Abstract:
  In Statistical Relational Artificial Intelligence, a branch of AI and machine
learning which combines the logical and statistical schools of AI, one uses the
concept {\em para\-metrized probabilistic graphical model (PPGM)} to model
(conditional) dependencies between random variables and to make probabilistic
inferences about events on a space of "possible worlds". The set of possible
worlds with underlying domain $D$ (a set of objects) can be represented by the
set $\mathbf{W}_D$ of all first-order structures (for a suitable signature)
with domain $D$. Using a formal logic we can describe events on $\mathbf{W}_D$.
By combining a logic and a PPGM we can also define a probability distribution
$\mathbb{P}_D$ on $\mathbf{W}_D$ and use it to compute the probability of an
event. We consider a logic, denoted $PLA$, with truth values in the unit
interval, which uses aggregation functions, such as arithmetic mean, geometric
mean, maximum and minimum instead of quantifiers. However we face the problem
of computational efficiency and this problem is an obstacle to the wider use of
methods from Statistical Relational AI in practical applications. We address
this problem by proving that the described probability will, under certain
assumptions on the PPGM and the sentence $\varphi$, converge as the size of $D$
tends to infinity. The convergence result is obtained by showing that every
formula $\varphi(x_1, \ldots, x_k)$ which contains only "admissible"
aggregation functions (e.g. arithmetic and geometric mean, max and min) is
asymptotically equivalent to a formula $\psi(x_1, \ldots, x_k)$ without
aggregation functions.


Abstract:
  An analytical derivation of the vibrational density of states (DOS) of
liquids, and in particular of its characteristic linear in frequency low-energy
regime, has always been elusive because of the presence of an infinite set of
purely imaginary modes -- the instantaneous normal modes (INMs). By combining
an analytic continuation of the Plemelj identity to the complex plane with the
purely overdamped dynamics of the INMs, we derive a closed-form analytic
expression for the low-frequency DOS of liquids. The obtained result explains
from first principles the widely observed linear in frequency term of the DOS
in liquids, whose slope appears to increase with the average lifetime of the
INMs. The analytic results are robustly confirmed by fitting simulations data
for Lennard-Jones liquids, and they also recover the Arrhenius law for the
average relaxation time of the INMs, as expected.


Abstract:
  Inferring the causal structure of a system typically requires interventional
data, rather than just observational data. Since interventional experiments can
be costly, it is preferable to select interventions that yield the maximum
amount of information about a system. We propose a novel Bayesian method for
optimal experimental design by sequentially selecting interventions that
minimize the expected posterior entropy as rapidly as possible. A key feature
is that the method can be implemented by computing simple summaries of the
current posterior, avoiding the computationally burdensome task of repeatedly
performing posterior inference on hypothetical future datasets drawn from the
posterior predictive. After deriving the method in a general setting, we apply
it to the problem of inferring causal networks. We present a series of
simulation studies in which we find that the proposed method performs favorably
compared to existing alternative methods. Finally, we apply the method to real
and simulated data from a protein-signaling network.


Abstract:
  Hand gesture is a new and promising interface for locomotion in virtual
environments. While several previous studies have proposed different hand
gestures for virtual locomotion, little is known about their differences in
terms of performance and user preference in virtual locomotion tasks. In the
present paper, we presented three different hand gesture interfaces and their
algorithms for locomotion, which are called the Finger Distance gesture, the
Finger Number gesture and the Finger Tapping gesture. These gestures were
inspired by previous studies of gesture-based locomotion interfaces and are
typical gestures that people are familiar with in their daily lives.
Implementing these hand gesture interfaces in the present study enabled us to
systematically compare the differences between these gestures. In addition, to
compare the usability of these gestures to locomotion interfaces using
gamepads, we also designed and implemented a gamepad interface based on the
Xbox One controller. We compared these four interfaces through two virtual
locomotion tasks. These tasks assessed their performance and user preference on
speed control and waypoints navigation. Results showed that user preference and
performance of the Finger Distance gesture were comparable to that of the
gamepad interface. The Finger Number gesture also had close performance and
user preference to that of the Finger Distance gesture. Our study demonstrates
that the Finger Distance gesture and the Finger Number gesture are very
promising interfaces for virtual locomotion. We also discuss that the Finger
Tapping gesture needs further improvements before it can be used for virtual
walking.


Abstract:
  Harvesting waste heat with temperatures lower than 100 oC can improve system
efficiency and reduce greenhouse gas emissions, yet it has been a longstanding
and challenging task. Electrochemical methods for harvesting low-grade heat
have attracted research interest in recent years due to the relatively high
effective temperature coefficient of the electrolytes (> 1 mV/K) compared with
the thermopower of traditional thermoelectric devices. Comparing with other
electrochemical devices such as temperature-variation based thermally
regenerative electrochemical cycle and temperature-difference based
thermogalvanic cells, the thermally regenerative flow battery (TRFB) has the
advantages of providing a continuous power output, decoupling the heat source
and heat sink and recuperating heat, and compatible with stacking for scaling
up. However, TRFB suffers from the issue of stable operation due to the
challenge of pH matching between catholyte and anolyte solutions with desirable
temperature coefficients. In this work, we demonstrate a PH-neutral TRFB based
on KI/KI3 and K3Fe(CN)6/K4Fe(CN)6 as the catholyte and anolyte, respectively,
with a cell temperature coefficient of 1.9 mV/K and a power density of 9
uW/cm2. This work also presents a comprehensive model with a coupled analysis
of mass transfer and reaction kinetics in a porous electrode that can
accurately capture the flow rate dependence of power density and energy
conversion efficiency. We estimate that the efficiency of the pH-neutral TRFB
can reach 11% of the Carnot efficiency at the maximum power output with a
temperature difference of 37 K. Via analysis, we identify that the mass
transfer overpotential inside the porous electrode and the resistance of the
ion exchange membrane are the two major factors limiting the efficiency and
power density, pointing to directions for future improvements.


Abstract:
  We investigate dynamical changes and its corresponding phase space complexity
in a stochastic red blood cell system. The system is obtained by incorporating
power noise with the associated sinusoidal flow. Both chaotic and non-chaotic
dynamics of sinusoidal flow in red blood cell are identified by 0-1 test.
Furthermore, dynamical complexity of the sinusoidal flow in the system is
investigated by heterogeneous recurrence based entropy. The numerical
simulation is performed to quantify the existence of chaotic dynamics and
complexity for the sinusoidal blood flow.


Abstract:
  This paper presents a Domain Specific Language (DSL) for generically
describing cyber attacks, agnostic to specific system-under-test(SUT). The
creation of the presented DSL is motivated by an automotive use case. The
concepts of the DSL are generic such thatattacks on arbitrary systems can be
addressed.The ongoing trend to improve the user experience of vehicles with
connected services implies an enhanced connectivity as well asremote accessible
interface opens potential attack vectors. This might also impact safety and the
proprietary nature of potential SUTs.Reusing tests of attack vectors to
industrialize testing them on multiple SUTs mandates an abstraction mechanism
to port an attackfrom one system to another. The DSL therefore generically
describes attacks for the usage with a test case generator (and
executionenvironment) also described in this paper. The latter use this
description and a database with SUT-specific information to generateattack
implementations for a multitude of different (automotive) SUTs.


Abstract:
  We prove the rationality of the Poincar\'e series of multiplier ideals in any
dimension and thus extending the main results for surfaces of Galindo and
Monserrat and Alberich-Carrami\~nana et al. Our results also hold for
Poincar\'e series of test ideals. In order to do so, we introduce a theory of
Hilbert functions indexed over $\mathbb{R}$ which gives an unified treatment of
both cases.


Abstract:
  We study the transverse geometric behavior of 2-dimensional foliations in
3-manifolds. We show that an R-covered transversely orientable foliation with
Gromov hyperbolic leaves in a closed 3-manifold admits a regulating, transverse
pseudo-Anosov flow (in the appropriate sense) in each atoroidal piece of the
manifold. The flow is a blow of a one prong pseudo-Anosov flow. In addition we
show that there is a regulating flow for the whole foliation. We also determine
how deck transformations act on the universal circle of the foliation.


Abstract:
  Quantum computers promise tremendous impact across applications -- and have
shown great strides in hardware engineering -- but remain notoriously error
prone. Careful design of low-level controls has been shown to compensate for
the processes which induce hardware errors, leveraging techniques from optimal
and robust control. However, these techniques rely heavily on the availability
of highly accurate and detailed physical models which generally only achieve
sufficient representative fidelity for the most simple operations and generic
noise modes. In this work, we use deep reinforcement learning to design a
universal set of error-robust quantum logic gates on a superconducting quantum
computer, without requiring knowledge of a specific Hamiltonian model of the
system, its controls, or its underlying error processes. We experimentally
demonstrate that a fully autonomous deep reinforcement learning agent can
design single qubit gates up to $3\times$ faster than default DRAG operations
without additional leakage error, and exhibiting robustness against calibration
drifts over weeks. We then show that $ZX(-\pi/2)$ operations implemented using
the cross-resonance interaction can outperform hardware default gates by over
$2\times$ and equivalently exhibit superior calibration-free performance up to
25 days post optimization using various metrics. We benchmark the performance
of deep reinforcement learning derived gates against other black box
optimization techniques, showing that deep reinforcement learning can achieve
comparable or marginally superior performance, even with limited hardware
access.


Abstract:
  A sender sells an object of unknown quality to a receiver who pays his
expected value for it. Sender and receiver might hold different priors over
quality. The sender commits to a monotonic categorization of quality. We
characterize the sender's optimal monotonic categorization. Using our
characterization, we study the optimality of full pooling or full separation,
the alternation of pooling and separation, and make precise a sense in which
pooling is dominant relative to separation. We discuss applications, extensions
and generalizations, among them the design of a grading scheme by a
profit-maximizing school which seeks to signal student qualities and
simultaneously incentivize students to learn. Such incentive constraints force
monotonicity, and can also be embedded as a distortion of the school's prior
over student qualities, generating a categorization problem with distinct
sender and receiver priors.


Abstract:
  We present a new radiative transfer method (SPH-M1RT) that is coupled
dynamically with smoothed particle hydrodynamics (SPH). We implement it in the
(task-based parallel) SWIFT galaxy simulation code but it can be
straightforwardly implemented in other SPH codes. Our moment-based method
simultaneously solves the radiation energy and flux equations in SPH, making it
adaptive in space and time. We modify the M1 closure relation to stabilize
radiation fronts in the optically thin limit. We also introduce anisotropic
artificial viscosity and high-order artificial diffusion schemes, which allow
the code to handle radiation transport accurately in both the optically thin
and optically thick regimes. Non-equilibrium thermo-chemistry is solved using a
semi-implicit sub-cycling technique. The computational cost of our method is
independent of the number of sources and can be lowered further by using the
reduced speed of light approximation. We demonstrate the robustness of our
method by applying it to a set of standard tests from the cosmological
radiative transfer comparison project of Iliev et al. The SPH-M1RT scheme is
well-suited for modelling situations in which numerous sources emit ionising
radiation, such as cosmological simulations of galaxy formation or simulations
of the interstellar medium.


Abstract:
  In this research paper, I will elaborate on a method to evaluate machine
translation models based on their performance on underlying syntactical
phenomena between English and Arabic languages. This method is especially
important as such "neural" and "machine learning" are hard to fine-tune and
change. Thus, finding a way to evaluate them easily and diversely would greatly
help the task of bettering them.


Abstract:
  Many clinical studies evaluate the benefit of treatment based on both
survival and other ordinal/continuous clinical outcomes, such as neurocognitive
scores or quality-of-life scores. In these studies, there are situations when
the clinical outcomes are truncated by death, where subjects die before their
clinical outcome is measured. Treating outcomes as "missing" or "censored" due
to death can be misleading for treatment effect evaluation. We show that if we
use the median in the survivors or in the always-survivors to summarize
clinical outcomes, we may conclude a trade-off exists between the probability
of survival and good clinical outcomes, even in settings where both the
probability of survival and the probability of any good clinical outcome are
better for one treatment. Therefore, we advocate not always treating death as a
mechanism through which clinical outcomes are missing, but rather as part of
the outcome measure. To account for the survival status, we describe the
survival-incorporated median as an alternative summary measure for outcomes in
the presence of death. The survival-incorporated median is the threshold such
that 50\% of the population is alive with an outcome above that threshold. We
use conceptual examples to show that the survival-incorporated median provides
a simple and useful summary measure to inform clinical practice.


Abstract:
  The unified set of yields of particles produced in proton-proton collisions
at $\sqrt{s}$ = 17.3 GeV (laboratory beam momentum 158 GeV/c) is evaluated,
combining the experimental results of the NA49 and NA61/SHINE collaborations at
the CERN SPS. With the statistical hadronization code Thermal-Fist we confirm
the unacceptably high value of $\chi^2$, both in the canonical and grand
canonical - strangeness canonical approach, and the common volume for all the
hadrons. The use of the energy-dependent width of the Breit-Wigner
parametrization for the mass distributions of unstable particles improves the
quality of the description of particle yields only slightly. We confirm the
observation that exclusion of the $\phi$ meson yield makes the fit result
acceptable. The complete experimental data set of particle yields can be
reasonably fitted if the canonical volumes of hadrons without and with open
strangeness are allowed to vary independently. The canonical volume of
strangeness was found larger than that for non-strange hadrons, which is
compatible with the femtoscopy measurements of p+p system at $\sqrt{s} = $ 27.4
MeV and 900 MeV. The model with the best-fit parameters allows to predict the
yields of several not yet measured particles emitted from p+p at $\sqrt{s}$ =
17.3 GeV.


Abstract:
  In this paper, we extend a recently introduced multi-fidelity control variate
for the uncertainty quantification of the Boltzmann equation to the case of
kinetic models arising in the study of multiagent systems. For these phenomena,
where the effect of uncertainties is particularly evident, several models have
been developed whose equilibrium states are typically unknown. In particular,
we aim to develop efficient numerical methods based on solving the kinetic
equations in the phase space by Direct Simulation Monte Carlo (DSMC) coupled to
a Monte Carlo sampling in the random space. To this end, exploiting the
knowledge of the corresponding mean-field approximation we develop novel
mean-field Control Variate (MFCV) methods that are able to strongly reduce the
variance of the standard Monte Carlo sampling method in the random space. We
verify these observations with several numerical examples based on classical
models , including wealth exchanges and opinion formation model for collective
phenomena.


Abstract:
  We demonstrate an on-demand source of microwave single photons with 71--99\%
intrinsic quantum efficiency. The source is narrowband (300\unite{kHz}) and
tuneable over a 600 MHz range around 5.2 GHz. Such a device is an important
element in numerous quantum technologies and applications. The device consists
of a superconducting transmon qubit coupled to the open end of a transmission
line. A $\pi$-pulse excites the qubit, which subsequently rapidly emits a
single photon into the transmission line. A cancellation pulse then suppresses
the reflected $\pi$-pulse by 33.5 dB, resulting in 0.005 photons leaking into
the photon emission channel. We verify strong antibunching of the emitted
photon field and determine its Wigner function. Non-radiative decay and $1/f$
flux noise both affect the quantum efficiency. We also study the device
stability over time and identify uncorrelated discrete jumps of the pure
dephasing rate at different qubit frequencies on a time scale of hours, which
we attribute to independent two-level system defects in the device dielectrics,
dispersively coupled to the qubit.


Abstract:
  The heavy fermion state with Kondo-hybridization (KH), usually manifested in
f-electron systems with lanthanide or actinide elements, was recently
discovered in several 3d transition metal compounds without f-electrons.
However, KH has not yet been observed in 4d/5d transition metal compounds,
since more extended 4d/5d orbitals do not usually form flat bands that supply
localized electrons appropriate for Kondo pairing. Here, we report a doping-
and temperature-dependent angle-resolved photoemission study on 4d
Ca2-xSrxRuO4, which shows the signature of KH. We observed a spectral weight
transfer in the {\gamma}-band, reminiscent of an orbital-selective Mott phase
(OSMP). The Mott localized {\gamma}-band induces KH with the itinerant
\b{eta}-band, resulting in spectral weight suppression around the Fermi level.
Our work is the first to demonstrate the evolution of the OSMP with possible KH
among 4d electrons, and thereby expands the material boundary of Kondo physics
to 4d multi-orbital systems.


Abstract:
  Excellent thermoelectric performance in the out-of-layer n-doped SnSe has
been observed experimentally (Chang et al., Science 360, 778-783 (2018)).
However, a first-principles investigation of the dominant scattering mechanisms
governing all thermoelectric transport properties is lacking. In the present
work, by applying extensive first-principles calculations of electron-phonon
coupling associated with the calculation of the scattering by ionized
impurities, we investigate the reasons behind the superior figure of merit as
well as the enhancement of zT above 600 K in n-doped out-of-layer SnSe, as
compared to p-doped SnSe with similar carrier densities. For the n-doped case,
the relaxation time is dominated by ionized impurity scattering and increases
with temperature, a feature that maintains the power factor at high values at
higher temperatures and simultaneously causes the carrier thermal conductivity
at zero electric current (k_el) to decrease faster for higher temperatures,
leading to an ultrahigh-zT = 3.1 at 807 K. We rationalize the roles played by
k_el and k^0 (the thermal conductivity due to carrier transport under
isoelectrochemical conditions) in the determination of zT. Our results show the
ratio between k^0 and the lattice thermal conductivity indeed corresponds to
the upper limit for zT, whereas the difference between calculated zT and the
upper limit is proportional to k_el.


Abstract:
  We consider $\mathbb{Z}_2$-synchronization on the Euclidean lattice. Every
vertex of $\mathbb{Z}^d$ is assigned an independent symmetric random sign
$\theta_u$, and for every edge $(u,v)$ of the lattice, one observes the product
$\theta_u\theta_v$ flipped independently with probability $p$. The task is to
reconstruct products $\theta_u\theta_v$ for pairs of vertices $u$ and $v$ which
are arbitrarily far apart. Abb\'e, Massouli\'e, Montanari, Sly and Srivastava
(2018) showed that synchronization is possible if and only if $p$ is below a
critical threshold $\tilde{p}_c(d)$, and efficiently so for $p$ small enough.
We augment this synchronization setting with a model of side information
preserving the sign symmetry of $\theta$, and propose an \emph{efficient}
algorithm which synchronizes a randomly chosen pair of far away vertices on
average, up to a differently defined critical threshold $p_c(d)$. We conjecture
that $ p_c(d)=\tilde{p}_c(d)$ for all $d \ge 2$. Our strategy is to
\emph{renormalize} the synchronization model in order to reduce the effective
noise parameter, and then apply a variant of the multiscale algorithm of AMMSS.
The success of the renormalization procedure is conditional on a plausible but
unproved assumption about the regularity of the free energy of an Ising spin
glass model on $\mathbb{Z}^d$.


Abstract:
  Graph Neural Networks (GNNs) are the subject of intense focus by the machine
learning community for problems involving relational reasoning. GNNs can be
broadly divided into spatial and spectral approaches. Spatial approaches use a
form of learned message-passing, in which interactions among vertices are
computed locally, and information propagates over longer distances on the graph
with greater numbers of message-passing steps. Spectral approaches use
eigendecompositions of the graph Laplacian to produce a generalization of
spatial convolutions to graph structured data which access information over
short and long time scales simultaneously. Here we introduce the Spectral Graph
Network, which applies message passing to both the spatial and spectral
domains. Our model projects vertices of the spatial graph onto the Laplacian
eigenvectors, which are each represented as vertices in a fully connected
"spectral graph", and then applies learned message passing to them. We apply
this model to various benchmark tasks including a graph-based variant of MNIST
classification, molecular property prediction on MoleculeNet and QM9, and
shortest path problems on random graphs. Our results show that the Spectral GN
promotes efficient training, reaching high performance with fewer training
iterations despite having more parameters. The model also provides robustness
to edge dropout and outperforms baselines for the classification tasks. We also
explore how these performance benefits depend on properties of the dataset.


Abstract:
  Cookie banners are devices implemented by websites to allow users to manage
their privacy settings with respect to the use of cookies. They are part of a
user's daily web browsing experience since legislation in Europe requires
websites to show such notices. In this paper, we carry out a large-scale study
of more than 17,000 websites including more than 7,500 cookie banners in Greece
and the UK to determine compliance and tracking transparency levels. Our
analysis shows that although more than 60% of websites store third-party
cookies in both countries, only less than 50% show a cookie notice and hence a
substantial proportion do not comply with the law even at the very basic level.
We find only a small proportion of the surveyed websites providing a direct
opt-out option, with an overwhelming majority either nudging users towards
privacy-intrusive choices or making cookie rejection much harder than consent.
Our results differ significantly in some cases from previous smaller-scale
studies and hence underline the importance of large-scale studies for a better
understanding of the big picture in cookie practices.


Abstract:
  We present an analytic computation of the two-loop QCD corrections to
$u\bar{d}\to W^+b\bar{b}$ for an on-shell $W$-boson using the leading colour
and massless bottom quark approximations. We perform an integration-by-parts
reduction of the unpolarised squared matrix element using finite field
reconstruction techniques and identify an independent basis of special
functions that allows an analytic subtraction of the infrared and ultraviolet
poles. This basis is valid for all planar topologies for five-particle
scattering with an off-shell leg.


Abstract:
  Operator spreading under unitary time evolution has attracted a lot of
attention recently, as a way to probe many-body quantum chaos. While quantities
such as out-of-time-ordered correlators (OTOC) do distinguish interacting from
non-interacting systems, it has remained unclear to what extent they can truly
diagnose chaotic {\it vs} integrable dynamics in many-body quantum systems.
Here, we analyze operator spreading in generic 1D many-body quantum systems
using a combination of matrix product operator (MPO) and analytical techniques,
focusing on the operator {\em right-weight}. First, we show that while small
bond dimension MPOs allow one to capture the exponentially-decaying tail of the
operator front, in agreement with earlier results, they lead to significant
quantitative and qualitative errors for the actual front -- defined by the
maximum of the right-weight. We find that while the operator front broadens
diffusively in both integrable and chaotic interacting spin chains, the precise
shape and scaling of the height of the front in integrable systems is anomalous
for all accessible times. We interpret these results using a quasiparticle
picture. This provides a sharp, though rather subtle signature of many-body
quantum chaos in the operator front.


Abstract:
  We developed a theory of electric and thermoelectric conductivity of lightly
doped SrTiO$_3$ in the non-degenerate region $k_B T \geq E_F$, assuming that
the major source of electron scattering is their interaction with soft
transverse optical phonons present due to proximity to ferroelectric
transition. We have used kinetic equation approach within relaxation-time
approximation and we have determined energy-dependent transport relaxation time
$\tau(E)$ by the iterative procedure. Using electron effective mass $m$ and
electron-transverse phonon coupling constant $\lambda$ as two fitting
parameters, we are able to describe quantitatively a large set of the measured
temperature dependences of resistivity $R(T)$ and Seebeck coefficient
$\mathcal{S}(T)$ for a broad range of electron densities studied experimentally
in recent paper [1]. In addition, we calculated Nernst ratio $\nu=N/B$ in the
linear approximation over weak magnetic field in the same temperature range.


Abstract:
  Recently, it has been argued that encoder-decoder models can be made more
interpretable by replacing the softmax function in the attention with its
sparse variants. In this work, we introduce a novel, simple method for
achieving sparsity in attention: we replace the softmax activation with a ReLU,
and show that sparsity naturally emerges from such a formulation. Training
stability is achieved with layer normalization with either a specialized
initialization or an additional gating function. Our model, which we call
Rectified Linear Attention (ReLA), is easy to implement and more efficient than
previously proposed sparse attention mechanisms. We apply ReLA to the
Transformer and conduct experiments on five machine translation tasks. ReLA
achieves translation performance comparable to several strong baselines, with
training and decoding speed similar to that of the vanilla attention. Our
analysis shows that ReLA delivers high sparsity rate and head diversity, and
the induced cross attention achieves better accuracy with respect to
source-target word alignment than recent sparsified softmax-based models.
Intriguingly, ReLA heads also learn to attend to nothing (i.e. 'switch off')
for some queries, which is not possible with sparsified softmax alternatives.


Abstract:
  Gradient-descent based iterative algorithms pervade a variety of problems in
estimation, prediction, learning, control, and optimization. Recently iterative
algorithms based on higher-order information have been explored in an attempt
to lead to accelerated learning. In this paper, we explore a specific a
high-order tuner that has been shown to result in stability with time-varying
regressors in linearly parametrized systems, and accelerated convergence with
constant regressors. We show that this tuner continues to provide bounded
parameter estimates even if the gradients are corrupted by noise. Additionally,
we also show that the parameter estimates converge exponentially to a compact
set whose size is dependent on noise statistics. As the HT algorithms can be
applied to a wide range of problems in estimation, filtering, control, and
machine learning, the result obtained in this paper represents an important
extension to the topic of real-time and fast decision making.


Abstract:
  A result due to Williams, Stampfli and Fillmore shows that an essential
isometry $T$ on a Hilbert space $\mathcal{H}$ is a compact perturbation of an
isometry if and only if ind$(T)\le 0$. A recent result of S. Chavan yields an
analogous characterization of essential spherical isometries
$T=(T_1,\dots,T_n)\in\mathcal{B}(\mathcal{H})^n$ with
dim($\bigcap_{i=1}^n\ker(T_i))\le$ dim$(\bigcap_{i=1}^n\ker(T_i^*))$. In the
present note we show that in dimension $n>1$ the result of Chavan holds without
any condition on the dimensions of the joint kernels of $T$ and $T^*$.


Abstract:
  Collective phenomena in the Tavis-Cummings model has been widely studied,
focusing on the phase transition features. In many occasions, it has been used
variational approaches that consider separated radiation-matters systems. In
this paper, we examine the role of the quantum entanglement of an assembly of
two-level emitters coupled to a single-mode cavity; this allows us to
characterise the quantum correlated state for each regime. Statistical
properties of the system, e.g., the first four statistical moments, show
clearly the structure of the light and matter distributions. Even though the
second order correlation function goes to one in some regimes, the statistical
analysis evidence a sharp departure from coherent behaviour, contrarily to the
common understanding.


Abstract:
  In this paper we prove existence of nonnegative solutions to parabolic
Cauchy-Dirichlet problems with superlinear gradient terms which are possibly
singular. The model equation is \[
  u_t - \Delta_pu=g(u)|\nabla u|^q+h(u)f(t,x)\qquad \text{in
}(0,T)\times\Omega, \] where $\Omega$ is an open bounded subset of
$\mathbb{R}^N$ with $N>2$, $0<T<+\infty$, $1<p<N$, and $q<p$ is superlinear.
The functions $g,\,h$ are continuous and possibly satisfying $g(0) = +\infty$
and/or $h(0)= +\infty$, with different rates. Finally, $f$ is nonnegative and
it belongs to a suitable Lebesgue space. We investigate the relation among the
superlinear threshold of $q$, the regularity of the initial datum and the
forcing term, and the decay rates of $g,\,h$ at infinity.


Abstract:
  The recent progress of artificial intelligence(AI) has shown great potentials
for alleviating human burden in various complex tasks. From the view of
software engineering, AI techniques can be seen in many fundamental aspects of
development, such as source code comprehension, in which state-of-the-art
models are implemented to extract and express the meaning of code snippets
automatically. However, such technologies are still struggling to tackle and
comprehend the complex structures within industrial code, thus far from
real-world applications. In the present work, we built an innovative and
systematical framework, emphasizing the problem of complexity in code
comprehension and further software engineering. Upon automatic data collection
from the latest Linux kernel source code, we modeled code structures as complex
networks through token extraction and relation parsing. Comprehensive analysis
of complexity further revealed the density and scale of network-based code
representations. Our work constructed the first large-scale dataset from
industrial-strength software code for downstream software engineering tasks
including code comprehension, and incorporated complex network theory into
code-level investigations of software development for the first time. In the
longer term, the proposed methodology could play significant roles in the
entire software engineering process, powering software design, coding,
debugging, testing, and sustaining by redefining and embracing complexity.


Abstract:
  Static and spherically symmetric wormhole solutions can be reconstructed in
the framework of curvature based Extended Theories of Gravity. In particular,
extensions of the General Relativity, in metric and curvature formalism give
rise to modified gravitational potentials, constituted by the classical
Newtonian potential and Yukawa-like corrections, whose parameters can be, in
turn, gauged by the observations. Such an approach allows to reconstruct the
spacetime out of the wormhole throat considering the asymptotic flatness as a
physical property for the related gravitational field. Such an argument can be
applied for a large class of curvature theories characterising the wormholes
through the parameters of the potentials. According to this procedure, possible
wormhole solutions could be observationally constrained. On the other hand,
stable and traversable wormholes could be a direct probe for this class of
Extended Theories of Gravity.


Abstract:
  Mutation analysis is an effective technique to evaluate a test suite adequacy
in terms of revealing unforeseen bugs in software. Traditional source- or
IR-level mutation analysis is not applicable to the software only available in
binary format. This paper proposes a practical binary mutation analysis via
binary rewriting, along with a rich set of mutation operators to represent more
realistic bugs. We implemented our approach using two state-of-the-art binary
rewriting tools and evaluated its effectiveness and scalability by applying
them to SPEC CPU benchmarks. Our analysis revealed that the richer mutation
operators contribute to generating more diverse mutants, which, compared to
previous works leads to a higher mutation score for the test harness. We also
conclude that the reassembleable disassembly rewriting yields better
scalability in comparison to lifting to an intermediate representation and
performing a full translation.


Abstract:
  Let $R=K[G]$ be a group ring of a group $G$ over a field $K$. The Ore
condition says that for any $a,b\in R$ there exist $u,v\in R$ such that
$au=bv$, where $u\ne0$ or $v\ne0$. It always holds whenever $G$ is amenable.
Recently it was shown that for R.\,Thompson's group $F$ the converse is also
true. So the famous amenability problem for $F$ is equivalent to the question
on the Ore condition for the group ring of the same group.
  It is easy to see that the problem on the Ore condition for $K[F]$ is
equivalent to the same property for the monoid ring $K[M]$, where $M$ is the
monoid of positive elements of $F$. In this paper we reduce the problem to the
case when $a$, $b$ are homogeneous elements of the same degree in the monoid
ring. We study the case of degree $1$ and find solutions of the Ore equation.
For the case of degree $2$, we study the case of linear combinations of
monomials from $S=\{x_0^2,x_0x_1,x_0x_2,x_1^2,x_1x_2\}$. This set is not
doubling, that is, there are nonempty finite subsets $X\subset M\subset F$ such
that $|SX| < 2|X|$. As a consequence, the Ore condition holds for linear
combinations of these monomials. We give an estimate for the degree of $u$, $v$
in the above equation.
  The case of monomials of higher degree is open as well as the case of degree
$2$ for monomials on $x_0,x_1,...,x_m$, where $m\ge3$. Recall that negative
answer to any of these questions will immediately imply non-amenability of $F$.


Abstract:
  The photogalvanic effect (PGE) occurring in noncentrosymmetric materials
enables the generation of a dc photocurrent at zero bias with a high
polarization sensitivity, which makes it very attractive in photodetection.
However, the magnitude of the PGE photocurrent is usually small, leading to a
low photoresponsivity, and therefore hampers its practical application in
photodetection. Here, we propose an approach to largely enhancing the PGE
photocurrent by applying an inhomogenous mechanical stretch, based on quantum
transport simulations. We model a two-dimensional photodetector consisting of
the wide-bandgap MgCl$_2$/ZnBr$_2$ vertical van der Waals heterojunction with
the noncentrosymmetric $C_{3v}$ symmetry. Polarization-sensitive PGE
photocurrent is generated under the vertical illumination of linearly polarized
light. By applying inhomogenous mechanical stretch on the lattice, the
photocurrent can be largely increased by up to 3 orders of magnitude due to the
significantly increased device asymmetry. Our results propose an effective way
to enhance the PGE by inhomogenous mechanical strain, showing the potential of
the MgCl$_2$/ZnBr$_2$ vertical heterojunction in the low-power UV
photodetection.


Abstract:
  We report magneto-transport measurements of quasi-1-dimensional (1D) Al-InAs
nanowires produced via etching of a hybrid superconductor-semiconductor
two-dimensional electron gas (2DEG). Tunnel spectroscopy measurements above the
superconducting gap provide a means of identifying the 1D sub-bands associated
with the confined 1D region. Fitting the data to a model that includes the
different components of the spin-orbit interaction (SOI) reveals their
strength, of interest for evaluating the suitability of
superconductor-semiconductor 2DEG for realizing Majorana qubits.


Abstract:
  This is our main paper in a series in which we prove the full, unconditional,
nonlinear stability of the Kerr family $Kerr(a, m)$ for small angular momentum,
i.e. $|a|/m\ll 1$, in the context of asymptotically flat solutions of the
Einstein vacuum equations (EVE). Three papers in the series, \cite{KS-GCM1} and
\cite{KS-GCM2} and \cite{GKS1} have already been released. We expect that the
remaining ones \cite{GKS2}, \cite{KS:Kerr-B} and \cite{Shen} will appear
shortly. Our work extends the strategy developed in \cite{KS}, in which only
axial polarized perturbations of Schwarzschild were treated, by developing new
geometric and analytic ideas on how to deal with with general perturbations of
Kerr. We note that the restriction to small angular momentum appears only in
connection to Morawetz type estimates in \cite{GKS2} and \cite{KS:Kerr-B}


Abstract:
  WALOP (Wide-Area Linear Optical Polarimeter)-South, to be mounted on the 1m
SAAO telescope in South Africa, is first of the two WALOP instruments currently
under development for carrying out the PASIPHAE survey. Scheduled for
commissioning in the year 2021, the WALOP instruments will be used to measure
the linear polarization of around $10^{6}$ stars in the SDSS-r broadband with
$0.1~\%$ polarimetric accuracy, covering 4000 square degrees in the Galactic
polar regions. The combined capabilities of one-shot linear polarimetry, high
polarimetric accuracy ($< 0.1~\%$) and polarimetric sensitivity ($< 0.05~\%$),
and a large field of view (FOV) of $35\times35~arcminutes$ make WALOP-South a
unique astronomical instrument. In a single exposure, it is designed to measure
the Stokes parameters $I$, $q$ and $u$ in the SDSS-r broadband and narrowband
filters between $500-700~nm$. During each measurement, four images of the full
field corresponding to the polarization angles of $0^{\circ}$, $45^{\circ}$,
$90^{\circ}$ and $135^{\circ}$ will be imaged on four detectors and carrying
out differential photometry on these images will yield the Stokes parameters.
Major challenges in designing WALOP-South instrument include- (a) in the
optical design, correcting for the spectral dispersion introduced by large
split angle Wollaston Prisms used as polarization analyzers as well as
aberrations from the wide field, and (b) making an optomechanical design
adherent to the tolerances required to obtain good imaging and polarimetric
performance under all temperature conditions as well as telescope pointing
positions. We present the optical and optomechanical design for WALOP-South
which overcomes these challenges.


Abstract:
  As a maintainer of an open source software project, you are usually happy
about contributions in the form of pull requests that bring the project a step
forward. Past studies have shown that when reviewing a pull request, not only
its content is taken into account, but also, for example, the social
characteristics of the contributor. Whether a contribution is accepted and how
long this takes therefore depends not only on the content of the contribution.
What we only have indications for so far, however, is that pull requests from
bots may be prioritized lower, even if the bots are explicitly deployed by the
development team and are considered useful.
  One goal of the bot research and development community is to design helpful
bots to effectively support software development in a variety of ways. To get
closer to this goal, in this GitHub mining study, we examine the measurable
differences in how maintainers interact with manually created pull requests
from humans compared to those created automatically by bots.
  About one third of all pull requests on GitHub currently come from bots.
While pull requests from humans are accepted and merged in 72.53% of all cases,
this applies to only 37.38% of bot pull requests. Furthermore, it takes
significantly longer for a bot pull request to be interacted with and for it to
be merged, even though they contain fewer changes on average than human pull
requests. These results suggest that bots have yet to realize their full
potential.


Abstract:
  The structure of protostellar cores can often be approximated by isothermal
Bonnor-Ebert spheres (BES) which are stabilized by an external pressure. For
the typical pressure of $10^4k_B\,\mathrm{K\,cm^{-3}}$ to
$10^5k_B\,\mathrm{K\,cm^{-3}}$ found in molecular clouds, cores with masses
below $1.5\,{\rm M_\odot}$ are stable against gravitational collapse. In this
paper, we analyze the efficiency of triggering a gravitational collapse by a
nearby stellar wind, which represents an interesting scenario for triggered
low-mass star formation. We derive analytically a new stability criterion for a
BES compressed by a stellar wind, which depends on its initial nondimensional
radius $\xi_{max}$. If the stability limit is violated the wind triggers a core
collapse. Otherwise, the core is destroyed by the wind. We estimate its
validity range to $2.5<\xi_{max}<4.2$ and confirm this in simulations with the
SPH Code GADGET-3. The efficiency to trigger a gravitational collapse strongly
decreases for $\xi_{max}<2.5$ since in this case destruction and acceleration
of the whole sphere begin to dominate. We were unable to trigger a collapse for
$\xi_{max}<2$, which leads to the conclusion that a stellar wind can move the
smallest unstable stellar mass to $0.5\,\mathrm{M_\odot}$ and destabilizing
even smaller cores would require an external pressure larger than
$10^5k_B\,\mathrm{K\,cm^{-3}}$. For $\xi_{max}>4.2$ the expected wind strength
according to our criterion is small enough so that the compression is slower
than the sound speed of the BES and sound waves can be triggered. In this case
our criterion underestimates somewhat the onset of collapse and detailed
numerical analyses are required.


Abstract:
  Among the ODEs peculiarities -- specially those of Mechanics -- besides the
problem of leading them to quadratures and to solve them either in series or in
closed form, one is faced with the inversion. E.g. when one wishes to pass from
time as function of lagrangian coordinates to these last as functions of time.
This paper solves in almost closed form the system of non linear ODEs of the
2D-motion (say, co-ordinates $\theta$ and $\psi$) of a gravity-free double
pendulum (GFDP) not subjected to any force. In such a way its movement is ruled
by initial conditions only. The relevant strongly non linear ODEs, have been
put back to hyper-elliptic quadratures which, through the Integral
Representation Theorem (hereinafter IRT) have been driven to the Lauricella
hypergeometric functions $F_D^{(j)}, j=3, 4, 5, 6 $. The IRT has been applied
after a change of variable which improves their use and accelerates the series
convergence. The $\psi$ is given in terms of $F_D^{(4)}$ -- which is inverted
by means of the Fourier Series tool and put as an argument inside the
$F_D^{(5)}$ -- in such a way allowing the $\theta$ computations. We succeed in
a insight knowledge of time laws and trajectories of both bobs forming the
GFDP, which -- after the inversion -- is therefore completely solved in
explicit closed form. Suitable sample problems of the three possible cases of
motion are carried out and their analysis closes the work. The Lauricella
functions employed here to solve the differential equations -- in lack of
specific SW packages -- have been implemented thanks to some reduction theorems
which will form the object of a next paper. To our best knowledge, this work
adds a new contribution as it concerns detection and inversion of solutions of
nonlinear hamiltonian systems.


Abstract:
  We show that adding differential privacy to Explainable Boosting Machines
(EBMs), a recent method for training interpretable ML models, yields
state-of-the-art accuracy while protecting privacy. Our experiments on multiple
classification and regression datasets show that DP-EBM models suffer
surprisingly little accuracy loss even with strong differential privacy
guarantees. In addition to high accuracy, two other benefits of applying DP to
EBMs are: a) trained models provide exact global and local interpretability,
which is often important in settings where differential privacy is needed; and
b) the models can be edited after training without loss of privacy to correct
errors which DP noise may have introduced.


Abstract:
  Human personality traits are the key drivers behind our decision-making,
influencing our life path on a daily basis. Inference of personality traits,
such as Myers-Briggs Personality Type, as well as an understanding of
dependencies between personality traits and users' behavior on various social
media platforms is of crucial importance to modern research and industry
applications. The emergence of diverse and cross-purpose social media avenues
makes it possible to perform user personality profiling automatically and
efficiently based on data represented across multiple data modalities. However,
the research efforts on personality profiling from multi-source multi-modal
social media data are relatively sparse, and the level of impact of different
social network data on machine learning performance has yet to be
comprehensively evaluated. Furthermore, there is not such dataset in the
research community to benchmark. This study is one of the first attempts
towards bridging such an important research gap. Specifically, in this work, we
infer the Myers-Briggs Personality Type indicators, by applying a novel
multi-view fusion framework, called "PERS" and comparing the performance
results not just across data modalities but also with respect to different
social network data sources. Our experimental results demonstrate the PERS's
ability to learn from multi-view data for personality profiling by efficiently
leveraging on the significantly different data arriving from diverse social
multimedia sources. We have also found that the selection of a machine learning
approach is of crucial importance when choosing social network data sources and
that people tend to reveal multiple facets of their personality in different
social media avenues. Our released social multimedia dataset facilitates future
research on this direction.


Abstract:
  In his 1987 paper, Todorcevic remarks that Sierpinski's onto mapping
principle (1932) and the Erdos-Hajnal-Milner negative Ramsey relation (1966)
are equivalent to each other, and follow from the existence of a Luzin set.
Recently, Guzman and Miller showed that these two principles are also
equivalent to the existence of a nonmeager set of reals of cardinality
$\aleph_1$. We expand this circle of equivalences and show that these
propositions are equivalent also to the high-dimensional version of the
Erdos-Hajnal-Milner negative Ramsey relation, thereby improving a CH theorem of
Galvin (1980).
  Then we consider the validity of these relations in the context of strong
colorings over partitions and prove the consistency of a positive Ramsey
relation, as follows: It is consistent with the existence of both a Luzin set
and of a Souslin tree that for some countable partition p, all colorings are
p-special.


Abstract:
  Mastery of order-disorder processes in highly non-equilibrium nanostructured
oxides has significant implications for the development of emerging energy
technologies. However, we are presently limited in our ability to quantify and
harness these processes at high spatial, chemical, and temporal resolution,
particularly in extreme environments. Here we describe the percolation of
disorder at the model oxide interface LaMnO$_3$ / SrTiO$_3$, which we visualize
during in situ ion irradiation in the transmission electron microscope. We
observe the formation of a network of disorder during the initial stages of ion
irradiation and track the global progression of the system to full disorder. We
couple these measurements with detailed structural and chemical probes,
examining possible underlying defect mechanisms responsible for this unique
percolative behavior.


Abstract:
  The Principal-Agent Theory model is widely used to explain governance role
where there is a separation of ownership and control, as it defines clear
boundaries between governance and executives. However, examination of recent
corporate failure reveals the concerning contribution of the Board of Directors
to such failures and calls into question governance effectiveness in the
presence of a powerful and charismatic CEO. This study proposes a framework for
analyzing the relationship between the Board of Directors and the CEO, and how
certain relationships affect the power structure and behavior of the Board,
which leads to a role reversal in the Principal-Agent Theory, as the Board
assumes the role of the CEO's agent. This study's results may help create a red
flag for a board and leader's behavior that may result in governance failure.


Abstract:
  We prove that there are at least as many exact embedded Lagrangian fillings
as seeds for Legendrian links of affine type $\tilde{\mathsf{D}}
\tilde{\mathsf{E}}$. We also provide as many Lagrangian fillings with certain
symmetries as seeds of type $\tilde{\mathsf{B}}_n$, $\tilde{\mathsf{F}}_4$,
$\tilde{\mathsf{G}}_2$, and $\mathsf{E}_6^{(2)}$. These families are the first
known Legendrian links with infinitely many fillings that exhaust all seeds in
the corresponding cluster structures. Furthermore, we show that Legendrian
realization of Coxeter mutation of type $\tilde{\mathsf{D}}$ corresponds to the
Legendrian loop considered by Casals and Ng.


Abstract:
  Most modern unsupervised domain adaptation (UDA) approaches are rooted in
domain alignment, i.e., learning to align source and target features to learn a
target domain classifier using source labels. In semi-supervised domain
adaptation (SSDA), when the learner can access few target domain labels, prior
approaches have followed UDA theory to use domain alignment for learning. We
show that the case of SSDA is different and a good target classifier can be
learned without needing alignment. We use self-supervised pretraining (via
rotation prediction) and consistency regularization to achieve well separated
target clusters, aiding in learning a low error target classifier. With our
Pretraining and Consistency (PAC) approach, we achieve state of the art target
accuracy on this semi-supervised domain adaptation task, surpassing multiple
adversarial domain alignment methods, across multiple datasets. PAC, while
using simple techniques, performs remarkably well on large and challenging SSDA
benchmarks like DomainNet and Visda-17, often outperforming recent state of the
art by sizeable margins. Code for our experiments can be found at
https://github.com/venkatesh-saligrama/PAC


Abstract:
  In this paper we derive quantitative estimates in the context of stochastic
homogenization for integral functionals defined on finite partitions, where the
random surface integrand is assumed to be stationary. Requiring the integrand
to satisfy in addition a multiscale functional inequality, we control
quantitatively the fluctuations of the asymptotic cell formulas defining the
homogenized surface integrand. As a byproduct we obtain a simplified cell
formula where we replace cubes by almost flat hyperrectangles.


Abstract:
  Searches for periodicity in time series are often done with models of
periodic signals, whose statistical significance is assessed via false alarm
probabilities or Bayes factors. However, a statistically significant periodic
model might not originate from a strictly periodic source. In astronomy in
particular, one expects transient signals that show periodicity for a certain
amount of time before vanishing. This situation is encountered for instance in
the search for planets in radial velocity data. While planetary signals are
expected to have a stable phase, amplitude and frequency - except when strong
planet-planet interactions are present - signals induced by stellar activity
will typically not exhibit the same stability. In the present article, we
explore the use of periodic functions multiplied by time windows to diagnose
whether an apparently periodic signal is truly so. We suggest diagnostics to
check whether a signal is consistently present in the time series, and has a
stable phase, amplitude and period. The tests are expressed both in a
periodogram and Bayesian framework. Our methods are applied to the Solar
HARPS-N data as well as HD 215152, HD 69830 and HD 13808. We find that (i) the
HARPS-N Solar data exhibits signals at the Solar rotation period and its first
harmonic ($\sim$ 13.4 days). The frequency and phase of the 13.4 days signal
appear constant within the estimation uncertainties, but its amplitude presents
significant variations which can be mapped to activity levels. (ii) as
previously reported, we find four, three and two planets orbiting HD 215152, HD
69830 and HD 13808.


Abstract:
  The task of multi-label image classification is to recognize all the object
labels presented in an image. Though advancing for years, small objects,
similar objects and objects with high conditional probability are still the
main bottlenecks of previous convolutional neural network(CNN) based models,
limited by convolutional kernels' representational capacity. Recent vision
transformer networks utilize the self-attention mechanism to extract the
feature of pixel granularity, which expresses richer local semantic
information, while is insufficient for mining global spatial dependence. In
this paper, we point out the three crucial problems that CNN-based methods
encounter and explore the possibility of conducting specific transformer
modules to settle them. We put forward a Multi-label Transformer
architecture(MlTr) constructed with windows partitioning, in-window pixel
attention, cross-window attention, particularly improving the performance of
multi-label image classification tasks. The proposed MlTr shows
state-of-the-art results on various prevalent multi-label datasets such as
MS-COCO, Pascal-VOC, and NUS-WIDE with 88.5%, 95.8%, and 65.5% respectively.
The code will be available soon at https://github.com/starmemda/MlTr/


Abstract:
  The purpose of this study is to examine Olympic champions' characteristics on
Instagram to first understand whether differences exist between male and female
athletes and then to find possible correlations between these characteristics.
We utilized a content analytic method to analyze Olympic gold medalists'
photographs on Instagram. By this way we fetched data from Instagram pages of
all those Rio2016 Olympic gold medalists who had their account publicly
available. The analysis of data revealed the existence of a positive monotonic
relationship between the ratio of following/follower and the ratio of
engagement to follower for men gold medalists, and a strong negative monotonic
relationship between age and ratio of self-presenting post of both men and
women gold medalists which even take a linear form for men. These findings
aligned with the relative theories and literature may come together to help the
athletes to manage and expand their personal brand in social media.


Abstract:
  The evolution of young stars and disks is driven by the interplay of several
processes, notably accretion and ejection of material. Critical to correctly
describe the conditions of planet formation, these processes are best probed
spectroscopically. About five-hundred orbits of the Hubble Space Telescope
(HST) are being devoted in 2020-2022 to the ULLYSES public survey of about 70
low-mass (M<2Msun) young (age<10 Myr) stars at UV wavelengths. Here we present
the PENELLOPE Large Program that is being carried out at the ESO Very Large
Telescope (VLT) to acquire, contemporaneous to HST, optical ESPRESSO/UVES
high-resolution spectra to investigate the kinematics of the emitting gas, and
UV-to-NIR X-Shooter medium-resolution flux-calibrated spectra to provide the
fundamental parameters that HST data alone cannot provide, such as extinction
and stellar properties. The data obtained by PENELLOPE have no proprietary
time, and the fully reduced spectra are made available to the whole community.
Here, we describe the data and the first scientific analysis of the accretion
properties for the sample of thirteen targets located in the Orion OB1
association and in the sigma-Orionis cluster, observed in Nov-Dec 2020. We find
that the accretion rates are in line with those observed previously in
similarly young star-forming regions, with a variability on a timescale of days
of <3. The comparison of the fits to the continuum excess emission obtained
with a slab model on the X-Shooter spectra and the HST/STIS spectra shows a
shortcoming in the X-Shooter estimates of <10%, well within the assumed
uncertainty. Its origin can be either a wrong UV extinction curve or due to the
simplicity of this modelling, and will be investigated in the course of the
PENELLOPE program. The combined ULLYSES and PENELLOPE data will be key for a
better understanding of the accretion/ejection mechanisms in young stars.


Abstract:
  Advances in imagery at atomic and near-atomic resolution, such as cryogenic
electron microscopy (cryo-EM), have led to an influx of high resolution images
of proteins and other macromolecular structures to data banks worldwide.
Producing a protein structure from the discrete voxel grid data of cryo-EM maps
involves interpolation into the continuous spatial domain. We present a novel
data format called the neural cryo-EM map, which is formed from a set of neural
networks that accurately parameterize cryo-EM maps and provide native,
spatially continuous data for density and gradient. As a case study of this
data format, we create graph-based interpretations of high resolution
experimental cryo-EM maps. Normalized cryo-EM map values interpolated using the
non-linear neural cryo-EM format are more accurate, consistently scoring less
than 0.01 mean absolute error, than a conventional tri-linear interpolation,
which scores up to 0.12 mean absolute error. Our graph-based interpretations of
115 experimental cryo-EM maps from 1.15 to 4.0 Angstrom resolution provide high
coverage of the underlying amino acid residue locations, while accuracy of
nodes is correlated with resolution. The nodes of graphs created from atomic
resolution maps (higher than 1.6 Angstroms) provide greater than 99% residue
coverage as well as 85% full atomic coverage with a mean of than 0.19 Angstrom
root mean squared deviation (RMSD). Other graphs have a mean 84% residue
coverage with less specificity of the nodes due to experimental noise and
differences of density context at lower resolutions. This work may be
generalized for transforming any 3D grid-based data format into non-linear,
continuous, and differentiable format for the downstream geometric deep
learning applications.


Abstract:
  Many real-life applications involve estimation of curves that exhibit
complicated shapes including jumps or varying-frequency oscillations. Practical
methods have been devised that can adapt to a locally varying complexity of an
unknown function (e.g. variable-knot splines, sparse wavelet reconstructions,
kernel methods or trees/forests). However, the overwhelming majority of
existing asymptotic minimaxity theory is predicated on homogeneous smoothness
assumptions. Focusing on locally Holderian functions, we provide new locally
adaptive posterior concentration rate results under the supremum loss for
widely used Bayesian machine learning techniques in white noise and
non-parametric regression. In particular, we show that popular spike-and-slab
priors and Bayesian CART are uniformly locally adaptive. In addition, we
propose a new class of repulsive partitioning priors which relate to variable
knot splines and which are exact-rate adaptive. For uncertainty quantification,
we construct locally adaptive confidence bands whose width depends on the local
smoothness and which achieve uniform asymptotic coverage under local
self-similarity. To illustrate that spatial adaptation is not at all automatic,
we provide lower-bound results showing that popular hierarchical Gaussian
process priors fall short of spatial adaptation.


Abstract:
  Back-translation is an effective strategy to improve the performance of
Neural Machine Translation~(NMT) by generating pseudo-parallel data. However,
several recent works have found that better translation quality of the
pseudo-parallel data does not necessarily lead to better final translation
models, while lower-quality but more diverse data often yields stronger
results. In this paper, we propose a novel method to generate pseudo-parallel
data from a pre-trained back-translation model. Our method is a meta-learning
algorithm which adapts a pre-trained back-translation model so that the
pseudo-parallel data it generates would train a forward-translation model to do
well on a validation set. In our evaluations in both the standard datasets WMT
En-De'14 and WMT En-Fr'14, as well as a multilingual translation setting, our
method leads to significant improvements over strong baselines. Our code will
be made available.


Abstract:
  We investigate the problem of fast-forwarding quantum evolution, whereby the
dynamics of certain quantum systems can be simulated with gate complexity that
is sublinear in the evolution time. We provide a definition of fast-forwarding
that considers the model of quantum computation, the Hamiltonians that induce
the evolution, and the properties of the initial states. Our definition
accounts for any asymptotic complexity improvement of the general case and we
use it to demonstrate fast-forwarding in several quantum systems. In
particular, we show that some local spin systems whose Hamiltonians can be
taken into block diagonal form using an efficient quantum circuit, such as
those that are permutation-invariant, can be exponentially fast-forwarded. We
also show that certain classes of positive semidefinite local spin systems,
also known as frustration-free, can be polynomially fast-forwarded, provided
the initial state is supported on a subspace of sufficiently low energies.
Last, we show that all quadratic fermionic systems and number-conserving
quadratic bosonic systems can be exponentially fast-forwarded in a model where
quantum gates are exponentials of specific fermionic or bosonic operators,
respectively. Our results extend the classes of physical Hamiltonians that were
previously known to be fast-forwarded, while not necessarily requiring methods
that diagonalize the Hamiltonians efficiently. We further develop a connection
between fast-forwarding and precise energy measurements that also accounts for
polynomial improvements.


Abstract:
  Social Networks' omnipresence and ease of use has revolutionized the
generation and distribution of information in today's world. However, easy
access to information does not equal an increased level of public knowledge.
Unlike traditional media channels, social networks also facilitate faster and
wider spread of disinformation and misinformation. Viral spread of false
information has serious implications on the behaviors, attitudes and beliefs of
the public, and ultimately can seriously endanger the democratic processes.
Limiting false information's negative impact through early detection and
control of extensive spread presents the main challenge facing researchers
today. In this survey paper, we extensively analyze a wide range of different
solutions for the early detection of fake news in the existing literature. More
precisely, we examine Machine Learning (ML) models for the identification and
classification of fake news, online fake news detection competitions,
statistical outputs as well as the advantages and disadvantages of some of the
available data sets. Finally, we evaluate the online web browsing tools
available for detecting and mitigating fake news and present some open research
challenges.


Abstract:
  To accommodate the explosive growth of the Internet-of-Things (IoT),
incorporating interference alignment (IA) into existing multiple access (MA)
schemes is under investigation. However, when it is applied in MIMO networks to
improve the system compacity, the incoming problem regarding information delay
arises which does not meet the requirement of low-latency. Therefore, in this
paper, we first propose a new metric, degree of delay (DoD), to quantify the
issue of information delay, and characterize DoD for three typical transmission
schemes, i.e., TDMA, beamforming based TDMA (BD-TDMA), and retrospective
interference alignment (RIA). By analyzing DoD in these schemes, its value
mainly depends on three factors, i.e., delay sensitive factor, size of data
set, and queueing delay slot. The first two reflect the relationship between
quality of service (QoS) and information delay sensitivity, and normalize time
cost for each symbol, respectively. These two factors are independent of the
transmission schemes, and thus we aim to reduce the queueing delay slot to
improve DoD. Herein, three novel joint IA schemes are proposed for MIMO
downlink networks with different number of users. That is, hybrid antenna array
based partial interference elimination and retrospective interference
regeneration scheme (HAA-PIE-RIR), HAA based improved PIE and RIR scheme
(HAA-IPIE-RIR), and HAA based cyclic interference elimination and RIR scheme
(HAA-CIE-RIR). Based on the first scheme, the second scheme extends the
application scenarios from $2$-user to $K$-user while causing heavy
computational burden. The third scheme relieves such computational burden,
though it has certain degree of freedom (DoF) loss due to insufficient
utilization of space resources.


Abstract:
  We present new H$\alpha$ photometry for the Star-Formation Reference Survey
(SFRS), a representative sample of star-forming galaxies in the local Universe.
Combining these data with the panchromatic coverage of the SFRS, we provide
calibrations of H$\alpha$-based star-formation rates (SFRs) with and without
correction for the contribution of [$\rm N_{^{II}}$] emission. We consider the
effect of extinction corrections based on the Balmer decrement, infrared excess
(IRX), and spectral energy distribution (SED) fits. We compare the SFR
estimates derived from SED fits, polycyclic aromatic hydrocarbons, hybrid
indicators such as 24 $\mu$m + H$\alpha$, 8 $\mu$m + H$\alpha$, FIR + FUV, and
H$\alpha$ emission for a sample of purely star-forming galaxies. We provide a
new calibration for 1.4 GHz-based SFRs by comparing to the H$\alpha$ emission,
and we measure a dependence of the radio-to-H$\alpha$ emission ratio based on
galaxy stellar mass. Active galactic nuclei introduce biases in the
calibrations of different SFR indicators but have only a minimal effect on the
inferred SFR densities from galaxy surveys. Finally, we quantify the
correlation between galaxy metallicity and extinction.


Abstract:
  Fungi cells are capable of sensing extracellular cues through reception,
transduction and response systems which allow them to communicate with their
host and adapt to their environment. They display effective regulatory protein
expressions which enhance and regulate their response and adaptation to a
variety of triggers such as stress, hormones, light, chemicals and host
factors. In our recent studies, we have shown that $Pleurotus$ oyster fungi
generate electrical potential impulses in the form of spike events as a result
of their exposure to environmental, mechanical and chemical triggers,
demonstrating that it is possible to discern the nature of stimuli from the
fungi electrical responses. Harnessing the power of fungi sensing and
intelligent capabilities, we explored the communication protocols of fungi as
reporters of human chemical secretions such as hormones, addressing the
question if fungi can sense human signals. We exposed $Pleurotus$ oyster fungi
to cortisol, directly applied to a surface of a hemp shavings substrate
colonised by fungi, and recorded the electrical activity of fungi. The response
of fungi to cortisol was also supplementary studied through the application of
X-ray to identify changes in the fungi tissue, where receiving cortisol by the
substrate can inhibit the flow of calcium and, in turn, reduce its
physiological changes. This study could pave the way for future research on
adaptive fungal wearables capable for detecting physiological states of humans
and biosensors made of living fungi.


Abstract:
  Developers of AI-Intensive Systems--i.e., systems that involve both
"traditional" software and Artificial Intelligence"are recognizing the need to
organize development systematically and use engineered methods and tools. Since
an AI-Intensive System (AIIS) relies heavily on software, it is expected that
Software Engineering (SE) methods and tools can help. However, AIIS development
differs from the development of "traditional" software systems in a few
substantial aspects. Hence, traditional SE methods and tools are not suitable
or sufficient by themselves and need to be adapted and extended. A quest for
"SE for AI" methods and tools has started. We believe that, in this effort, we
should learn from experience and avoid repeating some of the mistakes made in
the quest for SE in past years. To this end, a fundamental instrument is a set
of concepts and a notation to deal with AIIS and the problems that characterize
their development processes. In this paper, we propose to describe AIIS via a
notation that was proposed for SE and embeds a set of concepts that are
suitable to represent AIIS as well. We demonstrate the usage of the notation by
modeling some characteristics that are particularly relevant for AIIS.


Abstract:
  Tables on the web constitute a valuable data source for many applications,
like factual search and knowledge base augmentation. However, as genuine tables
containing relational knowledge only account for a small proportion of tables
on the web, reliable genuine web table classification is a crucial first step
of table extraction. Previous works usually rely on explicit feature
construction from the HTML code. In contrast, we propose an approach for web
table classification by exploiting the full visual appearance of a table, which
works purely by applying a convolutional neural network on the rendered image
of the web table. Since these visual features can be extracted automatically,
our approach circumvents the need for explicit feature construction. A new hand
labeled gold standard dataset containing HTML source code and images for 13,112
tables was generated for this task. Transfer learning techniques are applied to
well known VGG16 and ResNet50 architectures. The evaluation of CNN image
classification with fine tuned ResNet50 (F1 93.29%) shows that this approach
achieves results comparable to previous solutions using explicitly defined HTML
code based features. By combining visual and explicit features, an F-measure of
93.70% can be achieved by Random Forest classification, which beats current
state of the art methods.


Abstract:
  We show an application of a subdiffusion equation with Caputo fractional time
derivative with respect to another function $g$ to describe subdiffusion in a
medium having a structure evolving over time. In this case a continuous
transition from subdiffusion to other type of diffusion may occur. The process
can be interpreted as "ordinary" subdiffusion with fixed subdiffusion parameter
(subdiffusion exponent) $\alpha$ in which time scale is changed by the function
$g$. As example, we consider the transition from "ordinary" subdiffusion to
ultraslow diffusion. The function $g$ generates the additional aging process
superimposed on the "standard" aging generated by "ordinary" subdiffusion. The
aging process is analyzed using coefficient of relative aging of
$g$--subdiffusion with respect to "ordinary" subdiffusion. The method of
solving the $g$-subdiffusion equation is also presented.


Abstract:
  Galaxies can be classified as passive ellipticals or star-forming discs.
Ellipticals dominate at the high end of the mass range, and therefore there
must be a mechanism responsible for the quenching of star-forming galaxies.
This could either be due to the secular processes linked to the mass and star
formation of galaxies or to external processes linked to the surrounding
environment. In this paper, we analytically model the processes that govern
galaxy evolution and quantify their contribution. We have specifically studied
the effects of mass quenching, gas stripping, and mergers on galaxy quenching.
To achieve this, we first assumed a set of differential equations that describe
the processes that shape galaxy evolution. We then modelled the parameters of
these equations by maximising likelihood. These equations describe the
evolution of galaxies individually, but the parameters of the equations are
constrained by matching the extrapolated intermediate-redshift galaxies with
the low-redshift galaxy population. In this study, we modelled the processes
that change star formation and stellar mass in massive galaxies from the GAMA
survey between z~0.4 and the present. We identified and quantified the
contributions from mass quenching, gas stripping, and mergers to galaxy
quenching. The quenching timescale is on average 1.2 Gyr and a closer look
reveals support for the slow-then-rapid quenching scenario. The major merging
rate of galaxies is about once per 10~Gyr, while the rate of ram pressure
stripping is significantly higher. In galaxies with decreasing star formation,
we show that star formation is lost to fast quenching mechanisms such as ram
pressure stripping and is countered by mergers, at a rate of about 41%
Gyr$^{-1}$ and to mass quenching 49% Gyr$^{-1}$. (abridged)


Abstract:
  Low-rank tensors are an established framework for high-dimensional
least-squares problems. We propose to extend this framework by including the
concept of block-sparsity. In the context of polynomial regression each
sparsity pattern corresponds to some subspace of homogeneous multivariate
polynomials. This allows us to adapt the ansatz space to align better with
known sample complexity results. The resulting method is tested in numerical
experiments and demonstrates improved computational resource utilization and
sample efficiency.


Abstract:
  We construct the global phase portraits of inflationary dynamics in
teleparallel gravity models with a scalar field nonminimally coupled to torsion
scalar. The adopted set of variables can clearly distinguish between different
asymptotic states as fixed points, including the kinetic and inflationary
regimes. The key role in the description of inflation is played by the
heteroclinic orbits which run from the asymptotic saddle points to the late
time attractor point and are approximated by nonminimal slow roll conditions.
To seek the asymptotic fixed points we outline a heuristic method in terms of
the "effective potential" and "effective mass", which can be applied for any
nonminimally coupled theories. As particular examples we study positive
quadratic nonminimal couplings with quadratic and quartic potentials, and note
how the portraits differ qualitatively from the known scalar-curvature
counterparts. For quadratic models inflation can only occur at small nonminimal
coupling to torsion, as for larger coupling the asymptotic de Sitter saddle
point disappears from the physical phase space. Teleparallel models with
quartic potentials are not viable for inflation at all, since for small
nonminimal coupling the asymptotic saddle point exhibits weaker than
exponential expansion, and for larger coupling disappears too.


Abstract:
  We consider a dynamic network of individuals that may hold one of two
different opinions in a two-party society. As a dynamical model, agents can
endlessly create and delete links to satisfy a preferred degree, and the
network is shaped by \emph{homophily}, a form of social interaction.
Characterized by the parameter $J \in [-1,1]$, the latter plays a role similar
to Ising spins: agents create links to others of the same opinion with
probability $(1+J)/2$, and delete them with probability $(1-J)/2$. Using Monte
Carlo simulations and mean-field theory, we focus on the network structure in
the steady state. We study the effects of $J$ on degree distributions and the
fraction of cross-party links. While the extreme cases of homophily or
heterophily ($J= \pm 1$) are easily understood to result in complete
polarization or anti-polarization, intermediate values of $J$ lead to
interesting features of the network. Our model exhibits the intriguing feature
of an "overwhelming transition" occurring when communities of different sizes
are subject to sufficient heterophily: agents of the minority group are
oversubscribed and their average degree greatly exceeds that of the majority
group. In addition, we introduce an original measure of polarization which
displays distinct advantages over the commonly used average edge homogeneity.


Abstract:
  Learning based representation has become the key to the success of many
computer vision systems. While many 3D representations have been proposed, it
is still an unaddressed problem how to represent a dynamically changing 3D
object. In this paper, we introduce a compositional representation for 4D
captures, i.e. a deforming 3D object over a temporal span, that disentangles
shape, initial state, and motion respectively. Each component is represented by
a latent code via a trained encoder. To model the motion, a neural Ordinary
Differential Equation (ODE) is trained to update the initial state conditioned
on the learned motion code, and a decoder takes the shape code and the updated
state code to reconstruct the 3D model at each time stamp. To this end, we
propose an Identity Exchange Training (IET) strategy to encourage the network
to learn effectively decoupling each component. Extensive experiments
demonstrate that the proposed method outperforms existing state-of-the-art deep
learning based methods on 4D reconstruction, and significantly improves on
various tasks, including motion transfer and completion.


Abstract:
  In this paper, we discuss the In\"on\"u-Winger contraction of the conformal
algebra. We start with the light-cone form of the Poincar\'e algebra and extend
it to write down the conformal algebra in $d$ dimensions. To contract the
conformal algebra, we choose five dimensions for simplicity and compactify the
third transverse direction in to a circle of radius $R$ following Kaluza-Klein
dimensional reduction method. We identify the inverse radius, $1/R$, as the
contraction parameter. After the contraction, the resulting representation is
found to be the continuous spin representation in four dimensions. Even though
the scaling symmetry survives the contraction, but the special conformal
translation vector changes and behaves like the four-momentum vector. We also
discussed the generalization to $d$ dimensions.


Abstract:
  Predicting (1) when the next hospital admission occurs and (2) what will
happen in the next admission about a patient by mining electronic health record
(EHR) data can provide granular readmission predictions to assist clinical
decision making. Recurrent neural network (RNN) and point process models are
usually employed in modelling temporal sequential data. Simple RNN models
assume that sequences of hospital visits follow strict causal dependencies
between consecutive visits. However, in the real-world, a patient may have
multiple co-existing chronic medical conditions, i.e., multimorbidity, which
results in a cascade of visits where a non-immediate historical visit can be
most influential to the next visit. Although a point process (e.g., Hawkes
process) is able to model a cascade temporal relationship, it strongly relies
on a prior generative process assumption. We propose a novel model, MEDCAS, to
address these challenges. MEDCAS combines the strengths of RNN-based models and
point processes by integrating point processes in modelling visit types and
time gaps into an attention-based sequence-to-sequence learning model, which is
able to capture the temporal cascade relationships. To supplement the patients
with short visit sequences, a structural modelling technique with graph-based
methods is used to construct the markers of the point process in MEDCAS.
Extensive experiments on three real-world EHR datasets have been performed and
the results demonstrate that \texttt{MEDCAS} outperforms state-of-the-art
models in both tasks.


Abstract:
  The use of non-differentiable priors in Bayesian statistics has become
increasingly popular, in particular in Bayesian imaging analysis. Current state
of the art methods are approximate in the sense that they replace the posterior
with a smooth approximation via Moreau-Yosida envelopes, and apply
gradient-based discretized diffusions to sample from the resulting
distribution. We characterize the error of the Moreau-Yosida approximation and
propose a novel implementation using underdamped Langevin dynamics. In
misson-critical cases, however, replacing the posterior with an approximation
may not be a viable option. Instead, we show that Piecewise-Deterministic
Markov Processes (PDMP) can be utilized for exact posterior inference from
distributions satisfying almost everywhere differentiability. Furthermore, in
contrast with diffusion-based methods, the suggested PDMP-based samplers place
no assumptions on the prior shape, nor require access to a computationally
cheap proximal operator, and consequently have a much broader scope of
application. Through detailed numerical examples, including a
non-differentiable circular distribution and a non-convex genomics model, we
elucidate the relative strengths of these sampling methods on problems of
moderate to high dimensions, underlining the benefits of PDMP-based methods
when accurate sampling is decisive.


Abstract:
  Ultra sparse-view computed tomography (CT) algorithms can reduce radiation
exposure of patients, but those algorithms lack an explicit cycle consistency
loss minimization and an explicit log-likelihood maximization in testing. Here,
we propose X2CT-FLOW for the maximum a posteriori (MAP) reconstruction of a
three-dimensional (3D) chest CT image from a single or a few two-dimensional
(2D) projection images using a progressive flow-based deep generative model,
especially for ultra low-dose protocols. The MAP reconstruction can
simultaneously optimize the cycle consistency loss and the log-likelihood. The
proposed algorithm is built upon a newly developed progressive flow-based deep
generative model, which is featured with exact log-likelihood estimation,
efficient sampling, and progressive learning. We applied X2CT-FLOW to
reconstruction of 3D chest CT images from biplanar projection images without
noise contamination (assuming a standard-dose protocol) and with strong noise
contamination (assuming an ultra low-dose protocol). With the standard-dose
protocol, our images reconstructed from 2D projected images and 3D ground-truth
CT images showed good agreement in terms of structural similarity (SSIM, 0.7675
on average), peak signal-to-noise ratio (PSNR, 25.89 dB on average), mean
absolute error (MAE, 0.02364 on average), and normalized root mean square error
(NRMSE, 0.05731 on average). Moreover, with the ultra low-dose protocol, our
images reconstructed from 2D projected images and the 3D ground-truth CT images
also showed good agreement in terms of SSIM (0.7008 on average), PSNR (23.58 dB
on average), MAE (0.02991 on average), and NRMSE (0.07349 on average).


Abstract:
  In this paper, we have studied the performance and role of local optimizers
in quantum variational circuits. We studied the performance of the two most
popular optimizers and compared their results with some popular classical
machine learning algorithms. The classical algorithms we used in our study are
support vector machine (SVM), gradient boosting (GB), and random forest (RF).
These were compared with a variational quantum classifier (VQC) using two sets
of local optimizers viz AQGD and COBYLA. For experimenting with VQC, IBM
Quantum Experience and IBM Qiskit was used while for classical machine learning
models, sci-kit learn was used. The results show that machine learning on noisy
immediate scale quantum machines can produce comparable results as on classical
machines. For our experiments, we have used a popular restaurant sentiment
analysis dataset. The extracted features from this dataset and then after
applying PCA reduced the feature set into 5 features. Quantum ML models were
trained using 100 epochs and 150 epochs on using EfficientSU2 variational
circuit. Overall, four Quantum ML models were trained and three Classical ML
models were trained. The performance of the trained models was evaluated using
standard evaluation measures viz, Accuracy, Precision, Recall, F-Score. In all
the cases AQGD optimizer-based model with 100 Epochs performed better than all
other models. It produced an accuracy of 77% and an F-Score of 0.785 which were
highest across all the trained models.


Abstract:
  Classification algorithms have been widely adopted to detect anomalies for
various systems, e.g., IoT, cloud and face recognition, under the common
assumption that the data source is clean, i.e., features and labels are
correctly set. However, data collected from the wild can be unreliable due to
careless annotations or malicious data transformation for incorrect anomaly
detection. In this paper, we extend a two-layer on-line data selection
framework: Robust Anomaly Detector (RAD) with a newly designed ensemble
prediction where both layers contribute to the final anomaly detection
decision. To adapt to the on-line nature of anomaly detection, we consider
additional features of conflicting opinions of classifiers, repetitive
cleaning, and oracle knowledge. We on-line learn from incoming data streams and
continuously cleanse the data, so as to adapt to the increasing learning
capacity from the larger accumulated data set. Moreover, we explore the concept
of oracle learning that provides additional information of true labels for
difficult data points. We specifically focus on three use cases, (i) detecting
10 classes of IoT attacks, (ii) predicting 4 classes of task failures of big
data jobs, and (iii) recognising 100 celebrities faces. Our evaluation results
show that RAD can robustly improve the accuracy of anomaly detection, to reach
up to 98.95% for IoT device attacks (i.e., +7%), up to 85.03% for cloud task
failures (i.e., +14%) under 40% label noise, and for its extension, it can
reach up to 77.51% for face recognition (i.e., +39%) under 30% label noise. The
proposed RAD and its extensions are general and can be applied to different
anomaly detection algorithms.


Abstract:
  So far the null results from axion searches have enforced a huge hierarchy
between the Peccei-Quinn and electroweak symmetry breaking scales. Then the
inevitable Higgs portal poses a large fine tuning on the standard model Higgs
scalar. Now we find if the Peccei-Quinn global symmetry has a set of residually
discrete symmetries, these global and discrete symmetries can achieve a chain
breaking at low scales such as the accessible TeV scale. This novel mechanism
can accommodate some new phenomena including a sizable coupling of the standard
model Higgs boson to the axion.


Abstract:
  BaNi$_{2}$As$_{2}$ is a non-magnetic analogue of BaFe$_{2}$As$_{2}$, the
parent compound of a prototype ferro-pnictide high-temperature superconductor.
Recent diffraction studies on BaNi$_{2}$As$_{2}$ demonstrate the existence of
two types of periodic lattice distortions above and below the tetragonal to
triclinic phase transition, suggesting charge-density-wave (CDW) order to
compete with superconductivity. We apply time-resolved optical spectroscopy and
demonstrate the existence of collective CDW amplitude modes. The smooth
evolution of these modes through the structural phase transition implies the
CDW order in the triclinic phase smoothly evolves from the unidirectional CDW
in the tetragonal phase and suggests that the CDW order drives the structural
phase transition.


Abstract:
  The solenoid scan is one of the most common methods for the in-situ
measurement of the thermal emittance of a photocathode in an rf photoinjector.
The fringe field of the solenoid overlaps with the gun rf field in quite a
number of photoinjectors, which makes accurate knowledge of the transfer matrix
challenging, thus increases the measurement uncertainty of the thermal
emittance. This paper summarizes two methods that have been used to solve the
overlap issue and explains their deficiencies. Furthermore, we provide a new
method to eliminate the measurement error due to the overlap issue in solenoid
scans. The new method is systematically demonstrated using theoretical
derivations, beam dynamics simulations, and experimental data based on the
photoinjector configurations from three different groups, proving that the
measurement error with the new method is very small and can be ignored in most
of the photoinjector configurations.


Abstract:
  Reinforcement learning (RL) research focuses on general solutions that can be
applied across different domains. This results in methods that RL practitioners
can use in almost any domain. However, recent studies often lack the
engineering steps ("tricks") which may be needed to effectively use RL, such as
reward shaping, curriculum learning, and splitting a large task into smaller
chunks. Such tricks are common, if not necessary, to achieve state-of-the-art
results and win RL competitions. To ease the engineering efforts, we distill
descriptions of tricks from state-of-the-art results and study how well these
tricks can improve a standard deep Q-learning agent. The long-term goal of this
work is to enable combining proven RL methods with domain-specific tricks by
providing a unified software framework and accompanying insights in multiple
domains.


Abstract:
  We study private synthetic data generation for query release, where the goal
is to construct a sanitized version of a sensitive dataset, subject to
differential privacy, that approximately preserves the answers to a large
collection of statistical queries. We first present an algorithmic framework
that unifies a long line of iterative algorithms in the literature. Under this
framework, we propose two new methods. The first method, private entropy
projection (PEP), can be viewed as an advanced variant of MWEM that adaptively
reuses past query measurements to boost accuracy. Our second method, generative
networks with the exponential mechanism (GEM), circumvents computational
bottlenecks in algorithms such as MWEM and PEP by optimizing over generative
models parameterized by neural networks, which capture a rich family of
distributions while enabling fast gradient-based optimization. We demonstrate
that PEP and GEM empirically outperform existing algorithms. Furthermore, we
show that GEM nicely incorporates prior information from public data while
overcoming limitations of PMW^Pub, the existing state-of-the-art method that
also leverages public data.


Abstract:
  We show that a one-dimensional regular continuous Markov process \(\X\) with
scale function \(s\) is a Feller--Dynkin process precisely if the space
transformed process \(s (X)\) is a martingale when stopped at the boundaries of
its state space. As a consequence, the Feller--Dynkin and the martingale
property are equivalent for regular diffusions on natural scale with open state
space. By means of a counterexample, we also show that this equivalence fails
for multi-dimensional diffusions. Moreover, for It\^o diffusions we discuss
relations to Cauchy problems.


Abstract:
  Recent facial image synthesis methods have been mainly based on conditional
generative models. Sketch-based conditions can effectively describe the
geometry of faces, including the contours of facial components, hair
structures, as well as salient edges (e.g., wrinkles) on face surfaces but lack
effective control of appearance, which is influenced by color, material,
lighting condition, etc. To have more control of generated results, one
possible approach is to apply existing disentangling works to disentangle face
images into geometry and appearance representations. However, existing
disentangling methods are not optimized for human face editing, and cannot
achieve fine control of facial details such as wrinkles. To address this issue,
we propose DeepFaceEditing, a structured disentanglement framework specifically
designed for face images to support face generation and editing with
disentangled control of geometry and appearance. We adopt a local-to-global
approach to incorporate the face domain knowledge: local component images are
decomposed into geometry and appearance representations, which are fused
consistently using a global fusion module to improve generation quality. We
exploit sketches to assist in extracting a better geometry representation,
which also supports intuitive geometry editing via sketching. The resulting
method can either extract the geometry and appearance representations from face
images, or directly extract the geometry representation from face sketches.
Such representations allow users to easily edit and synthesize face images,
with decoupled control of their geometry and appearance. Both qualitative and
quantitative evaluations show the superior detail and appearance control
abilities of our method compared to state-of-the-art methods.


Abstract:
  We provide comprehensive regularity results and optimal conditions for a
general class of functionals involving Orlicz multi-phase of the type
\begin{align}
  \label{abst:1}
  v\mapsto \int_{\Omega} F(x,v,Dv)\,dx, \end{align} exhibiting non-standard
growth conditions and non-uniformly elliptic properties.
  The model functional under consideration is given by the Orlicz multi-phase
integral \begin{align}
  \label{abst:2}
  v\mapsto \int_{\Omega} f(x,v)\left[ G(|Dv|) +
\sum\limits_{k=1}^{N}a_k(x)H_{k}(|Dv|) \right]\,dx,\quad N\geqslant 1,
\end{align} where $G,H_{k}$ are $N$-functions and $ 0\leqslant a_{k}(\cdot)\in
L^{\infty}(\Omega) $ with $0 < \nu \leqslant f(\cdot) \leqslant L$. Its
ellipticity ratio varies according to the geometry of the level sets
$\{a_{k}(x)=0\}$ of the modulating coefficient functions $a_{k}(\cdot)$ for
every $k\in \{1,\ldots,N\}$.
  We give a unified treatment to show various regularity results for such
multi-phase problems with the coefficient functions
$\{a_{k}(\cdot)\}_{k=1}^{N}$ not necessarily H\"older continuous even for a
lower level of the regularity. Moreover, assuming that minima of the functional
above belong to better spaces such as $C^{0,\gamma}(\Omega)$ or
$L^{\kappa}(\Omega)$ for some $\gamma\in (0,1)$ and $\kappa\in (1,\infty]$, we
address optimal conditions on nonlinearity for each variant under which we
build comprehensive regularity results.
  On the other hand, since there is a lack of homogeneity properties in the
nonlinearity, we consider an appropriate scaling with keeping the structures of
the problems under which we apply Harmonic type approximation in the setting
varying on the a priori assumption on minima. We believe that the methods and
proofs developed in this paper are suitable to build regularity theorems for a
larger class of non-autonomous functionals.


Abstract:
  We study thermodynamic processes in contact with a heat bath that may have an
arbitrary time-varying periodic temperature profile. Within the framework of
stochastic thermodynamics, and for models of thermo-dynamic engines in the
idealized case of underdamped particles in the low-friction regime, we derive
explicit bounds as well as optimal control protocols that draw maximum power
and achieve maximum efficiency at any specified level of power.