences if the mixing angle $\theta_{13}\neq 0$ is the
possibility of the CP violation on the neutrino sector. In order to obtain
nonzero $\theta_{13}$ mixing angle, we break the neutrino mass matrix that obey
$\mu-\tau$ symmetry by introducing a complex parameter and determine the
Jarlskog invariant as a measure of CP violation existence. By using the
experimental data as input, we determine the Dirac phase $\delta$ as function
of mixing angle $\theta_{13}$


  Initial steps are presented towards understanding which finitely generated
groups are almost surely generated as semigroups by the path of a random walk
on the group.


  In each dimension n>2 there are many projective simplicial toric varieties
whose Grothendieck groups of vector bundles are at least as big as the ground
field. In particular, the conjecture that the Grothendieck groups of locally
trivial sheaves and coherent sheaves on such varieties are rationally
isomorphic fails badly.


  In the framework of the theory of open systems based on completely positive
quantum dynamical semigroups, we determine the degree of quantum decoherence of
a harmonic oscillator interacting with a thermal bath. It is found that the
system manifests a quantum decoherence which is more and more significant in
time. We also calculate the decoherence time and show that it has the same
scale as the time after which thermal fluctuations become comparable with
quantum fluctuations. Then we solve the master equation for two independent
harmonic oscillators interacting with an environment in the asymptotic
long-time regime. We give a description of the continuous-variable asymptotic
entanglement in terms of the covariance matrix of the quantum states of the
considered system for an arbitrary Gaussian input state. Using the Peres-Simon
necessary and sufficient condition for separability of two-mode Gaussian
states, we show that the two non-interacting systems immersed in a common
environment become asymptotically entangled for certain environments, so that
in the long-time regime they manifest non-local quantum correlations.


  This paper describes an initial dataset and automatic natural language
processing (NLP) method for extracting concepts related to precision oncology
from biomedical research articles. We extract five concept types: Cancer,
Mutation, Population, Treatment, Outcome. A corpus of 250 biomedical abstracts
were annotated with these concepts following standard double-annotation
procedures. We then experiment with BERT-based models for concept extraction.
The best-performing model achieved a precision of 63.8%, a recall of 71.9%, and
an F1 of 67.1. Finally, we propose additional directions for research for
improving extraction performance and utilizing the NLP system in downstream
precision oncology applications.


  The observation of a possible electromagnetic counterpart by the Fermi GBM
group to the aLIGO detection of the merger of a black hole binary has spawned a
number of ideas about its source. Furthermore, observations of fast radio
bursts (FRBs) have similarly resulted in a range of new models that might endow
black hole binaries with electromagnetic signatures. In this context, even the
unlikely idea that astrophysical black holes may have significant charge is
worth exploring, and here we present results from the simulation of weakly
charged black holes as they orbit and merge. Our simulations suggest that a
black hole binary with mass comparable to that observed in GW150914 could
produce the level of electromagnetic luminosity observed by Fermi GBM
($10^{49}$ ergs/s) with a non-dimensional charge of $q \equiv Q/M = 10^{-4}$
assuming good radiative efficiency. However even a charge such as this is
difficult to imagine avoiding neutralization long enough for the binary to
produce its electromagnetic counterpart, and so this value would likely serve
simply as an upper bound. On the other hand, one can equivalently consider the
black holes as having acquired a magnetic monopole charge that would be easy to
maintain and would generate an identical electromagnetic signature as the
electric charges. The observation of such a binary would have significant
cosmological implications, not the least of which would be an explanation for
the quantization of charge itself. We also study such a magnetically charged
binary in the force-free regime and find it much more radiative, reducing even
further the requirements to produce the counterpart.


  In this paper, we propose a new characterization for elementary trapping sets
(ETSs) of variable-regular low-density parity-check (LDPC) codes. Recently,
Karimi and Banihashemi proposed a characterization of ETSs, which was based on
viewing an ETS as a layered superset (LSS) of a short cycle in the code's
Tanner graph. A notable advantage of LSS characterization is that it
corresponds to a simple LSS-based search algorithm that starts from short
cycles of the graph and finds the ETSs with LSS structure efficiently. Compared
to the LSS-based characterization of Karimi and Banihashemi, which is based on
a single LSS expansion technique, the new characterization involves two
additional expansion techniques. The introduction of the new techniques
mitigates two problems that LSS-based characterization/search suffers from: (1)
exhaustiveness, (2) search efficiency. We prove that using the three expansion
techniques, any ETS structure can be obtained starting from a simple cycle, no
matter how large the size of the structure $a$ or the number of its unsatisfied
check nodes $b$ are. We also demonstrate that for the proposed
characterization/search to exhaustively cover all the ETS structures within the
$(a,b)$ classes with $a \leq a_{max}, b \leq b_{max}$, for any value of
$a_{max}$ and $b_{max}$, the length of the short cycles required to be
enumerated is less than that of the LSS-based characterization/search. We also
prove that the three expansion techniques, proposed here, are the only
expansions needed for characterization of ETS structures starting from simple
cycles in the graph, if one requires each and every intermediate sub-structure
to be an ETS as well. Extensive simulation results are provided to show that,
compared to LSS-based search, significant improvement in search speed and
memory requirements can be achieved.


  A superconducting state coexists with static electrical fields under the
formation of phase-slips and kinematic vortices (kV 's). Besides that, such a
resistive state installed in the superconductor is not desirable in many
applications. Then, it is essential to know the ultrafast kV dynamics for
controlling the fluxonics of the system. Thus, in this work, we studied the
dynamics of the kV 's in a mesoscopic superconductor under the influence of
external magnetic fields (H) and transport currents (Jtr). As a result, the kV
dynamics are affected by increasing H. With those significant changes, it was
possible to build a Jtr(H) phase diagram. There, in between the Meissner and
the normal states, the kV 's present three distinct behaviors. For high fields,
the vortices behave as Abrikosov-like ones, with velocities two orders of
magnitude lower than those at low fields regime. Besides that, one demonstrates
how the interplay between Jtr and the shielding currents, its controlled by H,
allowing for some quantitative predictions of boundaries in the phase diagram.
Additionally, for H's where only a kV is nucleated, for tenths of picoseconds,
a surface barrier effect acts over the instantaneous velocity of the kV.


  Traditionally, mechanism design focuses on simultaneous-move games (e.g.,
Myerson (1981)). In this paper, we study mechanism design with sequential-move
games, and provide two results on revelation principles for general solution
concepts (e.g., perfect Bayesian equilibrium, obvious dominance, strong-obvious
dominance). First, if a solution concept is additive, implementation in
sequential-move games is equivalent to implementation in simultaneous-move
games. Second, for any solution concept \r{ho} and any social choice function
f, we identify a canonical operator {\gamma}^{(\r{ho},f)}, which is defined on
primitives. We prove that, if \r{ho} is monotonic, f can be implemented by a
sequential-move game if and only if {\gamma}^{(\r{ho},f)} is achievable, which
translates a complicated mechanism design problem into checking some conditions
defined on primitives. Most of the existing solution concepts are either
additive or monotonic.


  The photospheric spatial distribution of the main magnetic polarities of
bipolar active regions (ARs) presents during their emergence deformations are
known as magnetic tongues. They are attributed to the presence of twist in the
toroidal magnetic flux-tubes that form the ARs. The aim of this article is to
study the twist of newly emerged ARs from the evolution of magnetic tongues
observed in photospheric line-of-sight magnetograms. We apply the procedure
described by Poisson et al. (2015, Solar Phys. 290, 727) to ARs observed over
the full Solar Cycle 23 and the beginning of Cycle 24. Our results show that
the hemispherical rule obtained using the tongues as a proxy of the twist has a
weak sign-dominance (53 % in the southern hemisphere and 58 % in the northern
hemisphere). By defining the variation of the tongue angle, we characterize the
strength of the magnetic tongues during different phases of the AR emergence.
We find that there is a tendency of the tongues to be stronger during the
beginning of the emergence and to become weaker as the AR reaches its maximum
magnetic flux. We compare this evolution with the emergence of a toroidal
flux-rope model with non-uniform twist. The variety of evolution of the tongues
in the analyzed ARs can only be reproduced when using a broad range of twist
profiles, in particular having a large variety of twist gradient in the
direction vertical to the photosphere. Although the analytical model used is a
special case, selected to minimize the complexity of the problem, the results
obtained set new observational constraints to theoretical models of flux-rope
emergence that form bipolar ARs.


  In normal version of combinatorial game theory, all games are invertible,
whereas only the empty game is invertible in mis\`ere version. For this reason,
several restricted universes were earlier considered for their study, in which
more games are invertible. We here study combinatorial games in mis\`ere
version, in particular universes where no player would like to pass their turn
In these universes, we prove that having one extra condition makes all games
become invertible. We then focus our attention on a specific quotient, called
Q_Z, and show that all sums of universes whose quotient is Q_Z also have Q_Z as
their quotient.


  Today's intelligent applications can achieve high performance accuracy using
machine learning (ML) techniques, such as deep neural networks (DNNs).
Traditionally, in a remote DNN inference problem, an edge device transmits raw
data to a remote node that performs the inference task. However, this may incur
high transmission energy costs and puts data privacy at risk. In this paper, we
propose a technique to reduce the total energy bill at the edge device by
utilizing model compression and time-varying model split between the edge and
remote nodes. The time-varying representation accounts for time-varying
channels and can significantly reduce the total energy at the edge device while
maintaining high accuracy (low loss). We implement our approach in an image
classification task using the MNIST dataset, and the system environment is
simulated as a trajectory navigation scenario to emulate different channel
conditions. Numerical simulations show that our proposed solution results in
minimal energy consumption and $CO_2$ emission compared to the considered
baselines while exhibiting robust performance across different channel
conditions and bandwidth regime choices.


  Data collection is a fundamental problem in the scenario of big data, where
the size of sampling sets plays a very important role, especially in the
characterization of data structure. This paper considers the information
collection process by taking message importance into account, and gives a
distribution-free criterion to determine how many samples are required in big
data structure characterization. Similar to differential entropy, we define
differential message importance measure (DMIM) as a measure of message
importance for continuous random variable. The DMIM for many common densities
is discussed, and high-precision approximate values for normal distribution are
given. Moreover, it is proved that the change of DMIM can describe the gap
between the distribution of a set of sample values and a theoretical
distribution. In fact, the deviation of DMIM is equivalent to
Kolmogorov-Smirnov statistic, but it offers a new way to characterize the
distribution goodness-of-fit. Numerical results show some basic properties of
DMIM and the accuracy of the proposed approximate values. Furthermore, it is
also obtained that the empirical distribution approaches the real distribution
with decreasing of the DMIM deviation, which contributes to the selection of
suitable sampling points in actual system.


  We present a non-physical interpretation of the Cosmological Constant based
on a particular algebraic analysis. This also introduces some novel algebraic
structures, such as ``unital norms", ``uncurling metrics", and ``partial wedge
products".


  In these notes we give a simple proof of the second-order Boltzmann-Gibbs
Principle, which is the main tool in order to prove that the equilibrium
fluctuations of the WASEP are given, in the regime of the critical strength
asymmetry, by the Stochastic Burgers equation.


  The Gaia-ESO Survey (GES) is a public, high-resolution spectroscopic survey
with FLAMES@VLT. GES targeted in particular a large sample of open clusters
(OCs) of all ages. The different kinds of OCs are useful to reach the main
science goals, which are the study of the OC structure and dynamics, the use of
OCs to constrain and improve stellar evolution models, and the definition of
Galactic disc properties (e.g. metallicity distribution). GES is organised in
19 working groups (WGs). We describe here the work of three of them, WG4 in
charge of the selection of the targets within each cluster), WG1 responsible
for defining the most probable candidate members, and WG6 in charge of the
preparation of the observations. As GES has been conducted before Gaia DR2, we
could not make use of the Gaia astrometry to define cluster members. We made
use of public and private photometry to select the stars to be observed with
FLAMES. Candidate target selection was based on ground-based proper motions,
radial velocities, and X-ray properties when appropriate, and it was mostly
used to define the position of the clusters' evolutionary sequences in the
colour-magnitude diagrams. Targets for GIRAFFE were selected near the sequences
in an unbiased way. We used available information on membership only for the
few UVES stars. We collected spectra for 62 confirmed OCs (a few more were
taken from the ESO archive). Among them are very young clusters, where the main
targets are pre-main sequence stars, clusters with very hot and massive stars
currently on the main sequence, intermediate-age and old clusters where evolved
stars are the main targets. The selection of targets was as inclusive and
unbiased as possible and we observed a representative fraction of all possible
targets, thus collecting the largest, most accurate, and most homogeneous
spectroscopic data set on ever achieved. [abridged]


  This paper addresses the study and applications of polyhedral duality of
locally convex topological vector (LCTV) spaces. We first revisit the classical
Rockafellar's proper separation theorem for two convex sets one which is
polyhedral and then present its LCTV extension with replacing the relative
interior by its quasi-relative interior counterpart. Then we apply this result
to derive enhanced calculus rules for normals to convex sets, coderivatives of
convex set-valued mappings, and subgradients of extended-real-valued functions
under certain polyhedrality requirements in LCTV spaces by developing a
geometric approach. We also establish in this way new results on conjugate
calculus and duality in convex optimization with relaxed qualification
conditions in polyhedral settings. Our developments contain significant
improvements to a number of existing results obtained by Ng and Song in [31].


  Street demonstrations occur across the world. In Rio de Janeiro, June/July
2013, they reach beyond one million people. A wrathful reader of \textit{O
Globo}, leading newspaper in the same city, published a letter \cite{OGlobo}
where many social questions are stated and answered Yes or No. These million
people of street demonstrations share opinion consensus about a similar set of
social issues. But they did not reach this consensus within such a huge
numbered meetings. Earlier, they have met in diverse small groups where some of
them could be convinced to change mind by other few fellows. Suddenly, a
macroscopic consensus emerges. Many other big manifestations are widespread all
over the world in recent times, and are supposed to remain in the future. The
interesting questions are: 1) How a binary-option opinion distributed among
some population evolves in time, through local changes occurred within
small-group meetings? and 2) Is there some natural selection rule acting upon?
Here, we address these questions through an agent-based model.


  We prove a Dold-Kan type correspondence between the category of dendroidal
abelian groups and a suitably constructed category of dendroidal complexes. Our
result naturally extends the classical Dold-Kan correspondence between the
category of simplicial abelian groups and the category of non-negatively graded
chain complexes.


  We applied a recently published modified Fock-Schwinger (MFS) method to find
the exact solution of the propagator equation for a charged vector boson in the
presence of a constant magnetic field directly in the momentum space as a sum
over Landau levels in arbitrary $\xi$-gauge. In contrast to the standard
approaches for finding propagators, MFS method demonstrated several
improvements in terms of computational complexity reduction and revealed simple
internal structures in intermediate and final expressions, thus allowing to
obtain new useful representations of the propagator.


  We present results of the first unbiased search for extended UV (XUV)-disk
galaxies undertaken to determine the space density of such galaxies. Our sample
contains 561 local (0.001 < z < 0.05) galaxies that lie in the intersection of
available GALEX deep imaging (exposure time > 1.5 x 10^4 s) and SDSS DR7
footprints. We explore modifications to the standard classification scheme for
our sample that includes both disk- and bulge-dominated galaxies. Visual
classification of each galaxy in the sample reveals an XUV-disk frequency of up
to 20% for the most nearby portion of our sample. On average over the entire
sample (out to z=0.05) the frequency ranges from a hard limit of 4% to 14%. The
GALEX imaging allows us to detect XUV-disks beyond 100 Mpc. The XUV regions
around XUV-disk galaxies are consistently bluer than the main bodies. We find a
surprisingly high frequency of XUV emission around luminous red (NUV-r > 5) and
green valley (3 < NUV-r < 5) galaxies. The XUV-disk space density in the local
universe is > 1.5-4.2 x 10^-3 Mpc^-3. Using the XUV emission as an indicator of
recent gas accretion, we estimate that the cold gas accretion rate onto these
galaxies is > 1.7-4.6 x 10^-3 Msun Mpc^-3 yr^-1. The number of XUV-disks in the
green valley and the estimated accretion rate onto such galaxies points to the
intriguing possibility that 7%-18% of galaxies in this population are
transitioning away from the red sequence.


  Cycloidal spin orders are common in multiferroics. One of the prototypical
examples is BiFeO3 (BFO) which shows a large polarization and a cycloidal
antiferromagnetic order at room temperature. Here we employ Landau theory and
phase-field simulations to analyze the coupled switching dynamics of
polarization and cycloidal antiferromagnetic orders in BFO. We are able to
identify 14 types of transitional spin structures between two cycloids and 9
electric-field-induced spin switching paths. We demonstrate the
electric-field-induced rotation of wave vectors of the cycloidal spins and
discover 2 types of cycloidal spin switching dynamics: fast local spin flips
and slow rotation of wave vectors. Also, we construct road maps to achieve the
switching between any two spin cycloids through multi-step applications of
electric fields. The work provides a theoretical framework for the
phenomenological description of spin cycloids and a fundamental understanding
of the switching mechanisms to achieve electrical control of magnetic orders.


  Quantum optics experiments frequently involve interfering single photons and
coherent states. In the case of multi-photon experiments this requires that all
photons are frequency degenerate. We report a simple and practical approach to
generate coherent states that can be readily tuned to any wavelength required,
for example by non-degenerate photon pair creation. We demonstrate this by
performing a two-photon (Hong-Ou-Mandel) interference experiment between a
coherent state and a pure heralded single photon source. No spectral filtering
is required on either source, the coherent state constrained by the pump and
seed lasers and the heralded photon exploits non-local filtering. We expect
that such an approach can find a wide range of applications in photonic based
quantum information science.


  Let $\mathbb{E}$ be a quadratic extension of a number field $\mathbb{F}$. Let
$E(g, s)$ be an Eisenstein series on $GL_2(\mathbb{E})$, and let $F$ be a
cuspidal automorphic form on $GL_2(\mathbb{F})$. We will consider in this paper
the following automorphic integral: $$\int_{Z_{A}GL_{2}(\mathbb{F})\backslash
GL_{2}(\mathbb{A}_{\mathbb{F}})} F(g)E(g,s) dg.$$ This is in some sense the
complementary case to the well-known Rankin-Selberg integral and the triple
product formula. We will approach this integral by Waldspurger's formula. We
will discuss when the integral is automatically zero, and otherwise the
L-function it represents. We will calculate local integrals at some ramified
places, where the level of the ramification can be arbitrarily large.


  Gamma-ray burst sources appear to fulfill all the conditions for being
efficient cosmic ray accelerators, and being extremely compact, are also
expected to produce multi-GeV to PeV neutrinos. I review the basic model
predictions for the expected neutrino fluxes in classical GRBs as well as in
low luminosity and choked bursts, discussing the recent IceCube observational
constraints and implications from the observed diffuse neutrino flux.


  We investigate the non-resonant, 3-D (spatial) model of the hierarchical
system composed of point-mass stellar (or sub-stellar) binary and a low-mass
companion (a circumbinary planet or a brown dwarf). We take into account the
leading relativistic corrections to the Newtonian gravity. The secular model of
the system relies on the expansion of the perturbing Hamiltonian in terms of
the ratio of semi-major axes $\alpha$, averaged over the mean anomalies. We
found that the low-mass object in a distant orbit may excite large eccentricity
of the inner binary when the mutual inclination of the orbits is larger than
about of 60 deg. This is related to strong instability caused by a phenomenon
which acts similarly to the Lidov-Kozai resonance (LKR). The secular system may
be strongly chaotic and its dynamics unpredictable over the long-term time
scale. Our study shows that in the Jupiter-- or brown dwarf-- mass regime of
the low-massive companion, the restricted model does not properly describe the
long-term dynamics in the vicinity of the LKR. The relativistic correction is
significant for the parametric structure of a few families of stationary
solutions in this problem, in particular, for the direct orbits configurations
(with the mutual inclination less than 90 degrees). We found that the dynamics
of hierarchical systems with small $\alpha \sim 0.01$ may be qualitatively
different in the realm of the Newtonian (classic) and relativistic models. This
holds true even for relatively large masses of the secondaries.


  We describe the interface between measure theoretic probability and causal
inference by constructing causal models on probability spaces within the
potential outcomes framework. We find that measure theory provides a precise
and instructive language for causality and that consideration of the
probability spaces underlying causal models offers clarity into central
concepts of causal inference. By closely studying simple, instructive examples,
we demonstrate insights into causal effects, causal interactions, matching
procedures, and randomization. Additionally, we introduce a simple technique
for visualizing causal models on probability spaces that is useful both for
generating examples and developing causal intuition. Finally, we provide an
axiomatic framework for causality and make initial steps towards a formal
theory of general causal models.


  Several particle algorithms admit a Feynman-Kac representation such that the
potential function may be expressed as a recursive function which depends on
the complete state trajectory. An important example is the mixture Kalman
filter, but other models and algorithms of practical interest fall in this
category. We study the asymptotic stability of such particle algorithms as time
goes to infinity. As a corollary, practical conditions for the stability of the
mixture Kalman filter, and a mixture GARCH filter, are derived. Finally, we
show that our results can also lead to weaker conditions for the stability of
standard particle algorithms, such that the potential function depends on the
last state only.


  The following proceedings contain a theory perspective on rare Kaon decays. I
review rare kaon decays in the LHC era: we discuss interplay with B-anomalies
and possible New Physics in direct CP violation in $K\to 2\pi$: very rare kaon
decays like $K \to \pi \nu \bar{\nu}$ are very important to this purpose. We
discuss also the decays $K^0 \to \mu ^+ \mu ^-$ due to the LHCB measurement


  Let M_1 and M_2 be compact, orientable 3-manifolds, and M the manifold
obtained by gluing some component F of \bdy M_1 to some component of \bdy M_2
by a homeomorphism \phi. We show that when \phi is "sufficiently complicated"
then (1) the amalgamation of low genus, unstabilized, boundary-unstabilized
Heegaard splittings of M_i is an unstabilized splitting of M, (2) every low
genus, unstabilized Heegaard splitting of M can be expressed as an amalgamation
of unstabilized, boundary-unstabilized splittings of M_i, and possibly a Type
II splitting of F \times I, and (3) if there is no Type II splitting in such an
expression then it is unique.


  We propose a dual cavity Fabry-Perot interferometer as a wavelength
calibrator and a stability tracking device for astronomical spectrograph. The
FPI consists of two adjoining cavities engraved on a low expansion monoblock
spacer. A low-finesse astro-cavity is intended for generating a uniform grid of
reference lines to calibrate the spectrograph and a high-finesse lock-cavity is
meant for tracking the stability of the reference lines using optical frequency
standards. The differential length changes in two cavities due to temperature
and vibration perturbations are quantitatively analyzed using finite element
method. An optimized mounting geometry with fractional length changes $\Delta
L/L \approx 1.5\times 10^{-12}$ is suggested. We also identify conditions
necessary to suppress relative length variations between two cavities well
below 10$^{-10}$~m, thus facilitating accurate dimension tracking and
generation of stable reference spectra for Doppler measurement at 10 cms$^{-1}$
level.


  As stellar compositions evolve over time in the Milky Way, so will the
resulting planet populations. In order to place planet formation in the context
of Galactic chemical evolution, we make use of a large ($N = 5\,325$) stellar
sample representing the thin and thick discs, defined chemically, and the halo,
and we simulate planet formation by pebble accretion around these stars. We
build a chemical model of their protoplanetary discs, taking into account the
relevant chemical transitions between vapour and refractory minerals, in order
to track the resulting compositions of formed planets. We find that the masses
of our synthetic planets increase on average with increasing stellar
metallicity [Fe/H] and that giant planets and super-Earths are most common
around thin-disc ($\alpha$-poor) stars since these stars have an overall higher
budget of solid particles. Giant planets are found to be very rare
($\lesssim$1\%) around thick-disc ($\alpha$-rich) stars and nearly non-existent
around halo stars. This indicates that the planet population is more diverse
for more metal-rich stars in the thin disc. Water-rich planets are less common
around low-metallicity stars since their low metallicity prohibits efficient
growth beyond the water ice line. If we allow water to oxidise iron in the
protoplanetary disc, this results in decreasing core mass fractions with
increasing [Fe/H]. Excluding iron oxidation from our condensation model instead
results in higher core mass fractions, in better agreement with the core-mass
fraction of Earth, that increase with increasing [Fe/H]. Our work demonstrates
how the Galactic chemical evolution and stellar parameters, such as stellar
mass and chemical composition, can shape the resulting planet population.


  General equations for one- particle Green function in non- magic nuclei have
been derived where a pairing mechanism, which is analogous to the Bardeen-
Cooper- Schrieffer one, has been singled out explicitely. A "refining"
procedure for the phenomenological single- particle energies and pairing gaps
is formulated to avoid double counting of the phonon mixing if the initial data
of the problem are these phenomenological quantities. The general equations
obtained have been written in the squared phonon creation amplitude
approximation. This corresponds to taking quasiparticle$\otimes$phonon
configurations into account and, as shown in the work, is a good approximation
for semi- magic nuclei. Using this approximation the secular equation to
calculate excitations of odd non- magic nuclei has been obtained. The equation
takes ground state correlations into account completely and contains only the
squared energy variable. Distributions of single- particle strength in 119Sn
and 121}Sn have been calculated and reasonable agreement with the available
experiment has been obtained.


  We investigate the effect of adiabatic regularization on both the tensor- and
scalar-perturbation power spectra in \textit{nonminimally} coupled chaotic
inflation. Similar to that of the \textit{minimally} coupled general
single-field inflation, we find that the subtraction term is suppressed by an
exponentially decaying factor involving the number of $ e $-folds. By following
the subtraction term long enough beyond horizon crossing, the regularized power
spectrum tends to the "bare" power spectrum. This study justifies the use of
the unregularized ("bare") power spectrum in standard calculations.


  The notion of strict singular characteristics is important in the
wellposedness issue of singular dynamics on the cut locus of the viscosity
solutions. We provide an intuitive and rigorous proof of the existence of the
strict singular characteristics of Hamilton-Jacobi equation $H(x,Du(x),u(x))=0$
in two dimensional case. We also proved if $\mathbf{x}$ is a strict singular
characteristic, then we really have the right-differentiability of $\mathbf{x}$
and the right-continuity of $\dot{\mathbf{x}}^+(t)$ for every $t$. Such a
strict singular characteristic must give a selection $p(t)\in
D^+u(\mathbf{x}(t))$ such that $p(t)=\arg\min_{p\in
D^+u(\mathbf{x}(t))}H(\mathbf{x}(t),p,u(\mathbf{x}(t)))$.


  We study the problem of eliciting the preferences of a decision-maker through
a moderate number of pairwise comparison queries to make them a high quality
recommendation for a specific problem. We are motivated by applications in high
stakes domains, such as when choosing a policy for allocating scarce resources
to satisfy basic needs (e.g., kidneys for transplantation or housing for those
experiencing homelessness) where a consequential recommendation needs to be
made from the (partially) elicited preferences. We model uncertainty in the
preferences as being set based and} investigate two settings: a) an offline
elicitation setting, where all queries are made at once, and b) an online
elicitation setting, where queries are selected sequentially over time in an
adaptive fashion. We propose robust optimization formulations of these problems
which integrate the preference elicitation and recommendation phases with aim
to either maximize worst-case utility or minimize worst-case regret, and study
their complexity. For the offline case, where active preference elicitation
takes the form of a two and half stage robust optimization problem with
decision-dependent information discovery, we provide an equivalent
reformulation in the form of a mixed-binary linear program which we solve via
column-and-constraint generation. For the online setting, where active
preference learning takes the form of a multi-stage robust optimization problem
with decision-dependent information discovery, we propose a conservative
solution approach. Numerical studies on synthetic data demonstrate that our
methods outperform state-of-the art approaches from the literature in terms of
worst-case rank, regret, and utility. We showcase how our methodology can be
used to assist a homeless services agency in choosing a policy for allocating
scarce housing resources of different types to people experiencing
homelessness.


  A derivation is given from first principles of the fact that the SU(2) gauge
theory is in a confining phase for all values of the coupling $0 < g < \infty$
defined at lattice spacing (UV regulator) $a$, and space-time dimension $d \leq
4$. The strategy is to employ approximate RG decimation transformations of the
potential moving type which give both upper and lower bounds on the partition
function at each successive decimation step. By interpolation between these
bounds an exact representation of the partition function is obtained on
progressively coarser lattices. In the same manner, one obtains a
representation of the partition function in the presence of external center
flux. Under successive decimations the flow of the effective action in these
representations is constrained by that in the upper and lower bounds which are
easily explicitly computable. Confining behavior for the vortex free energy
order parameter (ratio of partition functions with and without external flux),
hence `area law' for the Wilson loop, is the result for any initial coupling.
Keeping the string tension fixed determines the dependence $g(a)$, which is
such that $g(a) \to 0$ for $a \to 0$.


  Let G be a finite group and let k be a field of characteristic p. Given a
finitely generated indecomposable non-projective kG-module M, we conjecture
that if the Tate cohomology $\HHHH^*(G, M)$ of G with coefficients in M is
finitely generated over the Tate cohomology ring $\HHHH^*(G, k)$, then the
support variety V_G(M) of M is equal to the entire maximal ideal spectrum
V_G(k). We prove various results which support this conjecture. The converse of
this conjecture is established for modules in the connected component of k in
the stable Auslander-Reiten quiver for kG, but it is shown to be false in
general. It is also shown that all finitely generated kG-modules over a group G
have finitely generated Tate cohomology if and only if G has periodic
cohomology.


  The Semiinfinite Flag Space appeared in the works of B.Feigin and E.Frenkel,
and under different disguises was found by V.Drinfeld and G.Lusztig in the
early 80-s. Another recent discovery (Beilinson-Drinfeld Grassmannian) turned
out to conceal a new incarnation of Semiinfinite Flags. We write down these and
other results scattered in folklore. We define the local semiinfinite flag
space attached to a semisimple group $G$ as the quotient $G((z))/HN((z))$ (an
ind-scheme), where $H$ and $N$ are a Cartan subgroup and the unipotent radical
of a Borel subgroup of $G$. The global semiinfinite flag space attached to a
smooth complete curve $C$ is a union of Quasimaps from $C$ to the flag variety
of $G$. In the present work we use $C=P^1$ to construct the category $PS$ of
certain collections of perverse sheaves on Quasimaps spaces, with factorization
isomorphisms. We construct an exact convolution functor from the category of
perverse sheaves on affine Grassmannian, constant along Iwahori orbits, to the
category $PS$. Conjecturally, this functor should correspond to the restriction
functor from modules over quantum group with divided powers to modules over the
small quantum group.


  We investigate the geometry of \'etale $4:1$ coverings of smooth complex
genus 2 curves with the monodromy group isomorphic to the Klein four-group.
There are two cases, isotropic and non-isotropic depending on the values of the
Weil pairing restricted to the group defining the covering. We recall from our
previous work \cite{bo} the results concerning the non-isotropic case and fully
describe the isotropic case. We show that the necessary information to
construct the Klein coverings is encoded in the 6 points on $\mathbb{P}^1$
defining the genus 2 curve. The main result of the paper is the fact that, in
both cases the Prym map associated to these coverings is injective.
Additionally, we provide a concrete description of the closure of the image of
the Prym map inside the corresponding moduli space of polarised abelian
varieties.


  A graph is equitably $k$-colorable if its vertices can be partitioned into
$k$ independent sets in such a way that the number of vertices in any two sets
differ by at most one. The smallest $k$ for which such a coloring exists is
known as the \emph{equitable chromatic number} of $G$ and it is denoted by
$\chi_{=}(G)$. In this paper the problem of determinig $\chi_=$ for coronas of
cubic graphs is studied. Although the problem of ordinary coloring of coronas
of cubic graphs is solvable in polynomial time, the problem of equitable
coloring becomes NP-hard for these graphs. We provide polynomially solvable
cases of coronas of cubic graphs and prove the NP-hardness in a general case.
As a by-product we obtain a simple linear time algorithm for equitable coloring
of such graphs which uses $\chi_=(G)$ or $\chi_=(G)+1$ colors. Our algorithm is
best possible, unless $P=NP$. Consequently, cubical coronas seem to be the only
known class of graphs for which equitable coloring is harder than ordinary
coloring.


  Ultra-cool dwarfs as wide companions to subgiants, giants, white dwarfs and
main sequence stars can be very good benchmark objects, for which we can infer
physical properties with minimal reference to theoretical models, through
association with the primary stars. We have searched for benchmark ultra-cool
dwarfs in widely separated binary systems using SDSS, UKIDSS, and 2MASS. We
then estimate spectral types using SDSS spectroscopy and multi-band colors,
place constraints on distance, and perform proper motions calculations for all
candidates which have sufficient epoch baseline coverage. Analysis of the
proper motion and distance constraints show that eight of our ultra-cool dwarfs
are members of widely separated binary systems. Another L3.5 dwarf, SDSS 0832,
is shown to be a companion to the bright K3 giant Eta Cancri. Such primaries
can provide age and metallicity constraints for any companion objects, yielding
excellent benchmark objects. This is the first wide ultra-cool dwarf + giant
binary system identified.


  Sentence representation from vanilla BERT models does not work well on
sentence similarity tasks. Sentence-BERT models specifically trained on STS or
NLI datasets are shown to provide state-of-the-art performance. However,
building these models for low-resource languages is not straightforward due to
the lack of these specialized datasets. This work focuses on two low-resource
Indian languages, Hindi and Marathi. We train sentence-BERT models for these
languages using synthetic NLI and STS datasets prepared using machine
translation. We show that the strategy of NLI pre-training followed by STSb
fine-tuning is effective in generating high-performance sentence-similarity
models for Hindi and Marathi. The vanilla BERT models trained using this simple
strategy outperform the multilingual LaBSE trained using a complex training
strategy. These models are evaluated on downstream text classification and
similarity tasks. We evaluate these models on real text classification datasets
to show embeddings obtained from synthetic data training are generalizable to
real datasets as well and thus represent an effective training strategy for
low-resource languages. We also provide a comparative analysis of sentence
embeddings from fast text models, multilingual BERT models (mBERT, IndicBERT,
xlm-RoBERTa, MuRIL), multilingual sentence embedding models (LASER, LaBSE), and
monolingual BERT models based on L3Cube-MahaBERT and HindBERT. We release
L3Cube-MahaSBERT and HindSBERT, the state-of-the-art sentence-BERT models for
Marathi and Hindi respectively. Our work also serves as a guide to building
low-resource sentence embedding models.


  When a disaster occurs, maintaining and restoring community lifelines
subsequently require collective efforts from various stakeholders. Aiming at
reducing the efforts associated with generating Stakeholder Collaboration
Networks (SCNs), this paper proposes a systematic approach to reliable
information extraction for stakeholder collaboration and automated network
generation. Specifically, stakeholders and their interactions are extracted
from texts through Named Entity Recognition (NER), one of the techniques in
natural language processing. Once extracted, the collaboration information is
transformed into structured datasets to generate the SCNs automatically. A case
study of stakeholder collaboration during Hurricane Harvey was investigated and
it had demonstrated the feasibility and applicability of the proposed method.
Hence, the proposed approach was proved to significantly reduce practitioners'
interpretation and data collection workloads. In the end, discussions and
future work are provided.


  We introduce a simplified model for a three-terminal quantum thermocouple
consisting of two strongly-coupled quantum dots. To elucidate spin-dependent
Seebeck and Peltier effects, we employ a microscopic Hamiltonian and map the
Lindblad master equation onto a quantum transition network, capturing the key
working principles for both reciprocal effects. Our analysis reveals quantum
thermodynamic networks encompassing both Coulomb interaction and spin-flipping
processes, lead to the emergence of spin-thermolectric effects. Using algebraic
graph theory, we recover the phenomenological law of irreversible
thermodynamics from the stochastic version of the entropy production rate
expressed in terms of cycle flux and cycle forces. Remarkably, Onsager
reciprocity and Kelvin relation for transport coefficients find their premises
in the properties of cycle flux trajectories within the quantum transition
network. This underscores the universal generality of thermodynamic principles
across classical and quantum realms, despite their fundamentally different
basis from classical laws of irreversible thermodynamics relying on local
equilibrium assumptions.


  We give a distribution-free testing algorithm for decision lists with
$\tilde{O}(n^{11/12}/\varepsilon^3)$ queries. This is the first sublinear
algorithm for this problem, which shows that, unlike halfspaces, testing is
strictly easier than learning for decision lists. Complementing the algorithm,
we show that any distribution-free tester for decision lists must make
$\tilde{\Omega}(\sqrt{n})$ queries, or draw $\tilde{\Omega}(n)$ samples when
the algorithm is sample-based.


  This paper presents an approach to the study of cognitive activities in
collaborative software development. This approach has been developed by a
multidisciplinary team made up of software engineers and cognitive
psychologists. The basis of this approach is to improve our understanding of
software development by observing professionals at work. The goal is to derive
lines of conduct or good practices based on observations and analyses of the
processes that are naturally used by software engineers. The strategy involved
is derived from a standard approach in cognitive science. It is based on the
videotaping of the activities of software engineers, transcription of the
videos, coding of the transcription, defining categories from the coded
episodes and defining cognitive behaviors or dialogs from the categories. This
project presents two original contributions that make this approach generic in
software engineering. The first contribution is the introduction of a formal
hierarchical coding scheme, which will enable comparison of various types of
observations. The second is the merging of psychological and statistical
analysis approaches to build a cognitive model. The details of this new
approach are illustrated with the initial data obtained from the analysis of
technical review meetings.


  In recent years, there has been growing concern over the vulnerability of
convolutional neural networks (CNNs) to image perturbations. However, achieving
general robustness against different types of perturbations remains
challenging, in which enhancing robustness to some perturbations (e.g.,
adversarial perturbations) may degrade others (e.g., common corruptions). In
this paper, we demonstrate that adversarial training with an emphasis on phase
components significantly improves model performance on clean, adversarial, and
common corruption accuracies. We propose a frequency-based data augmentation
method, Adversarial Amplitude Swap, that swaps the amplitude spectrum between
clean and adversarial images to generate two novel training images: adversarial
amplitude and adversarial phase images. These images act as substitutes for
adversarial images and can be implemented in various adversarial training
setups. Through extensive experiments, we demonstrate that our method enables
the CNNs to gain general robustness against different types of perturbations
and results in a uniform performance against all types of common corruptions.


  This paper aims to numerically verify the large Reynolds number asymptotic
theory of magneto-hydrodynamic (MHD) flows proposed in the companion paper
Deguchi (2019). To avoid any complexity associated with the chaotic nature of
turbulence and flow geometry, nonlinear steady solutions of the
viscous-resistive magneto-hydrodynamic equations in plane Couette flow have
been utilised. Two classes of nonlinear MHD states, which convert kinematic
energy to magnetic energy effectively, have been determined. The first class of
nonlinear states can be obtained when a small spanwise uniform magnetic field
is applied to the known hydrodynamic solution branch of the plane Couette flow.
The nonlinear states are characterised by the hydrodynamic/magnetic roll-streak
and the resonant layer at which strong vorticity and current sheets are
observed. These flow features, and the induced strong streamwise magnetic
field, are fully consistent with the vortex/Alfv\'en wave interaction theory
proposed in Deguchi (2019). When the spanwise uniform magnetic field is
switched off, the solutions become purely hydrodynamic. However, the second
class of `self-sustained shear driven dynamos' at the zero-external magnetic
field limit can be found by homotopy via the forced states subject to a
spanwise uniform current field. The discovery of the dynamo states has
motivated the corresponding large Reynolds number matched asymptotic analysis
in Deguchi (2019). Here, the reduced equations derived by the asymptotic theory
have been solved numerically. The asymptotic solution provides remarkably good
predictions for the finite Reynolds number dynamo solutions.


  We discuss how to calculate Yukawa couplings in string-derived orbifold GUTs.
As an application we investigate the quark mass hierarchies in these models. An
interplay of different mechanisms derived from string theory leads to an
interesting pattern. We discuss concrete examples in the context of heterotic
orbifold compactifications.


  The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory,
Tennessee, provides an intense flux of neutrinos in the few tens-of-MeV range,
with a sharply-pulsed timing structure that is beneficial for background
rejection. In this white paper, we describe how the SNS source can be used for
a measurement of coherent elastic neutrino-nucleus scattering (CENNS), and the
physics reach of different phases of such an experimental program (CSI:
Coherent Scattering Investigations at the SNS).


  The relativistic motion of a compact binary system moving in circular orbit
is investigated using the post-Newtonian (PN) approximation and the
perturbative self-force (SF) formalism. A particular gauge-invariant observable
quantity is computed as a function of the binary's orbital frequency. The
conservative effect induced by the gravitational SF is obtained numerically
with high precision, and compared to the PN prediction developed to high order.
The PN calculation involves the computation of the 3PN regularized metric at
the location of the particle. Its divergent self-field is regularized by means
of dimensional regularization. The poles proportional to 1/(d-3) which occur
within dimensional regularization at the 3PN order disappear from the final
gauge-invariant result. The leading 4PN and next-to-leading 5PN conservative
logarithmic contributions originating from gravitational-wave tails are also
obtained. Making use of these exact PN results, some previously unknown PN
coefficients are measured up to the very high 7PN order by fitting to the
numerical self-force data. Using just the 2PN and new logarithmic terms, the
value of the 3PN coefficient is also confirmed numerically with very high
precision. The consistency of this cross-cultural comparison provides a crucial
test of the very different regularization methods used in both SF and PN
formalisms, and illustrates the complementarity of these approximation schemes
when modelling compact binary systems.


  Wireless communications for status update are becoming increasingly
important, especially for machine-type control applications. Existing work has
been mainly focused on Age of Information (AoI) optimizations. In this paper, a
status-aware predictive wireless interface design, networking and
implementation are presented which aim to minimize the status recovery error of
a wireless networked system by leveraging online status model predictions. Two
critical issues of predictive status update are addressed: practicality and
usefulness. Link-level experiments on a Software-Defined-Radio (SDR) testbed
are conducted and test results show that the proposed design can significantly
reduce the number of wireless transmissions while maintaining a low status
recovery error. A Status-aware Multi-Agent Reinforcement learning neTworking
solution (SMART) is proposed to dynamically and autonomously control the
transmit decisions of devices in an ad hoc network based on their individual
statuses. System-level simulations of a multi dense platooning scenario are
carried out on a road traffic simulator. Results show that the proposed schemes
can greatly improve the platooning control performance in terms of the minimum
safe distance between successive vehicles, in comparison with the AoI-optimized
status-unaware and communication latency-optimized schemes---this demonstrates
the usefulness of our proposed status update schemes in a real-world
application.


  Diphoton searches at high invariant mass are an integral part of the
experimental high energy frontier. Using the analyses of the 750\,GeV diphoton
resonance as a case study, we examine the methodology currently employed by the
experimental analyses in estimating the dominant standard model backgrounds. We
assess the dependence of the significance associated with the excess on the
background modeling. In particular, we show that close to the high energy tails
of the distributions, estimates of the jet faking backgrounds relying on
functional extrapolations or Monte Carlo estimates of the challenging
photon-jet contributions introduce a large uncertainty. Analyses with loose
photon cuts, low photon $p_T$ cuts and those susceptible to high photon
rapidity regions are especially affected. Given that diphoton-based searches
beyond 1\,TeV are highly motivated as discovery modes, these considerations are
relevant and applicable in the future.
  We first consider a physics-driven deformation of the shape of the diphoton
faking photon-jet spectrum by next-to-leading order effects combined with a
rapidity and transverse momentum dependent fake rate. We show that the
resulting local significance of the excess is reduced due to such a
deformation. Then using a simple, but more general, ansatz to modify the
contribution of the jet faking backgrounds at high invariant masses but keeping
the inclusive and differential sample purities within experimental uncertainty
estimates, we demonstrate that the originally reported local 750\,GeV excess
significances could have been overestimated by more than one standard
deviation. We furthermore cross-check our analysis by comparing fit results
based on the smaller 2015 and the larger 2016 LHC datasets. Finally we employ
our methodology on the complete 13\,TeV LHC dataset to asses the systematics
involved in the current diphoton searches beyond the TeV region.


  The inelastic magnetic scattering of neutrons from thin superconducting films
irradiated with microwave radiations, is analyzed. This process is due to the
interaction of the magnetic field generated by the neutron, with the
oscillating current induced by the electromagnetic field penetrated into the
film. In the Born approximation, the neutron energy can both increase and
decrease by the energy quantum of the field. The scattering cross section,
which increases with decreasing the field frequency, is derived, and its
numerical calculations are carried out. Measurements of this cross section give
directly the frequency-dependent supercurrents in the films.


  Terahertz (THz) communications with multi-GHz bandwidth are envisioned as a
key technology for 6G systems. Ultra-massive (UM) MIMO with hybrid beamforming
architectures are widely investigated to provide a high array gain to overcome
the huge propagation loss. However, most of the existing hybrid beamforming
architectures can only utilize the multiplexing offered by the multipath
components, i.e., inter-path multiplexing, which is very limited due to the
spatially sparse THz channel. In this paper, a widely-spaced multi-subarray
(WSMS) hybrid beamforming architecture is proposed, which improves the
multiplexing gain by exploiting a new type of intra-path multiplexing provided
by the spherical-wave propagation among k widely-spaced subarrays, in addition
to the inter-path multiplexing. The resulting multiplexing gain of WSMS
architecture is k times of the existing architectures. To harness WSMS hybrid
beamforming, a novel design problem is formulated by optimizing the number of
subarrays, subarray spacing, and hybrid beamforming matrices to maximize the
spectral efficiency, which is decomposed into two subproblems. An optimal
closed-form solution is derived for the first hybrid beamforming subproblem,
while a dominant-line-of-sight-relaxation algorithm is proposed for the second
array configuration subproblem. Extensive simulation results demonstrate that
the WSMS architecture and proposed algorithms substantially enhance the
spectral efficiency and energy efficiency.


  Although a generic uniformly distributed sequence has Poissonian pair
correlations, only one explicit example has been found up to now. Additionally,
it is even known that many classes of uniformly distributed sequences, like van
der Corput sequences, Kronecker sequences and LS sequences, do not have
Poissonian pair correlations. In this paper, we show that van der Corput
sequences and the Kronecker sequence for the golden mean are as close to having
Poissonian pair correlations as possible: they both have $\alpha$-pair
correlations for all $0 < \alpha < 1$ but not for $\alpha = 1$ which
corresponds to Poissonian pair correlations.


  In this study, we conducted experiments on CaC6 for elucidating the
Na-catalyzed formation mechanism and achieving rapid mass synthesis of graphite
intercalation compounds (GICs). Rapidly synthesized CaC6 was characterized by
analysis of its crystal structure and physical properties. We found that the
formation of the reaction intermediate Na-GIC (NaCx, x = 64) requires a larger
amount of Na than is intercalated between the graphite interlayers. The
requirement for excess Na may provide insights into the mechanism of
Na-catalyzed GIC formation. A Na-to-C molar mixing ratio of 1.5-2.0:6 was
suitable for the efficient formation of CaC6 under heat treatment at 250{\deg}C
for 2 h, and the catalytic Na remaining in the sample was demonstrably reduced
to a Na:Ca ratio of approximately 3:97. The upper critical field Hc2 was
enhanced approximately three times compared to those of previous reports. Based
on X-ray diffraction and experimental parameter analysis, we concluded that the
enhancement of Hc2 was attributed to the disordered stacking sequence in CaC6,
possibly because of the rapid and low-temperature formation. Physical
properties derived from specific heat measurements were comparable to those of
high-quality CaC6, which is slowly synthesized using the molten Li-Ca alloy
method. This study provides new avenues for future research and exploration in
the rapid mass synthesis of GICs as practical materials, for applications such
as battery electrodes and superconducting wires.


  By introducing the "comparison and replacement" (CNR) operation, we propose a
general-purpose pure quantum approximate optimization algorithm and derive its
core optimization mechanism quantitatively. The algorithm is constructed to a
$p$-level divide-and-conquer structure based on the CNR operations. The quality
of approximate optimization improves with the increase of $p$. For sufficiently
general optimization problems, the algorithm can work and produce the
near-optimal solutions as expected with considerably high probability.
Moreover, we demonstrate that the algorithm is scalable to be applied to large
size problems. Our algorithm is applied to two optimization problems with
significantly different degeneracy, the Gaussian weighted 2-edge graph and
MAX-2-XOR, and then we show the algorithm performance in detail when the
required qubits number of the two optimization problems is 10.


  In this paper, the construction of finite-length binary sequences whose
nonlinear complexity is not less than half of the length is investigated. By
characterizing the structure of the sequences, an algorithm is proposed to
generate all binary sequences with length $n$ and nonlinear complexity
$c_{n}\geq n/2$, where $n$ is an integer larger than $2$. Furthermore, a
formula is established to calculate the exact number of these sequences. The
distribution of nonlinear complexity for these sequences is thus completely
determined.


  A beam of holes formed in graphene by a collimating contact is imaged using a
liquid-He cooled scanning probe microscope (SPM). The mean free path of holes
is greater than the device dimensions. A zigzag shaped pattern on both sides of
the collimating contact absorb holes that enter at large angles. The image
charge beneath the SPM tip defects holes, and the pattern of flow is imaged by
displaying the change in conductance between contacts on opposite sides, as the
tip is raster scanned across the sample. Collimation is confirmed by bending
hole trajectories away from the receiving contact with an applied magnetic
field. The SPM images agree well with ray-tracing simulations.


  Pre-trained seq2seq models excel at graph semantic parsing with rich
annotated data, but generalize worse to out-of-distribution (OOD) and long-tail
examples. In comparison, symbolic parsers under-perform on population-level
metrics, but exhibit unique strength in OOD and tail generalization. In this
work, we study compositionality-aware approach to neural-symbolic inference
informed by model confidence, performing fine-grained neural-symbolic reasoning
at subgraph level (i.e., nodes and edges) and precisely targeting subgraph
components with high uncertainty in the neural parser. As a result, the method
combines the distinct strength of the neural and symbolic approaches in
capturing different aspects of the graph prediction, leading to well-rounded
generalization performance both across domains and in the tail. We empirically
investigate the approach in the English Resource Grammar (ERG) parsing problem
on a diverse suite of standard in-domain and seven OOD corpora. Our approach
leads to 35.26% and 35.60% error reduction in aggregated Smatch score over
neural and symbolic approaches respectively, and 14% absolute accuracy gain in
key tail linguistic categories over the neural model, outperforming prior
state-of-art methods that do not account for compositionality or uncertainty.


  We have studied the relationship between three different versions of the
sunspot number (Group, International and American sunspot number) and the
number of active days (i.e., the number of days with spots on the solar disk).
We have detected an approximately linear relationship for low solar activity
conditions. However, this relationship for the International sunspot number is
very different to the ones obtained with the other versions of the sunspot
number. The discordant values correspond to older observations.


  An incomparable feature of the chiral quark soliton model as compared with
many other effective models like the MIT bag model is that it can give
reasonable predictions not only for quark distributions but also for antiquark
distributions. This will be exemplified by the argument on the positivity
constraint for $\bar{u} (x) + \bar{d} (x)$ as well as the Soffer inequality for
quark and antiquark distributions. We also explain how the model can resolve
the so-called nucleon spin puzzle without assuming large gluon polarization at
the low energy scale.


  In recent years, there has been a growing interest in mapping data from
different domains to graph structures. Among others, neural network models such
as the multi-layer perceptron (MLP) can be modeled as graphs. In fact, MLPs can
be represented as directed acyclic graphs. Graph neural networks (GNNs) have
recently become the standard tool for performing machine learning tasks on
graphs. In this work, we show that an MLP is equivalent to an asynchronous
message passing GNN model which operates on the MLP's graph representation. We
then propose a new machine learning model for tabular data, the so-called Graph
Neural Machine (GNM), which replaces the MLP's directed acyclic graph with a
nearly complete graph and which employs a synchronous message passing scheme.
We show that a single GNM model can simulate multiple MLP models. We evaluate
the proposed model in several classification and regression datasets. In most
cases, the GNM model outperforms the MLP architecture.


  Within the framework of light-front field theory, we reassess the
electromagnetic form factors of the pion and kaon. Comparison with experiment
is made for the full range of momentum transfer, q^2<0, including recent data.
The light-front model's single regulator mass, m_R, of the \bar qq bound-state
vertex function is initially adjusted to reproduce the weak decay constants,
f_\pi\ and f_K, and both meson's charge radii, r_\pi\ and r_K. We study the
behavior of these observables under variation of the quark masses and find an
optimized parameter set, m_u=m_d, m_s and m_R, for which they are in sensibly
better agreement with experiment than in a previous analysis; a feature also
observed for the elastic form factors, in particular at small q^2. This model
refinement is important in view of an extension to vector and heavy-light
mesons.


  This paper addresses the problem of decentralized learning to achieve a
high-performance global model by asking a group of clients to share local
models pre-trained with their own data resources. We are particularly
interested in a specific case where both the client model architectures and
data distributions are diverse, which makes it nontrivial to adopt conventional
approaches such as Federated Learning and network co-distillation. To this end,
we propose a new decentralized learning method called Decentralized Learning
via Adaptive Distillation (DLAD). Given a collection of client models and a
large number of unlabeled distillation samples, the proposed DLAD 1) aggregates
the outputs of the client models while adaptively emphasizing those with higher
confidence in given distillation samples and 2) trains the global model to
imitate the aggregated outputs. Our extensive experimental evaluation on
multiple public datasets (MNIST, CIFAR-10, and CINIC-10) demonstrates the
effectiveness of the proposed method.