  The article analyses two potential metamaterial designs, the metafoundation
and the metabarrier, capable to attenuate seismic waves on buildings or
structural components in a frequency band between 3.5 to 8 Hz. The
metafoundation serves the dual purpose of reducing the seismic response and
supporting the superstructure. Conversely the metabarrier surrounds and shields
the structure from incoming waves. The two solutions are based on a cell layout
of local resonators whose dynamic properties are tuned using finite element
simulations combined with Bloch-Floquet boundary conditions. To enlarge the
attenuation band, a graded design where the resonant frequency of each cell
varies spatially is employed. If appropriately enlarged or reduced, the
metamaterial designs could be used to attenuate lower frequency seismic waves
or groundborne vibrations respectively. A sensitivity analysis over various
design parameters including size, number of resonators, soil type and source
directivity, carried out by computing full 3D numerical simulations in time
domain for horizontal shear waves is proposed. Overall, the metamaterial
solutions discussed here can reduce the spectral amplification of the
superstructure between approx. 15 to 70% depending on several parameters
including the metastructure size and the properties of the soil. Pitfalls and
advantages of each configuration are discussed in detail. The role of damping,
crucial to avoid multiple resonant coupling, and the analogies between graded
metamaterials and tuned mass dampers is also investigated.


  Best present experimental achievements in double beta decay are presented.
Possible progress in this field in the near and far future is discussed.


  In our previous work, we have shown that many of the properties of the
Florida power grid are reproduced by deterministic network growth models based
on the minimization of energy dissipation $E_\mathrm{diss}$. As there is no $a~
priori$ best $E_\mathrm{diss}$ minimizing growth model, we here present a tool,
called the "centrality fingerprint," for probing the behavior of different
growth models. The centrality fingerprints are comparisons of the current flow
into/out of the network with the values of various centrality measures
calculated at every step of the growth process. Finally, we discuss
applications to the Maryland power grid.


  Define $s (n) := n^{- 1} \sigma (n)$ ($\sigma (n):=\sum_{d|n}d )$ and
$\omega(n)$ is the number of prime divisors of $n$. One of the properties of
$s$ plays a central role: $s (p^a) > s (q^b)$ if $p < q$ are prime numbers,
with no special condition on $a, b$ other than $a, b \geqslant 1$. This result,
combined with the Multiplicity Permutation theorem, will help us establish
properties of the next counterexample (say $c$) to Robin's inequality $s (n) <
e^{\gamma} \log \log n$. The number $c$ is superabundant, and $\omega(c)$ must
be greater than a number close to one billion. In addition, the ratio
$p_{\omega (c)} / \log c$ has a lower and upper bound. At most $\omega(c)/14$
multiplicity parameters are greater than $1$. Last but not least, we apply
simple methods to sharpen Robin's inequality for various categories of numbers.


  How can we bring both privacy and energy-efficiency to a neural system? In
this paper, we propose PrivateSNN, which aims to build low-power Spiking Neural
Networks (SNNs) from a pre-trained ANN model without leaking sensitive
information contained in a dataset. Here, we tackle two types of leakage
problems: 1) Data leakage is caused when the networks access real training data
during an ANN-SNN conversion process. 2) Class leakage is caused when
class-related features can be reconstructed from network parameters. In order
to address the data leakage issue, we generate synthetic images from the
pre-trained ANNs and convert ANNs to SNNs using the generated images. However,
converted SNNs remain vulnerable to class leakage since the weight parameters
have the same (or scaled) value with respect to ANN parameters. Therefore, we
encrypt SNN weights by training SNNs with a temporal spike-based learning rule.
Updating weight parameters with temporal data makes SNNs difficult to be
interpreted in the spatial domain. We observe that the encrypted PrivateSNN
eliminates data and class leakage issues with a slight performance drop (less
than ~2) and significant energy-efficiency gain (about 55x) compared to the
standard ANN. We conduct extensive experiments on various datasets including
CIFAR10, CIFAR100, and TinyImageNet, highlighting the importance of
privacy-preserving SNN training.


  3D decomposition/segmentation still remains a challenge as large-scale 3D
annotated data is not readily available. Contemporary approaches typically
leverage 2D machine-generated segments, integrating them for 3D consistency.
While the majority of these methods are based on NeRFs, they face a potential
weakness that the instance/semantic embedding features derive from independent
MLPs, thus preventing the segmentation network from learning the geometric
details of the objects directly through radiance and density. In this paper, we
propose ClusteringSDF, a novel approach to achieve both segmentation and
reconstruction in 3D via the neural implicit surface representation,
specifically Signal Distance Function (SDF), where the segmentation rendering
is directly integrated with the volume rendering of neural implicit surfaces.
Although based on ObjectSDF++, ClusteringSDF no longer requires the
ground-truth segments for supervision while maintaining the capability of
reconstructing individual object surfaces, but purely with the noisy and
inconsistent labels from pre-trained models.As the core of ClusteringSDF, we
introduce a high-efficient clustering mechanism for lifting the 2D labels to 3D
and the experimental results on the challenging scenes from ScanNet and Replica
datasets show that ClusteringSDF can achieve competitive performance compared
against the state-of-the-art with significantly reduced training time.


  The characteristic frequencies of a system provide important information on
the phenomena that govern its physical properties. In this framework, there has
recently been renewed interest in cryogenic microwave characterization for
condensed matter systems since it allows to probe energy scales of the order of
a few $\mu$eV. However, broadband measurements of the absolute value of a
sample response in this frequency range are extremely sensitive to its
environment and require a careful calibration. In this paper, we present an
\textit{in situ} calibration method for cryogenic broadband microwave
reflectometry experiments that is both simple to implement and through which
the effect of the sample electromagnetic environment can be minimized. The
calibration references are here provided by the sample itself, at three
reference temperatures where its impedance is assumed or measured, and not by
external standards as is usual. We compare the frequency-dependent complex
impedance (0.1--2 GHz) of an a-Nb$_{15}$Si$_{85}$ superconducting thin film
obtained through this Sample-Based Calibration (SBC) and through an
Open-Short-Load Standard Calibration (SC) when working at very low temperature
(0.02--4 K) and show that the SBC allows us to obtain the absolute response of
the sample. This method brings the calibration planes as close as possible to
the sample, so that the environment electrodynamic response does not affect the
measurement, provided it is temperature independent. This results in a
heightened sensitivity, for a given experimental set--up.


  Chemical Species Tomography (CST) has been widely used for in situ imaging of
critical parameters, e.g. species concentration and temperature, in reactive
flows. However, even with state-of-the-art computational algorithms the method
is limited due to the inherently ill-posed and rank-deficient tomographic data
inversion, and by high computational cost. These issues hinder its application
for real-time flow diagnosis. To address them, we present here a novel
CST-based convolutional neural Network (CSTNet) for high-fidelity, rapid, and
simultaneous imaging of species concentration and temperature. CSTNet
introduces a shared feature extractor that incorporates the CST measurement and
sensor layout into the learning network. In addition, a dual-branch
architecture is proposed for image reconstruction with crosstalk decoders that
automatically learn the naturally correlated distributions of species
concentration and temperature. The proposed CSTNet is validated both with
simulated datasets, and with measured data from real flames in experiments
using an industry-oriented sensor. Superior performance is found relative to
previous approaches, in terms of robustness to measurement noise and
millisecond-level computing time. This is the first time, to the best of our
knowledge, that a deep learning-based algorithm for CST has been experimentally
validated for simultaneous imaging of multiple critical parameters in reactive
flows using a low-complexity optical sensor with severely limited number of
laser beams.


  Supersonic isothermal turbulence establishes a network of transient dense
shocks that sweep up material and have a density profile described by balance
between ram pressure of the background fluid versus the magnetic and gas
pressure gradient behind the shock. These rare, densest regions of a turbulent
environment can become Jeans unstable and collapse to form pre-stellar cores.
Using numerical simulations of magneto-gravo-turbulence, we describe the
structural properties of dense shocks, which are the seeds of gravitational
collapse, as a function of magnetic field strength. In the regime of a weak
magnetic field, the collapse is isotropic. Strong magnetic field strengths lead
to significant anisotropy in the shocked distribution and collapse occurs
preferentially parallel to the field lines. Our work provides insight into
analyzing the magnetic field topology and density structures of young
protostellar collapse, which the theory presented here predicts are associated
with large-scale strong shocks that persist for at least a free-fall time.


  We present strategies to search for heavy neutral Higgs bosons decaying to
top quark pairs, as often occurs at low $\tan \beta$ in type II two Higgs
doublet models such as the Higgs sector of the MSSM. The resonant production
channel is unsatisfactory due to interference with the SM background. We
instead propose to utilize same-sign dilepton signatures arising from the
production of heavy Higgs bosons in association with one or two top quarks and
subsequent decay to a top pair. We find that for heavier neutral Higgs bosons
the production in association with one top quark provides greater sensitivity
than production in association with two top quarks. We obtain current limits at
the LHC using Run I data at 8 TeV and forecast the sensitivity of a dedicated
analysis during Run II at 14 TeV. Then we perform a detailed BDT study for the
14 TeV LHC and a future 100 TeV collider.


  By analyzing the large-angle Bhabha scattering events $e^{+}e^{-}$ $\to$
($\gamma$)$e^{+}e^{-}$ and diphoton events $e^{+}e^{-}$ $\to$ $\gamma\gamma$
for the data sets collected at center-of-mass (c.m.) energies between 2.2324
and 4.5900 GeV (131 energy points in total) with the upgraded Beijing
Spectrometer (BESIII) at the Beijing Electron-Positron Collider (BEPCII), the
integrated luminosities have been measured at the different c.m. energies,
individually. The results are the important inputs for R value and $J/\psi$
resonance parameter measurements.


  This paper proposes and analyzes a new weak Galerkin method for the
eigenvalue problem by using the shifted-inverse power technique. A high order
lower bound can be obtained at a relatively low cost via the proposed method.
The error estimates for both eigenvalue and eigenfunction are provided and
asymptotic lower bounds are shown as well under some conditions. Numerical
examples are presented to validate the theoretical analysis.


  In the context of branch-and-bound (B&B) for integer programming (IP)
problems, a direction along which the polyhedron of the IP has minimum width is
termed a thin direction. We demonstrate that a thin direction need not always
be a good direction to branch on for solving the problem efficiently. Further,
the integer width, which is the number of B&B nodes created when branching on
the direction, may also not be an accurate indicator of good branching
directions.


  Some new results on magnetic dipole (M1) transitions in heavy quarkonium from
nonrelativistic effective field theories of QCD are briefly reported. This
model-independent approach not only facilitates a systematic and lucid way to
investigate the relativistic corrections, it also clarifies some inconsistent
treatment in previous potential model approach. The impact of our formalism on
J/Psi -> eta_c gamma, Upsilon(Upsilon') -> eta_b gamma and h_c -> chi_{c0}
gamma are discussed.


  In the VIP2 (VIolation of the Pauli Exlusion Principle) experiment at the
Gran Sasso underground laboratory (LNGS) we are searching for possible
violations of standard quantum mechanics predictions. With high precision we
investigate the Pauli Exclusion Principle and the collapse of the wave function
(collapse models). We will present our experimental method of searching for
possible small violations of the Pauli Exclusion Principle for electrons, via
the search for "anomalous" X-ray transitions in copper atoms, produced by "new"
electrons (brought inside a copper bar by circulating current) which could have
the probability to undergo Pauli-forbidden transition to the ground state (1 s
level) already occupied by two electrons. We will describe the concept of the
VIP2 experiment taking data at LNGS presently. The goal of VIP2 is to test the
PEP for electrons with unprecedented accuracy, down to a limit in the
probability that PEP is violated at the level of 10$^{-31}$. We will show
preliminary experimental results obtained at LNGS and discuss implications of a
possible violation.


  The spatial 't Hooft loop measuring the electric flux and the spatial
Wilsonloop measuring the magnetic flux are analyzed in hot SU(N) gauge theory.
Both display area laws. On one hand the tension of the 't Hooft loop is
perturbatively calculable, in the same sense as the pressure. We show that the
O(g^3) contribution is absent. The ratio of multi-charged 't Hooft loops have a
remarkably simple dependence on the charge, true up to, but not including,
O(g^4). This dependence follows also from a simple model of free screened
colour charges. On the other hand the surface tension of the Wilsonloop is
non-perturbative. But in a model of screened free monopoles at very high
temperature the known area law follows. The density of these monopoles starts
to contribute to O(g^6) to the pressure. The ratio of the multicharged Wilson
loops is calculable and identical to that of the 't Hooft loops.


  Getting acquainted with a large codebase can be a daunting task for software
developers, both new and seasoned. The description of a codebase and its
development should be the purpose of its documentation. However, software
documentation, if it exists at all, is usually textual and accompanied only by
simple static diagrams. It is also time-consuming to maintain manually. Even an
API reference, which can be generated automatically from the codebase itself,
has many drawbacks. It is limited to what it can extract from the codebase, is
cumbersome to navigate, and fails to capture the interwoven nature of code. We
explore an alternative approach centered around a node-link diagram
representing the structure of a codebase. The diagram is interactive and
filterable, providing details on demand. It is designed for automation, relying
on static analysis of the codebase, and thus produces results quickly and
offers a viable alternative to missing or outdated documentation. To evaluate
this approach, we implemented a prototype named Helveg that is able to analyze
and visualize C# code. Testing with five professional programmers provided
feedback on the approach's benefits and challenges, which we discuss in detail.


  We present a sweeping preconditioner for quasi-optimal Domain Decomposition
Methods (DDM) applied to Helmholtz transmission problems in periodic layered
media. Quasi-optimal DD (QO DD) for Helmholtz equations rely on transmission
operators that are approximations of Dirichlet-to-Neumann (DtN) operators.
Employing shape perturbation series, we construct approximations of DtN
operators corresponding to periodic domains, which we then use as transmission
operators in a non-overlapping DD framework. The Robin-to-Robin (RtR) operators
that are the building blocks of DDM are expressed via robust boundary integral
equation formulations. We use Nystr\"om discretizations of quasi-periodic
boundary integral operators to construct high-order approximations of RtR.
Based on the premise that the quasi-optimal transmission operators should act
like perfect transparent boundary conditions, we construct an approximate LU
factorization of the tridiagonal QO DD matrix associated with periodic layered
media, which is then used as a double sweep preconditioner. We present a
variety of numerical results that showcase the effectiveness of the sweeping
preconditioners applied to QO DD for the iterative solution of Helmholtz
transmission problems in periodic layered media.


  High-quality single crystals of KCo$_2$As$_2$ with the body-centered
tetragonal ThCr$_2$Si$_2$ structure were grown using KAs self flux. Structural,
magnetic, thermal, and electrical transport were investigated. No clear
evidence for any phase transitions was found in the temperature range 2 to 300
K. The in-plane electrical resistivity $\rho$ versus temperature $T$ is highly
unusual, showing a $T^4$ behavior below 30 K and an anomalous positive
curvature up to 300 K which is different from the linear behavior expected from
the Bloch-Gr\"uneisen theory for electron scattering by acoustic phonons. This
positive curvature has been previously observed in the in-plane resistivity of
high-conductivity layered delafossites such as PdCoO$_2$ and PtCoO$_2$. The
in-plane $\rho(T\to0) = 0.36~\mu\Omega$ cm of KCo$_2$As$_2$ is exceptionally
small for this class of compounds. The material also exhibits a nearly linear
magnetoresistance at low $T$ which attains a value of about 40% at $T=2$K and
magnetic field $H= 80$ kOe. The magnetic susceptibility $\chi$ of KCo$_2$As$_2$
is isotropic and about an order of magnitude smaller than the values for the
related compounds SrCo$_2$As$_2$ and BaCo$_2$As$_2$. The $\chi$ increases above
100 K which is found from our first-principles calculations to arise from a
sharp peak in the electronic density of states just above the Fermi energy
$E_{\rm F}$. Heat capacity $C_{\rm p}(T)$ data at low $T$ yield an electronic
density of states $N(E_{\rm F})$ that is about 36% larger than predicted by the
first-principles theory. The $C_{\rm p}(T)$ data near room temperature suggest
the presence of excited optic vibration modes which may also be the source of
the positive curvature in $\rho(T)$. Our results show that KCo$_2$As$_2$
provides a new avenue for investigating the physics of high-purity metals.


  The effective properties of piezoelectric laminates have been analyzed, based
on the calculation of internal fields and making use of a simple matrix
manipulation method. The results are expressed in a compact notation which is
convenient for numerical implementation and at the same time suitable for
further analytical treatments. A detailed analysis of fully compatible
ferroelectric domain structures shows, that the results for arbitrary
piezoelectric laminates can be further simplified and specific property
relationships for rank-1 laminates of tetragonal and rhombohedral crystals are
derived. The method is finally applied to the analysis of various hierarchical
domain structures. Detailed orientation relationships between the particular
domains in some important domain pattern are given to make these structures
accessible for the presented method. Some numerical results for tetragonal
barium titanate illustrate the effects of different domain arrangements on the
effective properties.


  We design a library for binary field arithmetic and we supply a core API
which is completely developed in DLAL, extended with a fix point formula. Since
DLAL is a restriction of linear logic where only functional programs with
polynomial evaluation cost can be typed, we obtain the core of a functional
programming setting for binary field arithmetic with built-in polynomial
complexity.


  Nearly a decade of research in software engineering has focused on automating
mobile app testing to help engineers in overcoming the unique challenges
associated with the software platform. Much of this work has come in the form
of Automated Input Generation tools (AIG tools) that dynamically explore app
screens. However, such tools have repeatedly been demonstrated to achieve
lower-than-expected code coverage - particularly on sophisticated proprietary
apps. Prior work has illustrated that a primary cause of these coverage
deficiencies is related to so-called tarpits, or complex screens that are
difficult to navigate.
  In this paper, we take a critical step toward enabling AIG tools to
effectively navigate tarpits during app exploration through a new form of
automated semantic screen understanding. We introduce AURORA, a technique that
learns from the visual and textual patterns that exist in mobile app UIs to
automatically detect common screen designs and navigate them accordingly. The
key idea of AURORA is that there are a finite number of mobile app screen
designs, albeit with subtle variations, such that the general patterns of
different categories of UI designs can be learned. As such, AURORA employs a
multi-modal, neural screen classifier that is able to recognize the most common
types of UI screen designs. After recognizing a given screen, it then applies a
set of flexible and generalizable heuristics to properly navigate the screen.
We evaluated AURORA both on a set of 12 apps with known tarpits from prior
work, and on a new set of five of the most popular apps from the Google Play
store. Our results indicate that AURORA is able to effectively navigate tarpit
screens, outperforming prior approaches that avoid tarpits by 19.6% in terms of
method coverage. The improvements can be attributed to AURORA's UI design
classification and heuristic navigation techniques.


  The nearby and young M star AU Mic is surrounded by a debris disk in which we
previously identified a series of large-scale arch-like structures that have
never been seen before in any other debris disk and that move outward at high
velocities. We initiated a monitoring program with the following objectives: 1)
track the location of the structures and better constrain their projected
speeds, 2) search for new features emerging closer in, and ultimately 3)
understand the mechanism responsible for the motion and production of the disk
features. AU Mic was observed at 11 different epochs between August 2014 and
October 2017 with the IR camera and spectrograph of SPHERE. These high-contrast
imaging data were processed with a variety of angular, spectral, and
polarimetric differential imaging techniques to reveal the faintest structures
in the disk. We measured the projected separations of the features in a
systematic way for all epochs. We also applied the very same measurements to
older observations from the Hubble Space Telescope (HST) with the visible
cameras STIS and ACS. The main outcomes of this work are 1) the recovery of the
five southeastern broad arch-like structures we identified in our first study,
and confirmation of their fast motion (projected speed in the range 4-12 km/s);
2) the confirmation that the very first structures observed in 2004 with ACS
are indeed connected to those observed later with STIS and now SPHERE; 3) the
discovery of two new very compact structures at the northwest side of the disk
(at 0.40" and 0.55" in May 2015) that move to the southeast at low speed; and
4) the identification of a new arch-like structure that might be emerging at
the southeast side at about 0.4" from the star (as of May 2016). Abridged.


  The current understanding of the turbulent dissipation in stellar convective
zones is based on the assumption that the turbulence follows Kolmogorov
scaling. This assumption is valid for some cases in which the time frequency of
the external shear is high (e.g., solar p modes). However, for many cases of
astrophysical interest (e.g., binary orbits, stellar pulsations, etc.), the
timescales of interest lie outside the regime of applicability of Kolmogorov
scaling. We present direct calculations of the dissipation efficiency of the
turbulent convective flow in this regime, using simulations of anelastic
convection with external forcing. We show that the effects of the turbulent
flow are well represented by an effective viscosity coefficient, we provide the
values of the effective viscosity as a function of the perturbation frequency
and compare our results to the perturbative method for finding the effective
viscosity of Penev et al. that can be applied to actual simulations of the
surface convective zones of stars.


  We present the main theoretical aspects of the quantum mechanical
string+${}^3P_0$ model of polarized quark fragmentation for pseudoscalar and
vector meson production. The model is an extension of the Lund string
fragmentation model that systematically includes the quark spin degree of
freedom assuming the ${}^3P_0$ mechanism of quark pair production at string
breaking. The recently achieved introduction of vector mesons accounts for the
spin correlations between the latter and the quark of the fragmentation chain.
The string+${}^3P_0$ model reproduces transverse spin effects such as the
Collins effect and the dihadron asymmetry, and longitudinal spin effects such
as the jet handedness.


  Axisymmetric magnetic strings with a fixed sense of rotation and nanometer
sizes (chiral magnetic vortices or Skyrmions) have been predicted to exist in a
large group of non-centrosymmetric crystals more than two decades ago. Recently
these extraordinary magnetic states have been directly observed in thin layers
of cubic helimagnet (Fe,Co)Si. In this report we apply our earlier theoretical
findings to review main properties of chiral Skyrmions, to elucidate their
physical nature, and to analyse these recent experimental results on
magnetic-field-driven evolution of Skyrmions and helicoids in chiral
helimagnets.


  A new generalization of Dawson's integral function based on the link between
a Riccati nonlinear differential equation and a second-order ordinary
differential equation is reported. The MacLaurin expansion of this generalized
function is built up and to this end an explicit formula for a generic cofactor
of a triangular matrix is deduced.


  Sensor network localization (SNL) is a challenging problem due to its
inherent non-convexity and the effects of noise in inter-node ranging
measurements and anchor node position. We formulate a non-convex SNL problem as
a multi-player non-convex potential game and investigate the existence and
uniqueness of a Nash equilibrium (NE) in both the ideal setting without
measurement noise and the practical setting with measurement noise. We first
show that the NE exists and is unique in the noiseless case, and corresponds to
the precise network localization. Then, we study the SNL for the case with
errors affecting the anchor node position and the inter-node distance
measurements. Specifically, we establish that in case these errors are
sufficiently small, the NE exists and is unique. It is shown that the NE is an
approximate solution to the SNL problem, and that the position errors can be
quantified accordingly. Based on these findings, we apply the results to case
studies involving only inter-node distance measurement errors and only anchor
position information inaccuracies.


  Remote spatial indistinguishability of identical subsystems as a direct
controllable quantum resource at distant sites has not been yet experimentally
proven. We design a setup capable to tune the spatial indistinguishability of
two photons by independently adjusting their spatial distribution in two
distant regions, which leads to polarization entanglement starting from
uncorrelated photons. The amount of entanglement uniquely depends on the degree
of remote spatial indistinguishability, quantified by an entropic measure
$\mathcal{I}$, which enables teleportation with fidelities above the classical
threshold. This experiment realizes a basic nonlocal entangling gate by the
inherent nature of identical quantum subsystems.


  Multimodal machine learning algorithms aim to learn visual-textual
correspondences. Previous work suggests that concepts with concrete visual
manifestations may be easier to learn than concepts with abstract ones. We give
an algorithm for automatically computing the visual concreteness of words and
topics within multimodal datasets. We apply the approach in four settings,
ranging from image captions to images/text scraped from historical books. In
addition to enabling explorations of concepts in multimodal datasets, our
concreteness scores predict the capacity of machine learning algorithms to
learn textual/visual relationships. We find that 1) concrete concepts are
indeed easier to learn; 2) the large number of algorithms we consider have
similar failure cases; 3) the precise positive relationship between
concreteness and performance varies between datasets. We conclude with
recommendations for using concreteness scores to facilitate future multimodal
research.


  Large, pre-trained neural networks consisting of self-attention layers
(transformers) have recently achieved state-of-the-art results on several
speech emotion recognition (SER) datasets. These models are typically
pre-trained in self-supervised manner with the goal to improve automatic speech
recognition performance -- and thus, to understand linguistic information. In
this work, we investigate the extent in which this information is exploited
during SER fine-tuning. Using a reproducible methodology based on open-source
tools, we synthesise prosodically neutral speech utterances while varying the
sentiment of the text. Valence predictions of the transformer model are very
reactive to positive and negative sentiment content, as well as negations, but
not to intensifiers or reducers, while none of those linguistic features impact
arousal or dominance. These findings show that transformers can successfully
leverage linguistic information to improve their valence predictions, and that
linguistic analysis should be included in their testing.


  Sequential recommendation aims to model dynamic user behavior from historical
interactions. Self-attentive methods have proven effective at capturing
short-term dynamics and long-term preferences. Despite their success, these
approaches still struggle to model sparse data, on which they struggle to learn
high-quality item representations. We propose to model user dynamics from
shopping intents and interacted items simultaneously. The learned intents are
coarse-grained and work as prior knowledge for item recommendation. To this
end, we present a coarse-to-fine self-attention framework, namely CaFe, which
explicitly learns coarse-grained and fine-grained sequential dynamics.
Specifically, CaFe first learns intents from coarse-grained sequences which are
dense and hence provide high-quality user intent representations. Then, CaFe
fuses intent representations into item encoder outputs to obtain improved item
representations. Finally, we infer recommended items based on representations
of items and corresponding intents. Experiments on sparse datasets show that
CaFe outperforms state-of-the-art self-attentive recommenders by 44.03% NDCG@5
on average.


  We describe the iterated monodromy groups associated with post-critically
finite quadratic polynomials, and explicit their connection to the `kneading
sequence' of the polynomial.
  We then give recursive presentations by generators and relations for these
groups, and study some of their properties, like torsion and `branchness'.


  A long-standing challenge for search and conversational assistants is query
intention detection in ambiguous queries. Asking clarifying questions in
conversational search has been widely studied and considered an effective
solution to resolve query ambiguity. Existing work have explored various
approaches for clarifying question ranking and generation. However, due to the
lack of real conversational search data, they have to use artificial datasets
for training, which limits their generalizability to real-world search
scenarios. As a result, the industry has shown reluctance to implement them in
reality, further suspending the availability of real conversational search
interaction data. The above dilemma can be formulated as a cold start problem
of clarifying question generation and conversational search in general.
Furthermore, even if we do have large-scale conversational logs, it is not
realistic to gather training data that can comprehensively cover all possible
queries and topics in open-domain search scenarios. The risk of fitting bias
when training a clarifying question retrieval/generation model on
incomprehensive dataset is thus another important challenge.
  In this work, we innovatively explore generating clarifying questions in a
zero-shot setting to overcome the cold start problem and we propose a
constrained clarifying question generation system which uses both question
templates and query facets to guide the effective and precise question
generation. The experiment results show that our method outperforms existing
state-of-the-art zero-shot baselines by a large margin. Human annotations to
our model outputs also indicate our method generates 25.2\% more natural
questions, 18.1\% more useful questions, 6.1\% less unnatural and 4\% less
useless questions.


  A Bessel excursion is a Bessel process that begins at the origin and first
returns there at some given time $T$. We study the distribution of the area
under such an excursion, which recently found application in the context of
laser cooling. The area $A$ scales with the time as $A \sim T^{3/2}$,
independent of the dimension, $d$, but the functional form of the distribution
does depend on $d$. We demonstrate that for $d=1$, the distribution reduces as
expected to the distribution for the area under a Brownian excursion, known as
the Airy distribution, deriving a new expression for the Airy distribution in
the process. We show that the distribution is symmetric in $d-2$, with
nonanalytic behavior at $d=2$. We calculate the first and second moments of the
distribution, as well as a particular fractional moment. We also analyze the
analytic continuation from $d<2$ to $d>2$. In the limit where $d\to 4$ from
below, this analytically continued distribution is described by a one-sided
L\'evy $\alpha$-stable distribution with index $2/3$ and a scale factor
proportional to $[(4-d)T]^{3/2}$.


  This paper is threefold. First, we provide a new "universal" definition for
the Racah algebra of rank-2 as an extension of the rank-1 Racah algebra where
the generators are indexed by subsets and any three disjoint indexing sets
define a subalgebra isomorphic to the rank-1 case. With this definition, we
explore some of the properties of the algebra including verifying that these
natural assumptions are equivalent to other defining relations in the
literature. Second, we look at the symmetries of the generators of the rank-2
Racah algebra. Those symmetries allows us to partially make abstraction of the
choice of the generators and write relations and properties in a different
format. Last, we provide a novel representation of the Racah algebra. This new
representation requires only one generator to be diagonal and is based on an
expansion of the split basis representation from the rank-1 Racah algebra.


  Unmanned aerial vehicles (UAVs) are the emerging vital components of
millimeter wave (mmWave) wireless systems. Accurate beam alignment is essential
for efficient beam-based mmWave communications of UAVs with base stations
(BSs). Conventional beam sweeping approaches often have large overhead due to
the high mobility and autonomous operation of UAVs. Learning-based approaches
greatly reduce the overhead by leveraging UAV data, like position to identify
optimal beam directions. In this paper, we propose a reinforcement learning
(RL)-based framework for UAV-BS beam alignment using deep Q-Network (DQN) in a
mmWave setting. We consider uplink communications where the UAV hovers around
5G new radio (NR) BS coverage area, with varying channel conditions. The
proposed learning framework uses the location information to maximize data rate
through the optimal beam-pairs efficiently, upon every communication request
from UAV inside the multi-location environment. We compare our proposed
framework against the Multi-Armed Bandit (MAB) learning-based approach and the
traditional exhaustive approach, respectively, and also analyse the training
performance of DQN-based beam alignment over different coverage area
requirements and channel conditions. Our results show that the proposed
DQN-based beam alignment converges faster and generic for different
environmental conditions. The framework can also learn optimal beam alignment
comparable to the exhaustive approach in an online manner under real-time
conditions.


  A rich literature has been produced on the quantum states of atoms and
molecules confined into infinite potential wells with a specified symmetry.
Apart from their interest as basic quantum systems, confined atoms and
molecules are useful models for extreme high pressure states of matter,
spectroscopically active defects in solid lattices and chemical species in
molecular cages. A most important case is that of H$_2^+$ for which little or
no results are available in the case of polyhedral confinement. The approach of
the authors makes use of the Diffusion Monte Carlo (DMC) method. The advantage
of this method is that previously developed codes are readily adapted to new,
even complex, well geometries and nuclear positions. In this paper the
potential energy surface (PES) of H$_2^+$ confined inside an octahedral well is
reported for restricted D$_{4h}$ and D$_{3d}$ geometries and different well
widths. The results are discussed using the concept of electron compression and
the correlation with semi-confined atomic orbitals.


  In a superconductor with a sublattice degree of freedom, we find
unconventional Andreev bound states whose charge density oscillates in sign
between the two sublattices. The appearance of these oscillating-charged
Andreev bound states is characterized by a Zak phase, rather than a
conventional topological invariant. In contrast to conventional Andreev bound
states, for oscillating-charged Andreev bound states the proportionality
between the electron-like spectral function, the local density of states and
the tunneling conductance is broken. We examine the possible appearance of
these novel Andreev bound states in UTe$_2$ and locally noncentrosymmetric
superconductors.


  Topological phases and their topological features are enriched by the
fundamental time-reversal, particle-hole, and chiral as well as crystalline
symmetries. While one-dimensional (1D) generalized Su-Schrieffer-Heeger (SSH)
systems show various topological phenomena such as topological solitons and
topological charge pumping, it remains unclear how such symmetry protects and
relates such topological phenomena. Here we show that the generalized
time-reversal, particle-hole, and chiral symmetry operators consistently
explain not only the symmetry transformation properties between the ground
states but also the topological features of the topological solitons in
prototypical quasi-1D systems such as the SSH, Rice-Mele, and double-chain
models. As a consequence, we classify generalized essential operators into
three groups: Class I and class II operators connect ground states in between
after spontaneous symmetry breaking while class III operators give the
generalized particle-hole and chiral symmetries to ground states. Furthermore,
class I operators endow the equivalence relation between topological solitons
while class II and III operators do the particle-hole relations. Finally, we
demonstrate three distinct types of topological charge pumping and soliton
chirality from the viewpoint of class I, II, and III operators. We build a
general framework to explore the topological features of the generalized 1D
electronic system, which can be easily applied in various condensed matter
systems as well as photonic crystal and cold atomic systems.


  We present an observational and theoretical study of the primary ionizing
agents (cosmic rays and X-rays) in the TW Hya protoplanetary disk. We use a set
of resolved and unresolved observations of molecular ions and other molecular
species, encompassing eleven lines total, in concert with a grid of disk
chemistry models. The molecular ion constraints comprise new data from the
Submillimeter Array on HCO$^+$, acquired at unprecedented spatial resolution,
and data from the literature, including ALMA observations of N$_2$H$^+$. We
vary the model incident CR flux and stellar X-ray spectra and find that TW
Hya's HCO$^+$ and N$_2$H$^+$ emission are best fit by a moderately hard X-ray
spectra, as would be expected during the "flaring" state of the star, and a low
CR ionization rate, $\zeta_{\rm CR}\lesssim10^{-19}$ s$^{-1}$. This low CR rate
is the first indication of the presence of CR exclusion by winds and/or
magnetic fields in an actively accreting T Tauri disk system. With this new
constraint, our best fit ionization structure predicts a low turbulence
"dead-zone" extending from the inner edge of the disk out to $50-65$ AU. This
region coincides with an observed concentration of millimeter grains, and we
propose that the inner region of TW Hya is a dust (and possibly planet) growth
factory as predicted by previous theoretical work.


  The entanglement spectrum provides crucial information about correlated
quantum systems. We show that the study of the block-like nature of the reduced
density matrix in number sectors and the partition dependence of the spectrum
in finite systems leads to interesting unexpected insights, which we illustrate
for the case of a 1D extended Hubbard model. We show that block symmetry
provides an intuitive understanding of the spectral double degeneracy of the
Haldane-insulator, revealing as well partial double degeneracy for the
Mott-insulator. Moreover, surprisingly, the partition dependence of the
spectral degeneracy in the Haldane- and Mott-insulator is directly linked to
the, in principle unrelated, density-density correlations, and presents an
intriguing periodic behavior in superfluid and supersolid phases.


  We explore the use of real fermionization as a test case for understanding
how specific features of phenomenological interest in the low-energy effective
superpotential are realized in exact solutions to heterotic superstring theory.
We present pedagogic examples of models which realize SO(10) as a level two
current algebra on the world-sheet, and discuss in general how higher level
current algebras can be realized in the tensor product of simple constituent
conformal field theories. We describe formal developments necessary to compute
couplings in models built using real fermionization. This allows us to isolate
cases of spin structures where the standard prescription for real
fermionization may break down.


  We present a coherent and operational strategy to calculate, in a
nonperturbative way, physical observables in light-front dynamics. This
strategy is based on the decomposition of the state vector of any compound
system in Fock components, and on the covariant formulation of light-front
dynamics, together with the so-called Fock sector dependent renormalization
scheme. We apply our approach to the calculation of the electromagnetic form
factors of a fermion in the Yukawa model, in the nontrivial three-body Fock
space truncation, for rather large values of the coupling constant. We find
that, once the renormalization conditions are properly taken into account, the
form factors do not depend on the regularization scale, when the latter is much
larger than the physical masses. We then extend the Fock space by including
antifermion degrees of freedom.


  A large class of quintessential inflationary models, recently proposed by
Peebles and Vilenkin, leads to post-inflationary phases whose effective
equation of state is stiffer than radiation. The expected gravitational waves
logarithmic energy spectra are tilted towards high frequencies and
characterized by two parameters: the inflationary curvature scale at which the
transition to the stiff phase occurs and the number of (non conformally
coupled) scalar degrees of freedom whose decay into fermions triggers the onset
of a gravitational reheating of the Universe. Depending upon the parameters of
the model and upon the different inflationary dynamics (prior to the onset of
the stiff evolution) the relic gravitons energy density can be much more
sizeable than in standard inflationary models, for frequencies larger than 1
Hz. We estimate the required sensitivity for detection of the predicted
spectral amplitude and show that the allowed region of our parameter space
leads to a signal smaller (by one 1.5 orders of magnitude) than the advanced
LIGO sensitivity at a frequency of 0.1 KHz. The maximal signal, in our context,
is expected in the GHz region where the energy density of relic gravitons in
critical units (i.e. $h_0^2 \Omega_{GW}$) is of the order of $10^{-6}$, roughly
eight orders of magnitude larger than in ordinary inflationary models. Smaller
detectors (not necessarily interferometers) can be relevant for detection
purposes in the GHz frequency window. We suggest/speculate that future
measurements through microwave cavities can offer interesting perspectives.


  Since the spring of 2012 there have been many papers published using Fermi
LAT public data that claim evidence for narrow spectral lines coming from the
region of the Galactic center. This study uses non-Galactic center Fermi-LAT
data from survey mode observations, and Earth limb Fermi data to test the dark
matter interpretation of this feature and better understand its origins.


  In this paper, new extremal odd unimodular lattices in dimension $36$ are
constructed. Some new odd unimodular lattices in dimension $36$ with long
shadows are also constructed.


  The Milky Way is surrounded by dozens of ultra-faint (< $10^5$ solar
luminosities) dwarf satellite galaxies. They are the surviving remnants of the
earliest galaxies, as confirmed by their ancient (~13 billion years old) and
chemically primitive stars. Simulations suggest that these systems formed
within extended dark matter halos and experienced early galaxy mergers and
supernova feedback. However, the signatures of these events would lie outside
their core regions (>2 half-light radii), which are spectroscopically unstudied
due to the sparseness of their distant stars. Here we identify members of the
Tucana II ultra-faint dwarf galaxy in its outer region (up to 9 half-light
radii), demonstrating the system to be dramatically more spatially extended and
chemically primitive than previously found. These distant stars are extremely
metal-poor (<[Fe/H]>=-3.02; less than ~1/1000th of the solar iron abundance),
affirming Tucana II as the most metal-poor known galaxy. We observationally
establish, for the first time, an extended dark matter halo surrounding an
ultra-faint dwarf galaxy out to one kiloparsec, with a total mass of >$10^7$
solar masses. This measurement is consistent with the expected ~2x$10^7$ solar
masses using a generalized NFW density profile. The extended nature of Tucana
II suggests that it may have undergone strong bursty feedback or been the
product of an early galactic merger. We demonstrate that spatially extended
stellar populations, which other ultra-faint dwarfs hint at hosting as well,
are observable in principle and open the possibility for detailed studies of
the stellar halos of relic galaxies.


  Vanadium dichalcogenides have attracted increasing interests for the charge
density wave phenomena and possible ferromagnetism. Here, we report on the
multiphase behavior and gap opening in monolayer VTe2 grown by molecular beam
epitaxy. Scanning tunneling microscopy and spectroscopy study revealed the
(4*4) metallic and gapped (2root3*2root3)charge-density wave pahses with an
energy gap of ~40 meV. Through the in-plane condensation of vanadium atoms, the
typical star-of-David clusters and truncated triangle-shaped clusters are
formed in (4*4) and (2root3*2root3) phases respectively, resulting in different
surface morphologies and electronic structures as confirmed by density
functional theory calculations with on-site coulomb repulsion. The CDW-driven
reorganization of the atomic structure weakens the ferromagnetic superexchange
coupling and strengthens the antiferromagnetic exchange coupling on the
contrary, suppressing the long-range magnetic order in monolayer VTe2.


  Large linear systems are ubiquitous in modern computational science. The main
recipe for solving them is iterative solvers with well-designed
preconditioners. Deep learning models may be used to precondition residuals
during iteration of such linear solvers as the conjugate gradient (CG) method.
Neural network models require an enormous number of parameters to approximate
well in this setup. Another approach is to take advantage of small graph neural
networks (GNNs) to construct preconditioners of the predefined sparsity
pattern. In our work, we recall well-established preconditioners from linear
algebra and use them as a starting point for training the GNN. Numerical
experiments demonstrate that our approach outperforms both classical methods
and neural network-based preconditioning. We also provide a heuristic
justification for the loss function used and validate our approach on complex
datasets.


  In this paper, we prove that the total expected curvature for random spatial
equilateral quadrilaterals with diameter at most $r$ decreases as $r$
increases. To do so, we prove several curvature monotonicity inequalities and
stochastic ordering lemmas in terms the of the action-angle coordinates. Using
these, we can use Baddeley's extension of Crofton's differential equation to
show that the derivative of the expected total curvature is non-positive.


  The cultural integration of immigrants conditions their overall
socio-economic integration as well as natives' attitudes towards globalisation
in general and immigration in particular. At the same time, excessive
integration -- or acculturation -- can be detrimental in that it implies
forfeiting one's ties to the home country and eventually translates into a loss
of diversity (from the viewpoint of host countries) and of global connections
(from the viewpoint of both host and home countries). Cultural integration can
be described using two dimensions: the preservation of links to the home
country and culture, which we call home attachment, and the creation of new
links together with the adoption of cultural traits from the new residence
country, which we call destination attachment. In this paper we introduce a
means to quantify these two aspects based on Twitter data. We build home and
destination attachment indexes and analyse their possible determinants (e.g.,
language proximity, distance between countries), also in relation to Hofstede's
cultural dimension scores. The results stress the importance of host language
proficiency to explain destination attachment, but also the link between
language and home attachment. In particular, the common language between home
and destination countries corresponds to increased home attachment, as does low
proficiency in the host language. Common geographical borders also seem to
increase both home and destination attachment. Regarding cultural dimensions,
larger differences among home and destination country in terms of
Individualism, Masculinity and Uncertainty appear to correspond to larger
destination attachment and lower home attachment.


  We investigate the properties of three entanglement measures that quantify
the statistical distinguishability of a given state with the closest
disentangled state that has the same reductions as the primary state. In
particular, we concentrate on the relative entropy of entanglement with
reversed entries. We show that this quantity is an entanglement monotone which
is strongly additive, thereby demonstrating that monotonicity under local
quantum operations and strong additivity are compatible in principle. In
accordance with the presented statistical interpretation which is provided,
this entanglement monotone, however, has the property that it diverges on pure
states, with the consequence that it cannot distinguish the degree of
entanglement of different pure states. We also prove that the relative entropy
of entanglement with respect to the set of disentangled states that have
identical reductions to the primary state is an entanglement monotone. We
finally investigate the trace-norm measure and demonstrate that it is also a
proper entanglement monotone.


  We update the indirect bounds on anomalous triple gauge couplings coming from
the non-universal one-loop contributions to the $Z \to b \bar{b}$ width. These
bounds, which are independent of the Higgs boson mass, are in agreement with
the standard model predictions for the gauge boson self-couplings since the
present value of $R_b$ agrees fairly well with the theoretical estimates.
Moreover, these indirect constraints on $\Delta g^Z_1$ and $g^Z_5$ are more
stringent than the present direct bounds on these quantities, while the
indirect limit on $\lambda_Z$ is weaker than the available experimental data.


  In this paper, we establish spectral inequalities on measurable sets of
positive Lebesgue measure for the Stokes operator, as well as an observability
inequalities on space-time measurable sets of positive measure for
non-stationary Stokes system. Furthermore, we provide their applications in the
theory of shape optimization and time optimal control problems.


  We present measurements of the intensity of the Cosmic X-ray Background (CXB)
with the NuSTAR telescope in the 3-20 keV energy range. Our method uses spatial
modulation of the CXB signal on the NuSTAR detectors through the telescope's
side aperture. Based on the NuSTAR observations of selected extragalactic
fields with a total exposure of 7 Ms, we have estimated the CXB 3-20 keV flux
to be 2.8E-11 erg/s/cm^2/deg^2, which is ~8 higher than measured with HEAO-1
and consistent with the INTEGRAL measurement. The inferred CXB spectral shape
in the 3-20 keV energy band is consistent with the canonical model of Gruber et
al. We demonstrate that the spatially modulated CXB signal measured by NuSTAR
is not contaminated by systematic noise and is limited by photon statistics.
The measured relative scatter of the CXB intensity between different sky
directions is compatible with cosmic variance, which opens new possibilities
for studying CXB anisotropy over the whole sky with NuSTAR.


  It is shown that topologically stable cosmic strings can, in fact, appear to
end or to break, even in theories without monopoles. This can occur whenever
the spatial topology of the universe is nontrivial. For the case of
Abelian-Higgs strings, we describe the gauge and scalar field configurations
necessary for a string to end on a black hole. We give a lower bound for the
rate at which a cosmic string will break via black hole pair production, using
an instanton calculation based on the Euclidean C-metric.


  The mechanisms limiting the spin coherence time of electrons are of great
importance for spintronics. We present electron spin resonance (ESR) and
transport measurements of six different two dimensional electron gases in
silicon/silicon-germanium (Si/SiGe 2DEGs). The spin decoherence time $T_2^*$ is
presented in conjunction with the 2DEG density $n_e$ and momentum scattering
time $\tau_p$ as measured from transport experiments. A pronounced dependence
of $T_2^*$ on the orientation of the applied magnetic field with respect to
2DEG layer is found which is not consistent with that expected from any
mechanism described in the literature.


  We present a comprehensive study of a model where the dark matter is composed
of a singlet real scalar that couples to the Standard Model predominantly via a
Yukawa interaction with a light quark and a colored vector-like fermion. A
distinctive feature of this scenario is that thermal freeze-out in the early
universe may be driven by annihilation both into gluon pairs at one-loop ($gg$)
and by virtual internal Bremsstrahlung of a gluon ($q \bar{q} g$). Such a dark
matter candidate may also be tested through direct and indirect detection and
at the LHC; viable candidates have either a mass nearly degenerate with that of
the fermionic mediator or a mass above about 2 TeV.


  Distributed optimization aims to optimize a global objective formed by a sum
of coupled local convex functions over a graph via only local computation and
communication. In this paper, we propose the Bregman parallel direction method
of multipliers (PDMM) based on a generalized averaging step named mirror
averaging. We establish the global convergence and $O(1/T)$ convergence rate of
the Bregman PDMM, along with its $O(n/\ln n)$ improvement over existing PDMM,
where $T$ denotes the number of iterations and $n$ the dimension of solution
variable. In addition, we can enhance its performance by optimizing the
spectral gap of the averaging matrix. We demonstrate our results via a
numerical example.


  We report a transient absorption microscopy study of charge carrier dynamics
in bulk MoS2 crystals at room temperature. Charge carriers are injected by
interband absorption of a 555-nm pulse, and probed by measuring differential
reflection of a time-delayed and spatially scanned 660-nm pulse. We find an
intervalley transfer time of about 0.35 ps, an energy relaxation time of hot
carriers on the order of 50 ps, and a carrier lifetime of 180 ps. By monitoring
the spatiotemporal dynamics of carriers, we obtained a diffusion coefficient of
thermalized electrons of 4.2 cm2/s, corresponding to a mobility of 170 cm2/Vs.
We also observed a time-varying diffusion coefficient of hot carriers.


  We study theoretically the optical response of the surface states of a
topological insulator, especially the generation of helicity-dependent direct
current by circularly polarized light. Interestingly, the dominant current, due
to an interband transition, is controlled by the Berry curvature of the surface
bands. This extends the connection between photocurrents and Berry curvature
beyond the quasiclassical approximation where it has been shown to hold.
Explicit expressions are derived for the (111) surface of the topological
insulator Bi_{2}Se_{3} where we find significant helicity dependent
photocurrents when the rotational symmetry of the surface is broken by an
in-plane magnetic field or a strain. Moreover, the dominant current grows
linearly with time until a scattering occurs, which provides a means for
determining the scattering time. The dc spin generated on the surface is also
dominated by a linear-in-time, Berry curvature dependent contribution.


  The main problem faced by smart contract platforms is the amount of time and
computational power required to reach consensus. In a classical blockchain
model, each operation is in fact performed by each node, both to update the
status and to validate the results of the calculations performed by others. In
this short survey we sketch some state-of-the-art approaches to obtain an
efficient and scalable computation of smart contracts. Particular emphasis is
given to sharding, a promising method that allows parallelization and therefore
a more efficient management of the computational resources of the network.


  Consider Hermitian and symmetric random band matrices $H=(\sigma_{xy}A_{xy})$
on the $d$-dimensional lattice $\left(\mathbb{Z}/{L\mathbb{Z}}\right)^d$, where
$A_{xy}=\overline{A_{yx}}$ are independent uniformly distributed random
variables on $S^1$ or $\{+1, -1\}$, and the variance profile $\sigma^{2}_{xy}$
is characterized by the bandwidth $W$ and $\alpha$-stable density with
$\alpha\in (0,2]$. We investigate local eigenvalue statistics at the spectral
edge as $W\to \infty$ and observe the critical dimension $d_c=3\alpha$ and the
critical bandwidth $W_c=L^{(1-\frac{d}{3\alpha})_{+}}$, possibly with a $\log
L$ correction when $d=\alpha$ or $2\alpha$. In the Hermitian case, we establish
that (i) when $d<2\alpha$, GUE edge, interpolating, and Poisson statistics
emerge in the supercritical ($W\gg W_c$), critical ($W\sim W_c$), and
subcritical ($W\ll W_c$) regimes, respectively; (ii) when $d\ge 2\alpha$, as
long as $W\ge L^{\frac{1}{3}+\epsilon}$ for a small constant $\epsilon>0$, GUE
edge universality holds. In the symmetric case, we also establish similar but
subtle phenomena. In both $d=1$ and $\alpha=2$, the subcritical and
supercritical results have been proven by Sodin for the band model with a
cutoff variance profile \cite{sodin2010spectral}. Our proof builds upon Sodin's
program and new techniques of taming the singularity of Feynman diagrams and
graph integrals through a connection to the $\phi^3$ model.


  Gravitational waves from binary neutron star (BNS) mergers can constrain
nuclear matter models predicting the neutron star's equation of state (EOS).
Matter effects on the inspiral-merger signal are encoded in the multipolar
tidal polarizability parameters, whose leading order combination is sufficient
to capture to high accuracy the key features of the merger waveform (e.g.~the
merger frequency). Similar EOS-insensitive relations exist for the post-merger
signal and can be used to model the emission from the remnant. Several works
suggested that the appearance of new degrees of freedom or phase transitions in
high-density post-merger matter can be inferred by observing a violation of
these EOS-insensitive relations. Here, we demonstrate a Bayesian method to test
such an EOS-insensitive relation between the tidal polarizability parameters
(or any other equivalent parameter) and the dominant post-merger frequency,
using information either up to merger or from the post-merger signal.
Technically, the method is similar to tests of General Relativity with binary
black holes that verify the inspiral-merger-ringdown consistency. However,
differently from the latter, BNS pre/post-merger consistency tests are
conceptually less informative and they only address the consistency (or the
breaking) of the assumed EOS-insensitive relation. Specifically, we discuss how
such tests cannot conclusively discriminate between an EOS not respecting such
relation and the appearance of new degrees of freedom (or phase transitions) in
high-density matter.


  The sub-gap density of states of amorphous indium gallium zinc oxide
($a$-IGZO) is obtained using the ultrabroadband photoconduction (UBPC) response
of thin-film transistors (TFTs). Density functional theory simulations classify
the origin of the measured sub-gap density of states peaks as a series of
donor-like oxygen vacancy states and acceptor-like Zn vacancy states. Donor
peaks are found both near the conduction band and deep in the sub-gap, with
peak densities of $10^{17}-10^{18}$ cm$^{-3}$eV$^{-1}$. Two deep acceptor-like
metal vacancy peaks with peak densities in the range of $10^{18}$
cm$^{-3}$eV$^{-1}$ and lie adjacent to the valance band Urbach tail region at
2.0 to 2.5 eV below the conduction band edge. By applying detailed charge
balance, we show increasing the density of metal vacancy deep-acceptors
strongly shifts the $a$-IGZO TFT threshold voltage to more positive values.
Photoionization (h$\nu$ > 2.0 eV) of metal vacancy acceptors is one cause of
transfer curve hysteresis in $a$-IGZO TFTs owing to longer recombination
lifetimes as they get captured into acceptor-like vacancies.


  We present a study of the colors, structural properties, and star formation
histories for a sample of ~1600 dwarfs over look-back times of ~3 Gyr
(z=0.002-0.25). The sample consists of 401 distant dwarfs drawn from the Galaxy
Evolution from Morphologies and SEDs (GEMS) survey, which provides high
resolution Hubble Space Telescope (HST) Advanced Camera for Surveys (ACS)
images and accurate redshifts, and of 1291 dwarfs at 10-90 Mpc compiled from
the Sloan Digitized Sky Survey (SDSS). The sample is complete down to an
effective surface brightness of 22 mag arcsec^-2 in z and includes dwarfs with
M_g=-18.5 to -14 mag. Rest-frame luminosities in Johnson UBV and SDSS ugr
filters are provided by the COMBO-17 survey and structural parameters have been
determined by S\'ersic fits. We find that the GEMS dwarfs are bluer than the
SDSS dwarfs by ~0.13 mag in g-r, which is consistent with the color evolution
over ~2 Gyr of star formation histories involving moderate starbursts and long
periods of continuous star formation. The full color range of the samples
cannot be reproduced by single starbursts of different masses or long periods
of continuous star formation alone. Furthermore, an estimate of the mechanical
luminosities needed for the gas in the GEMS dwarfs to be completely removed
from the galaxies shows that a significant number of low luminosity dwarfs are
susceptible to such a complete gas loss, if they would experience a starburst.
On the other hand, a large fraction of more luminous dwarfs is likely to retain
their gas. We also estimate the star formation rates per unit area for the GEMS
dwarfs and find good agreement with the values for local dwarfs.


  Employing concrete examples from nuclear physics it is shown that low energy
nuclear reactions can and have been induced by all of the four fundamental
interactions (i) (stellar) gravitational, (ii) strong, (iii) electromagnetic
and (iv) weak. Differences are highlighted through the great diversity in the
rates and similarity through the nature of the nuclear reactions initiated by
each.


  This paper is a short review on the foundations and recent advances in the
microscopic Fermi-liquid (FL) theory. We demonstrate that this theory is built
on five identities, which follow from conservation of total charge (particle
number), spin, and momentum in a translationally and $SU(2)$-invariant FL.
These identities allows one to express the effective mass and quasiparticle
residue in terms of an exact vertex function and also impose constraints on the
"quasiparticle" and "incoherent" (or "low-energ" and "high-energy")
contributions to the observable quantities. Such constraints forbid certain
Pomeranchuk instabilities of a FL, e.g., towards phases with order parameters
that coincide with charge and spin currents. We provide diagrammatic
derivations of these constraints and of the general (Leggett) formula for the
susceptibility in arbitrary angular momentum channel, and illustrate the
general relations through simple examples treated in the perturbation theory.


  We provide a co-free construction which adds elementary structure to a
primary doctrine. We show that the construction preserves comprehensions and
all the logical operations which are in the starting doctrine, in the sense
that it maps a first order many-sorted theory into a the same theory formulated
with equality. As a corollary it forces an implicational doctrine to have an
extentional entailment.


  After a short review of microscopic electrodynamics in materials, we
investigate the relation of the microscopic dielectric tensor to the current
response tensor and to the full electromagnetic Green function. Subsequently,
we give a systematic overview of microscopic electromagnetic wave equations in
materials, which can be formulated in terms of the microscopic dielectric
tensor.


  We introduce an operator linked with the radial index in the Laguerre-Gauss
modes of a two-dimensional harmonic oscillator in cylindrical coordinates. We
discuss ladder operators for this variable, and confirm that they obey the
commutation relations of the su(1,1) algebra. Using this fact, we examine how
basic quantum optical concepts can be recast in terms of radial modes.


  The rapid integration of variable energy sources (VRES) into power grids
increases variability and uncertainty of the net demand, making the power
system operation challenging. Operating reserve is used by system operators to
manage and hedge against such variability and uncertainty. Traditionally,
reserve requirements are determined by rules-of-thumb (static reserve
requirements, e.g., NERC Reliability Standards), and more recently, dynamic
reserve requirements from tools and methods which are in the adoption process
(e.g., DynADOR, DRD, and RESERVE, among others). While these methods/tools
significantly improve the static rule-of-thumb approaches, they rely
exclusively on deterministic data (i.e., best guess only). Consequently, these
methods disregard the probabilistic uncertainty thresholds associated with
specific days and their weather conditions (i.e., best guess plus probabilistic
uncertainty).
  This work presents practical approaches to determine the operating reserve
requirements leveraging the wealth information from probabilistic forecasts.
Proposed approaches are validated and tested using actual data from the CAISO
system. Results show the benefits in terms of risk reduction of considering the
probabilistic forecast information into the dimensioning process of operating
reserve requirements.


  At the end of the inflationary stage of the early universe, profuse particle
production leads to the reheating of the universe. Such explosive particle
production is due to parametric amplification of quantum fluctuations for the
unbroken symmetry case (appropriate for chaotic inflation), or spinodal
instabilities in the broken symmetry phase(which is the case in new inflation).
The self-consistent methods presented in these lectures are the only
approaches, so far, that lead to reliable quantitative results on the reheating
mechanism in the inflationary universe. They fully use the field theoretical
Schwinger-Keldysh out of equilibrium method in a non-perturbative way. These
approaches take into account the non-linear interaction between the quantum
modes and exactly conserve energy (covariantly). Simplified analysis that do
not include the full backreaction and do not conserve energy, result in unbound
particle production and lead to quantitatively erroneous results. For
spontaneously broken theories the issue of whether the symmetry may be restored
or not by the quantum fluctuations is analyzed. The precise criterion for
symmetry restoration is presented. The field dynamics is symmetric when the
energy density in the initial state is larger than the top of the tree level
potential. When the initial energy density is below the top of the tree level
potential, the symmetry is broken.


  We present the FlipDyn, a dynamic game in which two opponents (a defender and
an adversary) choose strategies to optimally takeover a resource that involves
a dynamical system. At any time instant, each player can take over the resource
and thereby control the dynamical system after incurring a state-dependent and
a control-dependent costs. The resulting model becomes a hybrid dynamical
system where the discrete state (FlipDyn state) determines which player is in
control of the resource. Our objective is to compute the Nash equilibria of
this dynamic zero-sum game. Our contributions are four-fold. First, for any
non-negative costs, we present analytical expressions for the saddle-point
value of the FlipDyn game, along with the corresponding Nash equilibrium (NE)
takeover strategies. Second, for continuous state, linear dynamical systems
with quadratic costs, we establish sufficient conditions under which the game
admits a NE in the space of linear state-feedback policies. Third, for scalar
dynamical systems with quadratic costs, we derive the NE takeover strategies
and saddle-point values independent of the continuous state of the dynamical
system. Fourth and finally, for higher dimensional linear dynamical systems
with quadratic costs, we derive approximate NE takeover strategies and control
policies which enable the computation of bounds on the value functions of the
game in each takeover state. We illustrate our findings through a numerical
study involving the control of a linear dynamical system in the presence of an
adversary.


  We study the numerical resolution of the time-dependent Gross-Pitaevskii
equation, a non-linear Schroedinger equation used to simulate the dynamics of
Bose-Einstein condensates. Considering condensates trapped in harmonic
potentials, we present an efficient algorithm by making use of a spectral
Galerkin method, using a basis set of harmonic oscillator functions, and the
Gauss-Hermite quadrature. We apply this algorithm to the simulation of
condensate breathing and scissors modes.


  Measurements of the electrical and thermal conductivities, thermopower and
paramagnetic susceptibility have been performed on single crystal samples of
Bi1/2Sr1/2MnO3 and complemented with the X-ray powder diffraction data. A
pronounced hysteretic behavior, observed below the cubic-to- orthorhombic
transition at Tcrit = 535 K, is related to the onset of long-range charge order
at TCO = 450 K and its further evolution down to about 330 K. The diffraction
data suggest that the charge ordered state is formed by Zener pairs,
represented by Mn4+ dimers linked by one extra eg electron, and is possibly
stabilized by cooperative Bi,Sr displacements. An extremely low thermal
conductivity is observed down to the lowest temperatures, without any recovery
at the anti- ferromagnetic ordering temperature TN = 150 K. Such be- havior
points to a presence of strong scatterers of phonons. Their possible origin can
be linked to "optical-like" oscilla- tions which are associated with
fluctuating charges within the Zener pairs.


  It is proved that if an almost K\"ahler manifold of dimension greater or
equal to 8 is of pointwise constant antiholomorphic sectional curvature, then
it is a complex space form.


  We investigate the boundary between chemotaxis driven by spatial estimation
of gradients and chemotaxis driven by temporal estimation. While it is well
known that spatial chemotaxis becomes disadvantageous for small organisms at
high noise levels, it is unclear whether there is a discontinuous switch of
optimal strategies or a continuous transition exists. Here, we employ deep
reinforcement learning to study the possible integration of spatial and
temporal information in an a priori unconstrained manner. We parameterize such
a combined chemotactic policy by a recurrent neural network and evaluate it
using a minimal theoretical model of a chemotactic cell. By comparing with
constrained variants of the policy, we show that it converges to purely
temporal and spatial strategies at small and large cell sizes, respectively. We
find that the transition between the regimes is continuous, with the combined
strategy outperforming in the transition region both the constrained variants
as well as models that explicitly integrate spatial and temporal information.
Finally, by utilizing the attribution method of integrated gradients, we show
that the policy relies on a non-trivial combination of spatially and temporally
derived gradient information in a ratio that varies dynamically during the
chemotactic trajectories.


  This paper presents a model of capital accumulation for a large number of
heterogenous producer-consumers in an exchange space in which interactions
depend on agents' positions. Each agent is described by his production,
consumption, stock of capital, as well as the position he occupies in this
abstract space. Each agent produces one differentiated good whose price is
fixed by market clearing conditions. Production functions are Cobb-Douglas, and
capital stocks follow the standard capital accumulation dynamic equation.
Agents consume all goods but have a preference for goods produced by their
closest neighbors. Agents in the exchange space are subject both to attractive
and repulsive forces. Exchanges drive agents closer, but beyond a certain level
of proximity, agents will tend to crowd out more distant agents. The present
model uses a formalism based on statistical field theory developed earlier by
the authors. This approach allows the analytical treatment of economic models
with an arbitrary number of agents, while preserving the system's interactions
and complexity at the individual level. Our results show that the dynamics of
capital accumulation and agents' position in the exchange space are correlated.
Interactions in the exchange space induce several phases of the system. A first
phase appears when attractive forces are limited. In this phase, an initial
central position in the exchange space favors capital accumulation in average
and leads to a higher level of capital, while agents far from the center will
experience a slower accumulation process. A high level of initial capital
drives agents towards a central position, i.e. improve the terms of their
exchanges: they experience a higher demand and higher prices for their product.
As usual, high capital productivity favors capital accumulation, while higher
rates of capital depreciation reduce capital stock. In a second phase,
attractive forces are predominant. The previous results remain, but an
additional threshold effect appears. Even though no restriction was imposed
initially on the system, two types of agents emerge, depending on their initial
stock of capital. One type of agents will remain above the capital threshold
and occupy and benefit from a central position. The other type will remain
below the threshold, will not be able to break it and will remain at the
periphery of the exchange space. In this phase, capital distribution is less
homogenous than in the first phase.


  Using the technique of Fra\"iss\'e theory, for every constant $K\ge 1$ we
consruct a universal object in the class of Banach spaces with normalized
$K$-suppression unconditional Schauder bases.


  The prospect of quantum simulating lattice gauge theories opens exciting
possibilities for understanding fundamental forms of matter. Here, we show that
trapped ions represent a promising platform in this context when simultaneously
exploiting internal pseudo-spins and external phonon vibrations. We illustrate
our ideas with two complementary proposals for simulating lattice-regularized
quantum electrodynamics (QED) in (1+1) space-time dimensions. The first scheme
replaces the gauge fields by local vibrations with a high occupation number. By
numerical finite-size scaling, we demonstrate that this model recovers Wilson's
lattice gauge theory in a controlled way. Its implementation can be scaled up
to tens of ions in an array of micro-traps. The second scheme represents the
gauge fields by spins 1/2, and thus simulates a quantum link model. As we show,
this allows the fermionic matter to be replaced by bosonic degrees of freedom,
permitting small-scale implementations in a linear Paul trap. Both schemes work
on energy scales significantly larger than typical decoherence rates in
experiments, thus enabling the investigation of phenomena such as string
breaking, Coleman's quantum phase transition, and false-vacuum decay. The
underlying ideas of the proposed analog simulation schemes may also be adapted
to other platforms, such as superconducting qubits.


  We propose an approach to the existence problem for locally conformally
K\"ahler metrics on compact complex manifolds by introducing and studying a
functional that is different according to whether the complex dimension of the
manifold is $2$ or higher.


  It is beneficial to calibrate the period Wesenheit metallicity relation
(PWZR) of Delta Cephei stars (DCEPs), i.e., classical Cepheids, using accurate
parallaxes of associated open clusters (OCs) from Gaia data release 3 (DR3). To
this aim, we obtain a total of 43 OC-DCEPs (including 33 fundamental mode, 9
first overtone mode, and 1 multimode DCEPs.) and calibrate the PWZR as
$W_G=(-3.356 \,\pm\, 0.033) \,(\log{P-1})+(-5.947 \,\pm\, 0.025)+(-0.285
\,\pm\, 0.064)[\textrm{Fe/H}]$. The concurrently obtained residual parallax
offset in OC, $zp = -4\pm5\,\mu\textrm{as}$, demonstrate the adequacy of the
parallax corrections within the magnitude range of OC member stars. By
comparing the field DCEPs' DR3 parallaxes with their photometric parallaxes
derived by our PWZR, we estimated the residual parallax offset in field DCEPs
as $zp = -15\pm3\,\mu\textrm{as}$. Using our PWZR, we estimate the distance
modulus of the Large Magellanic Cloud to be $18.482 \,\pm\, 0.040$ mag, which
aligns well with the most accurate published value obtained through geometric
methods.


  We consider a family of second-order parabolic operators
$\partial_t+\mathcal{L}_\varepsilon$ in divergence form with rapidly
oscillating, time-dependent and almost-periodic coefficients. We establish
uniform interior and boundary H\"older and Lipschitz estimates as well as
convergence rate. The estimates of fundamental solution and Green's function
are also established. In contrast to periodic case, the main difficulty is that
the corrector equation $
(\partial_s+\mathcal{L}_1)(\chi^\beta_{j})=-\mathcal{L}_1(P^\beta_j) $ in
$\mathbb{R}^{d+1}$ may not be solvable in the almost periodic setting for
linear functions $P(y)$ and $\partial_t \chi_S$ may not in
$B^2(\mathbb{R}^{d+1})$. Our results are new even in the case of
time-independent coefficients.


  In this paper we study Markov chains associated with the Metropolis-Hastings
algorithm. We consider conditions under which the sequence of the successive
densities of such a chain converges to the target density according to the
total variation distance for any choice of the initial density. In particular
we prove that the positiveness of the proposal density is enough for the chain
to converge. The content of this work basically presents a stand alone proof
that the reversibility along with the kernel positivity imply the convergence.


  We suggest a fast method to find possibly overlapping network communities of
a desired size and link density. Our method is a natural generalization of the
finite-$T$ superparamegnetic Potts clustering introduced by Blatt, Wiseman, and
Domany (Phys. Rev. Lett. v.76, 3251 (1996) and the recently suggested by
Reichard and Bornholdt (Phys. Rev. Lett. v.93, 21870 (2004)) annealing of Potts
model with global antiferromagnetic term. Similarly to both preceding works,
the proposed generalization is based on ordering of ferromagnetic Potts model;
the novelty of the proposed approach lies in the adjustable dependence of the
antiferromagnetic term on the population of each Potts state, which
interpolates between the two previously considered cases. This adjustability
allows to empirically tune the algorithm to detect the maximum number of
communities of the given size and link density. We illustrate the method by
detecting protein complexes in high-throughput protein binding networks.


  The quest to know the structure of matter has resulted in various theoretical
speculations wherein additional colored fermions are postulated. Arising either
as Kaluza-Klein excitations of ordinary quarks, or as excited states in
scenarios wherein the quarks themselves are composites, or even in theories
with extended gauge symmetry, the presence of such fermions ($q^*$) can
potentially be manifested in $\gamma + jet$ final states at the LHC. Using
unitarized amplitudes and the CMS setup, we demonstrate that in the initial
phase of LHC operation (with an integrated luminosity of $200 \pb^{-1}$) one
can discover such states for a mass upto 2.0 TeV. The discovery of a $q^*$ with
a mass as large as $\sim$5 TeV can be acheived for an integrated luminosity of
$\sim 140 \fb^{-1}$. We also comment on the feasibility of mass determination.


  Edge computing has been emerging as a popular scenario for model inference.
However, the inference performance on edge devices (e.g., Multi-Core DSP, FGPA,
etc.) suffers from inefficiency due to the lack of highly optimized inference
frameworks. Previous model inference frameworks are mainly developed in an
operator-centric way, which provides insufficient acceleration to edge-based
inference. Besides, the operator-centric framework incurs significant costs for
continuous development and maintenance.
  In this paper, we propose Xenos, which can automatically conduct
dataflow-centric optimization of the computation graph and accelerate inference
in two dimensions. Vertically, Xenos develops operator linking technique to
improve data locality by restructuring the inter-operator dataflow.
Horizontally, Xenos develops DSP-aware operator split technique to enable
higher parallelism across multiple DSP units. Our evaluation proves the
effectiveness of vertical and horizontal dataflow optimization, which reduce
the inference time by 21.2\%--84.9\% and 17.9\%--96.2\% , respectively.
Besides, Xenos also outperforms the widely-used TVM by
3.22$\times$--17.92$\times$. Moreover, we extend Xenos to a distributed
solution, which we call d-Xenos. d-Xenos employs multiple edge devices to
jointly conduct the inference task and achieves a speedup of 3.68x--3.78x
compared with the single device.


  In two papers, B\"urgisser and Ikenmeyer (STOC 2011, STOC 2013) used an
adaption of the geometric complexity theory (GCT) approach by Mulmuley and
Sohoni (Siam J Comput 2001, 2008) to prove lower bounds on the border rank of
the matrix multiplication tensor. A key ingredient was information about
certain Kronecker coefficients. While tensors are an interesting test bed for
GCT ideas, the far-away goal is the separation of algebraic complexity classes.
The role of the Kronecker coefficients in that setting is taken by the
so-called plethysm coefficients: These are the multiplicities in the coordinate
rings of spaces of polynomials. Even though several hardness results for
Kronecker coefficients are known, there are almost no results about the
complexity of computing the plethysm coefficients or even deciding their
positivity.
  In this paper we show that deciding positivity of plethysm coefficients is
NP-hard, and that computing plethysm coefficients is #P-hard. In fact, both
problems remain hard even if the inner parameter of the plethysm coefficient is
fixed. In this way we obtain an inner versus outer contrast: If the outer
parameter of the plethysm coefficient is fixed, then the plethysm coefficient
can be computed in polynomial time.
  Moreover, we derive new lower and upper bounds and in special cases even
combinatorial descriptions for plethysm coefficients, which we consider to be
of independent interest. Our technique uses discrete tomography in a more
refined way than the recent work on Kronecker coefficients by Ikenmeyer,
Mulmuley, and Walter (Comput Compl 2017). This makes our work the first to
apply techniques from discrete tomography to the study of plethysm
coefficients. Quite surprisingly, that interpretation also leads to new
equalities between certain plethysm coefficients and Kronecker coefficients.


  Measurements of the resistivity anisotropy can provide crucial information
about the electronic structure and scattering processes in anisotropic and
low-dimensional materials, but quantitative measurements by conventional means
often suffer very significant systematic errors. Here we describe a novel
approach to measuring the resistivity anisotropy of orthorhombic materials,
using a single crystal and a single measurement, that is derived from a
$\frac{\pi}{4}$ rotation of the measurement frame relative to the
crystallographic axes. In this new basis the transverse resistivity gives a
direct measurement of the resistivity anisotropy, which combined with the
longitudinal resistivity also gives the in-plane elements of the conventional
resistivity tensor via a 5-point contact geometry. This is demonstrated through
application to the charge-density wave compound ErTe$_3$, and it is concluded
that this method presents a significant improvement on existing techniques in
many scenarios.


  A drop impacting a solid surface with sufficient velocity will emit many
small droplets creating a splash. However, splashing is completely suppressed
if the surrounding gas pressure is lowered. The mechanism by which the gas
affects splashing remains unknown. We use high-speed interference imaging to
measure the air beneath all regions of a spreading viscous drop as well as
optical absorption to measure the drop thickness. Although an initial air
bubble is created on impact, no significant air layer persists until the time a
splash is created. This suggests that splashing in our experimentally
accessible range of viscosities is initiated at the edge of the drop as it
encroaches into the surrounding gas.


  Let $(R,\m)$ be a formally unmixed local ring of positive prime
characteristic and dimension $d$. We examine the implications of having small
Hilbert-Kunz multiplicity (i.e., close to 1). In particular, we show that if
$R$ is not regular, there exists a lower bound, strictly greater than one,
depending only on $d$, for its Hilbert-Kunz multiplicity.


  This work is devoted to the study of some exactly solvable quantum problems
of four, five and six bodies moving on the line. We solve completely the
corresponding stationary Schr\"odinger equation for these systems confined in
an harmonic trap, and interacting pairwise, in clusters of two and three
particles, by two-body inverse square Calogero potential. Both translationaly
and non-translationaly invariant multi-body potentials are added. In each case,
the full solutions are provided, namely the normalized regular eigensolutions
and the eigenenergies spectrum. The irregular solutions are also studied. We
discuss the domains of coupling constants for which these irregular solutions
are square integrable. The case of a "Coulomb-type" confinement is investigated
only for the bound states of the four-body systems.


  We prove quasi-polynomiality for monotone and strictly monotone orbifold
Hurwitz numbers. The second enumerative problem is also known as enumeration of
a special kind of Grothendieck's dessins d'enfants or $r$-hypermaps. These
statements answer positively two conjectures proposed by Do-Karev and
Do-Manescu. We also apply the same method to the usual orbifold Hurwitz numbers
and obtain a new proof of the quasi-polynomiality in this case.
  In the second part of the paper we show that the property of
quasi-polynomiality is equivalent in all these three cases to the property that
the $n$-point generating function has a natural representation on the $n$-th
cartesian powers of a certain algebraic curve. These representations are the
necessary conditions for the Chekhov-Eynard-Orantin topological recursion.


  This and companion papers by Harrington et al. and Blecic et al. present the
Bayesian Atmospheric Radiative Transfer (BART) code, an open-source,
open-development package to characterize extrasolar-planet atmospheres. BART
combines a thermochemical equilibrium abundances (TEA), a radiative-transfer
(Transit), and a Bayesian statistical (MC3) module to constrain atmospheric
temperatures and molecular abundances for given spectroscopic observations.
Here, we describe the Transit radiative-transfer package, an efficient
line-by-line radiative-transfer C code for one-dimensional atmospheres,
developed by P. Rojo and further modified by the UCF exoplanet group. This code
produces transmission and hemisphere-integrated emission spectra. Transit
handles line-by-line opacities from HITRAN, Partridge \& Schwenke ({\water}),
Schwenke (TiO), and Plez (VO); and collision-induced absorption from Borysow,
HITRAN, and ExoMol. Transit emission-spectra models agree with models from C.
Morley (priv. comm.) within a few percent. We applied BART to the {\Spitzer}
and {\Hubble} transit observations of the Neptune-sized planet HAT-P-11b. Our
results generally agree with those from previous studies, constraining the
{\water} abundance and finding an atmosphere enhanced in heavy elements.
Different conclusions start to emerge when we make different assumptions from
other studies. The BART source code and documentation are available at
https://github.com/exosports/BART.


  Temporal knowledge graphs (TKGs) inherently reflect the transient nature of
real-world knowledge, as opposed to static knowledge graphs. Naturally,
automatic TKG completion has drawn much research interests for a more realistic
modeling of relational reasoning. However, most of the existing mod-els for TKG
completion extend static KG embeddings that donot fully exploit TKG structure,
thus lacking in 1) account-ing for temporally relevant events already residing
in the lo-cal neighborhood of a query, and 2) path-based inference that
facilitates multi-hop reasoning and better interpretability. In this paper, we
propose T-GAP, a novel model for TKG completion that maximally utilizes both
temporal information and graph structure in its encoder and decoder. T-GAP
encodes query-specific substructure of TKG by focusing on the temporal
displacement between each event and the query times-tamp, and performs
path-based inference by propagating attention through the graph. Our empirical
experiments demonstrate that T-GAP not only achieves superior performance
against state-of-the-art baselines, but also competently generalizes to queries
with unseen timestamps. Through extensive qualitative analyses, we also show
that T-GAP enjoys from transparent interpretability, and follows human
intuition in its reasoning process.


  Sequential experimental design to discover interventions that achieve a
desired outcome is a key problem in various domains including science,
engineering and public policy. When the space of possible interventions is
large, making an exhaustive search infeasible, experimental design strategies
are needed. In this context, encoding the causal relationships between the
variables, and thus the effect of interventions on the system, is critical for
identifying desirable interventions more efficiently. Here, we develop a causal
active learning strategy to identify interventions that are optimal, as
measured by the discrepancy between the post-interventional mean of the
distribution and a desired target mean. The approach employs a Bayesian update
for the causal model and prioritizes interventions using a carefully designed,
causally informed acquisition function. This acquisition function is evaluated
in closed form, allowing for fast optimization. The resulting algorithms are
theoretically grounded with information-theoretic bounds and provable
consistency results for linear causal models with known causal graph. We apply
our approach to both synthetic data and single-cell transcriptomic data from
Perturb-CITE-seq experiments to identify optimal perturbations that induce a
specific cell state transition. The causally informed acquisition function
generally outperforms existing criteria allowing for optimal intervention
design with fewer but carefully selected samples.


  In the influence maximization (IM) problem, we are given a social network and
a budget $k$, and we look for a set of $k$ nodes in the network, called seeds,
that maximize the expected number of nodes that are reached by an influence
cascade generated by the seeds, according to some stochastic model for
influence diffusion. In this paper, we study the adaptive IM, where the nodes
are selected sequentially one by one, and the decision on the $i$th seed can be
based on the observed cascade produced by the first $i-1$ seeds. We focus on
the full-adoption feedback in which we can observe the entire cascade of each
previously selected seed and on the independent cascade model where each edge
is associated with an independent probability of diffusing influence.
  Our main result is the first sub-linear upper bound that holds for any graph.
Specifically, we show that the adaptivity gap is upper-bounded by $\lceil
n^{1/3}\rceil $, where $n$ is the number of nodes in the graph. Moreover, we
improve over the known upper bound for in-arborescences from
$\frac{2e}{e-1}\approx 3.16$ to $\frac{2e^2}{e^2-1}\approx 2.31$. Finally, we
study $\alpha$-bounded graphs, a class of undirected graphs in which the sum of
node degrees higher than two is at most $\alpha$, and show that the adaptivity
gap is upper-bounded by $\sqrt{\alpha}+O(1)$. Moreover, we show that in
0-bounded graphs, i.e. undirected graphs in which each connected component is a
path or a cycle, the adaptivity gap is at most $\frac{3e^3}{e^3-1}\approx
3.16$. To prove our bounds, we introduce new techniques to relate adaptive
policies with non-adaptive ones that might be of their own interest.


  Accelerating learning processes for complex tasks by leveraging previously
learned tasks has been one of the most challenging problems in reinforcement
learning, especially when the similarity between source and target tasks is
low. This work proposes REPresentation And INstance Transfer (REPAINT)
algorithm for knowledge transfer in deep reinforcement learning. REPAINT not
only transfers the representation of a pre-trained teacher policy in the
on-policy learning, but also uses an advantage-based experience selection
approach to transfer useful samples collected following the teacher policy in
the off-policy learning. Our experimental results on several benchmark tasks
show that REPAINT significantly reduces the total training time in generic
cases of task similarity. In particular, when the source tasks are dissimilar
to, or sub-tasks of, the target tasks, REPAINT outperforms other baselines in
both training-time reduction and asymptotic performance of return scores.


  Li metal is an ideal anode material for use in state-of-the-art secondary
batteries. However, Li-dendrite growth is a safety concern and results in low
coulombic efficiency, which significantly restricts the commercial application
of Li secondary batteries. Unfortunately, the Li deposition (growth) mechanism
is poorly understood on the atomic scale. Here, we used machine learning to
construct a Li potential model with quantum-mechanical computational accuracy.
Molecular dynamics simulations in this study with this model revealed two
self-healing mechanisms in a large Li-metal system, viz. surface self-healing
and bulk self-healing, and identified three Li-dendrite morphologies under
different conditions, viz. "needle", "mushroom", and "hemisphere". Finally, we
introduce the concepts of local current density and variance in local current
density to supplement the critical current density when evaluating the
probability of self-healing.