  Given a graph $G$ with vertices $\{v_1,\ldots,v_n\}$, we define
$\mathcal{S}(G)$ to be the set of symmetric matrices $A=[a_{i,j}]$ such that
for $i\ne j$ we have $a_{i,j}\ne 0$ if and only if $v_iv_j\in E(G)$. Motivated
by the Graph Complement Conjecture, we say that a graph $G$ is complementary
vanishing if there exist matrices $A \in \mathcal{S}(G)$ and $B \in
\mathcal{S}(\overline{G})$ such that $AB=O$. We provide combinatorial
conditions for when a graph is or is not complementary vanishing, and we
characterize which graphs are complementary vanishing in terms of certain
minimal complementary vanishing graphs. In addition to this, we determine which
graphs on at most $8$ vertices are complementary vanishing.


  In a system containing a large number of interacting stochastic processes,
there will typically be many non-zero correlation coefficients. This makes it
difficult to either visualize the system's inter-dependencies, or identify its
dominant elements. Such a situation arises in Foreign Exchange (FX) which is
the world's biggest market. Here we develop a network analysis of these
correlations using Minimum Spanning Trees (MSTs). We show that not only do the
MSTs provide a meaningful representation of the global FX dynamics, but they
also enable one to determine momentarily dominant and dependent currencies. We
find that information about a country's geographical ties emerges from the raw
exchange-rate data. Most importantly from a trading perspective, we discuss how
to infer which currencies are `in play' during a particular period of time.


  We introduce a tropical geometric framework that allows us to define $\psi$
classes for moduli spaces of tropical curves of arbitrary genus. We prove
correspondence theorems between algebraic and tropical $\psi$ classes for some
one-dimensional families of genus-one tropical curves.


  To understand the short-distance vs long-distance final-state interactions,
we have performed a detailed amplitude analysis for the two-body decay, J/psi
into vector and pseudoscalar mesons. The current data favor a large relative
phase nearly 90 degrees between the three-gluon and one-photon decay
amplitudes. The source of this phase is apparently in the long-distance
final-state interaction. Nothing anomalous is found in the magnitudes of the
three-gluon and one-photon amplitudes. We discuss implications of this large
relative phase in the weak decay of heavy particles.


  The Robotics community has started to heavily rely on increasingly realistic
3D simulators for large-scale training of robots on massive amounts of data.
But once robots are deployed in the real world, the simulation gap, as well as
changes in the real world (e.g. lights, objects displacements) lead to errors.
In this paper, we introduce Sim2RealViz, a visual analytics tool to assist
experts in understanding and reducing this gap for robot ego-pose estimation
tasks, i.e. the estimation of a robot's position using trained models.
Sim2RealViz displays details of a given model and the performance of its
instances in both simulation and real-world. Experts can identify environment
differences that impact model predictions at a given location and explore
through direct interactions with the model hypothesis to fix it. We detail the
design of the tool, and case studies related to the exploit of the regression
to the mean bias and how it can be addressed, and how models are perturbed by
the vanish of landmarks such as bikes.


  Alkali-metal (potassium) adsorption on FeSe thin films with thickness from
two unit cells (UC) to 4-UC on SrTiO3 grown by molecular beam epitaxy is
investigated with a low-temperature scanning tunneling microscope. At
appropriate potassium coverage (0.2-0.3 monolayer), the tunneling spectra of
the films all exhibit a superconducting-like gap larger than 11 meV (five times
the gap value of bulk FeSe), and two distinct features of characteristic phonon
modes at 11 meV and 21 meV. The results reveal the critical role of the
interface enhanced electron-phonon coupling for possible high temperature
superconductivity in the system and is consistent with recent theories. Our
study provides compelling evidence for the conventional pairing mechanism for
this type of heterostructure superconducting systems.


  On September 14, 2015, the newly upgraded Laser Interferometer
Gravitational-wave Observatory (LIGO) recorded a loud gravitational-wave (GW)
signal, emitted a billion light-years away by a coalescing binary of two
stellar-mass black holes. The detection was announced in February 2016, in time
for the hundredth anniversary of Einstein's prediction of GWs within the theory
of general relativity (GR). The signal represents the first direct detection of
GWs, the first observation of a black-hole binary, and the first test of GR in
its strong-field, high-velocity, nonlinear regime. In the remainder of its
first observing run, LIGO observed two more signals from black-hole binaries,
one moderately loud, another at the boundary of statistical significance. The
detections mark the end of a decades-long quest, and the beginning of GW
astronomy: finally, we are able to probe the unseen, electromagnetically dark
Universe by listening to it. In this article, we present a short historical
overview of GW science: this young discipline combines GR, arguably the
crowning achievement of classical physics, with record-setting, ultra-low-noise
laser interferometry, and with some of the most powerful developments in the
theory of differential geometry, partial differential equations,
high-performance computation, numerical analysis, signal processing,
statistical inference, and data science. Our emphasis is on the synergy between
these disciplines, and how mathematics, broadly understood, has historically
played, and continues to play, a crucial role in the development of GW science.
We focus on black holes, which are very pure mathematical solutions of
Einstein's gravitational-field equations that are nevertheless realized in
Nature, and that provided the first observed signals.


  This paper focus on the convergence of stochastic approximation with Nesterov
momentum. Nesterov acceleration has proven effective in machine learning for
its ability to reduce computational complexity. The issue of delayed
information in the acceleration term remains a challenge to achieving the
almost sure convergence. Based on the delayed supermatingale convergence
lemmas, we give a series of framework for almost sure convergence. Our
framework applies to several widely-used random iterative methods, such as
stochastic subgradient methods, the proximal Robbins-Monro method for general
stochastic optimization, and the proximal stochastic subgradient method for
composite optimization. Through the applications of our framework, these
methods with Nesterov acceleration achieve almost sure convergence. And three
groups of numerical experiments is to check out theoretical results.


  Structured sparsity has been proposed as an efficient way to prune the
complexity of modern Machine Learning (ML) applications and to simplify the
handling of sparse data in hardware. The acceleration of ML models - for both
training and inference - relies primarily on equivalent matrix multiplications
that can be executed efficiently on vector processors or custom matrix engines.
The goal of this work is to incorporate the simplicity of structured sparsity
into vector execution, thereby accelerating the corresponding matrix
multiplications. Toward this objective, a new vector index-multiply-accumulate
instruction is proposed, which enables the implementation of lowcost indirect
reads from the vector register file. This reduces unnecessary memory traffic
and increases data locality. The proposed new instruction was integrated in a
decoupled RISCV vector processor with negligible hardware cost. Extensive
evaluation demonstrates significant speedups of 1.80x-2.14x, as compared to
state-of-the-art vectorized kernels, when executing layers of varying sparsity
from state-of-the-art Convolutional Neural Networks (CNNs).


  In this article we discuss flows in shallow, stratified horizontal layers of
two immiscible fluids. The top layer is an electrolyte which is
electromagnetically driven and the bottom layer is a dielectric fluid. Using a
quasi-two-dimensional approximation, we derive the depth-averaged
two-dimensional (2D) vorticity equation which includes a prefactor to the
advection term, previously unaccounted for. In addition, we study how the
horizontal components of velocity vary in the vertical direction. For a
Kolmogorov-like flow, we evaluate analytical expressions for the coefficients
in the generalized 2D vorticity equation, uncovering their dependence on
experimental parameters. To test the accuracy of these estimates, we
experimentally measure the horizontal velocity fields at the free-surface and
at the electrolyte-dielectric interface using particle image velocimetry (PIV).
We show that there is excellent agreement between the analytical predictions
and the experimental measurements. Our analysis shows that by increasing the
viscosity of the electrolyte relative to that of the dielectric, one may
significantly improve the uniformity of the flow along the vertical direction.


  We present a collective coordinate approach to study the collective behaviour
of a finite ensemble of $N$ stochastic Kuramoto oscillators using two degrees
of freedom; one describing the shape dynamics of the oscillators and one
describing their mean phase. Contrary to the thermodynamic limit $N\to\infty$
in which the mean phase of the cluster of globally synchronized oscillators is
constant in time, the mean phase of a finite-size cluster experiences Brownian
diffusion with a variance proportional to $1/N$. This finite-size effect is
quantitatively well captured by our collective coordinate approach.


  We show that there is a randomized algorithm that, when given a small
constant-depth Boolean circuit $C$ made up of gates that compute
constant-degree Polynomial Threshold functions or PTFs (i.e., Boolean functions
that compute signs of constant-degree polynomials), counts the number of
satisfying assignments to $C$ in significantly better than brute-force time.
  Formally, for any constants $d,k$, there is an $\epsilon > 0$ such that the
algorithm counts the number of satisfying assignments to a given depth-$d$
circuit $C$ made up of $k$-PTF gates such that $C$ has size at most
$n^{1+\epsilon}$. The algorithm runs in time $2^{n-n^{\Omega(\epsilon)}}$.
  Before our result, no algorithm for beating brute-force search was known even
for a single degree-$2$ PTF (which is a depth-$1$ circuit of linear size).
  The main new tool is the use of a learning algorithm for learning degree-$1$
PTFs (or Linear Threshold Functions) using comparison queries due to Kane,
Lovett, Moran and Zhang (FOCS 2017). We show that their ideas fit nicely into a
memoization approach that yields the #SAT algorithms.


  Emotion recognition has received considerable attention from the Computer
Vision community in the last 20 years. However, most of the research focused on
analyzing the six basic emotions (e.g. joy, anger, surprise), with a limited
work directed to other affective states. In this paper, we tackle
sentimentality (strong feeling of heartwarming or nostalgia), a new emotional
state that has few works in the literature, and no guideline defining its
facial markers. To this end, we first collect a dataset of 4.9K videos of
participants watching some sentimental and non-sentimental ads, and then we
label the moments evoking sentimentality in the ads. Second, we use the
ad-level labels and the facial Action Units (AUs) activation across different
frames for defining some weak frame-level sentimentality labels. Third, we
train a Multilayer Perceptron (MLP) using the AUs activation for sentimentality
detection. Finally, we define two new ad-level metrics for evaluating our model
performance. Quantitative and qualitative results show promising results for
sentimentality detection. To the best of our knowledge this is the first work
to address the problem of sentimentality detection.


  RF cavities used in modern particle accelerators operate in TM$_{m10}$-like
modes composed of a single, dominant multipole of order $m$; $m=0$ modes are
used for the longitudinal acceleration of a particle beam and $m\neq0$ modes
for controlling transverse beam dynamics. The practical design of the latter,
however, can be complex and require extensive analysis through the iteration of
both approximate mathematical models and computationally expensive simulations
to optimise the performance of the structure. In this paper we present a new,
systematic method for designing azimuthally modulated RF cavities that support
modes composed of any number and magnitude of user-specified transverse
multipoles, either with or without a longitudinally accelerating component. Two
case studies are presented of RF cavity designs that support modes composed of
a longitudinally accelerating field in addition to a single transverse
multipole, and designs that support modes composed of two transverse
multipoles. We discuss generalising the discoveries and conclusions from the
two case studies to designing cavities that support modes composed of any
number of multipoles. The theoretical work is verified with analysis of 3D
simulations and experimental measurements are presented of a cavity operating
in a 3 GHz mode that simultaneously longitudinally accelerates and transversely
focuses a beam.


  After reviewing briefly the upper bounds on the mass of the lightest Higgs
boson in the most general unconstrained minimal supersymmetric extension of the
Standard Model, we discuss various arguments which reduce the parameter space
of the model and give stronger predictions for $M_{h^0}$. First, the
constraints from the presently available experimental data are summarized.
Next, the role of of several additional theoretical assumptions is studied,
after extrapolating the model to high energy scales. The most important ones
are: perturbative validity up to the GUT scale and the electroweak symmetry
breaking. A Higgs boson with $M_{h^0}<$100 GeV is predicted in several
scenarios. Its absence in that mass range will have important implications for
the parameter space of the model.


  Superconductivity involving finite momentum pairing can lead to spatial gap
and pair density modulations, as well as Bogoliubov Fermi states within the
superconducting gap. However, the experimental realization of their intertwined
relations has been challenging. Here, we detect chiral kagome superconductivity
modulations with residual Fermi arcs in KV3Sb5 and CsV3Sb5 by normal and
Josephson scanning tunneling microscopy down to 30mK with resolved electronic
energy difference at microelectronvolt level. We observe a U-shaped
superconducting gap with flat residual in-gap states. This gap exhibits chiral
2 by 2 spatial modulations with magnetic field tunable chirality, which align
with the chiral 2 by 2 pair density modulations observed through Josephson
tunneling. These findings demonstrate a chiral pair density wave (PDW) that
breaks time-reversal symmetry. Quasiparticle interference imaging of the in-gap
zero-energy states reveals segmented arcs, with high-temperature data linking
them to parts of the reconstructed V d-orbital states within the charge order.
The detected residual Fermi arcs can be explained by the partial suppression of
these d-orbital states through an interorbital 2 by 2 PDW and thus serve as
candidate Bogoliubov Fermi states. Additionally, we differentiate the observed
PDW order from impurity-induced gap modulations. Our observations not only
uncover a chiral PDW order with orbital-selectivity, but also illuminate the
fundamental space-momentum correspondence inherent in finite momentum paired
superconductivity.


  A simple model for nuclear structure functions in the region of small x and
small and moderate $Q^2$, is presented. It is a parameter-free extension, in
the Glauber-Gribov approach to nuclear collisions, of a saturation model for
the nucleon. A reasonable agreement with experimental data on ratios of nuclear
structure functions is obtained. Nuclear effects in the
longitudinal-to-transverse cross section ratios are found to be small.
Predictions of the model for values of $x$ smaller than those available to
present experiments are given. The unintegrated gluon distribution and the
behaviour of the saturation scale which result from this model are shown and
discussed.


  Microwave phased array antennas (PAAs) are very attractive to defense
applications and high-speed wireless communications for their abilities of fast
beam scanning and complex beam pattern control. However, traditional PAAs based
on phase shifters suffer from the beam-squint problem and have limited
bandwidths. True-time-delay (TTD) beamforming based on low-loss photonic delay
lines can solve this problem. But it is still quite challenging to build
large-scale photonic TTD beamformers due to their high hardware complexity. In
this paper, we demonstrate a photonic TTD beamforming network based on a
miniature microresonator frequency comb (microcomb) source and dispersive time
delay. A method incorporating optical phase modulation and programmable
spectral shaping is proposed for positive and negative apodization weighting to
achieve arbitrary microwave beam pattern control. The experimentally
demonstrated TTD beamforming network can support a PAA with 21 elements. The
microwave frequency range is $\mathbf{8\sim20\ {GHz}}$, and the beam scanning
range is $\mathbf{\pm 60.2^\circ}$. Detailed measurements of the microwave
amplitudes and phases are performed. The beamforming performances of Gaussian,
rectangular beams and beam notch steering are evaluated through simulations by
assuming a uniform radiating antenna array. The scheme can potentially support
larger PAAs with hundreds of elements by increasing the number of comb lines
with broadband microcomb generation.


  By combining angle-resolved photoemission spectroscopy and quantum
oscillation measurements, we performed a comprehensive investigation on the
electronic structure of LaSb, which exhibits near-quadratic extremely large
magnetoresistance (XMR) without any sign of saturation at magnetic fields as
high as 40 T. We clearly resolve one spherical and one intersecting-ellipsoidal
hole Fermi surfaces (FSs) at the Brillouin zone (BZ) center $\Gamma$ and one
ellipsoidal electron FS at the BZ boundary $X$. The hole and electron carriers
calculated from the enclosed FS volumes are perfectly compensated, and the
carrier compensation is unaffected by temperature. We further reveal that LaSb
is topologically trivial but share many similarities with the Weyl semimetal
TaAs family in the bulk electronic structure. Based on these results, we have
examined the mechanisms that have been proposed so far to explain the
near-quadratic XMR in semimetals.


  We report the reduction in residual stress of AlN thin films and also the
crystal structure, surface morphology and nanomechanical properties of
magnetron sputtered as a function of substrate temperature (Ts, 35 - 600 ?C).
The residual stress of these films was calculated by sin2 technique and found
that they are varying from tensile to compression with temperature (Ts).
Evolution of crystalline growth of AlN films was studied by GIXRD and
transmission electron microscopy (TEM) and a preferred a-axis orientation was
observed at 400 ?C. The cross-sectional TEM micrograph and selected area
electron difraction (SAED) of this film exhibited a high degree of orientation
as well as a columnar structure. Hardness (H) measured by Nanoindentation
technique on these films ranged between 12.8 - 19 GPa.


  We address the problem of coherent state discrimination in the presence of
phase diffusion. We investigate the role of the hybrid near-optimum receiver
(HYNORE) we proposed in [J. Opt. Soc. Am. B 40, 705-714 (2023)] in the task of
mitigating the noise impact. We prove the HYNORE to be a robust receiver,
outperforming the displacement photon-number-resolving (DPNR) receiver and
beating the standard quantum limit in particular regimes. We introduce the
maximum tolerable phase noise $\sigma_{\mathrm{max}}$ as a figure of merit for
the receiver robustness and show that HYNORE increases its value with respect
to the DPNR receiver.


  Quantum computers (QC) could harbor the potential to significantly advance
materials simulations, particularly at the atomistic scale involving strongly
correlated fermionic systems where an accurate description of quantum many-body
effects scales unfavorably with size. While a full-scale treatment of condensed
matter systems with currently available noisy quantum computers remains
elusive, quantum embedding schemes like dynamical mean-field theory (DMFT)
allow the mapping of an effective, reduced subspace Hamiltonian to available
devices to improve the accuracy of ab initio calculations such as density
functional theory (DFT). Here, we report on the development of a hybrid
quantum-classical DFT+DMFT simulation framework which relies on a quantum
impurity solver based on the Lehmann representation of the impurity Green's
function. Hardware experiments with up to 14 qubits on the IBM Quantum system
are conducted, using advanced error mitigation methods and a novel calibration
scheme for an improved zero-noise extrapolation to effectively reduce adverse
effects from inherent noise on current quantum devices. We showcase the utility
of our quantum DFT+DMFT workflow by assessing the correlation effects on the
electronic structure of a real material, Ca2CuO2Cl2, and by carefully
benchmarking our quantum results with respect to exact reference solutions and
experimental spectroscopy measurements.


  We establish a new criterion for the existence of a global cross section to a
non-singular volume-preserving flow on a compact manifold. Namely, if $\Phi$ is
a non-singular smooth flow on a compact, connected manifold $M$ with a smooth
invariant volume form $\Omega$, then $\Phi$ admits a global cross section if
and only if the $(n-1)$-form $i_X \Omega$ is intrinsically harmonic, that is,
harmonic with respect to some Riemannian metric on $M$.


  We study the following nonlinear and nonlocal elliptic equation in~$\R^n$ $$
(-\Delta)^s u = \epsilon\,h\,u^q + u^p \ {\mbox{ in }}\R^n, $$
where~$s\in(0,1)$, $n>2s$, $\epsilon>0$ is a small parameter,
$p=\frac{n+2s}{n-2s}$, $q\in(0,1)$, and~$h\in L^1(\R^n)\cap L^\infty(\R^n)$.
The problem has a variational structure, and this allows us to find a positive
solution by looking at critical points of a suitable energy functional. In
particular, in this paper, we find a local minimum and a mountain pass solution
of this functional. One of the crucial ingredient is a
Concentration-Compactness principle.
  Some difficulties arise from the nonlocal structure of the problem and from
the fact that we deal with an equation in the whole of~$\R^n$ (and this causes
lack of compactness of some embeddings). We overcome these difficulties by
looking at an equivalent extended problem.


  Topic models extract representative word sets - called topics - from word
counts in documents without requiring any semantic annotations. Topics are not
guaranteed to be well interpretable, therefore, coherence measures have been
proposed to distinguish between good and bad topics. Studies of topic coherence
so far are limited to measures that score pairs of individual words. For the
first time, we include coherence measures from scientific philosophy that score
pairs of more complex word subsets and apply them to topic scoring.


  In a meta-analysis, it is important to specify a model that adequately
describes the effect-size distribution of the underlying population of studies.
The conventional normal fixed-effect and normal random-effects models assume a
normal effect-size population distribution, conditionally on parameters and
covariates. For estimating the mean overall effect size, such models may be
adequate, but for prediction they surely are not if the effect size
distribution exhibits non-normal behavior. To address this issue, we propose a
Bayesian nonparametric meta-analysis model, which can describe a wider range of
effect-size distributions, including unimodal symmetric distributions, as well
as skewed and more multimodal distributions. We demonstrate our model through
the analysis of real meta-analytic data arising from behavioral-genetic
research. We compare the predictive performance of the Bayesian nonparametric
model against various conventional and more modern normal fixed-effects and
random-effects models.


  This is the first of two papers dedicated to the computation of the reduced
C*-algebra of a connected, linear, real reductive group up to Morita
equivalence, and the verification of the Connes-Kasparov conjecture for these
groups. These results were originally announced by Antony Wassermann in 1987.
In Part I we shall give details of the C*-algebraic Morita equivalence, and
then compute the Connes-Kasparov morphism subject to some results in tempered
representation theory that we shall prove in Part II using tools from David
Vogan's classification of the tempered dual.


  Even though convolutional neural networks have become the method of choice in
many fields of computer vision, they still lack interpretability and are
usually designed manually in a cumbersome trial-and-error process. This paper
aims at overcoming those limitations by proposing a deep neural network, which
is designed in a systematic fashion and is interpretable, by integrating
multiresolution analysis at the core of the deep neural network design. By
using the lifting scheme, it is possible to generate a wavelet representation
and design a network capable of learning wavelet coefficients in an end-to-end
form. Compared to state-of-the-art architectures, the proposed model requires
less hyper-parameter tuning and achieves competitive accuracy in image
classification tasks


  Radio map in general refers to the geographical signal power spectrum
density, formed by the superposition of concurrent wireless transmissions, as a
function of location, frequency and time. It contains rich and useful
information regarding the spectral activities and propagation channels in
wireless networks. Such information can be leveraged to not only improve the
performance of existing wireless services (e.g., via proactive resource
provisioning) but also enable new applications, such as wireless spectrum
surveillance. However, practical implementation of radio maps in wireless
communication is often difficult due to the heterogeneity of modern wireless
networks and the sheer amount of wireless data to be collected, stored, and
processed. In this article, we provide an overview on the state-of-the-art
techniques for constructing radio maps as well as applying them to achieve
efficient and secure resource management in wireless networks. Some key
research challenges are highlighted to motivate future investigation.


  The NA61/SHINE experiment aims to discover the Critical Point of strongly
interacting matter and study the properties of the onset of deconfinement.
These goals are to be achieved by per- forming a two-dimensional phase diagram
($T-\mu_{B}$) scan by measurements of hadron production properties in
proton-proton, proton-nucleus and nucleus-nucleus interactions as a function of
col- lision energy and system size. Close to the Critical Point an increase of
fluctuations is predicted. This contribution presents preliminary results on
transverse momentum and multiplicity fluctua- tions expressed in terms of
strongly intensive quantities from the Be+Be energy scan. The data are fully
corrected for contributions from non-target interactions. The Be+Be results are
compared with NA61/SHINE measurements from the p+p energy scan, with NA49
results from central Pb+Pb collisions as well as with model predictions.


  An important open question today is the understanding of the relevance that
dark matter (DM) halo substructure may have for DM searches. In the standard
cosmological framework, subhalos are predicted to be largely abundant inside
larger halos, i.e., galaxies like ours, and are thought to form first and later
merge to form larger structures. Dwarf satellite galaxies -- the most massive
exponents of halo substructure in our own galaxy -- are already known to be
excellent targets and, indeed, they are constantly scrutinized by current
gamma-ray experiments in their search for DM annihilation signals. Lighter
subhalos not massive enough to have a visible baryonic counterpart may be good
targets as well given their typical number densities and distances. In
addition, the clumpy distribution of subhalos residing in larger halos may
boost the DM signals considerably. In an era in which gamma-ray experiments
possess, for the first time, the exciting potential of reaching the most
relevant regions of the DM parameter space, a profound knowledge of the DM
targets and scenarios being tested at present is mandatory if we aim for
accurate predictions of DM-induced fluxes, for investing significant telescope
observing time to selected targets, and for deriving robust conclusions from
our DM search efforts. In this regard, a precise characterization of the
statistical and structural properties of subhalos becomes critical. With the
Special Issue "The Role of Halo Substructure in Gamma-Ray Dark Matter Searches"
[https://www.mdpi.com/journal/galaxies/special_issues/Gamma-RayDMS], we aimed
to summarize where we stand today on our knowledge of the different aspects of
the DM halo substructure; to identify what are the remaining big questions, how
we could address these and, by doing so, to find new avenues for research.


  Diffusion over a network refers to the phenomenon of a change of state of a
cross-sectional unit in one period leading to a change of state of its
neighbors in the network in the next period. One may estimate or test for
diffusion by estimating a cross-sectionally aggregated correlation between
neighbors over time from data. However, the estimated diffusion can be
misleading if the diffusion is confounded by omitted covariates. This paper
focuses on the measure of diffusion proposed by He and Song (2022), provides a
method of decomposition analysis to measure the role of the covariates on the
estimated diffusion, and develops an asymptotic inference procedure for the
decomposition analysis in such a situation. This paper also presents results
from a Monte Carlo study on the small sample performance of the inference
procedure.


  The large model size, high computational operations, and vulnerability
against membership inference attack (MIA) have impeded deep learning or deep
neural networks (DNNs) popularity, especially on mobile devices. To address the
challenge, we envision that the weight pruning technique will help DNNs against
MIA while reducing model storage and computational operation. In this work, we
propose a pruning algorithm, and we show that the proposed algorithm can find a
subnetwork that can prevent privacy leakage from MIA and achieves competitive
accuracy with the original DNNs. We also verify our theoretical insights with
experiments. Our experimental results illustrate that the attack accuracy using
model compression is up to 13.6% and 10% lower than that of the baseline and
Min-Max game, accordingly.


  The CMB is perhaps the cleanest cosmological observable. Given a cosmology
model, the angular spectrum of the CMB can be computed to percent accuracy. On
the observational side, as far as we know, there is little that stands in the
way between an accurate measurement and a rigorous confrontation with theory.
In this article, we review the state of the data and indicate future
directions.
  The data clearly show a rise in the angular spectrum to a peak of roughly
$\delta T_l=(l(l+1)C_l/2\pi)^{1/2}\approx 85~\mu$K at $l\approx 200$ and a fall
at higher $l$. In particular, $\delta T_l$ at $l=400$ is significantly less
than at $l=200$. This is shown by a combined analysis of data sets and by the
TOCO data alone.
  In the simplest open models with $\Omega_m = 0.35$, one expects a peak in the
angular spectrum near $l=400$. For spatially flat models, a peak near $l=200$
is indicated and thus this model is preferred by the data. The combination of
this, along with the growing body of evidence that $\Omega_m \approx 0.3$,
suggests a cosmological constant is required. Further evidence for a
cosmological constant is provided by the height of the peak. This conclusion is
independent of the supernovae data.


  We analyze the geometry of scaling limits of near-critical 2D percolation,
i.e., for $p=p_c+\lambda\delta^{1/\nu}$, with $\nu=4/3$, as the lattice spacing
$\delta \to 0$. Our proposed framework extends previous analyses for $p=p_c$,
based on $SLE_6$. It combines the continuum nonsimple loop process describing
the full scaling limit at criticality with a Poissonian process for marking
double (touching) points of that (critical) loop process. The double points are
exactly the continuum limits of "macroscopically pivotal" lattice sites and the
marked ones are those that actually change state as $\lambda$ varies. This
structure is rich enough to yield a one-parameter family of near-critical loop
processes and their associated connectivity probabilities as well as related
processes describing, e.g., the scaling limit of 2D minimal spanning trees.


  For quantum theories with a classical limit (which includes the large N
limits of typical field theories), we derive a hierarchy of evolution equations
for equal time correlators which systematically incorporate corrections to the
limiting classical evolution. Explicit expressions are given for
next-to-leading order, and next-to-next-to-leading order time evolution. The
large N limit of N-component vector models, and the usual semiclassical limit
of point particle quantum mechanics are used as concrete examples. Our
formulation directly exploits the appropriate group structure which underlies
the construction of suitable coherent states and generates the classical phase
space. We discuss the growth of truncation error with time, and argue that
truncations of the large-N evolution equations are generically expected to be
useful only for times short compared to a ``decoherence'' time which scales
like N^{1/2}.


  We put forth a unifying formalism for the description of the thermodynamics
of continuously monitored systems, where measurements are only performed on the
environment connected to a system. We show, in particular, that the conditional
and unconditional entropy production, which quantify the degree of
irreversibility of the open system's dynamics, are related to each other by the
Holevo quantity. This, in turn, can be further split into an information gain
rate and loss rate, which provide conditions for the existence of informational
steady-states (ISSs), i.e. stationary states of a conditional dynamics that are
maintained owing to the unbroken acquisition of information. We illustrate the
applicability of our framework through several examples.


  In a graph G, a dissociation set is a subset of vertices which induces a
subgraph with vertex degree at most 1. Finding a dissociation set of maximum
cardinality in a graph is NP-hard even for bipartite graphs and is called the
maximum dissociation set problem. The complexity of maximum dissociation set
problem in various subclasses of graphs has been extensively studied in the
literature. In this paper, we study the maximum dissociation problem from
different perspectives and characterize the vertices belonging to all maximum
dissociation sets, and to no maximum dissociation set of a tree. We present a
linear time recognition algorithm which can determine whether a given vertex in
a tree is contained in all (or no) maximum dissociation sets of the tree. Thus
for a tree with n vertices, we can find all vertices belonging to all (or no)
maximum dissociation sets of the tree in O(n^2) time.


  Supernovae (SNe) powered by interaction with circumstellar material provide
evidence for intense stellar mass loss during the final years leading up to
core collapse. We have argued that during and after core neon burning, internal
gravity waves excited by core convection can tap into the core fusion power and
transport a super-Eddington energy flux out to the stellar envelope,
potentially unbinding up to ~ 1 solar mass of material. In this work, we
explore the internal conditions of SN progenitors using the MESA 1-D stellar
evolution code, in search of those most susceptible to wave-driven mass loss.
We focus on simple, order of magnitude considerations applicable to a wide
range of progenitors. Wave-driven mass loss during core neon and oxygen fusion
happens preferentially in either lower mass (<~ 20 solar mass ZAMS) stars or
massive, sub-solar metallicity stars. Roughly 20 per cent of the SN progenitors
we survey can excite ~ 10^46 - 10^48 erg of energy in waves that can
potentially drive mass loss within a few months to a decade of core collapse.
This energy can generate a circumstellar environment with 10^-3 - 1 solar mass
reaching ~ 100 AU before explosion. We predict a correlation between the energy
associated with pre-SN mass ejection and the time to core collapse, with the
most intense mass loss preferentially happening closer to core collapse. During
silicon burning, a ~ 5 day long phase for our progenitor models, wave energy
may inflate ~ 10^-3 - 1 solar mass of the stellar envelope to ~ 10 - 100s of
solar radii. This suggests that some nominally compact SN progenitors (Type Ibc
progenitors) will have a significantly different SN shock breakout signature
than traditionally assumed. We discuss the implications of our results for the
core-collapse SN mechanism, Type IIn SNe, Type IIb SNe from extended
progenitors (e.g., SNe 1993j and 2011dh), and observed pre-SN outbursts.


  Revealing the hardening and strengthening mechanisms is crucial for
facilitating the design of superhard and high-strength high-entropy ceramics
(HECs). Here, we take high-entropy diborides (HEB$_2$) as the prototype to
thoroughly investigate the hardening and strengthening mechanisms of HECs.
Specifically, the equiatomic 4- to 9-cation single-phase HEB$_2$ ceramics
(4-9HEB$_2$) are fabricated by an ultra-fast high-temperature sintering method.
The as-fabricated 4-9HEB$_2$ samples possess similar grain sizes, comparable
relative densities (up to ~98%), uniform compositions, and clean grain
boundaries without any impurities. The experimental results show that the
hardness and flexural strength of the as-fabricated 4-9HEB$_2$ samples have an
increasing tendency with the increase of metal components. The first-principles
calculations find that lattice distortion is essential to the hardness and
strength of HEB$_2$. With the increase of metal components, an aggravation of
lattice distortion accompanied by B-B bond strengthening is determined,
resulting in the enhancement of the hardness and flexural strength. Moreover,
the correlation between other potential indicators and the hardness/flexural
strength of HEB$_2$ has been disproved, including valence electron
concentration, electronegativity mismatch, and metallic states. Our results
unravel the hardening and strengthening mechanisms of HECs by intensifying
lattice distortion, which may provide guidance for developing superhard and
high-strength HECs.


  In this paper we study the large N_c limit of SO(N_c) gauge theory coupled to
a Majorana field and a real scalar field in 1+1 dimensions extending ideas of
Rajeev. We show that the phase space of the resulting classical theory of
bilinears, which are the mesonic operators of this theory, is OSp_1(H|H
)/U(H_+|H_+), where H|H refers to the underlying complex graded space of
combined one-particle states of fermions and bosons and H_+|H_+ corresponds to
the positive frequency subspace. In the begining to simplify our presentation
we discuss in detail the case with Majorana fermions only (the purely bosonic
case is treated in our earlier work). In the Majorana fermion case the phase
space is given by O_1(H)/U(H_+), where H refers to the complex one-particle
states and H_+ to its positive frequency subspace. The meson spectrum in the
linear approximation again obeys a variant of the 't Hooft equation. The linear
approximation to the boson/fermion coupled case brings an additonal bound state
equation for mesons, which consists of one fermion and one boson, again of the
same form as the well-known 't Hooft equation.


  Most softwarized telco services are conveniently framed as Service Function
Chains (SFCs). Indeed, being structured as a combination of interconnected
nodes, service chains may suffer from the single point of failure problem,
meaning that an individual node malfunctioning could compromise the whole chain
operation. To guarantee "highly available" (HA) levels, service providers are
required to introduce redundancy strategies to achieve specific availability
demands, where cost constraints have to be taken into account as well. Along
these lines we propose HASFC (standing for High Availability SFC), a framework
designed to support, through a dedicated REST interface, the MANO
infrastructure in deploying SFCs with an optimal availability-cost trade off.
Our framework is equipped with: i) an availability model builder aimed to
construct probabilistic models of the SFC nodes in terms of failure and repair
actions; ii) a chaining and selection module to compose the possible redundant
SFCs, and extract the best candidates thereof. Beyond providing architectural
details, we demonstrate the functionalities of HASFC through a use case which
considers the IP Multimedia Subsystem, an SFC-like structure adopted to manage
multimedia contents within 4G and 5G networks.


  The number of mobile devices (e.g., smartphones, wearable technologies) is
rapidly growing. In line with this trend, a massive amount of spatial data is
being collected since these devices allow users to geo-tag user-generated
content. Clearly, a scalable computing infrastructure is needed to manage such
large datasets. Meanwhile, Cloud Computing service providers (e.g., Amazon,
Google, and Microsoft) allow users to lease computing resources. However, most
of the existing spatial indexing techniques are designed for the centralized
paradigm which is limited to the capabilities of a single sever. To address the
scalability shortcomings of existing approaches, we provide a study that focus
on generating a distributed spatial index structure that not only scales out to
multiple servers but also scales up since it fully exploits the multi-core CPUs
available on each server using Voronoi diagram as the partitioning and indexing
technique which we also use to process spatial queries effectively. More
specifically, since the data objects continuously move and issue position
updates to the index structure, we collect the latest positions of objects and
periodically generate a read-only index to eliminate costly distributed
updates. Our approach scales near-linearly in index construction and query
processing, and can efficiently construct an index for millions of objects
within a few seconds. In addition to scalability and efficiency, we also aim to
maximize the server utilization that can support the same workload with less
number of servers. Server utilization is a crucial point while using Cloud
Computing because users are charged based on the total amount of time they
reserve each server, with no consideration of utilization.


  Variable Conductance Heat Pipes (VCHP) are mainly employed to cool down
electronic systems in spacecraft applications, as they can handle high
temperature fluctuations in their cold source, preventing thus the systems from
damaging. These fluctuations, as well as ultra-low temperatures, are always
present in outer space, and one of the key steps in a VCHP design is therefore
to make sure that they endure these conditions. However, not much has been
written about their resilience during and after a long exposition to
subfreezing conditions, i.e. temperatures lower than the freezing point of the
working fluid. In this paper we implement and validate a computational routine
based on a modified Flat-Front Approach to predict the VCHP temperature profile
and to determine the location of the gas-vapor front. Then we continuously
expose an ammonia/stainless-steel VCHP to temperatures below the ammonia
freezing point for 211 hours, to later examine the formation and subsequent
dynamics of a thin block of frozen ammonia which is diffused into the inactive
part of the heat pipe condenser. We describe as well how a strong correlation
between the adiabatic section and the reservoir temperatures is maintained (or
broken) upon the occurrence (or absence) of the blockage of frozen ammonia.


  We consider the problem of estimating the marginal independence structure of
a Bayesian network from observational data, learning an undirected graph we
call the unconditional dependence graph. We show that unconditional dependence
graphs of Bayesian networks correspond to the graphs having equal independence
and intersection numbers. Using this observation, a Gr\"obner basis for a toric
ideal associated to unconditional dependence graphs of Bayesian networks is
given and then extended by additional binomial relations to connect the space
of all such graphs. An MCMC method, called GrUES (Gr\"obner-based Unconditional
Equivalence Search), is implemented based on the resulting moves and applied to
synthetic Gaussian data. GrUES recovers the true marginal independence
structure via a penalized maximum likelihood or MAP estimate at a higher rate
than simple independence tests while also yielding an estimate of the
posterior, for which the $20\%$ HPD credible sets include the true structure at
a high rate for data-generating graphs with density at least $0.5$.


  We study non-topological solitons, so called Q-balls, which carry a
non-vanishing Noether charge and arise as lump solutions of self-interacting
complex scalar field models. Explicit examples of new axially symmetric
non-spinning Q-ball solutions that have not been studied so far are constructed
numerically. These solutions can be interpreted as angular excitations of the
fundamental $Q$-balls and are related to the spherical harmonics.
Correspondingly, they have higher energy and their energy densities possess two
local maxima on the positive z-axis.
  We also study two Q-balls interacting via a potential term in (3+1)
dimensions and construct examples of stationary, solitonic-like objects in
(3+1)-dimensional flat space-time that consist of two interacting global scalar
fields. We concentrate on configurations composed of one spinning and one
non-spinning Q-ball and study the parameter-dependence of the energy and
charges of the configuration.
  In addition, we present numerical evidence that for fixed values of the
coupling constants two different types of 2-Q-ball solutions exist: solutions
with defined parity, but also solutions which are asymmetric with respect to
reflexion through the x-y-plane.


  Some recent supernovae studies have extended the distance versus velocity
Hubble plot to very high redshift, and have revealed the apparent presence of a
cosmic repulsion. We show that such a repulsion has a natural origin within
conformal gravity, a fully covariant candidate alternative to standard gravity,
and that it arises from the gravitational field itself via the negative spatial
curvature which conformal cosmology is required to possess. In this same
cosmology the flatness, horizon and cosmological constant problems are all
resolved, and no need is found for dark matter.


  Recently, transformers have become incredibly popular in computer vision and
vision-language tasks. This notable rise in their usage can be primarily
attributed to the capabilities offered by attention mechanisms and the
outstanding ability of transformers to adapt and apply themselves to a variety
of tasks and domains. Their versatility and state-of-the-art performance have
established them as indispensable tools for a wide array of applications.
However, in the constantly changing landscape of machine learning, the
assurance of the trustworthiness of transformers holds utmost importance. This
paper conducts a thorough examination of vision-language transformers,
employing three fundamental principles of responsible AI: Bias, Robustness, and
Interpretability. The primary objective of this paper is to delve into the
intricacies and complexities associated with the practical use of transformers,
with the overarching goal of advancing our comprehension of how to enhance
their reliability and accountability.


  Digitized archives contain and preserve the knowledge of generations of
scholars in millions of documents. The size of these archives calls for
automatic analysis since a manual analysis by specialists is often too
expensive. In this paper, we focus on the analysis of handwriting in scanned
documents from the art-historic archive of the WPI. Since the archive consists
of documents written in several languages and lacks annotated training data for
the creation of recognition models, we propose the task of handwriting
classification as a new step for a handwriting OCR pipeline. We propose a
handwriting classification model that labels extracted text fragments, eg,
numbers, dates, or words, based on their visual structure. Such a
classification supports historians by highlighting documents that contain a
specific class of text without the need to read the entire content. To this
end, we develop and compare several deep learning-based models for text
classification. In extensive experiments, we show the advantages and
disadvantages of our proposed approach and discuss possible usage scenarios on
a real-world dataset.


  We prove that $\tilde{\Theta}(k d^2 / \varepsilon^2)$ samples are necessary
and sufficient for learning a mixture of $k$ Gaussians in $\mathbb{R}^d$, up to
error $\varepsilon$ in total variation distance. This improves both the known
upper bounds and lower bounds for this problem. For mixtures of axis-aligned
Gaussians, we show that $\tilde{O}(k d / \varepsilon^2)$ samples suffice,
matching a known lower bound. Moreover, these results hold in the
agnostic-learning/robust-estimation setting as well, where the target
distribution is only approximately a mixture of Gaussians.
  The upper bound is shown using a novel technique for distribution learning
based on a notion of `compression.' Any class of distributions that allows such
a compression scheme can also be learned with few samples. Moreover, if a class
of distributions has such a compression scheme, then so do the classes of
products and mixtures of those distributions. The core of our main result is
showing that the class of Gaussians in $\mathbb{R}^d$ admits a small-sized
compression scheme.


  In this paper, we address the theoretical resolution of the Vlasov-Gauss
system from the linear regime to the strongly nonlinear one, when significant
trapping has occurred. The electric field is that of a sinusoidal electron
plasma wave (EPW) which is assumed to grow from the noise level, and to keep
growing at least up to the amplitude when linear theory in no longer valid
(while the wave evolution in the nonlinear regime may be arbitrary). The ions
are considered as a neutralizing fluid, while the electron response to the wave
is derived by matching two different techniques. We make use of a perturbation
analysis similar to that introduced to prove the Kolmogorov-Arnold- Moser
theorem, up to amplitudes large enough for neo-adiabatic results to be valid.
Our theory is applied to the growth and saturation of the beam-plasma
instability, and to the three-dimensional propagation of a driven EPW in a
non-uniform and non-stationary plasma. For the latter example, we lay a special
emphasis on nonlinear collisionless dissipation. We provide an explicit
theoretical expression for the nonlinear Landau-like damping rate which, in
some instances, is amenable to a simple analytic formula. We also insist on the
irreversible evolution of the electron distribution function, which is nonlocal
in the wave amplitude and phase velocity. This makes trapping an effective
means of dissipation for the electrostatic energy, and also makes the wave
dispersion relation nonlocal. Our theory is generalized to allow for stimulated
Raman scattering, which we address up to saturation by accounting for plasma
inhomogeneity and non-stationarity, nonlinear kinetic effects, and interspeckle
coupling.


  We consider the effect of interactions on electrons confined to two
dimensions at Landau level filling $\nu=2$, with the specific aim to determine
the range of parameters where the fully polarized state is stable. We calculate
the charge and the spin density collective modes in random phase approximation
(RPA) including vertex corrections (also known as time dependent Hartree Fock),
and treating the Landau level mixing accurately within the subspace of a single
particle hole pair. It is found that the spin wave excitation mode of the fully
polarized state has a roton minimum which deepens as a result of the
interaction induced Landau level mixing, and the energy of the roton vanishes
at a critical Zeeman energy signaling an instability of the fully polarized
state at still lower Zeeman energies. The feasibility of the experimental
observation of the roton minimum in the spin wave mode and its softening will
be discussed. The spin and charge density collective modes of the unpolarized
state are also considered, and a phase diagram for the $\nu =2$ state as a
function of $r_{S}$ and the Zeeman energy is obtained.


  The rapid development of IoT applications and their use in various fields of
everyday life has resulted in an escalated number of different possible
cyber-threats, and has consequently raised the need of securing IoT devices.
Collecting Cyber-Threat Intelligence (e.g., zero-day vulnerabilities or
trending exploits) from various online sources and utilizing it to proactively
secure IoT systems or prepare mitigation scenarios has proven to be a promising
direction. In this work, we focus on social media monitoring and investigate
real-time Cyber-Threat Intelligence detection from the Twitter stream.
Initially, we compare and extensively evaluate six different machine-learning
based classification alternatives trained with vulnerability descriptions and
tested with real-world data from the Twitter stream to identify the
best-fitting solution. Subsequently, based on our findings, we propose a novel
social media monitoring system tailored to the IoT domain; the system allows
users to identify recent/trending vulnerabilities and exploits on IoT devices.
Finally, to aid research on the field and support the reproducibility of our
results we publicly release all annotated datasets created during this process.


  A critical decision point when training predictors using multiple studies is
whether these studies should be combined or treated separately. We compare two
multi-study learning approaches in the presence of potential heterogeneity in
predictor-outcome relationships across datasets. We consider 1) merging all of
the datasets and training a single learner, and 2) multi-study ensembling,
which involves training a separate learner on each dataset and combining the
predictions resulting from each learner. In a linear regression setting, we
show analytically and confirm via simulation that merging yields lower
prediction error than ensembling when the predictor-outcome relationships are
relatively homogeneous across studies. However, as cross-study heterogeneity
increases, there exists a transition point beyond which ensembling outperforms
merging. We provide analytic expressions for the transition point in various
scenarios, study asymptotic properties, and illustrate how transition point
theory can be used for deciding when studies should be combined with an
application from metabolomics.


  Just as parallel threebranes on a smooth manifold are related to string
theory on $AdS_5\times {\bf S}^5$, parallel threebranes near a conical
singularity are related to string theory on $AdS_5\times X_5$, for a suitable
$X_5$. For the example of the conifold singularity, for which $X_5=(SU(2)\times
SU(2))/U(1)$, we argue that string theory on $AdS_5\times X_5$ can be described
by a certain ${\cal N}=1$ supersymmetric gauge theory which we describe in
detail.


  The dynamics of magnetic fields with amplitude of several tens of Megagauss,
generated at both sides of a solid target irradiated with a high intensity (?
1019W/cm2) picosecond laser pulse, has been spatially and temporally resolved
using a proton imaging technique. The amplitude of the magnetic fields is
sufficiently large to have a constraining effect on the radial expansion of the
plasma sheath at the target surfaces. These results, supported by numerical
simulations and simple analytical modeling, may have implications for ion
acceleration driven by the plasma sheath at the rear side of the target as well
as for the laboratory study of self-collimated high-energy plasma jets.


  Deep learning models have been used to support analytics beyond simple
aggregation, where deeper and wider models have been shown to yield great
results. These models consume a huge amount of memory and computational
operations. However, most of the large-scale industrial applications are often
computational budget constrained. In practice, the peak workload of inference
service could be 10x higher than the average cases, with the presence of
unpredictable extreme cases. Lots of computational resources could be wasted
during off-peak hours and the system may crash when the workload exceeds system
capacity. How to support deep learning services with a dynamic workload
cost-efficiently remains a challenging problem. In this paper, we address the
challenge with a general and novel training scheme called model slicing, which
enables deep learning models to provide predictions within the prescribed
computational resource budget dynamically. Model slicing could be viewed as an
elastic computation solution without requiring more computational resources.
Succinctly, each layer in the model is divided into groups of a contiguous
block of basic components (i.e. neurons in dense layers and channels in
convolutional layers), and then partially ordered relation is introduced to
these groups by enforcing that groups participated in each forward pass always
starts from the first group to the dynamically-determined rightmost group.
Trained by dynamically indexing the rightmost group with a single parameter
slice rate, the network is engendered to build up group-wise and residual
representation. Then during inference, a sub-model with fewer groups can be
readily deployed for efficiency whose computation is roughly quadratic to the
width controlled by the slice rate. Extensive experiments show that models
trained with model slicing can effectively support on-demand workload with
elastic inference cost.


  A discussion of several exotic models and how well they are able to describe
the data, with particular emphasis on atmospheric neutrinos.


  M-theory suggests the study of 11-dimensional space-times compactified on
some 7-manifolds. From its intimate relation to superstrings, one possible
class of such 7-manifolds are those that have Calabi-Yau threefolds as
boundary. In this article, we construct a special class of such 7-manifolds,
named as {\it K3-Thurston} (K3T) 7-manifolds. The factor from the K3 part of
the deformation space of these K3T 7-manifolds admits a K\"{a}hler structure,
while the factor of the deformation space from the Thurston part admits a
special K\"{a}hler structure. The latter rings with the nature of the scalar
manifold of a vector multiplet in an N=2 $d=4$ supersymmetric gauge theory.
Remarks and examples on more general K3T 7-manifolds and issues to possible
interfaces of K3T to M-theory are also discussed.


  In the present work, we demonstrate the possibility of controlling by an
external field the dynamics of collective excitations (excitons) of molecules
on an optical lattice. We show that a suitably chosen two-species mixture of
ultracold polar molecules loaded on an optical lattice forms a phononless
crystal, where exciton-impurity interactions can be controlled by applying an
external electric field. This can be used for the controlled creation of
many-body entangled states of ultracold molecules and the time-domain quantum
simulation of disorder-induced localization and delocalization of quantum
particles.


  The specific heat and the compressibility for the integrable t-J model are
calculated showing Luttinger liquid behavior for low temperatures. A
Trotter-Suzuki mapping and the quantum transfer matrix approach are utilized.
Using an algebraic Bethe ansatz this method permits the exact calculation of
the free energy and related quantities. A set of just two non-linear integral
equations determining these quantities is studied for various particle
densities and temperatures. The structure of the specific heat is discussed in
terms of the elementary charge as well as spin excitations.


  Heavy elements like gold, platinum or uranium are produced in the r-process,
which needs neutron-rich and explosive environments. Neutron star mergers are a
promising candidate for an r-process site. They exhibit three different
channels for matter ejection fulfilling these conditions: dynamic ejecta due to
tidal torques, neutrino-driven winds and evaporating matter from the accretion
disk. We present a first study of the integrated nucleosynthesis for a
neutrino-driven wind from a neutron star merger with a hyper-massive neutron
star. Trajectories from a recent hydrodynamical simulation are divided into
four different angle regions and post-processed with a reaction network. We
find that the electron fraction varies around $Y_e \approx 0.1 - 0.4$, but its
distribution differs for every angle of ejection. Hence, the wind ejecta do not
undergo a robust r-process, but rather possess distinct nucleosynthesis yields
depending on the angle range. Compared to the dynamic ejecta, a smaller amount
of neutron-rich matter gets unbound, but the production of lighter heavy
elements with $A \lesssim 130$ in the neutrino-driven wind can complement the
strong r-process of the dynamic ejecta.


  Among the chloride-based Li-ion solid electrolytes, Li$_2$ZrCl$_6$ (LZC) have
emerged as potential candidates due to their affordability, moisture stability,
and high ionic conductivity. LZC synthesized by solid-state heating exhibits
limited Li-ion conductivity while the mechanochemical ball-milled material is
more conductive. In this computational study, we integrate thermodynamic
modeling, using cluster-expansion Monte Carlo, and kinetic modeling, using
molecular dynamics, to investigate whether cation disorder can be achieved in
LZC, and how it affects Li-ion transport. Our results indicate that fast Li-ion
conductivity is induced by the activation of Li/vacancy disorder, which itself
depends on the degree of Zr disorder. We find that the very high-temperature
scale at which equilibrium Zr-disorder can form precludes any equilibrium
synthesis processes for achieving fast Li-ion conductivity, rationalizing why
only non-equilibrium synthesis methods, such as ball milling leads to good
conductivity. We identify as the critical mechanism the lack of Li/vacancy
disorder near room temperature when Zr is well-ordered. Our simulations further
show that the Li/vacancy order-disorder transition temperature is lowered by Zr
disorder, which is necessary for creating high Li diffusivity at room
temperature. The insights obtained from this study raise a challenge for the
large-scale production of these materials and the potential for the long-term
stability of their properties.


  Recent analyses of cosmic ray arrival directions have resulted in evidence
for a positive correlation with active galactic nuclei positions that has weak
significance against an isotropic source distribution. In this paper, we
explore the sample size needed to measure a highly statistically significant
correlation to a parent source catalogue. We compare several scenarios for the
directional scattering of ultra-high energy cosmic rays given our current
knowledge of the galactic and intergalactic magnetic fields. We find
significant correlations are possible for a sample of $>$1000 cosmic ray
protons with energies above 60 EeV.


  Neural networks often suffer from catastrophic interference (CI): performance
on previously learned tasks drops off significantly when learning a new task.
This contrasts strongly with humans, who can sequentially learn new tasks
without appreciably forgetting previous tasks. Prior work has explored various
techniques for mitigating CI such as regularization, rehearsal, generative
replay, and distillation methods. The current work takes a different approach,
one guided by cognitive science research showing that in naturalistic
environments, the probability of encountering a task decreases as a power-law
of the time since it was last performed. We argue that a realistic evaluation
of techniques for the mitigation of CI should be performed in simulated
naturalistic learning environments. Thus, we evaluate the extent of mitigation
of CI when training simple rehearsal-based methods in power-law environments
similar to the ones humans face. Our work explores this novel rehearsal-based
approach for a domain-incremental task: learning permutations in the MNIST
task. We compare our rehearsal environment with other baselines to show its
efficacy in promoting continual learning. Additionally, we investigate whether
this environment shows forward facilitation, i.e., faster learning of later
tasks. Next, we explore the robustness of our learning environment to the
number of tasks, model size, and amount of data rehearsed after each task.
Notably, our results show that the performance is comparable or superior to
that of models trained using popular regularization methods and also to
rehearsals in non-power-law environments. The benefits of this training
paradigm include simplicity and the lack of a need for extra neural circuitry.
In addition, because our method is orthogonal to other methods, future research
can combine training in power-law environments with other continual learning
mechanisms.


  Unlike popular modularized framework, end-to-end autonomous driving seeks to
solve the perception, decision and control problems in an integrated way, which
can be more adapting to new scenarios and easier to generalize at scale.
However, existing end-to-end approaches are often lack of interpretability, and
can only deal with simple driving tasks like lane keeping. In this paper, we
propose an interpretable deep reinforcement learning method for end-to-end
autonomous driving, which is able to handle complex urban scenarios. A
sequential latent environment model is introduced and learned jointly with the
reinforcement learning process. With this latent model, a semantic birdeye mask
can be generated, which is enforced to connect with a certain intermediate
property in today's modularized framework for the purpose of explaining the
behaviors of learned policy. The latent space also significantly reduces the
sample complexity of reinforcement learning. Comparison tests with a simulated
autonomous car in CARLA show that the performance of our method in urban
scenarios with crowded surrounding vehicles dominates many baselines including
DQN, DDPG, TD3 and SAC. Moreover, through masked outputs, the learned policy is
able to provide a better explanation of how the car reasons about the driving
environment. The codes and videos of this work are available at our github repo
and project website.


  Learned reweighting (LRW) approaches to supervised learning use an
optimization criterion to assign weights for training instances, in order to
maximize performance on a representative validation dataset. We pose and
formalize the problem of optimized selection of the validation set used in LRW
training, to improve classifier generalization. In particular, we show that
using hard-to-classify instances in the validation set has both a theoretical
connection to, and strong empirical evidence of generalization. We provide an
efficient algorithm for training this meta-optimized model, as well as a simple
train-twice heuristic for careful comparative study. We demonstrate that LRW
with easy validation data performs consistently worse than LRW with hard
validation data, establishing the validity of our meta-optimization problem.
Our proposed algorithm outperforms a wide range of baselines on a range of
datasets and domain shift challenges (Imagenet-1K, CIFAR-100, Clothing-1M,
CAMELYON, WILDS, etc.), with ~1% gains using VIT-B on Imagenet. We also show
that using naturally hard examples for validation (Imagenet-R / Imagenet-A) in
LRW training for Imagenet improves performance on both clean and naturally hard
test instances by 1-2%. Secondary analyses show that using hard validation data
in an LRW framework improves margins on test data, hinting at the mechanism
underlying our empirical gains. We believe this work opens up new research
directions for the meta-optimization of meta-learning in a supervised learning
context.


  We study the geometrical structure of the coadjoint orbits of an arbitrary
complex or real Lie algebra ${\mathfrak g}$ containing some ideal ${\mathfrak
n}$. It is shown that any coadjoint orbit in ${\mathfrak g}^*$ is a bundle with
the affine subspace of ${\mathfrak g}^*$ as its fibre. This fibre is an
isotropic submanifold of the orbit and is defined only by the coadjoint
representations of the Lie algebras ${\mathfrak g}$ and ${\mathfrak n}$ on the
dual space ${\mathfrak n}^*$. The use of this fact and an application of
methods of symplectic geometry give a new insight into the structure of
coadjoint orbits and allow us to generalize results derived earlier in the case
when ${\mathfrak g}$ is a split extension using the Abelian ideal ${\mathfrak
n}$ (a semidirect product). As applications, a new proof of the formula for the
index of Lie algebra and a necessary condition of integrality of a coadjoint
orbit are obtained.


  In this work, we study the stellar-dynamical hardening of unequal mass
supermassive black hole (SMBH) binaries in the central regions of merging
galactic nuclei. We present a comprehensive set of direct $N$-body simulations
of the problem, varying both the total mass and the mass ratio of the SMBH
binary (SMBHB). Simulations were carried out with the $\varphi-$GPU $N$-body
code, which enabled us to fully exploit supercomputers equipped with graphic
processing units (GPUs). As a model for the galactic nuclei, we adopted initial
axisymmetric, rotating models, aimed at reproducing the properties of a
galactic nucleus emerging from a galaxy merger event, containing two SMBHs
which were unbound initially. We found no 'final-parsec problem', as our SMBHs
tend to pair and shrink without showing significant signs of stalling. This
confirms earlier results and extends them to large particle numbers and
rotating systems. We find that the SMBHB hardening depends on the
binary-reduced mass ratio via a single parameter function. Our results suggest
that, at a fixed value for the SMBHB primary mass, the merger time of highly
asymmetric binaries is up to four order of magnitudes smaller than the
equal-mass binaries. This can significantly affect the population of SMBHs
potentially detectable as gravitational wave sources.


  Consideration of the model of the relativistic particle with curvature and
torsion in the three-dimensional space-time shows that the squaring of the
primary constraints entails a wrong result. The complete set of the Hamiltonian
constraints arising here correspond to another model with an action similar but
not identical with the initial action.


  Adversarial examples reveal the vulnerability and unexplained nature of
neural networks. Studying the defense of adversarial examples is of
considerable practical importance. Most adversarial examples that misclassify
networks are often undetectable by humans. In this paper, we propose a defense
model to train the classifier into a human-perception classification model with
shape preference. The proposed model comprising a texture transfer network
(TTN) and an auxiliary defense generative adversarial networks (GAN) is called
Human-perception Auxiliary Defense GAN (HAD-GAN). The TTN is used to extend the
texture samples of a clean image and helps classifiers focus on its shape. GAN
is utilized to form a training framework for the model and generate the
necessary images. A series of experiments conducted on MNIST, Fashion-MNIST and
CIFAR10 show that the proposed model outperforms the state-of-the-art defense
methods for network robustness. The model also demonstrates a significant
improvement on defense capability of adversarial examples.


  Cold, low-mass, field brown dwarfs are important for constraining the
terminus of the stellar mass function, and also for optimizing atmospheric
studies of exoplanets. In 2020 new model grids for such objects were made
available: Sonora-Bobcat and ATMO 2020. Also, new candidate cold brown dwarfs
were announced, and new spectroscopic observations at lambda ~4.8 um were
published. In this paper we present new infrared photometry for some of the
coldest brown dwarfs, and put the new data and models together to explore the
properties of these objects. We reconfirm the importance of mixing in these
atmospheres, which leads to CO and NH_3 abundances that differ by orders of
magnitude from chemical equilibrium values. We also demonstrate that the new
models retain the known factor >~3 discrepancy with observations at 2 <~ lambda
um <~ 4, for brown dwarfs cooler than 600 K. We show that the entire 1 <~
lambda um <~ 20 energy distribution of six brown dwarfs with 260 <= T_eff K <=
475 can be well reproduced, for the first time, by model atmospheres which
include dis-equilibrium chemistry as well as a photospheric temperature
gradient which deviates from the standard radiative/convective equilibrium
value. This change to the pressure-temperature profile is not unexpected for
rotating and turbulent atmospheres which are subject to diabatic processes. A
limited grid of modified-adiabat model colors is generated, and used to
estimate temperatures and metallicities for the currently known Y dwarfs. A
compilation of the photometric data used here is given in the Appendix.


  Context: The past decade has witnessed a large number of Galactic plane
surveys at angular resolutions below 20". However, no comparable
high-resolution survey exists at long radio wavelengths around 21cm in line and
continuum emission. Methods: Employing the Very Large Array (VLA) in the
C-array configuration and a large program, we observe the HI 21cm line, four OH
lines, nineteen Halpha radio recombination lines as well as the continuum
emission from 1 to 2GHz in full polarization over a large part of the first
Galactic quadrant. Results: Covering Galactic longitudes from 14.5 to 67.4deg
and latitudes between +-1.25deg, we image all of these lines and the continuum
at ~20" resolution. These data allow us to study the various components of the
interstellar medium (ISM): from the atomic phase, traced by the HI line, to the
molecular phase, observed by the OH transitions, to the ionized medium,
revealed by the cm continuum and the Halpha radio recombination lines.
Furthermore, the polarized continuum emission enables magnetic field studies.
In this overview paper, we discuss the survey outline and present the first
data release as well as early results from the different datasets. We now
release the first half of the survey; the second half will follow later after
the ongoing data processing has been completed. The data in fits format
(continuum images and line data cubes) can be accessed through the project
web-page http://www.mpia.de/thor. Conclusions: The HI/OH/Recombination line
survey of the Milky Way (THOR) opens a new window to the different parts of the
ISM. It enables detailed studies of molecular cloud formation, conversion of
atomic to molecular gas, and feedback from HII regions as well as the magnetic
field in the Milky Way. It is highly complementary to other surveys of our
Galaxy, and comparing different datasets allows us to address many open
questions.


  Nonlinear optical wave propagation manifests in a multitude of frequencies
generated from quantum-noise, and selecting desired nonlinear products usually
requires seeding the medium with extraneous waves, employing spatial or
spectral filters, or operation in resonant cavities. This is especially true
for multimode systems because of their high density of states. Here we report
the discovery of a self-action effect, originating from quantum noise, leading
to complete nonlinear optical conversion of an ultrashort soliton pulse between
two distinct, spatially coherent eigenmodes that are frequency-separated by one
Raman Stokes shift. That systematic nonlinear spatial reconfiguration occurs in
fibers with mode counts exceeding 10,000, which are often deemed to be chaotic,
points to the fundamental role of intermodal group velocity dispersion in the
selection rules for multimode nonlinear optics. We demonstrate wideband
spectral translations of ~70% of the carrier frequency, and the generation of
record, Megawatt peak-power pulses in the biologically crucial 1300-nm spectral
window, directly out of a flexible optical fiber. More generally, this novel
nonlinear coupling mechanism may be applied to fibers or on-chip waveguides,
and facilitate, power-scalable spatially coherent, ultrashort pulse generation
from the visible to the mid-IR.


  We present an adaptive refinement algorithm for T-splines on unstructured 2D
meshes. While for structured 2D meshes, one can refine elements alternatingly
in horizontal and vertical direction, such an approach cannot be generalized
directly to unstructured meshes, where no two unique global mesh directions can
be assigned. To resolve this issue, we introduce the concept of direction
indices, i.e., integers associated to each edge, which are inspired by theory
on higher-dimensional structured T-splines. Together with refinement levels of
edges, these indices essentially drive the refinement scheme. We combine these
ideas with an edge subdivision routine that allows for I-nodes, yielding a very
flexible refinement scheme that nicely distributes the T-nodes, preserving
global linear independence, analysis-suitability (local linear independence)
except in the vicinity of extraordinary nodes, sparsity of the system matrix,
and shape regularity of the mesh elements. Further, we show that the refinement
procedure has linear complexity in the sense of guaranteed upper bounds on a)
the distance between marked and additionally refined elements, and on b) the
ratio of the numbers of generated and marked mesh elements.


  Solutions of the wave equation in a space-time containing a thin cosmic
string are examined in the context of non-linear generalised functions.
Existence and uniqueness of solutions to the wave equation in the Colombeau
algebra G is established for a conical space-time and this solution is shown to
be associated to a distributional solution. A concept of generalised
hyperbolicity, based on test fields, can be defined for such singular
space-times and it is shown that a conical space-time is G-hyperbolic.


  Explicit expressions of the 3D velocity field in terms of the conserved
quantities of ideal fluid thermocline theory, namely Bernoulli function,
density, and potential vorticity, are generalised here to a compressible ocean
with a realistic nonlinear equation of state. The most general such expression
is the `inactive wind' solution, an exact nonlinear solution of the inviscid
compressible Navier-Stokes that satisfies the continuity equation as a
consequence of Ertel's potential vorticity theorem. Such expressions are shown
to be non-unique due to the non-uniqueness of the choice of Bernoulli function
and in general approximately differ by the magnitude of their vertical velocity
component. Due to the thermobaric nonlinearity of the equation of state, the
expression of the 3D velocity field for a compressible ocean is found to
resemble its ideal fluid counterpart only if constructed in terms of the
available form of Bernoulli function as per Lorenz theory of available
potential energy (APE). APE theory also naturally defines a quasi-material
approximately neutral density variable called Lorenz reference density, which
in turn defines a potential vorticity variable minimally affected by
thermobaric production, thus providing all necessary tools for extending most
results of ideal fluid thermocline theory to a compressible ocean.


  We introduce graphs associated to transport problems between discrete
marginals, that allow to characterize the set of all optimizers given one
primal optimizer. In particular, we establish that connectivity of those graphs
is a necessary and sufficient condition for uniqueness of the dual optimizers.
Moreover, we provide an algorithm that can efficiently compute the dual
optimizer that is the limit, as the regularization parameter goes to zero, of
the dual entropic optimizers. Our results find an application in a
Stackelberg-Cournot-Nash game, for which we obtain existence and
characterization of the equilibria.


  Cloud computing is an increasingly popular computing paradigm, now proving a
necessity for utility computing services. Each provider offers a unique service
portfolio with a range of resource configurations. Resource provisioning for
cloud services in a comprehensive way is crucial to any resource allocation
model. Any model should consider both computational resources and network
resources to accurately represent and serve practical needs. Another aspect
that should be considered while provisioning resources is energy consumption.
This aspect is getting more attention from industry and governments parties.
Calls of support for the green clouds are gaining momentum. With that in mind,
resource allocation algorithms aim to accomplish the task of scheduling virtual
machines on data center servers and then scheduling connection requests on the
network paths available while complying with the problem constraints. Several
external and internal factors that affect the performance of resource
allocation models are introduced in this paper. These factors are discussed in
detail and research gaps are pointed out. Design challenges are discussed with
the aim of providing a reference to be used when designing a comprehensive
energy aware resource allocation model for cloud computing data centers.


  We present a new stability and convergence analysis for the spatial
discretization of a time-fractional Fokker--Planck equation in a convex
polyhedral domain, using continuous, piecewise-linear, finite elements. The
forcing may depend on time as well as on the spatial variables, and the initial
data may have low regularity. Our analysis uses a novel sequence of energy
arguments in combination with a generalized Gronwall inequality. Although this
theory covers only the spatial discretization, we present numerical experiments
with a fully discrete scheme employing a very small time step, and observe
results consistent with the predicted convergence behavior.


  The measurement of the inclusive cross section for Wgamma production is
presented based on 36/ pb of data acquired with the CMS detector from 7 TeV LHC
collisions in 2010. Comparisons are made with the predictions of the standard
model. The W bosons are identified through their leptonic decays to electrons
and muons. The Wgamma cross section is sensitive to anomalous triple-gauge
couplings and hence this measurement probes physics beyond the standard model.


  We introduce DS-1000, a code generation benchmark with a thousand data
science problems spanning seven Python libraries, such as NumPy and Pandas.
Compared to prior works, DS-1000 incorporates three core features. First, our
problems reflect diverse, realistic, and practical use cases since we collected
them from StackOverflow. Second, our automatic evaluation is highly specific
(reliable) -- across all Codex-002-predicted solutions that our evaluation
accept, only 1.8% of them are incorrect; we achieve this with multi-criteria
metrics, checking both functional correctness by running test cases and
surface-form constraints by restricting API usages or keywords. Finally, we
proactively defend against memorization by slightly modifying our problems to
be different from the original StackOverflow source; consequently, models
cannot answer them correctly by memorizing the solutions from pre-training. The
current best public system (Codex-002) achieves 43.3% accuracy, leaving ample
room for improvement. We release our benchmark at
https://ds1000-code-gen.github.io.


  Combinatorial evolution - the creation of new things through the combination
of existing things - can be a powerful way to evolve rather than design
technical objects such as electronic circuits. Intriguingly, this seems to be
an ongoing and thus open-ended process creating novelty with increasing
complexity. Here, we employ combinatorial evolution in software development.
While current approaches such as genetic programming are efficient in solving
particular problems, they all converge towards a solution and do not create
anything new anymore afterwards. Combinatorial evolution of complex systems
such as languages and technology are considered open-ended. Therefore,
open-ended automatic programming might be possible through combinatorial
evolution. We implemented a computer program simulating combinatorial evolution
of code blocks stored in a database to make them available for combining.
Automatic programming in the sense of algorithm-based code generation is
achieved by evaluating regular expressions. We found that reserved keywords of
a programming language are suitable for defining the basic code blocks at the
beginning of the simulation. We also found that placeholders can be used to
combine code blocks and that code complexity can be described in terms of the
importance to the programming language. As in a previous combinatorial
evolution simulation of electronic circuits, complexity increased from simple
keywords and special characters to more complex variable declarations, class
definitions, methods, and classes containing methods and variable declarations.
Combinatorial evolution, therefore, seems to be a promising approach for
open-ended automatic programming.


  We study metanetworks arising in genotype and phenotype spaces, in the
context of a model population of Boolean graphs evolved under selection for
short dynamical attractors. We define the adjacency matrix of a graph as its
genotype, which gets mutated in the course of evolution, while its phenotype is
its set of dynamical attractors. Metanetworks in the genotype and phenotype
spaces are formed, respectively, by genetic proximity and by phenotypic
similarity, the latter weighted by the sizes of the basins of attraction of the
shared attractors. We find that populations of evolved networks form giant
clusters in genotype space, have Poissonian degree distributions but exhibit
hierarchically organized $\kappa$-core decompositions. Nevertheless, at large
scales, they form tree-like expander graphs. Random populations of Boolean
graphs are typically so far removed from each other genetically that they
cannot form a metanetwork. In phenotype space, the metanetworks of evolved
populations are super robust both under the elimination of weak connections and
random removal of nodes.


  The nature of superconductivity and its interplay with strong spin-orbit
coupling at the KTaO3(111) interfaces remains a subject of debate. To address
this problem, we grew epitaxial LaMnO3/KTaO3(111) heterostructures. We show
that superconductivity is robust against the in-plane magnetic field, with the
critical field of superconductivity reaching 25 T in optimally doped
heterostructures. The superconducting order parameter is highly sensitive to
carrier density. We argue that spin-orbit coupling drives the formation of
anomalous quasiparticles with vanishing magnetic moment, providing the
condensate significant immunity against magnetic fields beyond the Pauli
paramagnetic limit. These results offer design opportunities for
superconductors with extreme resilience against magnetic field.


  Posted price mechanisms (PPM) constitute one of the predominant practices to
price goods in online marketplaces and their revenue guarantees have been a
central object of study in the last decade. We consider a basic setting where
the buyers' valuations are independent and identically distributed and there is
a single unit on sale. It is well-known that this setting is equivalent to the
so-called i.i.d. prophet inequality, for which optimal guarantees are known and
evaluate to 0.745 in general (equivalent to a PPM with dynamic prices) and $1 -
1/e \approx 0.632$ in the fixed threshold case (equivalent to a fixed price
PPM). In this paper we consider an additional assumption, namely, that the
underlying market is very large. This is modeled by first fixing a valuation
distribution F and then making the number of buyers grow large, rather than
considering the worst distribution for each possible market size. In this
setting Kennedy and Kertz [Ann. Probab. 1991] breaks the 0.745 fraction
achievable in general with a dynamic threshold policy. We prove that this large
market benefit continue to hold when using fixed price PPMs, and show that the
guarantee of 0.632 actually improves to 0.712. We then move to study the case
of selling k identical units and we prove that the revenue gap of the fixed
price PPM approaches $1-1/\sqrt{ 2k\pi}$. As this bound is achievable without
the large market assumption, we obtain the somewhat surprising result that the
large market advantage vanishes as $k$ grows.


  We develop an effective field theory (EFT) for nuclear vibrations. The key
ingredients - quadrupole degrees of freedom, rotational invariance, and a
breakdown scale around the three-phonon level - are taken from data. The EFT is
developed for spectra and electromagnetic moments and transitions. We employ
tools from Bayesian statistics for the quantification of theoretical
uncertainties. The EFT consistently describes spectra and electromagnetic
transitions for $^{62}$Ni, $^{98,100}$Ru, $^{106,108}$Pd, $^{110,112,114}$Cd,
and $^{118,120,122}$Te within the theoretical uncertainties. This suggests that
these nuclei can be viewed as anharmonic vibrators.


  Internet users such as individuals and organizations are subject to different
types of epidemic risks such as worms, viruses, spams, and botnets. To reduce
the probability of risk, an Internet user generally invests in traditional
security mechanisms like anti-virus and anti-spam software, sometimes also
known as self-defense mechanisms. However, such software does not completely
eliminate risk. Recent works have considered the problem of residual risk
elimination by proposing the idea of cyber-insurance. In this regard, an
important research problem is the analysis of optimal user self-defense
investments and cyber-insurance contracts under the Internet environment. In
this paper, we investigate two problems and their relationship: 1) analyzing
optimal self-defense investments in the Internet, under optimal cyber-insurance
coverage, where optimality is an insurer objective and 2) designing optimal
cyber-insurance contracts for Internet users, where a contract is a (premium,
coverage) pair.


  Recently, from the new Spitzer Photometry and Accurate Rotation Curves
(SPARC) data, McGaugh et al. (2016) reported a tight Radial Acceleration
Relation (RAR) between the observed total acceleration and the acceleration
produced by baryons in spiral galaxies. The relation can be fitted by different
functions. However, these functions can be discerned if we express the data in
the form of halo acceleration relation (HAR). The data reveals a maximum in the
halo acceleration. We examined NFW (cusp) and Burkert (core) profiles in the
context of dark matter and different parameter families of the interpolating
function in the framework of Modified Newtonian Dynamics (MOND).


  We give a uniform geometric realization for the cluster algebra of an
arbitrary finite type with principal coefficients at an arbitrary acyclic seed.
This algebra is realized as the coordinate ring of a certain reduced double
Bruhat cell in the simply connected semisimple algebraic group of the same
Cartan-Killing type. In this realization, the cluster variables appear as
certain (generalized) principal minors.


  We give a short proof of the recent results that, for every $1\leq p<
\infty,$ the Ces{\`a}ro function space $Ces_p(I)$ is not a dual space, has the
weak Banach-Saks property and does not have the Radon-Nikodym property.


  We design a distributed coordinated guiding vector field (CGVF) for a group
of robots to achieve ordering-flexible motion coordination while maneuvering on
a desired two-dimensional (2D) surface. The CGVF is characterized by three
terms, i.e., a convergence term to drive the robots to converge to the desired
surface, a propagation term to provide a traversing direction for maneuvering
on the desired surface, and a coordinated term to achieve the surface motion
coordination with an arbitrary ordering of the robotic group. By setting the
surface parameters as additional virtual coordinates, the proposed approach
eliminates the potential singularity of the CGVF and enables both the global
convergence to the desired surface and the maneuvering on the surface from all
possible initial conditions. The ordering-flexible surface motion coordination
is realized by each robot to share with its neighbors only two virtual
coordinates, i.e. that of a given target and that of its own, which reduces the
communication and computation cost in multi-robot surface navigation. Finally,
the effectiveness of the CGVF is substantiated by extensive numerical
simulations.


  Resource allocation is still a difficult issue to deal with in wireless
networks. The unstable channel condition and traffic demand for Quality of
Service (QoS) raise some barriers that interfere with the process. It is
significant that an optimal policy takes into account some resources available
to each traffic class while considering the spectral efficiency and other
related channel issues. Reinforcement learning is a dynamic and effective
method to support the accomplishment of resource allocation properly
maintaining QoS levels for applications. The technique can track the system
state as feedback to enhance the performance of a given task. Herein, it is
proposed a simple reinforcement learning mechanism introduced in LTE-A networks
and aimed to choose and limit the number of resources allocated for each
traffic class, regarding the QoS Class Identifier (QCI), at each Transmission
Time Interval (TTI) along the scheduling procedure. The proposed mechanism
implements a Markov Decision Process (MDP) solved by the Q-Learning algorithm
to find an optimal action-state decision policy. The results obtained from
simulation exhibit good performance, especially for the real-time Video
application.


  We investigate the importance of the three-nucleon (3N) force contact terms
in elastic nucleon-deuteron (Nd) scattering by applying the N$^4$LO$^+$ chiral
semi-local momentum space (SMS) regularized nucleon-nucleon (NN) chiral
potential supplemented by N$^2$LO and all subleading N$^4$LO three-nucleon
force (3NF) contact terms. Strength parameters of the contact terms were
obtained by least squares fitting of theoretical predictions to cross section
and analyzing powers data at three energies of the impinging nucleon. Although
the N$^3$LO contributions to the 3N force were completely neglected, the
results calculated with the contact terms multiplied by the fitted strength
parameters yield an improved description of the elastic Nd scattering
observables in a wide range of incoming nucleon energies below the pion
production threshold.


  The thermoelectric power factor of hierarchically nanostructured materials is
investigated using the nonequilibrium Greens function method for quantum
transport, including interactions of electrons with acoustic and optical
phonons. We describe hierarchical nanostructuring by superlatticelike potential
barriers or wells, combined with quantum dot barriers or wells nanoinclusions
as well as voids in the intermediate region. We show that these structures can
be designed in a way that the power factor is not only largely immune to the
presence of the nanostructure features, but under certain conditions benefits
can be achieved as well. Interestingly, we show that these design approaches
are linked to the energy relaxation of the current flow and whether charge
carrier scattering is limited by elastic or inelastic processes. In particular,
when nanostructures form potential barriers, the power factor can be
substantially enhanced under elastic scattering conditions, irrespective of
nanostructuring density and potential barrier heights. When inelastic
scattering processes dominate, however, the power factor is inevitably
degraded. In the case in which nanostructures form potential wells, despite a
slight decrease, the power factor is quite resilient under either elastic or
inelastic scattering processes. These nanostructuring design approaches could
help open the path to the optimization of new generation nanostructured
thermoelectric materials by not only targeting reductions in thermal
conductivity, but simultaneous improvements in the power factor as well.


  In the paper we are dealing with metric measure spaces of diameter at most
one and of total measure one. Gromov introduced the sampling compactification
of the set of these spaces. He asked whether the metric measure space
invariants extend to the compactification. Using ideas of the newly developed
theory of graph limits we identify the elements of the compactification with
certain geometric objects and show how to extend various invariants to this
space. We will introduce the notion of ultralimit of metric measure spaces,
that will be the main technical tool of our paper.