  Supernovae of type IIn possess spectral signatures that indicate an intense
interaction between the supernova ejecta and surrounding dense circumstellar
material cast off by the star in pre-explosion mass-loss episodes. Studying
this interaction can yield clues to the nature of Type IIn progenitors and
their mass loss history. In particular, polarization spectra of Type IIn's show
complex line polarization and position angle features that arise from a
combination of geometrical and optical effects. I have constructed a Monte
Carlo code that simulates the transfer of the H alpha line through
circumstellar shells with various geometrical configurations and optical
characteristics. The superposition of broad and narrow line components produced
in different regions of the circumstellar environment and modified by electron
and line scattering, hydrogen absorption, thermal emission, and geometrical and
viewing angle effects gives rise to a variety of polarized line shapes in the
model spectra. Comparison of these results with recent high-quality
spectropolarimetric observations of Type IIn supernovae suggests that a model
"shock" region between the supernova photosphere and the circumstellar shell is
necessary to produce the narrow polarized emission features at the rest
wavelength of H alpha seen in some IIn's. Further model results point toward
other features in the polarized line profile that can be used to constrain the
characteristics of the circumstellar material in these intriguing objects. The
code's usefulness will be extended by the treatment of Doppler effects due to
expansion of the circumstellar scattering region, such as those that
characterize the polarized H alpha profiles of the Type IIn SN 1997eg.


  We present a novel approach to determine the optical properties of materials
in the nanoscale range using optical tweezers (OT). Fluorescent polymer-based
nanostructures (pdots) are optically trapped in a Gaussian beam OT and the trap
stiffness is studied as a function of various parameters of interest. We
explicitly show that properties such as the refractive index and the optical
anisotropy of these nanostructures can be determined with high accuracy by
comparing the experimental data to an optical force model. In particular, we
demonstrate that the effective optical properties of these pdots can be
modulated by changing the light wavelength that excites the sample, opening the
door for a fine tuning of their optical response, with possible applications in
the development of new sensors and/or other optoelectronic devices.


  Given a `genus' function $g=g(n)$, we let $\mathcal{E}^g$ be the class of all
graphs $G$ such that if $G$ has order $n$ (that is, has $n$ vertices) then it
is embeddable in a surface of Euler genus at most $g(n)$. Let the random graph
$R_n$ be sampled uniformly from the graphs in $\mathcal{E}^g$ on vertex set
$[n]=\{1,\ldots,n\}$. Observe that if $g(n)$ is 0 then $R_n$ is a random planar
graph, and if $g(n)$ is sufficiently large then $R_n$ is a binomial random
graph $G(n,\tfrac12)$. We investigate typical properties of $R_n$. We find that
for \emph{every} genus function $g$, with high probability at most one
component of $R_n$ is non-planar. In contrast, we find a transition for example
for connectivity: if $g$ is non-decreasing and $g(n) = O(n/\log n)$ then
$\liminf_{n \to \infty} \mathbb{P}(R_n \mbox{ is connected}) < 1$, and if $g(n)
\gg n$ then with high probability $R_n$ is connected. These results also hold
when we consider orientable and non-orientable surfaces separately. We also
investigate random graphs sampled uniformly from the `hereditary part' or the
`minor-closed' part of $\mathcal{E}^g$, and briefly consider corresponding
results for unlabelled graphs.


  The acceptance of immersive telepresence systems is impeded by the latency
that is present when mediating the realistic feeling of presence in a remote
environment to a local human user. A disagreement between the user's ego-motion
and the visual response provokes the emergence of motion sickness. Viewport or
head motion (HM) prediction techniques play a key role in compensating the
noticeable delay between the user and the remote site. We present a deep
learning-based viewport prediction paradigm that fuses past HM trajectories
with scene semantics in a late-fusion manner. Real HM profiles are used to
evaluate the proposed approach. A mean compensation rate as high as 99.99% is
obtained, clearly outperforming the state-of-the-art. An on-demand 360-degree
video streaming framework is presented to prove its general validity. The
proposed approach increases the perceived video quality while requiring a
significantly lower transmission rate.


  The new trend in multi-object tracking task is to track objects of interest
using natural language. However, the scarcity of paired prompt-instance data
hinders its progress. To address this challenge, we propose a high-quality yet
low-cost data generation method base on Unreal Engine 5 and construct a
brand-new benchmark dataset, named Refer-UE-City, which primarily includes
scenes from intersection surveillance videos, detailing the appearance and
actions of people and vehicles. Specifically, it provides 14 videos with a
total of 714 expressions, and is comparable in scale to the Refer-KITTI
dataset. Additionally, we propose a multi-level semantic-guided multi-object
framework called MLS-Track, where the interaction between the model and text is
enhanced layer by layer through the introduction of Semantic Guidance Module
(SGM) and Semantic Correlation Branch (SCB). Extensive experiments on
Refer-UE-City and Refer-KITTI datasets demonstrate the effectiveness of our
proposed framework and it achieves state-of-the-art performance. Code and
datatsets will be available.


  New calculations for rotational excitation of cyanoacetylene by collisions
with hydrogen molecules are performed to include the lowest 38 rotational
levels of HC3N and kinetic temperatures to 300 K. Calculations are based on the
interaction potential of Wernli et al. A&A, 464, 1147 (2007) whose accuracy is
checked against spectroscopic measurements of the HC3N-H2 complex. The quantum
coupled-channel approach is employed and complemented by quasi-classical
trajectory calculations. Rate coefficients for ortho-H2 are provided for the
first time. Hyperfine resolved rate coefficients are also deduced. Collisional
propensity rules are discussed and comparisons between quantum and classical
rate coefficients are presented. This collisional data should prove useful in
interpreting HC3N observations in the cold and warm ISM, as well as in
protoplanetary disks.


  Associated to any manifold equipped with a closed form of degree >1 is an
`L-infinity algebra of observables' which acts as a higher/homotopy analog of
the Poisson algebra of functions on a symplectic manifold. In order to study
Lie group actions on these manifolds, we introduce a theory of homotopy moment
maps. Such a map is a L-infinity morphism from the Lie algebra of the group
into the observables which lifts the infinitesimal action. We establish the
relationship between homotopy moment maps and equivariant de Rham cohomology,
and analyze the obstruction theory for the existence of such maps. This allows
us to easily and explicitly construct a large number of examples. These include
results concerning group actions on loop spaces and moduli spaces of flat
connections. Relationships are also established with previous work by others in
classical field theory, algebroid theory, and dg geometry. Furthermore, we use
our theory to geometrically construct various L-infinity algebras as higher
central extensions of Lie algebras, in analogy with Kostant's quantization
theory. In particular, the so-called `string Lie 2-algebra' arises this way.


  We propose the new method of fluid structure investigation which is based on
numerical analytical continuation of structural correlation functions with Pade
approximants. The method particularly allows extracting hidden structural
features of non-ordered condensed matter systems from experimental diffraction
data. The method has been applied to investigating the local order of liquid
gallium which has non-trivial stricture in both the liquid and solid states.
Processing the correlation functions obtained from molecular dynamic
simulations, we show the method proposed reveals non-trivial structural
features of liquid gallium such as the spectrum of length-scales and the
existence of different types of local clusters in the liquid.


  Multiorbital models are important to both the correlation physics and
topological behavior of quantum materials. LiFeAs is a prototype iron pnictide
suitable for indepth investigation of this issue. Its electronic structure is
strikingly different from the prediction of the noninteracting description.
Here, a multiorbital Hubbard model for this compound is studied using a $U(1)$
slave spin theory. We demonstrate a new mechanism for a large change in the
size of the Fermi surface, namely, orbital selectivity of the energy-level
renormalization cooperating with its counterpart in the quasiparticle spectral
weight. Using this effect, we show how the dominating features of the
electronic structure in LiFeAs are understood in terms of the local
correlations alone. Our results reveal a remarkable degree of universality out
of the seemingly complex multiorbital building blocks across a broad range of
strongly correlated superconductors.


  Skyrmions, topologically stable spin textures, are of great interest for new
generations of spintronic devices. In general, the stabilization of skyrmions
has been achieved in systems with broken inversion symmetry, where the
asymmetric Dzyaloshinskii-Moriya interaction (DMI) modifies the uniform
magnetic state to a swirling state. Depending on the crystal symmetries, two
distinct types of skyrmions, Bloch and N\'eel types, have been observed
experimentally. Here, we present, the experimental manifestation of a special
type of spin-swirling, namely antiskyrmions, in a family of acentric tetragonal
Heusler compounds with D2d crystal symmetry. A spiral magnetic ground-state,
which propagates in the tetragonal basal plane, is transformed into a skyrmion
lattice-state under magnetic fields applied along the tetragonal axis over a
wide temperature interval. Direct imaging by Lorentz Transmission Electron
Microscopy (LTEM) shows field stabilized antiskyrmion lattices and isolated
antiskyrmions between 100 K and 400 K, and zero-field metastable antiskyrmions
at low temperatures.


  We propose a framework for learned image and video compression using the
generative sparse visual representation (SVR) guided by fidelity-preserving
controls. By embedding inputs into a discrete latent space spanned by learned
visual codebooks, SVR-based compression transmits integer codeword indices,
which is efficient and cross-platform robust. However, high-quality (HQ)
reconstruction in the decoder relies on intermediate feature inputs from the
encoder via direct connections. Due to the prohibitively high transmission
costs, previous SVR-based compression methods remove such feature links,
resulting in largely degraded reconstruction quality. In this work, we treat
the intermediate features as fidelity-preserving control signals that guide the
conditioned generative reconstruction in the decoder. Instead of discarding or
directly transferring such signals, we draw them from a low-quality (LQ)
fidelity-preserving alternative input that is sent to the decoder with very low
bitrate. These control signals provide complementary fidelity cues to improve
reconstruction, and their quality is determined by the compression rate of the
LQ alternative, which can be tuned to trade off bitrate, fidelity and
perceptual quality. Our framework can be conveniently used for both learned
image compression (LIC) and learned video compression (LVC). Since SVR is
robust against input perturbations, a large portion of codeword indices between
adjacent frames can be the same. By only transferring different indices,
SVR-based LIC and LVC can share a similar processing pipeline. Experiments over
standard image and video compression benchmarks demonstrate the effectiveness
of our approach.


  Handling missing values in training datasets for constructing learning models
or extracting useful information is considered to be an important research task
in data mining and knowledge discovery in databases. In recent years, lot of
techniques are proposed for imputing missing values by considering attribute
relationships with missing value observation and other observations of training
dataset. The main deficiency of such techniques is that, they depend upon
single approach and do not combine multiple approaches, that why they are less
accurate. To improve the accuracy of missing values imputation, in this paper
we introduce a novel partial matching concept in association rules mining,
which shows better results as compared to full matching concept that we
described in our previous work. Our imputation technique combines the partial
matching concept in association rules with k-nearest neighbor approach. Since
this is a hybrid technique, therefore its accuracy is much better than as
compared to those techniques which depend upon single approach. To check the
efficiency of our technique, we also provide detail experimental results on
number of benchmark datasets which show better results as compared to previous
approaches.


  We present User-predictable Face Editing (UP-FacE) -- a novel method for
predictable face shape editing. In stark contrast to existing methods for face
editing using trial and error, edits with UP-FacE are predictable by the human
user. That is, users can control the desired degree of change precisely and
deterministically and know upfront the amount of change required to achieve a
certain editing result. Our method leverages facial landmarks to precisely
measure facial feature values, facilitating the training of UP-FacE without
manually annotated attribute labels. At the core of UP-FacE is a
transformer-based network that takes as input a latent vector from a
pre-trained generative model and a facial feature embedding, and predicts a
suitable manipulation vector. To enable user-predictable editing, a scaling
layer adjusts the manipulation vector to achieve the precise desired degree of
change. To ensure that the desired feature is manipulated towards the target
value without altering uncorrelated features, we further introduce a novel
semantic face feature loss. Qualitative and quantitative results demonstrate
that UP-FacE enables precise and fine-grained control over 23 face shape
features.


  We measure the thermodynamic magnetization of a low-disordered, strongly
correlated two-dimensional electron system in silicon in perpendicular magnetic
fields. A new, parameter-free method is used to directly determine the spectrum
characteristics (Lande g-factor and the cyclotron mass) when the Fermi level
lies outside the spectral gaps and the inter-level interactions between
quasiparticles are avoided. Intra-level interactions are found to strongly
modify the magnetization, without affecting the determined g* and m*.


  Reproducibility and comparability of empirical results are at the core tenet
of the scientific method in any scientific field. To ease reproducibility of
empirical studies, several benchmarks in software engineering research, such as
Defects4J, have been developed and widely used. For quantum software
engineering research, however, no benchmark has been established yet. In this
position paper, we propose a new benchmark -- named QBugs -- which will provide
experimental subjects and an experimental infrastructure to ease the evaluation
of new research and the reproducibility of previously published results on
quantum software engineering.


  This paper deals with the existence of a compact stellar object, precisely
strange (quark) star, in the framework of Einstein's General Theory of
Relativity with Tolman $V$ metric potential, which is one of the simplest forms
of potential among his proposals. The potential is given by $e^{\nu}=Kr^{2n}$,
where $K$ is the constant and $n$ is a parameter [R.C. Tolman, Phys. Rev.
{\bf55}, 364 (1939)]. Considering charged, static, spherically symmetric,
isotropic fluid sphere we have studied different physical features of some
strange star candidates namely $EXO\ 1785-248$, $LMC\ X-4$, $SMC\ X-1$, $SAX\
J1808.4-3658$, $4U\ 1538-52$ and $Her\ X-1$. To represent the strange quark
matter (SQM) distribution we have employed the simplest form of MIT bag
equation of state (EOS), which provides a linear relationship between pressure
and density of the matter through Bag constant $B$. We have done several tests
for the stability criteria and the physical acceptability of the proposed
model. The results show consistency with energy condition, TOV equation,
adiabatic index, etc. We have calculated different physical parameters of our
model for the three different consecutive values of Bag constant $B$ which are
$83\ MeV/fm^3$, $90\ MeV/fm^3$ and $100\ MeV/fm^3$. Among them with $B=90\
MeV/fm^3$ we have analyzed different properties of the proposed strange star
candidates.


  Ultra-faint dwarf galaxies (UFDs) are the faintest known galaxies and due to
their incredibly low surface brightness, it is difficult to find them beyond
the Local Group. We report a serendipitous discovery of an UFD, Fornax UFD1, in
the outskirts of NGC 1316, a giant galaxy in the Fornax cluster. The new galaxy
is located at a projected radius of 55 kpc in the south-east of NGC 1316. This
UFD is found as a small group of resolved stars in the Hubble Space Telescope
images of a halo field of NGC 1316, obtained as part of the Carnegie-Chicago
Hubble Program. Resolved stars in this galaxy are consistent with being mostly
metal-poor red giant branch (RGB) stars. Applying the tip of the RGB method to
the mean magnitude of the two brightest RGB stars, we estimate the distance to
this galaxy, 19.0 +- 1.3 Mpc. Fornax UFD1 is probably a member of the Fornax
cluster. The color-magnitude diagram of these stars is matched by a 12 Gyr
isochrone with low metallicity ([Fe/H] ~ -2.4). Total magnitude and effective
radius of Fornax UFD1 are Mv ~ -7.6 +- 0.2 mag and r_eff = 146 +- 9 pc, which
are similar to those of Virgo UFD1 that was discovered recently in the
intracluster field of Virgo by Jang & Lee (2014).Fornax UFD1 is the most
distant known UFD that is confirmed by resolved stars. This indicates that UFDs
are ubiquitous and that more UFDs remain to be discovered in the Fornax
cluster.


  Motivated by recent experiments on Mott insulators, in both iridates and
ultracold atoms, we theoretically study the effects of magnetic order on the
Mott-Hubbard excitons. In particular, we focus on spin-mediated doublon-holon
pairing in Hubbard materials. We use several complementary theoretical
techniques: mean-field theory to describe the spin degrees of freedom, the
self-consistent Born approximation to characterize individual charge
excitations across the Hubbard gap, and the Bethe-Salpeter equation to identify
bound states of doublons and holons. The binding energy of the Hubbard exciton
is found to increase with increasing the N{\'e}el order parameter, while the
exciton mass decreases. We observe that these trends rely significantly on the
retardation of the effective interaction, and require consideration of multiple
effects from changing the magnetic order. Our results are consistent with the
key qualitative trends observed in recent experiments on iridates. Moreover,
the findings could have direct implications on ultracold atom Mott insulators,
where the Hubbard model is the exact description of the system and the
microscopic degrees of freedom can be directly accessed.


  We give a natural-deduction-style type theory for symmetric monoidal
categories whose judgmental structure directly represents morphisms with tensor
products in their codomain as well as their domain. The syntax is inspired by
Sweedler notation for coalgebras, with variables associated to types in the
domain and terms associated to types in the codomain, allowing types to be
treated informally like "sets with elements" subject to global linearity-like
restrictions. We illustrate the usefulness of this type theory with various
applications to duality, traces, Frobenius monoids, and (weak) Hopf monoids.


  The calculation of the leading electroweak corrections to physical transition
matrix elements in powers of $M_H^2/v^2$ can be greatly simplified in the limit
$M_H^2\gg M_W^2,\, M_Z^2$ through the use of the Goldstone boson equivalence
theorem. This theorem allows the vector bosons $W^\pm$ and $Z$ to be replaced
by the associated scalar Goldstone bosons $w^\pm$, $z$ which appear in the
symmetry breaking sector of the Standard Model in the limit of vanishing gauge
couplings. In the present paper, we extend the equivalence theorem
systematically to include the Yukawa interactions between the fermions and the
Higgs and Goldstone bosons of the Standard Model. The corresponding Lagrangian
${\cal L}_{EQT}$ is given, and is formally renormalized to all orders. The
renormalization conditions are formulated both to make connection with physical
observables and to satisfy the requirements underlying the equivalence theorem.
As an application of this framework, we calculate the dominant radiative
corrections to fermionic Higgs decays at one loop including the virtual effects
of a heavy top quark. We apply the result to the decays $H\rightarrow t\bar{t}$
and $H\rightarrow b\bar{b}$, and find that the equivalence theorem results
including fermions are quite accurate numerically for Higgs-boson masses $M_H>
400\,(350)$ GeV, respectively, even for $m_t=175$ GeV.


  The advantage of recurrent neural networks (RNNs) in learning dependencies
between time-series data has distinguished RNNs from other deep learning
models. Recently, many advances are proposed in this emerging field. However,
there is a lack of comprehensive review on memory models in RNNs in the
literature. This paper provides a fundamental review on RNNs and long short
term memory (LSTM) model. Then, provides a surveys of recent advances in
different memory enhancements and learning techniques for capturing long term
dependencies in RNNs.


  This paper focuses on robotic reinforcement learning with sparse rewards for
natural language goal representations. An open problem is the
sample-inefficiency that stems from the compositionality of natural language,
and from the grounding of language in sensory data and actions. We address
these issues with three contributions. We first present a mechanism for
hindsight instruction replay utilizing expert feedback. Second, we propose a
seq2seq model to generate linguistic hindsight instructions. Finally, we
present a novel class of language-focused learning tasks. We show that
hindsight instructions improve the learning performance, as expected. In
addition, we also provide an unexpected result: We show that the learning
performance of our agent can be improved by one third if, in a sense, the agent
learns to talk to itself in a self-supervised manner. We achieve this by
learning to generate linguistic instructions that would have been appropriate
as a natural language goal for an originally unintended behavior. Our results
indicate that the performance gain increases with the task-complexity.


  The downward closure of a language $L$ of words is the set of all (not
necessarily contiguous) subwords of members of $L$. It is well known that the
downward closure of any language is regular. Although the downward closure
seems to be a promising abstraction, there are only few language classes for
which an automaton for the downward closure is known to be computable.
  It is shown here that for stacked counter automata, the downward closure is
computable. Stacked counter automata are finite automata with a storage
mechanism obtained by \emph{adding blind counters} and \emph{building stacks}.
Hence, they generalize pushdown and blind counter automata.
  The class of languages accepted by these automata are precisely those in the
hierarchy obtained from the context-free languages by alternating two closure
operators: imposing semilinear constraints and taking the algebraic extension.
The main tool for computing downward closures is the new concept of Parikh
annotations. As a second application of Parikh annotations, it is shown that
the hierarchy above is strict at every level.


It is considered a special, convex variant of Sperner lemma type .


  Proving capacity for networks without feedback or cooperation usually
involves two fundamental random coding techniques: superposition coding and
binning. Although conceptually very different, these two techniques often
achieve the same performance, suggesting an underlying similarity. In this
correspondence we propose a new random coding technique that generalizes
superposition coding and binning and provides new insight on relationship among
the two With this new theoretical tool, we derive new achievable regions for
three classical information theoretical models: multi-access channel, broadcast
channel, the interference channel, and show that, unfortunately, it does not
improve over the largest known achievable regions for these cases.


  We study the Ginzburg-Landau equations in order to describe a two-dimensional
superconductor in a bounded domain. Using the properties of a particular
integrability point ($\kappa = 1/ \sqrt2$) of these nonlinear equations which
allows vortex solutions, we obtain a closed expression for the energy of the
superconductor. The presence of the boundary provides a selection mechanism for
the number of vortices.
  A perturbation analysis around $\kappa = 1/ \sqrt2$ enables us to include the
effects of the vortex interactions and to describe quantitatively the
magnetization curves recently measured on small superconducting disks.
  We also calculate the optimal vortex configuration and obtain an expression
for the confining potential away from the London limit.


  We consider deformations of D-brane systems induced by a change in the closed
string background in the framework of bosonic open-closed string field theory,
where it is possible to unambiguously tame infrared divergences originating
from both open and closed string degenerations. A closed string classical
solution induces a tadpole for the open strings which shifts the open string
vacuum and generates a cosmological constant composed of two terms: one which
is directly related to the closed string solution and the other which depends
on the open string vacuum shift. We show that only the sum of these two terms
is invariant under closed SFT gauge transformations and therefore is an
observable. We conjecture that this observable is universally proportional to
the shift in the world-sheet disk partition function between the starting
D-brane in undeformed background and the final D-brane in deformed background,
which typically includes also a change in the string coupling constant. We test
the conjecture by considering a perturbative closed string solution describing
deformations of a Narain compactification and, from the SFT cosmological
constant, we reproduce the expected shift in the $g$-function of various
D-branes living in the compactification. In doing this we are also able to
identify a surprising change in the string coupling constant at second order in
the deformation.


  Using canonical transformations we obtain localized (in space) exact
solutions of the nonlinear Schr\"odinger equation (NLSE) with space and time
modulated nonlinearity and in the presence of an external potential depending
on space and time. In particular we obtain exact solutions of NLSE in the
presence of a number of non Hermitian $\mathcal{PT}$ symmetric external
potentials.


  We present spatially resolved mid-infrared spectroscopy of the class
I/flat-spectrum protostellar binary system SVS20 in the Serpens cloud core. The
spectra were obtained with the mid-infrared instrument T-ReCS on Gemini-South.
SVS20-South, the more luminous of the two sources, exhibits a mid-infrared
emission spectrum peaking near 11.3 \micron, while SVS20-North exhibits a
shallow amorphous silicate absorption spectrum with a peak optical depth of
$\tau \sim 0.3$. After removal of the the line-of-sight extinction by the
molecular common envelope, the ``protostar-only'' spectra are found to be
dominated by strong amorphous olivine emission peaking near 10 \micron. We also
find evidence for emission from crystalline forsterite and enstatite associated
with both SVS20-S and SVS20-N. The presence of crystalline silicate in such a
young binary system indicates that the grain processing found in more evolved
HAeBe and T Tauri pre-main sequence stars likely begins at a relatively young
evolutionary stage, while mass accretion is still ongoing.


  Recently, it has been found that complete resolution of the Hubble tension
might point to a scale-invariant Harrison-Zeldovich spectrum of primordial
scalar perturbation, i.e. $n_s=1$ for $H_0\sim 73$km/s/Mpc. We show that for
well-known slow-roll models, if inflation ends by a waterfall instability with
respect to another field in the field space while inflaton is still at a deep
slow-roll region, $n_s$ can be lifted to $n_s= 1$. A surprise of our result is
that with pre-recombination early dark energy, chaotic $\phi^2$ inflation,
ruled out by Planck+BICEP/Keck in standard $\Lambda$CDM, can be revived, which
is now well within testable region of upcoming cosmic microwave background
B-mode experiments.


  To fully understand the complexities of human emotion, the integration of
multiple physical features from different modalities can be advantageous.
Considering this, we present an analysis of 3D facial data, action units, and
physiological data as it relates to their impact on emotion recognition. We
analyze each modality independently, as well as the fusion of each for
recognizing human emotion. This analysis includes which features are most
important for specific emotions (e.g. happy). Our analysis indicates that both
3D facial landmarks and physiological data are encouraging for
expression/emotion recognition. On the other hand, while action units can
positively impact emotion recognition when fused with other modalities, the
results suggest it is difficult to detect emotion using them in a unimodal
fashion.


  We investigate pattern formation in self-oscillating systems forced by an
external periodic perturbation. Experimental observations and numerical studies
of reaction-diffusion systems and an analysis of an amplitude equation are
presented. The oscillations in each of these systems entrain to rational
multiples of the perturbation frequency for certain values of the forcing
frequency and amplitude. We focus on the subharmonic resonant case where the
system locks at one fourth the driving frequency, and four-phase rotating
spiral patterns are observed at low forcing amplitudes. The spiral patterns are
studied using an amplitude equation for periodically forced oscillating
systems. The analysis predicts a bifurcation (with increasing forcing) from
rotating four-phase spirals to standing two-phase patterns. This bifurcation is
also found in periodically forced reaction-diffusion equations, the
FitzHugh-Nagumo and Brusselator models, even far from the onset of oscillations
where the amplitude equation analysis is not strictly valid. In a
Belousov-Zhabotinsky chemical system periodically forced with light we also
observe four-phase rotating spiral wave patterns. However, we have not observed
the transition to standing two-phase patterns, possibly because with increasing
light intensity the reaction kinetics become excitable rather than oscillatory.


  [Abridged] We present deep optical and near-infrared UBVRIHKs imaging data
for 24 blue compact galaxies (BCGs). The sample contains luminous dwarf and
intermediate-mass BCGs which are predominantly metal-poor, although a few have
near-solar metallicities. We have analyzed isophotal and elliptical integration
surface brightness and color profiles, extremely deep (mu_B<29 mag arcsec^{-2})
contour maps and RGB images for each galaxy in the sample. The colors are
compared to different spectral evolutionary models. We detect extremely
extended low surface brightness (LSB) components dominant beyond the Holmberg
radius as well as optical bridges between companion galaxies at the mu_V~28th
mag arcsec^{-2} isophotal level. The central surface brightness mu_0 and scale
length h_r are derived from two radial ranges typically assumed to be dominated
by the underlying host galaxy. We find that mu_0 and h_r of the BCGs host
deviate from those of dwarf ellipticals (dE) and dwarf irregulars (dI) solely
due to a strong burst contribution to the surface brightness profile almost
down to the Holmberg radius. Structural parameters obtained from a fainter
region, mu_B=26-28 mag arcsec^{-2}, are consistent with those of true LSB
galaxies for the starbursting BCGs in our sample, and with dEs and dIs for the
BCGs with less vigorous star formation.


  Photonic integration is an enabling technology for photonic quantum science,
offering greater scalability, stability, and functionality than traditional
bulk optics. Here, we describe a scalable, heterogeneous III-V/silicon
integration platform to produce Si$_3$N$_4$ photonic circuits incorporating
GaAs-based nanophotonic devices containing self-assembled InAs/GaAs quantum
dots. We demonstrate pure singlephoton emission from individual quantum dots in
GaAs waveguides and cavities - where strong control of spontaneous emission
rate is observed - directly launched into Si$_3$N$_4$ waveguides with > 90 %
efficiency through evanescent coupling. To date, InAs/GaAs quantum dots
constitute the most promising solidstate triggered single-photon sources,
offering bright, pure and indistinguishable emission that can be electrically
and optically controlled. Si$_3$N$_4$ waveguides offer low-loss propagation,
tailorable dispersion and high Kerr nonlinearities, desirable for linear and
nonlinear optical signal processing down to the quantum level. We combine these
two in an integration platform that will enable a new class of scalable,
efficient and versatile integrated quantum photonic devices


  In the favored progenitor scenario, Type Ia supernovae arise from a white
dwarf accreting material from a non-degenerate companion star. Soon after the
white dwarf explodes, the ejected supernova material engulfs the companion
star; two-dimensional hydrodynamical simulations by Marietta et. al. show that,
in the interaction, the companion star carves out a conical hole of opening
angle 30-40 degrees in the supernova ejecta. In this paper we use
multi-dimensional Monte Carlo radiative transfer calculations to explore the
observable consequences of an ejecta-hole asymmetry. We calculate the variation
of the spectrum, luminosity, and polarization with viewing angle for the
aspherical supernova near maximum light. We find that the supernova looks
normal from almost all viewing angles except when one looks almost directly
down the hole. In the latter case, one sees into the deeper, hotter layers of
ejecta. The supernova is relatively brighter and has a peculiar spectrum
characterized by more highly ionized species, weaker absorption features, and
lower absorption velocities. The spectrum viewed down the hole is comparable to
the class of SN 1991T-like supernovae. We consider how the ejecta-hole
asymmetry may explain the current spectropolarimetric observations of SNe Ia,
and suggest a few observational signatures of the geometry. Finally, we discuss
the variety currently seen in observed SNe Ia and how an ejecta-hole asymmetry
may fit in as one of several possible sources of diversity.


  Within the frame of a novel treatment we make a complete mathematical
analysis of exactly solvable one-dimensional quantum systems with non-constant
mass, involving their ordering ambiguities. This work extends the results
recently reported in the literature and clarifies the relation between
physically acceptable effective mass Hamiltonians.


  We introduce VideoPrism, a general-purpose video encoder that tackles diverse
video understanding tasks with a single frozen model. We pretrain VideoPrism on
a heterogeneous corpus containing 36M high-quality video-caption pairs and 582M
video clips with noisy parallel text (e.g., ASR transcripts). The pretraining
approach improves upon masked autoencoding by global-local distillation of
semantic video embeddings and a token shuffling scheme, enabling VideoPrism to
focus primarily on the video modality while leveraging the invaluable text
associated with videos. We extensively test VideoPrism on four broad groups of
video understanding tasks, from web video question answering to CV for science,
achieving state-of-the-art performance on 31 out of 33 video understanding
benchmarks.


  We examine the stacking order of multilayer graphene grown on the
SiC$(000\bar{1})$ surface using low-energy electron diffraction and surface
X-ray diffraction. We show that the films contain a high density of rotational
stacking faults caused by three types of rotated graphene: sheets rotated
$30^\circ$ and $\pm 2.20^\circ$ relative to the SiC substrate. These angles are
unique because they correspond to commensurate phases of layered graphene, both
with itself and with the SiC substrate. {\it Ab intio} calculations show that
these rotational phases electronically decouple adjacent graphene layers. The
band structure from graphene at fault boundaries displays linear energy
dispersion at the $K$-point (Dirac cones), nearly identical to that of a single
graphene sheet.


  We construct an infinite-dimensional symplectic 2-groupoid as the integration
of an exact Courant algebroid. We show that every integrable Dirac structure
integrates to a "Lagrangian" sub-2-groupoid of this symplectic 2-groupoid. As a
corollary, we recover a result of Bursztyn-Crainic-Weinstein-Zhu that every
integrable Dirac structure integrates to a presymplectic groupoid.


  In the context of increasing demands for long-term multi-energy load
forecasting in real-world applications, this paper introduces Patchformer, a
novel model that integrates patch embedding with encoder-decoder
Transformer-based architectures. To address the limitation in existing
Transformer-based models, which struggle with intricate temporal patterns in
long-term forecasting, Patchformer employs patch embedding, which predicts
multivariate time-series data by separating it into multiple univariate data
and segmenting each of them into multiple patches. This method effectively
enhances the model's ability to capture local and global semantic dependencies.
The numerical analysis shows that the Patchformer obtains overall better
prediction accuracy in both multivariate and univariate long-term forecasting
on the novel Multi-Energy dataset and other benchmark datasets. In addition,
the positive effect of the interdependence among energy-related products on the
performance of long-term time-series forecasting across Patchformer and other
compared models is discovered, and the superiority of the Patchformer against
other models is also demonstrated, which presents a significant advancement in
handling the interdependence and complexities of long-term multi-energy
forecasting. Lastly, Patchformer is illustrated as the only model that follows
the positive correlation between model performance and the length of the past
sequence, which states its ability to capture long-range past local semantic
information.


  The concept of scale-free networks has been widely applied across natural and
physical sciences. Many claims are made about the properties of these networks,
even though the concept of scale-free is often vaguely defined. We present
tools and procedures to analyse the statistical properties of networks defined
by arbitrary degree distributions and other constraints. Doing so reveals the
highly likely properties, and some unrecognised richness, of scale-free
networks, and casts doubt on some previously claimed properties being due to a
scale-free characteristic.


  We study Haar unitary random matrices with permuted entries. For a sequence
of permutations $\left(\sigma_N\right)_N$, where $\sigma_N$ acts on $N\times N$
matrices we identify conditions under which the $\ast$--distribution of
permuted Haar unitary matrices $U_N^{\sigma_N}$ is asymptotically circular and
free from the unpermuted sequence $U_N$. We show that this convergence takes
place in the almost sure sense. Moreover we show that our conditions on the
sequence of permutations are generic in the sense that are almost surely
satisfied by a sequence of random permutations.


  Planets with non-zero obliquity and/or orbital eccentricity experience
seasonal variations of stellar irradiation at local latitudes. The extent of
the atmospheric response can be crudely estimated by the ratio between the
orbital timescale and the atmospheric radiative timescale. Given a set of
atmospheric parameters, we show that this ratio depends mostly on the stellar
properties and is independent of orbital distance and planetary equilibrium
temperature. For Jupiter-like atmospheres, this ratio is $\ll1$ for planets
around very-low-mass M dwarfs and $\gtrsim1$ when the stellar mass is greater
than about 0.6 solar mass. Complications can arise from various factors,
including varying atmospheric metallicity, clouds, and atmospheric dynamics.
Given the eccentricity and obliquity, the seasonal response is expected to be
systematically weaker for gaseous exoplanets around low-mass stars and stronger
for those around more massive stars. The amplitude and phase lag of atmospheric
seasonal variations as a function of host stellar mass are quantified by
idealized analytic models. At the infrared emission level in the photosphere,
the relative amplitudes of thermal flux and temperature perturbations are
negligible, and their phase lags are closed to $-90^{\circ}$ for Jupiter-like
planets around very-low-mass stars. The relative amplitudes and phase lags
increase gradually with increasing stellar mass. With a particular stellar
mass, the relative amplitude and phase lag decrease from low to high infrared
optical depth. We also present numerical calculations for a better illustration
of the seasonal behaviors. Lastly, we discuss implications for the atmospheric
circulation and future atmospheric characterization of exoplanets in systems
with different stellar masses.


  We simulate the hadroproduction of a top-antitop pair in association with a
bottom-antibottom pair at 14 TeV LHC using the PowHel package. We use the
generated events, stored according to the Les-Houches event format, to make
predictions for differential distributions formally at the next-to-leading
order (NLO) accuracy and we compare these to existing predictions accurate at
NLO.


  The magnetic moments of baryon decuplet are studied in vacuum as well as in
the symmetric nuclear matter at finite temperature using a chiral SU(3) quark
mean field model approach. The contributions coming from the valence quarks,
quark sea and the orbital angular momentum of the quark sea have been
considered to calculate magnetic moment of decuplet baryons. The decuplet
baryon masses decrease, whereas, the magnetic moments increase significantly
with the rise of baryonic density of the symmetric nuclear medium. This is
because of the reason that constituent quark magnetic moment and the quark spin
polarizations show considerable variation in the nuclear medium especially in
the low temperature and baryonic density regime.
  The increase is however quantitatively less as compared to the case of octet
baryon members.


  Safely integrating unmanned aerial vehicles into civil airspace is contingent
upon development of a trustworthy collision avoidance system. This paper
proposes an approach whereby a parameterized resolution logic that is
considered trusted for a given range of its parameters is adaptively tuned
online. Specifically, to address the potential conservatism of the resolution
logic with static parameters, we present a dynamic programming approach for
adapting the parameters dynamically based on the encounter state. We compute
the adaptation policy offline using a simulation-based approximate dynamic
programming method that accommodates the high dimensionality of the problem.
Numerical experiments show that this approach improves safety and operational
performance compared to the baseline resolution logic, while retaining
trustworthiness.


  A new image denoising algorithm to deal with the additive Gaussian white
noise model is given. Like the non-local means method, the filter is based on
the weighted average of the observations in a neighborhood, with weights
depending on the similarity of local patches. But in contrast to the non-local
means filter, instead of using a fixed Gaussian kernel, we propose to choose
the weights by minimizing a tight upper bound of mean square error. This
approach makes it possible to define the weights adapted to the function at
hand, mimicking the weights of the oracle filter. Under some regularity
conditions on the target image, we show that the obtained estimator converges
at the usual optimal rate. The proposed algorithm is parameter free in the
sense that it automatically calculates the bandwidth of the smoothing kernel;
it is fast and its implementation is straightforward. The performance of the
new filter is illustrated by numerical simulations.


  Long period (LP) earthquakes are common at active volcanoes, and are
ubiquitous at persistently active andesitic and dacitic subduction zone
volcanoes. They provide critical information regarding the state of volcanic
unrest, and their occurrence rates are key data for eruption forecasting. LPs
are commonly quasi-periodic or 'anti-clustered', unlike volcano-tectonic (VT)
earthquakes, so the existing Poisson point process methods used to model
occurrence rates of VT earthquakes are unlikely to be optimal for LP data. We
evaluate the performance of candidate formulations for LP data, based on
inhomogeneous point process models with four different inter-event time
distributions: exponential (IP), Gamma (IG), inverse Gaussian (IIG), and
Weibull (IW). We examine how well these models explain the observed data, and
the quality of retrospective forecasts of eruption time. We use a Bayesian MCMC
approach to fit the models. Goodness-of-fit is assessed using Quantile-Quantile
and Kolmogorov-Smirnov methods, and benchmarking against results obtained from
synthetic datasets. IG and IIG models were both found to fit the data well,
with the IIG model slightly outperforming the IG model. Retrospective
forecasting analysis shows that the IG model performs best, with the initial
preference for the IIG model controlled by catalogue incompleteness late in the
sequence. The IG model fits the data significantly better than the IP model,
and simulations show it produces better forecasts for highly periodic data.
Simulations also show that forecast precision increases with the degree of
periodicity of the earthquake process using the IG model, and so should be
better for LP earthquakes than VTs. These results provide a new framework for
point process modelling of volcanic earthquake time series, and verification of
alternative models.


  Jet isomorphism theorems for conformal geometry are discussed. A new proof of
the jet isomorphism theorem for odd-dimensional conformal geometry is outlined,
using an ambient realization of the conformal deformation complex. An infinite
order ambient lift for conformal densities in the case in which harmonic
extension is obstructed is described. A jet isomorphism theorem for even
dimensional conformal geometry is formulated using the inhomogeneous ambient
metrics recently introduced by the author and K. Hirachi.


  A permutation $\pi$ over alphabet $\Sigma = {1,2,3,\ldots,n}$, is a sequence
where every element $x$ in $\Sigma$ occurs exactly once. $S_n$ is the symmetric
group consisting of all permutations of length $n$ defined over $\Sigma$. $I_n$
= $(1, 2, 3,\ldots, n)$ and $R_n =(n, n-1, n-2,\ldots, 2, 1)$ are identity
(i.e. sorted) and reverse permutations respectively. An operation, that we call
as an $LRE$ operation, has been defined in OEIS with identity A186752. This
operation is constituted by three generators: left-rotation, right-rotation and
transposition(1,2). We call transposition(1,2) that swaps the two leftmost
elements as $Exchange$. The minimum number of moves required to transform $R_n$
into $I_n$ with $LRE$ operation are known for $n \leq 11$ as listed in OEIS
with sequence number A186752. For this problem no upper bound is known. OEIS
sequence A186783 gives the conjectured diameter of the symmetric group $S_n$
when generated by $LRE$ operations \cite{oeis}. The contributions of this
article are: (a) The first non-trivial upper bound for the number of moves
required to sort $R_n$ with $LRE$; (b) a tighter upper bound for the number of
moves required to sort $R_n$ with $LRE$; and (c) the minimum number of moves
required to sort $R_{10}$ and $R_{11}$ have been computed. Here we are
computing an upper bound of the diameter of Cayley graph generated by $LRE$
operation. Cayley graphs are employed in computer interconnection networks to
model efficient parallel architectures. The diameter of the network corresponds
to the maximum delay in the network.


  We construct C-algebras for a class of surfaces that are inverse images of
certain polynomials of arbitrary degree. By using the directed graph associated
to a matrix, the representation theory can be understood in terms of ``loop''
and ``string'' representations, which are closely related to the dynamics of an
iterated map in the plane. As a particular class of algebras we introduce the
``Henon algebras'', for which the dynamical map is a generalized Henon map, and
give an example where irreducible representations of all dimensions exist.


  Unitarity triangles and characteristic measurables of the $3\times 3$
Cabibbo-Kobayashi-Maskawa (CKM) matrix are discussed. Beyond the $3\times 3$
CKM scheme we make a rephasing-invariant generalization of the
Gronau-Wyler-Dunietz approach to determine the weak phase shift in
$B^{\pm}_u\rightarrow D K^{\pm}$ or $B^0_d\rightarrow D K^{*0}$ vs
$\bar{B}^0_d\rightarrow D \bar{K}^{*0}$, which is only sensitive to the
underlying new physics in $D^0-\bar{D}^0$ mixing. We also show that the weak
angle $\gamma\equiv \arg (-V^*_{ub}V^*_{cd}V_{ud}V_{cb})$ is possible to be
determined from the $CP$ asymmetries of some $B_d$ decays, even in a
non-standard model with an extended quark sector. Brief comments are given on
tests of unitarity of the $3\times 3$ CKM matrix.


  A path integral representation appropriate for further Monte Carlo
simulations is derived for the electron-phonon Holstein model in three spatial
dimensions. The model is studied within mean-field theory. Charge density wave
and superconducting phase transitions are discussed.


  Object State Changes (OSCs) are pivotal for video understanding. While humans
can effortlessly generalize OSC understanding from familiar to unknown objects,
current approaches are confined to a closed vocabulary. Addressing this gap, we
introduce a novel open-world formulation for the video OSC problem. The goal is
to temporally localize the three stages of an OSC -- the object's initial
state, its transitioning state, and its end state -- whether or not the object
has been observed during training. Towards this end, we develop VidOSC, a
holistic learning approach that: (1) leverages text and vision-language models
for supervisory signals to obviate manually labeling OSC training data, and (2)
abstracts fine-grained shared state representations from objects to enhance
generalization. Furthermore, we present HowToChange, the first open-world
benchmark for video OSC localization, which offers an order of magnitude
increase in the label space and annotation volume compared to the best existing
benchmark. Experimental results demonstrate the efficacy of our approach, in
both traditional closed-world and open-world scenarios.


  Reward engineering has long been a challenge in Reinforcement Learning (RL)
research, as it often requires extensive human effort and iterative processes
of trial-and-error to design effective reward functions. In this paper, we
propose RL-VLM-F, a method that automatically generates reward functions for
agents to learn new tasks, using only a text description of the task goal and
the agent's visual observations, by leveraging feedbacks from vision language
foundation models (VLMs). The key to our approach is to query these models to
give preferences over pairs of the agent's image observations based on the text
description of the task goal, and then learn a reward function from the
preference labels, rather than directly prompting these models to output a raw
reward score, which can be noisy and inconsistent. We demonstrate that RL-VLM-F
successfully produces effective rewards and policies across various domains -
including classic control, as well as manipulation of rigid, articulated, and
deformable objects - without the need for human supervision, outperforming
prior methods that use large pretrained models for reward generation under the
same assumptions. Videos can be found on our project website:
https://rlvlmf2024.github.io/


  Recently there has been much work on early phase cancer designs that
incorporate both toxicity and efficacy data, called Phase I-II designs because
they combine elements of both phases. However, they do not explicitly address
the Phase II hypothesis test of $H_0: p\le p_0$, where $p$ is the probability
of efficacy at the estimated maximum tolerated dose (MTD) $\widehat{\eta}$ from
Phase I and $p_0$ is the baseline efficacy rate. Standard practice for Phase II
remains to treat $p$ as a fixed, unknown parameter and to use Simon's 2-stage
design with all patients dosed at $\widehat{\eta}$. We propose a Phase I-II
design that addresses the uncertainty in the estimate $p=p(\widehat{\eta})$ in
$H_0$ by using sequential generalized likelihood theory. Combining this with a
Phase I design that incorporates efficacy data, the Phase I-II design provides
a common framework that can be used all the way from the first dose of Phase I
through the final accept/reject decision about $H_0$ at the end of Phase II,
utilizing both toxicity and efficacy data throughout. Efficient group
sequential testing is used in Phase II that allows for early stopping to show
treatment effect or futility. The proposed Phase I-II design thus removes the
artificial barrier between Phase I and Phase II, and fulfills the objectives of
searching for the MTD and testing if the treatment has an acceptable response
rate to enter into a Phase III trial.


  Coherent super-high-frequency (SHF) vibrations provide an excellent tool for
the modulation and control of excitations in semiconductors. Here, we
investigate the piezoelectric generation and propagation of longitudinal bulk
acoustic waves (LBAWs) with frequencies up to 20 GHz in GaAs crystals using
bulk acoustic wave resonators (BAWRs) based on piezoelectric thin ZnO films. We
show that the electro-acoustic conversion efficiency of the BAWRs depends
sensitively on the sputtering conditions of the ZnO films. The BAWRs were then
used for the study of the propagation properties of the LBAWs in GaAs in the
frequency and temperature ranges from 1 to 20 GHz and 10 and 300 K,
respectively, which have so far not been experimentally accessed. We found that
the intrinsic acoustic absorption of GaAs in the temperature range from 80 K to
300 K is dominated by scattering with thermal phonons. At lower temperatures,
in contrast, the intrinsic acoustic absorption saturates at a
frequency-dependent value. Experiments carried out with different propagation
lengths indicate that the saturation is associated with losses during
reflection at the sample boundaries. We also demonstrate devices with high
quality factor fabricated on top of acoustic Bragg-reflectors. The results
presented here prove the feasibility of high-quality acoustic resonators
embedding GaAs-based nanostructures, thus opening the way for the modulation
and control of their properties by electrically excited SHF LBAWs.


  We present a gauge-invariant density matrix description of non-equilibrium
superconductor (SC) states with spatial and temporal correlations driven by
intense terahertz (THz) lightwaves. We derive superconductor Bloch--Maxwell
equations of motion that extend Anderson pseudo-spin models to include the
Cooper pair center-of-mass motion and electromagnetic propagation effects. We
thus describe quantum control of dynamical phases, collective modes,
quasi-particle coherence, and high nonlinearities during cycles of carrier wave
oscillations, which relate to our recent experiments. Coherent photogeneration
of a nonlinear supercurrent with dc component via condensate acceleration by an
effective lightwave field dynamically breaks the equilibrium inversion
symmetry. Experimental signatures include high harmonic light emission at
equilibrium-symmetry-forbidden frequencies, Rabi--Higgs collective modes and
quasi-particle coherence, and non-equilibrium moving condensate states tuned by
few-cycle THz fields. We use such lightwaves as an oscillating accelerating
force that drives strong nonlinearities and anisotropic quasi-particle
populations to control and amplify different classes of collective modes, e.g.,
damped oscillations, persistent oscillations, and overdamped dynamics via Rabi
flopping. Recent phase-coherent nonlinear spectroscopy experiments can be
modeled by solving the full nonlinear quantum dynamics including
self-consistent light--matter coupling.


  A Banach space $X$ is said to be Q.H.I. if eve\-ry infinite dimensional
quo\-tient spa\-ce of $X$ is H.I.: that is, a space is Q.H.I. if the H.I.
property is not only stable passing to subspaces, but also passing to quotients
and to the dual. We show that Gowers-Maurey's space is Q.H.I.; then we provide
an example of a reflexive H.I. space ${\cal X}$ whose dual is not H.I., from
which it follows that $\cal X$ is not Q.H.I.


  Prediction of the action outcome is a new challenge for a robot
collaboratively working with humans. With the impressive progress in video
action recognition in recent years, fine-grained action recognition from video
data turns into a new concern. Fine-grained action recognition detects subtle
differences of actions in more specific granularity and is significant in many
fields such as human-robot interaction, intelligent traffic management, sports
training, health caring. Considering that the different outcomes are closely
connected to the subtle differences in actions, fine-grained action recognition
is a practical method for action outcome prediction. In this paper, we explore
the performance of convolutional gate recurrent unit (ConvGRU) method on a
fine-grained action recognition tasks: predicting outcomes of ball-pitching.
Based on sequences of RGB images of human actions, the proposed approach
achieved the performance of 79.17% accuracy, which exceeds the current
state-of-the-art result. We also compared different network implementations and
showed the influence of different image sampling methods, different fusion
methods and pre-training, etc. Finally, we discussed the advantages and
limitations of ConvGRU in such action outcome prediction and fine-grained
action recognition tasks.


  Proposing a certain category of bialgebroid maps we show that the balanced
depth 2 extensions appear as they were the finitary Galois extensions in the
context of quantum groupoid actions, i.e., actions by finite bialgebroids, weak
bialgebras or weak Hopf algebras. We comment on deformation of weak bialgebras,
on half grouplike elements, on uniqueness of weak Hopf algebra reconstructions
and discuss the example of separable field extensions.


  Some one- and two-parametric deformations of U[sl(2)] and their
representations are considered. Interestingly, a newly introduced
two-parametric deformation admits a class of infinite - dimensional
representations which have no classical (non-deformed) and one-parametric
deformation analogues, even at generic deformation parameters.


  We utilize a complete sample of RR Lyrae stars discovered by the QUEST survey
using light curves to design selection criteria based on SDSS colors. Thanks to
the sensitivity of the u-g color to surface gravity and of g-r color to
effective temperature, and to the small photometric errors (~0.02 mag)
delivered by SDSS, RR Lyrae stars can be efficiently and robustly recognized
even with single-epoch data. In a 100% complete color-selected sample, the
selection efficiency (the fraction of RR Lyrae stars in the candidate sample)
is 6%, and, by adjusting color cuts, it can be increased to 10% with a
completeness of 80%, and to 60% with 28% completeness. Such color selection
produces samples that are sufficiently clean for statistical studies of the
Milky Way's halo substructure, and we utilize it to select 3,643 candidate RR
Lyrae stars from SDSS Data Release 1. We demonstrate that this sample recovers
known clumps of RR Lyrae stars associated with the Sgr dwarf tidal tail, and
Pal 5 globular cluster, and use it to constrain the halo substructure away from
the Sgr dwarf tidal tail. These results suggest that it will be possible to
study the halo substructure out to ~70 kpc from the Galactic Center in the
entire area imaged by the SDSS, and not only in the multiply observed regions.


  We present the simplest theory of perfect tunneling of an excitation in a
Bose-Einstein condensate (BEC) through an impurity potential with an arbitrary
shape in the low-momentum limit. That is for the transverse spin wave in the
ferromagnetic phase of a spin-1 BEC. This mode obeys a Schr\"odinger-type
equation; yet, effects of the potential on its transmission coefficient $T$ and
on its scattering cross section $sigma$ vanish in that limit. The order
parameter determines $T$, and the momentum $p$-dependence of $sigma$ exhibits a
Rayleigh scattering type ($sigma propto p^{4}$). These properties are common
between two types of Nambu-Goldstone modes: this spin wave and the Bogoliubov
mode.


  Spatio-temporal shaping of electron beams is a bold frontier in electron
microscopy, enabling new routes toward spatial-resolution enhancement,
selective probing, low-dose imaging and faster data acquisition. Over the last
decade, shaping methods evolved from passive phase plates to low-speed
electrostatic and magnetostatic displays. Recently, higher shaping speed and
flexibility have become feasible by the advent of ultrafast electron
microscopy, embodying a swift change of paradigm that relies on using light to
control free electrons. Here, we experimentally demonstrate that arbitrary
transverse modulation of electron beams is possible without the need for
designing and fabricating complicated electron-optics elements or material
nanostructures, but rather resorting to shaping light beams reflected from a
planar thin film. We demonstrate arbitrary transverse modulation of electron
wavepackets via inelastic interaction with a shaped ultrafast light field
controlled by an external spatial light modulator (SLM). We illustrate this
method by generating Hermite-Gaussian (HG) electron beams with HG10 and HG01
symmetry and discuss their possible use in enhancing the imaging contrast of
microscopic features. Relative to current schemes, our approach adopting an
external SLM dramatically widens the range of patterns that can be imprinted on
the electron wave function and makes electron shaping a much easier task to
perform.


  The J\"uttner (covariant Boltzmann) distribution is provided for anisotropic
pressure (or temperature) tensors. Its manifestly covariant form follows from
its scalar property.


  In this article we will present pure two-dimensional analytic solutions for
the coupled non-compressible Newtoniain Navier-Stokes
  --- with Boussinesq approximation --- and the heat conduction equation. The
system was investigated from E.N. Lorenz half a century ago with Fourier series
and pioneered the way to the paradigm of chaos. We present a novel analysis of
the same system where the key idea is the two-dimensional generalization of the
well-known self-similar Ansatz of Barenblatt which will be interpreted in a
geometrical way. The results, the pressure, temperature and velocity fields are
all analytic and can be expressed with the help of the error functions. The
temperature field has a strongly damped oscillating behavior which is an
interesting feature.


  The Hurwitz chain gives a sequence of pairs of Farey approximations to an
irrational real number. Minkowski gave a criterion for a number to be algebraic
by using a certain generalization of the Hurwitz chain. We apply Minkowski's
generalization (the Minkowski chain) to give criteria for a real linear form to
be either badly approximable or singular. We also give a variant of Dirichlet's
approximation theorem for a real linear form that produces a whole basis of
approximating integral vectors rather than a single one. This result holds if
and only if the form is badly approximable. The proofs rely on properties of
successive minima and reduced bases of lattices.


  We have computed the hard dilepton production rate from a weakly magnetized
deconfined QCD medium within one-loop photon self-energy by considering one
hard and one thermomagnetic resummed quark propagator in the loop. In the
presence of the magnetic field, the resummed propagator leads to four
quasiparticle modes. The production of hard dileptons consists of rates when
all four quasiquarks originating from the poles of the propagator individually
annihilate with a hard quark coming from a bare propagator in the loop. Besides
these, there are also contributions from a mixture of pole and Landau cut part.
In weak field approximation, the magnetic field appears as a perturbative
correction to the thermal contribution. Since the calculation is very involved,
for a first effort as well as for simplicity, we obtained the rate up to first
order in the magnetic field, i.e., ${\cal O}[(eB)]$, which causes a marginal
improvement over that in the absence of magnetic field.


  During the last twenty-five years evidence has been mounting that a
black-hole surface area has a {\it discrete} spectrum. Moreover, it is widely
believed that area eigenvalues are {\it uniformally} spaced. There is, however,
no general agreement on the {\it spacing} of the levels. In this letter we use
Bohr's correspondence principle to provide this missing link. We conclude that
the area spacing of a black-hole is $4\hbar \ln 3$. This is the unique spacing
consistent both with the area-entropy {\it thermodynamic} relation for black
holes, with Boltzmann-Einstein formula in {\it statistical physics} and with
{\it Bohr's correspondence principle}.


  MeV neutrinos are produced in many astrophysical transients, such as stellar
collapses and high-energy jets, where they play a role in sustaining and
cooling energetic explosions. Detecting these neutrinos from sources outside
the Milky Way is very difficult due to the small neutrino-nucleon cross section
at MeV. Nevertheless, the non-observation of MeV neutrinos from high-energy
transients may provide useful constraints on related neutrino production
mechanisms where significant MeV production is expected. The IceCube Neutrino
Observatory, a cubic kilometer neutrino detector operating with nearly 100%
uptime at the South Pole, is sensitive to bursts of MeV neutrinos from
astrophysical sources in and beyond the Milky Way. In this work, we describe
the MeV neutrino detection system of IceCube and show results from several
categories of astrophysical transients.


  We work out the junction conditions for $f(R)$ gravity formulated in
metric-affine (Palatini) spaces using a tensor distributional approach. These
conditions are needed for building consistent models of gravitating bodies with
an interior and exterior regions matched at some hypersurface. Some of these
conditions depart from the standard Darmois-Israel ones of General Relativity
and from their metric $f(R)$ counterparts. In particular, we find that the
trace of the stress-energy momentum tensor in the bulk must be continuous
across the matching hypersurface, though its normal derivative need not to. We
illustrate the relevance of these conditions by considering the properties of
stellar surfaces in polytropic models, showing that the range of equations of
state with potentially pathological effects is shifted beyond the domain of
physical interest. This confirms, in particular, that neutron stars and white
dwarfs can be safely modelled within the Palatini $f(R)$ framework.


  We consider the problem of separating error messages generated in large
distributed data center networks into error events. In such networks, each
error event leads to a stream of messages generated by hardware and software
components affected by the event. These messages are stored in a giant message
log. We consider the unsupervised learning problem of identifying the
signatures of events that generated these messages; here, the signature of an
error event refers to the mixture of messages generated by the event. One of
the main contributions of the paper is a novel mapping of our problem which
transforms it into a problem of topic discovery in documents. Events in our
problem correspond to topics and messages in our problem correspond to words in
the topic discovery problem. However, there is no direct analog of documents.
Therefore, we use a non-parametric change-point detection algorithm, which has
linear computational complexity in the number of messages, to divide the
message log into smaller subsets called episodes, which serve as the
equivalents of documents. After this mapping has been done, we use a well-known
algorithm for topic discovery, called LDA, to solve our problem. We
theoretically analyze the change-point detection algorithm, and show that it is
consistent and has low sample complexity. We also demonstrate the scalability
of our algorithm on a real data set consisting of $97$ million messages
collected over a period of $15$ days, from a distributed data center network
which supports the operations of a large wireless service provider.


  We conduct kinematic analysis of the SDSS spectra of 568 obscured luminous
quasars, with the emphasis on the kinematic structure of the [OIII]5007
emission line. [OIII] emission tends to show blueshifts and blue excess, which
indicates that at least part of the narrow-line gas is undergoing an organized
outflow. The velocity width containing 90% of line power ranges from 370 to
4780 km/sec, suggesting outflow velocities up to 2000 km/sec. The velocity
width of the [OIII] emission is positively correlated with the radio luminosity
among the radio-quiet quasars. We propose that radio emission in radio-quiet
quasars is due to relativistic particles accelerated in the shocks within the
quasar-driven outflows; star formation in quasar hosts is insufficient to
explain the observed radio emission. The median radio luminosity of the sample
of nu L_nu[1.4GHz] = 10^40 erg/sec suggests a median kinetic luminosity of the
quasar-driven wind of L_wind=3x10^44 erg/sec, or about 4% of the estimated
median bolometric luminosity L_bol=8x10^45 erg/sec. Furthermore, the velocity
width of [OIII] is positively correlated with mid-infrared luminosity, which
suggests that outflows are ultimately driven by the radiative output of the
quasar. As the outflow velocity increases, some emission lines characteristic
of shocks in quasi-neutral medium increase as well, which we take as further
evidence of quasar-driven winds propagating into the interstellar medium of the
host galaxy. None of the kinematic components show correlations with the
stellar velocity dispersions of the host galaxies, so there is no evidence that
any of the gas in the narrow-line region of quasars is in dynamical equilibrium
with the host galaxy. Quasar feedback appears to operate above the threshold
luminosity of L_bol=3x10^45 erg/sec.


  The recent literature has discussed the use of the relaxed Second Order Cone
Programming (SOCP) to formulate Optimal Power Flow problems (OPF) for radial
power grids. However, if the shunt parameters of the lines, composing the power
grid, are considered the proposed methods do not provide sufficient conditions
that can be verified ex ante for the exactness of the optimal solutions.
Additionally, the same formulations have not correctly accounted for the
ampacity constraint of the lines. Similar to the inclusion of upper
voltage-magnitude limit, the SOCP relaxation faces difficulties when the
ampacity constraints of the lines are binding. In order to overcome these
limitations, we propose a convex formulation of the OPF problem applied to
radial power grids for which the AC-OPF equations, including the transverse
parameters, are considered. We augment the formulation with a new set of more
conservative constraints to limit the lines current together with the nodal
voltage-magnitudes. Sufficient conditions are provided to ensure the
feasibility and optimality of the proposed OPF solution. Furthermore, the
proofs of the exactness of the SOCP relaxation are provided. Using standard
power grids, we show that these conditions are mild and hold for real
distribution networks.


  Risk-based authentication (RBA) aims to protect users against attacks
involving stolen passwords. RBA monitors features during login, and requests
re-authentication when feature values widely differ from previously observed
ones. It is recommended by various national security organizations, and users
perceive it more usable and equally secure than equivalent two-factor
authentication. Despite that, RBA is still only used by very few online
services. Reasons for this include a lack of validated open resources on RBA
properties, implementation, and configuration. This effectively hinders the RBA
research, development, and adoption progress.
  To close this gap, we provide the first long-term RBA analysis on a
real-world large-scale online service. We collected feature data of 3.3 million
users and 31.3 million login attempts over more than one year. Based on the
data, we provide (i) studies on RBA's real-world characteristics, and its
configurations and enhancements to balance usability, security, and privacy,
(ii) a machine learning based RBA parameter optimization method to support
administrators finding an optimal configuration for their own use case
scenario, (iii) an evaluation of the round-trip time feature's potential to
replace the IP address for enhanced user privacy, and (iv) a synthesized RBA
data set to reproduce this research and to foster future RBA research. Our
results provide insights on selecting an optimized RBA configuration so that
users profit from RBA after just a few logins. The open data set enables
researchers to study, test, and improve RBA for widespread deployment in the
wild.


  We prove that the low energy parts of the wave operators $W_\pm$ for
Schr\"odinger operators $H = -\lap + V(x)$ on $\R^4$ are bounded in $
L^p(\R^4)$ for $1<p\leq 2$ and are unbounded for $2<p\leq \infty$ if $H$ has
resonances at the threshold. If $H$ has eigenfunctions only at the threshold,
it has recently been proved that they are bounded in $L^p(\R^4)$ for $1\leq
p<4$ in general and for $1\leq p<\infty$ if all threshold eigenfunctions $\ph$
satisfy $\int_{\R^4}x_j V(x) \ph(x)dx=0$ for $1\leq j\leq 4$. We prove in this
case that they are unbounded in $L^p(\R^4)$ for $4<p<\infty$ unless the latter
condition is satisfied. It is long known that the high energy parts are bounded
in $L^p(\R^4)$ for all $1\leq p\leq \infty$ and that the same holds for $W_\pm$
if $H$ has no eigenfunctions nor resonances at the threshold.


  In this letter, we investigate the energy efficiency (EE) problem in a
millimeter wave (mmWave) massive MIMO (mMIMO) system with non-orthogonal
multiple access (NOMA). Multiple two-user clusters are formulated according to
their channel correlation and gain difference. Following this, we propose a
hybrid analog/digital precoding scheme for the low radio frequency (RF) chains
structure at the base station (BS). On this basis, we formulate a power
allocation problem aiming to maximize the EE under users' quality of service
(QoS) requirements and per-cluster power constraint. An iterative algorithm is
proposed to obtain the optimal power allocation. Simulation results show that
the proposed NOMA achieves superior EE performance than that of conventional
OMA.


  We have reformed a large lecture modern physics course for engineering majors
by radically changing both the content and the learning techniques implemented
in lecture and homework. Traditionally this course has been taught in a manner
similar to the equivalent course for physics majors, focusing on mathematical
solutions of abstract problems. Based on interviews with physics and
engineering professors, we developed a syllabus and learning goals focused on
content that was more useful to our actual student population: engineering
majors. The content of this course emphasized reasoning development, model
building, and connections to real world applications. In addition we
implemented a variety of PER-based learning techniques, including peer
instruction, collaborative homework sessions, and interactive simulations. We
have assessed the effectiveness of reforms in this course using pre/post
surveys on both content and beliefs. We have found significant improvements in
both content knowledge and beliefs compared with the same course before
implementing these reforms and a corresponding course for physics majors.


  Team Semantics is a generalization of Tarskian Semantics that can be used to
add to First Order Logic atoms and connectives expressing dependencies between
the possible values of variables. Some of these extensions are more expressive
than First Order Logic, while others are reducible to it. In this work, I fully
characterize the (relativizable) atoms and families of atoms that do not
increase the expressive power of First Order Logic with Team Semantics when
they and their negations are added to it, separately or jointly (or,
equivalently, when they are added to First Order Logic augmented with a
contradictory negation connective applicable only to literals and dependency
atoms).


  For all positive even integers $n$, graphs of order $n$ with degree sequence
\begin{equation*} S_{n}:1,2,\dots,n/2,n/2,n/2+1,n/2+2,\dots,n-1 \end{equation*}
naturally arose in the study of a labeling problem in \cite{IMO}. This fact
motivated the authors of the aforementioned paper to study these sequences and
as a result of this study they proved that there is a unique graph of order $n$
realizing $S_{n}$ for every even integer $n$. The main goal of this paper is to
generalize this result.


  Convolutional Neural Networks (CNNs) have made significant progress on
several computer vision benchmarks, but are fraught with numerous non-human
biases such as vulnerability to adversarial samples. Their lack of
explainability makes identification and rectification of these biases
difficult, and understanding their generalization behavior remains an open
problem. In this work we explore the relationship between the generalization
behavior of CNNs and the Singular Value Decomposition (SVD) of images. We show
that naturally trained and adversarially robust CNNs exploit highly different
features for the same dataset. We demonstrate that these features can be
disentangled by SVD for ImageNet and CIFAR-10 trained networks. Finally, we
propose Rank Integrated Gradients (RIG), the first rank-based feature
attribution method to understand the dependence of CNNs on image rank.


  The Blaschke-Lebesgue Theorem states that among all planar convex domains of
given constant width B the Reuleaux triangle has minimal area. It is the
purpose of the present note to give a direct proof of this theorem by analyzing
the underlying variational problem. The advantages of the proof are that it
shows uniqueness (modulo rigid deformations such as rotation and translation)
and leads analytically to the shape of the area-minimizing domain. Most
previous proofs have relied on foreknowledge of the minimizing domain. Key
parts of the analysis extend to the higher-dimensional situation, where the
convex body of given constant width and minimal volume is unknown.


  In this paper we consider some rational approximations to the fractional
powers of self-adjoint positive operators, arising from the Gauss-Laguerre
rules. We derive practical error estimates that can be used to select a priori
the number of Laguerre points necessary to achieve a given accuracy. We also
present some numerical experiments to show the effectiveness of our approaches
and the reliability of the estimates.


  In this paper, we newly formulate and solve the optimal density control
problem with Gromov-Wasserstein (GW) terminal cost in discrete-time linear
Gaussian systems. Differently from the Wasserstein or Kullback-Leibler
distances employed in the existing works, the GW distance quantifies the
difference in shapes of the distribution, which is invariant under translation
and rotation. Consequently, our formulation allows us to find small energy
inputs that achieve the desired shape of the terminal distribution, which has
practical applications, e.g., robotic swarms. We demonstrate that the problem
can be reduced to a Difference of Convex (DC) programming, which is efficiently
solvable through the DC algorithm. Through numerical experiments, we confirm
that the state distribution reaches the terminal distribution that can be
realized with the minimum control energy among those having the specified
shape.


  Radio and X-ray emission of AGN appears to be correlated. The details of the
underlying physical processes, however, are still not fully understood, i.e.,
to what extent is the X-ray and radio emission originating from the same
relativistic particles or from the accretion-disk or corona or both. We study
the cm radio emission of an SDSS/ROSAT/FIRST matched sample of 13 X-raying AGN
in the redshift range 0.11< z < 0.37 at high angular resolution with the goal
of searching for jet structures or diffuse, extended emission on sub-kpc
scales. We use MERLIN at 18 cm for all objects and Western EVN at 18 cm for
four objects to study the radio emission on scales of ~500 pc and ~40 pc for
the MERLIN and EVN observations, respectively. The detected emission is
dominated by compact nuclear radio structures. We find no kpc collimated jet
structures. The EVN data indicate for compact nuclei on 40 pc scales, with
brightness temperatures typical for accretion-disk scenarios. Comparison with
FIRST shows that the 18 cm emission is resolved out up to 50% by MERLIN.
Star-formation rates based on large aperture SDSS spectra are generally too
small to produce considerable contamination of the nuclear radio emission. We
can, therefore, assume the 18 cm flux densities to be produced in the nuclei of
the AGN. Together with the ROSAT soft X-ray luminosities and black hole mass
estimates from the literature, our sample objects follow closely the Merloni et
al. (2003) fundamental plane relation, which appears to trace the accretion
processes. Detailed X-ray spectral modeling from deeper hard X-ray observations
and higher angular resolution at radio wavelengths are required to further
proceed in the disentangling of jet and accretion related processes.


  In this paper, the non-linear modified epidemiological model of computer
viruses is illustrated. For this aim, two semi-analytical methods, the
differential transform method (DTM) and the Laplace-Adomian decomposition
method (LADM) are applied. The numerical results are estimated for different
values of iterations and compared to the results of the LADM and the homotopy
analysis transform method (HATM). Also, graphs of residual errors and phase
portraits of approximate solutions for $n=5,10,15$ are demonstrated. The
numerical approximations show the performance of the LADM in comparison to the
LADM and the HATM.


  It has recently been proposed [M. P. Hertzberg and A. Loeb, arXiv:2302.09090]
that the observed value of the cosmological constant can be related to the
physical parameters of the Standard Model. In the present compact note we point
out that the derivation of the claimed cosmological-constant-standard-model
relation presented in \cite{HL} is, unfortunately, erroneous.


  We present an exhaustive, quantitative comparison of all of the known
extinction curves in the Small and Large Magellanic Clouds (SMC and LMC) with
our understanding of the general behavior of Milky Way extinction curves. The
R_V dependent CCM relationship and the sample of extinction curves used to
derive this relationship is used to describe the general behavior of Milky Way
extinction curves. The ultraviolet portion of the SMC and LMC extinction curves
are derived from archival IUE data, except for one new SMC extinction curve
which was measured using HST/STIS observations. The optical extinction curves
are derived from new (for the SMC) and literature UBVRI photometry (for the
LMC). The near-infrared extinction curves are calculated mainly from 2MASS
photometry supplemented with DENIS and new JHK photometry. For each extinction
curve, we give R_V = A(V)/E(B-V) and N(HI) values which probe the same dust
column as the extinction curve. We compare the properties of the SMC and LMC
extinction curves with the CCM relationship three different ways: each curve by
itself, the behavior of extinction at different wavelengths with R_V, and
behavior of the extinction curve FM fit parameters with R_V. As has been found
previously, we find that a small number of LMC extinction curves are consistent
with the CCM relationship, but majority of the LMC and all of the SMC curves do
not follow the CCM relationship. For the first time, we find that the CCM
relationship seems to form a bound on the properties of all of the LMC and SMC
extinction curves. This result strengthens the picture of dust extinction
curves exhibit a continuum of properties between those found in the Milky Way
and the SMC Bar. (abridged)


  The discovery of effective hydrogen storage materials is fundamental for the
progress of a clean energy economy. Ammonia borane ($\mathrm{H_3BNH_3}$) has
attracted great interest as a promising candidate but the reaction path that
leads from its solid phase to hydrogen release is not yet fully understood. To
address the need for insights in the atomistic details of such a complex solid
state process, in this work we use \textit{ab-initio} molecular dynamics and
metadynamics to study the early stages of AB dehydrogenation. We show that the
formation of ammonia diborane ($\mathrm{H_3NBH_2(}$$\mu$$\mathrm{-H)BH_3}$)
leads to the release of $\mathrm{NH_4^+}$, which in turn triggers an
autocatalytic $\mathrm{H_2}$ production cycle. Our calculations provide a model
for how complex solid state reactions can be theoretically investigated and
rely upon the presence of multiple ammonia borane molecules, as substantiated
by standard quantum-mechanical simulations on a cluster.


  The random laser emission from the functionalized thienyl-S,S-dioxide
quinquethiophene (T5OCx) in confined patterns with different shapes is
demonstrated. Functional patterning of the light emitter organic material in
well defined features is obtained by spontaneous molecular self-assembly guided
by surface tension driven (STD) lithography. Such controlled supramolecular
nano-aggregates act as scattering centers allowing the fabrication of
one-component organic lasers with no external resonator and with desired shape
and efficiency. Atomic force microscopy shows that different geometric pattern
with different supramolecular organization obtained by the lithographic process
tailors the coherent emission properties by controlling the distribution and
the size of the random scatterers.


  Ridepooling services play an increasingly important role in modern
transportation systems. With soaring demand and growing fleet sizes, the
underlying route planning problems become increasingly challenging. In this
context, we consider the dial-a-ride problem (DARP): Given a set of
transportation requests with pick-up and delivery locations, passenger numbers,
time windows, and maximum ride times, an optimal routing for a fleet of
vehicles, including an optimized passenger assignment, needs to be determined.
We present tight mixed-integer linear programming (MILP) formulations for the
DARP by combining two state-of-the-art models into novel
location-augmented-event-based formulations. Strong valid inequalities and
lower and upper bounding techniques are derived to further improve the
formulations. We then demonstrate the theoretical and computational superiority
of the new model: First, the formulation is tight in the sense that, if time
windows shrink to a single point in time, the linear programming relaxation
yields integer (and hence optimal) solutions. Second, extensive numerical
experiments on benchmark instances show that computational times are on average
reduced by 49.7% compared to state-of-the-art event-based approaches.


  We present an analysis of the data on aging in the three-dimensional Edwards
Anderson spin glass model with nearest neighbor interactions, which is well
suited for the comparison with a recently developed dynamical mean field
theory. We measure the parameter $x(q)$ describing the violation of the
relation among correlation and response function implied by the fluctuation
dissipation theorem.


  Background and Purpose: This study was designed to compare the effects of
high-intensity interval training (HIT) and moderate-intensity continuous
training (MOD) on functional recovery and cerebral plasticity during the first
2 weeks following cerebral ischemia. Methods: Rats were randomized as follows:
Control (n=15), SHAM (n=9), MCAO (n=13), MCAO-D1 (n=7), MOD (n=13) and HIT
(n=13). Incremental tests were performed at day 1 (D1) and 14 (D14) to identify
the running speed associated with the lactate threshold (SLT) and the maximal
speed (Smax). Functional tests were performed at D1, D7 and D14. Microglia
form, cytokines, p75NTR, KCC2 and NKCC1 expression were made at D15.
Results-HIT was more effective to improve the endurance performance than MOD
and induced a fast recovery of the impaired forelimb grip force. The Iba-1
positive cells with amoeboid form and the pro- and anti-inflammatory cytokine
expression were lower in HIT group, mainly in the ipsilesional hemisphere. A
p75NTR overexpression is observed on the ipsilesional side together with a
restored NKCC1/KCC2 ratio on the contralesional side. Conclusions-Low-volume
HIT based on lactate threshold appears to be more effective after cerebral
ischemia than work-matched MOD to improve aerobic fitness, grip strength and
might promote cerebral plasticity.


  A mathematical model of a drug transport after rapid injection is given. It
takes into account three processes: - drug plasma protein binding in central
compartment - transport processes between the central compartment and the
peripheral compartment - elimination of a drug from the central compartment. .


  We study perfect matchings on the square-hexagon lattice with $1\times n$
periodic edge weights such that the boundary condition is given by either (1)
each remaining vertex on the bottom boundary is followed by $(m-1)$ removed
vertices; (2) the bottom boundary can be divided into finitely many alternating
line segments where all the vertices along each line segment are either removed
or remained. In Case (1), we show that under certain homeomorphism from the
liquid region to the upper half plane, the height fluctuations converge to the
Gaussian free field in the upper half plane. In Case (2), when the edge weights
$x_1,\ldots,x_n$ in one period satisfy the condition that
$x_{i+1}=O\left(\frac{x_i}{N^{\alpha}}\right)$, where $\alpha>0$ is a constant
independent of $N$, we show that the height fluctuations converge to an
independent Gaussian free field in each connected component of the liquid
region.


  S. K. Donaldson asked whether the lower bound of the Calabi functional is
achieved by a sequence the normalized Donaldson-Futaki invariants. We answer
the question for the Ricci curvature formalism, in place of the scalar
curvature. The principle is that the stability indicator is optimized by the
multiplier ideal sheaves of certain weak geodesic ray asymptotic to the
geometric flow. We actually prove it in the two case: the inverse Monge-Ampere
flow and the Kahler-Ricci flow.


  Taking advantage of both vehicle-to-everything (V2X) communication and
automated driving technology, connected and automated vehicles are quickly
becoming one of the transformative solutions to many transportation problems.
However, in a mixed traffic environment at signalized intersections, it is
still a challenging task to improve overall throughput and energy efficiency
considering the complexity and uncertainty in the traffic system. In this
study, we proposed a hybrid reinforcement learning (HRL) framework which
combines the rule-based strategy and the deep reinforcement learning (deep RL)
to support connected eco-driving at signalized intersections in mixed traffic.
Vision-perceptive methods are integrated with vehicle-to-infrastructure (V2I)
communications to achieve higher mobility and energy efficiency in mixed
connected traffic. The HRL framework has three components: a rule-based driving
manager that operates the collaboration between the rule-based policies and the
RL policy; a multi-stream neural network that extracts the hidden features of
vision and V2I information; and a deep RL-based policy network that generate
both longitudinal and lateral eco-driving actions. In order to evaluate our
approach, we developed a Unity-based simulator and designed a mixed-traffic
intersection scenario. Moreover, several baselines were implemented to compare
with our new design, and numerical experiments were conducted to test the
performance of the HRL model. The experiments show that our HRL method can
reduce energy consumption by 12.70% and save 11.75% travel time when compared
with a state-of-the-art model-based Eco-Driving approach.


  We study the geometry of equivariant, proper maps from homogeneous bundles
$G\times_P V$ over flag varieties $G/P$ to representations of $G$, called
collapsing maps. Kempf showed that, provided the bundle is completely
reducible, the image $G\cdot V$ of a collapsing map has rational singularities
in characteristic zero. We extend this result to positive characteristic and
show that for the analogous bundles the saturation $G\cdot V$ is strongly
$F$-regular if its coordinate ring has a good filtration. We further show that
in this case the images of collapsing maps of homogeneous bundles restricted to
Schubert varieties are $F$-rational in positive characteristic, and have
rational singularities in characteristic zero. We provide results on the
singularities and defining equations of saturations $G\cdot X$ for $P$-stable
closed subvarieties $X\subset V$. We give criteria for the existence of good
filtrations for the coordinate ring of $G\cdot X$. Our results give a uniform,
characteristic-free approach for the study of the geometry of a number of
important varieties: multicones over Schubert varieties, determinantal
varieties in the space of matrices, symmetric matrices, skew-symmetric
matrices, and certain matrix Schubert varieties therein, representation
varieties of radical square zero algebras (e.g. varieties of complexes),
subspace varieties, higher rank varieties, etc.


  We investigate the stability conditions for remote state estimation of
multiple linear time-invariant (LTI) systems over multiple wireless
time-varying communication channels. We answer the following open problem: what
is the fundamental requirement on the multi-sensor-multi-channel system to
guarantee the existence of a sensor scheduling policy that can stabilize the
remote estimation system? We propose a novel policy construction and analytical
framework and derive the necessary-and-sufficient stability condition in terms
of the LTI system parameters and the channel statistics.


  Continuing advancements in quantum information processing have caused a
paradigm shift from research mainly focused on testing the reality of quantum
mechanics to engineering qubit devices with numbers required for practical
quantum computation. One of the major challenges in scaling toward large-scale
solid-state systems is the limited input/output (I/O) connectors present in
cryostats operating at sub-kelvin temperatures required to execute quantum
logic with high-fidelity. This interconnect bottleneck is equally present in
the device fabrication-measurement cycle, which requires high-throughput and
cryogenic characterization to develop quantum processors. Here we multiplex
quantum transport of two-dimensional electron gases at sub-kelvin temperatures.
We use commercial off-the-shelf CMOS multiplexers to achieve an order of
magnitude increase in the number of wires. Exploiting this technology we
advance 300 mm epitaxial wafers manufactured in an industrial CMOS fab to a
record electron mobility of (3.9$\pm$0.6)$\times$10$^5$ cm$^2$\slash Vs and
percolation density of (6.9$\pm$0.4)$\times$10$^{10}$ cm$^{-2}$, representing a
key step toward large silicon qubit arrays. We envision that the demonstration
will inspire the development of cryogenic electronics for quantum information
and because of the simplicity of assembly, low-cost, yet versatility, we
foresee widespread use of similar cryo-CMOS circuits for high-throughput
quantum measurements and control of quantum engineered systems.


  We describe an example of an exact, quantitative Jeopardy-type quantum
mechanics problem. This problem type is based on the conditions in
one-dimensional quantum systems that allow an energy eigenstate for the
infinite square well to have zero curvature and zero energy when suitable Dirac
delta functions are added. This condition and its solution are not often
discussed in quantum mechanics texts and have interesting pedagogical
consequences.


  Real photon-photon scattering is a long-predicted phenomenon that is being
searched for in experiment in the form of a birefringent vacuum at optical and
X-ray frequencies. We present results of calculations and numerical simulations
for a scenario to measure this effect using multi-MeV photons generated in the
collision of electrons with a laser pulse. We find that the birefringence of
the vacuum should be measurable using experimental parameters attainable in the
near future.