  We investigate the challenge of parametrizing policies for reinforcement
learning (RL) in high-dimensional continuous action spaces. Our objective is to
develop a multimodal policy that overcomes limitations inherent in the
commonly-used Gaussian parameterization. To achieve this, we propose a
principled framework that models the continuous RL policy as a generative model
of optimal trajectories. By conditioning the policy on a latent variable, we
derive a novel variational bound as the optimization objective, which promotes
exploration of the environment. We then present a practical model-based RL
method, called Reparameterized Policy Gradient (RPG), which leverages the
multimodal policy parameterization and learned world model to achieve strong
exploration capabilities and high data efficiency. Empirical results
demonstrate that our method can help agents evade local optima in tasks with
dense rewards and solve challenging sparse-reward environments by incorporating
an object-centric intrinsic reward. Our method consistently outperforms
previous approaches across a range of tasks. Code and supplementary materials
are available on the project page https://haosulab.github.io/RPG/


  In a model with four neutrino mass eigenstates grouping in two narrow
doublets the condition of maximal mixing of the doublet neutrino components
leads to a simple four-neutrino mixing matrix which is related to two connected
effective two-neutrino mixing patterns. As a result, large values of the
neutrino oscillation amplitudes for both the solar and atmospheric neutrino
anomalies appear quite naturally if the LSND data are accepted.


  The fluctuation dissipation theorem (FDT) is the basis for a microscopic
description of the interaction between electromagnetic radiation and matter.By
assuming the electromagnetic radiation in thermal equilibrium and the
interaction in the linear response regime, the theorem interrelates the
spontaneous fluctuations of microscopic variables with the kinetic coefficients
that are responsible for energy dissipation.In the quantum form provided by
Callen and Welton in their pioneer paper of 1951 for the case of conductors,
electrical noise detected at the terminals of a conductor was given in terms of
the spectral density of voltage fluctuations, $S_V({\omega})$, and was related
to the real part of its impedance, $Re[Z({\omega})]$, by a simple relation.The
drawbacks of this relation concern with: (I) the appearance of a zero point
contribution which implies a divergence of the spectrum at increasing
frequencies; (ii) the lack of detailing the appropriate equivalent-circuit of
the impedance, (iii) the neglect of the Casimir effect associated with the
quantum interaction between zero-point energy and boundaries of the considered
physical system; (iv) the lack of identification of the microscopic noise
sources beyond the temperature model. These drawbacks do not allow to validate
the relation with experiments. By revisiting the FDT within a brief historical
survey, we shed new light on the existing drawbacks by providing further
properties of the theorem, focusing on the electrical noise of a two-terminal
sample under equilibrium conditions. Accordingly, we will discuss the duality
and reciprocity properties of the theorem, its applications to the ballistic
transport regime, to the case of vacuum and to the case of a photon gas.


  Privacy-preserving estimation of counts of items in streaming data finds
applications in several real-world scenarios including word auto-correction and
traffic management applications. Recent works of RAPPOR and Apple's count-mean
sketch (CMS) algorithm propose privacy preserving mechanisms for count
estimation in large volumes of data using probabilistic data structures like
counting Bloom filter and CMS. However, these existing methods fall short in
providing a sound solution for real-time streaming data applications. In this
work, we propose a novel (local) Differentially private mechanism that provides
high utility for the streaming data count estimation problem with similar or
even lower privacy budgets while providing: a) fuzzy counting to report counts
of related or similar items (for instance to account for typing errors and data
variations), and b) improved querying efficiency to reduce the response time
for real-time querying of counts. We provide formal proofs for privacy and
utility guarantees and present extensive experimental evaluation of our
algorithm using real and synthetic English words datasets for both the exact
and fuzzy counting scenarios. Our privacy preserving mechanism substantially
outperforms the prior work in terms of lower querying time, significantly
higher utility (accuracy of count estimation) under similar or lower privacy
guarantees, at the cost of communication overhead.


  Recent studies have shown how the distribution of $^{56}$Ni within the ejecta
of type Ia supernovae can have profound consequences on the observed light
curves. Observations at early times can therefore provide important details on
the explosion physics in thermonuclear supernovae. We present a series of
radiative transfer calculations that explore variations in the $^{56}$Ni
distribution. Our models also show the importance of the density profile in
shaping the light curve, which is often neglected in the literature. Using our
model set, we investigate the observations that are necessary to determine the
$^{56}$Ni distribution as robustly as possible within the current model set. We
find that this includes observations beginning at least $\sim$14 days before
$B$-band maximum, extending to approximately maximum light with a high
($\lesssim$3 day) cadence, and in at least one blue and one red band are
required (such as $B$ and $R$, or $g$ and $r$). We compare a number of
well-observed type Ia supernovae that meet these criteria to our models and
find that the light curves of $\sim$70-80\% of objects in our sample are
consistent with being produced solely by variations in the $^{56}$Ni
distributions. The remaining supernovae show an excess of flux at early times,
indicating missing physics that is not accounted for within our model set, such
as an interaction or the presence of short-lived radioactive isotopes.
Comparing our model light curves and spectra to observations and delayed
detonation models demonstrates that while a somewhat extended $^{56}$Ni
distribution is necessary to reproduce the observed light curve shape, this
does not negatively affect the spectra at maximum light. Investigating current
explosion models shows that observations typically require a shallower decrease
in the $^{56}$Ni mass towards the outer ejecta than is produced for models of a
given $^{56}$Ni mass.


  Cheap micro-controllers, such as the Arduino or other controllers based on
the Atmel AVR CPUs are being deployed in a wide variety of projects, ranging
from sensors networks to robotic submarines. In this paper, we investigate the
feasibility of using the Arduino as a true random number generator (TRNG). The
Arduino Reference Manual recommends using it to seed a pseudo random number
generator (PRNG) due to its ability to read random atmospheric noise from its
analog pins. This is an enticing application since true bits of entropy are
hard to come by. Unfortunately, we show by statistical methods that the
atmospheric noise of an Arduino is largely predictable in a variety of
settings, and is thus a weak source of entropy. We explore various methods to
extract true randomness from the micro-controller and conclude that it should
not be used to produce randomness from its analog pins.


  We consider the quantum Lobachevsky space ${\bf L}_q^3$, which is defined as
subalgebra of the Hopf algebra ${\cal A}_q(SL_2({\bf C}))$. The Iwasawa
decomposition of ${\cal A}_q(SL_2({\bf C}))$ introduced by Podles and
Woronowicz allows to consider the quantum analog of the horospheric coordinates
on ${\bf L}_q^3$. The action of the Casimir element, which belongs to the dual
to ${\cal A}_q$ quantum group $U_q(SL_2({\bf C}))$, on some subspace in ${\bf
L}_q^3$ in these coordinates leads to a second order difference operator on the
infinite one-dimensional lattice. In the continuos limit $q\rightarrow 1$ it is
transformed into the Schr\"{o}dinger Hamiltonian, which describes zero modes
into the Liouville field theory (the Liouville quantum mechanics). We calculate
the spectrum (Brillouin zones) and the eigenfunctions of this operator. They
are $q$-continuos Hermit polynomials, which are particular case of the
Macdonald or Rogers-Askey-Ismail polynomials. The scattering in this problem
corresponds to the scattering of first two level dressed excitations in the
$Z_N$ Baxter model in the very peculiar limit when the anisotropy parameter
$\ga$ and $N~\rightarrow\infty$, or, equivalently, $(\ga, N)\rightarrow 0$.


  We report on a micromagnetic numerical simulation study of dynamic couplings
between neighboring skyrmions in narrow-width nanostrips. We explored the
coupled gyration modes and their characteristic dispersions in terms of the
interdistance between the neighboring skyrmions. The application of
perpendicular magnetic fields, importantly, allows for control/modification of
the dispersion of the coupled modes. Coupled skyrmion gyration modes provide a
new type of efficient, reliable, fast, low-power-consumption information-signal
propagation in narrow-width straight and curved nanostrips, as driven
predominantly by the exchange interaction between individual nano-scale
skyrmions.


  There are several inequalities in the literature carrying the name of Ky Fan.
We survey these well-known Ky Fan inequalities and some other significant
inequalities generalized by Ky Fan and review some of their recent
developments.


  Standard Model predictions for the reactions with six fermions in the final
state relevant for top quark pair production and decay at linear colliders are
discussed. An issue of the double resonance signal versus non doubly resonant
background is addresed. Effects related to the off-mass-shell production of the
t\bar{t}-pair are discussed.


  The Chinese stock market experienced an abrupt crash in 2015, and over
one-third of its market value evaporated. Given its associations with fear and
the fine resolution with respect to frequency, the illiquidity of stocks may
offer a promising perspective for understanding and even signaling a market
crash. In this study, by connecting stocks with illiquidity comovements, an
illiquidity network is established to model the market. Compared to noncrash
days, on crash days, the market is more densely connected due to heavier but
more homogeneous illiquidity dependencies that facilitate abrupt collapses.
Critical stocks in the illiquidity network, particularly those in the finance
sector, are targeted for inspection because of their crucial roles in
accumulating and passing on illiquidity losses. The cascading failures of
stocks in market crashes are profiled as disseminating from small degrees to
high degrees that are usually located in the core of the illiquidity network
and then back to the periphery. By counting the days with random failures in
the previous five days, an early signal is implemented to successfully predict
more than half of the crash days, especially consecutive days in the early
phase. Additional evidence from both the Granger causality network and the
random network further testifies to the robustness of the signal. Our results
could help market practitioners such as regulators detect and prevent the risk
of crashes in advance.


  We study smooth integral curves of bidegree $(1,1)$, called \textit{smooth
conics}, in the flag threefold $\mathbb{F}$. The study is motivated by the fact
that the family of smooth conics contains the set of fibers of the twistor
projection $\mathbb{F}\to\mathbb{CP}^{2}$. We give a bound on the maximum
number of smooth conics contained in a smooth surface $S\subset\mathbb{F}$.
Then, we show qualitative properties of algebraic surfaces containing a
prescribed number of smooth conics. Lastly, we study surfaces containing
infinitely many twistor fibers. We show that the only smooth cases are surfaces
of bidegree $(1,1)$. Then, for any integer $a>1$, we exhibit a method to
construct an integral surface of bidegree $(a,a)$ containing infinitely many
twistor fibers.


  Let $\frak{p}$ be a prime ideal of $\mathbb{F}_q[T]$. Let $J_0(\frak{p})$ be
the Jacobian variety of the Drinfeld modular curve $X_0(\frak{p})$. Let $\Phi$
be the component group of $J_0(\frak{p})$ at the place $1/T$. We use graph
Laplacians to estimate the order of $\Phi$ as $\mathrm{deg}(\frak{p})$ goes to
infinity. This estimate implies that $\Phi$ is not annihilated by the
Eisenstein ideal of the Hecke algebra $\mathbb{T}(\frak{p})$ acting on
$J_0(\frak{p})$ once the degree of $\frak{p}$ is large enough. We also obtain
an asymptotic formula for the size of the discriminant of
$\mathbb{T}(\frak{p})$ by relating this discriminant to the order of $\Phi$; in
this problem the order of $\Phi$ plays a role similar to the Faltings height of
classical modular Jacobians. Finally, we bound the spectrum of the adjacency
operator of a finite subgraph of an infinite diagram in terms of the spectrum
of the adjacency operator of the diagram itself; this result has applications
to the gonality of Drinfeld modular curves.


  We consider the continuity equation $\partial_t \mu_t +
\mathop{\mathrm{div}}(b \mu_t) = 0$, where $\{\mu_t\}_{t \in \mathbb R}$ is a
measurable family of (possibily signed) Borel measures on $\mathbb R^d$ and $b
\colon \mathbb R \times \mathbb R^d \to \mathbb R^d$ is a bounded Borel vector
field (and the equation is understood in the sense of distributions). If the
measure-valued solution $\mu_t$ is non-negative, then the following
\emph{superposition principle} holds: $\mu_t$ can be decomposed into a
superposition of measures concentrated along the integral curves of $b$. For
smooth $b$ this result follows from the method of characteristics, and in the
general case it was established by L. Ambrosio. A partial extension of this
result for signed measure-valued solutions $\mu_t$ was obtained in \cite{AB},
where the following problem was proposed: does the superposition principle hold
for signed measure-valued solutions in presence of unique flow of
homeomorphisms solving the associated ordinary differential equation? We answer
to this question in the negative, presenting two counterexamples in which
uniqueness of the flow of the vector field holds but one can construct
non-trivial signed measure-valued solutions to the continuity equation with
zero initial data.


  Performance period determination and bad definition for credit scorecard has
been a mix of fortune for the typical data modeler. The lack of literature on
these matters led to a proliferation of approaches and techniques to solve the
problems. However, the most commonly accepted approach involves subjective
interpretations of the performance period and bad definition as well as being
chicken and egg problem. These complications result in poorly developed credit
scorecard with minimal benefits to the banks. In this paper, we will be
recommending a simple and effective approach to resolve these issues.


  We combine Very-Long-Baseline Interferometry (VLBI) data for $\sim100$ active
galactic nuclei (AGN) available from the Very Large Baseline Array (VLBA) 2 cm
imaging survey and optical spectroscopy to investigate the relationships in the
emission-line region--central engine--radio jet system. Here, we present the
diversity of spectral types among the brightest AGN in our sample. We also
discuss correlations between the mass of the central engine and properties of
the parsec-scale radio jet for 24 AGN selected by the presence of H$\beta$
broad-emission lines in their spectra.


  We introduce a method for calculating the stationary state of a translation
invariant array of weakly coupled cavities in the presence of dissipation and
coherent as well as incoherent drives. Instead of computing the full density
matrix our method directly calculates the correlation functions which are
relevant for obtaining all local quantities of interest. It considers an
expansion of the correlation functions and their equations of motion in powers
of the photon tunneling rate between adjacent cavities, leading to an exact
second order solution for any number of cavities. Our method provides a
controllable approximation for weak tunneling rates applicable to the strongly
correlated regime that is dominated by nonlinearities in the cavities and thus
of high interest.


  We plot the geometry of several distance-based quantifiers of coherence for
Bell-diagonal states. We find that along with both $l_{1}$ norm and relative
entropy of coherence changes continuously from zero to one, their surfaces move
from the separable regions to the entangled regions. Based on this fact, it is
more illuminating to use an intuitive geometry to explain quantum states with
nonzero coherence can be used for entanglement creation, rather than the other
way around. We find the necessary and sufficient conditions that quantum
discord of Bell-diagonal states equal to its relative entropy of coherence and
depict the surfaces of the equality. We give surfaces of relative entropy of
coherence for $X$ states. We show the surfaces of dynamics of relative entropy
of coherence for Bell-diagonal states under local nondissipative channels and
find that all coherence under local nondissipative channels decrease.


  The Veneziano ghost field has been proposed as an alternative source of dark
energy whose energy density is consistent with the cosmological observations.
In this model, the energy density of QCD ghost field is expressed in terms of
QCD degrees of freedom at zero temperature. We extend this model to finite
temperature to search the model predictions from late time to early universe.
We depict the variations of QCD parameters entering the calculations, dark
energy density, equation of state, Hubble and deceleration parameters on
temperature from zero to a critical temperature. We compare our results with
the observations and theoretical predictions existing at different eras. It is
found that this model safely defines the universe from quark condensation up to
now and its predictions are not in tension with those of the standard
cosmology. The EoS parameter of dark energy is dynamical and evolves from
$-1/3$ in the presence of radiation to $-1$ at late time. The finite
temperature ghost dark energy predictions on the Hubble parameter well fit to
those of $\Lambda$CDM and observations at late time.


  The introduction of multiple viewpoints in video scenes inevitably increases
the bitrates required for storage and transmission. To reduce bitrates,
researchers have developed methods to skip intermediate viewpoints during
compression and delivery, and ultimately reconstruct them using Side
Information (SI). Typically, depth maps are used to construct SI. However,
their methods suffer from inaccuracies in reconstruction and inherently high
bitrates. In this paper, we propose a novel multi-view video coding method that
leverages the image generation capabilities of Generative Adversarial Network
(GAN) to improve the reconstruction accuracy of SI. Additionally, we consider
incorporating information from adjacent temporal and spatial viewpoints to
further reduce SI redundancy. At the encoder, we construct a spatio-temporal
Epipolar Plane Image (EPI) and further utilize a convolutional network to
extract the latent code of a GAN as SI. At the decoder side, we combine the SI
and adjacent viewpoints to reconstruct intermediate views using the GAN
generator. Specifically, we establish a joint encoder constraint for
reconstruction cost and SI entropy to achieve an optimal trade-off between
reconstruction quality and bitrates overhead. Experiments demonstrate
significantly improved Rate-Distortion (RD) performance compared with
state-of-the-art methods.


  We propose a deep generative approach to sampling from a conditional
distribution based on a unified formulation of conditional distribution and
generalized nonparametric regression function using the noise-outsourcing
lemma. The proposed approach aims at learning a conditional generator so that a
random sample from the target conditional distribution can be obtained by the
action of the conditional generator on a sample drawn from a reference
distribution. The conditional generator is estimated nonparametrically with
neural networks by matching appropriate joint distributions using the
Kullback-Liebler divergence. An appealing aspect of our method is that it
allows either of or both the predictor and the response to be high-dimensional
and can handle both continuous and discrete type predictors and responses. We
show that the proposed method is consistent in the sense that the conditional
generator converges in distribution to the underlying conditional distribution
under mild conditions. Our numerical experiments with simulated and benchmark
image data validate the proposed method and demonstrate that it outperforms
several existing conditional density estimation methods.


  Ad Hoc networks with multi-hop topology are widely used in military and
civilian applications. One challenge for Ad Hoc networks is to design efficient
Media Access Control (MAC) protocols to ensure the quality of service (QoS). In
Ad Hoc networks, there is a kind of node called key node, which undertakes more
forwarding traffic than other surrounding nodes. The number of neighbor nodes
around key nodes is often large, and the surrounding channel environment and
interference are often more complex. Thus, the key nodes can hardly get enough
channel access opportunities, resulting in poor end-to-end performance.
Therefore, we propose an adaptive channel reservation MAC protocol based on
forwarding traffic of key nodes, which is aimed at alleviating the congestion
for key nodes. Nodes initiate reservations for future transmission time
according to the buffer status before sending packets and then calculate the
Weight of Reservation Ability (WRA). The node adaptively adjusts its
reservation opportunity by comparing the WRA with neighbor nodes, thus
improving the channel access efficiency and ensuring the transmission
opportunity of key nodes. Extensive simulation confirms that our proposed
FTKN-CRM provides significant improvements in end-to-end performance over the
IEEE 802.11ax protocol and other reservation access protocols.


  This letter investigates the problem of controlling an aerial manipulator,
composed of an omnidirectional tilting drone equipped with a
five-degrees-of-freedom robotic arm. The robot has to interact with the
environment to inspect structures and perform non-destructive measurements. A
parallel force-impedance control technique is developed to establish contact
with the designed surface with a desired force profile. During the interaction,
a pushing phase is required to create a vacuum between the surface and the
echometer sensor mounted at the end-effector, to measure the thickness of the
interaction surface. Repetitive measures are performed to show the
repeatability of the algorithm.


  This paper introduces a divide-and-conquer inspired adversarial learning
(DACAL) approach for photo enhancement. The key idea is to decompose the photo
enhancement process into hierarchically multiple sub-problems, which can be
better conquered from bottom to up. On the top level, we propose a
perception-based division to learn additive and multiplicative components,
required to translate a low-quality image or video into its high-quality
counterpart. On the intermediate level, we use a frequency-based division with
generative adversarial network (GAN) to weakly supervise the photo enhancement
process. On the lower level, we design a dimension-based division that enables
the GAN model to better approximates the distribution distance on multiple
independent one-dimensional data to train the GAN model. While considering all
three hierarchies, we develop multiscale and recurrent training approaches to
optimize the image and video enhancement process in a weakly-supervised manner.
Both quantitative and qualitative results clearly demonstrate that the proposed
DACAL achieves the state-of-the-art performance for high-resolution image and
video enhancement.


  Social-based recommendation systems exploit the selections of friends to
combat the data sparsity on user preferences, and improve the recommendation
accuracy of the collaborative filtering strategy. The main challenge is to
capture and weigh friends' preferences, as in practice they do necessarily
match. In this paper, we propose a Neural Attention mechanism for Social
collaborative filtering, namely NAS. We design a neural architecture, to
carefully compute the non-linearity in friends' preferences by taking into
account the social latent effects of friends on user behavior. In addition, we
introduce a social behavioral attention mechanism to adaptively weigh the
influence of friends on user preferences and consequently generate accurate
recommendations. Our experiments on publicly available datasets demonstrate the
effectiveness of the proposed NAS model over other state-of-the-art methods.
Furthermore, we study the effect of the proposed social behavioral attention
mechanism and show that it is a key factor to our model's performance.


  We describe a first attempt to calculate scalar 2-loop box-functions with
arbitrary internal masses, applying a novel method proposed in hep-ph/9407234.
Four of the eight integrals are accessible to integration by means of the
residue theorem, leaving a rational function in the remaining variables. The
result of the procedure is a three- or sometimes two-dimensional integral
representation over a finite volume that can be further evaluated using
numerical methods.


  We investigate the effects of Majorana bound states on the ac response of a
quantum resistor-capacitor circuit which is composed of a topological
superconducting wire whose two ends are tunnel-coupled to a lead and a spinless
quantum dot, respectively. The Majorana states formed at the two ends of the
wire are found to suppress completely or enhance greatly the dissipation,
depending on the strength of the overlap between two Majorana modes and/or the
dot level. We compare the relaxation resistance and the quantum capacitance of
the system with those of non-Majorana counterparts to find that the effects of
the Majorana state on the ac response are genuine and cannot be reproduced in
ordinary fermonic systems.


  We present results from optical, X-ray and radio observations of two X-ray
bright (L_X ~ 10^{45} erg/s) galaxy clusters. A1451 is at redshift z=0.1989 and
has line-of-sight velocity dispersion sigma_v = 1330 km/s as measured from 57
cluster galaxies. It has regular X-ray emission without signs of substructure,
a Gaussian velocity distribution, lack of a cooling flow region and significant
deviations from the observed scaling laws between luminosity, temperature and
velocity dispersion, indicating a possible merging shock. There is only one
spectroscopically confirmed cluster radio galaxy, which is close to the X-ray
peak. 1RXS J131423.6-251521 has z=0.2474 and sigma_v = 1100 km/s from 37
galaxies. There are two distinct galaxy groups with a projected separation of
\~700 kpc. The velocity histogram is bi-modal with a redshift-space separation
of ~1700 km/s, and the X-ray emission is double peaked. Although there are no
spectroscopically confirmed cluster radio galaxies, we have identified a
plausible relic source candidate.


  When domain experts are needed to perform data annotation for complex
machine-learning tasks, reducing annotation effort is crucial in order to cut
down time and expenses. For cases when there are no annotations available, one
approach is to utilize the structure of the feature space for clustering-based
active learning (AL) methods. However, these methods are heavily dependent on
how the samples are organized in the feature space and what distance metric is
used. Unsupervised methods such as contrastive predictive coding (CPC) can
potentially be used to learn organized feature spaces, but these methods
typically create high-dimensional features which might be challenging for
estimating data density. In this paper, we combine CPC and multiple
dimensionality reduction methods in search of functioning practices for
clustering-based AL. Our experiments for simulating speech emotion recognition
system deployment show that both the local and global topology of the feature
space can be successfully used for AL, and that CPC can be used to improve
clustering-based AL performance over traditional signal features. Additionally,
we observe that compressing data dimensionality does not harm AL performance
substantially, and that 2-D feature representations achieved similar AL
performance as higher-dimensional representations when the number of
annotations is not very low.


  A random walk is a basic stochastic process on graphs and a key primitive in
the design of distributed algorithms. One of the most important features of
random walks is that, under mild conditions, they converge to a stationary
distribution in time that is at most polynomial in the size of the graph. This
fundamental property, however, only holds if the graph does not change over
time, while on the other hand many distributed networks are inherently dynamic,
and their topology is subjected to potentially drastic changes.
  In this work we study the mixing (i.e., converging) properties of random
walks on graphs subjected to random changes over time. Specifically, we
consider the edge-Markovian random graph model: for each edge slot, there is a
two-state Markov chain with transition probabilities $p$ (add a non-existing
edge) and $q$ (remove an existing edge). We derive several positive and
negative results that depend on both the density of the graph and the speed by
which the graph changes. We show that if $p$ is very small (i.e., below the
connectivity threshold of Erd\H{o}s-R\'{e}nyi random graphs), random walks do
not mix (fast). When $p$ is larger, instead, we observe the following behavior:
if the graph changes slowly over time (i.e., $q$ is small), random walks enjoy
strong mixing properties that are comparable to the ones possessed by random
walks on static graphs; however, if the graph changes too fast (i.e., $q$ is
large), only coarse mixing properties are preserved.


  We study the nature of the [TiII] and [NiII] emission from the so-called
strontium filament found in the ejecta of eta Carinae. To this purpose we
employ multilevel models of the TiII and NiII systems which are used to
investigate the physical condition of the filament and the excitation
mechanisms of the observed lines. For the TiII ion, for which no atomic data
was previously available, we carry out ab initio calculations of radiative
transition rates and electron impact excitation rate coefficients. It is found
that the observed spectrum is consistent with the lines being excited in a
mostly neutral region with an electron density of the order of $10^7$ cm$^{-3}$
and a temperature around 6000 K. In analyzing three observations with different
slit orientations recorded between March~2000 and November~2001 we find line
ratios that change among various observations, in a way consistent with changes
of up to an order of magnitude in the strength of the continuum radiation
field. These changes result from different samplings of the extended filament,
due to the different slit orientations used for each observation, and yield
clues on the spatial extent and optical depth of the filament. The observed
emission indicates a large Ti/Ni abundance ratio relative to solar abundances.
It is suggested that the observed high Ti/Ni ratio in gas is caused by dust-gas
fractionation processes and does not reflect the absolute Ti/Ni ratio in the
ejecta of \etacar. We study the condensation chemistry of Ti, Ni and Fe within
the filament and suggest that the observed gas phase overabundance of Ti


  We have computed the first 30 even-N moments for the double fermionic (nf^2)
part of the quark-to-gluon splitting function P_{gq} at the fourth order of
perturbative QCD via the renormalization of off-shell operator matrix elements.
From these results we have determined the all-N form, and hence the exact
x-space expression, using systems of Diophantine equations for its
coefficients. The dominant and subdominant leading small-x nf^2 contributions
to P_{gq}^(3)(x) are of the form 1/x ln x and ln^4 x, respectively; the leading
large-x term is ln^4 (1-x). The coefficient of the first of these is new, the
other two agree with results obtained before and thus provide checks of our
results.


  While there is no consensus about the history of time since the beginning, in
this paper I will discuss some possibilities. We have a pretty clear picture of
cosmic history from the electroweak phase transition through the time of
recombination, a period which includes the QCD phase transition and big bang
nucleosynthesis. This paper includes a quantitative discussion of the age of
the universe, of the radiation-matter transition, and of hydrogen
recombination. There is much evidence that at earlier times the universe
underwent inflation, but the details of how and when inflation happened are
still far from certain. There is even more uncertainty about what happened
before inflation, and how inflation began. I will describe the possibility of
``eternal'' inflation, which proposes that our universe evolved from an
infinite tree of inflationary spacetime. Most likely, however, inflation can be
eternal only into the future, but still must have a beginning.


  By identifying a bosonic consistent truncation from the 1/4-BPS wrapped
M5-brane geometry of Maldacena, Strominger and Witten in D = 11 supergravity
and finding a supersymmetric extension, we recover an N = 2 D = 3 supergravity
theory. Reductions of a large class of supersymmetric solutions corresponding
to wrapped M2 and M5-branes lead to black strings and warped AdS3 solutions
preserving supersymmetry. With a view to AdS/CMT applications, we also
construct a numerical hairy BTZ black hole and, as a preliminary step in this
direction, determine the conductivity of the dual CFT.


  Preparing the ground state of a given Hamiltonian and estimating its ground
energy are important but computationally hard tasks. However, given some
additional information, these problems can be solved efficiently on a quantum
computer. We assume that an initial state with non-trivial overlap with the
ground state can be efficiently prepared, and the spectral gap between the
ground energy and the first excited energy is bounded from below. With these
assumptions we design an algorithm that prepares the ground state when an upper
bound of the ground energy is known, whose runtime has a logarithmic dependence
on the inverse error. When such an upper bound is not known, we propose a
hybrid quantum-classical algorithm to estimate the ground energy, where the
dependence of the number of queries to the initial state on the desired
precision is exponentially improved compared to the current state-of-the-art
algorithm proposed in [Ge et al. 2019]. These two algorithms can then be
combined to prepare a ground state without knowing an upper bound of the ground
energy. We also prove that our algorithms reach the complexity lower bounds by
applying it to the unstructured search problem and the quantum approximate
counting problem.


  The most efficient modern optical communication is known as coherent
communication and its standard quantum limit (SQL) is almost reachable with
current technology. Though it has been predicted for a long time that this SQL
could be overcome via quantum mechanically optimized receivers, such a
performance has not been experimentally realized so far. Here we demonstrate
the first unconditional evidence surpassing the SQL of coherent optical
communication. We implement a quantum receiver with a simple linear optics
configuration and achieve more than 90% of the total detection efficiency of
the system. Such an efficient quantum receiver will provide a new way of
extending the distance of amplification-free channels, as well as of realizing
quantum information protocols based on coherent states and the loophole-free
test of quantum mechanics.


  In this paper, some patterns of the Neuron Response of deep Convolutional
Neural Networks were observed.


  Edge intelligence is an emerging technology where the base stations located
at the edge of the network are equipped with computing units that provide
machine learning services to the end users. To provide high-quality services in
a cost-efficient way, the wireless and computing resources need to be
dimensioned carefully. In this paper, we address the problem of resource
dimensioning in a single-cell system that supports edge video analytics under
latency and accuracy constraints. We show that the resource-dimensioning
problem can be transformed into a convex optimization problem, and we provide
numerical results that give insights into the trade-offs between the wireless
and computing resources for varying cell sizes and for varying intensity of
incoming tasks. Overall, we observe that the wireless and computing resources
exhibit opposite trends; the wireless resources favor from smaller cells, where
high attenuation losses are avoided, and the computing resources favor from
larger cells, where statistical multiplexing allows for computing more tasks.
We also show that small cells with low loads have high per-request costs, even
when the wireless resources are increased to compensate for the low
multiplexing gain at the servers.


  We identity a by-far-unrecognized problem of Adam-style optimizers which
results from unnecessary coupling between momentum and adaptivity. The coupling
leads to instability and divergence when the momentum and adaptivity parameters
are mismatched. In this work, we propose a method, Laprop, which decouples
momentum and adaptivity in the Adam-style methods. We show that the decoupling
leads to greater flexibility in the hyperparameters and allows for a
straightforward interpolation between the signed gradient methods and the
adaptive gradient methods. We experimentally show that Laprop has consistently
improved speed and stability over Adam on a variety of tasks. We also bound the
regret of Laprop on a convex problem and show that our bound differs from that
of Adam by a key factor, which demonstrates its advantage.


  The pairing symmetry of an effective Hamiltonian for interacting fermions on
a twisted bilayer graphene superlattice is studied with the determinant quantum
Monte Carlo method. The model has the symmetry of a triangle lattice and a
nearly-flat low energy band, features which underlie the magic-angle twisted
bilayer graphene superlattice. We show that the low temperature phase is
insulating at half-filling, even for relatively weak interactions. The natures
of the spin and pairing correlations upon doping are determined, and exhibit an
electron-hole asymmetry consistent with experiment. Among the pairing
symmetries allowed, we demonstrate that the dominating channels are $d$-wave,
opening the possibility of condensation into an unconventional
$d_{x^2-y^2}+id_{xy}$ phase, which is characterized by an integer topological
invariant and gapless edge states.


  Let k and n be positive even integers. For a cuspidal Hecke eigenform h in
the Kohnen plus space of weight k-n/2+1/2, let f be the corresponding primitive
form of weight 2k-n for SL(2,Z) under the Shimura correspondence, and I(h) the
Duke-Imamoglu-Ikeda lift of h to the space of cusp forms of weight k of genus
n. Moreover, let FJ(I(h),1) be the first Fourier-Jacobi coefficient of I(h) and
s(FJ(I(h),1)) be the cusp form in the generalized Kohnen plus space of weight
k-1/2 corresponding to FJ(I(h),1) under the Ibukiyama isomorphism. We then give
an explicit formula for the Koecher-Maass series of s(FJ(I(h),1)) expressed in
terms of the usual L-functions of h and f.


  In this paper we discuss the diffusion of serious games and present reasons
for why Rogers traditional approach is limited in this context. We present an
alternative overview through the characteristics of relative advantage,
compatibility, complexity, trialability, and observability, that reflect on the
adoption decision and contributes on the commercialization of serious games.


  This article summarizes the proposal to address the cosmological constant
problem within the framework of supersymmetric large extra dimensions (SLED).
The proposed mechanism is described, emphasizing the relaxation mechanism which
ensures that low-energy particles like the electron do not contribute too large
a vacuum energy. This is followed by a preliminary discussion of outstanding
issues and observational consequences.


  Synthetic data is widely used in various domains. This is because many modern
algorithms require lots of data for efficient training, and data collection and
labeling usually are a time-consuming process and are prone to errors.
Furthermore, some real-world data, due to its nature, is confidential and
cannot be shared. Bayesian networks are a type of probabilistic graphical model
widely used to model the uncertainties in real-world processes. Dynamic
Bayesian networks are a special class of Bayesian networks that model temporal
and time series data. In this paper, we introduce the tsBNgen, a Python library
to generate time series and sequential data based on an arbitrary dynamic
Bayesian network. The package, documentation, and examples can be downloaded
from https://github.com/manitadayon/tsBNgen.


  We present nearly final results from our analysis of the form factors for
$B\to D^\ast\ell\nu$ decay at nonzero recoil. Our analysis includes 15 MILC
asqtad ensembles with $N_f=2+1$ flavors of sea quarks and lattice spacings
ranging from $a\approx0.15$ fm down to $0.045$ fm. The valence light quarks
employ the asqtad action, whereas the $b$ and $c$ quarks are treated using the
Fermilab action. We discuss the impact that our results will have on $\
|V_{cb}\ |$ and $R(D^\ast)$.


  In this paper we aim to determine the baryon numbers at which the minimal
energy Skyrmion has icosahedral symmetry. By comparing polyhedra which arise as
minimal energy Skyrmions with the dual of polyhedra that minimize the energy of
Coulomb charges on a sphere, we are led to conjecture a sequence of magic
baryon numbers, B=7,17,37,67,97,... at which the minimal energy Skyrmion has
icosahedral symmetry and unusually low energy. We present evidence for this
conjecture by applying a simulated annealing algorithm to compute energy
minimizing rational maps for all degrees upto 40. Further evidence is provided
by the explicit construction of icosahedrally symmetric rational maps of
degrees 37, 47, 67 and 97. To calculate these maps we introduce two new methods
for computing rational maps with Platonic symmetries.


  This is an elementary explanation of a cubic composition formula due to
Ramanujan.


  Federated learning is an approach to collaboratively training machine
learning models for multiple parties that prohibit data sharing. One of the
challenges in federated learning is non-IID data between clients, as a single
model can not fit the data distribution for all clients. Meta-learning, such as
Per-FedAvg, is introduced to cope with the challenge. Meta-learning learns
shared initial parameters for all clients. Each client employs gradient descent
to adapt the initialization to local data distributions quickly to realize
model personalization. However, due to non-convex loss function and randomness
of sampling update, meta-learning approaches have unstable goals in local
adaptation for the same client. This fluctuation in different adaptation
directions hinders the convergence in meta-learning. To overcome this
challenge, we use the historical local adapted model to restrict the direction
of the inner loop and propose an elastic-constrained method. As a result, the
current round inner loop keeps historical goals and adapts to better solutions.
Experiments show our method boosts meta-learning convergence and improves
personalization without additional calculation and communication. Our method
achieved SOTA on all metrics in three public datasets.


  We consider a discretization of Caputo derivatives resulted from deconvolving
a scheme for the corresponding Volterra integral. Properties of this
discretization, including signs of the coefficients, comparison principles, and
stability of the corresponding implicit schemes, are proved by its linkage to
Volterra integrals with completely monotone kernels. We then apply the backward
scheme corresponding to this discretization to two time fractional dissipative
problems, and these implicit schemes are helpful for the analysis of the
corresponding problems. In particular, we show that the overdamped generalized
Langevin equation with fractional noise has a unique limiting measure for
strongly convex potentials and establish the convergence of numerical solutions
to the strong solutions of time fractional gradient flows. The proposed scheme
and schemes derived using the same philosophy can be useful for many other
applications as well.


  Neuroimaging data, e.g. obtained from magnetic resonance imaging (MRI), is
comparably homogeneous due to (1) the uniform structure of the brain and (2)
additional efforts to spatially normalize the data to a standard template using
linear and non-linear transformations. Convolutional neural networks (CNNs), in
contrast, have been specifically designed for highly heterogeneous data, such
as natural images, by sliding convolutional filters over different positions in
an image. Here, we suggest a new CNN architecture that combines the idea of
hierarchical abstraction in neural networks with a prior on the spatial
homogeneity of neuroimaging data: Whereas early layers are trained globally
using standard convolutional layers, we introduce for higher, more abstract
layers patch individual filters (PIF). By learning filters in individual image
regions (patches) without sharing weights, PIF layers can learn abstract
features faster and with fewer samples. We thoroughly evaluated PIF layers for
three different tasks and data sets, namely sex classification on UK Biobank
data, Alzheimer's disease detection on ADNI data and multiple sclerosis
detection on private hospital data. We demonstrate that CNNs using PIF layers
result in higher accuracies, especially in low sample size settings, and need
fewer training epochs for convergence. To the best of our knowledge, this is
the first study which introduces a prior on brain MRI for CNN learning.


  We describe a fast method to simulate in a semi-analytical way consistent
maps of the thermal, kinetic and polarised Sunyaev-Zel'dovich effect, featuring
both cluster spatial correlations and large scale velocity flows.


  Multi-object tracking (MOT) is a challenging vision task that aims to detect
individual objects within a single frame and associate them across multiple
frames. Recent MOT approaches can be categorized into two-stage
tracking-by-detection (TBD) methods and one-stage joint detection and tracking
(JDT) methods. Despite the success of these approaches, they also suffer from
common problems, such as harmful global or local inconsistency, poor trade-off
between robustness and model complexity, and lack of flexibility in different
scenes within the same video. In this paper we propose a simple but robust
framework that formulates object detection and association jointly as a
consistent denoising diffusion process from paired noise boxes to paired
ground-truth boxes. This novel progressive denoising diffusion strategy
substantially augments the tracker's effectiveness, enabling it to discriminate
between various objects. During the training stage, paired object boxes diffuse
from paired ground-truth boxes to random distribution, and the model learns
detection and tracking simultaneously by reversing this noising process. In
inference, the model refines a set of paired randomly generated boxes to the
detection and tracking results in a flexible one-step or multi-step denoising
diffusion process. Extensive experiments on three widely used MOT benchmarks,
including MOT17, MOT20, and Dancetrack, demonstrate that our approach achieves
competitive performance compared to the current state-of-the-art methods.


  In this work, a functional variant of the polynomial analogue of the
classical Gandy's fixed point theorem is obtained. Sufficient conditions have
been found to ensure that the complexity of the recursive function does not go
beyond the polynomial. In 2021, we proved a polynomial analogue of the
classical Gandy's fixed point theorem. This became an important impetus for the
construction of p-complete programming languages. And such a language was first
built by us in 2022. The main result of that work was: a solution of the
problem P=L. Next are the followers of the works on building a new high-level
language and the idea of building a general programming methodology. But there
was one gap in our research: classes of recursive functions whose complexity
was polynomial were not described. In this work we found sufficient conditions
for such functions. In many ways, the main ideas of this work are similar to
the ideas that we used in the proof of the polynomial analogue of Gandy's fixed
point theorem. But there are also striking differences. Functions, as such,
differ quite strongly from predicates precisely in the multitude of their
values. If a predicate is either true or false, then a function can generally
take on a variety of values. Moreover, even if there are not many values, but
there is recursion and simple multiplication, then powers and factorials may
arise during the calculations, which, of course, can violate the polynomial
computational complexity of this function. Therefore, finding these
restrictions on recursive functions that would be soft enough for the class of
functions to be large, and at the same time tough enough not to go beyond
polynomiality, has been a problem for us for the last 3 years, after the proof
of the polynomial analogue Gandy's fixed point theorem in the case of predicate
extensions.


  We strengthen "The Free Will Theorem" [1] in several ways, by replacing the
axiom FIN by a weaker axiom MIN, and also by allowing the particles' responses
to depend on past half-spaces rather than on on past light cones. This change
allows us to directly apply our theorem to relativistic theories of the GRW
type.


  A statistical study is presented of (a) the frequency of narrow CIV lambda
1549 absorption lines in 1.5 <~ z <~ 3.6 radio-quiet and radio-loud quasars,
and of (b) the UV and radio properties of the absorbed quasars. The quasar
sample is unbiased with respect to absorption properties and the radio-quiet
and radio-loud subsamples are well matched in redshift and luminosity. A
similarly high incidence (>~50%) of narrow CIV absorbers is detected for the
radio-quiet and radio-loud quasars, and a constant ~25% of all the quasars,
irrespective of radio type display associated CIV absorbers stronger than a
rest equivalent width of 0.5A. Both radio-quiet and radio-loud quasars with
narrow absorption lines have systematically redder continua, especially
strongly absorbed objects. There is evidence of inclination dependent dust
reddening and absorption for the radio quasars. An additional key result is
that the most strongly absorbed radio quasars have the largest radio source
extent. This result is in stark contrast to a recent study of the low-frequency
selected Molonglo survey in which a connection between the strength of the
narrow absorbers and the (young) age of the radio source has been proposed. The
possible origin of these discrepant results is discussed and may be related to
the higher source luminosity for the quasars studied here.


  We construct a spectral triple on a quantum Heisenberg manifold, which
generalizes the results of Chakraborty and Shinha, and associate to it an
energy functional on the set of projections, following the approach of
Mathai-Rosenberg to non-linear sigma models. The spectral triples that we
construct extend the We derive a lower bound for this energy functional that is
linked on the topological charge of the projection which depends on the
curvature of a compatible connection. A detailed study of this lower bound is
given for the Kang projection in quantum Heisenberg manifolds. These results
display an intriguing interplay between non-linear sigma models and Yang-Mills
theory on quantum Heisenberg manifolds, unlike in the well-studied case of
noncommutative tori.


  The DT-operators are introduced, one for every pair (\mu,c) consisting of a
compactly supported Borel probability measure \mu on the complex plane and a
constant c>0. These are operators on Hilbert space that are defined as limits
in *-moments of certain upper triangular random matrices. The DT-operators
include Voiculescu's circular operator and elliptic deformations of it, as well
as the circular free Poisson operators. We show that every DT-operator is
strongly decomposable. We also show that a DT-operator generates a II_1-factor,
whose isomorphism class depends only on the number and sizes of atoms of \mu.
Those DT-operators that are also R-diagonal are identified. For a
quasi-nilpotent DT-operator T, we find the distribution of T^*T and a recursion
formula for general *-moments of T.


  A non-Abelian Born-Infeld theory is presented. The square root structure that
characterizes the Dirac-Born-Infeld (DBI) action does not appear. The procedure
is based on an Abelian theory proposed by Erwin Schr\"{o}dinger that, as he
showed, is equivalent to Born-Infeld theory. We briefly mention other possible
similar proposals. Our results could be of interest in connection with string
theory and possible extensions of well known physical results in the usual
Born-Infeld Abelian case.


  We extend here a previous investigation on the characteristics of Galactic
carbon stars using more accurate EDR3 astrometry measurements. Based on a much
larger statistics, we confirm that N- and SC-type carbon stars share a very
similar luminosity function, while the luminosities of J-type stars (Mbol) are
fainter by half a magnitude on average. R-hot type carbon stars have
luminosities throughout the RGB, which favours the hypothesis of an external
origin for their carbon enhancement. Moreover, the kinematic properties of a
significant fraction of the R-hot stars are compatible with the thick-disc
population, in contrast with that of N- and SC-type stars, which would belong
mostly to the thin disk. We also derive the luminosity function of a large
number of Galactic extrinsic and intrinsic (O-rich) S stars and show that the
luminosities of the latter are typically higher than the predicted onset of the
third dredge-up during the AGB for solar metallicity. This result is consistent
with these stars being genuine thermally pulsing AGB stars. On the other hand,
using the so-called Gaia-2MASS diagram, we show that the overwhelming majority
of the carbon stars identified in the LAMOST survey as AGB stars are probably
R-hot and/or CH-type stars. Finally, we report the identification of 2660 new
carbon stars candidates that we identified through their 2MASS photometry,
their Gaia astrometry, and their location in the Gaia-2MASS diagram.


  In September 1859 the Colaba observatory measured the most extreme
geomagnetic disturbance ever recorded at low latitudes related to solar
activity: the Carrington storm. This paper describes a geomagnetic disturbance
case with a profile extraordinarily similar to the disturbance of the
Carrington event at Colaba: the event on 29 October 2003 at Tihany magnetic
observatory in Hungary. The analysis of the H-field at different locations
during the "Carrington-like" event leads to a re-interpretation of the 1859
event. The major conclusions of the paper are the following: (a) the global Dst
or SYM-H, as indices based on averaging, missed the largest geomagnetic
disturbance in the 29 October 2003 event and might have missed the 1859
disturbance, since the large spike in the horizontal component (H) of
terrestrial magnetic field depends strongly on magnetic local time (MLT); (b)
the main cause of the large drop in H recorded at Colaba during the Carrington
storm was not the ring current but field-aligned currents (FACs), and (c) the
very local signatures of the H-spike imply that a Carrington-like event can
occur more often than expected.


  In many situations, sample data is obtained from a noisy or imperfect source.
In order to address such corruptions, this paper introduces the concept of a
sampling corrector. Such algorithms use structure that the distribution is
purported to have, in order to allow one to make "on-the-fly" corrections to
samples drawn from probability distributions. These algorithms then act as
filters between the noisy data and the end user.
  We show connections between sampling correctors, distribution learning
algorithms, and distribution property testing algorithms. We show that these
connections can be utilized to expand the applicability of known distribution
learning and property testing algorithms as well as to achieve improved
algorithms for those tasks.
  As a first step, we show how to design sampling correctors using proper
learning algorithms. We then focus on the question of whether algorithms for
sampling correctors can be more efficient in terms of sample complexity than
learning algorithms for the analogous families of distributions. When
correcting monotonicity, we show that this is indeed the case when also granted
query access to the cumulative distribution function. We also obtain sampling
correctors for monotonicity without this stronger type of access, provided that
the distribution be originally very close to monotone (namely, at a distance
$O(1/\log^2 n)$). In addition to that, we consider a restricted error model
that aims at capturing "missing data" corruptions. In this model, we show that
distributions that are close to monotone have sampling correctors that are
significantly more efficient than achievable by the learning approach.
  We also consider the question of whether an additional source of independent
random bits is required by sampling correctors to implement the correction
process.


  We combine orbital information from N-body simulations with an analytic model
for star formation quenching and SDSS observations to infer the differential
effect of the group/cluster environment on star formation in satellite
galaxies. We also consider a model for gas stripping, using the same input
supplemented with HI fluxes from the ALFALFA survey. The models are motivated
by and tested on the Hydrangea cosmological hydrodynamical simulation suite. We
recover the characteristic times when satellite galaxies are stripped and
quenched. Stripping in massive ($M_\mathrm{ vir}\sim
10^{14.5}\,\mathrm{M}_\odot$) clusters typically occurs at or just before the
first pericentric passage. Lower mass ($\sim10^{13.5}\,\mathrm{M}_\odot$)
groups strip their satellites on a significantly longer (by
$\sim3\,\mathrm{Gyr}$) timescale. Quenching occurs later: Balmer emission lines
typically fade $\sim3.5\,\mathrm{Gyr}$ ($5.5\,\mathrm{Gyr}$) after first
pericentre in clusters (groups), followed a few hundred $\mathrm{Myr}$ later by
reddenning in $(g-r)$ colour. These `delay timescales' are remarkably constant
across the entire satellite stellar mass range probed
($\sim10^{9.5}-10^{11}\,\mathrm{M}_\odot$), a feature closely tied to our
treatment of `group pre-processing'. The lowest mass groups in our sample
($\sim10^{12.5}\,\mathrm{M}_\odot$) strip and quench their satellites extremely
inefficiently: typical timescales may approach the age of the Universe. Our
measurements are qualitatively consistent with the `delayed-then-rapid'
quenching scenario advocated for by several other studies, but we find
significantly longer delay times. Our combination of a homogeneous analysis and
input catalogues yields new insight into the sequence of events leading to
quenching across wide intervals in host and satellite mass.


  Let {X_i} be a tower of discrete G-spectra, each of which is fibrant as a
spectrum, so that X=holim_i X_i is a continuous G-spectrum, with homotopy fixed
point spectrum X^{hG}. The E_2-term of the descent spectral sequence for
\pi_*(X^{hG}) cannot always be expressed as continuous cohomology. However, we
show that the E_2-term is always built out of a certain complex of spectra,
that, in the context of abelian groups, is used to compute the continuous
cochain cohomology of G with coefficients in lim_i M_i, where {M_i} is a tower
of discrete G-modules.


  We compare [O IV] 25.89 micron emission line luminosities with very hard
(10-200 keV) X-rays from Swift, Integral, and BeppoSAX for a complete sample of
89 Seyferts from the Revised Shapley-Ames sample. Using Seyfert 1s, we
calibrate [O IV] as a measure of AGN intrinsic luminosity, for particular use
in high-obscuration environments. With this calibration, we measure the average
decrement in 14-195 keV X-ray to [O IV] luminosity ratio for Seyfert 2s
compared to type 1s. We find a decrement of 3.1 +- 0.8 for Seyfert 2s, and a
decrement of 5.0 +- 2.7 for known Compton-thick Seyfert 2s. These decrements
imply column densities of approximately log N(H)=24.6 and 24.7 cm^-2,
respectively. Thus, we infer that the average Seyfert 2 is more highly obscured
and intrinsically more luminous than would be inferred even from the very hard
X-rays. We demonstrate two applications of the hard X-ray to [O IV] ratio. We
measure a column density for the extremely obscured NGC 1068 of log
N(H)=25.3-25.4 cm^-2. Finally, by comparing [O IV] luminosities to total
infrared luminosities for twelve bright ultraluminous infrared galaxies, we
find that four have substantial AGN contributions.


  Strings are extensively used in modern programming languages and constraints
over strings of unknown length occur in a wide range of real-world applications
such as software analysis and verification, testing, model checking, and web
security. Nevertheless, practically no CP solver natively supports string
constraints. We introduce string variables and a suitable set of string
constraints as builtin features of the MiniZinc modelling language.
Furthermore, we define an interpreter for converting a MiniZinc model with
strings into a FlatZinc instance relying on only integer variables. This
provides a user-friendly interface for modelling combinatorial problems with
strings, and enables both string and non-string solvers to actually solve such
problems.


  We present the prototype module of our extendible and robust multichannel
SQUID magnetometer system. A multi-module arrangement can be implemented by
using up to 7 modules. It is intended for high-precision measurements of
biomagnetism and spin precession. Further demanding applications are
magnetorelaxometry and ultra-low-field nuclear magnetic resonance (ULF NMR),
where pulsed fields of up to 100 mT are typically applied. The system is
operated inside the Berlin Magnetically Shielded Room and equipped with 18
magnetometers consisting of niobium (Nb) wire-wound pick-up coils. A total of
16 small pick-up coils with 17.1 mm diameter form a regular grid with
individual channels arranged to ensure system sensitivity covers all three
orthogonal spatial directions. Two large hexagonal pick-up coils with an
equivalent diameter of 74.5 mm sensitive in z-direction surround the grid at
two different heights and are suitable for the detection of deep sources. Each
coil is connected to the input of a thin-film Nb SQUID current sensor via a
detachable superconducting contact. The SQUIDs are equipped with integrated
input current limiters. Feedback into the pick-up coils minimizes crosstalk
between channels. The current sensor chip package includes a superconducting
shield of Nb. The field distortion of the prototype and a multi-module
arrangement was analysed by numerical simulation. The measured noise of the
small magnetometers was between 0.6 and 1.5 fT/Hz$^{1/2}$, and well below 1
fT/Hz$^{1/2}$ for the large ones. Using a software gradiometer, we achieved a
minimum noise level of 0.54 fT/Hz$^{1/2}$. We performed ULF NMR experiments,
verifying the system's robustness against pulsed fields, and
magnetoencephalographgy (MEG) on somatosensory evoked neuronal activity. The
low noise performance of our 18-channel prototype enabled the detection of
high-frequency components at around 1 kHz by MEG.


  It is a classical fact, that given an arbitrary n-dimensional convex body,
there exists an appropriate sequence of Minkowski symmetrizations (or Steiner
symmetrizations), that converges in Hausdorff metric to a Euclidean ball. Here
we provide quantitative estimates regarding this convergence, for both
Minkowski and Steiner symmetrizations. Our estimates are polynomial in the
dimension and in the logarithm of the desired distance to a Euclidean ball,
improving previously known exponential estimates. Inspired by a method of
Diaconis, our technique involves spherical harmonics. We also make use of an
earlier result by the author regarding ``isomorphic Minkowski symmetrization''.


  After an introduction recalling the theoretical motivation for low energy
(100 GeV to TeV scale) supersymmetry, this review describes the theory and
experimental implications of the soft supersymmetry-breaking Lagrangian of the
general minimal supersymmetric standard model (MSSM). Extensions to include
neutrino masses and nonminimal theories are also discussed. Topics covered
include models of supersymmetry breaking, phenomenological constraints from
electroweak symmetry breaking, flavor/CP violation, collider searches, and
cosmological constraints including dark matter and implications for
baryogenesis and inflation.


  In this work, we estimate the number of hyperedges in a hypergraph ${\cal
H}(U({\cal H}), {\cal F}({\cal H}))$, where $U({\cal H})$ denotes the set of
vertices and ${\cal F}({\cal H}))$ denotes the set of hyperedges. We assume a
query oracle access to the hypergraph ${\cal H}$. Estimating the number of
edges, triangles or small subgraphs in a graph is a well studied problem. Beame
\etal~and Bhattacharya \etal~gave algorithms to estimate the number of edges
and triangles in a graph using queries to the {\sc Bipartite Independent Set}
({\sc BIS}) and the {\sc Tripartite Independent Set} ({\sc TIS}) oracles,
respectively. We generalize the earlier works by estimating the number of
hyperedges using a query oracle, known as the {\bf Generalized $d$-partite
independent set oracle ({\sc GPIS})}, that takes $d$ (non-empty) pairwise
disjoint subsets of vertices $A_1,\ldots,A_d \subseteq U({\cal H})$ as input,
and answers whether there exists a hyperedge in ${\cal H}$ having (exactly) one
vertex in each $A_i, i \in \{1,2,\ldots,d\}$. We give a randomized algorithm
for the hyperedge estimation problem using the {\sc GPIS} query oracle to
output $\widehat{m}$ for $m({\cal H})$ satisfying $(1-\epsilon) \cdot m({\cal
H}) \leq \widehat{m} \leq (1+\epsilon) \cdot m({\cal H})$. The number of
queries made by our algorithm, assuming $d$ to be a constant, is
polylogarithmic in the number of vertices of the hypergraph.


  We relate the notions of spectral gap for unitary representations and
subfactors with definability of certain important sets in the corresponding
structures. We give several applications of this relationship.


  In the classic Correlation Clustering problem introduced by Bansal, Blum, and
Chawla~(FOCS 2002), the input is a complete graph where edges are labeled
either $+$ or $-$, and the goal is to find a partition of the vertices that
minimizes the sum of the +edges across parts plus the sum of the -edges within
parts. In recent years, Chawla, Makarychev, Schramm and Yaroslavtsev~(STOC
2015) gave a 2.06-approximation by providing a near-optimal rounding of the
standard LP, and Cohen-Addad, Lee, Li, and Newman~(FOCS 2022, 2023) finally
bypassed the integrality gap of 2 for this LP giving a $1.73$-approximation for
the problem.
  In order to create a simple and unified framework for Correlation Clustering
similar to those for {\em typical} approximate optimization tasks, we propose
the {\em cluster LP} as a strong linear program that might tightly capture the
approximability of Correlation Clustering. It unifies all the previous
relaxations for the problem.
  We demonstrate the power of the cluster LP by presenting a simple rounding
algorithm, and providing two analyses, one analytically proving a
1.49-approximation and the other solving a factor-revealing SDP to show a
1.437-approximation. Both proofs introduce principled methods by which to
analyze the performance of the algorithm, resulting in a significantly improved
approximation guarantee.
  Finally, we prove an integrality gap of $4/3$ for the cluster LP, showing our
1.437-upper bound cannot be drastically improved. Our gap instance directly
inspires an improved NP-hardness of approximation with a ratio $24/23 \approx
1.042$; no explicit hardness ratio was known before.


  Massively parallel architectures such as the GPU are becoming increasingly
important due to the recent proliferation of data. In this paper, we propose a
key class of hybrid parallel graphlet algorithms that leverages multiple CPUs
and GPUs simultaneously for computing k-vertex induced subgraph statistics
(called graphlets). In addition to the hybrid multi-core CPU-GPU framework, we
also investigate single GPU methods (using multiple cores) and multi-GPU
methods that leverage all available GPUs simultaneously for computing induced
subgraph statistics. Both methods leverage GPU devices only, whereas the hybrid
multi-core CPU-GPU framework leverages all available multi-core CPUs and
multiple GPUs for computing graphlets in large networks. Compared to recent
approaches, our methods are orders of magnitude faster, while also more cost
effective enjoying superior performance per capita and per watt. In particular,
the methods are up to 300 times faster than the recent state-of-the-art method.
To the best of our knowledge, this is the first work to leverage multiple CPUs
and GPUs simultaneously for computing induced subgraph statistics.


  Leveraging multiple cameras on Unmanned Aerial Vehicles (UAVs) to form a
variable-baseline stereo camera for collaborative perception is highly
promising. The critical steps include high-rate cross-camera feature
association and frame-rate relative pose estimation of multiple UAVs. To
accelerate the feature association rate to match the frame rate, we propose a
dual-channel structure to decouple the time-consuming feature detection and
match from the high-rate image stream. The novel design of periodic guidance
and fast prediction effectively utilizes each image frame to achieve a
frame-rate feature association. Real-world experiments are executed using
SuperPoint and SuperGlue on the NVIDIA NX 8G platform with a 30 Hz image
stream. Using single-channel SuperPoint and SuperGlue can only achieve 13 Hz
feature association. The proposed dual-channel method can improve the rate of
feature association from 13 Hz to 30 Hz, supporting the frame-rate requirement.
To accommodate the proposed feature association, we develop a Multi-State
Constrained Kalman Filter (MSCKF)-based relative pose estimator in the back-end
by fusing the local odometry from two UAVs together with the measurements of
common features. Experiments show that the dual-channel feature association
improves the rate of visual observation and enhances the real-time performance
of back-end estimator compared to the existing methods. Video -
https://youtu.be/UBAR1iP0GPk Supplementary video - https://youtu.be/nPq8EpVzJZM


  We prove that if a Borel probability measure (\mu) on (\T) is invariant under
the action of a "large" multiplicative semigroup (lower logarithmic density is
positive) and the action of the whole semigroup is ergodic then (\mu) is either
Lebesgue or has finite support.


  Assuming that the Generalized Riemann Hypothesis (GRH) holds, we prove an
explicit formula for the number of representations of an integer as a sum of
$k\geq 5$ primes. Our error terms in such a formula improve by some logarithmic
factors an analogous result by Friedlander-Goldston.


  Let $\operatorname{G}$ be a finite groupoid and $\alpha=(S_g,\alpha_g)_{g\in
\operatorname{G}}$ a unital partial action of group-type of $\operatorname{G}$
on a commutative ring $S=\oplus_{y\in\operatorname{G}_0}S_y$. We shall prove a
Galois correspondence between a class of wide subgroupoids of
$\operatorname{G}$ and a class of subrings of $S$. We recover known results for
global groupoid actions and we give several examples to illustrate the
correspondence.


  This paper presents a hypothesis testing method given independent samples
from a number of connected populations. The method is motivated by a forestry
project for monitoring change in the strength of lumber. Traditional practice
has been built upon nonparametric methods which ignore the fact that these
populations are connected. By pooling the information in multiple samples
through a density ratio model, the proposed empirical likelihood method leads
to a more efficient inference and therefore reduces the cost in applications.
The new test has a classical chi-square null limiting distribution. Its power
function is obtained under a class of local alternatives. The local power is
found increased even when some underlying populations are unrelated to the
hypothesis of interest. Simulation studies confirm that this test has better
power properties than potential competitors, and is robust to model
misspecification. An application example to lumber strength is included.


  AISHELL-1 is by far the largest open-source speech corpus available for
Mandarin speech recognition research. It was released with a baseline system
containing solid training and testing pipelines for Mandarin ASR. In AISHELL-2,
1000 hours of clean read-speech data from iOS is published, which is free for
academic usage. On top of AISHELL-2 corpus, an improved recipe is developed and
released, containing key components for industrial applications, such as
Chinese word segmentation, flexible vocabulary expension and phone set
transformation etc. Pipelines support various state-of-the-art techniques, such
as time-delayed neural networks and Lattic-Free MMI objective funciton. In
addition, we also release dev and test data from other channels(Android and
Mic). For research community, we hope that AISHELL-2 corpus can be a solid
resource for topics like transfer learning and robust ASR. For industry, we
hope AISHELL-2 recipe can be a helpful reference for building meaningful
industrial systems and products.


  We consider a certain family of Kudla-Rapoport cycles on an integral model of
a Shimura variety attached to a unitary group of signature (1,1), and prove
that the arithmetic degrees of these cycles can be identified with the Fourier
coefficients of the central derivative of an Eisenstein series of genus 2. The
integral model in question parametrizes abelian surfaces equipped with a
non-principal polarization and an action of an imaginary quadratic number ring,
and in this setting the cycles are degenerate: they may contain components of
positive dimension. This result can be viewed as confirmation, in the
degenerate setting and for dimension 2, of conjectures of Kudla and
Kudla-Rapoport that predict relations between the intersection numbers of
special cycles and the Fourier coefficients of automorphic forms.


  This paper deals with a challenging, frequently encountered, yet not properly
investigated problem in two-frame optical flow estimation. That is, the input
frames are compounds of two imaging layers -- one desired background layer of
the scene, and one distracting, possibly moving layer due to transparency or
reflection. In this situation, the conventional brightness constancy constraint
-- the cornerstone of most existing optical flow methods -- will no longer be
valid. In this paper, we propose a robust solution to this problem. The
proposed method performs both optical flow estimation, and image layer
separation. It exploits a generalized double-layer brightness consistency
constraint connecting these two tasks, and utilizes the priors for both of
them. Experiments on both synthetic data and real images have confirmed the
efficacy of the proposed method. To the best of our knowledge, this is the
first attempt towards handling generic optical flow fields of two-frame images
containing transparency or reflection.


  We explore a simple theoretical model to describe the properties of Bose
condensed para-excitons in Cu2O. Taking into account the exciton-phonon
interaction and introducing a coherent phonon part of the moving condensate, we
derive the dynamic equations for the exciton-phonon condensate. Within the Bose
approximation for excitons, we discuss the conditions for the moving
inhomogeneous condensate to appear in the crystal. We calculate the condensate
wave function and energy and a collective excitation spectrum in the
semiclassical approximation. The stability conditions of the moving condensate
are analyzed by use of Landau arguments, and two critical velocities appear in
the theory. Finally, we apply our model to describe the recently observed
interference between two coherent exciton-phonon packets in Cu2O.


  Ever since the development of GPT-3 in the natural language processing (NLP)
field, in-context learning (ICL) has played an essential role in utilizing
large language models (LLMs). By presenting the LM utterance-label
demonstrations at the input, the LM can accomplish few-shot learning without
relying on gradient descent or requiring explicit modification of its
parameters. This enables the LM to perform various downstream tasks in a
black-box manner. Despite the success of ICL in NLP, little work is exploring
the possibility of ICL in speech processing. This study is the first work
exploring ICL for speech classification tasks with textless speech LM. We first
show that the current speech LM lacks the ICL capability. We then perform
warmup training on the speech LM, equipping the LM with demonstration learning
capability. This paper explores and proposes the first speech LM capable of
performing unseen classification tasks in an ICL manner.


  We prove the Wiener-Hopf factorization for Markov Additive processes. We
derive also Spitzer-Rogozin theorem for this class of processes which serves
for obtaining Kendall's formula and Fristedt representation of the cumulant
matrix of the ladder epoch process. Finally, we also obtain the so-called
ballot theorem.


  We present 250~GHz continuum and H29alpha line data toward W49N:A2, a
hypercompact HII region ionized by an O9 star. The data obtained with ALMA at a
resolution of ~0"05 (600 au) confirmed the presence of an ionized ring with a
radius of~700 au inclined by ~50degree (0degree for pole-on). It has a width of
~1000 au and is relatively flat with a scale height of less than several
hundred au. The tilted ring, or the apparent ellipse, has a prominent velocity
difference between its NW and SE ridges along the minor axis, suggesting that
it is expanding in the equatorial plane at a velocity of 13.2 km/s. The ring
also shows a hint of rotation at 2.7 km/s, which is significantly (2.5sigma)
smaller than the Kepler velocity of 5.2 km/s at its radius around the 20 M$_sun
star. This can be interpreted that the ring gas has been transported from the
radius of ~170 au by conserving its original specific angular momentum that it
had there. The ionized ring may thus be a remnant of the accretion disk that
fed the O9 star, whose radiation or magnetic activities became so strong that
the disk accretion was reversed due to the intense thermal or
magneto-hydrodynamic pressure around the star. The data has revealed a rare
example of how a massive star terminates its accretion at the end of its
formation, transforming a hypercompact HII region into an ultracompact HII
region.


  In this work we solve the problem of robustly learning a high-dimensional
Gaussian mixture model with $k$ components from $\epsilon$-corrupted samples up
to accuracy $\widetilde{O}(\epsilon)$ in total variation distance for any
constant $k$ and with mild assumptions on the mixture. This robustness
guarantee is optimal up to polylogarithmic factors. The main challenge is that
most earlier works rely on learning individual components in the mixture, but
this is impossible in our setting, at least for the types of strong robustness
guarantees we are aiming for. Instead we introduce a new framework which we
call {\em strong observability} that gives us a route to circumvent this
obstacle.


  Gamma-ray binaries allow us to study physical processes such as particle
acceleration up to very-high energies and gamma-ray emission and absorption
with changing geometrical configurations on a periodic basis. These sources
produce outflows of radio-emitting particles whose structure can be imaged with
Very Long Baseline Interferometry (VLBI). We have studied the changing
morphology of the gamma-ray binaries LS I +61 303 and LS 5039, and we have
discovered the extended emission of PSR B1259-63 and HESS J0632+057. Based on
these results, we have established the basic properties and behaviour of the
radio emission of gamma-ray binaries on AU scales, and we have contributed to
find characteristics that are common to all of them. Here we present the most
relevant properties of each source and the general properties of gamma-ray
binaries, and we describe the implications on the nature of these binary
systems.


  Building accurate Deep Learning (DL) models for brain age prediction is a
very relevant topic in neuroimaging, as it could help better understand
neurodegenerative disorders and find new biomarkers. To estimate accurate and
generalizable models, large datasets have been collected, which are often
multi-site and multi-scanner. This large heterogeneity negatively affects the
generalization performance of DL models since they are prone to overfit
site-related noise. Recently, contrastive learning approaches have been shown
to be more robust against noise in data or labels. For this reason, we propose
a novel contrastive learning regression loss for robust brain age prediction
using MRI scans. Our method achieves state-of-the-art performance on the
OpenBHB challenge, yielding the best generalization capability and robustness
to site-related noise.


  We discuss the field independent additive constant in Wilson actions
carefully within the exact renormalization group formalism. The additive
constant does not affect the correlation functions of fields normalized by the
partition function, and for that reason it is often ignored. But it is an
essential part of the partition function, and in the limit where the UV cutoff
goes to zero, the constant gives a renormalized vacuum energy density. We
discuss two concrete examples: the Gaussian theory and the linear sigma model
in the large $N$ limit.


  We report initial results of the first flight of the Antarctic Impulsive
Transient Antenna (ANITA-1) 2006-2007 Long Duration Balloon flight, which
searched for evidence of a diffuse flux of cosmic neutrinos above energies of 3
EeV. ANITA-1 flew for 35 days looking for radio impulses due to the Askaryan
effect in neutrino-induced electromagnetic showers within the Antarctic ice
sheets. We report here on our initial analysis, which was performed as a blind
search of the data. No neutrino candidates are seen, with no detected physics
background. We set model-independent limits based on this result. Upper limits
derived from our analysis rule out the highest cosmogenic neutrino models. In a
background horizontal-polarization channel, we also detect six events
consistent with radio impulses from ultra-high energy extensive air showers.


  Stochastic optimization algorithms are widely used for large-scale data
analysis due to their low per-iteration costs, but they often suffer from slow
asymptotic convergence caused by inherent variance. Variance-reduced techniques
have been therefore used to address this issue in structured sparse models
utilizing sparsity-inducing norms or $\ell_0$-norms. However, these techniques
are not directly applicable to complex (non-convex) graph sparsity models,
which are essential in applications like disease outbreak monitoring and social
network analysis. In this paper, we introduce two stochastic variance-reduced
gradient-based methods to solve graph sparsity optimization: GraphSVRG-IHT and
GraphSCSG-IHT. We provide a general framework for theoretical analysis,
demonstrating that our methods enjoy a linear convergence speed. Extensive
experiments validate


  Lung ultrasound (LUS) is an important imaging modality used by emergency
physicians to assess pulmonary congestion at the patient bedside. B-line
artifacts in LUS videos are key findings associated with pulmonary congestion.
Not only can the interpretation of LUS be challenging for novice operators, but
visual quantification of B-lines remains subject to observer variability. In
this work, we investigate the strengths and weaknesses of multiple deep
learning approaches for automated B-line detection and localization in LUS
videos. We curate and publish, BEDLUS, a new ultrasound dataset comprising
1,419 videos from 113 patients with a total of 15,755 expert-annotated B-lines.
Based on this dataset, we present a benchmark of established deep learning
methods applied to the task of B-line detection. To pave the way for
interpretable quantification of B-lines, we propose a novel "single-point"
approach to B-line localization using only the point of origin. Our results
show that (a) the area under the receiver operating characteristic curve ranges
from 0.864 to 0.955 for the benchmarked detection methods, (b) within this
range, the best performance is achieved by models that leverage multiple
successive frames as input, and (c) the proposed single-point approach for
B-line localization reaches an F1-score of 0.65, performing on par with the
inter-observer agreement. The dataset and developed methods can facilitate
further biomedical research on automated interpretation of lung ultrasound with
the potential to expand the clinical utility.


  High-performance computing on shared-memory/multi-core architectures could
suffer from non-negligible performance bottlenecks due to coordination
algorithms, which are nevertheless necessary to ensure the overall correctness
and/or to support the execution of housekeeping operations, e.g. to recover
computing resources (e.g., memory). Although more complex in
design/development, a paradigm switch from classical coordination algorithms to
wait-free ones could significantly boost the performance of HPC applications.
  In this paper we explore the relevance of this paradigm shift in
shared-memory architectures, by focusing on the context of Parallel Discrete
Event Simulation, where the Global Virtual Time (GVT) represents a fundamental
coordination algorithm. It allows to compute the lower bound on the value of
the logical time passed through by all the entities participating in a
parallel/distributed computation. Hence it can be used to discriminate what
events belong to the past history of the computation---thus being considered as
committed---and allowing for memory recovery (e.g. of obsolete logs that were
taken in order to support state recoverability) and non-revokable operations
(e.g. I/O).
  We compare the reference (blocking) algorithm for shared memory, the one
proposed by by Fujimoto and Hybinette \cite{Fuj97}, with an innovative
wait-free implementation, emphasizing on what design choices must be made to
enforce this paradigm shift, and what are the performance implications of
removing critical sections in coordination algorithms.


  Data-dependent access patterns of an application to an untrusted storage
system are notorious for leaking sensitive information about the user's data.
Previous research has shown how an adversary capable of monitoring both read
and write requests issued to the memory can correlate them with the application
to learn its sensitive data. However, information leakage through only the
write access patterns is less obvious and not well studied in the current
literature. In this work, we demonstrate an actual attack on power-side-channel
resistant Montgomery's ladder based modular exponentiation algorithm commonly
used in public key cryptography. We infer the complete 512-bit secret exponent
in $\sim3.5$ minutes by virtue of just the write access patterns of the
algorithm to the main memory. In order to learn the victim algorithm's write
access patterns under realistic settings, we exploit a compromised DMA device
to take frequent snapshots of the application's address space, and then run a
simple differential analysis on these snapshots to find the write access
sequence. The attack has been shown on an Intel Core(TM) i7-4790 3.60GHz
processor based system. We further discuss a possible attack on McEliece
public-key cryptosystem that also exploits the write-access patterns to learn
the secret key.


  We describe some families of differentiable vector fields with the Hopf
bifurcation at infinity, without assuming the continuous differentiability.
These vector fields have isolated singular points on the plane, and the initial
families are obtained by special perturbations at infinity of a vector field
with some spectral property, for instance the dissipativity. The strong
domination imposed by the spectral condition in the differentiable vector field
is used, and then we do not apply the standard Poincar\'e compactification.
Moreover, the perturbation of planar systems with a global period annulus is
also considered.


  The Green-Schwarz covariant N=2 superstring action can be consistently
deduced as the action of the Wess-Zumino-Witten (WZW) sigma model defined on
the direct product of two N=1, D=10 Poincar\'e supertranslation groups.
Generalizing this result, we construct new WZW sigma models on the supergroups
with a nonabelian even part and interpret them as models of superstrings moving
on the supergroup manifolds. We show that these models are completely
integrable and in some special cases possess fermionic kappa-symmetry.


  A partial edge drawing (PED) of a graph is a variation of a node-link
diagram. PED draws a link, which is a partial visual representation of an edge,
and reduces visual clutter of the node-link diagram. However, more time is
required to read a PED to infer undrawn parts. The authors propose a morphing
edge drawing (MED), which is a PED that changes with time. In MED, links morph
between partial and complete drawings; thus, a reduced load for estimation of
undrawn parts in a PED is expected. Herein, a formalization of MED is shown
based on a formalization of PED. Then, requirements for the scheduling of
morphing are specified. The requirements inhibit morphing from crossing and
shorten the overall time for morphing the edges. Moreover, an algorithm for a
scheduling method implemented by the authors is illustrated and the
effectiveness of PED from a reading time viewpoint is shown through an
experimental evaluation.


  A graph is called diameter-$k$-critical if its diameter is $k$, and the
removal of any edge strictly increases the diameter. In this paper, we prove
several results related to a conjecture often attributed to Murty and Simon,
regarding the maximum number of edges that any diameter-$k$-critical graph can
have. In particular, we disprove a longstanding conjecture of Caccetta and
H\"aggkvist (that in every diameter-2-critical graph, the average edge-degree
is at most the number of vertices), which promised to completely solve the
extremal problem for diameter-2-critical graphs.
  On the other hand, we prove that the same claim holds for all higher
diameters, and is asymptotically tight, resolving the average edge-degree
question in all cases except diameter-2. We also apply our techniques to prove
several bounds for the original extremal question, including the correct
asymptotic bound for diameter-$k$-critical graphs, and an upper bound of
$(\frac{1}{6} + o(1))n^2$ for the number of edges in a diameter-3-critical
graph.


  We present a detailed study of the circumstellar envelope of the
post-asymptotic giant branch ``21 micron object'' HD 56126. We build a detailed
dust radiative transfer model of the circumstellar envelope in order to derive
the dust composition and mass, and the mass-loss history of the star. To model
the emission of the dust we use amorphous carbon, hydrogenated amorphous
carbon, magnesium sulfide and titanium carbide. We present a detailed
parametrisation of the optical properties of hydrogenated amorphous carbon as a
function of H/C content. The mid-infrared imaging and spectroscopy is best
reproduced by a single dust shell from 1.2 to 2.6 arcsec radius around the
central star. This shell originates from a short period during which the
mass-loss rate exceeded 10^(-4) M_sun/yr. We find that the strength of the
``21'' micron feature poses a problem for the TiC identification. The low
abundance of Ti requires very high absorption cross-sections in the ultraviolet
and visible wavelength range to explain the strength of the feature. Other
nano-crystalline metal carbides should be considered as well. We find that
hydrogenated amorphous carbon in radiative equilibrium with the local radiation
field does not reach a high enough temperature to explain the strength of the
3.3-3.4 and 6-9 micron hydrocarbon features relative to the 11-17 micron
hydrocarbon features. We propose that the carriers of these hydrocarbon
features are not in radiative equilibrium but are transiently heated to high
temperature. We find that 2 per cent of the dust mass is required to explain
the strength of the ``30'' micron feature, which fits well within the measured
atmospheric abundance of Mg and S. This further strengthens the MgS
identification of the ``30'' micron feature.


  Alcohol consumption interferes with the functioning of multiple organ
systems, causing changes in the chemistry, physiology and pathology of tissues
and cellular organelles. Although epigenetic modifications underlie the
development of cancer, exposure to carcinogenic chemicals, such as alcohol, can
also contribute to disease development. However, the effects of chronic
alcoholism on normal or pre-carcinogenic cells/tissues in different organelles
are not well understood. Therefore, we herein study the effect of alcohol
consumption on colonic nucleus using control and azoxymethane (AOM) and dextran
sulfate sodium (DSS) treated carcinogenic mice. Previous studies showed that
progression of carcinogenesis is associated with increase in the degree of
intranuclear nanoscale structural disorder. In the present work, we quantify
the degree of nanostructural disorder as a measure of carcinogenesis. To
accomplish this, transmission electron microscopy (TEM) imaging of respective
colonic epithelial cell nuclei are used to construct disordered optical
lattices, and the properties of nanoscale disorder are then studied by
analyzing the inverse participation ratio (IPR) of the spatially localized
eigenfunctions of these optical lattices. Nanoscale structural disorder
strength, as a marker of cancer progression, is measured in the length scale of
10 to 75 nm. Results show no significant visible effect in nanoscale structural
changes on colon cell nuclei from alcohol exposure. However, alcohol was found
to act as an enhancer of nanoscale disorder in precancerous cells and, hence,
carcinogenic processes. To the best of our knowledge, this is the first study
to quantify the effect of alcohol on early carcinogenic biological cells, using
mesoscopic condensed matter physics.


  Dijkgraaf and Vafa (DV) have conjectured that the exact superpotential for a
large class of N=1 SUSY gauge theories can be extracted from the planar limit
of a certain holomorphic matrix integral. We test their proposal against
existing knowledge for a family of deformations of N=4 SUSY Yang-Mills theory
involving an arbitrary polynomial superpotential for one of the three adjoint
chiral superfields. Specifically, we compare the DV prediction for these models
with earlier results based on the connection between SUSY gauge theories and
integrable systems. We find complete agreement between the two approaches. In
particular we show how the DV proposal allows the extraction of the exact
eigenvalues of the adjoint scalar in the confining vacuum and hence computes
all related condensates of the finite-N gauge theory. We extend these results
to include Leigh-Strassler deformations of the N=4 theory.


  New fragmentation functions (FFs) are extracted from a NLO QCD fit to the
preliminary COMPASS data on pion and kaon multiplicities. It is shown that the
new kaon FFs are very different from those of De Florian at al. (DSS) and Hirai
et al. (HKNS). The sensitivity of the extracted polarized PDFs to the new FFs
is discussed.


  We study the full moduli space of vacua of 6d worldvolume SCFTs on M5 branes
probing an $A$-type singularity, focusing on the geometric incarnation of the
discrete gauging mechanism which acts as a discrete quotient on the Higgs
branch fibered over the tensor branch. We combine insights from brane
constructions and magnetic quiver techniques, in which discrete gauging is
implemented through the concept of decoration introduced in [arXiv:2202.01218].
We discover and characterize new transverse slices between phases of 6d SCFTs,
identifying some of them with a family of isolated symplectic singularities
recently discovered in [arXiv:2112.15494], and conjecturing the existence of
two new isolated symplectic singularities.


  Due to the high efficiency of planet detections, current microlensing planet
searches focus on high-magnification events. High-magnification events are
sensitive to remote binary companions as well and thus a sample of
wide-separation binaries are expected to be collected as a byproduct. In this
paper, we show that characterizing binaries for a portion of this sample will
be difficult due to the degeneracy of the binary-lensing parameters. This
degeneracy arises because the perturbation induced by the binary companion is
well approximated by the Chang-Refsdal lensing for binaries with separations
greater than a certain limit. For binaries composed of equal mass lenses, we
find that the lens binarity can be noticed up to the separations of $\sim 60$
times of the Einstein radius corresponding to the mass of each lens. Among
these binaries, however, we find that the lensing parameters can be determined
only for a portion of binaries with separations less than $\sim 20$ times of
the Einstein radius.