  While many time-dependent network design problems can be formulated as
time-indexed formulations with strong relaxations, the size of these
formulations depends on the discretization of the time horizon and can become
prohibitively large. The recently-developed dynamic discretization discovery
(DDD) method allows many time-dependent problems to become more tractable by
iteratively solving instances of the problem on smaller networks where each
node has its own discrete set of departure times. However, in the current
implementation of DDD, all arcs departing a common node share the same set of
departure times. This causes DDD to be ineffective for solving problems where
all near-optimal solutions require many distinct departure times at the
majority of the high-degree nodes in the network. Region-based networks are one
such structure that often leads to many high-degree nodes, and their increasing
popularity underscores the importance of tailoring solution methods for these
networks.
  To improve methods for solving problems that require many departure times at
nodes, we develop a DDD framework where the set of departure times is
determined on the arc level rather than the node level. We apply this arc-based
DDD method to instances of the service network design problem (SND). We show
that an arc-based approach is particularly advantageous when instances arise
from region-based networks, and when candidate paths are fixed in the base
graph for each commodity. Moreover, our algorithm builds upon the existing DDD
framework and achieves these improvements with only benign modifications to the
original implementation.


  We study how dense multi-antenna millimeter wave (mmWave) cellular network
performance scales in terms of the base station (BS) spatial density $\lambda$,
by studying the signal-to-interference-plus-noise ratio (SINR) and the area
spectral efficiency (ASE). If the number of antennas at each BS scales at least
linearly with $\lambda$, which increases the number of possible beam
configurations and their main-lobe gain, and decreases their side-lobe gain, we
prove that the SINR approaches a finite random variable that is independent of
$\lambda$ and the ASE scales at least linearly with $\lambda$. In contrast, if
the number of antennas scales sub-linearly with $\lambda$, then the SINR decays
to zero and the ASE saturates to a constant. Thus, by moving to higher carrier
frequencies with successively smaller antennas, and exploiting the
correspondingly increased directionality, cellular operators can in principle
avoid the densification plateau (or collapse) in cellular networks and instead
continue to harvest linear sum throughput gains through BS densification.


  We describe the construction of a motorized optical rotation mount with a 40
mm clear aperture. The device is used to remotely control the power of large
diameter laser beams for a magneto-optical trap (MOT). A piezo-electric
ultrasonic motor on a printed circuit board (PCB) provides rotation with a
precision better than 0.03 deg and allows for a very compact design. The
rotation unit is controlled from a computer via serial communication, making
integration into most software control platforms straightforward.


  The quality of vocal delivery is one of the key indicators for evaluating
teacher enthusiasm, which has been widely accepted to be connected to the
overall course qualities. However, existing evaluation for vocal delivery is
mainly conducted with manual ratings, which faces two core challenges:
subjectivity and time-consuming. In this paper, we present a novel machine
learning approach that utilizes pairwise comparisons and a multimodal
orthogonal fusing algorithm to generate large-scale objective evaluation
results of the teacher vocal delivery in terms of fluency and passion. We
collect two datasets from real-world education scenarios and the experiment
results demonstrate the effectiveness of our algorithm. To encourage
reproducible results, we make our code public available at
\url{https://github.com/tal-ai/ML4VocalDelivery.git}.


  Based on the idea that the components of a cosmological metric may be
determined by the total gravitational potential of the universe, the scalar
field $\phi=1/G$ in the Jordan-Brans-Dicke (JBD) theory is introduced as
evolving with the inverse square of the scale factor. Since the gravitational
potential is related to the field $\phi$ resulting from Mach's principle and
depends on time due to the expansion of space, the temporal evolution of the
field should be in accord with the evolution of time and space intervals in the
metric tensor. For the same reason, the time dependence of the field makes
these comoving intervals relative for different points on the time axis. Thus,
it is shown that introduction of the cosmic gravitational potential as a time
dependent scalar field proportional to $1/a^2$ may resolve the flatness, the
horizon and the late-time accelerating expansion problems of the standard model
of cosmology. The luminosity distance vs redshift data of Type Ia supernovae is
in agreement with this approach.


  We use (versions of) the von Neumann inequality for Hilbert space
contractions to prove several Schwarz-Pick inequalities. Specifically, we
derive an alternate proof for a multi-point Schwarz-Pick inequality by Beardon
and Minda, along with a generalized version for operators. Connections with
model spaces and Peschl's invariant derivatives are established. Finally,
Schwarz-Pick inequalities for analytic functions on polydisks and for higher
order derivatives are discussed. An enhanced version of the Schwarz-Pick lemma,
using the notion of distinguished variety, is obtained for the bidisk.


  We report inelastic light scattering experiments in the spin-chain and ladder
system Sr_14-xCa_xCu_24O_41. A depletion of an electronic background is
observed for 0 < x < 5 at low temperatures and with light polarization both
parallel to the leg and rung directions. This points to the formation of 2D
charge density wave in the ladders. Furthermore, the Ca-substitution dependence
of the 305- and 249-cm-1 modes, sensitive to the lattice modulations of the
respective chain and ladder system, suggests that the charge dynamics in the
ladders is strongly influenced by the structural incommensurability.


  According to the classic Chv{\'{a}}tal's Lemma from 1977, a graph of minimum
degree $\delta(G)$ contains every tree on $\delta(G)+1$ vertices.
  Our main result is the following algorithmic "extension" of Chv\'{a}tal's
Lemma: For any $n$-vertex graph $G$, integer $k$, and a tree $T$ on at most
$\delta(G)+k$ vertices, deciding whether $G$ contains a subgraph isomorphic to
$T$, can be done in time $f(k)\cdot n^{\mathcal{O}(1)}$ for some function $f$
of $k$ only.
  The proof of our main result is based on an interplay between extremal graph
theory and parameterized algorithms.


  Prediction of the glass forming ability (GFA) of alloys remains a major
challenge. We are not able to predict the composition dependence of the GFA of
even binary alloys. To investigate the effect of each element's propensity to
form particular crystal structures on glass formation, we focus on binary
alloys composed of elements with the same size, but different atomic symmetries
using the patchy-particle model. For mixtures with atomic symmetries that
promote different crystal structures, the minimum critical cooling rate $R_c$
is only a factor of $5$ lower than that for the pure substances. For mixtures
with different atomic symmetries that promote local crystalline and icosahedral
order, the minimum $R_c$ is more than $3$ orders of magnitude lower than that
for pure substances. Results for $R_c$ for the patchy-particle model are in
agreement with those from embedded atom method simulations and sputtering
experiments of NiCu, TiAl, and high entropy alloys.


  In contrast to the advances in characterizing the sample complexity for
solving Markov decision processes (MDPs), the optimal statistical complexity
for solving constrained MDPs (CMDPs) remains unknown. We resolve this question
by providing minimax upper and lower bounds on the sample complexity for
learning near-optimal policies in a discounted CMDP with access to a generative
model (simulator). In particular, we design a model-based algorithm that
addresses two settings: (i) relaxed feasibility, where small constraint
violations are allowed, and (ii) strict feasibility, where the output policy is
required to satisfy the constraint. For (i), we prove that our algorithm
returns an $\epsilon$-optimal policy with probability $1 - \delta$, by making
$\tilde{O}\left(\frac{S A \log(1/\delta)}{(1 - \gamma)^3 \epsilon^2}\right)$
queries to the generative model, thus matching the sample-complexity for
unconstrained MDPs. For (ii), we show that the algorithm's sample complexity is
upper-bounded by $\tilde{O} \left(\frac{S A \, \log(1/\delta)}{(1 - \gamma)^5
\, \epsilon^2 \zeta^2} \right)$ where $\zeta$ is the problem-dependent Slater
constant that characterizes the size of the feasible region. Finally, we prove
a matching lower-bound for the strict feasibility setting, thus obtaining the
first near minimax optimal bounds for discounted CMDPs. Our results show that
learning CMDPs is as easy as MDPs when small constraint violations are allowed,
but inherently more difficult when we demand zero constraint violation.


  The calculation of electronic conductance of nano-scale junctions from first
principles is a long standing problem in molecular electronics. Here we
demonstrate excellent agreement with experiments for the transport properties
of the gold/alkanediamine benchmark system when electron-electron interactions
are described using the many-body GW approximation. The main difference from
standard density functional theory (DFT) calculations is a significant
reduction of the contact conductance, G_c, due an improved alignment of the
molecular energy levels with the metal Fermi energy. The molecular orbitals
involved in the tunneling process comprise states delocalized over the carbon
backbone and states localized on the amine end groups. We find that dynamical
screening effects renormalize the two types of states in qualitatively
different ways when the molecule is inserted in the junction. Consequently, the
GW transport results cannot be mimicked by DFT calculations employing a simple
scissors operator.


  Given a marked renewal point process (assuming that the marks are i.i.d.) we
say that an unbounded region is stable if it contains finitely many points of
the point process with probability one. In this paper we provide algorithms
that allow to sample these finitely many points efficiently. We explain how
exact simulation of the steady-state measure valued state descriptor of the
infinite server queue follows as a simple corollary of our algorithms. We
provide numerical evidence supporting that our algorithms are not only
theoretically sound but also practical. Finally, having simulation optimization
in mind, we also apply our results to gradient estimation of steady-state
performance measures.


  Ensuring safety in real-world robotic systems is often challenging due to
unmodeled disturbances and noisy sensor measurements. To account for such
stochastic uncertainties, many robotic systems leverage probabilistic state
estimators such as Kalman filters to obtain a robot's belief, i.e. a
probability distribution over possible states. We propose belief control
barrier functions (BCBFs) to enable risk-aware control synthesis, leveraging
all information provided by state estimators. This allows robots to stay in
predefined safety regions with desired confidence under these stochastic
uncertainties. BCBFs are general and can be applied to a variety of robotic
systems that use extended Kalman filters as state estimator. We demonstrate
BCBFs on a quadrotor that is exposed to external disturbances and varying
sensing conditions. Our results show improved safety compared to traditional
state-based approaches while allowing control frequencies of up to 1kHz.


  The Initial-Boundary Value Problem for the heat equation is solved by using a
new algorithm based on a random walk on heat balls. Even if it represents a
sophisticated generalization of the Walk on Spheres (WOS) algorithm introduced
to solve the Dirich-let problem for Laplace's equation, its implementation is
rather easy. The definition of the random walk is based on a new mean value
formula for the heat equation. The convergence results and numerical examples
permit to emphasize the efficiency and accuracy of the algorithm.


  COMPASS RICH-1 has used high-purity perfluorobutane as radiator gas since
2001. The operation and control of the radiator gas has evolved over years with
continuous improvements. We report on the experience gained in the 20 year-long
operation of perfluorobutane as COMPASS RICH radiator. Very accurate values for
the radiator gas refractive index are needed for high-performance particle
identification. The procedure has evolved over years and the one presently in
use, which provides refractive index estimate at the 1 ppm level, is discussed.
Perfluorobutane procurement is becoming challenging, and the minimization of
material waste is now a priority for the protection of the environment.
Commercially available perfluorobutane needs dedicated filtering before usage
and typical material losses in the filtering procedure were around 30%. Recent
efforts allowed us to reduce them to about 5%. A potential alternative to
fluorocarbon radiators in gaseous RICHes is also presented.


  We derive Taylor's Formula for conformable fractional derivatives. This is
then employed to extend some recent and classical integral inequalities to the
conformable fractional calculus, including the inequalities of Steffensen,
Chebychev, Hermite-Hadamard, Ostrowski, and Gruss.


  In a recent paper, Buniy et al. have argued that a possible discretization of
spacetime leads to an unavoidable discretization of the state space of quantum
mechanics. In this paper, we show that this conclusion is not limited to
quantum theory: in any classical, quantum, or more general probabilistic
theory, states (i.e. probabilities or corresponding amplitudes) become discrete
or fuzzy for observers, as long as time evolution is reversible and entropy is
locally bounded.
  Specifically, we show that the Bekenstein bound suggests that probabilities
in small closed regions of space carry an uncertainty inversely proportional to
the the square root of the system's effective radius and energy.


  In this thesis, we show the existence of a sequence of differential operators
starting with with the Dirac operator in k Clifford variables, $D=(D_1,...,
D_k)$, where $D_i=\sum_j e_j\cdot \partial_{ij}: C^\infty((\R^n)^k,\S)\to
C^\infty((\R^n)^k,\S)$ ($\S$ is the spinor module). This operator is the
Cauchy-Riemann operator for n=2 and its resolution is the Dolbeault complex.
For higher n, the resolution of D is not known in general. While this problem
was treated many times in the language of Clifford analysis and some partial
results are known, we give a description of this operator in Parabolic
geometry, which is a special type of Cartan geometry modeled on $G/P$, where P
is a Parabolic subgroup of G. We construct sequences of invariant differential
operators starting with the Dirac operator in several variables and assume that
these sequences coinside in some cases with the resolution. We describe the
structure of these sequences precisely in case the dimension $n$ is odd and
give a conjecture that these sequences have similar structure for n even,
$k\leq n/2$ (the s.c. {\it stable range}). We also give some information about
these sequences in case n even, k>n/2. In the last chapter, explicite formulas
for the operators are derived for the case k=2.


  It is argued that all notions associated with the origin of life should be
related with the participatory anthropic principle of Wheeler and must be
extended into the realm of the multiverse. Also discussed is the notion that
life can only be possible in a given universe during a finite period along
which such a universe expands in an accelerated fashion. We advance finally the
idea that life, cosmic accelerated expansion and quantum theory are nothing but
three distinct faces from a single, unique coin which describes the physical
reality.


  We consider the Cauchy problem of third order Benjamin-Ono type equations on
the torus. Nonlinear terms may yield derivative losses, which prevents us from
using the classical energy method. In order to overcome that difficulty, we add
a correction term into the energy. We also use the Bona-Smith type argument to
show the continuous dependence.


  We show, assuming the Strong Exponential Time Hypothesis, that for every
$\varepsilon > 0$, approximating directed Diameter on $m$-arc graphs within
ratio $7/4 - \varepsilon$ requires $m^{4/3 - o(1)}$ time. Our construction uses
nonnegative edge weights but even holds for sparse digraphs, i.e., for which
the number of vertices $n$ and the number of arcs $m$ satisfy $m = n
\log^{O(1)} n$. This is the first result that conditionally rules out a
near-linear time $5/3$-approximation for Diameter.


  We describe a construction of the Fukaya category of an exact symplectic
Lefschetz fibration, together with its closed-open string map.


  This article aims at clarifying the language and practice of scientific
experiment, mainly by hooking observability on calculability.


  Circular polarization is a relatively unexplored realm of CMB physics. Given
the substantial community effort towards building next generation CMB
polarization experiments, including those which will be sensitive to circular
polarization, it behooves theorists to understand the possible sources and
relevant physics of circular polarization, as encoded in the Stokes V
parameter. In this work we develop and derive the requisite formalism, namely
the Boltzmann hierarchy for V-mode scalar, vector, and tensor, anisotropies. We
derive the V-mode anisotropies induced by a general source term, and
demonstrate how existing proposals for the generation of V can be incorporated
as source terms in the Boltzmann hierarchy. A subset of these effects may be
correlated with 21cm intensity; we provide a worked example and derive an
estimator to extract this information from observations. We conclude by
computing the CMB TV cross-correlation generated by axions, and find a relation
between TV and VV spectra in axion models.


  This work reports information on the transience of hole doping in epitaxial
graphene devices when nitric acid is used as an adsorbent. Under vacuum
conditions, desorption processes are monitored by electrical and spectroscopic
means to extract the relevant timescales from the corresponding data. It is of
vital importance to understand the reversible nature of hole doping because
such device processing can be a suitable alternative to large-scale, metallic
gating. Most measurements are performed post-exposure at room temperature, and,
for some electrical transport measurements, at 1.5 K. Vacuum conditions are
applied to many measurements to replicate the laboratory conditions under which
devices using this doping method would be measured. The relevant timescales
from transport measurements are compared with results from X-ray photoelecton
spectroscopy and Fourier transform infrared spectroscopy measurements, with the
latter performed at ambient conditions and accompanied by calculations of the
spectra in the Reststrahlen band.


  Using renormalization group methods we study multifractality in directed
percolation. Our approach is based on random lattice networks consisting of
resistor like and diode like bonds with microscopic noise. These random
resistor diode networks capture the features of isotropic as well as directed
percolation. In this note we introduce a field theoretic Hamiltonian for the
multifractal properties at the transition from the non-percolating to the
directed percolating phase. We investigate the multifractal moments of the
current distribution and determine a family of critical exponents for these
moments to two-loop order.


  We propose that the fracton models with subsystem symmetry can be a class of
toy models for the holographic principle. The discovery of the anti-de
Sitter/conformal field theory correspondence as a concrete construction of
holography and the subsequent developments including the subregion duality and
Ryu-Takayanagi formula of entanglement entropy have revolutionized our
understanding of quantum gravity and provided powerful tool sets for solving
various strongly-coupled quantum field theory problems. To resolve many
mysteries of holography, toy models can be very helpful. One example is the
holographic tensor networks which illuminate the quantum error correcting
properties of gravity in the anti-de Sitter space. In this work we discuss a
classical toy model featuring subsystem symmetries and immobile fracton
excitations. We show that such a model defined on the hyperbolic lattice
satisfies some key properties of the holographic correspondence. The correct
subregion duality and Ryu-Takayanagi formula for mutual information are
established for a connected boundary region. A naively defined black hole's
entropy scales as its horizon area. We also present discussions on corrections
for more complicated boundary subregions, the possible generalizations of the
model, and a comparison with the holographic tensor networks.


  This paper proposes a self-supervised monocular image-to-depth prediction
framework that is trained with an end-to-end photometric loss that handles not
only 6-DOF camera motion but also 6-DOF moving object instances.
Self-supervision is performed by warping the images across a video sequence
using depth and scene motion including object instances. One novelty of the
proposed method is the use of the multi-head attention of the transformer
network that matches moving objects across time and models their interaction
and dynamics. This enables accurate and robust pose estimation for each object
instance. Most image-to-depth predication frameworks make the assumption of
rigid scenes, which largely degrades their performance with respect to dynamic
objects. Only a few SOTA papers have accounted for dynamic objects. The
proposed method is shown to outperform these methods on standard benchmarks and
the impact of the dynamic motion on these benchmarks is exposed. Furthermore,
the proposed image-to-depth prediction framework is also shown to be
competitive with SOTA video-to-depth prediction frameworks.


  In cellular networks, User Equipment (UE) handoff from one Base Station (BS)
to another, giving rise to the load balancing problem among the BSs. To address
this problem, BSs can work collaboratively to deliver a smooth migration (or
handoff) and satisfy the UEs' service requirements. This paper formulates the
load balancing problem as a Markov game and proposes a Robust Multi-agent
Attention Actor-Critic (Robust-MA3C) algorithm that can facilitate
collaboration among the BSs (i.e., agents). In particular, to solve the Markov
game and find a Nash equilibrium policy, we embrace the idea of adopting a
nature agent to model the system uncertainty. Moreover, we utilize the
self-attention mechanism, which encourages high-performance BSs to assist
low-performance BSs. In addition, we consider two types of schemes, which can
facilitate load balancing for both active UEs and idle UEs. We carry out
extensive evaluations by simulations, and simulation results illustrate that,
compared to the state-of-the-art MARL methods, Robust-\ours~scheme can improve
the overall performance by up to 45%.


  The ultraviolet (UV) spectral energy distributions of low-mass (K- and
M-type) stars play a critical role in the heating and chemistry of exoplanet
atmospheres, but are not observationally well-constrained. Direct observations
of the intrinsic flux of the Lyman alpha line (the dominant source of UV
photons from low-mass stars) are challenging, as interstellar HI absorbs the
entire line core for even the closest stars. To address the existing gap in
empirical constraints on the UV flux of K and M dwarfs, the MUSCLES HST
Treasury Survey has obtained UV observations of 11 nearby M and K dwarfs
hosting exoplanets. This paper presents the Lyman alpha and extreme-UV spectral
reconstructions for the MUSCLES targets. Most targets are optically inactive,
but all exhibit significant UV activity. We use a Markov Chain Monte Carlo
technique to correct the observed Lyman alpha profiles for interstellar
absorption, and we employ empirical relations to compute the extreme-UV
spectral energy distribution from the intrinsic Lyman alpha flux in ~100 {\AA}
bins from 100-1170 {\AA}. The reconstructed Lyman alpha profiles have 300 km/s
broad cores, while >1% of the total intrinsic Lyman alpha flux is measured in
extended wings between 300 km/s to 1200 km/s. The Lyman alpha surface flux
positively correlates with the MgII surface flux and negatively correlates with
the stellar rotation period. Stars with larger Lyman alpha surface flux also
tend to have larger surface flux in ions formed at higher temperatures, but
these correlations remain statistically insignificant in our sample of 11
stars. We also present HI column density measurements for 10 new sightlines
through the local interstellar medium.


  We investigate the long term behavior in terms of finite dimensional global
and exponential attractors, as time goes to infinity, of solutions to a
semilinear reaction-diffusion equation on non-smooth domains subject to
nonlocal Robin boundary conditions, characterized by the presence of fractional
diffusion on the boundary. Our results are of general character and apply to a
large class of irregular domains, including domains whose boundary is Holder
continuous and domains which have fractal-like geometry. In addition to
recovering most of the existing results on existence, regularity, uniqueness,
stability, attractor existence, and dimension, for the well-known
reaction-diffusion equation in smooth domains, the framework we develop also
makes possible a number of new results for all diffusion models in other
non-smooth settings.


  We introduce and study procedures and constructions in the theory of the
joint spectral radius that are related to the spectral theory. In particular we
devlop the theory of the scattered radical. Among applications we find some
sufficient conditions of continuity of the spectrum and spectral radii of
various types, and prove that in GCR C*-algebras the joint spectral radius is
continuous on precompact subsets and coincides with the Berger-Wang radius.


  We formulate a stochastic gauge fixing method to study the gauge dependence
of Abelian projection. In this method, one can change the gauge from the
maximally Abelian one to no gauge fixing continuously. We have found that the
linear part of the heavy quark potential from Abelian contribution depends
little on the gauge parameter. Similar results have been obtained for the
monopole contribution part.We also investigate the gauge dependence of the
length of monopole loop, which is known to be important for the confinement,
and monopole density. These results suggest that the picture that monopole
plays an important role for the confinement of QCD dose not depend on choice of
the gauge.


  Despite recent advances, object detection in aerial images is still a
challenging task. Specific problems in aerial images makes the detection
problem harder, such as small objects, densely packed objects, objects in
different sizes and with different orientations. To address small object
detection problem, we propose a two-stage object detection framework called
"Focus-and-Detect". The first stage which consists of an object detector
network supervised by a Gaussian Mixture Model, generates clusters of objects
constituting the focused regions. The second stage, which is also an object
detector network, predicts objects within the focal regions. Incomplete Box
Suppression (IBS) method is also proposed to overcome the truncation effect of
region search approach. Results indicate that the proposed two-stage framework
achieves an AP score of 42.06 on VisDrone validation dataset, surpassing all
other state-of-the-art small object detection methods reported in the
literature, to the best of authors' knowledge.


  O. Plamenevskaya associated to each transverse knot K an element of the
Khovanov homology of K. In this paper, we give two refinements of
Plamenevskaya's invariant, one valued in Bar-Natan's deformation of the
Khovanov complex and another as a cohomotopy element of the Khovanov spectrum.
We show that the first of these refinements is invariant under negative flypes
and SZ moves; this implies that Plamenevskaya's class is also invariant under
these moves. We go on to show that for small-crossing transverse knots K, both
refinements are determined by the classical invariants of K.


  In this paper we study the concurrence and the block-block entanglement in
the $S=1/2$ spin ladder with four-spin ring exchange by the exact
diagonalization method of finite cluster of spins. The relationship between the
global phase diagram and the ground-state entanglement is investigated. It is
shown that the block-block entanglement of different block size and geometry
manifests richer information of the system. We find that the extremal point of
the two-site block-block entanglement on the rung locates a transition point
exactly due to SU(4) symmetry at this point. The scaling behavior of the
block-block entanglement is discussed. Our results suggest that the block-block
entanglement can be used as a convenient marker of quantum phase transition in
some complex spin systems.


  As research and industry moves towards large-scale models capable of numerous
downstream tasks, the complexity of understanding multi-modal datasets that
give nuance to models rapidly increases. A clear and thorough understanding of
a dataset's origins, development, intent, ethical considerations and evolution
becomes a necessary step for the responsible and informed deployment of models,
especially those in people-facing contexts and high-risk domains. However, the
burden of this understanding often falls on the intelligibility, conciseness,
and comprehensiveness of the documentation. It requires consistency and
comparability across the documentation of all datasets involved, and as such
documentation must be treated as a user-centric product in and of itself. In
this paper, we propose Data Cards for fostering transparent, purposeful and
human-centered documentation of datasets within the practical contexts of
industry and research. Data Cards are structured summaries of essential facts
about various aspects of ML datasets needed by stakeholders across a dataset's
lifecycle for responsible AI development. These summaries provide explanations
of processes and rationales that shape the data and consequently the models,
such as upstream sources, data collection and annotation methods; training and
evaluation methods, intended use; or decisions affecting model performance. We
also present frameworks that ground Data Cards in real-world utility and
human-centricity. Using two case studies, we report on desirable
characteristics that support adoption across domains, organizational
structures, and audience groups. Finally, we present lessons learned from
deploying over 20 Data Cards.


  There have been debates in the literature about the existence of the first
overtone in the ringdown of GW150914. We develop a novel Bayesian framework to
reanalyze the data of this event, by incorporating a new technique, the
"rational filter" that can clean particular modes from the ringdown signal. We
examine the existence of the first overtone in GW150914 from multiple novel
perspectives. First, we confirm that the estimates of the remnant black hole
mass and spin are more consistent with those obtained from the full IMR signal
when including the first overtone at an early stage of the ringdown (right
after the inferred signal peak); such improvement fades away at later times.
Second, we formulate a new way to compare the ringdown models with and without
the first overtone by calculating the Bayes factor at different times during
the ringdown. We obtain a Bayes factor of 600 at the time when the signal
amplitude reaches its peak. The Bayes factor decreases sharply when moving away
from the peak time and eventually oscillates around a small value when the
overtone signal is expected to have decayed. Third, we clean the fundamental
mode from the ringdown of GW150914 and estimate the amplitudes of the modes
using the filtered data with MCMC. The inferred amplitude of the fundamental
mode is ~0 whereas the amplitude of the first overtone remains almost
unchanged, implying that the filtered data is consistent with a
first-overtone-only template. Similarly, if we remove the first overtone from
the GW150914 data, the filtered data are consistent with a
fundamental-mode-only template. Finally, after removing the fundamental mode,
we use MCMC to infer the remnant black hole mass and spin from the first
overtone alone. We find the posteriors are still informative and consistent
with those inferred from the fundamental mode.


  The Magellanic Clouds (MCs) offer a unique opportunity to study the stellar
evolution and nucleosynthesis of massive Asymptotic Giant Branch (AGB) stars in
low metallicity environments where distances are known. Rubidium is a key
element to distinguish between high mass AGB stars and low mass AGBs or other
type of astronomical objects such as massive red supergiant stars.
Theoretically, high mass AGBs are predicted to produce a lot of Rb. We present
the discovery of massive Rb-rich AGB stars in the MCs, confirming for the first
time that these stars also exist in other galaxies. Our findings show that
these stars are generally brighter than the standard adopted luminosity limit
(Mbol ~ -7.1) for AGB stars. The observations of massive MC AGBs are
qualitatively predicted by the present theoretical models. However, these
theoretical models are far from matching the extremely high Rb overabundances
observed. This might be related with an incomplete present understanding of the
atmospheres of these stars.


  We evolve isolated comparable mass spinning compact binaries experiencing
Schnittman's post-Newtonian spin-orbit resonances in an inertial frame
associated with $j_0$, the initial direction of the total angular momentum. We
argue that accurate gravitational wave (GW) measurements of the initial
orientations of the two spins and orbital angular momentum from $j_0$ should
allow us to distinguish between the two possible families of spin-orbit
resonances. Therefore, these measurements have the potential to provide direct
observational evidence of possible binary formation scenarios. The above
statements should also apply for binaries that do not remain in a resonant
plane when they become detectable by GW interferometers. The resonant plane,
characterized by the vanishing scalar triple product involving the two spins
and the orbital angular momentum, naturally appears in the one parameter family
of equilibrium solutions, discovered by Schnittman. We develop a prescription
to compute the time-domain inspiral templates for binaries residing in these
resonant configurations and explore their preliminary data analysis
consequences.


  Relativistic quantum systems that admit scattering experiments are
quantitatively described by effective field theories, where $S$-matrix
kinematics and symmetry considerations are encoded in the operator spectrum of
the EFT. In this paper we use the $S$-matrix to derive the structure of the EFT
operator basis, providing complementary descriptions in (i) position space
utilizing the conformal algebra and cohomology and (ii) momentum space via an
algebraic formulation in terms of a ring of momenta with kinematics implemented
as an ideal. These frameworks systematically handle redundancies associated
with equations of motion (on-shell) and integration by parts (momentum
conservation).
  We introduce a partition function, termed the Hilbert series, to enumerate
the operator basis--correspondingly, the $S$-matrix--and derive a matrix
integral expression to compute the Hilbert series. The expression is general,
easily applied in any spacetime dimension, with arbitrary field content and
(linearly realized) symmetries.
  In addition to counting, we discuss construction of the basis. Simple
algorithms follow from the algebraic formulation in momentum space. We
explicitly compute the basis for operators involving up to $n=5$ scalar fields.
This construction universally applies to fields with spin, since the operator
basis for scalars encodes the momentum dependence of $n$-point amplitudes.
  We discuss in detail the operator basis for non-linearly realized symmetries.
In the presence of massless particles, there is freedom to impose additional
structure on the $S$-matrix in the form of soft limits. The most na\"ive
implementation for massless scalars leads to the operator basis for pions,
which we confirm using the standard CCWZ formulation for non-linear
realizations.


  We show that confinement of quarks and gluons can be explained by their
interaction with the vacuum Abelian gauge field $A_{\sf{vac}}$, which is
implicitly introduced by the canonical commutation relations and generates the
vacuum force. The background gauge field $A_{\sf{vac}}$, linear in coordinates
of $\mathbb{R}^3$, is inherently present in quantum mechanics: it is introduced
during the canonical quantization of phase space $(T^*\mathbb{R}^3, \omega )$
of a nonrelativistic particle, when a potential $\theta$ of the symplectic
2-form $\omega =\mathrm{d}\theta$ on $T^*\mathbb{R}^3$ is mapped into a
connection $A_{\sf{vac}}=-\mathrm{i}\theta$ on a complex line bundle
$L_{\sf{v}}$ over $T^*\mathbb{R}^3$ with gauge group U(1)$_{\sf{v}}$ and
curvature $F_{\sf{vac}}=\mathrm{d} A_{\sf{vac}}=-\mathrm{i}\omega$.
Generalizing this correspondence to the relativistic phase space
$T^*\mathbb{R}^{3,1}$, we extend the Dirac equation from $\mathbb{R}^{3,1}$ to
$T^*\mathbb{R}^{3,1}$ while maintaining the condition that fermions depend only
on $x\in\mathbb{R}^{3,1}$. The generalized Dirac equation contains the
interaction of fermions with $A_{\sf{vac}}$ and has particle-like solutions
localized in space. Thus, the wave-particle duality can be explained by turning
on or off the interaction with the vacuum field $A_{\sf{vac}}$. Accordingly,
confinement of quarks and gluons can be explained by the fact that their
interaction with $A_{\sf{vac}}$ is always on and therefore they can only exist
in bound states in the form of hadrons.


  In this paper, we propose to combine formally noise and shape priors in
region-based active contours. On the one hand, we use the general framework of
exponential family as a prior model for noise. On the other hand, translation
and scale invariant Legendre moments are considered to incorporate the shape
prior (e.g. fidelity to a reference shape). The combination of the two prior
terms in the active contour functional yields the final evolution equation
whose evolution speed is rigorously derived using shape derivative tools.
Experimental results on both synthetic images and real life cardiac echography
data clearly demonstrate the robustness to initialization and noise,
flexibility and large potential applicability of our segmentation algorithm.


  To reduce training time of large-scale DNNs, scientists have started to
explore parallelization strategies like data-parallelism, model-parallelism,
and hybrid-parallelism. While data-parallelism has been extensively studied and
developed, several problems exist in realizing model-parallelism and
hybrid-parallelism efficiently. Four major problems we focus on are: 1)
defining a notion of a distributed model across processes, 2) implementing
forward/back-propagation across process boundaries that requires explicit
communication, 3) obtaining parallel speedup on an inherently sequential task,
and 4) achieving scalability without losing out on a model's accuracy. To
address these problems, we create HyPar-Flow --- a model-size/-type agnostic,
scalable, practical, and user-transparent system for hybrid-parallel training
by exploiting MPI, Keras, and TensorFlow. HyPar-Flow provides a single API that
can be used to perform data, model, and hybrid parallel training of any Keras
model at scale. We create an internal distributed representation of the
user-provided Keras model, utilize TF's Eager execution features for
distributed forward/back-propagation across processes, exploit pipelining to
improve performance and leverage efficient MPI primitives for scalable
communication. Between model partitions, we use send and recv to exchange
layer-data/partial-errors while allreduce is used to accumulate/average
gradients across model replicas. Beyond the design and implementation of
HyPar-Flow, we also provide comprehensive correctness and performance results
on three state-of-the-art HPC systems including TACC Frontera (#5 on
Top500.org). For ResNet-1001, an ultra-deep model, HyPar-Flow provides: 1) Up
to 1.6x speedup over Horovod-based data-parallel training, 2) 110x speedup over
single-node on 128 Stampede2 nodes, and 3) 481x speedup over single-node on 512
Frontera nodes.


  We establish an asymptotic formula for the first moment and derive an upper
bound for the second moment of L-functions associated with the complete family
of primitive cubic Dirichlet characters defined over the Eisenstein field. Our
results are unconditional, and indicate that there are infinitely many
characters within this family for which the L-function $L(s,\chi)$ does not
vanish at the central point $s=1/2$.


  We consider spacelike surfaces in the four-dimensional Minkowski space and
introduce geometrically an invariant linear map of Weingarten-type in the
tangent plane at any point of the surface under consideration. This allows us
to introduce principal lines and an invariant moving frame field. Writing
derivative formulas of Frenet-type for this frame field, we obtain eight
invariant functions. We prove a fundamental theorem of Bonnet-type, stating
that these eight invariants under some natural conditions determine the surface
up to a motion. We show that the basic geometric classes of spacelike surfaces
in the four-dimensional Minkowski space, determined by conditions on their
invariants, can be interpreted in terms of the properties of the two geometric
figures: the tangent indicatrix, and the normal curvature ellipse. We apply our
theory to a class of spacelike general rotational surfaces.


  This study addresses the evolving challenges in urban traffic monitoring
detection systems based on fisheye lens cameras by proposing a framework that
improves the efficacy and accuracy of these systems. In the context of urban
infrastructure and transportation management, advanced traffic monitoring
systems have become critical for managing the complexities of urbanization and
increasing vehicle density. Traditional monitoring methods, which rely on
static cameras with narrow fields of view, are ineffective in dynamic urban
environments, necessitating the installation of multiple cameras, which raises
costs. Fisheye lenses, which were recently introduced, provide wide and
omnidirectional coverage in a single frame, making them a transformative
solution. However, issues such as distorted views and blurriness arise,
preventing accurate object detection on these images. Motivated by these
challenges, this study proposes a novel approach that combines a
ransformer-based image enhancement framework and ensemble learning technique to
address these challenges and improve traffic monitoring accuracy, making
significant contributions to the future of intelligent traffic management
systems. Our proposed methodological framework won 5th place in the 2024 AI
City Challenge, Track 4, with an F1 score of 0.5965 on experimental validation
data. The experimental results demonstrate the effectiveness, efficiency, and
robustness of the proposed system. Our code is publicly available at
https://github.com/daitranskku/AIC2024-TRACK4-TEAM15.


  For a complex irreducible projective variety, the volume function and the
higher asymptotic cohomological functions have proven to be useful in
understanding the positivity of divisors as well as other geometric properties
of the variety. In this paper, we study the vanishing properties of these
functions on specific hypersurfaces of P^n \times P^n. In particular, we show
that very general hypersurfaces of bidegree (k,k) obey a very strong vanishing
property, which we define as asymptotic purity: at most one asymptotic
cohomological function is nonzero for each divisor. This provides evidence for
a conjecture of Bogomolov and also suggests some general conditions for
asymptotic purity.


  Achieving high-resolution novel view synthesis (HRNVS) from low-resolution
input views is a challenging task due to the lack of high-resolution data.
Previous methods optimize high-resolution Neural Radiance Field (NeRF) from
low-resolution input views but suffer from slow rendering speed. In this work,
we base our method on 3D Gaussian Splatting (3DGS) due to its capability of
producing high-quality images at a faster rendering speed. To alleviate the
shortage of data for higher-resolution synthesis, we propose to leverage
off-the-shelf 2D diffusion priors by distilling the 2D knowledge into 3D with
Score Distillation Sampling (SDS). Nevertheless, applying SDS directly to
Gaussian-based 3D super-resolution leads to undesirable and redundant 3D
Gaussian primitives, due to the randomness brought by generative priors. To
mitigate this issue, we introduce two simple yet effective techniques to reduce
stochastic disturbances introduced by SDS. Specifically, we 1) shrink the range
of diffusion timestep in SDS with an annealing strategy; 2) randomly discard
redundant Gaussian primitives during densification. Extensive experiments have
demonstrated that our proposed GaussainSR can attain high-quality results for
HRNVS with only low-resolution inputs on both synthetic and real-world
datasets. Project page: https://chchnii.github.io/GaussianSR/


  Using only undergraduate-level methods, we classify all groups of order
$p^4$, where $p$ is an odd prime.


  Information theory on a time-discrete setting in the framework of time series
analysis is generalized to the time-continuous case. Considerations of the
Roessler and Lorenz dynamics as well as the Ornstein-Uhlenbeck process yield
for time-continuous entropies a new possibility for the distinction of chaos
and noise. In the deterministic case an upper threshold of the joint
uncertainty in the limit of infinitely high sampling rate can be found and the
entropy rate can be calculated as a usual time derivative of the entropy. In a
three-dimensional representation the dependence of the joint entropy on space
resolution, discretization time step length and uncertainty-assessed time is
shown in a unified manner. Hence the dimension and the Kolmogorov-Sinai entropy
rate of any dynamics can be read out as limit cases from one single graph.


  Providing energy-efficient Internet of Things (IoT) connectivity has
attracted significant attention in fifth-generation (5G) wireless networks and
beyond. A potential solution for realizing a long-lasting network of IoT
devices is to equip each IoT device with a wake-up receiver (WuR) to have
always-accessible devices instead of always-on devices. WuRs typically comprise
a radio frequency demodulator, sequence decoder, and digital address decoder
and are provided with a unique authentication address in the network. Although
the literature on efficient demodulators is mature, it lacks research on fast,
low-power, and reliable address decoders. As this module continuously monitors
the received ambient energy for potential paging of the device, its
contribution to WuR's power consumption is crucial. Motivated by this need, a
low-power, reliable address decoder is developed in this paper. We further
investigate the integration of WuR in low-power uplink/downlink communications
and, using system-level energy analysis; we characterize operation regions in
which WuR can contribute significantly to energy saving. The device-level
energy analysis confirms the superior performance of our decoder. The results
show that the proposed decoder significantly outperforms the state-of-the-art
with a power consumption of 60 nW, at cost of compromising a negligible
increase in decoding delay.


  Image generation has been a long sought-after but challenging task, and
performing the generation task in an efficient manner is similarly difficult.
Often researchers attempt to create a "one size fits all" generator, where
there are few differences in the parameter space for drastically different
datasets. Herein, we present a new transformer-based framework, dubbed
StyleNAT, targeting high-quality image generation with superior efficiency and
flexibility. At the core of our model, is a carefully designed framework that
partitions attention heads to capture local and global information, which is
achieved through using Neighborhood Attention (NA). With different heads able
to pay attention to varying receptive fields, the model is able to better
combine this information, and adapt, in a highly flexible manner, to the data
at hand. StyleNAT attains a new SOTA FID score on FFHQ-256 with 2.046, beating
prior arts with convolutional models such as StyleGAN-XL and transformers such
as HIT and StyleSwin, and a new transformer SOTA on FFHQ-1024 with an FID score
of 4.174. These results show a 6.4% improvement on FFHQ-256 scores when
compared to StyleGAN-XL with a 28% reduction in the number of parameters and
56% improvement in sampling throughput. Code and models will be open-sourced at
https://github.com/SHI-Labs/StyleNAT.


  An artificial neural network architecture, parameterization networks, is
proposed for simulating extrapolated dynamics beyond observed data in dynamical
systems. Parameterization networks are used to ensure the long term integrity
of extrapolated dynamics, while careful tuning of model hyperparameters against
validation errors controls overfitting. A parameterization network is
demonstrated on the logistic map, where chaos and other nonlinear phenomena
consistent with the underlying model can be extrapolated from non-chaotic
training time series with good fidelity. The stated results are a lot less
fantastical than they appear to be because the neural network is only
extrapolating between quadratic return maps. Nonetheless, the results do
suggest that successful extrapolation of qualitatively different behaviors
requires learning to occur on a level of abstraction where the corresponding
behaviors are more similar in nature.


  In the framework of heavy quark effective theory, the leading order
Isgur-Wise form factors relevant to semileptonic decays of the ground state
$\bar{b}s$ meson $B_{s}$ into orbitally excited $D$-wave $\bar{c}s$ mesons,
including the newly observed narrow $D^{*}_{s1}(2860)$ and $D^{*}_{s3}(2860)$
states by the LHCb Collaboration, are calculated with the QCD sum rule method.
With these universal form factors, the decay rates and branching ratios are
estimated. We find that the decay widths are $\Gamma(B_s\rightarrow
D^{*}_{s1}\ell\bar{\nu}) =1.25^{+0.80}_{-0.60}\times10^{-19} \mbox{GeV}$,
$\Gamma(B_s\rightarrow D^{'}_{s2}\ell\bar{\nu})
=1.49^{+0.97}_{-0.73}\times10^{-19} \mbox{GeV}$, $\Gamma(B_s\rightarrow
D_{s2}\ell\bar{\nu}) =4.48^{+1.05}_{-0.94}\times10^{-17} \mbox{GeV}$, and
$\Gamma(B_s\rightarrow D^{*}_{s3}\ell\bar{\nu}) =
1.52^{+0.35}_{-0.31}\times10^{-16} \mbox{GeV}$. The corresponding branching
ratios are $\mathcal {B}(B_s\rightarrow D^{*}_{s1}\ell\bar{\nu})
=2.85^{+1.82}_{-1.36}\times 10^{-7}$, $\mathcal {B}(B_s\rightarrow
D^{'}_{s2}\ell\bar{\nu}) =3.40^{+2.21}_{-1.66}\times 10^{-7}$, $\mathcal
{B}(B_{s}\rightarrow D_{s2}\ell\bar{\nu}) =1.02^{+0.24}_{-0.21}\times 10^{-4}$,
and $\mathcal {B}(B_s\rightarrow D^{*}_{s3}\ell\bar{\nu}) =
3.46^{+0.80}_{-0.70}\times 10^{-4}$. The decay widths and branching ratios of
corresponding $B^{*}_{s}$ semileptonic processes are also predicted.


  The orienteering problem is a well-studied and fundamental problem in
transportation science. In the problem, we are given a graph with prizes on the
nodes and lengths on the edges, together with a budget on the overall tour
length. The goal is to find a tour that respects the length budget and
maximizes the collected prizes. In this work, we introduce the orienteering
interdiction game, in which a competitor (the leader) tries to minimize the
total prize that the follower can collect within a feasible tour. To this end,
the leader interdicts some of the nodes so that the follower cannot collect
their prizes. The resulting interdiction game is formulated as a bilevel
optimization problem, and a single-level reformulation is obtained based on
interdiction cuts. A branch-and-cut algorithm with several enhancements,
including the use of a solution pool, a cut pool and a heuristic method for the
follower's problem, is proposed. In addition to this exact approach, a genetic
algorithm is developed to obtain high-quality solutions in a short computing
time. In a computational study based on instances from the literature for the
orienteering problem, the usefulness of the proposed algorithmic components is
assessed, and the branch-and-cut and genetic algorithms are compared in terms
of solution time and quality.


  For a quantitative interpretation of reconstructed jet properties in
heavy-ion collisions it is paramount to characterize the contribution from the
underlying event and the influence of background fluctuations on the jet
signal. In addition to the pure number fluctuations, region-to-region
correlated background within one event can enhance or deplete locally the level
of background and modify the jet energy. We show a first detailed assessment of
background effects using different probes embedded into heavy-ion data and
quantify their influence on the reconstructed jet spectrum.


  This article aims to present a neighborhood semantic for a modal logic,
namely, propositional logic of the plausible. As a subnormal character modal
logic, not admits a Kripke semantics. However, the authors show that this
neighborhood semantic, reach a relational semantics for propositional logic of
the plausible.


  We have detected stellar halo streams in the solar neighborhood using data
from the 7th public data release of the Sloan Digital Sky Survey (SDSS), which
includes the directed stellar program SEGUE: Sloan Extension For Galactic
Understanding and Exploration. In order to derive distances to each star, we
used the metallicity-dependent photometric parallax relation from Ivezic et al.
(2008) for which we examine and quantify the accuracy. Our final sample
consists of 22,321 nearby (d < 2 kpc), metal-poor ([Fe/H] < -0.5) main-sequence
stars with 6D estimates of position and space velocity. We characterize the
orbits of these stars through suitable kinematic proxies for their "effective"
integrals of motion, angular momentum, eccentricity, and orbital polar angle
and compare the observed distribution to expectations from a smooth
distribution in four [Fe/H] bins. On this basis we identify at least five
significant "phase-space overdensities" of stars on very similar orbits in the
solar neighborhood to which we can assign unambiguously peaked [Fe/H]
distributions. Three of them have been identified previously, including the
halo stream discovered by Helmi et al. (1999) at a significance level of 12.0.
In addition, we find at least two new genuine halo streams, judged by their
kinematics and [Fe/H], at significance levels of 2.9 and 4.8, respectively. For
one stream the stars even show coherence in configuration space, matching a
spatial overdensity of stars found by Juric et al. (2008) at (R,z) \approx
(9.5,0.8) kpc. Our results demonstrate the practical power of our search method
to detect substructure in the phase-space distribution of nearby stars without
making a-priori assumptions about the detailed form of the gravitational
potential.


  Gerry Brown initiated some early studies on the coexistence of different
nuclear shapes. The subject has continued to be of interest and is crucial for
understanding nuclear fission. We now have a very good picture of the potential
energy surface with respect to shape degrees of freedom in heavy nuclei, but
the dynamics remain problematic. In contrast, the early studies on light nuclei
were quite successful in describing the mixing between shapes. Perhaps a new
approach in the spirit of the old calculations could better elucidate the
character of the fission dynamics and explain phenomena that current theory
does not model well.


  This article aims to study efficient/trace optimal designs for crossover
trials with multiple responses recorded from each subject in the time periods.
A multivariate fixed effects model is proposed with direct and carryover
effects corresponding to the multiple responses. The corresponding error
dispersion matrix is chosen to be either of the proportional or the generalized
Markov covariance type, permitting the existence of direct and
cross-correlations within and between the multiple responses. The corresponding
information matrices for direct effects under the two types of dispersions are
used to determine efficient designs. The efficiency of orthogonal array designs
of Type $I$ and strength $2$ is investigated for a wide choice of covariance
functions, namely, Mat($0.5$), Mat($1.5$) and Mat($\infty$). To motivate these
multivariate crossover designs, a gene expression dataset in a $3 \times 3$
framework is utilized.


  This paper is intended to investigate Grassmann and Clifford algebras over
Peano spaces, introducing their respective associated extended algebras, and to
explore these concepts also from the counterspace viewpoint. The exterior
(regressive) algebra is shown to share the exterior (progressive) algebra in
the direct sum of chiral and achiral subspaces. The duality between scalars and
volume elements, respectively under the progressive and the regressive products
is shown to have chirality, in the case when the dimension n of the Peano space
is even. In other words, the counterspace volume element is shown to be a
scalar or a pseudoscalar, depending on the dimension of the vector space to be
respectively odd or even. The de Rham cochain associated with the differential
operator is constituted by a sequence of exterior algebra homogeneous subspaces
subsequently chiral and achiral. Thus we prove that the exterior algebra over
the space and the exterior algebra constructed on the counterspace are only
pseudoduals each other, when we introduce chirality. The extended Clifford
algebra is introduced in the light of the periodicity theorem of Clifford
algebras context, wherein the Clifford and extended Clifford algebras Cl(p,q)
can be embedded in Cl(p+1,q+1), which is shown to be exactly the extended
Clifford algebra. Clifford algebras are constructed over the counterspace, and
the duality between progressive and regressive products is presented using the
dual Hodge star operator. The differential and codifferential operators are
also defined for the extended exterior algebras from the regressive product
viewpoint, and it is shown they uniquely tumble right out progressive and
regressive exterior products of 1-forms.


  Recently, we introduced several dispersive representations for the vector
$K\pi$ form factor and fitted them to the Belle spectrum of $\tau \to K \pi
\nu_\tau$. Here, we briefly present the model and discuss the results for the
slope and curvature of $F_+(s)$ arising from the best fit. Furthermore, we
compare the pole position of the charged $K^*(892)$ computed from our model
with other results in the literature. Finally, we discuss the prospects of a
simultaneous fit to $\tau \to K \pi \nu_\tau$ and $K_{e3}$ spectra.


  While it is often claimed that users are empowered via online technologies,
there is also a general feeling of privacy dis-empowerment. We investigate the
perception of privacy and sharing empowerment online, as well as the use of
privacy technologies, via a cross-national online study with N=907
participants. We find that perception of privacy empowerment differs from that
of sharing across dimensions of meaningfulness, competence and choice. We find
similarities and differences in privacy method preference between the US, UK
and Germany. We also find that non-technology methods of privacy protection are
among the most preferred methods, while more advanced and standalone privacy
technologies are least preferred.. By mapping the perception of privacy
dis-empowerment into patterns of privacy behavior online, and clarifying the
similarities and distinctions in privacy technology use, this paper provides an
important foundation for future research and the design of privacy
technologies. The findings may be used across disciplines to develop more
user-centric privacy technologies, that support and enable the user.


  Atomically thin layered two dimensional (2D) material has provided a rich
library for both fundamental research and device applications. One of the
special advantages is that, bandgap engineering and controlled material
response can be achieved by stacking different 2D materials. Recently several
types of excitonic lasers have been reported based on Transition metal
dichalcogenide (TMDC) monolayers, however, the emission is still the intrinsic
energy bandgap of the monolayers and lasers harnessing the flexibility of Van
der Waals heterostructures have not been demonstrated yet. Here, we report for
the first time a room temperature interlayer exciton laser with MoS2/WSe2
heterostructures. The onset of lasing action was identified by a combination of
distinct kink in the 'L-L' curve and the noticeable collapse of spectral
linewidth. Different from visible emission of intralayer excitons for both MoS2
and WSe2, our interlayer exciton laser works in the infrared range, which is
fully compatible with the well-established technologies in silicon photonics.
Thanks to the long lifetime of interlayer excitons, the requirement of the
cavity quality factor is relaxed by orders of magnitude. The demonstration of
room temperature interlayer exciton laser might open new perspectives for the
development of coherent light source with tailored optical properties on
silicon photonics platform.


  In this paper, we are concerned with backward solvabilities of heat
equations, in an abstract framework. We show that semigroups $T_t$ in Banach
spaces $X$, generated by heat operators, are extendable to groups in an
extended space $E$, which is obtained by considering a sequence of wider Banach
spaces containing $X$, i.e. $X$$/subset$$X_t$$/subset$$X_s$... $(t<s)$, under
the following two conditions. One is the density assumption on a subset $D$ of
$X$, the set of initial values $x$ from which $T_{-t}x$ exists for all $t>0$.
Another is the backward uniqueness of the semigroup $T_t$. For example, we
prove the holomorphic semigroup satisfies the above conditions, and thus is
extendable to a group in a larger functional space $E$. We also studied
structual properties of the extended space $E$.


  We compute the improvement coefficient $c_{sw}$ that multiplies the
Sheikholeslami-Wohlert term as a function of the bare gauge coupling for two
flavour QCD. We discuss several aspects concerning simulations with improved
dynamical Wilson fermions.


  We study the influence of higher-order effects such as third order dispersion
(TOD), fourth order dispersion (FOD), quintic nonlinearity (QN), self
steepening (SS) and second order nonlinear dispersion (SOND) on the dynamics of
dissipative soliton (DS) in metamaterials. Considering each higher-order effect
as a perturbation to the system and following Lagrangian variational method, we
demonstrate stable dynamics of DS as a result of the interplay between
different higher-order effects. We also perform numerical analysis to confirm
the analytical results.


  In galaxy clusters, the relations between observables in X-ray and millimeter
wave bands and the total mass have normalizations, slopes and redshift
evolutions that are simple to estimate in a self-similar scenario. We study
these scaling relations and show that they can be efficiently expressed, in a
more coherent picture, by fixing the normalizations and slopes to the
self-similar predictions, and advocating, as responsible of the observed
deviations, only three physical mass-dependent quantities: the gas clumpiness
$C$, the gas mass fraction $f_g$ and the logarithmic slope of the thermal
pressure profile $\beta_P$. We use samples of the observed gas masses,
temperature, luminosities, and Compton parameters in local clusters to
constrain normalization and mass dependence of these 3 physical quantities, and
measure: $C^{0.5} f_g = 0.110 (\pm 0.002 \pm 0.002) \left( E_z M / 5 \times
10^{14} M_{\odot} \right)^{0.198 (\pm 0.025 \pm 0.04)}$ and $\beta_P = -d \ln
P/d \ln r = 3.14 (\pm 0.04 \pm 0.02) \left( E_z M / 5 \times 10^{14} M_{\odot}
\right)^{0.071 (\pm 0.012 \pm 0.004)}$, where both a statistical and systematic
error (the latter mainly due to the cross-calibration uncertainties affecting
the \cxo\ and \xmm\ results used in the present analysis) are quoted. The
degeneracy between $C$ and $f_g$ is broken by using the estimates of the
Compton parameters. Together with the self-similar predictions, these estimates
on $C$, $f_g$ and $\beta_P$ define an inter-correlated internally-consistent
set of scaling relations that reproduces the mass estimates with the lowest
residuals.


  We show how lattice paths and the reflection principle can be used to give
easy proofs of unimodality results. In particular, we give a "one-line"
combinatorial proof of the unimodality of the binomial coefficients. Other
examples include products of binomial coefficients, polynomials related to the
Legendre polynomials, and a result connected to a conjecture of Simion.


  Lipid membranes, the barrier defining living cells and many of their
sub-compartments, bind to a wide variety of nano- and micro-meter sized
objects. In the presence of strong adhesive forces, membranes can strongly
deform and wrap the particles, an essential step in crossing the membrane for a
variety of health and disease-related processes. A large body of theoretical
and numerical work has focused on identifying the physical properties that
underly wrapping. Using a model system of micron-sized colloidal particles and
giant unilamellar lipid vesicles with tunable adhesive forces, we measure a
wrapping phase diagram and make quantitative comparisons to theoretical models.
Our data is consistent with a model of membrane-particle interactions
accounting for the adhesive energy per unit area, membrane bending rigidity,
particle size, and vesicle radius.


  In this chapter, we consider probabilistic drift-diffusion models and
Bayesian inference frameworks to address this issue, assisting better social
human decision-making. We provide details of the models, as well as
representative numerical examples, and discuss the decision-making process with
a representative example of the escape route decision-making phenomena by
further developing the drift-diffusion models and Bayesian inference
frameworks. In the latter context, we also give a review of recent developments
in human collective decision-making and its applications with brain network
models. Furthermore, we provide illustrative numerical examples to discuss the
role of neuromodulation, reinforcement learning in decision-making processes.
Finally, we call attention to existing challenges, open problems, and promising
approaches in studying social dynamics and collective human decision-making,
including those arising from nonequilibrium considerations of the associated
processes.


  Reconfigurable intelligent surface (RIS) is a promising technique for
millimeter wave (mmWave) positioning systems. In this paper, we consider
multiple mobile users (MUs) positioning problem in the multiple-input
multiple-output (MIMO) time-division duplex (TDD) mmWave systems aided by the
RIS. We derive the expression for the space-time channel response vector
(STCRV) as a novel type of fingerprint. The STCRV consists of the multipath
channel characteristics, e.g., time delay and angle of arrival (AOA), which is
related to the position of the MU. By using the STCRV as input, we propose a
novel residual convolution network regression (RCNR) learning algorithm to
output the estimated three-dimensional (3D) position of the MU. Specifically,
the RCNR learninng algorithm includes a data processing block to process the
input STCRV, a normal convolution block to extract the features of STCRV, four
residual convolution blocks to further extract the features and protect the
integrity of the features, and a regression block to estimate the 3D position.
Extensive simulation results are also presented to demonstrate that the
proposed RCNR learning algorithm outperforms the traditional convolution neural
network (CNN).


  Neural network quantum states (NQS), incorporating with variational Monte
Carlo (VMC) method, are shown to be a promising way to investigate quantum
many-body physics. Whereas vanilla VMC methods perform one gradient update per
sample, we introduce a novel objective function with proximal optimization (PO)
that enables multiple updates via reusing the mismatched samples. Our VMC-PO
method keeps the advantage of the previous importance sampling gradient
optimization algorithm [L. Yang, {\it et al}, Phys. Rev. Research {\bf 2},
012039(R)(2020)] that efficiently uses sampled states. PO mitigates the
numerical instabilities during network updates, which is similar to stochastic
reconfiguration (SR) methods, but achieves an alternative and simpler implement
with lower computational complexity. We investigate the performance of our
VMC-PO algorithm for ground-state searching with a 1-dimensional
transverse-field Ising model and 2-dimensional Heisenberg antiferromagnet on a
square lattice, and demonstrate that the reached ground-state energies are
comparable to state-of-the-art results.


  We compute the one-loop effective action in unimodular gravity, starting from
two different classical formulations of the theory. We find that the effective
action is the same in both cases, and agrees with the one of General
Relativity.


  We address the questions of identifying pairs of interacting neurons from the
observation of their spiking activity. The neuronal network is modeled by a
system of interacting point processes with memory of variable length. The
influence of a neuron on another can be either excitatory or inhibitory. To
identify the existence and the nature of an interaction we propose an algorithm
based only on the observation of joint activity of the two neurons in
successive time slots. This reduces the amount of computation and storage
required to run the algorithm, thereby making the algorithm suitable for the
analysis of real neuronal data sets. We obtain computable upper bounds for the
probabilities of false positive and false negative detection. As a corollary we
prove the consistency of the identification algorithm.


  The recent obervation of a Kondo-like temperature dependence in the Li NMR
Knight shift in Ca- and Li- cosubstituted YBa2Cu3O7-d was attributed by Bobroff
et al. to be evidence of Li-induced local moments in overdoped Y123. It is
shown from Y-NMR measurements on Y0.8Ca0.2Ba2Cu3O7-d and heat capacity studies
on Bi2Sr2CaCu2O8 that such a Kondo-like temperature dependence is an intrinsic
overdoping effect quite generally present in cuprate superconductors which are
free of strong scatterers such as Zn and Li. It is probable that Zn and Li only
induce local moments in the underdoped pseudogap region and not in the
overdoped region.


  The multifractal properties of the electronic spectrum of a general
quasiperiodic chain are studied in first order in the quasiperiodic potential
strength. Analytical expressions for the generalized dimensions are found and
are in good agreement with numerical simulations. These first order results do
not depend on the irrational incommensurability.


  We present the first broadband X-ray observations of four Giga-Hertz Peaked
Spectrum (GPS) radio galaxies at redshift <~1 performed by Chandra and
XMM-Newton. These observations more than double the number of members of this
class with measured spectra in hard (E > 2 keV) X-rays. All sources were
detected. Their radio-to-X-ray spectral energy distributions are similar,
except for PKS0941-080, which is X-ray under-luminous by about two orders of
magnitude. The comparison between the full sample of GPS galaxies with
measurements in hard X-rays and a control sample of radio galaxies rules out
intrinsic X-ray weakness as causing a lower detection rate of GPS sources in
X-ray surveys. Four out of seven GPS galaxies exhibit high X-ray column
densities, whereas for the remaining three this measurement is hampered by the
poor spectral statistics. Bearing in mind the low number statistics in both the
GPS and the control sample, the average column density measured in GPS galaxies
is larger than in FRI or Broad Line Region FR II radio galaxies, but consistent
with that measured in High-Excitation FRII galaxies. This leads to a location
the absorbing gas in an obscuring ``torus'', which prevents us from observing
the nuclear region along lines-of-sight perpendicular to the radio axis. This
interpretation is supported by the discovery of rapid (timescale ~1000 s) X-ray
variability in the GPS galaxy COINSJ0029+3456, and by an almost
order-of-magnitude difference between the HI column density measured in radio
and X-rays in PKS0500+019.


  In this paper, we investigate the fractal uncertainty principle (FUP) for
discrete Cantor sets, which are determined by an alphabet from a base of
digits. Consider the base of M digits and the alphabets of cardinality A such
that all the corresponding Cantor sets have a fixed dimension 0<log A/log
M<2/3. We prove that the FUP with an improved exponent over Dyatlov-Jin
(arXiv:1608.02238) holds for almost all alphabets, asymptotically as M tends to
infinity. Our result provides the best possible exponent when the Cantor sets
enjoy either the strongest Fourier decay assumption or strongest additive
energy assumption. The proof is based on a concentration of measure phenomenon
in the space of alphabets.


  We present revised stellar properties for 172 K2 target stars that were
identified as possible hosts of transiting planets during Campaigns 1-17. Using
medium-resolution near-infrared spectra acquired with the NASA Infrared
Telescope Facility/SpeX and Palomar/TripleSpec, we found that 86 of our targets
were bona fide cool dwarfs, 74 were hotter dwarfs, and 12 were giants.
Combining our spectroscopic metallicities with Gaia parallaxes and archival
photometry, we derived photometric stellar parameters and compared them to our
spectroscopic estimates. Although our spectroscopic and photometric radius and
temperature estimates are consistent, our photometric mass estimates are
systematically 0.11 solar masses (34%) higher than our spectroscopic mass
estimates for the least massive stars (photometric mass estimates < 0.4 solar
masses). Adopting the photometric parameters and comparing our results to
parameters reported in the Ecliptic Plane Input Catalog, our revised stellar
radii are 0.15 solar radii (40%) larger and our revised stellar effective
temperatures are roughly 65K cooler. Correctly determining the properties of K2
target stars is essential for characterizing any associated planet candidates,
estimating the planet search sensitivity, and calculating planet occurrence
rates. Even though Gaia parallaxes have increased the power of photometric
surveys, spectroscopic characterization remains essential for determining
stellar metallicities and investigating correlations between stellar
metallicity and planetary properties.


  It was shown recently by Cordoba, Faraco and Gancedo that the 2D porous media
equation admits weak solutions with compact support in time. The proof, based
on the convex integration framework, uses ideas from the theory of laminates,
in particular T4 configurations. In this note we calculate the explicit
relaxation of IPM, thus avoiding T4 configurations. We then use this to
construct weak solutions to the unstable interface problem (the Muskat
problem), as a byproduct shedding new light on the gradient flow approach
introduced by Otto.


  We derive a novel multiple integral representation for a generating function
of the $\s^z$-$\s^z$ correlation functions of the spin-$\2$ XXZ chain at finite
temperature and finite, longitudinal magnetic field. Our work combines
algebraic Bethe ansatz techniques for the calculation of matrix elements with
the quantum transfer matrix approach to thermodynamics.


  Density functional theory (DFT) is one of the most widely used tools to solve
the many-body Schrodinger equation. The core uncertainty inside DFT theory is
the exchange-correlation (XC) functional, the exact form of which is still
unknown. Therefore, the essential part of DFT success is based on the progress
in the development of XC approximations. Traditionally, they are built upon
analytic solutions in low- and high-density limits and result from quantum
Monte Carlo numerical calculations. However, there is no consistent and general
scheme of XC interpolation and functional representation. Many different
developed parametrizations mainly utilize a number of phenomenological rules to
construct a specific XC functional. In contrast, the neural network (NN)
approach can provide a general way to parametrize an XC functional without any
a priori knowledge of its functional form. In this work, we develop NN XC
functionals and prove their applicability to 3-dimensional physical systems. We
show that both the local density approximation (LDA) and generalized gradient
approximation (GGA) are well reproduced by the NN approach. It is demonstrated
that the local environment can be easily considered by changing only the number
of neurons in the first layer of the NN. The developed NN XC functionals show
good results when applied to systems that are not presented in the
training/test data. The generalizability of the formulated NN XC framework
leads us to believe that it could give superior results in comparison with
traditional XC schemes provided training data from high-level theories such as
the quantum Monte Carlo and post-Hartree-Fock methods.


  We aimed at finding a physical explanation for the occurrence of
macroturbulence in the atmospheres of hot massive stars, a phenomenon found in
observations since more than a decade but yet unexplained. We computed time
series of line profiles for evolved massive stars broadened by rotation and by
hundreds of low-amplitude nonradial gravity-mode pulsations which are predicted
to be excited for evolved massive stars. In general, line profiles based on
macrotubulent broadening can mimic those subject to pulsational broadening. In
several cases, though, good fits require macroturbulent velocities that pass
the speed of sound for realistic pulsation amplitudes. Moreover, we find that
the rotation velocity can be seriously underestimated by using a simple
parameter description for macroturbulence rather than an appropriate
pulsational model description to fit the line profiles. We conclude that
macroturbulence is a likely signature of the collective effect of pulsations.
We provide line diagnostics and their typical values to decide whether or not
pulsational broadening is present in observed line profiles, as well as a
procedure to avoid an inaccurate estimation of the rotation velocity.


  ESO and ESA agreed to establish a number of Working Groups to explore
possible synergies between these two major European astronomical institutions.
This Working Group's mandate was to concentrate on fundamental questions in
cosmology, and the scope for tackling these in Europe over the next ~15 years.
One major resulting recommendation concerns the provision of new generations of
imaging survey, where the image quality and near-IR sensitivity that can be
attained only in space are naturally matched by ground-based imaging and
spectroscopy to yield massive datasets with well-understood photometric
redshifts (photo-z's). Such information is essential for a range of new
cosmological tests using gravitational lensing, large-scale structure, clusters
of galaxies, and supernovae. Great scope in future cosmology also exists for
ELT studies of the intergalactic medium and space-based studies of the CMB and
gravitational waves; here the synergy is less direct, but these areas will
remain of the highest mutual interest to the agencies. All these recommended
facilities will produce vast datasets of general applicability, which will have
a tremendous impact on broad areas of astronomy.


  We study the localization of probability distribution in a discrete quantum
random walk on an infinite chain. With a phase defect introduced in any
position of the quantum random walk (QRW), we have found that the localization
of the probability distribution in the QRW emerges. Different localized
behaviors of the probability distribution in the QRW are presented when the
defect occupies different positions. Given that the coefficients of the
localized stationary eigenstates relies on the coin operator, we reveal that
when the defect occupies different positions, the amplitude of localized
probability distribution in the QRW exhibits a non-trivial dependence on the
coin operator.


  We describe an interference setup which seems to defy the No-Communication
theorem. We discuss its potential flaw and derive a relation implied by quantum
mechanics that limits the size of the detector given the particle's state
width.


  Program comprehension concerns the ability of an individual to make an
understanding of an existing software system to extend or transform it.
Software systems comprise of data that are noisy and missing, which makes
program understanding even more difficult. A software system consists of
various views including the module dependency graph, execution logs,
evolutionary information and the vocabulary used in the source code, that
collectively defines the software system. Each of these views contain unique
and complementary information; together which can more accurately describe the
data. In this paper, we investigate various techniques for combining different
sources of information to improve the performance of a program comprehension
task. We employ state-of-the-art techniques from learning to 1) find a suitable
similarity function for each view, and 2) compare different multi-view learning
techniques to decompose a software system into high-level units and give
component-level recommendations for refactoring of the system, as well as
cross-view source code search. The experiments conducted on 10 relatively large
Java software systems show that by fusing knowledge from different views, we
can guarantee a lower bound on the quality of the modularization and even
improve upon it. We proceed by integrating different sources of information to
give a set of high-level recommendations as to how to refactor the software
system. Furthermore, we demonstrate how learning a joint subspace allows for
performing cross-modal retrieval across views, yielding results that are more
aligned with what the user intends by the query. The multi-view approaches
outlined in this paper can be employed for addressing problems in software
engineering that can be encoded in terms of a learning problem, such as
software bug prediction and feature location.


  A compact semisimple Lie algebra $\mathfrak{g}$ induces a Poisson structure
$\pi$ on the unit sphere $S$ in $\mathfrak{g}^*$. We compute the moduli space
of Poisson structures on $S$ around $\pi$. This is the first explicit
computation of a Poisson moduli space in dimension greater or equal than three
around a degenerate (i.e. not symplectic) Poisson structure.


  Superfluidity in an ultracold Fermi gas is usually associated with either a
negative scattering length, or the presence of a two-body bound state. We show
that none of these ingredients is necessary to achieve superfluidity. Using a
narrow Feshbach resonance with strong repulsive background interactions, the
effective interactions can be repulsive for small energies and attractive for
energies around the Fermi energy, similar to the effective interactions between
electrons in a metallic superconductor. This can result in BCS-type
superfluidity while the scattering length is positive.


  Object detection is an essential technique for autonomous driving. The
performance of an object detector significantly degrades if the weather of the
training images is different from that of test images. Domain adaptation can be
used to address the domain shift problem so as to improve the robustness of an
object detector. However, most existing domain adaptation methods either handle
single target domain or require domain labels. We propose a novel unsupervised
domain classification method which can be used to generalize single-target
domain adaptation methods to multi-target domains, and design a
weather-invariant object detector training framework based on it. We conduct
the experiments on Cityscapes dataset and its synthetic variants, i.e. foggy,
rainy, and night. The experimental results show that the object detector
trained by our proposed method realizes robust object detection under different
weather conditions.


  Current state-of-the-art deep learning based face recognition (FR) models
require a large number of face identities for central training. However, due to
the growing privacy awareness, it is prohibited to access the face images on
user devices to continually improve face recognition models. Federated Learning
(FL) is a technique to address the privacy issue, which can collaboratively
optimize the model without sharing the data between clients. In this work, we
propose a FL based framework called FedFR to improve the generic face
representation in a privacy-aware manner. Besides, the framework jointly
optimizes personalized models for the corresponding clients via the proposed
Decoupled Feature Customization module. The client-specific personalized model
can serve the need of optimized face recognition experience for registered
identities at the local device. To the best of our knowledge, we are the first
to explore the personalized face recognition in FL setup. The proposed
framework is validated to be superior to previous approaches on several generic
and personalized face recognition benchmarks with diverse FL scenarios. The
source codes and our proposed personalized FR benchmark under FL setup are
available at https://github.com/jackie840129/FedFR.


  We present RTAMT, an online monitoring library for Signal Temporal Logic
(STL) and its interface-aware variant (IA-STL), providing both discrete- and
dense-time interpretation of the logic. We also introduce RTAMT4ROS, a tool
that integrates RTAMT with Robotic Operating System (ROS), a common environment
for developing robotic applications. We evaluate RTAMT and RTAMT4ROS on two
robotic case studies.


  Periodic quantum oscillations of a rectified dc voltage Vdc(B) vs the
perpendicular magnetic field B were measured near the critical temperature Tc
in a single superconducting aluminum almost symmetric ring (without specially
created circular asymmetry) biased by alternating current with a zero dc
component. With varying bias current and temperature, these Vdc(B) oscillations
behave like the Vdc(B) oscillations observed in a circular-asymmetric ring but
are of smaller amplitude. The Fourier spectra of the Vdc(B) functions exhibit a
fundamental frequency, corresponding to the ring area, and its higher
harmonics. Unexpectedly, satellite frequencies depending on the structure
geometry and external parameters were found next to the fundamental frequency
and around its higher harmonics.


  The softmax attention mechanism has emerged as a noteworthy development in
the field of Artificial Intelligence research, building on the successes of
Transformer-based architectures. However, their ever increasing sizes
necessitate ever increasing computational memory, that limits their usage. We
propose KgV, a sigmoid gating mechanism that, in conjunction with softmax
attention, significantly boosts performance without increasing architecture
size. To amend the size requirements, we leverage Tensor Chains to identify and
prune the excess parameters. We find that such excess resides primarily within
the embedding layer, and not in the output linear layer. To further improve
embedding and significantly reduce parameters, we introduce H-SoftPOS, a
hierarchical embedding layer which simultaneously enhances performance.
Remarkably, on the WMT14 English-German validation set, our approach yields a
threefold reduction in perplexity, surpassing the current state-of-the-art,
while reducing parameter counts also by a factor of 3. When we further reduce
the number of parameters up to sevenfold, we can still achieve a 21\% decrease
in perplexity with respect to the baseline Transformer. To understand
generalization capabilities, we conduct experiments on the 7 language pairs of
the WMT17 dataset. Our method outperforms existing techniques in terms of test
loss while simultaneously halving the number of parameters. Moreover, we
observe a 70 times reduction in variance with respect to the prior
state-of-the-art. In conclusion, our proposed method yields significant
improvements in performance and much lower memory cost. We call the resulting
architecture Anthe.


  For many infectious disease outbreaks, the at-risk population changes their
behavior in response to the outbreak severity, causing the transmission
dynamics to change in real-time. Behavioral change is often ignored in epidemic
modeling efforts, making these models less useful than they could be. We
address this by introducing a novel class of data-driven epidemic models which
characterize and accurately estimate behavioral change. Our proposed model
allows time-varying transmission to be captured by the level of "alarm" in the
population, with alarm specified as a function of the past epidemic trajectory.
We investigate the estimability of the population alarm across a wide range of
scenarios, applying both parametric functions and non-parametric functions
using splines and Gaussian processes. The model is set in the data-augmented
Bayesian framework to allow estimation on partially observed epidemic data. The
benefit and utility of the proposed approach is illustrated through
applications to data from real epidemics.


  The purpose of this paper is to complement the results in [LS-1] by showing
the dense definability of the Cauchy-Leray transform for the domains that give
the counterexamples of [LS-1], where $L^p$-boundedness is shown to fail when
either the "near" $C^2$ boundary regularity, or the strong $\mathbb C$-linear
convexity assumption is dropped.


  We have fabricated and measured ballistic graphene transistors with two
oblique gates that can be independently biased. The gate lengths are about 38
nm and are separated by a distance of 30 nm, the tilting angle being of 45o
with respect to source and drain electrodes distanced at 190 nm. Electric
measurements reveal specific properties of ballistic carrier transport, i.e.
nonlinear drain voltage-drain current dependence, showing a saturation region,
and negative differential resistance at certain bias voltages, which cannot be
explained without physical mechanisms related to ballistic transport. Tens of
ballistic transistors, with very large transconductances, were fabricated on a
chip cut from a 4 inch graphene wafer. Such double-gate transistor
configurations can be used also as extremely efficient, state-of-the-art
photodetectors.


  In this short note, we construct solutions to quantum tetrahedron equation of
the kind "with variables on the edges". Each of these variables takes just two
values, called sometimes "colors". We propose two different constructions. The
first of them involves, in particular, two $\mathcal R$-operators each
depending on one parameter, while these parameters are independent from each
other; the number of nonvanishing matrix entries in these two $\mathcal
R$-operators is either 12 or 14. In the second construction, we use what may be
called "modified tetrahedron in a direct sum" to produce, again, quantum
tetrahedron solutions. All matrix entries of our $\mathcal R$-operators are
nonnegative, if the relevant parameters are chosen properly.


  We investigate the computational complexity for determining various
properties of a finite transformation semigroup given by generators. We
introduce a simple framework to describe transformation semigroup properties
that are decidable in $\mathsf{AC^0}$. This framework is then used to show that
the problems of deciding whether a transformation semigroup is a group,
commutative or a semilattice are in $\mathsf{AC^0}$. Deciding whether a
semigroup has a left (resp.right) zero is shown to be $\mathsf{NL}$-complete,
as are the problems of testing whether a transformation semigroup is nilpotent,
$\mathcal{R}$-trivial or has central idempotents. We also give $\mathsf{NL}$
algorithms for testing whether a transformation semigroup is idempotent,
orthodox, completely regular, Clifford or has commuting idempotents. Some of
these algorithms are direct consequences of the more general result that
arbitrary fixed semigroup equations can be tested in~$\mathsf{NL}$. Moreover,
we show how to compute left and right identities of a transformation semigroup
in polynomial time. Finally, we show that checking whether an element is
regular is $\mathsf{PSPACE}$-complete. \


  Frontal Polymerization (FP) was recently demonstrated as a faster, more
energy-efficient way to manufacture fiber-reinforced
thermosetting-polymer-matrix composites. FP uses heat from the exothermic
reaction of the solution of monomer and initiator to generate a selfpropagating
polymerization front. In most cases, the polymerization front propagates in a
steady fashion. However, under some conditions, the front experiences
instabilities, which do affect the quality of the manufactured composite part.
In this work, we use a coupled thermo-chemical model and an adaptive nonlinear
finite element solver to simulate FP-driven instabilities in dicyclopentadiene
(DCPD) and in carbon-fiber DCPD-matrix composites. With the aid of 1-D
transient simulations, we investigate how the initial temperature and the
carbon fiber volume fraction affect the amplitude and wavelength of the thermal
instabilities. We also extract the range of processing conditions for which the
instabilities are predicted to appear. The last part of this work investigates
the effect of convective heat loss on the FP-driven instabilities in both neat
resin and composite cases.


  Randomized controlled trials (RCTs) provide strong internal validity compared
with observational studies. However, selection bias threatens the external
validity of randomized trials. Thus, RCT results may not apply to either broad
public policy populations or narrow populations, such as specific insurance
pools. Some researchers use propensity scores (PSs) to generalize results from
an RCT to a target population. In this scenario, a PS is defined as the
probability of participating in the trial conditioning on observed covariates.
We study a model-free inverse probability weighted estimator (IPWE) of the
average treatment effect in a target population with data from a randomized
trial. We present variance estimators and compare the performance of our method
with that of model-based approaches. We examine the robustness of the
model-free estimators to heterogeneous treatment effects.


  The propagation of nonlinear electron- acoustic waves (EAWs) in an
unmagnetized collision- less plasma system consisting of a cold electron fluid,
superthermal hot electrons and stationary ions is investigated. A reductive
perturbation method is employed to obtain a modified Korteweg-de Vries (mKdV)
equa- tion for the first-order potential. The small amplitude electron-acoustic
solitary wave, e.g., soliton and dou- ble layer (DL) solutions are presented,
and the effects of superthermal electrons on the nature of the solitons are
also discussed. But the results shows that the weak stationary EA DLs cannot be
supported by the present model.


  This paper summarizes the contents of a plenary talk at the Pan African
Congress of Mathematics held in Rabat in July 2017. We provide a survey of
recent results on spectral properties of Schr\"odinger operators with singular
interactions supported by manifolds of codimension one and of Robin billiards
with the focus on the geometrically induced discrete spectrum and its
asymptotic expansions in term of the model parameters.


  The interplay between structure and function is crucial in determining some
emerging properties of many natural systems. Here we use an adaptive neural
network model inspired in observations of synaptic pruning that couples
activity and topological dynamics and reproduces experimental temporal profiles
of synaptic density, including an initial transient period of relatively high
synaptic connectivity. Using a simplified framework, we prove that the
existence of this transient is critical in providing ordered stationary states
that have the property of being able to store stable memories. In fact, there
is a discontinuous phase transition between the ordered memory phase and a
disordered one as a function of the initial transient synaptic density. We also
show that intermediate synaptic density values are optimal in order to obtain
these stable memory states with a minimum energy consumption, and that
ultimately it is the transient heterogeneity in the network what determines the
stationary state. Our results here could explain why the pruning curves
observed in actual brain areas present their characteristic temporal profiles
and, eventually, anomalies such as autism and schizophrenia associated,
respectively, with a deficit or an excess of pruning.


  We show that Rashba spin-orbit coupling at the interface between a
superconductor and a ferromagnet should produce a spontaneous current in the
atomic thickness region near the interface. This current is counter-balanced by
the superconducting screening current flowing in the region of the width of the
London penetration depth near the interface. Such current carrying state
creates a magnetic field near the superconductor surface, generates a stray
magnetic field outside the sample edges, changes the slope of the temperature
dependence of the critical field $H_{c3}$ and may generate the spontaneous
Abrikosov vortices near the interface.