  Domain generalization (DG) has been a hot topic in image recognition, with a
goal to train a general model that can perform well on unseen domains.
Recently, federated learning (FL), an emerging machine learning paradigm to
train a global model from multiple decentralized clients without compromising
data privacy, brings new challenges, also new possibilities, to DG. In the FL
scenario, many existing state-of-the-art (SOTA) DG methods become ineffective,
because they require the centralization of data from different domains during
training. In this paper, we propose a novel domain generalization method for
image recognition under federated learning through cross-client style transfer
(CCST) without exchanging data samples. Our CCST method can lead to more
uniform distributions of source clients, and thus make each local model learn
to fit the image styles of all the clients to avoid the different model biases.
Two types of style (single image style and overall domain style) with
corresponding mechanisms are proposed to be chosen according to different
scenarios. Our style representation is exceptionally lightweight and can hardly
be used for the reconstruction of the dataset. The level of diversity is also
flexible to be controlled with a hyper-parameter. Our method outperforms recent
SOTA DG methods on two DG benchmarks (PACS, OfficeHome) and a large-scale
medical image dataset (Camelyon17) in the FL setting. Last but not least, our
method is orthogonal to many classic DG methods, achieving additive performance
by combined utilization.


  Here we report on the formation of hybrid organic-metal composites via the
incorporation of selected amino acids into the crystal lattice of single
crystals of gold. We used electron microscopy, chemical analysis, and high
resolution synchrotron powder XRD to examine these composites. Crystal shape,
as well as atomic concentrations of occluded amino acids and their impact on
the crystal structure of gold, were determined.


  In this paper, we aim to address issues of (1) joint spatial-temporal
modeling and (2) side information injection for deep-learning based in-loop
filter. For (1), we design a deep network with both progressive rethinking and
collaborative learning mechanisms to improve quality of the reconstructed
intra-frames and inter-frames, respectively. For intra coding, a Progressive
Rethinking Network (PRN) is designed to simulate the human decision mechanism
for effective spatial modeling. Our designed block introduces an additional
inter-block connection to bypass a high-dimensional informative feature before
the bottleneck module across blocks to review the complete past memorized
experiences and rethinks progressively. For inter coding, the current
reconstructed frame interacts with reference frames (peak quality frame and the
nearest adjacent frame) collaboratively at the feature level. For (2), we
extract both intra-frame and inter-frame side information for better context
modeling. A coarse-to-fine partition map based on HEVC partition trees is built
as the intra-frame side information. Furthermore, the warped features of the
reference frames are offered as the inter-frame side information. Our PRN with
intra-frame side information provides 9.0% BD-rate reduction on average
compared to HEVC baseline under All-intra (AI) configuration. While under
Low-Delay B (LDB), Low-Delay P (LDP) and Random Access (RA) configuration, our
PRN with inter-frame side information provides 9.0%, 10.6% and 8.0% BD-rate
reduction on average respectively. Our project webpage is
https://dezhao-wang.github.io/PRN-v2/.


  The chain rule for the Shannon and von Neumann entropy, which relates the
total entropy of a system to the entropies of its parts, is of central
importance to information theory. Here we consider the chain rule for the more
general smooth min- and max-entropy, used in one-shot information theory. For
these entropy measures, the chain rule no longer holds as an equality, but
manifests itself as a set of inequalities that reduce to the chain rule for the
von Neumann entropy in the i.i.d. case.


  Many models of supersymmetry breaking involve particles with weak scale mass
and Planck mass suppressed couplings. Coherent production of such particles in
the early universe destroys the successful predictions of nucleosynthesis. We
show that this problem may be solved by a brief period of weak scale inflation.
Furthermore the inflaton potential for such an inflation naturally arises from
the same assumptions which lead to the cosmological problem. Successful
baryogenesis and preservation of density fluctuations for large scale structure
formation are also possible in this scenario.


  This paper has been withdrawn by the authors owing to a mistake in the proof
of the basic lemma.


  Gibbs statistical mechanics is derived for the Hamiltonian system coupling
self-consistently a wave to N particles. This identifies Landau damping with a
regime where a second order phase transition occurs. For nonequilibrium initial
data with warm particles, a critical initial wave intensity is found: above it,
thermodynamics predicts a finite wave amplitude in the limit of infinite N;
below it, the equilibrium amplitude vanishes. Simulations support these
predictions providing new insight on the long-time nonlinear fate of the wave
due to Landau damping in plasmas.


  We investigate the interplay between the quantum coherence and statistics in
electrically driven nano-structures. We obtain expression for the admittance
and the current noise for a driven nano-capacitor in terms of the Floquet
scattering matrix and derive a non-equilibrium fluctuation-dissipation
relation. As an interplay between the quantum phase coherence and the many-body
correlation, the admittance has peak values whenever the noise power shows a
step as a function of near-by gate voltage.
  Our theory is demonstrated by calculating the admittance and noise of driven
double quantum dots.


  Photoinduced spin-charge interconversion in semiconductors with spin-orbit
coupling could provide a route to optically addressable spintronics without the
use of external magnetic fields. A central question is whether the resulting
spin-associated charge currents are robust to structural disorder, which is
inherent to polycrystalline semiconductors that are desirable for device
applications. Using femtosecond circular polarization-resolved pump-probe
microscopy on polycrystalline halide perovskite thin films, we observe the
photoinduced ultrafast formation of spin-polarized positive and, unexpectedly,
negative spin domains on the micron scale formed through lateral currents.
Further, the polarization of these domains and lateral transport direction is
switched upon switching the polarization of the pump helicity. Micron scale
variations in the intensity of optical second-harmonic generation and vertical
piezoresponse suggest that the spin domain formation is driven by the presence
of strong local inversion symmetry breaking via inter-grain structural
disorder. We propose that this leads to spatially varying Rashba-like spin
textures that drive spin-momentum locked currents, leading to local spin
accumulation. Our results establish ultrafast spin domain formation in
polycrystalline semiconductors as a new optically addressable platform for
nanoscale spin-device physics.


  We give some applications of augmentation quotients of free group rings in
group theory.


  We report the results of an X-ray observing campaign on the massive, evolved
star Eta Carinae, concentrating on the 2003 X-ray minimum as seen by the
XMM-Newton observatory. These are the first spatially-resolved X-ray monitoring
observations of the stellar X-ray spectrum during the minimum. The hard X-ray
emission, believed to be associated with the collision of Eta Carinae's wind
with the wind from a massive companion star, varied strongly in flux on
timescales of days, but not significantly on timescales of hours. The lowest
X-ray flux in the 2-10 keV band seen by XMM-Newton was only 0.7% of the maximum
seen by RXTE just before the X-ray minimum. The slope of the X-ray continuum
above 5 keV did not vary in any observation, which suggests that the electron
temperature of the hottest plasma associated with the stellar source did not
vary significantly at any phase. Through the minimum, the absorption to the
stellar source increased by a factor of 5-10 to NH ~3-4E23 cm-2. The thermal Fe
XXV emission line showed significant excesses on both the red and blue sides of
the line outside the minimum and exhibited an extreme red excess during the
minimum. The Fe fluorescence line at 6.4 keV increased in equivalent width from
100 eV outside the minimum to 200 eV during the minimum. From these observed
features, we discuss two possible causes of the X-ray minimum; the eclipse of
the X-ray plasma and an intrinsic fading of the X-ray emissivity. The drop in
the colliding wind X-ray emission also revealed the presence of an additional
X-ray component which exhibited no variation on timescales of weeks to years.
This component may be produced by the collision of high speed outflows at v
\~1000-2000 km s-1 from Eta Carinae with ambient gas within a few thousand AU
from the star.


  I propose that the relatively high eccentricity found in some tidally
strongly interacting binary systems, where the mass-losing star is an evolved
giant star, e.g., an asymptotic giant branch star, is caused by an enhanced
mass loss rate during periastron passages. Tidal interaction by itself will
circularize the orbits of these systems in a relatively short time, hence a
mechanism which increases the eccentricity on a shorter time scale is required.
The proposed scenario predicts that the nebula formed by the mass loss process
possesses a prominent departure from axisymmetrical structure. Such a departure
is observed in the Red Rectangle, which has a central binary system, HD 44179,
with an orbital period of 318 days, and an eccentricity of e=0.38.


  In this paper, we present a dynamic non-diagonal regularization for interior
point methods. The non-diagonal aspect of this regularization is implicit,
since all the off-diagonal elements of the regularization matrices are
cancelled out by those elements present in the Newton system, which do not
contribute important information in the computation of the Newton direction.
Such a regularization has multiple goals. The obvious one is to improve the
spectral properties of the Newton system solved at each iteration of the
interior point method. On the other hand, the regularization matrices introduce
sparsity to the aforementioned linear system, allowing for more efficient
factorizations. We also propose a rule for tuning the regularization
dynamically based on the properties of the problem, such that sufficiently
large eigenvalues of the non-regularized system are perturbed insignificantly.
This alleviates the need of finding specific regularization values through
experimentation, which is the most common approach in literature. We provide
perturbation bounds for the eigenvalues of the non-regularized system matrix
and then discuss the spectral properties of the regularized matrix. Finally, we
demonstrate the efficiency of the method applied to solve standard small and
medium-scale linear and convex quadratic programming test problems.


  George Lusztig conjectured that asymptotic affine Hecke algebra of a simply
connected group can be explicitly described in terms of convolution algebras.
Main Theorem of this note (which is a continuation of RT/0010089) is a weak
version of this Conjecture. This version is strong enough to reprove all
previously known results (due to Nanhua Xi) in this direction, for example the
case of type $\tilde A_n$, see QA/0010159.


  Sparse and short news headlines can be arbitrary, noisy, and ambiguous,
making it difficult for classic topic model LDA (latent Dirichlet allocation)
designed for accommodating long text to discover knowledge from them.
Nonetheless, some of the existing research about text-based crude oil
forecasting employs LDA to explore topics from news headlines, resulting in a
mismatch between the short text and the topic model and further affecting the
forecasting performance. Exploiting advanced and appropriate methods to
construct high-quality features from news headlines becomes crucial in crude
oil forecasting. To tackle this issue, this paper introduces two novel
indicators of topic and sentiment for the short and sparse text data. Empirical
experiments show that AdaBoost.RT with our proposed text indicators, with a
more comprehensive view and characterization of the short and sparse text data,
outperforms the other benchmarks. Another significant merit is that our method
also yields good forecasting performance when applied to other futures
commodities.


  Type Ia supernova explosions (SNIa) are fundamental sources of elements for
the chemical evolution of galaxies. They efficiently produce intermediate-mass
(with Z between 11 and 20) and iron group elements - for example, about 70% of
the solar iron is expected to be made by SNIa. In this work, we calculate
complete abundance yields for 39 models of SNIa explosions, based on three
progenitors - a 1.4M deflagration detonation model, a 1.0 double detonation
model and a 0.8 M double detonation model - and 13 metallicities, with 22Ne
mass fractions of 0, 1x10-7, 1x10-6, 1x10-5, 1x10-4, 1x10-3, 2x10-3, 5x10-3,
1x10-2, 1.4x10-2, 5x10-2, and 0.1 respectively. Nucleosynthesis calculations
are done using the NuGrid suite of codes, using a consistent nuclear reaction
network between the models. Complete tables with yields and production factors
are provided online at Zenodo: Yields. We discuss the main properties of our
yields in the light of the present understanding of SNIa nucleosynthesis,
depending on different progenitor mass and composition. Finally, we compare our
results with a number of relevant models from the literature.


  A major challenge in scene graph classification is that the appearance of
objects and relations can be significantly different from one image to another.
Previous works have addressed this by relational reasoning over all objects in
an image or incorporating prior knowledge into classification. Unlike previous
works, we do not consider separate models for perception and prior knowledge.
Instead, we take a multi-task learning approach, where we implement the
classification as an attention layer. This allows for the prior knowledge to
emerge and propagate within the perception model. By enforcing the model also
to represent the prior, we achieve a strong inductive bias. We show that our
model can accurately generate commonsense knowledge and that the iterative
injection of this knowledge to scene representations leads to significantly
higher classification performance. Additionally, our model can be fine-tuned on
external knowledge given as triples. When combined with self-supervised
learning and with 1% of annotated images only, this gives more than 3%
improvement in object classification, 26% in scene graph classification, and
36% in predicate prediction accuracy.


  Instead of adhering to the usual explanation of the Cronin effect in terms of
the broadening of the parton transverse momentum in the initial state, we show
that the enhancement of hadron production at moderate $p_T$ in d+Au collisions
is due to the recombination of soft and shower partons in the final state. Such
a mechanism can readily explain the decrease of the Cronin effect with
increasing rapidity. Furthermore, the effect should be larger for protons than
for pions.


  Ranking documents using Large Language Models (LLMs) by directly feeding the
query and candidate documents into the prompt is an interesting and practical
problem. However, researchers have found it difficult to outperform fine-tuned
baseline rankers on benchmark datasets. We analyze pointwise and listwise
ranking prompts used by existing methods and argue that off-the-shelf LLMs do
not fully understand these challenging ranking formulations. In this paper, we
propose to significantly reduce the burden on LLMs by using a new technique
called Pairwise Ranking Prompting (PRP). Our results are the first in the
literature to achieve state-of-the-art ranking performance on standard
benchmarks using moderate-sized open-sourced LLMs. On TREC-DL 2019&2020, PRP
based on the Flan-UL2 model with 20B parameters performs favorably with the
previous best approach in the literature, which is based on the blackbox
commercial GPT-4 that has 50x (estimated) model size, while outperforming other
LLM-based solutions, such as InstructGPT which has 175B parameters, by over 10%
for all ranking metrics. By using the same prompt template on seven BEIR tasks,
PRP outperforms supervised baselines and outperforms the blackbox commercial
ChatGPT solution by 4.2% and pointwise LLM-based solutions by more than 10% on
average NDCG@10. Furthermore, we propose several variants of PRP to improve
efficiency and show that it is possible to achieve competitive results even
with linear complexity.


  The glitch size, $ \Delta\nu/\nu $, inter-glitch time interval, $ t_{i} $ and
frequency of glitches in pulsars are key parameters in discussing glitch
phenomena. In this paper, the glitch sizes and inter-glitch time intervals were
statistically analysed in a sample of 168 pulsars with a total of 483 glitches.
The glitches were broadly divided into two groups. Those with $ \Delta\nu/\nu <
10^{-7} $ are regarded as small size glitches, while those with $ \Delta\nu/\nu
\geq 10^{-7} $ are regarded as relatively large size glitches. In the ensemble
of glitches, the distribution of $ \Delta\nu/\nu $ is seen to be bimodal as
usual. The distribution of inter-glitch time intervals is unimodal and the
inter-glitch time intervals between small and large size glitches are not
significantly different from each other. This observation shows that
inter-glitch time intervals are size independent. In addition, the distribution
of the ratio $ \Delta\nu/\nu:t_{i} $ in both small and large size glitches has
the same pattern. This observation suggests that a parameter which depends on
time, which could be the spin-down rate of a pulsar plays a similar role in the
processes that regulate both small and large size glitches. Equally this could
be an indication that a single physical mechanism, which could produce varying
glitch sizes at similar time-intervals could be responsible for both classes of
glitch sizes.


  Rotor-stator cavity flows are known to exhibit unsteady flow structures in
the form of circular and spiral rolls. While the origin of the spirals is well
understood, that of the circular rolls is not. In the present study the
axisymmetric flow in an aspect ratio $R/H=10$ cavity is revisited {numerically
using recent concepts and tools from bifurcation theory}. It is confirmed that
a linear instability takes place at a finite critical Reynolds number
$Re=Re_c$, and that there exists a subcritical branch of large amplitude
chaotic solutions. This motivates the search for subcritical finite-amplitude
solutions. The branch of periodic states born in a Hopf bifurcation at
$Re=Re_c$, identified using a Self-Consistent Method (SCM) and arclength
continuation, is found to be supercritical. The associated solutions only
exist, however, in a very narrow range of $Re$ and do not explain the
subcritical chaotic rolls. Another subcritical branch of periodic solutions is
found using the Harmonic Balance Method with an initial guess obtained by SCM.
In addition, edge states separating the steady laminar and chaotic regimes are
identified using a bisection algorithm. These edge states are bi-periodic in
time for most values of $Re$, {where} their dynamics is {analysed in detail}.
Both solution branches fold around at approximately the same value of $Re$,
which is lower than $Re_c$ yet still larger than the values reported in
experiments. This suggests that, at least in the absence of external forcing,
sustained chaotic rolls have their origin in the bifurcations from these
unstable solutions.


  Let Kv be the complete graph of order v and F be a set of 1-factors of Kv. In
this article we study the existence of a resolvable decomposition of Kv - F
into 3-stars when F has the minimum number of 1-factors. We completely solve
the case in which F has the minimum number of 1- factors, with the possible
exception of v in {40, 44, 52, 76, 92, 100, 280, 284, 328, 332, 428, 472, 476,
572}.


  3GPP LWIP Release 13 technology and its prestandard version Wi-Fi Boost have
recently emerged as an efficient LTE and Wi-Fi integration at the IP layer,
allowing uplink on LTE and downlink on Wi-Fi. This solves all the contention
problems of Wi-Fi and allows an optimum usage of the unlicensed band for
downlink. In this paper, we present a new feature of Wi-Fi Boost, its radio
link management, which allows to steer the downlink traffic between both LTE
and Wi-Fi upon congestion detection in an intelligent manner. This customised
congestion detection algorithm is based on IP probing, and can work with any
Wi-Fi access point. Simulation results in a typical enterprise scenario show
that LWIP R13 and Wi-Fi Boost can enhance network performance up to 5x and 6x
over LTE-only, and 4x and 5x over Wi-Fi only networks, respectively, and that
the the proposed radio link management can further improve Wi-Fi Boost
performance over LWIP R13 up to 19 %. Based on the promising results, this
paper suggests to enhance LWIP R13 user feedback in future LTE releases.


  A cylindrical pipe with closed ends and with a partition in-between exhibits
acoustic modes in the two, thus formed, one-dimensional cavities at certain
frequencies. A partial transmission through the partition leads to interaction
between the two cavities' modes and to multiple avoided crossings between
modes' frequencies as a function of the partition position. This is analogous
to a quantum system that has two multi-level and interacting sub-systems and
thus exhibits multiple avoided crossings. Such an acoustic analog is realized
and studied by measuring the sound transmission as a function of frequency
through a pipe with a partially transmitting and movable partition. An
excellent agreement is obtained between the experimental results and a simple
model based on sound wave transmission and reflection at different interfaces.


  How were surfaces evaluated before the invention of the sexagesimal place
value notation in Mesopotamia? This chapter examines a group of five tablets
containing tables for surfaces of squares and rectangles dated to the Early
Dynastic period (ca. 2600-2350 BCE) and unearthed in southern Mesopotamia. In
order to capture the methods used by ancient scribes to quantify surfaces,
special attention is paid to the layout and organization of the tables, as well
as to the way in which measurement values are written down. It is argued that
these methods vary according to the dimensions of the squares or rectangles
concerned: the quantification of small surfaces does not use the same
mathematical tools as the quantification of large parcels of land. The chapter
shows a reciprocal influence between the metrological systems adopted by the
ancient scribes and the methods of calculation of surface they implemented.
Some methods may reflect ancient land-surveying practices, and others may
testify the emergence of new mathematical concepts applied to all kinds of
surfaces, large or small. Ultimately, several different conceptualizations of
the notion of surface emerge from the examination of these tables.


  Particle settling is a pervasive process in nature, and centrifugation is a
much versatile separation technique. Yet, the results of settling and
ultracentrifugation experiments often appear to contradict the very law on
which they are based: Archimedes Principle - arguably, the oldest Physical Law.
The purpose of this paper is delving at the very roots of the concept of
buoyancy by means of a combined experimental-theoretical study on sedimentation
profiles in colloidal mixtures. Our analysis shows that the standard
Archimedes' principle is only a limiting approximation, valid for mesoscopic
particles settling in a molecular fluid, and we provide a general expression
for the actual buoyancy force. This "Generalized Archimedes Principle" accounts
for unexpected effects, such as denser particles floating on top of a lighter
fluid, which in fact we observe in our experiments.


  We study energy transport in the integrable $\mathbb Z_3$ parafermionic chain
using the partitioning protocol. By exploiting the Bethe-ansatz solution for
the thermodynamics of the system, we develop a generalized hydrodynamic
description of the non-equilibrium steady states, which we benchmark using
numerical simulations based on matrix product states. The model features a
low-energy conformal limit with central charge $c=4/5$, which affects the
low-temperature energy current, as we explicitly show. Moreover, we exploit
that, for energies close to the maximally excited state, the system is also
critical and described by a conformal field theory with $c=1$. By considering
the two halves prepared at two temperatures both low in value but opposite in
sign, we are able to investigate in an exact and controlled way the junction
between two conformal field theories with different central charges.
Notwithstanding the absence of global conformal invariance, we find results
that approximate to a high degree those of out-of-equilibrium conformal field
theories. Our study extends the generalized hydrodynamics to a novel framework,
where it can be profitably used for exploring new physical phenomena.


  Mediation analysis is widely used for investigating direct and indirect
causal pathways through which an effect arises. However, many mediation
analysis studies are challenged by missingness in the mediator and outcome. In
general, when the mediator and outcome are missing not at random, the direct
and indirect effects are not identifiable without further assumptions. In this
work, we study the identifiability of the direct and indirect effects under
some interpretable mechanisms that allow for missing not at random in the
mediator and outcome. We evaluate the performance of statistical inference
under those mechanisms through simulation studies and illustrate the proposed
methods via the National Job Corps Study.


  Multi-agent systems can be extremely efficient when working concurrently and
collaboratively, e.g., for transportation, maintenance, search and rescue.
Coordination of such teams often involves two aspects: (i) selecting
appropriate sub-teams for different tasks; (ii) designing collaborative control
strategies to execute these tasks. The former aspect can be combinatorial
w.r.t. the team size, while the latter requires optimization over joint
state-spaces under geometric and dynamic constraints. Existing work often
tackles one aspect by assuming the other is given, while ignoring their close
dependency. This work formulates such problems as combinatorial-hybrid
optimizations (CHO), where both the discrete modes of collaboration and the
continuous control parameters are optimized simultaneously and iteratively. The
proposed framework consists of two interleaved layers: the dynamic formation of
task coalitions and the hybrid optimization of collaborative behaviors. Overall
feasibility and costs of different coalitions performing various tasks are
approximated at different granularities to improve the computational
efficiency. At last, a Nash-stable strategy for both task assignment and
execution is derived with provable guarantee on the feasibility and quality.
Two non-trivial applications of collaborative transportation and dynamic
capture are studied against several baselines.


  The scaling physics of quantum Hall transport in optimized topological
insulators with a plateau precision of ~1/1000 e2/h is considered. Two
exponential scaling regimes are observed in temperature-dependent transport
dissipation, one of which accords with thermal activation behavior with a gap
of 2.8 meV (> 20 K), the other being attributed to variable range hopping (1-20
K). Magnetic field-driven plateau-to-plateau transition gives scaling relations
of (dR$_{xy}$/dB)$^{max}$ \propto T$^{-\kappa}$ and \DeltaB$^{-1}$ \propto
T$^{-\kappa}$ with a consistent exponent of \kappa ~ 0.2, which is half the
universal value for a conventional two-dimensional electron gas. This is
evidence of percolation assisted by quantum tunneling, and reveals the
dominance of electron-electron interaction of the topological surface states.


  The generalized least square (GLS) is one of the most basic tools in
regression analyses. A major issue in implementing the GLS is estimation of the
conditional variance function of the error term, which typically requires a
restrictive functional form assumption for parametric estimation or smoothing
parameters for nonparametric estimation. In this paper, we propose an
alternative approach to estimate the conditional variance function under
nonparametric monotonicity constraints by utilizing the isotonic regression
method. Our GLS estimator is shown to be asymptotically equivalent to the
infeasible GLS estimator with knowledge of the conditional error variance, and
involves only some tuning to trim boundary observations, not only for point
estimation but also for interval estimation or hypothesis testing. Our analysis
extends the scope of the isotonic regression method by showing that the
isotonic estimates, possibly with generated variables, can be employed as first
stage estimates to be plugged in for semiparametric objects. Simulation studies
illustrate excellent finite sample performances of the proposed method. As an
empirical example, we revisit Acemoglu and Restrepo's (2017) study on the
relationship between an aging population and economic growth to illustrate how
our GLS estimator effectively reduces estimation errors.


  A short and simple text carrying no emotion can represent some strong
emotions when reading along with its context, i.e., the same sentence can
express extreme anger as well as happiness depending on its context. In this
paper, we propose a Contextual Affect Detection (CAD) framework which learns
the inter-dependence of words in a sentence, and at the same time the
inter-dependence of sentences in a dialogue. Our proposed CAD framework is
based on a Gated Recurrent Unit (GRU), which is further assisted by contextual
word embeddings and other diverse hand-crafted feature sets. Evaluation and
analysis suggest that our model outperforms the state-of-the-art methods by
5.49% and 9.14% on Friends and EmotionPush dataset, respectively.


  Let $FI$ be a skeleton of the category of finite sets and injective maps, and
$FI^m$ the product of $m$ copies of $FI$. We prove that if an $FI^m$-module is
generated in degree $\leqslant d$ and related in degree $\leqslant r$, then its
regularity is bounded above by a function of $m$, $d$, and $r$.


  Light scalars may be ubiquitous in nature, and their quantum fluctuations can
produce large non-Gaussianity in the cosmic microwave background temperature
anisotropy. The non-Gaussianity may be accompanied with a small admixture of
isocurvature perturbations, which often have correlations with the curvature
perturbations. We present a general method to calculate the non-Gaussianity in
the adiabatic and isocurvature perturbations with and without correlations, and
see how it works in several explicit examples. We also show that they leave
distinct signatures on the bispectrum of the cosmic microwave background
temperature fluctuations.


  We investigate the impact of the surface brightness (SB) limit on the galaxy
stellar mass functions (GSMFs) using mock surveys generated from the Horizon
Run 5 (HR5) simulation. We compare the stellar-to-halo-mass relation, GSMF, and
size-stellar mass relation of the HR5 galaxies with empirical data and other
cosmological simulations. The mean SB of simulated galaxies are computed using
their effective radii, luminosities, and colors. To examine the cosmic SB
dimming effect, we compute $k$-corrections from the spectral energy
distributions of individual simulated galaxy at each redshift, apply the
$k$-corrections to the galaxies, and conduct mock surveys based on the various
SB limits. We find that the GSMFs are significantly affected by the SB limits
at a low-mass end. This approach can ease the discrepancy between the GSMFs
obtained from simulations and observations at $0.625\le z\le 2$. We also find
that a redshift survey with a SB selection limit of $\left<\mu_r\right>^e =$ 28
mag arcsec${}^{-2}$ will miss 20% of galaxies with $M_\star^g=10^{9}~{\rm
M_\odot}$ at $z=0.625$. The missing fraction of low-surface-brightness galaxies
increases to 50%, 70%, and 98% at $z=0.9$, 1.1, and 1.9, respectively, at the
SB limit.


  An optical vortex (OV) is a beam with spiral wave front and screw phase
dislocation. This kind of beams is attracting rising interest in various
fields. Here we theoretically proposed and experimentally realized a novel but
easy approach to generate optical vortices. We leverage the inherent
topological vortex structures of polarization around bound states in the
continuum (BIC) in the momentum space of two dimensional periodic structures,
e.g. photonic crystal slabs, to induce Pancharatnam-Berry phases to the beams.
This new class of OV generators operates in the momentum space, meaning that
there is no real-space center of structure. Thus, not only the fabrication but
also the practical alignment would be greatly simplified. Any even order of OV,
which is actually a quasi-non-diffractive high-order quasi-Bessel beam, at any
desired working wavelength could be achieved in principle. The proposed
approach expands the application of bound states in the continuum and
topological photonics.


  We model the evolution of the snow line in a protoplanetary disc. If the
magneto-rotational instability (MRI) drives turbulence throughout the disc,
there is a unique snow line outside of which the disc is icy. The snow line
moves closer to the star as the infall accretion rate drops. Because the snow
line moves inside the radius of the Earth's orbit, the formation of our
water-devoid planet is difficult with this model. However, protoplanetary discs
are not likely to be sufficiently ionised to be fully turbulent. A dead zone at
the mid-plane slows the flow of material through the disc and a steady state
cannot be achieved. We therefore model the evolution of the snow line also in a
time-dependent disc with a dead zone. As the mass is accumulating, the outer
parts of the dead zone become self gravitating, heat the massive disc and thus
the outer snow line does not come inside the radius of the Earth's orbit,
contrary to the fully turbulent disc model. There is a second, inner icy
region, within the dead zone, that moves inwards of the Earth's orbit after a
time of about 10^6 yr. With this model there is sufficient time and mass in the
disc for the Earth to form from water-devoid planetesimals at a radius of 1 AU.
Furthermore, the additional inner icy region predicted by this model may allow
for the formation of giant planets close to their host star without the need
for much migration.


  Air quality has major impact on a country's socio-economic position and
identifying major air pollution sources is at the heart of tackling the issue.
Spatially and temporally distributed air quality data acquisition across a
country as varied as India has been a challenge to such analysis. The launch of
the Sentinel-5P satellite has helped in the observation of a wider variety of
air pollutants than measured before at a global scale on a daily basis. In this
chapter, spatio-temporal multi pollutant data retrieved from Sentinel-5P
satellite is used to cluster states as well as districts in India and
associated average monthly pollution signature and trends depicted by each of
the clusters are derived and presented.The clustering signatures can be used to
identify states and districts based on the types of pollutants emitted by
various pollution sources.


  We consider a sample of 107 Gamma Ray Bursts (GRBs) for which early UV
emission was measured by Swift, and extrapolate the photon intensity to lower
energies. Protons accelerated in the GRB jet may interact with such photons to
produce charged pions and subsequently ultra high energy neutrinos
$\varepsilon_\nu\geq 10^{16}$ eV. We use simple energy conversion efficiency
arguments to predict the maximal neutrino flux expected from each GRB. We
estimate the neutrino detection rate at large area radio based neutrino
detectors and conclude that the early afterglow neutrino emission is too weak
to be detected even by next generation neutrino observatories.


  We study the effect of modified gravity on weak lensing in a class of
scalar-tensor theory that includes $f(R)$ gravity as a special case. These
models are designed to satisfy local gravity constraints by having a large
scalar-field mass in a region of high curvature. Matter density perturbations
in these models are enhanced at small redshifts because of the presence of a
coupling $Q$ that characterizes the strength between dark energy and
non-relativistic matter. We compute a convergence power spectrum of weak
lensing numerically and show that the spectral index and the amplitude of the
spectrum in the linear regime can be significantly modified compared to the
$\Lambda$CDM model for large values of $|Q|$ of the order of unity. Thus weak
lensing provides a powerful tool to constrain such large coupling scalar-tensor
models including $f(R)$ gravity.


  Galaxy clusters appear as extended sources in XMM-Newton images, but not all
extended sources are clusters. So, their proper classification requires visual
inspection with optical images, which is a slow process with biases that are
almost impossible to model. We tackle this problem with a novel approach, using
convolutional neural networks (CNNs), a state-of-the-art image classification
tool, for automatic classification of galaxy cluster candidates. We train the
networks on combined XMM-Newton X-ray observations with their optical
counterparts from the all-sky Digitized Sky Survey. Our data set originates
from the X-CLASS survey sample of galaxy cluster candidates, selected by a
specially developed pipeline, the XAmin, tailored for extended source detection
and characterisation. Our data set contains 1 707 galaxy cluster candidates
classified by experts. Additionally, we create an official Zooniverse citizen
science project, The Hunt for Galaxy Clusters, to probe whether citizen
volunteers could help in a challenging task of galaxy cluster visual
confirmation. The project contained 1 600 galaxy cluster candidates in total of
which 404 overlap with the expert's sample. The networks were trained on expert
and Zooniverse data separately. The CNN test sample contains 85
spectroscopically confirmed clusters and 85 non-clusters that appear in both
data sets. Our custom network achieved the best performance in the binary
classification of clusters and non-clusters, acquiring accuracy of 90 %,
averaged after 10 runs. The results of using CNNs on combined X-ray and optical
data for galaxy cluster candidate classification are encouraging and there is a
lot of potential for future usage and improvements.


  Recently, several works have studied the problem of view selection in graph
databases. However, existing methods cannot fully exploit the graph properties
of views, e.g., supergraph views and common subgraph views, which leads to a
low view utility and duplicate view content. To address the problem, we propose
an end-to-end graph view selection tool, G-View, which can judiciously generate
a view set from a query workload by exploring the graph properties of candidate
views and considering their efficacy. Specifically, given a graph query set and
a space budget, G-View translates each query to a candidate view pattern and
checks the query containment via a filtering-and-verification framework. G-View
then selects the views using a graph gene algorithm (GGA), which relies on a
three-phase framework that explores graph view transformations to reduce the
view space and optimize the view benefit. Finally, G-View generates the
extended graph views that persist all the edge-induced subgraphs to answer the
subgraph and supergraph queries simultaneously. Extensive experiments on
real-life and synthetic datasets demonstrated G-View achieved averagely 21x and
2x query performance speedup over two view-based methods while having 2x and 5x
smaller space overhead, respectively. Moreover, the proposed selection
algorithm, GGA, outperformed other selection methods in both effectiveness and
efficiency.


  With the growth of residential rooftop PV adoption in recent decades, the
problem of effective layout design has become increasingly important in recent
years. Although a number of automated methods have been introduced, these tend
to rely on simplifying assumptions and heuristics to improve computational
tractability. We demonstrate a fully automated layout design pipeline that
attempts to solve a more general formulation with greater geometric flexibility
that accounts for shading losses. Our approach generates rooftop areas from
satellite imagery and uses MINLP optimization to select panel positions,
azimuth angles and tilt angles on an individual basis rather than imposing any
predefined layouts. Our results demonstrate that shading plays a critical role
in automated rooftop PV optimization and significantly changes the resulting
layouts. Additionally, they suggest that, although several common heuristics
are often effective, they may not be universally suitable due to complications
resulting from geometric restrictions and shading losses. Finally, we evaluate
a few specific heuristics from the literature and propose a potential new rule
of thumb that may help improve rooftop solar energy potential when shading
effects are considered.


  A normal $k$-edge-coloring of a cubic graph is an edge-coloring with $k$
colors having the additional property that when looking at the set of colors
assigned to any edge $e$ and the four edges adjacent it, we have either exactly
five distinct colors or exactly three distinct colors. We denote by
$\chi'_{N}(G)$ the smallest $k$, for which $G$ admits a normal
$k$-edge-coloring. Normal $k$-edge-colorings were introduced by Jaeger in order
to study his well-known Petersen Coloring Conjecture. More precisely, it is
known that proving $\chi'_{N}(G)\leq 5$ for every bridgeless cubic graph is
equivalent to proving Petersen Coloring Conjecture and then, among others,
Cycle Double Cover Conjecture and Berge-Fulkerson Conjecture. Considering the
larger class of all simple cubic graphs (not necessarily bridgeless), some
interesting questions naturally arise. For instance, there exist simple cubic
graphs, not bridgeless, with $\chi'_{N}(G)=7$. On the other hand, the known
best general upper bound for $\chi'_{N}(G)$ was $9$. Here, we improve it by
proving that $\chi'_{N}(G)\leq7$ for any simple cubic graph $G$, which is best
possible. We obtain this result by proving the existence of specific no-where
zero $\mathbb{Z}_2^2$-flows in $4$-edge-connected graphs.


  We investigate a spectrum-sharing system with non-severely faded mutual
interference links, where both the secondary-to-primary and
primary-to-secondary channels have a Line-of-Sight (LoS) component. Based on a
Rician model for the LoS channels, we show, analytically and numerically, that
LoS interference hinders the achievable secondary user capacity. This is caused
by the poor dynamic range of the interference channels fluctuations when a
dominant LoS component exists. In order to improve the capacity of such system,
we propose the usage of an Electronically Steerable Parasitic Array Radiator
(ESPAR) antenna at the secondary terminals. An ESPAR antenna requires a single
RF chain and has a reconfigurable radiation pattern that is controlled by
assigning arbitrary weights to M orthonormal basis radiation patterns. By
viewing these orthonormal patterns as multiple virtual dumb antennas, we
randomly vary their weights over time creating artificial channel fluctuations
that can perfectly eliminate the undesired impact of LoS interference. Because
the proposed scheme uses a single RF chain, it is well suited for compact and
low cost mobile terminals.


  Generic sentence embeddings provide a coarse-grained approximation of
semantic textual similarity but ignore specific aspects that make texts
similar. Conversely, aspect-based sentence embeddings provide similarities
between texts based on certain predefined aspects. Thus, similarity predictions
of texts are more targeted to specific requirements and more easily
explainable. In this paper, we present AspectCSE, an approach for aspect-based
contrastive learning of sentence embeddings. Results indicate that AspectCSE
achieves an average improvement of 3.97% on information retrieval tasks across
multiple aspects compared to the previous best results. We also propose using
Wikidata knowledge graph properties to train models of multi-aspect sentence
embeddings in which multiple specific aspects are simultaneously considered
during similarity predictions. We demonstrate that multi-aspect embeddings
outperform single-aspect embeddings on aspect-specific information retrieval
tasks. Finally, we examine the aspect-based sentence embedding space and
demonstrate that embeddings of semantically similar aspect labels are often
close, even without explicit similarity training between different aspect
labels.


  In autonomous systems, a motion planner generates reference trajectories
which are tracked by a low-level controller. For safe operation, the motion
planner should account for inevitable controller tracking error when generating
avoidance trajectories. In this article we present a method for generating
provably safe tracking error bounds, while reducing over-conservatism that
exists in existing methods. We achieve this goal by restricting possible
behaviors for the motion planner. We provide an algebraic method based on
sum-of-squares programming to define restrictions on the motion planner and
find small bounds on the tracking error. We demonstrate our method on two case
studies and show how we can integrate the method into already developed motion
planning techniques. Results suggest that our method can provide acceptable
tracking error wherein previous work were not applicable.


  Ultra-SAXS can enhance the capabilities of existing SAXS/WAXS beamlines and
laboratory instruments. A compact Ultra-SAXS module has been developed, which
extends the measurable $q$-range with $0.0015 \leq q\mathrm{(nm^{-1})} \leq
0.2$, allowing structural dimensions between $30 \leq D$(nm)$ \leq 4000$ to be
probed in addition to the range covered by a high-end SAXS/WAXS instrument. By
shifting the module components in and out on their respective motor stages,
SAXS/WAXS measurements can be easily and rapidly interleaved with USAXS
measurements.
  In this paper, the design considerations, realization and synchrotron
findings are presented. Measurements of silica spheres, an alumina membrane,
and a porous carbon catalyst are provided as application examples.


  The vision of pervasive artificial intelligence (AI) services can be realized
by training an AI model on time using real-time data collected by internet of
things (IoT) devices. To this end, IoT devices require offloading their data to
an edge server in proximity. However, transmitting high-dimensional and
voluminous data from energy-constrained IoT devices poses a significant
challenge. To address this limitation, we propose a novel offloading
architecture, called joint data deepening-and-prefetching (JD2P), which is
feature-by-feature offloading comprising two key techniques. The first one is
data deepening, where each data sample's features are sequentially offloaded in
the order of importance determined by the data embedding technique such as
principle component analysis (PCA). Offloading is terminated once the already
transmitted features are sufficient for accurate data classification, resulting
in a reduction in the amount of transmitted data. The criteria to offload data
are derived for binary and multi-class classifiers, which are designed based on
support vector machine (SVM) and deep neural network (DNN), respectively. The
second one is data prefetching, where some features potentially required in the
future are offloaded in advance, thus achieving high efficiency via precise
prediction and parameter optimization. We evaluate the effectiveness of JD2P
through experiments using the MNIST dataset, and the results demonstrate its
significant reduction in expected energy consumption compared to several
benchmarks without degrading learning accuracy.


  Effectively inseparable pairs and their properties play an important role in
the meta-mathematics of arithmetic and incompleteness. Different notions are
introduced and shown in the literature to be equivalent to effective
inseparability. We give a much simpler proof of these equivalences using the
strong double recursion theorem. Then we prove some results about the
application of effective inseparability in meta-mathematics.


  Linear chirp-based underwater acoustic communication has been widely used due
to its reliability and long-range transmission capability. However, unlike the
counterpart chirp technology in wireless -- LoRa, its throughput is severely
limited by the number of modulated chirps in a symbol. The fundamental
challenge lies in the underwater multi-path channel, where the delayed copied
of one symbol may cause inter-symbol and intra-symbol interfere. In this paper,
we present UWLoRa+, a system that realizes the same chirp modulation as LoRa
with higher data rate, and enhances LoRa's design to address the multi-path
challenge via the following designs: a) we replace the linear chirp used by
LoRa with the non-linear chirp to reduce the signal interference range and the
collision probability; b) we design an algorithm that first demodulates each
path and then combines the demodulation results of detected paths; and c) we
replace the Hamming codes used by LoRa with the non-binary LDPC codes to
mitigate the impact of the inevitable collision.Experiment results show that
the new designs improve the bit error rate (BER) by 3x, and the packet error
rate (PER) significantly, compared with the LoRa's naive design. Compared with
an state-of-the-art system for decoding underwater LoRa chirp signal, UWLoRa+
improves the throughput by up to 50 times.


  Artificial spin ices are periodic arrangements of interacting nanomagnets
successfully used to investigate emergent phenomena in the presence of
geometric frustration. Recently, it has been shown that artificial spin ices
can be used as building blocks for creating functional materials, such as
magnonic crystals, and support a large number of programmable magnetic states.
We investigate the magnetization dynamics in a system exhibiting anisotropic
magnetostatic interactions owing to locally broken structural inversion
symmetry. We find a rich spin-wave spectrum and investigate its evolution in an
external magnetic field. We determine the evolution of individual modes, from
building blocks up to larger arrays, highlighting the role of symmetry breaking
in defining the mode profiles. Moreover, we demonstrate that the mode spectra
exhibit signatures of long-range interactions in the system. These results
contribute to the understanding of magnetization dynamics in spin ices beyond
the kagome and square ice geometries and are relevant for the realization of
reconfigurable magnonic crystals based on spin ices.


  We study framed translation surfaces corresponding to meromorphic
differentials on compact Riemann surfaces, for which a horizontal separatrix is
marked for each pole or zero. Such geometric structures naturally appear when
studying flat geometry surfaces "near" the Deligne-Mumford boundary.We compute
the number of connected components of the corresponding strata, and give a
simple topological invariant that distinguishes them. In particular we see that
for $g>0$, there are at most two such components, except in the hyperelliptic
case.


  This paper presents a description and analysis of a rational cubic spline FIF
(RCSFIF) that has two shape parameters in each subinterval when it is defined
implicitly. To be precise, we consider the iterated function system (IFS) with
$q_n=\frac{P_n}{Q_n}$, $n \in \mathbb{N}_{N-1}$, where $P_n(x)$ are cubic
polynomials to be determined through interpolatory conditions of the
corresponding FIF and $Q_n(x)$ are preassigned quadratic polynomials each
containing two free shape/rationality parameters. We establish the convergence
of the proposed RCSFIF $g$ to the original function $\Phi \in \mathcal{C}^3(I)$
with respect to the uniform norm. We also provide the sufficient conditions for
an automatic selection of the rational IFS parameters to preserve monotonicity
and convexity of a prescribed set of data points. We consider some examples to
illustrate the developed fractal interpolation scheme and its shape preserving
aspects.


  The extragalactic distance scale relies heavily on Cepheids. However, it has
become clear from observations and pulsation models that the slope and zero
point of their P-L relations differ from galaxy to galaxy. This makes the
determination of Cepheid distances complex and calls for an independent test of
their differences. The test is provided by RR Lyrae star distances of 24
galaxies which calibrate the tip of the red-giant branch (TRGB; M_I = -4.05),
which in turn confirms the adopted Cepheids distances on our 2006 distance
scale in 18 cases to within 0.1 mag on average. Relative SN Ia and velocity
distances deny a remaining significant metallicity effect of the adopted
distances. The new support for these Cepheid distances increases the weight of
our previous calibration of the SN Ia luminosity and of the 21cm line width -
luminosity (TF) relation. The value of H_0 = 62.3 (+/-5) is confirmed on all
scales.


  We study the prime pair counting functions $\pi_{2k}(x),$ and their averages
over $2k.$ We show that good results can be achieved with relatively little
effort by considering averages. We prove an asymptotic relation for longer
averages of $\pi_{2k}(x)$ over $2k \leq x^\theta,$ $\theta > 7/12,$ and give an
almost sharp lower bound for fairly short averages over $k \leq C \log x,$ $C
>1/2.$ We generalize the ideas to other related problems.


  We consider the model of ``Chain Inflation,'' in which the period of
inflation in our universe took the form of a long sequence of quantum tunneling
events. We find that in the simplest such scenario, in which the tunneling
processes are uniform, approximately 10^4 vacua per e-folding of inflation are
required in order that the density perturbations produced are of an acceptable
size. We arrive at this conclusion through a combination of analytic and
numerical techniques, which could also serve as starting points for
calculations with more general sets of assumptions.


  The star chromatic index of a multigraph $G$, denoted $\chi'_{st}(G)$, is the
minimum number of colors needed to properly color the edges of $G$ such that no
path or cycle of length four is bicolored. We survey the results of determining
the star chromatic index, present the interesting proofs and techniques, and
collect many open problems and conjectures.


  This paper considers the pricing of equity-linked life insurance contracts
with death and survival benefits in a general model with multiple stochastic
risk factors: interest rate, equity, volatility, unsystematic and systematic
mortality. We price the equity-linked contracts by assuming that the insurer
hedges the risks to reduce the local variance of the net asset value process
and requires a compensation for the non-hedgeable part of the liability in the
form of an instantaneous standard deviation risk margin. The price can then be
expressed as the solution of a system of non-linear partial differential
equations. We reformulate the problem as a backward stochastic differential
equation with jumps and solve it numerically by the use of efficient neural
networks. Sensitivity analysis is performed with respect to initial parameters
and an analysis of the accuracy of the approximation of the true price with our
neural networks is provided.


  This paper addresses the problem of managing rotational load shedding
schedules for a power distribution network with multiple load zones. An integer
optimization problem is formulated to find the optimal number and duration of
planned power outages. Various types of damage costs are proposed to capture
the heterogeneous load shedding preferences of different zones. The McCormick
relaxation along with an effective procedure feasibility recovery is developed
to solve the resulting bilinear integer program, which yields a high-quality
suboptimal solution. Extensive simulation results corroborate the merit of the
proposed approach, which has a substantial edge over existing load shedding
schemes.


  Human drivers have distinct driving techniques, knowledge, and sentiments due
to unique driving traits. Driver drowsiness has been a serious issue
endangering road safety; therefore, it is essential to design an effective
drowsiness detection algorithm to bypass road accidents. Miscellaneous research
efforts have been approached the problem of detecting anomalous human driver
behaviour to examine the frontal face of the driver and automobile dynamics via
computer vision techniques. Still, the conventional methods cannot capture
complicated driver behaviour features. However, with the origin of deep
learning architectures, a substantial amount of research has also been executed
to analyze and recognize driver's drowsiness using neural network algorithms.
This paper introduces a novel framework based on vision transformers and YoloV5
architectures for driver drowsiness recognition. A custom YoloV5 pre-trained
architecture is proposed for face extraction with the aim of extracting Region
of Interest (ROI). Owing to the limitations of previous architectures, this
paper introduces vision transformers for binary image classification which is
trained and validated on a public dataset UTA-RLDD. The model had achieved
96.2\% and 97.4\% as it's training and validation accuracies respectively. For
the further evaluation, proposed framework is tested on a custom dataset of 39
participants in various light circumstances and achieved 95.5\% accuracy. The
conducted experimentations revealed the significant potential of our framework
for practical applications in smart transportation systems.


  The credit crisis of 2007 and 2008 has thrown much focus on the models used
to price mortgage backed securities. Many institutions have relied heavily on
the credit ratings provided by credit agency. The relationships between
management of credit agencies and debt issuers may have resulted in conflict of
interest when pricing these securities which has lead to incorrect risk
assumptions and value expectations from institutional buyers. Despite the
existence of sophisticated models, institutional buyers have relied on these
ratings when considering the risks involved with these products. Institutional
investors interested in non-agency MBS are particularly vulnerable due to both
the credit risks as well as prepayment risks. This paper describes a simple
simulation model that model non-agency MBS and CMO. The simulation model builds
on existing models for agency MBS. It incorporates credit risks of mortgage
buyers using existing models used in capital requirements as specified by the
Basel II Accord.


  We prove a conditional decoupling inequality for the model of random
interlacements in dimension $d\geq 3$: the conditional law of random
interlacements on a box (or a ball) $A_1$ given the (not very "bad")
configuration on a "distant" set $A_2$ does not differ a lot from the
unconditional law.


  We construct two classes of magnetohydrostatic (MHS) equilibria for an
axisymmetric vertical flux tube spanning from the photosphere to the lower part
of the transition region within a realistic stratified solar atmosphere subject
to solar gravity. We assume a general quadratic expression of the magnetic flux
function for the gas pressure and poloidal current and solve the Grad-Shafranov
equation analytically. The solution is a combination of a homogeneous and a
particular part where the former is separable by a Coulomb function in $r$ and
exponential in $z$, while the particular part is an open configuration that has
no $z$ dependence. We also present another open field solution by using a
self-similar formulation with two different profile functions and incorporating
stratified solar gravity to maintain the magnetohydrostatic equilibria, which
is a modification of earlier self-similar models with a twist. We study the
admitted parameter space that is consistent with the conditions in the solar
atmosphere and derive magnetic and the thermodynamic structures inside the flux
tube that are reasonably consistent with the photospheric magnetic bright
points (MBPs) for both open and closed field Coulomb function and self-similar
models as estimated from observations and simulations. The obtained open and
closed field flux tube solutions can be used as the background conditions for
the numerical simulations for the study of the wave propagation through the
flux tubes. The solutions can also be used to construct realistic magnetic
canopies.


  Network performance problems are notoriously difficult to diagnose. Prior
profiling systems collect performance statistics by keeping information about
each network flow, but maintaining per-flow state is not scalable on
resource-constrained NIC and switch hardware. Instead, we propose sketch-based
performance monitoring using memory that is sublinear in the number of flows.
Existing sketches estimate flow monitoring metrics based on flow sizes. In
contrast, performance monitoring typically requires combining information
across pairs of packets, such as matching a data packet with its acknowledgment
to compute a round-trip time. We define a new class of \emph{lean} algorithms
that use memory sublinear in both the size of input data and the number of
flows. We then introduce lean algorithms for a set of important statistics,
such as identifying flows with high latency, loss, out-of-order, or
retransmitted packets. We implement prototypes of our lean algorithms on a
commodity programmable switch using the P4 language. Our experiments show that
lean algorithms detect $\sim$82\% of top 100 problematic flows among real-world
packet traces using just 40KB memory.


  We examine the Lorentz non-invariance ambiguity in longitudinal weak-boson
scatterings and the precise conditions for the validity of the Equivalence
Theorem (ET). {\it Safe} Lorentz frames for applying the ET are defined, and
the intrinsic connection between the longitudinal weak-boson scatterings and
probing the symmetry breaking sector is analyzed. A universal precise
formulation of the ET is presented for both the Standard Model and the Chiral
Lagrangian formulated Electro-Weak Theories. It is shown that in electroweak
theories with strongly interacting symmetry breaking sector, the longitudinal
weak-boson scattering amplitude {\it under proper conditions} can be replaced
by the corresponding Goldstone-boson scattering amplitude in which all the
internal weak-boson lines and fermion loops are ignored.


  When the DNA double helix is subjected to external forces it can stretch
elastically to elongations reaching 100% of its natural length. These
distortions, imposed at the mesoscopic or macroscopic scales, have a dramatic
effect on electronic properties at the atomic scale and on electrical transport
along DNA. Accordingly, a multiscale approach is necessary to capture the
electronic behavior of the stretched DNA helix. To construct such a model, we
begin with accurate density-functional-theory calculations for electronic
states in DNA bases and base pairs in various relative configurations
encountered in the equilibrium and stretched forms. These results are
complemented by semi-empirical quantum mechanical calculations for the states
of a small size [18 base pair poly(CG)-poly(CG)] dry, neutral DNA sequence,
using previously published models for stretched DNA. The calculated electronic
states are then used to parametrize an effective tight-binding model that can
describe electron hopping in the presence of environmental effects, such as the
presence of stray water molecules on the backbone or structural features of the
substrate. These effects introduce disorder in the model hamiltonian which
leads to electron localization. The localization length is smaller by several
orders of magnitude in stretched DNA relative to that in the unstretched
structure.


  The efficient excitation of quantum sources such as quantum dots or single
molecules requires high NA optics which is often a challenge in cryogenics, or
in ultrafast optics. Here we propose a 3.2 um wide parabolic mirror, with a 0.8
um focal length, fabricated by direct laser writing on CdSe/CdS colloidal
quantum dots, capable of focusing the excitation light to a sub-wavelength spot
and to extract the generated emission by collimating it into a narrow beam.
This mirror is fabricated via in-situ volumetric optical lithography, which can
be aligned to individual emitters, and it can be easily adapted to other
geometries beyond the paraboloid. This compact solid-state transducer from
far-field to the emitter has important applications in objective-free quantum
technologies.


  In this paper, a simple field function is presented for facilitating the
solution of complex and dynamic fluid-solid systems on Cartesian grids with
interface-resolved fluid-fluid, fluid-solid, and solid-solid interactions. For
a Cartesian-grid-discretized computational domain segmented by a set of solid
bodies, this field function explicitly tracks each subdomain with multiple
resolved interfacial node layers. As a result, the presented field function
enables low-memory-cost multidomain node mapping, efficient node remapping,
fast collision detection, and expedient surface force integration.
Implementation algorithms for the field function and its described
functionalities are also presented. Equipped with a deterministic multibody
collision model, numerical experiments involving fluid-solid systems with flow
conditions ranging from subsonic to supersonic states are conducted to validate
and illustrate the applicability of the proposed field function.


  Although a plethora of techniques are now available for controlling the group
velocity of an optical wave packet, there are very few options for creating
accelerating or decelerating wave packets whose group velocity varies
controllably along the propagation axis. Here we show that `space-time' wave
packets in which each wavelength is associated with a prescribed spatial
bandwidth enable the realization of optical acceleration and deceleration in
free space. Endowing the field with precise spatio-temporal structure leads to
group-velocity changes as high as $\sim c$ observed over a distance of $\sim20$
mm in free space, which represents a boost of at least $\sim4$ orders of
magnitude over X-waves and Airy pulses. The acceleration implemented is in
principle independent of the initial group velocity, and we have verified this
effect in both the subluminal and superluminal regimes.


  The splitting of the Kondo resonance in the density of states of an Anderson
impurity in finite magnetic field is calculated from the exact Bethe-ansatz
solution. The result gives an estimate of the electron spectral function for
nonzero magnetic field and Kondo temperature, with consequences for transport
experiments on quantum dots in the Kondo regime. The strong correlations of the
Kondo ground state cause a significant low-temperature reduction of the peak
splitting. Explicit formulae are found for the shift and broadening of the
Kondo peaks. A likely cause of the problems of large-N approaches to spin-1/2
impurities at finite magnetic field is suggested.


  In this paper, we introduce a novel algorithm that can dramatically reduce
the number of antenna elements needed to accurately predict the direction of
arrival (DOA) for multiple input multiple output (MIMO) radar. The new proposed
algorithm predicts the received signal of a large antenna setup using reduced
number of antenna by using coupled dictionary learning. Hence, this enables the
MIMO radar to resolve more paths, which could not be resolved by the fewer
antennas. Specifically, we overcome the problem of inaccurate DOA estimation
due to a small virtual array setup. For example, we can use dictionary learning
to predict 100 virtual array elements using only 25. To evaluate our algorithm,
we used multiple signal classification (MUSIC) as a DOA estimation technique to
estimate the DOA for non coherent multiple targets. The results show that using
the predicted received signal, the proposed algorithm could resolve all the
targets in the scene, which could not been resolved using only the received
signal from the reduced antenna setup.


  Being able to identify software discussions that are primarily about design,
which we call design mining, can improve documentation and maintenance of
software systems. Existing design mining approaches have good classification
performance using natural language processing (NLP) techniques, but the
conclusion stability of these approaches is generally poor. A classifier
trained on a given dataset of software projects has so far not worked well on
different artifacts or different datasets. In this study, we replicate and
synthesize these earlier results in a meta-analysis. We then apply recent work
in transfer learning for NLP to the problem of design mining. However, for our
datasets, these deep transfer learning classifiers perform no better than less
complex classifiers. We conclude by discussing some reasons behind the transfer
learning approach to design mining.


  We propose a new version of the matrix product (MP) states approach to the
description of quantum spin chains, which allows one to construct MP states
with certain total spin and its z-projection. We show that previously known MP
wavefunctions for integer-spin antiferromagnetic chains and ladders correspond
to some particular cases of our general ansatz. Our method allows to describe
systems with spontaneously broken rotational symmetry, like quantum
ferrimagnetic chains whose ground state has nonzero total spin. We apply this
approach to describe the ground state properties of the isotropic ferrimagnetic
Heisenberg chain with alternating spins 1 and 1/2 and compare our variational
results with the high-precision numerical data obtained by means of the quantum
Monte Carlo (QMC) method. For both the ground state energy and the correlation
functions we obtain very good agreement between the variational results and the
QMC data.


  Abundances of Fe, Si, Ni, Ti, Na, Mg, Cu, Zn, Mn, Cr and Ca in the atmosphere
of the K-dwarf HD 77338 are determined and discussed. HD 77338 hosts a hot
Uranus-like planet and is currently the most metal-rich single star to host any
planet. Determination of abundances was carried out in the framework of a
self-consistent approach developed by Pavlenko et al. (2012). Abundances were
computed iteratively by the program ABEL8, and the process converged after 4
iterations. We find that most elements follow the iron abundance, however some
of the iron peak elements are found to be over-abundant in this star.


  We study six groups and clusters of galaxies suggested in the literature to
be `fossil' systems (i.e. to have luminous diffuse X-ray emission and a
magnitude gap of at least 2 mag-R between the first and the second ranked
member within half of the virial radius), each having good quality X-ray data
and SDSS spectroscopic or photometric coverage out to the virial radius. The
poor cluster AWM4 is clearly established as a fossil system, and we confirm the
fossil nature of four other systems (RXJ1331.5+1108, RXJ1340.6+4018,
RXJ1256.0+2556 and RXJ1416.4+2315), while the cluster RXJ1552.2+2013 is
disqualified as fossil system. For all systems we present the luminosity
functions within 0.5 and 1 virial radius that are consistent, within the
uncertainties, with the universal luminosity function of clusters. For the five
bona fide fossil systems, having a mass range 2x10^13-3x10^14 M_Sun, we compute
accurate cumulative substructure distribution functions (CSDFs) and compare
them with the CSDFs of observed and simulated groups/clusters available in the
literature. We demonstrate that the CSDFs of fossil systems are consistent with
those of normal observed clusters and do not lack any substructure with respect
to simulated galaxy systems in the cosmological LambdaCDM framework. In
particular, this holds for the archetype fossil group RXJ1340.6+4018 as well,
contrary to earlier claims.


  The recrystallized grain size and texture in alloys can be controlled via the
microchemistry state during thermomechanical processing. The influence of
concurrent precipitation on recovery and recrystallization is here analyzed by
directly correlating (sub)grains of P, CubeND or Cube orientation with
second-phase particles in a cold-rolled and non-isothermally annealed Al-Mn
alloy. The recrystallized state is dominated by coarse elongated grains with a
strong P, weaker CubeND and even weaker Cube texture. The correlated data
enables orientation dependent quantification of the density and size of
dispersoids on sub-boundaries and subgrains in the deformation zones around
large constituent particles. A new modified expression for the Smith-Zener drag
from dispersoids on sub-boundaries is derived and used. The results show that
the drag on (sub)grain boundaries from dispersoids is orientation dependent,
with Cube subgrains experiencing the highest drag after recovery and partial
recrystallization. The often observed size advantage of Cube subgrains in Al
alloys is not realized due to the increased drag, thereby promoting
particle-stimulated nucleation (PSN). Relatively fewer and larger dispersoids
in deformation zones around large particles give a reduced Smith-Zener drag on
PSN nuclei, thus further strengthening the effect of PSN. Observations
substantiating the stronger P texture compared to the CubeND texture are a
higher frequency of P subgrains and a faster growth of these subgrains. The
applied methodology enables a better understanding of the mechanisms behind the
orientation dependent nucleation and growth behavior during recovery and
recrystallization with strong concurrent precipitation in Al-Mn alloys. In
particular, the methodology gives new insights into the strong P and CubeND
textures compared to the Cube texture.


  The majority of atomic hydrogen Br{\gamma} emission detected in the spectra
of young stellar objects (YSOs) is believed to arise from the recombination
regions associated with the magnetospheric accretion of circumstellar disk
material onto the forming star. In this paper, we present the results of a
K-band IFU spectroscopic study of Br{\gamma} emission in eight young
protostars: CW Tau, DG Tau, Haro 6-10, HL Tau, HV Tau C, RW Aur, T Tau and XZ
Tau. We spatially resolve Br{\gamma} emission structures in half of these young
stars and find that most of the extended emission is consistent with the
location and velocities of the known Herbig-Haro flows associated with these
systems. At some velocities through the Br{\gamma} line profile, the spatially
extended emission comprises 20% or more of the integrated flux in that spectral
channel. However, the total spatially extended Br{\gamma} is typically less
than ~10% of the flux integrated over the full emission profile. For DG Tau and
Haro 6-10 S, we estimate the mass outflow rate using simple assumptions about
the hydrogen emission region, and compare this to the derived mass accretion
rate. We detect extended Br{\gamma} in the vicinity of the more obscured
targets in our sample and conclude that spatially extended Br{\gamma} emission
may exist toward other stars, but unattenuated photospheric flux probably
limits its detectability.


  Halide perovskites have attracted increasing research attention regarding
their outstanding optoelectronic applications. Owing to its low activation
energy, ion migration is implicated in the long-term stability and many unusual
transport behaviors of halide perovskite devices. However, precise control of
the ionic transport in halide perovskite crystals remains challenging. Here we
visualized and quantified the electric-field-induced halide ion migration in an
axial CsPbBr$_3$-CsPbCl$_3$ nanowire heterostructure and demonstrated a
solid-state ionic rectification, which is due to the non-uniform distribution
of the ionic vacancies in the nanowire that results from a competition between
electrical screening and their creation and destruction at the electrode
interface. The asymmetric heterostructure characteristics add an additional
knob to the ion-movement manipulation in the design of advanced ionic circuits
with halide perovskites as building blocks.


  We address the problem of detecting bipartite Bell nonlocality whenever the
only experimental information are the intensities produced in each run of the
experiment by an unknown number of particles. We point out that this scenario
naturally occurs in Bell experiments with parametric down-conversion when the
crystal is pumped by strong pulses, in Bell tests with distant sources and in
which particles suffer different delays during their flight, in Bell
experiments using living cells as photo detectors, and in Bell experiments
where the pairing information is physically removed. We show that, although
Bell nonlocality decreases as the number of particles increases, if the parties
can distinguish arbitrarily small differences of intensities and the visibility
is larger than $0.98$, then Bell nonlocality can still be experimentally
detected with fluxes of up to $15$ particles. We show that this prediction can
be tested with current equipment in a Bell experiment where pairing information
is physically removed, but requires the assumption of fair sampling.


  In this article we study conditions to be a continuous or a measurable
eigenvalue of finite rank minimal Cantor systems, that is, systems given by an
ordered Bratteli diagram with a bounded number of vertices per level. We prove
that continuous eigenvalues always come from the stable subspace associated to
the incidence matrices of the Bratteli diagram and we study rationally
independent generators of the additive group of continuous eigenvalues. Given
an ergodic probability measure, we provide a general necessary condition to be
a measurable eigenvalue. Then we consider two families of examples. A first one
to illustrate that measurable eigenvalues do not need to come from the stable
space. Finally we study Toeplitz type Cantor minimal systems of finite rank. We
recover classical results in the continuous case and we prove measurable
eigenvalues are always rational but not necessarily continuous.


  Renard (Relationships Extraction from NARrative Documents) is a Python
library that allows users to define custom natural language processing (NLP)
pipelines to extract character networks from narrative texts. Contrary to the
few existing tools, Renard can extract dynamic networks, as well as the more
common static networks. Renard pipelines are modular: users can choose the
implementation of each NLP subtask needed to extract a character network. This
allows users to specialize pipelines to particular types of texts and to study
the impact of each subtask on the extracted network.


  Fickian diffusion into a core-shell geometry is modeled. The interior core
mimics pancreatic Langerhan islets and the exterior shell acts as inert
protection. The consumption of oxygen diffusing into the cells is approximated
using Michaelis-Menten kinetics. The problem is transformed to dimensionless
units and solved numerically. Two regimes are identified, one that is diffusion
limited and the other consumption limited. A regression is fit that describes
the concentration at the center of the cells as a function of the relevant
physical parameters. It is determined that, in a cell culture environment, the
cells will remain viable as long as the islet has a radius of around $142 \mu
m$ or less and the encapsulating shell has a radius of less than approximately
$283 \mu m$. When the islet is on the order of $100 \mu m$ it is possible for
the cells to remain viable in environments with as little as $4.6\times10^{-2}
mol/m^{-3}$ $O_2$. These results indicate such an encapsulation scheme may be
used to prepare artificial pancreas to treat diabetes.


  We check a commonly used approximation in which a baryon with a heavy quark
is described as a heavy quark-light diquark system. The heavy quark influences
the diquark internal motion reducing the average distance between the two light
quarks. Besides, we show how the average distance between the heavy quark and
any of the light quarks, and that between the heavy quark and the center of
mass of the light diquark, are smaller than the distance between the two light
quarks, which seems to contradict the heavy quark-light diquark picture. This
latter result is in agreement with expectations from QCD sum rules and lattice
QCD calculations. Our results also show that the diquark approximations
produces larger masses than the ones obtained in a full calculation.


  In this note it is worked out a new set of Laplace-Like equations for
quaternions through Riemann-Cauchy hypercomplex relations otained earlier
\cite{BorgesZeMarcio}. As in the theory of functions of a complex variable, it
is expected that this new set of Laplace-Like equations might be applied to a
large number of Physical problems, providing new insights in the Classical
Theory Fields.


  We give a complete classification of foliations on open contact manifolds
whose leaves are contact submanifolds of the ambient manifold. The results are
analogues of Haefliger's classification of foliations on open manifold.


  Nucleon-nucleon potentials evolved to low momentum, which show great promise
in few- and many-body calculations, have generally been formulated with a sharp
cutoff on relative momenta. However, a sharp cutoff has technical disadvantages
and can cause convergence problems at the 10-100 keV level in the deuteron and
triton. This motivates using smooth momentum-space regulators as an
alternative. We generate low-momentum interactions with smooth cutoffs both
through energy-independent renormalization group methods and using a multi-step
process based on the Bloch-Horowitz approach. We find greatly improved
convergence for calculations of the deuteron and triton binding energies in a
harmonic oscillator basis compared to results with a sharp cutoff. Even a
slight evolution of chiral effective field theory interactions to lower momenta
is beneficial. The renormalization group preserves the long-range part of the
interaction, and consequently the renormalization of long-range operators, such
as the quadrupole moment, the radius and 1/r, is small. This demonstrates that
low-energy observables in the deuteron are reproduced without short-range
correlations in the wave function.


  A self-consistent formalism for the double beta decay of Fermi type is
provided. The particle-particle channel of the two-body interaction is
considered first in the mean field equations and then in the QRPA. The
resulting approach is called the QRPA with a self-consistent mean field
(QRPASMF). The mode provided by QRPASMF, does not collapse for any strength of
the particle-particle interaction. The transition amplitude for double beta
decay is almost insensitive to the variation of the particle-particle
interaction. Comparing it with the result of the standard pnQRPA, it is smaller
by a factor 6. The prediction for transition amplitude agrees quite well with
the exact result. The present approach is the only one which produces a strong
decrease of the amplitude and at the same time does not alter the stability of
the ground state.


  Forecasting the dynamics of large complex networks from previous time-series
data is important in a wide range of contexts. Here we present a machine
learning scheme for this task using a parallel architecture that mimics the
topology of the network of interest. We demonstrate the utility and scalability
of our method implemented using reservoir computing on a chaotic network of
oscillators. Two levels of prior knowledge are considered: (i) the network
links are known; and (ii) the network links are unknown and inferred via a
data-driven approach to approximately optimize prediction.


  Question answering is an effective method for obtaining information from
knowledge bases (KB). In this paper, we propose the Neural-Symbolic Complex
Question Answering (NS-CQA) model, a data-efficient reinforcement learning
framework for complex question answering by using only a modest number of
training samples. Our framework consists of a neural generator and a symbolic
executor that, respectively, transforms a natural-language question into a
sequence of primitive actions, and executes them over the knowledge base to
compute the answer. We carefully formulate a set of primitive symbolic actions
that allows us to not only simplify our neural network design but also
accelerate model convergence. To reduce search space, we employ the copy and
masking mechanisms in our encoder-decoder architecture to drastically reduce
the decoder output vocabulary and improve model generalizability. We equip our
model with a memory buffer that stores high-reward promising programs. Besides,
we propose an adaptive reward function. By comparing the generated trial with
the trials stored in the memory buffer, we derive the curriculum-guided reward
bonus, i.e., the proximity and the novelty. To mitigate the sparse reward
problem, we combine the adaptive reward and the reward bonus, reshaping the
sparse reward into dense feedback. Also, we encourage the model to generate new
trials to avoid imitating the spurious trials while making the model remember
the past high-reward trials to improve data efficiency. Our NS-CQA model is
evaluated on two datasets: CQA, a recent large-scale complex question answering
dataset, and WebQuestionsSP, a multi-hop question answering dataset. On both
datasets, our model outperforms the state-of-the-art models. Notably, on CQA,
NS-CQA performs well on questions with higher complexity, while only using
approximately 1% of the total training samples.


  We use computer algebra to study the 512-dimensional associative algebra Q
B_3, the rational monoid algebra of 3 x 3 Boolean matrices. We obtain a basis
for the radical in bijection with the 42 non-regular elements of B_3. The
center of the 470-dimensional semisimple quotient has dimension 14; we use a
splitting algorithm to find a basis of orthogonal primitive idempotents. We
show that the semisimple quotient is the direct sum of simple two-sided ideals
isomorphic to d x d rational matrix algebras for d = 1, 1, 1, 2, 3, 3, 3, 3, 6,
6, 7, 9, 9, 12. We construct the irreducible representations of B_3 over Q by
calculating the representation matrices for a minimal set of generators.


  Truly, kinematics in the Solar neighborhood has provided important
information both for the structure and for the evolution of the Galaxy since
the early 20th century. The relationship between Oort constants and the ratio
of the velocity dispersion are important quantities in stellar kinematics. In
the present paper, we calculated the kinematical parameters and the Oort
constants of various samples of late to intermediate M-type stars. We
calculated the velocity dispersion ({\sigma}_1,{\sigma}_2,{\sigma}_3 ) in units
of km s-1 for the samples under study. The longitude of the vertex (l_2 )
having negative values with our analysis; i.e., the program I (538 stars), l_2
=-0_.^o 5410, program II (100 stars), l_2=-0_.^o 4937 and program III (60
stars), l_2=-0_.^o 9495. We calculate the Oort constants as A=14.69+-0.61 km
s-1 kpc-1 and B=-16.70+-0.67 km s-1 kpc-1, and the rotational velocity
V_o=257.38+-9.40 km s^(-1). A possible explanation for the overestimated values
of the second Oort constants has been presented.


  We study contact effects on electron transport across a molecular wire
sandwiched between two semi-infinite (carbon) nanotube leads as a model for
nanoelectrodes. Employing the Landauer scattering matrix approach we find that
the conductance is very sensitive to parameters such as the coupling strength
and geometry of the contact. The conductance exhibits markedly different
behavior in the two limiting scenarios of single contact and multiple contacts
between the molecular wire and the nanotube interfacial atoms. In contrast to a
single contact the multiple-contact configuration acts as a filter selecting
single transport channels. It exhibits a scaling law for the conductance as a
function of coupling strength and tube diameter. We also observe an unusual
narrow-to-broad-to-narrow behavior of conductance resonances upon decreasing
the coupling.


  In this paper, a centralized Power Control (PC) scheme and an interference
channel learning method are jointly tackled to allow a Cognitive Radio Network
(CRN) access to the frequency band of a Primary User (PU) operating based on an
Adaptive Coding and Modulation (ACM) protocol. The learning process enabler is
a cooperative Modulation and Coding Classification (MCC) technique which
estimates the Modulation and Coding scheme (MCS) of the PU. Due to the lack of
cooperation between the PU and the CRN, the CRN exploits this multilevel MCC
sensing feedback as implicit channel state information (CSI) of the PU link in
order to constantly monitor the impact of the aggregated interference it
causes. In this paper, an algorithm is developed for maximizing the CRN
throughput (the PC optimization objective) and simultaneously learning how to
mitigate PU interference (the optimization problem constraint) by using only
the MCC information. Ideal approaches for this problem setting with high
convergence rate are the cutting plane methods (CPM). Here, we focus on the
analytic center cutting plane method (ACCPM) and the center of gravity cutting
plane method (CGCPM) whose effectiveness in the proposed simultaneous PC and
interference channel learning algorithm is demonstrated through numerical
simulations.


  We prove that, for all $\ell$ and $s$, every graph of sufficiently large
tree-width contains either a complete bipartite graph $K_{s,s}$ or a chordless
cycle of length greater than $\ell$.


  We give a criterion when an expansion of the ordered set of real numbers
defines the image of the expansion of the real field by the set of natural
numbers under a semialgebraic injection. In particular, we show that for a
non-quadratic irrational number a, the expansion of the ordered Q(a)-vector
space of real numbers by the set of natural numbers defines multiplication on
the real numbers.


  We examine the last-iterate convergence rate of Bregman proximal methods -
from mirror descent to mirror-prox and its optimistic variants - as a function
of the local geometry induced by the prox-mapping defining the method. For
generality, we focus on local solutions of constrained, non-monotone
variational inequalities, and we show that the convergence rate of a given
method depends sharply on its associated Legendre exponent, a notion that
measures the growth rate of the underlying Bregman function (Euclidean,
entropic, or other) near a solution. In particular, we show that boundary
solutions exhibit a stark separation of regimes between methods with a zero and
non-zero Legendre exponent: the former converge at a linear rate, while the
latter converge, in general, sublinearly. This dichotomy becomes even more
pronounced in linearly constrained problems where methods with entropic
regularization achieve a linear convergence rate along sharp directions,
compared to convergence in a finite number of steps under Euclidean
regularization.


  A matching of a graph is a set of edges without common end vertex. A graph is
called 1-planar if it admits a drawing in the plane such that each edge is
crossed at most once. Recently, Biedl and Wittnebel proved that every 1-planar
graph with minimum degree 3 and $n\geq 7$ vertices has a matching of size at
least $\frac{n+12}{7}$, which is tight for some graphs. They also provided
tight lower bounds for the sizes of matchings in 1-planar graphs with minimum
degree 4 or 5. In this paper, we show that any 1-planar graph with minimum
degree 6 and $n \geq 36$ vertices has a matching of size at least
$\frac{3n+4}{7}$, and this lower bound is tight. Our result confirms a
conjecture posed by Biedl and Wittnebel.


  We present the first complete far-infrared spectrum (43 to 197 um) of M82,
the brightest infrared galaxy in the sky, taken with the Long Wavelength
Spectrometer of the Infrared Space Observatory (ISO). We detected seven fine
structure emission lines, [OI] 63 and 145 um, [OIII] 52 and 88 um, [NII] 122
um, [NIII] 57 um and [CII] 158 um, and fit their ratios to a combination
starburst and photo-dissociation region (PDR) model. The best fit is obtained
with HII regions with n = 250 cm^{-3} and an ionization parameter of 10^{-3.5}
and PDRs with n = 10^{3.3} cm^{-3} and a far-ultraviolet flux of G_o =
10^{2.8}. We applied both continuous and instantaneous starburst models, with
our best fit being a 3-5 Myr old instantaneous burst model with a 100 M_o
cut-off. We also detected the ground state rotational line of OH in absorption
at 119.4 um. No excited level OH transitions are apparent, indicating that the
OH is almost entirely in its ground state with a column density ~ 4x10^{14}
cm^{-2}. The spectral energy distribution over the LWS wavelength range is well
fit with a 48 K dust temperature and an optical depth, tau_{Dust} proportional
to lambda^{-1}.


  Computational differential privacy (CDP) is a natural relaxation of the
standard notion of (statistical) differential privacy (SDP) proposed by Beimel,
Nissim, and Omri (CRYPTO 2008) and Mironov, Pandey, Reingold, and Vadhan
(CRYPTO 2009). In contrast to SDP, CDP only requires privacy guarantees to hold
against computationally-bounded adversaries rather than
computationally-unbounded statistical adversaries. Despite the question being
raised explicitly in several works (e.g., Bun, Chen, and Vadhan, TCC 2016), it
has remained tantalizingly open whether there is any task achievable with the
CDP notion but not the SDP notion. Even a candidate such task is unknown.
Indeed, it is even unclear what the truth could be!
  In this work, we give the first construction of a task achievable with the
CDP notion but not the SDP notion, under the following strong but plausible
cryptographic assumptions: (1) Non-Interactive Witness Indistinguishable
Proofs, (2) Laconic Collision-Resistant Keyless Hash Functions, (3)
Differing-Inputs Obfuscation for Public-Coin Samplers. In particular, we
construct a task for which there exists an $\varepsilon$-CDP mechanism with
$\varepsilon = O(1)$ achieving $1-o(1)$ utility, but any $(\varepsilon,
\delta)$-SDP mechanism, including computationally-unbounded ones, that achieves
a constant utility must use either a super-constant $\varepsilon$ or an
inverse-polynomially large $\delta$.
  To prove this, we introduce a new approach for showing that a mechanism
satisfies CDP: first we show that a mechanism is "private" against a certain
class of decision tree adversaries, and then we use cryptographic constructions
to "lift" this into privacy against computationally bounded adversaries. We
believe this approach could be useful to devise further tasks separating CDP
from SDP.


  The most important part of the order $\alpha_s^2$ corrections to the charm
component of the charged-current structure functions $F_2(x,Q^2)$ and
$F_3(x,Q^2)$ have been calculated. This calculation is based on the asymptotic
form of the heavy-quark coefficient functions corresponding to the higher order
corrections to the W-boson-gluon fusion process. These coefficient functions
which are in principle only valid for $Q^2 \gg m^2$ can be also used to
estimate the order $\alpha_s^2$ contributions at lower $Q^2$ values provided $x
< 0.1$. It turns out that the above corrections are appreciable in the large
$Q^2$-region and they explain the discrepancy found for the structure functions
between the fixed-flavour scheme (FFS) and the variable-flavour-number scheme
(VFNS). These corrections also hamper the extraction of the strange-quark
density from the data obtained for the charged-current and the
electromagnetic-current processes.


  Let $R$ be a finitely generated positively graded algebra over a Noetherian
local ring $B$, and $\mathfrak{m} = [R]_+$ be the graded irrelevant ideal of
$R$. We provide a local criterion characterizing the $B$-freeness of all the
local cohomology modules $\text{H}_\mathfrak{m}^i(M)$ of a finitely generated
graded $R$-module $M$. We show that fiber-full modules are exactly the ones
that satisfy this criterion. When we change $B$ by an arbitrary Noetherian ring
$A$, we study the fiber-full locus of a module in $\text{Spec}(A)$: we show
that the fiber-full locus is always an open subset of $\text{Spec}(A)$ and that
it is dense when $A$ is generically reduced.


  Curved single crystals are widely employed in spectrometer designs in the
hard X-ray regime. Due to their large solid angle coverage and focusing
properties, toroidally bent crystals are extremely useful in applications where
the output of photons is low. Spherically bent crystals, a subgroup of
toroidally bent crystals, particularly have found their way in many instruments
at synchtrotrons and free electron laser lightsource end-stations but also in
the re-emerging field of high-resolution laboratory-scale X-ray spectroscopy. A
solid theoretical understanding of the diffraction properties of such crystals
is essential when aiming for optimal spectrometer performance. In this work, we
present a general method to calculate the internal stress and strain fields of
toroidally bent crystals and how to apply it to predict their diffraction
properties. Solutions are derived and discussed for circular and rectangular
spherically bent wafers due to their prevalence in contemporary
instrumentation.