Abstract:
  In this paper, we argue that models coming from a variety of fields share a
common structure that we call matching function equilibria with partial
assignment. This structure revolves around an aggregate matching function and a
system of nonlinear equations. This encompasses search and matching models,
matching models with transferable, non-transferable and imperfectly
transferable utility, and matching with peer effects. We provide a proof of
existence and uniqueness of an equilibrium as well as an efficient algorithm to
compute it. We show how to estimate parametric versions of these models by
maximum likelihood. We also propose an approach to construct counterfactuals
without estimating the matching functions for a subclass of models. We
illustrate our estimation approach by analyzing the impact of the elimination
of the Social Security Student Benefit Program in 1982 on the marriage market
in the United States.


Abstract:
  We prove existence and regularity results for free transmission problems
governed by fully nonlinear elliptic equations with nonhomogeneous
degeneracies.


Abstract:
  Non-minimally coupled scalar field models are well-known for providing
interesting cosmological features. These include a late time dark energy
behavior, a phantom dark energy evolution without singularity, an early time
inflationary universe, scaling solutions, convergence to the standard
$\Lambda$CDM, etc. While the usual stability analysis helps us determine the
evolution of a model geometrically, bifurcation theory allows us to precisely
locate the parameters' values describing the global dynamics without a
fine-tuning of initial conditions. Using the center manifold theory and
bifurcation analysis, we show that the general model undergoes a transcritical
bifurcation, which predicts us to tune our models to have certain desired
dynamics. We obtained a class of models and a range of parameters capable of
describing a cosmic evolution from an early radiation era towards a late time
dark energy era over a wide range of initial conditions. There is also a
possible scenario of crossing the phantom divide line. We also find a class of
models where the late time attractor mechanism is indistinguishable from that
of a structurally stable general relativity based model; thus, we can elude the
big rip singularity generically. Therefore, bifurcation theory allows us to
select models that are viable with cosmological observations.


Abstract:
  The objective of this paper is to present some results about viscosity
subsolutions of the contact Hamiltonian-Jacobi equations on connected, closed
manifold $M$ $$ H(x,\partial_x u,u)= 0, \quad x\in M. $$ Based on implicit
variational principles introduced in [12,14], we focus on the monotonicity of
the solution semigroups on viscosity subsolutions and the positive invariance
of the epigraph for viscosity subsolutions. Besides, we show a similar
consequence for strict viscosity subsolutions on $M$.


Abstract:
  We analyze the influence of the surface passivation produced by oxides on the
superconducting properties of $\gamma$-Mo$_2$N ultra-thin films. The
superconducting critical temperature of thin films grown directly on Si (100)
with those using a buffer and a capping layer of AlN are compared. The results
show that the cover layer avoids the presence of surface oxides, maximizing the
superconducting critical temperature for films with thicknesses of a few
nanometers. We characterize the flux-flow instability measuring current-voltage
curves in a 6.4 nm thick Mo$_2$N film with a superconducting critical
temperature of 6.4 K. The data is analyzed using the Larkin and Ovchinnikov
model. Considering self-heating effects due to finite heat removal from the
substrate, we determine a fast quasiparticle relaxation time $\approx$ 45 ps.
This value is promising for its applications in single-photon detectors.


Abstract:
  Understanding the biological function of knots in proteins and their folding
process is an open and challenging question in biology. Recent studies classify
the topology and geometry of knotted proteins by analysing the distribution of
a protein's planar projections using topological objects called knotoids. We
approach the analysis of proteins with the same topology by introducing a
topologically inspired statistical metric between their knotoid distributions.
We detect geometric differences between trefoil proteins by characterising
their entanglement and we recover a clustering by sequence similarity. By
looking directly at the geometry and topology of their native states, we are
able to probe different folding pathways for proteins forming open-ended
trefoil knots. Interestingly, our pipeline reveals that the folding pathway of
shallow knotted Carbonic Anhydrases involves the creation of a double-looped
structure, differently from what was previously observed for deeply knotted
trefoil proteins. We validate this with Molecular Dynamics simulations.


Abstract:
  Moving clouds affect the global solar irradiance that reaches the surface of
the Earth. As a consequence, the amount of resources available to meet the
energy demand in a smart grid powered using Photovoltaic (PV) systems depends
on the shadows projected by passing clouds. This research introduces an
algorithm for tracking clouds to predict Sun occlusion. Using thermal images of
clouds, the algorithm is capable of estimating multiple wind velocity fields
with different altitudes, velocity magnitudes and directions.


Abstract:
  In Quantum Field Theory, we discuss the main features of the (non-local)
contour gauge which extends the local axial-type gauge used in most approaches.
Based on the gluon geometry, we demonstrate that the contour gauge does not
suffer from the residual gauge. We discuss the useful correspondence between
the contour gauge conception and the Hamiltonian (Lagrangian) formalism. Having
compared the local and non-local gauges, we again advocate the advantage of the
contour gauge use.


Abstract:
  Two decades after its unexpected discovery, the properties of the $X(3872)$
exotic resonance are still under intense scrutiny. In particular, there are
doubts about its nature as an ensemble of mesons or having any other internal
structure. We use a Diffusion Monte Carlo method to solve the many-body
Schr\"odinger equation that describes this state as a $c \bar c n \bar n$
($n=u$ or $d$ quark) system. This approach accounts for multi-particle
correlations in physical observables avoiding the usual quark-clustering
assumed in other theoretical techniques. The most general and accepted pairwise
Coulomb$\,+\,$linear-confining$\,+\,$hyperfine spin-spin interaction, with
parameters obtained by a simultaneous fit of around 100 masses of mesons and
baryons, is used. The $X(3872)$ contains light quarks whose masses are given by
the mechanism responsible of the dynamical breaking of chiral symmetry. The
same mechanisms gives rise to Goldstone-boson exchange interactions between
quarks that have been fixed in the last 10-20 years reproducing hadron,
hadron-hadron and multiquark phenomenology. It appears that a meson-meson
molecular configuration is preferred but, contrary to the usual assumption of
$D^0\bar{D}^{\ast0}$ molecule for the $X(3872)$, our formalism produces $\omega
J/\psi$ and $\rho J/\psi$ clusters as the most stable ones, which could explain
in a natural way all the observed features of the $X(3872)$.


Abstract:
  We propose a method to evaluate and improve the validity of required
specifications by comparing models from different viewpoints. Inconsistencies
are automatically extracted from the model in which the analyst defines the
service procedure based on the initial requirement; thereafter, the analyst
automatically compares it with a state transition model from the same initial
requirement that has been created by an evaluator who is different from the
analyst. The identified inconsistencies are reported to the analyst to enable
the improvement of the required specifications. We develop a tool for
extraction and comparison and then discuss its effectiveness by applying the
method to a requirements specification example.


Abstract:
  Kappa distributions and with loss cone features have been frequently observed
with flares emissions with the signatures of Lower hybrid waves. We have
analysed the plasma with Kappa distributions and with loss cone features for
the drift wave instabilities in perpendicular propagation for Large flare and
Normal flare and Coronal condition . While analysing the growth/damping rate,
we understand that the growth of propagation of EM waves increases with kappa
distribution index for all the three cases. In comparing the propagation large
flare shows lesser growth in compared with the normal and the coronal plasmas.
When added the loss cone features to Kappa distributions, we find that the
damping of EM wave propagation takes place. The damping rate EM waves is
increases with perpendicular temperature and loss cone index l, in all the
three cases but damping is very high for large flare and then normal in
comparision with coronal condition. This shows that the lower hybrid damping
may be the source of coronal heating.


Abstract:
  Many software engineering studies or tasks rely on categorizing software
engineering artifacts. In practice, this is done either by defining simple but
often imprecise heuristics, or by manual labelling of the artifacts.
Unfortunately, errors in these categorizations impact the tasks that rely on
them. To improve the precision of these categorizations, we propose to gather
heuristics in a collaborative heuristic repository, to which researchers can
contribute a large amount of diverse heuristics for a variety of tasks on a
variety of SE artifacts. These heuristics are then leveraged by
state-of-the-art weak supervision techniques to train high-quality classifiers,
thus improving the categorizations. We present an initial version of the
heuristic repository, which we applied to the concrete task of commit
classification.


Abstract:
  Graph embedding is a general approach to tackling graph-analytic problems by
encoding nodes into low-dimensional representations. Most existing embedding
methods are transductive since the information of all nodes is required in
training, including those to be predicted. In this paper, we propose a novel
inductive embedding method for semi-supervised learning on graphs. This method
generates node representations by learning a parametric function to aggregate
information from the neighborhood using an attention mechanism, and hence
naturally generalizes to previously unseen nodes. Furthermore, adversarial
training serves as an external regularization enforcing the learned
representations to match a prior distribution for improving robustness and
generalization ability. Experiments on real-world clean or noisy graphs are
used to demonstrate the effectiveness of this approach.


Abstract:
  We propose a new diffusion-asymptotic analysis for sequentially randomized
experiments, including those that arise in solving multi-armed bandit problems.
In an experiment with $ n $ time steps, we let the mean reward gaps between
actions scale to the order $1/\sqrt{n}$ so as to preserve the difficulty of the
learning task as $n$ grows. In this regime, we show that the behavior of a
class of sequentially randomized Markov experiments converges to a diffusion
limit, given as the solution of a stochastic differential equation. The
diffusion limit thus enables us to derive refined, instance-specific
characterization of the stochastic dynamics of adaptive experiments. As an
application of this framework, we use the diffusion limit to obtain several new
insights on the regret and belief evolution of Thompson sampling. We show that
a version of Thompson sampling with an asymptotically uninformative prior
variance achieves nearly-optimal instance-specific regret scaling when the
reward gaps are relatively large. We also demonstrate that, in this regime, the
posterior beliefs underlying Thompson sampling are highly unstable over time.


Abstract:
  The Embedded-Atom Model (EAM) provides a phenomenological description of
atomic arrangements in metallic systems. It consists of a configurational
energy depending on atomic positions and featuring the interplay of two-body
atomic interactions and nonlocal effects due to the corresponding electronic
clouds. The purpose of this paper is to mathematically investigate the
minimization of the EAM energy among lattices in two and three dimensions. We
present a suite of analytical and numerical results under different reference
choices for the underlying interaction potentials. In particular, Gaussian,
inverse-power, and Lennard-Jones-type interactions are addressed.


Abstract:
  We present a statistical analysis for the characteristics and spatial
evolution of the interplanetary discontinuities (IDs) in the solar wind, from
0.13 to 0.9 au, by using the Parker Solar Probe measurements on Orbits 4 and 5.
3948 IDs have been collected, including 2511 rotational discontinuities (RDs)
and 557 tangential discontinuities (TDs), with the remnant unidentified. The
statistical results show that (1) the ID occurrence rate decreases from 200
events/day at 0.13 au to 1 events/day at 0.9 au, following a spatial scaling
r-2.00, (2) the RD to TD ratio decreases quickly with the heliocentric
distance, from 8 at r<0.3 au to 1 at r>0.4 au, (3) the magnetic field tends to
rotate across the IDs, 45{\deg} for TDs and 30{\deg} for RDs in the pristine
solar wind within 0.3 au, (4) a special subgroup of RDs exist within 0.3 au,
characterized by small field rotation angles and parallel or antiparallel
propagations to the background magnetic fields, (5) the TD thicknesses
normalized by local ion inertial lengths (di) show no clear spatial scaling and
generally range from 5 to 35 di, and the normalized RD thicknesses follow
r-1.09 spatial scaling, (6) the outward (anti-sunward) propagating RDs
predominate in all RDs, with the propagation speeds in the plasma rest frame
proportional to r-1.03. This work could improve our understandings for the ID
characteristics and evolutions and shed light on the study of the turbulent
environment in the pristine solar wind.


Abstract:
  The paper addresses the problem of defining families of ordered sequences
$\{x_i\}_{i\in N}$ of elements of a compact subset $X$ of $R^d$ whose prefixes
$X_n=\{x_i\}_{i=1}^{n}$, for all orders $n$, have good space-filling properties
as measured by the dispersion (covering radius) criterion. Our ultimate aim is
the definition of incremental algorithms that generate sequences $X_n$ with
small optimality gap, i.e., with a small increase in the maximum distance
between points of $X$ and the elements of $X_n$ with respect to the optimal
solution $X_n^\star$. The paper is a first step in this direction, presenting
incremental design algorithms with proven optimality bound for one-parameter
families of criteria based on coverings and spacings that both converge to
dispersion for large values of their parameter. The examples presented show
that the covering-based method outperforms state-of-the-art competitors,
including coffee-house, suggesting that it inherits from its guaranteed 50\%
optimality gap.


Abstract:
  Barchan dunes, or simply barchans, are crescent-shaped dunes found in diverse
environments such as the bottom of rivers, Earth's deserts and the surface of
Mars. In a recent paper [Phys. Rev. E 101, 012905 (2020)], we investigated the
evolution of subaqueous barchans by using computational fluid dynamics -
discrete element method (CFD-DEM), and our simulations captured well the
evolution of an initial pile toward a barchan dune in both the bedform and
grain scales. The numerical method having shown to be adequate, we obtain now
the forces acting on each grain, isolate the contact interactions, and
investigate how forces are distributed and transmitted in a barchan dune. We
present force maps and probability density functions (PDFs) for values in the
streamwise and spanwise directions, and show that stronger forces are
experienced by grains at neither the crest nor leading edge of the barchan, but
in positions just upstream the dune centroid on the periphery of the dune. We
show also that a great part of grains undergo longitudinal forces of the order
of 10$^{-7}$ N, with negative values around the crest, resulting in
decelerations and grain deposition in that region. These data show that the
force distribution tends to route a great part of grains toward the crest and
horns of subaqueous barchans, being fundamental to comprehend their
morphodynamics. However, to the best of the authors' knowledge, they are not
accessible from current experiments, making of our results an important step
toward understanding the behavior of barchan dunes.


Abstract:
  In this paper, we prove that a compact quasi-Einstein manifold
$(M^n,\,g,\,u)$ of dimension $n\geq 4$ with boundary $\partial M,$ nonnegative
sectional curvature and zero radial Weyl tensor is either isometric, up to
scaling, to the standard hemisphere $\Bbb{S}^n_+,$ or $g=dt^{2}+\psi
^{2}(t)g_{L}$ and $u=u(t),$ where $g_{L}$ is Einstein with nonnegative Ricci
curvature. A similar classification result is obtained by assuming a
fourth-order vanishing condition on the Weyl tensor. Moreover, a new example is
presented in order to justify our assumptions. In addition, the case of
dimension $n=3$ is also discussed.


Abstract:
  Normal mode decomposition of atomic vibrations has been used to provide
microscopic under-standing of thermal transport in amorphous solids for
decades. In normal mode methods, it is naturally assumed that atoms vibrate
around their equilibrium positions and that individual normal modes are the
fundamental vibrational excitations transporting heat. With the abundance of
predictions from normal mode methods and experimental measurements now
available, we care-fully analyze these calculations in amorphous silicon, a
model amorphous solid. We find a number of discrepancies, suggesting that
treating individual normal modes as fundamental heat carriers may not be
accurate in amorphous solids. Further, our classical and ab-initio molecular
dynamics simulations of amorphous silicon demonstrate a large degree of atomic
diffusion, especially at high temperatures, leading to the conclusion that
thermal transport in amorphous solids could be better described starting from
the perspective of liquid dynamics rather than from crystalline solids


Abstract:
  Recent research has confirmed the feasibility of backdoor attacks in deep
reinforcement learning (RL) systems. However, the existing attacks require the
ability to arbitrarily modify an agent's observation, constraining the
application scope to simple RL systems such as Atari games. In this paper, we
migrate backdoor attacks to more complex RL systems involving multiple agents
and explore the possibility of triggering the backdoor without directly
manipulating the agent's observation. As a proof of concept, we demonstrate
that an adversary agent can trigger the backdoor of the victim agent with its
own action in two-player competitive RL systems. We prototype and evaluate
BACKDOORL in four competitive environments. The results show that when the
backdoor is activated, the winning rate of the victim drops by 17% to 37%
compared to when not activated.


Abstract:
  To complete a previous work, the probability density functions for the errors
in the center-of-gravity as positioning algorithm are derived with the usual
methods of the cumulative distribution functions. These methods introduce
substantial complications compared to the approaches used in a previous
publication on similar problems. The combinations of random variables
considered are: $X_{g3}=\theta(x_2-x_1) (x_1-x_3)/(x_1+x_2+x_3) +
\theta(x_1-x_2)(x_1+2x_4)/(x_1+x_2+x_4)$ and
$X_{g4}=(\theta(x_4-x_5)(2x_4+x_1-x_3)/(x_1+x_2+x_3+x_4)+
\theta(x_5-x_4)(x_1-x_3-2x_5)/(x_1+x_2+x_3+x_5)$ The complete and partial forms
of the probability density functions of these expressions of the
center-of-gravity algorithms are calculated for general probability density
functions of the observation noise. The cumulative probability distributions
are the essential steps in this study, never calculated elsewhere.


Abstract:
  In this work we derive the junction conditions for the matching between two
spacetimes at a separation hypersurface in the perfect-fluid version of
$f\left(R,T\right)$ gravity, not only in the usual geometrical representation
but also in a dynamically equivalent scalar-tensor representation. We start
with the general case in which a thin-shell separates the two spacetimes at the
separation hypersurface, for which the general junction conditions are deduced,
and the particular case for smooth matching is considered when the
stress-energy tensor of the thin-shell vanishes. The set of junction conditions
is similar to the one previously obtained for $f\left(R\right)$ gravity but
features also constraints in the continuity of the trace of the stress-energy
tensor $T_{ab}$ and its partial derivatives, which force the thin-shell to
satisfy the equation of state of radiation $\sigma=2p_t$. As a consequence, a
necessary and sufficient condition for spherically symmetric thin-shells to
satisfy all the energy conditions is the positivity of its energy density
$\sigma$. For specific forms of the function $f\left(R,T\right)$, the
continuity of $R$ and $T$ ceases to be mandatory but a gravitational
double-layer arises at the separation hypersurface. The Martinez thin-shell
system and a thin-shell surrounding a central black-hole are provided as
examples of application.


Abstract:
  We highlight shortcomings of the dynamical dark energy (DDE) paradigm. For
parametric models with equation of state (EOS), $w(z) = w_0 + w_a f(z)$ for a
given function of redshift $f(z)$, we show that the errors in $w_a$ are
sensitive to $f(z)$: if $f(z)$ increases quickly with redshift $z$, then errors
in $w_a$ are smaller, and vice versa. As a result, parametric DDE models suffer
from a degree of arbitrariness and focusing too much on one model runs the risk
that DDE may be overlooked. In particular, we show the ubiquitous
Chevallier-Polarski-Linder model is one of the least sensitive to DDE. We also
comment on ``wiggles" in $w(z)$ uncovered in non-parametric reconstructions.
Concretely, we isolate the most relevant Fourier modes in the wiggles, model
them and fit them back to the original data to confirm the wiggles at
$\lesssim2\sigma$. We delve into the assumptions going into the reconstruction
and argue that the assumed correlations, which clearly influence the wiggles,
place strong constraints on field theory models of DDE.


Abstract:
  Planning to support widespread transportation electrification depends on
detailed estimates for the electricity demand from electric vehicles in both
uncontrolled and controlled or smart charging scenarios. We present a modeling
approach to rapidly generate charging estimates that include control for
large-scale scenarios with millions of individual drivers. We model
uncontrolled charging demand using statistical representations of real charging
sessions. We model the effect of load modulation control on aggregate charging
profiles with a novel machine learning approach that replaces traditional
optimization approaches. We demonstrate its performance modeling workplace
charging control with multiple electricity rate schedules, achieving small
errors (2.5% to 4.5%), while accelerating computations by more than 4000 times.
We illustrate the methodology by generating scenarios for California's 2030
charging demand including multiple charging segments and controls, with
scenarios run locally in under 50 seconds, and for assisting rate design
modeling the large-scale impact of a new workplace charging rate.


Abstract:
  The effects of internal adaptation dynamics on the self-organized aggregation
of chemotactic bacteria are investigated by Monte Carlo (MC) simulations based
on a two-stream kinetic transport equation coupled with a reaction-diffusion
equation of the chemoattractant that bacteria produce. A remarkable finding is
a nonmonotonic behavior of the peak aggregation density with respect to the
adaptation time; more specifically, aggregation is the most enhanced when the
adaptation time is comparable to or moderately larger than the mean run time of
bacteria. Another curious observation is the formation of a trapezoidal
aggregation profile occurring at a very large adaptation time, where the biased
motion of individual cells is rather hindered at the plateau regimes due to the
boundedness of the tumbling frequency modulation. Asymptotic analysis of the
kinetic transport system is also carried out, and a novel asymptotic equation
is obtained at the large adaptation-time regime while the Keller-Segel type
equations are obtained when the adaptation time is moderate. Numerical
comparison of the asymptotic equations with MC results clarifies that
trapezoidal aggregation is well described by the novel asymptotic equation, and
the nonmonotonic behavior of the peak aggregation density is interpreted as the
transient of the asymptotic solutions between different adaptation time
regimes.


Abstract:
  We investigate the invariance of the Gibbs measure for the fractional
Schrodinger equation of exponential type (expNLS) $i\partial_t u +
(-\Delta)^{\frac{\alpha}2} u = 2\gamma\beta e^{\beta|u|^2}u$ on $d$-dimensional
compact Riemannian manifolds $\mathcal{M}$, for a dispersion parameter
$\alpha>d$, some coupling constant $\beta>0$, and $\gamma\neq 0$. (i) We first
study the construction of the Gibbs measure for (expNLS). We prove that in the
defocusing case $\gamma>0$, the measure is well-defined in the whole regime
$\alpha>d$ and $\beta>0$ (Theorem 1.1 (i)), while in the focusing case
$\gamma<0$ its partition function is always infinite for any $\alpha>d$ and
$\beta>0$, even with a mass cut-off of arbitrary small size (Theorem 1.1 (ii)).
(ii) We then study the dynamics (expNLS) with random initial data of low
regularity. We first use a compactness argument to prove weak invariance of the
Gibbs measure in the whole regime $\alpha>d$ and $0<\beta < \beta^\star_\alpha$
for some natural parameter $0<\beta^\star_\alpha\sim (\alpha-d)$ (Theorem 1.3
(i)). In the large dispersion regime $\alpha>2d$, we can improve this result by
constructing a local deterministic flow for (expNLS) for any $\beta>0$. Using
the Gibbs measure, we prove that solutions are almost surely global for
$0<\beta \ll\beta^\star_\alpha$, and that the Gibbs measure is invariant
(Theorem 1.3 (ii)). (iii) Finally, in the particular case $d=1$ and
$\mathcal{M}=\mathbb{T}$, we are able to exploit some probabilistic multilinear
smoothing effects to build a probabilistic flow for (expNLS) for
$1+\frac{\sqrt{2}}2<\alpha \leq 2$, locally for arbitrary $\beta>0$ and
globally for $0<\beta \ll \beta^\star_\alpha$ (Theorem 1.5).


Abstract:
  We propose a novel approximation hierarchy for cardinality-constrained,
convex quadratic programs that exploits the rank-dominating eigenvectors of the
quadratic matrix. Each level of approximation admits a min-max characterization
whose objective function can be optimized over the binary variables
analytically, while preserving convexity in the continuous variables.
Exploiting this property, we propose two scalable optimization algorithms,
coined as the "best response" and the "dual program", that can efficiently
screen the potential indices of the nonzero elements of the original program.
We show that the proposed methods are competitive with the existing screening
methods in the current sparse regression literature, and it is particularly
fast on instances with high number of measurements in experiments with both
synthetic and real datasets.


Abstract:
  This paper completely characterizes the standard Young tableaux that can be
reconstructed from their sets or multisets of $1$-minors. In particular, any
standard Young tableau with at least $5$ entries can be reconstructed from its
set of $1$-minors.


Abstract:
  We consider the problem of forecasting the daily number of hospitalized
COVID-19 patients at a single hospital site, in order to help administrators
with logistics and planning. We develop several candidate hierarchical Bayesian
models which directly capture the count nature of data via a generalized
Poisson likelihood, model time-series dependencies via autoregressive and
Gaussian process latent processes, and share statistical strength across
related sites. We demonstrate our approach on public datasets for 8 hospitals
in Massachusetts, U.S.A. and 10 hospitals in the United Kingdom. Further
prospective evaluation compares our approach favorably to baselines currently
used by stakeholders at 3 related hospitals to forecast 2-week-ahead demand by
rescaling state-level forecasts.


Abstract:
  We study theoretically two vibrating quantum emitters trapped near a
one-dimensional waveguide and interacting with propagating photons. We
demonstrate, that in the regime of strong optomechanical interaction the
light-induced coupling of emitter vibrations can lead to formation of spatially
localized vibration modes, exhibiting parity-time (PT ) symmetry breaking.
These localized vibrations can be interpreted as topological defects in the
quasiclassical energy spectrum.


Abstract:
  The origin of p-type conductivity and the mechanism responsible for low
carrier mobility was investigated in pyrite (FeS2) thin films. Temperature
dependent resistivity measurements were performed on polycrystalline and
nanostructured thin films prepared by three different methods. Films have a
high hole density and low mobility regardless of the method used for their
preparation. The charge transport mechanism is determined to be nearest
neighbour hopping (NNH) at near room temperature with Mott-type variable range
hopping (VRH) of holes via localized states occurring at lower temperatures.
Density functional theory (DFT) predicts that sulfur vacancy induced localized
defect states will be situated within the band gap with the charge remaining
localized around the defect. The data indicate that the electronic properties
including hopping transport in pyrite thin films can be correlated to sulfur
vacancy related defect. The results provide insights on electronic properties
of pyrite thin films and its implications for charge transport


Abstract:
  In one-shot weight sharing for NAS, the weights of each operation (at each
layer) are supposed to be identical for all architectures (paths) in the
supernet. However, this rules out the possibility of adjusting operation
weights to cater for different paths, which limits the reliability of the
evaluation results. In this paper, instead of counting on a single supernet, we
introduce $K$-shot supernets and take their weights for each operation as a
dictionary. The operation weight for each path is represented as a convex
combination of items in a dictionary with a simplex code. This enables a matrix
approximation of the stand-alone weight matrix with a higher rank ($K>1$). A
\textit{simplex-net} is introduced to produce architecture-customized code for
each path. As a result, all paths can adaptively learn how to share weights in
the $K$-shot supernets and acquire corresponding weights for better evaluation.
$K$-shot supernets and simplex-net can be iteratively trained, and we further
extend the search to the channel dimension. Extensive experiments on benchmark
datasets validate that K-shot NAS significantly improves the evaluation
accuracy of paths and thus brings in impressive performance improvements.


Abstract:
  We derive the main classical gravitational tests for a recently found vacuum
solution with spin and dilation charges in the framework of Metric-Affine gauge
theory of gravity. Using the results of the perihelion precession of the star
S2 by the GRAVITY collaboration and the gravitational redshift of Sirius B
white dwarf we constrain the corrections provided by the torsion and
nonmetricity fields for these effects.


Abstract:
  We present the results of photometry, linear spectropolarimetry, and imaging
circular polarimetry ofcomet C/2009 P1 (Garradd) performed at the 6-m telescope
BTA of the Special Astrophysical Observatory(Russia) equipped by the multi-mode
focal reducer SCORPIO-2. The comet was observed at two epochspost-perihelion:
on February 2-14, 2012 at r=1.6 au and {\alpha}=36 {\deg}; and on April 14-21,
2012 at r=2.2 au and {\alpha}=27 deg. The spatial maps of the relative
intensity and circular polarization as well as the spectral distribution of
linear polarization are presented. There were two features (dust and gas tails)
orientedin the solar and antisolar directions on February 2 and 14 that allowed
us to determine rotation periodof the nucleus as 11.1 hours. We detected
emissions of C2 , C3 , CN, CH, NH2 molecules as well as CO+ and H2O+ ions,
along with a high level of the dust continuum. On February 2, the degree of
linear polarization in the continuum, within the wavelength range of 0.67-0.68
{\mu}m, was about 5% in the near-nucleus region up to near 6000 km and
decreased to about 3% at near 40,000 km. The left-handed (negative) circular
polarization at the level approximately from -0.06% to -0.4% was observed at
the distances up to 3*10^4 km from the nucleus on February 14 and April 21,
respectively.


Abstract:
  Prospects of the Cherenkov Telescope Array (CTA) for the study of very high
energy gamma-ray emission from nearby star-forming galaxies are investigated.
In the previous work, we constructed a model to calculate luminosity and energy
spectrum of pion-decay gamma-ray emission produced by cosmic-ray interaction
with the interstellar medium (ISM), from four physical quantities of galaxies
[star formation rate (SFR), gas mass, stellar mass, and effective radius]. The
model is in good agreement with the observed GeV--TeV emission of several
nearby galaxies. Applying this model to nearby galaxies that are not yet
detected in TeV (mainly from the KINGFISH catalog), their hadronic gamma-ray
luminosities and spectra are predicted. We identify galaxies of the highest
chance of detection by CTA, including NGC 5236, M33, NGC 6946, and IC 342.
Concerning gamma-ray spectra, NGC 1482 is particularly interesting because our
model predicts that this galaxy is close to the calorimetric limit and its
gamma-ray spectral index in GeV--TeV is close to that of cosmic-ray protons
injected into ISM. Therefore this galaxy may be detectable by CTA even though
its GeV flux is below the {\it Fermi} Large Area Telescope sensitivity limit.
In the TeV regime, most galaxies are not in the calorimetric limit, and the
predicted TeV flux is lower than that assuming a simple relation between the
TeV luminosity and SFR of M82 and NGC 253, typically by a factor of 15. This
means that a more sophisticated model beyond the calorimetric limit assumption
is necessary to study TeV emission from star-forming galaxies.


Abstract:
  Motivated by the needs from an airline crew scheduling application, we
introduce structured convolutional kernel networks (Struct-CKN), which combine
CKNs from Mairal et al. (2014) in a structured prediction framework that
supports constraints on the outputs. CKNs are a particular kind of
convolutional neural networks that approximate a kernel feature map on training
data, thus combining properties of deep learning with the non-parametric
flexibility of kernel methods. Extending CKNs to structured outputs allows us
to obtain useful initial solutions on a flight-connection dataset that can be
further refined by an airline crew scheduling solver. More specifically, we use
a flight-based network modeled as a general conditional random field capable of
incorporating local constraints in the learning process. Our experiments
demonstrate that this approach yields significant improvements for the
large-scale crew pairing problem (50,000 flights per month) over standard
approaches, reducing the solution cost by 17% (a gain of millions of dollars)
and the cost of global constraints by 97%.


Abstract:
  We study a general convergence theory for the numerical solutions of
compressible viscous and electrically conducting fluids with a focus on
numerical schemes that preserve the divergence free property of magnetic field
exactly. Our strategy utilizes the recent concepts of dissipative weak
solutions and consistent approximations. First, we show the dissipative
weak--strong uniqueness principle, meaning a dissipative weak solution
coincides with a classical solution as long as they emanate from the same
initial data. Next, we show the convergence of consistent approximation towards
the dissipative weak solution and thus the classical solution. Upon
interpreting the consistent approximation as the stability and consistency of
suitable numerical solutions we have established a generalized Lax equivalence
theory: convergence $\Longleftrightarrow$ stability and consistency. Further,
to illustrate the application of this theory, we propose two novel mixed finite
volume-finite element methods with exact divergence-free magnetic field.
Finally, by showing solutions of these two schemes are consistent
approximations, we conclude their convergence towards the dissipative weak
solution and the classical solution.


Abstract:
  We study the duality between JT gravity and the double-scaled matrix model
including their respective deformations. For these deformed theories we relate
the thermal partition function to the generating function of topological
gravity correlators that are determined as solutions to the KdV hierarchy. We
specialise to those deformations of JT gravity coupled to a gas of defects,
which conforms with known results in the literature. We express the
(asymptotic) thermal partition functions in a low temperature limit, in which
non-perturbative corrections are suppressed and the thermal partition function
becomes exact. In this limit we demonstrate that there is a Hawking-Page phase
transition between connected and disconnected surfaces for this instance of JT
gravity with a transition temperature affected by the presence of defects.
Furthermore, the calculated spectral form factors show the qualitative
behaviour expected for a Hawking-Page phase transition. The considered
deformations cause the ramp to be shifted along the real time axis. Finally, we
comment on recent results related to conical Weil-Petersson volumes and the
analytic continuation to two-dimensional de Sitter space.


Abstract:
  In this paper, we extend the article that Minkowski problem in Gaussian
probability space of Huang et al. to $L_p$-Gaussian Minkowski problem, and
obtain the existence and uniqueness of $o$-symmetry weak solution in case of
$p\geq1$.


Abstract:
  Clustering data into meaningful subsets is a major task in scientific data
analysis. To date, various strategies ranging from model-based approaches to
data-driven schemes, have been devised for efficient and accurate clustering.
One important class of clustering methods that is of a particular interest is
the class of exemplar-based approaches. This interest primarily stems from the
amount of compressed information encoded in these exemplars that effectively
reflect the major characteristics of the respective clusters. Affinity
propagation (AP) has proven to be a powerful exemplar-based approach that
refines the set of optimal exemplars by iterative pairwise message updates.
However, a critical limitation is its inability to capitalize on known
networked relations between data points often available for various scientific
datasets. To mitigate this shortcoming, we propose geometric-AP, a novel
clustering algorithm that effectively extends AP to take advantage of the
network topology. Geometric-AP obeys network constraints and uses max-sum
belief propagation to leverage the available network topology for generating
smooth clusters over the network. Extensive performance assessment reveals a
significant enhancement in the quality of the clustering results when compared
to benchmark clustering schemes. Especially, we demonstrate that geometric-AP
performs extremely well even in cases where the original AP fails drastically.


Abstract:
  The effectiveness of fingerprint-based authentication systems on good quality
fingerprints is established long back. However, the performance of standard
fingerprint matching systems on noisy and poor quality fingerprints is far from
satisfactory. Towards this, we propose a data uncertainty-based framework which
enables the state-of-the-art fingerprint preprocessing models to quantify noise
present in the input image and identify fingerprint regions with background
noise and poor ridge clarity. Quantification of noise helps the model two
folds: firstly, it makes the objective function adaptive to the noise in a
particular input fingerprint and consequently, helps to achieve robust
performance on noisy and distorted fingerprint regions. Secondly, it provides a
noise variance map which indicates noisy pixels in the input fingerprint image.
The predicted noise variance map enables the end-users to understand erroneous
predictions due to noise present in the input image. Extensive experimental
evaluation on 13 publicly available fingerprint databases, across different
architectural choices and two fingerprint processing tasks demonstrate
effectiveness of the proposed framework.


Abstract:
  We encounter variables with little variation often in educational data mining
(EDM) due to the demographics of higher education and the questions we ask.
Yet, little work has examined how to analyze such data. Therefore, we conducted
a simulation study using logistic regression, penalized regression, and random
forest. We systematically varied the fraction of positive outcomes, feature
imbalances, and odds ratios. We find the algorithms treat features with the
same odds ratios differently based on the features' imbalance and the outcome
imbalance. While none of the algorithms fully solved how to handle imbalanced
data, penalized approaches such as Firth and Log-F reduced the difference
between the built-in odds ratio and value determined by the algorithm. Our
results suggest that EDM studies might contain false negatives when determining
which variables are related to an outcome. We then apply our findings to a
graduate admissions data set. We end by proposing recommendations that
researchers should consider penalized regression for data sets on the order of
hundreds of cases and should include more context about their data in
publications such as the outcome and feature imbalances.


Abstract:
  The COVID-19 pandemic started in China in December 2019 and quickly spread to
several countries. The consequences of this pandemic are incalculable, causing
the death of millions of people and damaging the global economy. To achieve
large-scale control of this pandemic, fast tools for detection and treatment of
patients are needed. Thus, the demand for alternative tools for the diagnosis
of COVID-19 has increased dramatically since accurated and automated tools are
not available. In this paper we present the ongoing work on a system for
COVID-19 detection using ultrasound imaging and using Deep Learning techniques.
Furthermore, such a system is implemented on a Raspberry Pi to make it portable
and easy to use in remote regions without an Internet connection.


Abstract:
  We prove that an inclusion-exclusion inspired expression of Schubert
polynomials of permutations that avoid the patterns 1432 and 1423 is
nonnegative. Our theorem implies a partial affirmative answer to a recent
conjecture of Yibo Gao about principal specializations of Schubert polynomials.
We propose a general framework for finding inclusion-exclusion inspired
expression of Schubert polynomials of all permutations.


Abstract:
  It is shown that the slopes of the superhorizon hypermagnetic spectra
produced by the variation of the gauge couplings are practically unaffected by
the relative strength of the parity-breaking terms. A new method is proposed
for the estimate of the gauge power spectra in the presence of pseudoscalar
interactions during inflation. To corroborate the general results, various
concrete examples are explicitly analyzed. Since the large-scale gauge spectra
also determine the late-time magnetic fields it turns out that the pseudoscalar
contributions have little impact on the magnetogenesis requirement. Conversely
the parity-breaking terms crucially affect the gyrotropic spectra that may
seed, in certain models, the baryon asymmetry of the Universe. In the most
interesting regions of the parameter space the modes reentering prior to
symmetry breaking lead to a sufficiently large baryon asymmetry while the
magnetic power spectra associated with the modes reentering after symmetry
breaking may even be of the order of a few hundredths of a nG over typical
length scales comparable with the Mpc prior to the collapse of the protogalaxy.
From the viewpoint of the effective field theory description of magnetogenesis
scenarios these considerations hold generically for the whole class of
inflationary models where the inflaton is not constrained by any underlying
symmetry.


Abstract:
  We study the representation theory of non-admissible simple affine vertex
algebra $L_{-5/2} (sl(4))$. We determine an explicit formula for the singular
vector of conformal weight four in the universal affine vertex algebra
$V^{-5/2} (sl(4))$, and show that it generates the maximal ideal in $V^{-5/2}
(sl(4))$. We classify irreducible $L_{-5/2} (sl(4))$--modules in the category
${\mathcal O}$, and determine the fusion rules between irreducible modules in
the category of ordinary modules $KL_{-5/2}$. It turns out that this fusion
algebra is isomorphic to the fusion algebra of $KL_{-1}$. We also prove that
$KL_{-5/2}$ is a semi-simple, rigid braided tensor category.
  In our proofs we use the notion of collapsing level for the affine
$\mathcal{W}$--algebra, and the properties of conformal embedding $gl(4)
\hookrightarrow sl(5)$ at level $k=-5/2$ from arXiv:1509.06512. We show that
$k=-5/2$ is a collapsing level with respect to the subregular nilpotent element
$f_{subreg}$, meaning that the simple quotient of the affine
$\mathcal{W}$--algebra $W^{-5/2}(sl(4), f_{subreg})$ is isomorphic to the
Heisenberg vertex algebra $M_J(1)$. We prove certain results on vanishing and
non-vanishing of cohomology for the quantum Hamiltonian reduction functor
$H_{f_{subreg}}$. It turns out that the properties of $H_{f_{subreg}}$ are more
subtle than in the case of minimal reducition.


Abstract:
  Over the last years, the number of cyber-attacks on industrial control
systems has been steadily increasing. Among several factors, proper software
development plays a vital role in keeping these systems secure. To achieve
secure software, developers need to be aware of secure coding guidelines and
secure coding best practices. This work presents a platform geared towards
software developers in the industry that aims to increase awareness of secure
software development. The authors also introduce an interactive game component,
a virtual coach, which implements a simple artificial intelligence engine based
on the laddering technique for interviews. Through a survey, a preliminary
evaluation of the implemented artifact with real-world players (from academia
and industry) shows a positive acceptance of the developed platform.
Furthermore, the players agree that the platform is adequate for training their
secure coding skills. The impact of our work is to introduce a new automatic
challenge evaluation method together with a virtual coach to improve existing
cybersecurity awareness training programs. These training workshops can be
easily held remotely or off-line.


Abstract:
  The long wavelength moir\'e superlattices in twisted 2D structures have
emerged as a highly tunable platform for strongly correlated electron physics.
We study the moir\'e bands in twisted transition metal dichalcogenide
homobilayers, focusing on WSe$_2$, at small twist angles using a combination of
first principles density functional theory, continuum modeling, and
Hartree-Fock approximation. We reveal the rich physics at small twist angles
$\theta<4^\circ$, and identify a particular magic angle at which the top
valence moir\'e band achieves almost perfect flatness. In the vicinity of this
magic angle, we predict the realization of a generalized Kane-Mele model with a
topological flat band, interaction-driven Haldane insulator, and Mott
insulators at the filling of one hole per moir\'e unit cell. The combination of
flat dispersion and uniformity of Berry curvature near the magic angle holds
promise for realizing fractional quantum anomalous Hall effect at fractional
filling. We also identify twist angles favorable for quantum spin Hall
insulators and interaction-induced quantum anomalous Hall insulators at other
integer fillings.


Abstract:
  Due to the rapid emergence of short videos and the requirement for content
understanding and creation, the video captioning task has received increasing
attention in recent years. In this paper, we convert traditional video
captioning task into a new paradigm, \ie, Open-book Video Captioning, which
generates natural language under the prompts of video-content-relevant
sentences, not limited to the video itself. To address the open-book video
captioning problem, we propose a novel Retrieve-Copy-Generate network, where a
pluggable video-to-text retriever is constructed to retrieve sentences as hints
from the training corpus effectively, and a copy-mechanism generator is
introduced to extract expressions from multi-retrieved sentences dynamically.
The two modules can be trained end-to-end or separately, which is flexible and
extensible. Our framework coordinates the conventional retrieval-based methods
with orthodox encoder-decoder methods, which can not only draw on the diverse
expressions in the retrieved sentences but also generate natural and accurate
content of the video. Extensive experiments on several benchmark datasets show
that our proposed approach surpasses the state-of-the-art performance,
indicating the effectiveness and promising of the proposed paradigm in the task
of video captioning.


Abstract:
  The $\phi^4$ double-well theory admits a kink solution, whose rich
phenomenology is strongly affected by the existence of a single bound
excitation called the shape mode. We find that the leading quantum correction
to the energy needed to excite the shape mode is $-0.115567\lambda/m$ in terms
of the coupling $\lambda/4$ and the meson mass $m$ evaluated at the minimum of
the potential. On the other hand, the correction to the continuum threshold is
$-0.433\lambda/m$. A naive extrapolation to finite coupling then suggests that
the shape mode melts into the continuum at the modest coupling of
$\lambda/4\sim 0.106 m^2$, where the $\mathbb{Z}_2$ symmetry is still broken.


Abstract:
  Most of the existing formation algorithms for multiagent systems are fully
label-specified, i.e., the desired position for each agent in the formation is
uniquely determined by its label, which would inevitably make the formation
algorithms vulnerable to agent failures. To address this issue, in this paper,
we propose a dynamic leader-follower approach to solving the line marching
problem for a swarm of planar kinematic robots. In contrast to the existing
results, the desired positions for the robots in the line are not fully
label-specified, but determined in a dynamic way according to the current state
of the robot swarm. By constantly forming a chain of leader-follower pairs,
exact formation can be achieved by pairwise leader-following tracking. Since
the order of the chain of leader-follower pairs is constantly updated, the
proposed algorithm shows strong robustness against robot failures.
Comprehensive numerical results are provided to evaluate the performance of the
proposed algorithm.


Abstract:
  A central theme in condensed matter physics is to create and understand the
exotic states of matter by incorporating magnetism into topological materials.
One prime example is the quantum anomalous Hall (QAH) state. Recently, MnBi2Te4
has been demonstrated to be an intrinsic magnetic topological insulator and the
QAH effect was observed in exfoliated MnBi2Te4 flakes. Here, we used molecular
beam epitaxy (MBE) to grow MnBi2Te4 films with thickness down to 1 septuple
layer (SL) and performed thickness-dependent transport measurements. We
observed a non-square hysteresis loop in the antiferromagnetic state for films
with thickness greater than 2 SL. The hysteresis loop can be separated into two
AH components. Through careful analysis, we demonstrated that one AH component
with the larger coercive field is from the dominant MnBi2Te4 phase, while the
other AH component with the smaller coercive field is from the minor Mn-doped
Bi2Te3 phase in the samples. The extracted AH component of the MnBi2Te4 phase
shows a clear even-odd layer-dependent behavior, a signature of
antiferromagnetic thin films. Our studies reveal insights on how to optimize
the MBE growth conditions to improve the quality of MnBi2Te4 films, in which
the QAH and other exotic states are predicted.


Abstract:
  We present the KMOS Galaxy Evolution Survey (KGES), a $K$-band Multi-Object
Spectrograph (KMOS) study of the H$\alpha$ and [NII] emission from 288 $K$
band-selected galaxies at $1.2 \lesssim z \lesssim 1.8$, with stellar masses in
the range $\log_{10}(M_{*}/\rm{M}_{\odot})\approx$9-11.5. In this paper, we
describe the survey design, present the sample, and discuss the key properties
of the KGES galaxies. We combine KGES with appropriately matched samples at
lower redshifts from the KMOS Redshift One Spectroscopic Survey (KROSS) and the
SAMI Galaxy Survey. Accounting for the effects of sample selection, data
quality, and analysis techniques between surveys, we examine the kinematic
characteristics and angular momentum content of star-forming galaxies at
$z\approx1.5$, $\approx1$ and $\approx0$. We find that stellar mass, rather
than redshift, most strongly correlates with the disc fraction amongst
star-forming galaxies at $z \lesssim 1.5$, observing only a modest increase in
the prevalence of discs between $z\approx1.5$ and $z\approx0.04$ at fixed
stellar mass. Furthermore, typical star-forming galaxies follow the same median
relation between specific angular momentum and stellar mass, regardless of
their redshift, with the normalisation of the relation depending more strongly
on how disc-like a galaxy's kinematics are. This suggests that massive
star-forming discs form in a very similar manner across the $\approx$ 10 Gyr
encompassed by our study and that the inferred link between the angular
momentum of galaxies and their haloes does not change significantly across the
stellar mass and redshift ranges probed in this work.


Abstract:
  A variety of wireless channel estimation methods, e.g., MUSIC and ESPRIT,
rely on prior knowledge of the model order. Therefore, it is important to
correctly estimate the number of multipath components (MPCs) which compose such
channels. However, environments with many scatterers may generate MPCs which
are closely spaced. This clustering of MPCs in addition to noise makes the
model order selection task difficult in practice to currently known algorithms.
In this paper, we exploit the multidimensional characteristics of MIMO
orthogonal frequency division multiplexing (OFDM) systems and propose a machine
learning (ML) method capable of determining the number of MPCs with a higher
accuracy than state of the art methods in almost coherent scenarios. Moreover,
our results show that our proposed ML method has an enhanced reliability.


Abstract:
  That neural networks may be pruned to high sparsities and retain high
accuracy is well established. Recent research efforts focus on pruning
immediately after initialization so as to allow the computational savings
afforded by sparsity to extend to the training process. In this work, we
introduce a new `DCT plus Sparse' layer architecture, which maintains
information propagation and trainability even with as little as 0.01% trainable
kernel parameters remaining. We show that standard training of networks built
with these layers, and pruned at initialization, achieves state-of-the-art
accuracy for extreme sparsities on a variety of benchmark network architectures
and datasets. Moreover, these results are achieved using only simple heuristics
to determine the locations of the trainable parameters in the network, and thus
without having to initially store or compute with the full, unpruned network,
as is required by competing prune-at-initialization algorithms. Switching from
standard sparse layers to DCT plus Sparse layers does not increase the storage
footprint of a network and incurs only a small additional computational
overhead.


Abstract:
  In this paper we investigate the one dimensional (1D) logarithmic diffusion
equation with nonlinear Robin boundary conditions, namely, \[ \left\{
\begin{array}{l} \partial_t u=\partial_{xx} \log u\quad \mbox{in}\quad
\left[-l,l\right]\times \left(0, \infty\right)\\ \displaystyle \partial_x
u\left(\pm l, t\right)=\pm 2\gamma u^{p}\left(\pm l, t\right), \end{array}
\right. \] where $\gamma$ is a constant. Let $u_0>0$ be a smooth function
defined on $\left[-l,l\right]$, and which satisfies the compatibility condition
$$\partial_x \log u_0\left(\pm l\right)= \pm 2\gamma u_0^{p-1}\left(\pm
l\right).$$ We show that for $\gamma > 0$, $p\leq \frac{3}{2}$ solutions to the
logarithmic diffusion equation above with initial data $u_0$ are global and
blow-up in infinite time, and for $p>2$ there is finite time blow-up. Also, we
show that in the case of $\gamma<0$, $p\geq \frac{3}{2}$, solutions to the
logarithmic diffusion equation with initial data $u_0$ are global and blow-down
in infinite time, but if $p\leq 1$ there is finite time blow-down. For some of
the cases mentioned above, and some particular families of examples, we provide
blow-up and blow-down rates. Our approach is partly based on studying the Ricci
flow on a cylinder endowed with a $\mathbb{S}^1$-symmetric metric. Then, we
bring our ideas full circle by proving a new long time existence result for the
Ricci flow on a cylinder without any symmetry assumption. Finally, we show a
blow-down result for the logarithmic diffusion equation on a disc.


Abstract:
  We present a dynamical mean-field study of antiferromagnetic magnons in one-,
two- and three-orbital Hubbard model of square and bcc cubic lattice at
intermediate coupling strength. Weinvestigate the effect of anisotropy
introduced by an external magnetic field or single-ion anisotropy.For the
latter we tune continuously between the easy-axis and easy-plane models. We
also analyzea model with spin-orbit coupling in cubic site-symmetry setting.
The ordered states as well as themagnetic excitations are sensitive to even a
small breaking ofSU(2)symmetry of the model andfollow the expectations of
spin-wave theory as well as general symmetry considerations.


Abstract:
  With Rydberg dipole interactions, a mesoscopic atomic ensemble may behave
like a two-level single atom, resulting in the so-called picture of superatom.
It is in potential a strong candidate as a qubit in quantum information
science, especially for efficient coupling with single photons via collective
enhancement that is essential for building quantum internet to connect remote
quantum computers. Previously, preliminary studies have been carried out in
demonstrating basic concept of Rydberg superatom, a single-photon source, and
entanglement with a single photon, etc. While a crucial element of single-shot
qubit measurement is still missing. Here we realize the deterministic
measurement of a superatom qubit via photon burst in a single shot. We make use
of a low-finesse ring cavity to enhance the atom-photon interaction and obtain
an in-fiber retrieval efficiency of 44%. Harnessing dipole interaction between
two Rydberg levels, we may either create a sequence of multiple single photons
or nothing, conditioned on the initial qubit state. We achieve a single-shot
measurement fidelity of 93.2% in 4.8 us. Our work complements the experimental
toolbox of harnessing Rydberg superatom for quantum information applications.


Abstract:
  The Wiedemann-Franz law states that the charge conductance and the electronic
contribution to the heat conductance are proportional. This sets stringent
constraints on efficiency bounds for thermoelectric applications, which seek
for large charge conduction in response to a small heat flow. We present
experiments based on a quantum dot formed inside a semiconducting InAs nanowire
transistor, in which the heat conduction can be tuned significantly below the
Wiedemann-Franz prediction. Comparison with scattering theory shows that this
is caused by quantum confinement and the resulting energy-selective transport
properties of the quantum dot. Our results open up perspectives for tailoring
independently the heat and electrical conduction properties in semiconductor
nanostructures.


Abstract:
  Despite its simple manifestation, triboelectric charging is believed to be
unthinkably complex because of the unresolvable interfacial interaction between
two rubbing materials. Here, we for the first time reveal a simple physics of
triboelectric charging and the origin of triboelectric series based on the
causality of friction-originated thermoelectric charging effects at the
interface, characterized by the density (\rho), specific heat (c), thermal
conductivity (k), and Seebeck coefficient (S) of each material. We demonstrate
that energy dissipational heat at the interface induces temperature variations
in the materials and thus develops electrostatic potentials that will initiate
thermoelectric charging across the interface. We discovered that the trends and
quantities of triboelectric charging for various polymers, metals,
semiconductors, and even lightning clouds are simply governed by the
triboelectric factor, S/\sqrt{{\rho}ck}. Our findings will bring significant
new opportunities for understanding and management of static electrification.


Abstract:
  In many problem settings, most notably in game playing, an agent receives a
possibly delayed reward for its actions. Often, those rewards are handcrafted
and not naturally given. Even simple terminal-only rewards, like winning equals
one and losing equals minus one, can not be seen as an unbiased statement,
since these values are chosen arbitrarily, and the behavior of the learner may
change with different encodings. It is hard to argue about good rewards and the
performance of an agent often depends on the design of the reward signal. In
particular, in domains where states by nature only have an ordinal ranking and
where meaningful distance information between game state values is not
available, a numerical reward signal is necessarily biased. In this paper we
take a look at MCTS, a popular algorithm to solve MDPs, highlight a reoccurring
problem concerning its use of rewards, and show that an ordinal treatment of
the rewards overcomes this problem. Using the General Video Game Playing
framework we show dominance of our newly proposed ordinal MCTS algorithm over
other MCTS variants, based on a novel bandit algorithm that we also introduce
and test versus UCB.


Abstract:
  The identification of defects in crystal structures is crucial for the
analysis of atomistic simulations. Many methods to characterize defects that
are based on the classification of local atomic arrangement are available for
simple crystalline structures. However, there is currently no method to
identify both, the crystal structures and internal defects of topologically
close-packed (TCP) phases such as Laves phases. We propose a new method, Laves
phase Crystal Analysis (LaCA), to characterize the atomic arrangement in Laves
crystals by interweaving existing structural analysis algorithms. The new
method can identify the polytypes C14 and C15 of Laves phases, typical
crystallographic defects in these phases, and common deformation mechanisms
such as synchroshear and non-basal dislocations. Defects in the C36 Laves phase
are detectable through deviations from the periodic arrangement of the C14 and
C15 structures that make up this phase. LaCA is robust and extendable to other
TCP phases.


Abstract:
  It is established existence of ground and bound state solutions for Choquard
equation considering concave-convex nonlinearities in the following form
  $$
  \begin{array}{rcl}
  -\Delta u +V(x) u &=& (I_\alpha* |u|^p)|u|^{p-2}u+ \lambda |u|^{q-2}u, \, u
\in H^1(\mathbb{R}^{N}),
  \end{array}
  $$ where $\lambda > 0, N \geq 3, \alpha \in (0, N)$. The potential $V$ is a
continuous function and $I_\alpha$ denotes the standard Riesz potential. Assume
also that $1 < q < 2,~2_{\alpha} < p < 2^*_\alpha$ where
$2_\alpha=(N+\alpha)/N$, $2_\alpha=(N+\alpha)/(N-2)$. Our main contribution is
to consider a specific condition on the parameter $\lambda > 0$ taking into
account the nonlinear Rayleigh quotient. More precisely, there exists
$\lambda_n > 0$ such that our main problem admits at least two positive
solutions for each $\lambda \in (0, \lambda_n]$. In order to do that we combine
Nehari method with a fine analysis on the nonlinear Rayleigh quotient. The
parameter $\lambda_n > 0$ is optimal in some sense which allow us to apply the
Nehari method.


Abstract:
  We investigate thin and thick stellar disc formation in Milky-Way-mass
galaxies using twelve FIRE-2 cosmological zoom-in simulations. All simulated
galaxies experience an early period of bursty star formation that transitions
to a late-time steady phase of near-constant star formation. Stars formed
during the late-time steady phase have more circular orbits and thin-disc-like
morphology at $z=0$, whilst stars born during the bursty phase have more radial
orbits and thick-disc structure. The median age of thick-disc stars at $z=0$
correlates strongly with this transition time. We also find that galaxies with
an earlier transition from bursty to steady star formation have a higher
thin-disc fractions at $z=0$. Three of our systems have minor mergers with
LMC-size satellites during the thin-disc phase. These mergers trigger short
starbursts but do not destroy the thin disc nor alter broad trends between the
star formation transition time and thin/thick disc properties. If our
simulations are representative of the Universe, then stellar archaeological
studies of the Milky Way (or M31) provide a window into past star-formation
modes in the Galaxy. Current age estimates of the Galactic thick disc would
suggest that the Milky Way transitioned from bursty to steady phase $\sim$6.5
Gyr ago; prior to that time the Milky Way likely lacked a recognisable thin
disc.


Abstract:
  Detection and localization of actions in videos is an important problem in
practice. A traffic analyst might be interested in studying the patterns in
which vehicles move at a given intersection. State-of-the-art video analytics
systems are unable to efficiently and effectively answer such action queries.
The reasons are threefold. First, action detection and localization tasks
require computationally expensive deep neural networks. Second, actions are
often rare events. Third, actions are spread across a sequence of frames. It is
important to take the entire sequence of frames into context for effectively
answering the query. It is critical to quickly skim through the irrelevant
parts of the video to answer the action query efficiently.
  In this paper, we present Zeus, a video analytics system tailored for
answering action queries. We propose a novel technique for efficiently
answering these queries using a deep reinforcement learning agent. Zeus trains
an agent that learns to adaptively modify the input video segments to an action
classification network. The agent alters the input segments along three
dimensions -- sampling rate, segment length, and resolution. Besides
efficiency, Zeus is capable of answering the query at a user-specified target
accuracy using a query optimizer that trains the agent based on an
accuracy-aware reward function. Our evaluation of Zeus on a novel action
localization dataset shows that it outperforms the state-of-the-art frame- and
window-based techniques by up to 1.4x and 3x, respectively. Furthermore, unlike
the frame-based technique, it satisfies the user-specified target accuracy
across all the queries, at up to 2x higher accuracy, than frame-based methods.


Abstract:
  The impact of global warming and the imperative to limit climate change have
stimulated the need to develop new solutions based on renewable energy sources.
One of the emerging trends in this endeavor are the Electric Vehicles (EVs),
which use electricity instead of traditional fossil fuels as a power source,
relying on the Vehicle-to-Grid (V2G) paradigm. The novelty of such a paradigm
requires careful analysis to avoid malicious attempts. An attacker can exploit
several surfaces, such as the remote connection between the Distribution Grid
and Charging Supply or the authentication system between the charging Supply
Equipment and the Electric Vehicles. However, V2G architecture's high cost and
complexity in implementation can restrain this field's research capability. In
this paper, we approach this limitation by proposing MiniV2G, an open-source
emulator to simulate Electric Vehicle Charging (EVC) built on top of Mininet
and RiseV2G. MiniV2G is particularly suitable for security researchers to study
and test real V2G charging scenarios. MiniV2G can reproduce with high fidelity
a V2G architecture to easily simulate an EV charging process. Finally, we
present a MiniV2G application and show how MiniV2G can be used to study V2G
communication and develop attacks and countermeasures that can be applied to
real systems. Since we believe our tool can be of great help for research in
this field, we also made it freely available.


Abstract:
  This note explores state space search to find efficient instruction sequences
that perform particular data manipulations. Once found, the instruction
sequences are hard-wired in the code generator that needs these data
manipulations. Since state space is only searched while developing the
compiler, search time is not at a premium, which allows exhaustively searching
for the best possible instruction sequences.


Abstract:
  As neural networks become more popular, the need for accompanying uncertainty
estimates increases. The current testing methodology focusses on how good the
predictive uncertainty estimates explain the differences between predictions
and observations in a previously unseen test set. Intuitively this is a logical
approach. The current setup of benchmark data sets also allows easy comparison
between the different methods. We demonstrate, however, through both
theoretical arguments and simulations that this way of evaluating the quality
of uncertainty estimates has serious flaws. Firstly, it cannot disentangle the
aleatoric from the epistemic uncertainty. Secondly, the current methodology
considers the uncertainty averaged over all test samples, implicitly averaging
out overconfident and underconfident predictions. When checking if the correct
fraction of test points falls inside prediction intervals, a good score on
average gives no guarantee that the intervals are sensible for individual
points. We demonstrate through practical examples that these effects can result
in favoring a method, based on the predictive uncertainty, that has undesirable
behaviour of the confidence intervals. Finally, we propose a simulation-based
testing approach that addresses these problems while still allowing easy
comparison between different methods.


Abstract:
  Acoustic focusing plays a pivotal role in a wide variety of applications
ranging from medical science to nondestructive testing. Previous works have
shown that acoustic metagratings can overcome the inherent efficiency
limitations of gradient metasurfaces in beam steering. In this work, we propose
a new design principle for acoustic metalenses, based on metagratings, to
achieve efficient ultrasonic focusing. We achieve beam focusing by locally
controlling the excitation of a single diffraction order with the use of
adiabatically varying metagratings over the lens aperture. A set of
metagratings is optimized by a semi-analytical approach using a genetic
algorithm, enabling efficient anomalous reflection for a wide range of
reflection angles. Numerical results reveal that our metalens can effectively
focus impinging ultrasonic waves to a focal point of FWHM = 0.364{\lambda}. The
focusing performance of the metalens is demonstrated experimentally, validating
our proposed approach.


Abstract:
  Internet and social media offer firms novel ways of managing their marketing
strategy and gain competitive advantage. The groups of users expressing
themselves on the Internet about a particular topic, product, or brand are
frequently called a virtual tribe or E-tribe. However, there are no automatic
tools for identifying and studying the characteristics of these virtual tribes.
Towards this aim, this paper presents Tribefinder, a system to reveal Twitter
users' tribal affiliations, by analyzing their tweets and language use. To show
the potential of this instrument, we provide an example considering three
specific tribal macro-categories: alternative realities, lifestyle, and
recreation. In addition, we discuss the different characteristics of each
identified tribe, in terms of use of language and social interaction metrics.
Tribefinder illustrates the importance of adopting a new lens for studying
virtual tribes, which is crucial for firms to properly design their marketing
strategy, and for scholars to extend prior marketing research.


Abstract:
  We study potential security vulnerabilities of a single-photon detector based
on superconducting transition-edge sensor. In a simple experiment, we show that
an adversary could fake a photon number result at a certain wavelength by
sending a larger number of photons at a longer wavelength. In another
experiment, we show that the detector can be blinded by bright continuous-wave
light and then, a controlled response simulating single-photon detection can be
produced by applying a bright light pulse. We model an intercept-and-resend
attack on a quantum key distribution system that exploits the latter
vulnerability and, under certain assumptions, succeeds to steal the key.


Abstract:
  Ultrafast laser pulses spatially shaped as Bessel beams in dielectrics create
high aspect ratio plasma channels whose relaxation can lead to the formation of
nanochannels. We report a strong enhancement of the nanochannel drilling
efficiency with illumination by double pulses separated by a delay between 10
to 500 ps. This enables the formation of nanochannels with diameters down to
100 nm. Experimental absorption measurements demonstrate that the increase of
drilling efficiency is due to an increase of the confinement of the energy
deposition. Nanochannel formation corresponds to a drastic change in absorption
of the second pulse demonstrating the occurrence of a phase change produced by
the first pulse. This creates a highly absorbing long-living state. Our
measurements show that it is compatible with the semi-metallization of warm
dense glass which takes place within a timescale of <10 ps after the first
laser pulse illumination.


Abstract:
  We introduce a class of Sparse, Physics-based, and partially Interpretable
Neural Networks (SPINN) for solving ordinary and partial differential equations
(PDEs). By reinterpreting a traditional meshless representation of solutions of
PDEs we develop a class of sparse neural network architectures that are
partially interpretable. The SPINN model we propose here serves as a seamless
bridge between two extreme modeling tools for PDEs, namely dense neural network
based methods like Physics Informed Neural Networks (PINNs) and traditional
mesh-free numerical methods, thereby providing a novel means to develop a new
class of hybrid algorithms that build on the best of both these viewpoints. A
unique feature of the SPINN model that distinguishes it from other neural
network based approximations proposed earlier is that it is (i) interpretable,
in a particular sense made precise in the work, and (ii) sparse in the sense
that it has much fewer connections than typical dense neural networks used for
PDEs. Further, the SPINN algorithm implicitly encodes mesh adaptivity and is
able to handle discontinuities in the solutions. In addition, we demonstrate
that Fourier series representations can also be expressed as a special class of
SPINN and propose generalized neural network analogues of Fourier
representations. We illustrate the utility of the proposed method with a
variety of examples involving ordinary differential equations, elliptic,
parabolic, hyperbolic and nonlinear partial differential equations, and an
example in fluid dynamics.


Abstract:
  Combinatorial Game Theory has also been called `additive game theory',
whenever the analysis involves sums of independent game components. Such {\em
disjunctive sums} invoke comparison between games, which allows abstract values
to be assigned to them. However, there are rulesets with {\em entailing moves}
that break the alternating play axiom and/or restrict the other player's
options within the disjunctive sum components. These situations are exemplified
in the literature by a ruleset such as {\sc nimstring}, a normal play variation
of the classical children's game {\sc dots \& boxes}, and {\sc top~entails}, an
elegant ruleset introduced in the classical work Winning Ways, by Berlekamp
Conway and Guy. Such rulesets fall outside the scope of the established normal
play theory. Here, we axiomatize normal play via two new terminating games,
$\infty$ (Left wins) and $\overline\infty$ (Right wins), and a more general
theory is achieved. We define {\em affine impartial}, which extends classical
impartial games, and we analyze their algebra by extending the established
Sprague-Grundy theory, with an accompanying minimum excluded rule. Solutions of
{\sc nimstring} and {\sc top~entails} are given to illustrate the theory.


Abstract:
  This short paper briefly presents our methodology details of automatic
intracranial aneurysms segmentation from brain MR scans. We use ensembles of
multiple models trained from different loss functions. Our method ranked first
place in the ADAM challenge segmentation task. The code and trained models are
publicly available at https://github.com/JunMa11/ADAM2020.


Abstract:
  Van der Waals heterostructures (vdWHs) allow the assembly of high-crystalline
two-dimensional (2D) materials in order to explore dimensionality effects in
strongly correlated systems and the emergence of potential new physical
scenarios. In this work, it is illustrated the feasibility to integrate 2D
materials in-between 2D superconductors. Particularly, it is presented the
fabrication and electrical characterization of vertical vdWHs based on
air-unstable atomically-thin transition metal dichalcogenides formed by
NbSe2/TaS2/NbSe2 stacks, with TaS2 being the insulator 1T-TaS2 or the metal
2H-TaS2. Phase transitions as 1T-TaS2 charge density wave and NbSe2
superconductivity are detected. An enhancement of the vdWH resistance due to
Andreev reflections is observed below the superconducting transition
temperature of the NbSe2 flakes. Moreover, in the NbSe2 superconducting state,
the field and temperature dependence of the normalized conductance is analyzed
within the Dynes' model and the overall behavior is consistent with the
Bardeen-Cooper-Schrieffer theory. This vdWH approach can be extended to other
2D materials, such as 2D magnets or topological insulators, with the aim of
exploring the new emergent properties that may arise from such combinations.


Abstract:
  In this work, we propose a tube-based model predictive control (MPC) scheme
for state and input constrained linear systems subject to dynamic uncertainties
described by integral quadratic constraints (IQCs). In particular, we extend
the framework of \r{ho}-hard IQCs for exponential stability analysis to
consider external inputs. This allows us to show that the model error between
the true uncertain system and the nominal prediction model is bounded by an
exponentially stable scalar system. In the proposed tube-based MPC scheme, the
state of this error bounding system is predicted along with the nominal model
and used as a scaling parameter for the tube size. We prove that this MPC
scheme achieves robust constraint satisfaction and input-to-state stability in
the presence of dynamic uncertainties and additive bounded disturbances. In a
numerical example, we demonstrate the flexibility of the proposed tube dynamics
and the reduced conservatism of the IQC approach compared to other
characterizations of dynamic uncertainties that are used in robust MPC.


Abstract:
  Semiquantitative group testing (SQGT) is a pooling method in which the test
outcomes represent bounded intervals for the number of defectives.
Alternatively, it may be viewed as an adder channel with quantized outputs.
SQGT represents a natural choice for Covid-19 group testing as it allows for a
straightforward interpretation of the cycle threshold values produced by
polymerase chain reactions (PCR). Prior work on SQGT did not address the need
for adaptive testing with a small number of rounds as required in practice. We
propose conceptually simple methods for 2-round and nonadaptive SQGT that
significantly improve upon existing schemes by using ideas on nonbinary
measurement matrices based on expander graphs and list-disjunct matrices.


Abstract:
  In causal graphical models based on directed acyclic graphs (DAGs), directed
paths represent causal pathways between the corresponding variables. The
variable at the beginning of such a path is referred to as an ancestor of the
variable at the end of the path. Ancestral relations between variables play an
important role in causal modeling. In existing literature on structure
learning, these relations are usually deduced from learned structures and used
for orienting edges or formulating constraints of the space of possible DAGs.
However, they are usually not posed as immediate target of inference. In this
work we investigate the graphical characterization of ancestral relations via
CPDAGs and d-separation relations. We propose a framework that can learn
definite non-ancestral relations without first learning the skeleton. This
frame-work yields structural information that can be used in both score- and
constraint-based algorithms to learn causal DAGs more efficiently.


Abstract:
  In this article, the hydrodynamic characteristics of Autonomous Underwater
Vehicles (AUVs) are investigated and analyzed under the influence of rotating
flow fields, that were generated in a recirculating water tank via a rotating
propeller. Initially, experiments were carried out for the measurement of flow
field variables and Quantities of Interest across the AUV in the presence of
the rotating propeller while varying the rotational speed and the extent of
rotational flow strength. The flow field across the AUV was measured using an
Acoustic Doppler Velocimeter (ADV). These measured turbulent flow statistics
were used to validate the Reynolds Stress Model (RSM) based numerical
predictions in a commercial CFD solver. After preliminary validation of the
turbulent flow statistics with the numerical predictions, a series of numerical
simulations were performed to investigate the effect of the rotational flow
field of the propeller on the drag, skin friction and pressure coefficients of
the AUV. The operating speed and location of the propeller were also varied to
check their affects on the hydrodynamic performance of AUV. The results
provided in this article will be useful for the design optimization of AUVs
cruising in shallow water where the flow is highly rotational because of
wave-current interactions. Additionally the results and analysis are relevant
to study the design and operation of AUVs that have to operate in a group of
unmanned underwater vehicles or near submarines and ships where the flow field
is highly complex and such rotational effects are present.


Abstract:
  In this work, we propose iterative access point (AP) selection (APS), linear
minimum mean-square error (MMSE) precoding and power allocation techniques for
Cell-Free Massive multiple-input multiple-output (MIMO) systems. We consider
the downlink channel with single-antenna users and multiple-antenna APs. We
derive sum-rate expressions for the proposed iterative APS techniques followed
by MMSE precoding and optimal, adaptive, and uniform power allocation schemes.
Simulations show that the proposed approach outperforms existing conjugate
beamforming (CB) and zero-forcing (ZF) schemes and that performance remains
excellent with APS, in the presence of perfect and imperfect channel state
information (CSI).


Abstract:
  The recent growth in multi-fidelity uncertainty quantification has given rise
to a large set of variance reduction techniques that leverage information from
model ensembles to provide variance reduction for estimates of the statistics
of a high-fidelity model. In this paper we provide two contributions: (1) we
utilize an ensemble estimator to account for uncertainties in the optimal
weights of approximate control variate (ACV) approaches and derive lower bounds
on the number of samples required to guarantee variance reduction; and (2) we
extend an existing multi-fidelity importance sampling (MFIS) scheme to leverage
control variates. As such we make significant progress towards both increasing
the practicality of approximate control variates$-$for instance, by accounting
for the effect of pilot samples$-$and using multi-fidelity approaches more
effectively for estimating low-probability events. The numerical results
indicate our hybrid MFIS-ACV estimator achieves up to 50% improvement in
variance reduction over the existing state-of-the-art MFIS estimator, which had
already shown outstanding convergence rate compared to the Monte Carlo method,
on several problems of computational mechanics.


Abstract:
  A general treatment of the spectral problem of quantum graphs and
tight-binding models in finite Hilbert spaces is given. The direct spectral
problem and the inverse spectral problem are written in terms of simple
algebraic equations containing information on the topology of a quantum graph.
The inverse problem is shown to be combinatorial, and some low dimensional
examples are explicitly solved. For a {\it window\ }graph, a commutator and
anticommutator algebra (superalgebra) is identified as the culprit behind
accidental degeneracy in the form of triplets, where configurational symmetry
{\it alone\ }fails to explain the result. For a M\"obius cycloacene graph, it
is found that the accidental triplet cannot be explained with a superalgebra,
but that the graph can be built unambiguously from the spectrum using
combinatorial methods. These examples are compared with a more symmetric but
less degenerate system, i.e. a {\it car wheel\ } graph which possesses neither
triplets, nor superalgebra.


Abstract:
  In E-commerce, a key challenge in text generation is to find a good trade-off
between word diversity and accuracy (relevance) in order to make generated text
appear more natural and human-like. In order to improve the relevance of
generated results, conditional text generators were developed that use input
keywords or attributes to produce the corresponding text. Prior work, however,
do not finely control the diversity of automatically generated sentences. For
example, it does not control the order of keywords to put more relevant ones
first. Moreover, it does not explicitly control the balance between diversity
and accuracy. To remedy these problems, we propose a fine-grained controllable
generative model, called~\textit{Apex}, that uses an algorithm borrowed from
automatic control (namely, a variant of the \textit{proportional, integral, and
derivative (PID) controller}) to precisely manipulate the diversity/accuracy
trade-off of generated text. The algorithm is injected into a Conditional
Variational Autoencoder (CVAE), allowing \textit{Apex} to control both (i) the
order of keywords in the generated sentences (conditioned on the input keywords
and their order), and (ii) the trade-off between diversity and accuracy.
Evaluation results on real-world datasets show that the proposed method
outperforms existing generative models in terms of diversity and relevance.
Apex is currently deployed to generate production descriptions and item
recommendation reasons in Taobao owned by Alibaba, the largest E-commerce
platform in China. The A/B production test results show that our method
improves click-through rate (CTR) by 13.17\% compared to the existing method
for production descriptions. For item recommendation reason, it is able to
increase CTR by 6.89\% and 1.42\% compared to user reviews and top-K item
recommendation without reviews, respectively.


Abstract:
  Most restless Markovian bandits problems in infinite horizon can be solved
quasioptimally: the famous Whittle index policy is known to become
asymptotically optimal exponentially fast as the number of arms grows, at least
under certain conditions (including having a so-called indexable problem). For
restless Markovian bandits problems in finite horizons no such optimal policy
is known. In this paper, we define a new policy, based on a linear program
relaxation of the finite horizon problem (called the LP-filling policy), that
is asymptotically optimal under no condition. Furthermore we show that for
regular problems (defined in the paper) the LP-filling policy becomes an index
policy (called the LP-regular policy) and becomes optimal exponentially fast in
the number of arms. We also introduce the LP-update policy that significantly
improves the performance compared to the LP-filling policy for large time
horizons. We provide numerical studies that show the prevalence of our
LP-policies over previous solutions.


Abstract:
  With the advent of deep optical-to-near-infrared extragalactic imaging on the
degree scale, samples of high-redshift sources are being selected that contain
both bright star-forming (SF) galaxies and faint active galactic nuclei (AGN).
In this study we investigate the transition between SF and AGN-dominated
systems at $z \simeq 4$ in the rest-frame UV. We find a rapid transition to
AGN-dominated sources bright-ward of $M_{\rm UV} \simeq -23.2$. The effect is
observed in the rest-frame UV morphology and size-luminosity relation, where
extended clumpy systems become point-source dominated, and also in the
available spectra for the sample. These results allow us to derive the
rest-frame UV luminosity function for the SF and AGN-dominated sub-samples. We
find the SF-dominated LF is best fit with a double-power law, with a lensed
Schechter function being unable to explain the existence of extremely luminous
SF galaxies at $M_{\rm UV} \simeq -23.5$. If we identify AGN-dominated sources
according to a point-source morphology criterion we recover the relatively flat
faint-end slope of the AGN LF determined in previous studies. If we instead
separate the LF according to the current spectroscopic AGN fraction, we find a
steeper faint-end slope of $\alpha = -1.83 \pm 0.11$. Using a simple model to
predict the rest-frame AGN LF from the $z = 4 $ galaxy LF we find that the
increasing impact of host galaxy light on the measured morphology of faint AGN
can explain our observations.


Abstract:
  Ultrafinitism postulates that we can only compute on relatively short
objects, and numbers beyond certain value are not available. This approach
would also forbid many forms of infinitary reasoning and allow to remove
certain paradoxes stemming from enumeration theorems.
  However, philosophers still disagree of whether such a finitist logic would
be consistent. We present preliminary work on a proof system based on
Curry-Howard isomorphism. We also try to present some well-known theorems that
stop being true in such systems, whereas opposite statements become provable.
  This approach presents certain impossibility results as logical paradoxes
stemming from a profligate use of transfinite reasoning.


Abstract:
  We examine the computational complexity of problems in which we are given
generators for a partial bijection semigroup and asked to check properties of
the generated semigroup. We prove that the following problems are in AC$^0$:
(1) enumerating left and right identities and (2) checking if the semigroup is
completely regular. We prove that checking membership of a given idempotent is
a PSPACE-complete problem. We also describe a nondeterministic logspace
algorithm for checking if an inverse semigroup given by generators satisfies a
given semigroup identity that may involve a unary inverse operation.


Abstract:
  Personalized conversation models (PCMs) generate responses according to
speaker preferences. Existing personalized conversation tasks typically require
models to extract speaker preferences from user descriptions or their
conversation histories, which are scarce for newcomers and inactive users. In
this paper, we propose a few-shot personalized conversation task with an
auxiliary social network. The task requires models to generate personalized
responses for a speaker given a few conversations from the speaker and a social
network. Existing methods are mainly designed to incorporate descriptions or
conversation histories. Those methods can hardly model speakers with so few
conversations or connections between speakers. To better cater for newcomers
with few resources, we propose a personalized conversation model (PCM) that
learns to adapt to new speakers as well as enabling new speakers to learn from
resource-rich speakers. Particularly, based on a meta-learning based PCM, we
propose a task aggregator (TA) to collect other speakers' information from the
social network. The TA provides prior knowledge of the new speaker in its
meta-learning. Experimental results show our methods outperform all baselines
in appropriateness, diversity, and consistency with speakers.


Abstract:
  Important phenomena such as magnetostriction, magnetocaloric, and
magnetoelectric effects arise from, or could be enhanced by, the coupling of
magnetic and structural degrees of freedom. The coupling of spin and lattice
also influence transport and structural properties in magnetic materials in
particular around phase transitions. In this paper we propose a method for
screening materials for a strong magneto-structural coupling by assessing the
effect of the local magnetic configuration on the atomic forces using density
functional theory (DFT). We have employed the disordered local moment approach
in a supercell formulation to probe different magnetic local configurations and
their forces and performed a high-throughput search on binary and ternary
compounds available in the Crystallographic Open Database. We identify a list
of materials with a strong spin-lattice coupling out of which several are
already known to display magneto-lattice coupling-phenomena like Fe3O4 and CrN.
Others, such as Mn2CrO4 and CaFe7O11 have been less studied and are yet to
reveal their potentials in experiments and applications.


Abstract:
  We show a few basic results about moduli spaces of semistable modules over
Lie algebroids. The first result shows that such moduli spaces exist for
relative projective morphisms of noetherian schemes, removing some earlier
constraints. The second result proves general separatedness Langton type
theorem for such moduli spaces. More precisely, we prove S-completness of some
moduli stacks of semistable modules. In some special cases this result
identifies closed points of the moduli space of Gieseker semistable sheaves on
a projective scheme and of the Donaldson--Uhlenbeck compactification of the
moduli space of slope stable locally free sheaves on a smooth projective
surface. The last result generalizes properness of Hitchin's morphism and it
shows properness of so called Hodge-Hitchin morphism defined in positive
characteristic on the moduli space of Gieseker semistable integrable
t-connections in terms of the p-curvature morphism. This last result was proven
in the curve case by de Cataldo and Zhang using completely different methods.


Abstract:
  We discuss the energy loss due to gravitational radiation of binaries
composed of exotic objects whose horizon boundary conditions are replaced with
reflective ones. Our focus is on the extreme mass-ratio inspirals, in which the
central heavier black hole is replaced with an exotic compact object. We show,
in this case, a modulation of the energy loss rate depending on the evolving
orbital frequency occurs and leads to two different types of modifications to
the gravitational wave phase evolution; the oscillating part directly
corresponding to the modulation in the energy flux, and the non-oscillating
part coming from the quadratic order in the modulation. This modification can
be sufficiently large to detect with future space-borne detectors.


Abstract:
  The metastable Helium (He*) lines near $10830~{\rm A}$ are ideal probes of
atmospheric erosion--a common phenomenon of close-in exoplanet evolution. A
handful of exoplanet observations yielded well-resolved He* absorption features
in transits, yet they were mostly analyzed with 1D isothermal models
prescribing mass-loss rates. This work devises 3D hydrodynamics co-evolved with
ray-tracing radiative transfer and non-equilibrium thermochemsitry. Starting
from the observed stellar/planetary properties with reasonable assumptions
about the host's high energy irradiation, we predict from first principle the
mass loss rate, the temperature and ionization profiles, and 3D outflow
kinematics. Our simulations well reproduce the observed He* line profiles and
light curves of WASP-69b. We further investigate the dependence of He*
observables on simulation conditions and host radiation. The key findings are:
(1) Simulations reveal a photoevaporative outflow ($\sim 0.5~M_{\oplus}~{\rm
Gyr}^{-1}$) for WASP-69b without a prominent comet-like tail, consistent with
the symmetric transit shape (Vissapragada et al. 2020). (2) 3D simulations are
mandatory for hydrodynamic features, including Coriolis force, advection, and
kinematic line broadening. (3) EUV ($>13.6~{\rm eV}$) photons dominate
photoevaporative outflows and populate He* via recombination; FUV is also
detrimental by destroying He*; X-ray plays a secondary role. (4) K stars hit
the sweet spot of EUV/FUV balance for He* line observation, while G and M stars
are also worthy targets. (5) Stellar flars create characteristic responses in
the He* line profiles.