Abstract:
  Process discovery methods have obtained remarkable achievements in Process
Mining, delivering comprehensible process models to enhance management
capabilities. However, selecting the suitable method for a specific event log
highly relies on human expertise, hindering its broad application. Solutions
based on Meta-learning (MtL) have been promising for creating systems with
reduced human assistance. This paper presents a MtL solution for recommending
process discovery methods that maximize model quality according to
complementary dimensions. Thanks to our MtL pipeline, it was possible to
recommend a discovery method with 92% of accuracy using light-weight features
that describe the event log. Our experimental analysis also provided
significant insights on the importance of log features in generating
recommendations, paving the way to a deeper understanding of the discovery
algorithms.


Abstract:
  Prediction based on Irregularly Sampled Time Series (ISTS) is of wide concern
in the real-world applications. For more accurate prediction, the methods had
better grasp more data characteristics. Different from ordinary time series,
ISTS is characterised with irregular time intervals of intra-series and
different sampling rates of inter-series. However, existing methods have
suboptimal predictions due to artificially introducing new dependencies in a
time series and biasedly learning relations among time series when modeling
these two characteristics. In this work, we propose a novel Time Encoding (TE)
mechanism. TE can embed the time information as time vectors in the complex
domain. It has the the properties of absolute distance and relative distance
under different sampling rates, which helps to represent both two
irregularities of ISTS. Meanwhile, we create a new model structure named Time
Encoding Echo State Network (TE-ESN). It is the first ESNs-based model that can
process ISTS data. Besides, TE-ESN can incorporate long short-term memories and
series fusion to grasp horizontal and vertical relations. Experiments on one
chaos system and three real-world datasets show that TE-ESN performs better
than all baselines and has better reservoir property.


Abstract:
  Rendering tactile effects on a touch screen via electrovibration has many
potential applications. However, our knowledge on tactile perception of change
in friction and the underlying contact mechanics are both very limited. In this
study, we investigate the tactile perception and the contact mechanics for a
step change in friction under electrovibration during a relative sliding
between finger and the surface of a capacitive touchscreen. First, we conduct
magnitude estimation experiments to investigate the role of normal force and
sliding velocity on the perceived tactile intensity for a step increase and
decrease in friction, called as rising friction (RF) and falling friction (FF).
To investigate the contact mechanics involved in RF and FF, we then measure the
frictional force, the apparent contact area, and the strains acting on the
fingerpad during sliding at a constant velocity under three different normal
loads using a custom-made experimental set-up. The results show that the
participants perceived RF stronger than FF, and both the normal force and
sliding velocity significantly influenced their perception. These results are
supported by our mechanical measurements; the relative change in friction, the
apparent contact area, and the strain in the sliding direction were all higher
for RF than those for FF, especially for low normal forces. Taken together, our
results suggest that different contact mechanics take place during RF and FF
due to the viscoelastic behavior of fingerpad skin, and those differences
influence our tactile perception of a step change in friction.


Abstract:
  With the advances of data-driven machine learning research, a wide variety of
prediction problems have been tackled. It has become critical to explore how
machine learning and specifically deep learning methods can be exploited to
analyse healthcare data. A major limitation of existing methods has been the
focus on grid-like data; however, the structure of physiological recordings are
often irregular and unordered which makes it difficult to conceptualise them as
a matrix. As such, graph neural networks have attracted significant attention
by exploiting implicit information that resides in a biological system, with
interactive nodes connected by edges whose weights can be either temporal
associations or anatomical junctions. In this survey, we thoroughly review the
different types of graph architectures and their applications in healthcare. We
provide an overview of these methods in a systematic manner, organized by their
domain of application including functional connectivity, anatomical structure
and electrical-based analysis. We also outline the limitations of existing
techniques and discuss potential directions for future research.


Abstract:
  We study the renormalization group running of axion couplings while taking
into account that the Standard Model can be extended to its supersymmetric
extension at a certain energy scale below the axion decay constant. We then
apply our results to three different classes of axion models, i.e. KSVZ-like,
DFSZ-like, and string-theoretic axions, and examine if string-theoretic axions
can be distinguished from others by having a different pattern of low energy
couplings to the photon, nucleons and electron. We find that the low energy
couplings of string-theoretic axions have a similar pattern as those of
KSVZ-like axions but yet reveal a sizable difference which might be testable in
future axion search experiments. We also note that the coupling of KSVZ-like
QCD axions to the electron is dominated by a three-loop contribution involving
the exotic heavy quark, gluons, top quark and Higgs field.


Abstract:
  Previous work by Mora and Sala provides the reduced Groebner basis of the
ideal formed by the elementary symmetric polynomials in $n$ variables of
degrees $k=1,\dots,n$, $\langle e_{1,n}(x), \dots, e_{n,n}(x) \rangle$.
Haglund, Rhoades, and Shimonozo expand upon this, finding the reduced Groebner
basis of the ideal of elementary symmetric polynomials in $n$ variables of
degree $d$ for $d=n-k+1,\dots,n$ for $k\leq n$. In this paper, we further
generalize their findings by using symbolic computation and experimentation to
construct the reduced Groebner basis for the ideal generated by the elementary
symmetric polynomials in $n$ variables of arbitrary degrees.


Abstract:
  Optimizing inspection and maintenance (I&M) plans for a large deteriorating
structure is a computationally challenging task, in particular if one considers
interdependences among its components. This is due to the sheer number of
possible decision alternatives over the lifetime of the structure and the
uncertainty surrounding the deterioration processes, the structural performance
and the outcomes of inspection and maintenance actions. To address this
challenge, Luque and Straub (2019) proposed a heuristic approach in which I&M
plans for structural systems are defined through a set of simple decision
rules. Here, we formalize the optimization of these decision rules and extend
the approach to enable adaptive planning. The initially optimal I&M plan is
successively adapted throughout the service life, based on past inspection and
monitoring results. The proposed methodology uses stochastic deterioration
models and accounts for the interdependence among structural components. The
heuristic-based adaptive planning is illustrated for a structural frame
subjected to fatigue.


Abstract:
  Using the density matrix equations (DME) for high scale leptogenesis based on
the type I seesaw mechanism, in which the CP violation (CPV) is provided by the
low-energy Dirac or/and Majorana phases of the neutrino mixing (PMNS) matrix,
we investigate the 1-to-2 and the 2-to-3 flavour regime transitions, where the
1, 2 and 3 leptogenesis flavour regimes in the generation of the baryon
asymmetry of the Universe $\eta_B$ are described by the Boltzmann equations.
Concentrating on the 1-to-2 flavour transition we determine the general
conditions under which $\eta_B$ goes through zero and changes sign in the
transition. Analysing in detail the behaviour of $\eta_B$ in the transition in
the case of two heavy Majorana neutrinos $N_{1,2}$ with hierarchical masses,
$M_1 \ll M_2$, we find, in particular, that i) the Boltzmann equations in many
cases fail to describe correctly the generation of $\eta_B$ in the 1, 2 and 3
flavour regimes, ii) the 2-flavour regime can persist above (below) $\sim
10^{12}$ GeV ($\sim 10^9$ GeV), iii) the flavour effects in leptogenesis
persist beyond the typically considered maximal for these effects leptogenesis
scale of $10^{12}$ GeV. We further determine the minimal scale
$M_{1\text{min}}$ at which we can have successful leptogenesis when the CPV is
provided only by the Dirac or Majorana phases of the PMNS matrix as well as the
ranges of scales and values of the phases for having successful leptogenesis.
We show, in particular, that when the CPV is due to the Dirac phase $\delta$,
there is a direct relation between the sign of $\sin \delta$ and the sign of
$\eta_B$ in the regions of viable leptogenesis in the case of normal
hierarchical light neutrino mass spectrum; for the inverted hierarchical
spectrum the same result holds for $M_1 \lesssim 10^{13}$ GeV. The considered
scenarios of leptogenesis are testable and falsifiable in low-energy neutrino
experiments.


Abstract:
  Concurrent analysis of composite materials can provide the interaction among
scales for better composite design, analysis, and performance prediction. A
data-driven concurrent n-scale modeling theory ($\textrm{FExSCA}^\textrm{n-1}$)
is proposed in this paper utilizing a mechanistic reduced order model (ROM)
called self-consistent clustering analysis (SCA). We demonstrated this theory
with a $\textrm{FExSCA}^2$ approach to study the 3-scale woven carbon fiber
reinforced polymer (CFRP) laminate structure. $\textrm{FExSCA}^2$ significantly
reduced expensive 3D nested composite representative volume elements (RVEs)
computation for woven and unidirectional (UD) composite structures by
developing a material database. The modeling procedure is established by
integrating the material database into a woven CFRP structural numerical model,
formulating a concurrent 3-scale modeling framework. This framework provides an
accurate prediction for the structural performance (e.g., nonlinear structural
behavior under tensile load), as well as the woven and UD physics field
evolution. The concurrent modeling results are validated against physical tests
that link structural performance to the basic material microstructures. The
proposed methodology provides a comprehensive predictive modeling procedure
applicable to general composite materials aiming to reduce laborious
experiments needed.


Abstract:
  Bacterial cells navigate around their environment by directing their movement
along chemical gradients. This process, known as chemotaxis, can promote the
rapid expansion of bacterial populations into previously unoccupied
territories. However, despite numerous experimental and theoretical studies on
this classical topic, chemotaxis-driven population expansion is not understood
in quantitative terms. Building on recent experimental progress, we here
present a detailed analytical study that provides a quantitative understanding
of how chemotaxis and cell growth lead to rapid and stable expansion of
bacterial populations. We provide analytical relations that accurately describe
the dependence of the expansion speed and density profile of the expanding
population on important molecular, cellular, and environmental parameters. In
particular, expansion speeds can be boosted by orders of magnitude when the
environmental availability of chemicals relative to the cellular limits of
chemical sensing is high. As analytical understanding of such complex
spatiotemporal dynamic processes is rare, the results derived here provide a
mathematical framework for further investigations of the different roles
chemotaxis plays in diverse ecological contexts.


Abstract:
  For ensembles of Hamiltonians that fall under the Dyson classification of
random matrices with $\beta \in \{1,2,4\}$, the low-temperature mean entropy
can be shown to vanish as $\langle S(T)\rangle\sim \kappa T^{\beta+1}$. A
similar relation holds for Altland-Zirnbauer ensembles. JT gravity has been
shown to be dual to the double-scaling limit of a $\beta =2$ ensemble, with a
classical eigenvalue density $\propto e^{S_0}\sqrt{E}$ when $0 < E \ll 1$. We
use universal results about the distribution of the smallest eigenvalues in
such ensembles to calculate $\kappa$ up to corrections that we argue are doubly
exponentially small in $S_0$.


Abstract:
  Aiming at developing high thermal conductivity copper/diamond composite, an
unconventional approach applying self-assembled monolayer (SAM) prior to the
high-temperature sintering of copper/diamond composite was utilized to enhance
the thermal boundary conductance (TBC) between copper and diamond. The
enhancement was first systematically confirmed on a model interface system by
detailed SAM morphology characterization and TBC measurements. TBC
significantly depends on the SAM coverage and ordering, and the formation of
high-quality SAM promoted the TBC to 73 MW/m^2-K from 27 MW/m^2-K, the value
without SAM. With the help of molecular dynamics simulations, the TBC
enhancement was identified to be determined by the number of SAM bridges and
the overlap of vibrational density of states. The diamond particles of 210
{\micro\metre} in size were simultaneously functionalized by SAM with the
condition giving the highest TBC in the model system and sintered together with
the copper to fabricate isotropic copper/diamond composite of 50% volume
fraction. The measured thermal conductivity marked 711 W/m-K at room
temperature, the highest value among the ones with similar diamond-particles
volume fraction and size. This work demonstrates a novel strategy to enhance
the thermal conductivity of composite materials by SAM functionalization.


Abstract:
  This work is devoted to deriving the Onsager-Machlup action functional for a
class of stochastic differential equations with (non-Gaussian) L\'{e}vy process
as well as Brownian motion in high dimensions. This is achieved by applying the
Girsanov transformation for probability measures and then by a path
representation. The Poincar\'{e} lemma is essential to handle such path
representation problem in high dimensions. We provide a sufficient condition on
the vector field such that this path representation holds in high dimensions.
Moreover, this Onsager-Machlup action functional may be considered as the
integral of a Lagrangian. Finally, by a variational principle, we investigate
the most probable transition pathways analytically and numerically.


Abstract:
  We show that the notions of weak solution to the total variation flow based
on the Anzellotti pairing and the variational inequality coincide under some
restrictions on the boundary data. The key ingredient in the argument is a
duality result for the total variation functional, which is based on an
approximation of the total variation by area-type functionals.


Abstract:
  Precision determinations of Standard Model (SM) Electro-Weak (EW) parameters
at the Large Hadron Collider (LHC) are dominated by uncertainties due to Parton
Distribution Functions (PDFs). Reweighting and profiling techniques are
routinely employed to treat this. We explore approaches based on combining
measurements of charged current and neutral current Drell-Yan (DY) asymmetries
to improve PDF uncertainties. We present the results of a numerical analysis
performed with the open-source platform xFitter. PDF uncertainties are examined
for lepton-charge and forward-backward asymmetries in regions of transverse and
invariant masses near the vector-boson peak, based on LHC Run III and HL-LHC
luminosity scenarios. We discuss the complementarity of the asymmetries in
reducing PDF uncertainties in observables relevant to both SM and Beyond the SM
(BSM) physics.


Abstract:
  This paper considers the problem of approximating the "maximal" region of
attraction (the set that contains all asymptotically stable sets) of any given
set of locally exponentially stable nonlinear Ordinary Differential Equations
(ODEs) with a sufficiently smooth vector field. Given a locally exponential
stable ODE with a differentiable vector field, we show that there exists a
globally Lipschitz continuous converse Lyapunov function whose 1-sublevel set
is equal to the maximal region of attraction of the ODE. We then propose a
sequence of d-degree Sum-of-Squares (SOS) programming problems that yields a
sequence of polynomials that converges to our proposed converse Lyapunov
function uniformly from above in the L1 norm. We show that each member of the
sequence of 1-sublevel sets of the polynomial solutions to our proposed
sequence of SOS programming problems are certifiably contained inside the
maximal region of attraction of the ODE, and moreover, we show that this
sequence of sublevel sets converges to the maximal region of attraction of the
ODE with respect to the volume metric. We provide numerical examples of
estimations of the maximal region of attraction for the Van der Pol oscillator
and a three dimensional servomechanism.


Abstract:
  Erythrosiderites with the formula A2FeX5H2O, where A = Rb, K, and (NH4) and X
= Cl and Br are intriguing systems that possess various magnetic and electric
phases, as well as multiferroic phases in which magnetism and ferroelectricity
are coupled. In this report, we study the magnetic phase diagram of
erythrosiderites as a function of superexchange interactions. To this end, we
perform classical Monte Carlo simulations on magnetic Hamiltonians that contain
five different superexchange interactions with single-ion anisotropies. Our
phase diagram contains all magnetic ground states that have been experimentally
observed in these materials. We argue that the ground states can be explained
by varying the ratio of J4/J2. For J4/J2 > 0.95 a cycloidal spins structure is
stabilized as observed in (NH4)2FeCl5H2O and otherwise, a collinear spin
structure is stabilized as observed in (K,Rb)2FeCl5H2O. We also show that the
difference in the single-ion anisotropy along a- and c- axes is essential to
stabilize the intermediate state observed in (NH)2FeCl5H2O.


Abstract:
  Big data has been used widely in many areas including the transportation
industry. Using various data sources, traffic states can be well estimated and
further predicted for improving the overall operation efficiency. Combined with
this trend, this study presents an up-to-date survey of open data and big data
tools used for traffic estimation and prediction. Different data types are
categorized and the off-the-shelf tools are introduced. To further promote the
use of big data for traffic estimation and prediction tasks, challenges and
future directions are given for future studies.


Abstract:
  We re-examine the jet probes of the nucleon spin and flavor structures. We
find for the first time the time-reversal odd (T-odd) component of a jet,
conventionally thought to vanish, can survive due to the non-perturbative
fragmentation and hadronization effects and could be testable. This additional
contribution of a jet will lead to novel jet phenomena relevant for unlocking
the access to several spin structures of the nucleon, which were thought to be
impossible by using jets. As examples, we show how the T-odd constituent can
couple to the proton transversity at the Electron Ion Collider (EIC) and can
give rise to the anisotropy in the jet production in $e^+e^-$ annihilations. We
expect the T-odd contribution of the jet to have broad applications in high
energy nuclear physics.


Abstract:
  In Navier-Stokes turbulence, energy and helicity injected at large scales are
subject to a joint direct cascade, with both quantities exhibiting a spectral
scaling $\propto k^{-5/3}$. We demonstrate via direct numerical simulations
that the two cascades are compatible due to the existence of a strong
scale-dependent phase alignment between velocity and vorticity fluctuations,
with the phase alignment angle scaling as $\cos\alpha_k\propto k^{-1}$.


Abstract:
  We present the results of a molecular survey of comet 46P/Wirtanen undertaken
with the IRAM 30-m and NOEMA radio telescopes in December 2018. Observations at
IRAM 30-m during the 12-18 Dec. period comprise a 2 mm spectral survey covering
25 GHz and a 1 mm survey covering 62 GHz. The gas outflow velocity and kinetic
temperature have been accurately constrained by the observations. We derive
abundances of 11 molecules, some being identified remotely for the first time
in a Jupiter-family comet, including complex organic molecules such as
formamide, ethylene glycol, acetaldehyde, or ethanol. Sensitive upper limits on
the abundances of 24 other molecules are obtained. The comet is found to be
relatively rich in methanol (3.4 percent relative to water), but relatively
depleted in CO, CS, HNC, HNCO, and HCOOH.


Abstract:
  In this article, the temperature-density relation of the intergalactic medium
was studied in the region $1.6 \leq z < 2.0$ divided into two bins. For this
purpose, the Ly-$\alpha$ forest decomposition into individual absorption
profiles was used for the study of 35 publicly available quasar spectra
obtained by the Ultraviolet and Visual Echelle Spectrograph (UVES) on the Very
Large Telescope (ESO) and by the High Resolution Echelle Spectrometer (HIRES)
on the Keck Telescope. For the determination of the thermal state sensitive
cut-off position in the $b - N_{HI}$ distribution, the iterative fitting
procedure was adopted. The measurements were calibrated using mock Ly-$\alpha$
forest data generated by 23 hydrodynamical simulations with different thermal
histories. The value of the temperature at mean density corresponds to the
decreasing trend predicted by various models at the lower redshifts. In the
case of power law index, determined values are close to 1.6, which is expected
after all reionization events in various models assuming the balance of
photoheating with adiabatic cooling.


Abstract:
  The temperature associated with gravitation as presented in the Unruh effect
and Hawking temperature serves to link different areas of physics, such as
gravity, statistical mechanics, and quantum physics. In this paper,
teleparallel gravity is considered to study temperature effects on the de
Sitter space-time. The effects of temperature are introduced using the Thermo
Field Dynamics (TFD) formalism. The gravitational Stefan-Boltzmann law is
obtained. Then the temperature of gravitation in the de Sitter space-time is
calculated. Here an Unruh-type effect is discussed. This effect relates the
temperature and the acceleration of a particle in the de Sitter space-time. The
gravitational Casimir effect is calculated. The result shows that there is a
transition between an attractive and a repulsive Casimir effect.


Abstract:
  Given a $d$-dimensional tensor $T:A_1\times\dots\times A_d\rightarrow
\mathbb{F}$ (where $\mathbb{F}$ is a field), the $i$-flattening rank of $T$ is
the rank of the matrix whose rows are indexed by $A_{i}$, columns are indexed
by $B_{i}=A_1\times\dots\times A_{i-1}\times A_{i+1}\times\dots\times A_{d}$
and whose entries are given by the corresponding values of $T$. The
max-flattening rank of $T$ is defined as $\text{mfrank}(T)=\max_{i\in
[d]}\text{frank}_{i}(T)$. A tensor $T:A^{d}\rightarrow\mathbb{F}$ is called
semi-diagonal, if $T(a,\dots,a)\neq 0$ for every $a\in A$, and
$T(a_{1},\dots,a_{d})=0$ for every $a_{1},\dots,a_{d}\in A$ that are all
distinct. In this paper we prove that if $T:A^{d}\rightarrow\mathbb{F}$ is
semi-diagonal, then $\text{mfrank}(T)\geq \frac{|A|}{d-1}$, and this bound is
the best possible.
  We give several applications of this result, including a generalization of
the celebrated Frankl-Wilson theorem on forbidden intersections. Also,
addressing a conjecture of Aharoni and Berger, we show that if the edges of an
$r$-uniform multi-hypergraph $\mathcal{H}$ are colored with $z$ colors such
that each colorclass is a matching of size $t$, then $\mathcal{H}$ contains a
rainbow matching of size $t$ provided $z>(t-1)\binom{rt}{r}$. This improves
previous results of Alon and Glebov, Sudakov and Szab\'o.


Abstract:
  The recent availability of high-resolution far-infrared (FIR) polarization
observations of galaxies using HAWC+/SOFIA has facilitated studies of
extragalactic magnetic fields in the cold and dense molecular disks.We
investigate if any significant structural differences are detectable in the
kpc-scale magnetic field of the grand design face-on spiral galaxy M51 when
traced within the diffuse (radio) and the dense and cold (FIR) interstellar
medium (ISM). Our analysis reveals a complex scenario where radio and FIR
polarization observations do not necessarily trace the same magnetic field
structure. We find that the magnetic field in the arms is wrapped tighter at
154um than at 3 and 6 cm; statistically significant lower values for the
magnetic pitch angle are measured at FIR in the outskirts (R > 7 kpc) of the
galaxy. This difference is not detected in the interarm region. We find strong
correlations of the polarization fraction and total intensity at FIR and radio
with the gas column density and 12CO(1-0) velocity dispersion. We conclude that
the arms show a relative increase of small-scale turbulent B-fields at regions
with increasing column density and dispersion velocities of the molecular gas.
No correlations are found with HI neutral gas. The star formation rate shows a
clear correlation with the radio polarized intensity, which is not found in
FIR, pointing to a small-scale dynamo-driven B-field amplification scenario.
This work shows that multi-wavelength polarization observations are key to
disentangling the interlocked relation between star formation, magnetic fields,
and gas kinematics in the multi-phase ISM.


Abstract:
  This paper introduces a new concept called "transferable visual words"
(TransVW), aiming to achieve annotation efficiency for deep learning in medical
image analysis. Medical imaging--focusing on particular parts of the body for
defined clinical purposes--generates images of great similarity in anatomy
across patients and yields sophisticated anatomical patterns across images,
which are associated with rich semantics about human anatomy and which are
natural visual words. We show that these visual words can be automatically
harvested according to anatomical consistency via self-discovery, and that the
self-discovered visual words can serve as strong yet free supervision signals
for deep models to learn semantics-enriched generic image representation via
self-supervision (self-classification and self-restoration). Our extensive
experiments demonstrate the annotation efficiency of TransVW by offering higher
performance and faster convergence with reduced annotation cost in several
applications. Our TransVW has several important advantages, including (1)
TransVW is a fully autodidactic scheme, which exploits the semantics of visual
words for self-supervised learning, requiring no expert annotation; (2) visual
word learning is an add-on strategy, which complements existing self-supervised
methods, boosting their performance; and (3) the learned image representation
is semantics-enriched models, which have proven to be more robust and
generalizable, saving annotation efforts for a variety of applications through
transfer learning. Our code, pre-trained models, and curated visual words are
available at https://github.com/JLiangLab/TransVW.


Abstract:
  Local coexistence of species in large ecosystems is traditionally explained
within the broad framework of niche theory. However, its rationale hardly
justifies rich biodiversity observed in nearly homogeneous environments. Here
we consider a consumer-resource model in which a coarse-graining procedure
accounts for a variety of ecological mechanisms and leads to effective spatial
effects which favour species coexistence. Herein, we provide conditions for
several species to live in an environment with very few resources. In fact, the
model displays two different phases depending on whether the number of
surviving species is larger or smaller than the number of resources. We obtain
conditions whereby a species can successfully colonize a pool of coexisting
species. Finally, we analytically compute the distribution of the population
sizes of coexisting species. Numerical simulations as well as empirical
distributions of population sizes support our analytical findings.


Abstract:
  By combining neutron scattering and magnetization measurements down to 80 mK,
we determine the $(H,T)$ phase diagram of the Nd$_2$(Zr$_{1-x}$Ti$_x$)$_2$O$_7$
pyrochlore magnet compounds. In those samples, Zr is partially substituted by
Ti, hence tuning the exchange parameters and testing the robustness of the
various phases. In all samples, the ground state remains "all in / all out",
while the field induces phase transitions towards new states characterized by
"2 in - 2 out" or "1 out - 3 in / 1 in - 3 out" configurations. These
transitions manifest as metamagnetic singularities in the magnetization vs
field measurements. Strikingly, it is found that moderate substitution
reinforces the stability of the "all in / all out" phase: the N\'eel
temperature, the metamagnetic fields along with the ordered magnetic moment are
higher in substituted samples with $x <$ 10\%.


Abstract:
  The results on the initial boundary value problem for Einstein's vacuum field
equation obtained in \cite{friedrich:nagy} rely on an unusual gauge. One of the
defining gauge source functions represents the mean extrinsic curvature of the
time-like leaves of a foliation that includes the boundary and covers a
neighbourhoood of it. The others steer the development of a frame field and
coordinates on the leaves. In general their combined action is needed to
control in the context of the reduced field equations the evolution of the
leaves. In this article are derived the hyperbolic equations implicit in that
gauge. It is shown that the latter are independent of the Einstein equations
and well defined on arbitrary space-times. The analysis simplifies if boundary
conditions with constant mean extrinsic curvature are stipulated. It simplifies
further if the boundary is required to be totally geodesic.


Abstract:
  Water (H$_{2}$O), in all forms, is an important constituent in planetary
bodies, controlling habitability and influencing geological activity. Under
conditions found in the interior of many planets, as the pressure increases,
the H-bonds in water gradually weaken and are replaced by ionic bonds. Recent
experimental measurements of the water equation of state (EOS) showed both a
new phase of H-bonded water ice, ice-VII$_t$, and a relatively low transition
pressure just above 30 GPa to ionic bonded ice-X, which has a bulk modulus 2.5
times larger. The higher bulk modulus of ice-X produces larger planets for a
given mass, thereby either reducing the atmospheric contribution to the volume
of many exoplanets or limiting their water content. We investigate the impact
of the new EOS measurements on the planetary mass-radius relation and interior
structure for water-rich planets. We find that the change in the planet
mass-radius relation caused by the systematic differences between previous and
new experimental EOS measurements are comparable to the observational
uncertainties in some planet sizes -- an issue that will become more important
as observations continue to improve.


Abstract:
  The occurrence of stochastic resonance in bistable systems undergoing
anomalous diffusions, which arise from density-dependent fluctuations, is
investigated with emphasis on the analytical formulation of the problem as well
as a possible analytical derivation of key quantifiers of stochastic resonance.
The nonlinear Fokker-Planck equation describing the system dynamics, together
with the corresponding Ito-Langevin equation, are formulated. In the
linear-response regime analytical expressions of the spectral amplification, of
the signal-to-noise ratio and of the hysteresis loop area are derived as
quantifiers of stochastic resonance. These quantifiers are found to be strongly
dependent on the parameters controlling the type of diffusion, in particular
the peak characterizing the signal-to-noise ratio occurs only in close ranges
of parameters. Results introduce the relevant information that taking into
consideration the interactions of anomalous diffusive systems with a periodic
signal, can provide a better understanding of the physics of stochastic
resonance in bistable systems driven by periodic forces.


Abstract:
  In a previous paper, the first two named authors established an isomorphism
between the moduli space of framed flags of sheaves on the projective plane and
the moduli space of stable representations of a certain quiver. In the present
note, we substitute one of the claims made, namely [5, Theorem 17], for a
weaker claim regarding the existence of unobstructed points in the quiver
moduli space. We also extend some of the results of the cited paper, concerning
the maximal stability chamber within which the isomorphism mentioned holds, and
the existence of a perfect obstruction theory for the quiver moduli space.


Abstract:
  Using the policy gradient algorithm, we train a single-hidden-layer neural
network to balance a physically accurate simulation of a single inverted
pendulum. The trained weights and biases can then be transferred to a physical
agent, where they are robust enough to to balance a real inverted pendulum.
This hybrid approach of training a simulation allows thousands of trial runs to
be completed orders of magnitude faster than would be possible in the real
world, resulting in greatly reduced training time and more iterations,
producing a more robust model. When compared with existing reinforcement
learning methods, the resulting control is smoother, learned faster, and able
to withstand forced disturbances.


Abstract:
  In this work, we study the binary neural networks (BNNs) of which both the
weights and activations are binary (i.e., 1-bit representation). Feature
representation is critical for deep neural networks, while in BNNs, the
features only differ in signs. Prior work introduces scaling factors into
binary weights and activations to reduce the quantization error and effectively
improves the classification accuracy of BNNs. However, the scaling factors not
only increase the computational complexity of networks, but also make no sense
to the signs of binary features. To this end, Self-Distribution Binary Neural
Network (SD-BNN) is proposed. Firstly, we utilize Activation Self Distribution
(ASD) to adaptively adjust the sign distribution of activations, thereby
improve the sign differences of the outputs of the convolution. Secondly, we
adjust the sign distribution of weights through Weight Self Distribution (WSD)
and then fine-tune the sign distribution of the outputs of the convolution.
Extensive experiments on CIFAR-10 and ImageNet datasets with various network
structures show that the proposed SD-BNN consistently outperforms the
state-of-the-art (SOTA) BNNs (e.g., achieves 92.5% on CIFAR-10 and 66.5% on
ImageNet with ResNet-18) with less computation cost. Code is available at
https://github.com/ pingxue-hfut/SD-BNN.


Abstract:
  Quantum coherent control of ultrafast bond making and the subsequent
molecular dynamics is crucial for the realization of a new photochemistry,
where a shaped laser field is actively driving the chemical system in a
coherent way from the thermal initial state of the reactants to the final state
of the desired products. We demonstrate here coherent control over the relative
yields of Mg$_2$ molecules that are generated via photoassociation and
subsequently photodriven into different groups of final states. The
strong-field process involves non-resonant multiphoton femtosecond
photoassociation of a pair of thermally hot magnesium atoms into a bound
Mg$_{2}$ molecule and subsequent molecular dynamics on electronically excited
states. The branching-ratio control is achieved with linearly chirped laser
pulses, utilizing the different chirp dependence that various groups of final
molecular states display for their post-pulse population. Our study establishes
the feasibility of high degree coherent control over quantum molecular dynamics
that is initiated by femtosecond photoassociation of thermal atoms.


Abstract:
  In this paper we study the statistical properties of Laplacian smoothing, a
graph-based approach to nonparametric regression. Under standard regularity
conditions, we establish upper bounds on the error of the Laplacian smoothing
estimator $\widehat{f}$, and a goodness-of-fit test also based on
$\widehat{f}$. These upper bounds match the minimax optimal estimation and
testing rates of convergence over the first-order Sobolev class
$H^1(\mathcal{X})$, for $\mathcal{X}\subseteq \mathbb{R}^d$ and $1 \leq d < 4$;
in the estimation problem, for $d = 4$, they are optimal modulo a $\log n$
factor. Additionally, we prove that Laplacian smoothing is manifold-adaptive:
if $\mathcal{X} \subseteq \mathbb{R}^d$ is an $m$-dimensional manifold with $m
< d$, then the error rate of Laplacian smoothing (in either estimation or
testing) depends only on $m$, in the same way it would if $\mathcal{X}$ were a
full-dimensional set in $\mathbb{R}^d$.


Abstract:
  Ammonia is predicted to be one of the major components in the depths of the
ice giant planets Uranus and Neptune. Their dynamics, evolution, and interior
structure are insufficiently understood and models rely imperatively on data
for equation of state and transport properties. Despite its great significance,
the experimentally accessed region of the ammonia phase diagram today is still
very limited in pressure and temperature. Here we push the probed regime to
unprecedented conditions, up to $\sim$350 GPa and $\sim$40000 K. Along the
Hugoniot, the temperature measured as a function of pressure shows a subtle
change in slope at $\sim$7000 K and $\sim$90 GPa, in agreement with ab initio
simulations we have performed. This feature coincides with the gradual
transition from a molecular liquid to a plasma state. Additionally, we
performed reflectivity measurements, providing the first experimental evidence
of electronic conduction in high-pressure ammonia. Shock reflectance
continuously rises with pressure above 50 GPa and reaches saturation values
above 120 GPa. Corresponding electrical conductivity values are up to 1 order
of magnitude higher than in water in the 100 GPa regime, with possible
significant contributions of the predicted ammonia-rich layers to the
generation of magnetic dynamos in ice giant interiors.


Abstract:
  We construct an example of a Hilbert C*-module which shows that Troitsky's
theorem on the geometrical essence of A-compact operators between Hilbert
C*-modules is not extendable to a not countably generated module case (even in
the case of a stronger uniform structure, which is also introduced). In
addition, the constructed module admits no frames.


Abstract:
  We discuss high-order calculations in perturbative effective field theory for
fermions at low energy scales. The Fermi-momentum or $k_{\rm F} a_s$ expansion
for the ground-state energy of the dilute Fermi gas is calculated to fourth
order, both in cutoff regularization and in dimensional regularization. For the
case of spin one-half fermions we find from a Bayesian analysis that the
expansion is well-converged at this order for ${| k_{\rm F} a_s | \lesssim
0.5}$. Further, we show that Pad{\'e}-Borel resummations can improve the
convergence for ${| k_{\rm F} a_s | \lesssim 1}$. Our results provide important
constraints for nonperturbative calculations of ultracold atoms and dilute
neutron matter.


Abstract:
  The idea of constructing a time machine is not new and even received a boost
thanks to the realization that a traversable wormhole could be converted to a
time machine. This also implied that it would be impossible to travel back in
time beyond the creation of the time machine. This paper addresses these
issues, as well as the concomitant causality violations, by starting with a
physically acceptable model, a spacetime that is anti-de Sitter due to an extra
time-like dimension, thereby allowing the existence of closed time-like curves.
By assuming that the extra dimension is independent of the radial coordinate,
the wormhole retains its basic geometric properties regardless of its location
and can therefore serve as a shortcut for any closed time-like curve, which, in
turn, can extend indefinitely into the past. The same independence of the
radial coordinate suggests that the wormhole could connect a region in the
present to a local region that (1) lies in the past and (2) does not contain
any closed time-like curves. Previous studies have suggested that if such a
region is sufficiently localized, it may be possible to avoid a causality
violation.


Abstract:
  Land use mix is one of the central concepts in the urban planning field,
though its measure has been found to have many fallacies. In this study, we
propose multiple alternative methods to the Conventional Shannon Entropy land
use mix index, which is generally employed to measure land use diversity. The
study attempted to measure land use mix from two new perspectives and test the
performance of the new measures with economic, transportation, and social
outcomes. We first offered an improved single measure, entropy-based land use
mix index weighted by adding surrounding land use attributes and the regional
land use context to mitigate the modifiable area unit problem (MAUP) and equal
composition issue. Second, we designed a multi-measure method to describe land
use patterns via clustering. We found that the new single measure method is
more effective than the existing Conventional Shannon Entropy index in
accurately delivering the vision of land use mix that Jane Jacobs originally
proposed. We also found the multi-measure clustering method performed much
better in the regression model on various effect variables, compared with both
conventional and other new index. Upon further review, we found that land use
pattern complexity's relationship with various outcomes makes a single
definition measure very difficult to represent true land use fully. Therefore,
we recommend that future research measure whether or not land use is mixed to
then assess land use pattern.


Abstract:
  We investigate the observational viability of a class of interacting dark
energy (iDE) models in the light of the latest Cosmic Microwave Background
(CMB), type Ia supernovae (SNe) and SH0ES Hubble parameter measurements. Our
analysis explores the assumption of a non-zero spatial curvature, the
correlation between the interaction parameter $\alpha$ and the current
expansion rate $H_0$, and updates the results reported in \cite{micol}.
Initially, assuming a spatially flat universe, the results show that the
best-fit of our joint analysis clearly favours a positive interaction, i.e., an
energy flux from dark matter to dark energy, with $\alpha \approx 0.2$, while
the non-interacting case, $\alpha = 0$, is ruled out by more than $3\sigma$
confidence level. On the other hand, considering a non-zero spatial curvature,
we find a slight preference for a negative value of the curvature parameter,
which seems to relax the correlation between the parameters $\alpha$ and $H_0$,
as well as between $H_0$ and the normalization of the matter power spectrum on
scales of 8$h^{-1}$ Mpc ($\sigma_8$). Finally, we discuss the influence of
considering the SH$0$ES prior on $H_0$ in the joint analyses, and find that
such a choice does not change considerably the standard cosmology predictions
but has a significant influence on the results of the iDE model.


Abstract:
  Crowdsourced design feedback systems are emerging resources for getting large
amounts of feedback in a short period of time. Traditionally, the feedback
comes in the form of a declarative statement, which often contains positive or
negative sentiment. Prior research has shown that overly negative or positive
sentiment can strongly influence the perceived usefulness and acceptance of
feedback and, subsequently, lead to ineffective design revisions. To enhance
the effectiveness of crowdsourced design feedback, we investigate a new
approach for mitigating the effects of negative or positive feedback by
combining open-ended and thought-provoking questions with declarative feedback
statements. We conducted two user studies to assess the effects of
question-based feedback on the sentiment and quality of design revisions in the
context of graphic design. We found that crowdsourced question-based feedback
contains more neutral sentiment than statement-based feedback. Moreover, we
provide evidence that presenting feedback as questions followed by statements
leads to better design revisions than question- or statement-based feedback
alone.


Abstract:
  In scientific computing and machine learning applications, matrices and more
general multidimensional arrays (tensors) can often be approximated with the
help of low-rank decompositions. Since matrices and tensors of fixed rank form
smooth Riemannian manifolds, one of the popular tools for finding low-rank
approximations is to use Riemannian optimization. Nevertheless, efficient
implementation of Riemannian gradients and Hessians, required in Riemannian
optimization algorithms, can be a nontrivial task in practice. Moreover, in
some cases, analytic formulas are not even available. In this paper, we build
upon automatic differentiation and propose a method that, given an
implementation of the function to be minimized, efficiently computes Riemannian
gradients and matrix-by-vector products between an approximate Riemannian
Hessian and a given vector.


Abstract:
  Given two relations containing multiple measurements - possibly with
uncertainties - our objective is to find which sets of attributes from the
first have a corresponding set on the second, using exclusively a sample of the
data. This approach could be used even when the associated metadata is damaged,
missing or incomplete, or when the volume is too big for exact methods. This
problem is similar to the search of Inclusion Dependencies (IND), a type of
rule over two relations asserting that for a set of attributes X from the
first, every combination of values appears on a set Y from the second. Existing
IND can be found exploiting the existence of a partial order relation called
specialization. However, this relation is based on set theory, requiring the
values to be directly comparable. Statistical tests are an intuitive possible
replacement, but it has not been studied how would they affect the underlying
assumptions. In this paper we formally review the effect that a statistical
approach has over the inference rules applied to IND discovery. Our results
confirm the intuitive thought that statistical tests can be used, but not in a
directly equivalent manner. We provide a workable alternative based on a
"hierarchy of null hypotheses", allowing for the automatic discovery of
multi-dimensional equally distributed sets of attributes.


Abstract:
  In previous work by this author, the screening paradox - the loss of
predictive power of screening tests over time $t$ - was mathematically
formalized using Bayesian theory. Where $J$ is Youden's statistic, $b$ is the
specificity of the screening test and $\phi$ is the prevalence of disease, the
ratio of positive predictive values at subsequent time $k$, $\rho(\phi_{k})$,
over the original $\rho(\phi_{0})$ at $t_0$ is given by:
  $\zeta(\phi_{0},k) = \frac{\rho(\phi_{k})}{\rho(\phi_{0})}
=\frac{\phi_k(1-b)+J\phi_0\phi_k}{\phi_0(1-b)+J\phi_0\phi_k}$
  Herein, we modify the traditional Kermack-McKendrick SIR Model to include the
fluctuation of the positive predictive value $\rho(\phi)$ (PPV) of a screening
test over time as a function of the prevalence threshold $\phi_e$. We term this
modified model the SIR-P model. Where a = sensitivity, b = specificity, $S$ =
number susceptible, $I$ = number infected, $R$ = number recovered/dead, $\beta$
= infectious rate, $\gamma$ = recovery rate, and $N$ is the total number in the
population, the predictive value $\rho(\phi,t)$ over time $t$ is given by:
  $\rho(\phi,t) = \frac{a[\frac{\beta IS}{N}-\gamma I]}{ a[\frac{\beta
IS}{N}-\gamma I]+(1-b)(1-[\frac{\beta IS}{N}-\gamma I])}$
  Otherwise stated:
  $\rho(\phi,t) = \frac{a\frac{dI}{dt}}{
a\frac{dI}{dt}+(1-b)(1-\frac{dI}{dt})}$ where $\frac{dI}{dt}$ is the
fluctuation of infected individuals over time $t$.


Abstract:
  The transformation of graphs and graph-like structures is ubiquitous in
computer science. When a system is described by graph-transformation rules, it
is often desirable that the rules are both terminating and confluent so that
rule applications in an arbitrary order produce unique resulting graphs.
However, there are application scenarios where the rules are not globally
confluent but confluent on a subclass of graphs that are of interest. In other
words, non-resolvable conflicts can only occur on graphs that are considered as
"garbage". In this paper, we introduce the notion of confluence up to garbage
and generalise Plump's critical pair lemma for double-pushout graph
transformation, providing a sufficient condition for confluence up to garbage
by non-garbage critical pair analysis. We apply our results in two case studies
about efficient language recognition: we present backtracking-free graph
reduction systems which recognise a class of flow diagrams and a class of
labelled series-parallel graphs, respectively. Both systems are non-confluent
but confluent up to garbage. We also give a critical pair condition for
subcommutativity up to garbage which, together with closedness, implies
confluence up to garbage even in non-terminating systems.


Abstract:
  Extensive molecular simulations are applied to characterize the equilibrium
dynamics, entanglement topology, and nonlinear extensional rheology of
symmetric ring-linear polymer blends with systematically varied ring fraction
$\phi_R$. Chains with degree of entanglement $Z\approx14$ mixed to produce 10
well-entangled systems with $\phi_R$ varying from neat linear to neat ring
melts. Primitive path analysis are used to visualize and quantify the structure
of the composite ring-linear entanglement network. We directly measure the
quantity of ring-linear threading and linear-linear entanglement as a function
of $\phi_R$, and identify with simple arguments a ring fraction
$\phi_R\approx0.4$ where the topological constraints of the entanglement
network are maximized. These topological analyses are used to rationalize the
$\phi_R$-dependence of ring and linear chain dynamics, conformations, and blend
viscosity. Complimentary simulations of startup uniaxial elongation flows
demonstrate the extensional stress overshoot observed in recent filament
stretching experiments, and characterize how it depends on the blend
composition and entanglement topology. The overshoot is driven by an
overstretching and recoil of ring polymer conformations that is caused by the
convective unthreading of rings from linear chains. This produces significant
changes in the entanglement structure of blends that we directly visualize and
quantify with primitive path analyses during flow.


Abstract:
  This paper provides the details of implementing two important policy gradient
methods to solve the inverted pendulum problem. These are namely the Deep
Deterministic Policy Gradient (DDPG) and the Proximal Policy Optimization (PPO)
algorithm. The problem is solved by using an actor-critic model where an
actor-network is used to learn the policy function and a critic network is to
evaluate the actor-network by learning to estimate the Q function. Apart from
briefly explaining the mathematics behind these two algorithms, the details of
python implementation are provided which helps in demystifying the underlying
complexity of the algorithm. In the process, the readers will be introduced to
OpenAI/Gym, Tensorflow 2.x and Keras utilities used for implementing the above
concepts.


Abstract:
  High-dimensional omics data contains intrinsic biomedical information that is
crucial for personalised medicine. Nevertheless, it is challenging to capture
them from the genome-wide data due to the large number of molecular features
and small number of available samples, which is also called 'the curse of
dimensionality' in machine learning. To tackle this problem and pave the way
for machine learning aided precision medicine, we proposed a unified multi-task
deep learning framework named OmiEmbed to capture biomedical information from
high-dimensional omics data with the deep embedding and downstream task
modules. The deep embedding module learnt an omics embedding that mapped
multiple omics data types into a latent space with lower dimensionality. Based
on the new representation of multi-omics data, different downstream task
modules were trained simultaneously and efficiently with the multi-task
strategy to predict the comprehensive phenotype profile of each sample.
OmiEmbed support multiple tasks for omics data including dimensionality
reduction, tumour type classification, multi-omics integration, demographic and
clinical feature reconstruction, and survival prediction. The framework
outperformed other methods on all three types of downstream tasks and achieved
better performance with the multi-task strategy comparing to training them
individually. OmiEmbed is a powerful and unified framework that can be widely
adapted to various application of high-dimensional omics data and has a great
potential to facilitate more accurate and personalised clinical decision
making.


Abstract:
  In an empirical study of a two-sided large matching market (such as a college
admissions problem), the primary statistics of interest are often the empirical
matching probabilities which represent the fraction of students of a given type
who match with colleges of a given type. This paper studies the
concentration-of-measure phenomenon for the empirical matching probabilities
and related statistics when the matching is generated from a student-optimal
stable matching mechanism. We introduce a notion of partial homogeneity of
preferences to express the degree of alignment in the preferences of colleges
over students, and show that the more the preferences are aligned, the more the
empirical matching frequencies tend to concentrate on their expected value
given the colleges' types. This means that the empirical matching probabilities
become more robust to realizations of students' preferences when the colleges'
preferences are more aligned.


Abstract:
  In the field of machine learning, the well-trained model is assumed to be
able to recover the training labels, i.e. the synthetic labels predicted by the
model should be as close to the ground-truth labels as possible. Inspired by
this, we propose a self-guided curriculum strategy to encourage the learning of
neural machine translation (NMT) models to follow the above recovery criterion,
where we cast the recovery degree of each training example as its learning
difficulty. Specifically, we adopt the sentence level BLEU score as the proxy
of recovery degree. Different from existing curricula relying on linguistic
prior knowledge or third-party language models, our chosen learning difficulty
is more suitable to measure the degree of knowledge mastery of the NMT models.
Experiments on translation benchmarks, including WMT14
English$\Rightarrow$German and WMT17 Chinese$\Rightarrow$English, demonstrate
that our approach can consistently improve translation performance against
strong baseline Transformer.


Abstract:
  Equilibrium Propagation (EP) is an algorithm intrinsically adapted to the
training of physical networks, thanks to the local updates of weights given by
the internal dynamics of the system. However, the construction of such a
hardware requires to make the algorithm compatible with existing neuromorphic
CMOS technologies, which generally exploit digital communication between
neurons and offer a limited amount of local memory. In this work, we
demonstrate that EP can train dynamical networks with binary activations and
weights. We first train systems with binary weights and full-precision
activations, achieving an accuracy equivalent to that of full-precision models
trained by standard EP on MNIST, and losing only 1.9% accuracy on CIFAR-10 with
equal architecture. We then extend our method to the training of models with
binary activations and weights on MNIST, achieving an accuracy within 1% of the
full-precision reference for fully connected architectures and reaching the
full-precision accuracy for convolutional architectures. Our extension of EP to
binary networks opens new solutions for on-chip learning and provides a compact
framework for training BNNs end-to-end with the same circuitry as for
inference.


Abstract:
  The COVID-19 pandemic considerably affects public health systems around the
world. The lack of knowledge about the virus, the extension of this phenomenon,
and the speed of the evolution of the infection are all factors that highlight
the necessity of employing new approaches to study these events. Artificial
intelligence techniques may be useful in analyzing data related to areas
affected by the virus. The aim of this work is to investigate any possible
relationships between air quality and confirmed cases of COVID-19 in Italian
districts. Specifically, we report an analysis of the correlation between daily
COVID-19 cases and environmental factors, such as temperature, relative
humidity, and atmospheric pollutants. Our analysis confirms a significant
association of some environmental parameters with the spread of the virus. This
suggests that machine learning models trained on the environmental parameters
to predict the number of future infected cases may be accurate. Predictive
models may be useful for helping institutions in making decisions for
protecting the population and contrasting the pandemic.


Abstract:
  Here we outline a description of paraxial light propagation from a modal
perspective. By decomposing the initial transverse field into a spatial basis
whose elements have known and analytical propagation characteristics, we are
able to analytically propagate any desired field, making the calculation fast
and easy. By selecting a basis other than that of planes waves, we overcome the
problem of numerical artefacts in the angular spectrum approach and at the same
time are able to offer an intuitive understanding for why certain classes of
fields propagate as they do. We outline the concept theoretically, compare it
to the numerical angular spectrum approach, and confirm its veracity
experimentally using a range of instructive examples. We believe that this
modal approach to propagating light will be a useful addition to toolbox for
propagating optical fields.


Abstract:
  Given positive integers $p$ and $q$, a $(p,q)$-coloring of the complete graph
$K_n$ is an edge-coloring in which every $p$-clique receives at least $q$
colors. Erd\H{o}s and Shelah posed the question of determining $f(n,p,q)$, the
minimum number of colors needed for a $(p,q)$-coloring of $K_n$. In this paper,
we expand on the color energy technique introduced by Pohoata and Sheffer to
prove new lower bounds on this function, making explicit the connection between
bounds on extremal numbers and $f(n,p,q)$. Using results on the extremal
numbers of subdivided complete graphs, theta graphs, and subdivided complete
bipartite graphs, we generalize results of Fish, Pohoata, and Sheffer, giving
the first nontrivial lower bounds on $f(n,p,q)$ for some pairs $(p,q)$ and
improving previous lower bounds for other pairs.


Abstract:
  This work considers a novel information design problem and studies how the
craft of payoff-relevant environmental signals solely can influence the
behaviors of intelligent agents. The agents' strategic interactions are
captured by a Markov game, in which each agent first selects one external
signal from multiple signal sources as additional payoff-relevant information
and then takes an action. There is a rational information designer (principal)
who possesses one signal source and aims to influence the equilibrium behaviors
of the agents by designing the information structure of her signals sent to the
agents. We propose a direct information design approach that incentivizes each
agent to select the signal sent by the principal, such that the design process
avoids the predictions of the agents' strategic selection behaviors. We then
introduce the design protocol given a goal of the designer which we refer to as
obedient implementability (OIL) and characterize the OIL in a class of obedient
sequential Markov perfect equilibria (O-SMPE). A design regime is proposed
based on an approach which we refer to as the fixed-point alignment that
incentivizes the agents to choose the signal sent by the principal, guarantees
that the agents' policy profile of taking actions is the policy component of an
O-SMPE and the principal's goal is achieved. We then formulate the principal's
optimal goal selection problem in terms of information design and characterize
the optimization problem by minimizing the fixed-point misalignments. The
proposed approach can be applied to elicit desired behaviors of multi-agent
systems in competing as well as cooperating settings and be extended to
heterogeneous stochastic games in the complete- and the incomplete-information
environments.


Abstract:
  We investigate the interaction between a moving detector and a quantum field,
especially about how the trajectory of the detector would affect the vacuum
fluctuations when the detector is moves in a quantum field (the Unruh effect).
We focus on two moving detectors system for future application in quantum
teleportation. We find that the rajectory of a uniformly accelerated detector
in Rindler space cannot be extended to a trajectory in which a detector moves
at constant velocity. Based on our previous work, we redo the calculations and
find that a term is missing from the past calculations, and we also find that
there are some restrictions on the values for the parameters in the solutions.
In addition, without inclusion of the missing term, the variance from the
quantum field for the inertial detector will be zero and is unlikely in such a
system. When all these points are combined, there is a difference in the
two-point correlation function between the inertial detector and the
accelerated detector in the early-time region. The influence of proper
acceleration can be seen in the two-point correlation functions. This might
play a role in the quantum teleportation process and be worth studying
thoroughly.


Abstract:
  An object's interior material properties, while invisible to the human eye,
determine motion observed on its surface. We propose an approach that estimates
heterogeneous material properties of an object from a monocular video of its
surface vibrations. Specifically, we show how to estimate Young's modulus and
density throughout a 3D object with known geometry. Knowledge of how these
values change across the object is useful for simulating its motion and
characterizing any defects. Traditional non-destructive testing approaches,
which often require expensive instruments, generally estimate only homogenized
material properties or simply identify the presence of defects. In contrast,
our approach leverages monocular video to (1) identify image-space modes from
an object's sub-pixel motion, and (2) directly infer spatially-varying Young's
modulus and density values from the observed modes. We demonstrate our approach
on both simulated and real videos.


Abstract:
  In this paper, we propose an approach for solving an energy-optimal goal
assignment problem to generate the desired formation in multi-agent systems.
Each agent solves a decentralized optimization problem with only local
information about its neighboring agents and the goals. The optimization
problem consists of two sub-problems. The first problem seeks to minimize the
energy for each agent to reach certain goals, while the second problem entreats
an optimal combination of goal and agent pairs that minimizes the energy cost.
By assuming the goal trajectories are given in a polynomial form, we prove the
solution to the formulated problem exists globally. Finally, the effectiveness
of the proposed approach is validated through the simulation.


Abstract:
  Let $f \in M_+(\mathbb{R}_+)$, the class of nonnegative, Lebesgure-measurable
functions on $\mathbb{R}_+=(0, \infty)$. We deal with integral operators of the
form \[ (T_Kf)(x)=\int_{\mathbb{R}_+}K(x,y)f(y)\, dy, \quad x \in \mathbb{R}_+,
\] with $K \in M_+(\mathbb{R}_+^2)$.


Abstract:
  In this paper we present the details of Women in Computer Vision Workshop -
WiCV 2020, organized in alongside virtual CVPR 2020. This event aims at
encouraging the women researchers in the field of computer vision. It provides
a voice to a minority (female) group in computer vision community and focuses
on increasingly the visibility of these researchers, both in academia and
industry. WiCV believes that such an event can play an important role in
lowering the gender imbalance in the field of computer vision. WiCV is
organized each year where it provides a.) opportunity for collaboration with
between researchers b.) mentorship to female junior researchers c.) financial
support to presenters to overcome monetary burden and d.) large and diverse
choice of role models, who can serve as examples to younger researchers at the
beginning of their careers. In this paper, we present a report on the workshop
program, trends over the past years, a summary of statistics regarding
presenters, attendees, and sponsorship for the current workshop.


Abstract:
  We investigate the effect of strong emission line galaxies on the performance
of empirical photometric redshift estimation methods. In order to artificially
control the contribution of photometric error and emission lines to total flux,
we develop a PCA-based stochastic mock catalogue generation technique that
allows for generating infinite signal-to-noise ratio model spectra with
realistic emission lines on top of theoretical stellar continua. Instead of
running the computationally expensive stellar population synthesis and nebular
emission codes, our algorithm generates realistic spectra with a statistical
approach, and - as an alternative to attempting to constrain the priors on
input model parameters - works by matching output observational parameters.
Hence, it can be used to match the luminosity, colour, emission line and
photometric error distribution of any photometric sample with sufficient
flux-calibrated spectroscopic follow-up. We test three simple empirical
photometric estimation methods and compare the results with and without
photometric noise and strong emission lines. While photometric noise clearly
dominates the uncertainty of photometric redshift estimates, the key findings
are that emission lines play a significant role in resolving colour space
degeneracies and good spectroscopic coverage of the entire colour space is
necessary to achieve good results with empirical photo-z methods. Template
fitting methods, on the other hand, must use a template set with sufficient
variation in emission line strengths and ratios, or even better, first estimate
the redshift empirically and fit the colours with templates at the best-fit
redshift to calculate the K-correction and various physical parameters.


Abstract:
  We present the first results of an MMT/Hectospec campaign to measure the
kinematics of globular clusters (GCs) around M49 -- the brightest galaxy in the
Virgo galaxy cluster, which dominates the Virgo B subcluster. The data include
kinematic tracers beyond 95 kpc (~5.2 effective radii) for M49 for the first
time, enabling us to achieve three key insights reported here. First, beyond
~20'-30' (~100-150 kpc), the GC kinematics sampled along the minor photometric
axis of M49 become increasingly hotter, indicating a transition from GCs
related to M49 to those representing the Virgo B intra-cluster medium. Second,
there is an anomaly in the line-of-sight radial velocity dispersion
($\sigma_{r,los}$) profile in an annulus ~10-15' (~50-90 kpc) from M49 in which
the kinematics cool by $\Delta \sigma_{r,los}~150$ km s$^{-1}$ relative to
those in- or outward. The kinematic fingerprint of a previous accretion event
is hinted at in projected phase-space, and we isolate GCs that both give rise
to this feature, and are spatially co-located with two prominent stellar shells
in the halo of M49. Third, we find a subsample of GCs with velocities
representative of the dwarf galaxy VCC1249 that is currently interacting with
M49. The spatial distribution of these GCs closely resembles the morphology of
VCC1249's isophotes, indicating that several of these GCs are likely in the act
of being stripped from the dwarf during its passage through M49's halo. Taken
together, these results point toward the opportunity of witnessing on-going
giant halo assembly in the depths of a cluster environment.


Abstract:
  Toda conformal field theories are natural generalizations of Liouville
conformal field theory that enjoy an enhanced level of symmetry. In Toda
conformal field theories this higher-spin symmetry can be made explicit, thanks
to a path integral formulation of the model based on a Lie algebra structure.
The purpose of the present document is to explain how this higher level of
symmetry can manifest itself within the rigorous probabilistic framework
introduced by R. Rhodes, V. Vargas and the first author. One of its features is
the existence of holomorphic currents that are introduced via a rigorous
derivation of the Miura transformation. More precisely, we prove that the
spin-three Ward identities, that encode higher-spin symmetry, hold in the
$\mathfrak{sl}_3$ Toda conformal field theory; as an original input we provide
explicit expressions for the descendent fields which were left unidentified in
the physics literature. This representation of the descendent fields provides a
new systematic method to find the degenerate fields of the $\mathfrak{sl}_3$
Toda (and Liouville) conformal field theory, which in turn implies that certain
four-point correlation functions are solutions of an hypergeometric
differential equation of the third order.


Abstract:
  Aims. We study the 27-day variations of galactic cosmic rays (GCRs) based on
neutron monitor (NM), ACE/CRIS, STEREO and SOHO/EPHIN measurements, in solar
minima 23/24 and 24/25 characterized by the opposite polarities of solar
magnetic cycle. Now there is an opportunity to reanalyze the polarity
dependence of the amplitudes of the recurrent GCR variations in 2007-2009 for
negative A < 0 solar magnetic polarity and to compare it with the clear
periodic variations related to solar rotation in 2017-2019 for positive A > 0.
Methods. We use the Fourier analysis method to study the periodicity in the GCR
fluxes. Since the GCR recurrence is a consequence of solar rotation, we analyze
not only GCR fluxes, but also solar and heliospheric parameters examining the
relationships between the 27-day GCR variations and heliospheric, as well as,
solar wind parameters. Results. We find that the polarity dependence of the
amplitudes of the 27-day variations of the GCR intensity and anisotropy for NMs
data is kept for the last two solar minima: 23/24 (2007-2009) and 24/25
(2017-2019) with greater amplitudes in positive A > 0 solar magnetic polarity.
ACE/CRIS, SOHO/EPHIN and STEREO measurements are not governed by this principle
of greater amplitudes in positive A > 0 polarity. GCR recurrence caused by the
solar rotation for low energy (< 1GeV) cosmic rays is more sensitive to the
enhanced diffusion effects, resulting in the same level of the 27-day
amplitudes for positive and negative polarities. While high energy (> 1GeV)
cosmic rays registered by NMs, are more sensitive to the large-scale drift
effect leading to the 22-year Hale cycle in the 27-day GCR variation, with the
larger amplitudes in the A > 0 polarity than in the A < 0.


Abstract:
  Spatio-temporal models for count data are required in a wide range of
scientific fields and they have become particularly crucial nowadays because of
their ability to analyse COVID-19-related data. Models for count data are
needed when the variable of interest take only non-negative integer values and
these integers arise from counting occurrences. Several R-packages are
currently available to deal with spatiotemporal areal count data. Each package
focuses on different models and/or statistical methodologies. Unfortunately,
the results generated by these models are rarely comparable due to differences
in notation and methods. The main objective of this paper is to present a
review describing the most important approaches that can be used to model and
analyse count data when questions of scientific interest concern both their
spatial and their temporal behaviour and we monitor their performance under the
same data set.
  For this review, we focus on the three R-packages that can be used for this
purpose and the different models assessed are representative of the two most
widespread methodologies used to analyse spatiotemporal count data: the
classical approach (based on Penalised Likelihood or Estimating Equations) and
the Bayesian point of view.
  A case study is analysed as an illustration of these different methodologies.
In this case study, these packages are used to model and predict daily
hospitalisations from COVID-19 in 24 health regions within the Valencian
Community (Spain), with data corresponding to the period from 28 June to 13
December 2020. Because of the current urgent need for monitoring and predicting
data in the COVID-19 pandemic, this case study is, in itself, of particular
importance and can be considered the secondary objective of this work.
Satisfactory and promising results have been obtained in this second goal.


Abstract:
  The accurate representation of variable renewable generation (RES, e.g.,
wind, solar PV) assets in capacity expansion planning (CEP) studies is
paramount to capture spatial and temporal correlations that may exist between
sites and impact both power system design and operation. However, it typically
has a high computational cost. This paper proposes a method to reduce the
spatial dimension of CEP problems while preserving an accurate representation
of renewable energy sources. A two-stage approach is proposed to this end. In
the first stage, relevant sites are identified via a screening routine that
discards the locations with little impact on system design. In the second
stage, the subset of relevant RES sites previously identified is used in a CEP
problem to determine the optimal configuration of the power system. The
proposed method is tested on a realistic EU case study and its performance is
benchmarked against a CEP set-up in which the entire set of candidate RES sites
is available. The method shows great promise, with the screening stage
consistently identifying 90% of the optimal RES sites while discarding up to
54% of the total number of candidate locations. This leads to a peak memory
reduction of up to 41% and solver runtime gains between 31% and 46%, depending
on the weather year considered.


Abstract:
  The rising adoption of machine learning in high energy physics and lattice
field theory necessitates the re-evaluation of common methods that are widely
used in computer vision, which, when applied to problems in physics, can lead
to significant drawbacks in terms of performance and generalizability. One
particular example for this is the use of neural network architectures that do
not reflect the underlying symmetries of the given physical problem. In this
work, we focus on complex scalar field theory on a two-dimensional lattice and
investigate the benefits of using group equivariant convolutional neural
network architectures based on the translation group. For a meaningful
comparison, we conduct a systematic search for equivariant and non-equivariant
neural network architectures and apply them to various regression and
classification tasks. We demonstrate that in most of these tasks our best
equivariant architectures can perform and generalize significantly better than
their non-equivariant counterparts, which applies not only to physical
parameters beyond those represented in the training set, but also to different
lattice sizes.


Abstract:
  Recently, reinforcement learning (RL) has been applied to task-oriented
dialogue systems by using latent actions to solve shortcomings of supervised
learning (SL). In this paper, we propose a multi-domain task-oriented dialogue
system, called Dialogue System with Optimizing a Recurrent Action Policy using
Efficient Context (DORA), that uses SL, with subsequently applied RL to
optimize dialogue systems using a recurrent dialogue policy. This dialogue
policy recurrently generates explicit system actions as a both word-level and
high-level policy. As a result, DORA is clearly optimized during both SL and RL
steps by using an explicit system action policy that considers an efficient
context instead of the entire dialogue history. The system actions are both
interpretable and controllable, whereas the latent actions are not. DORA
improved the success rate by 6.6 points on MultiWOZ 2.0 and by 10.9 points on
MultiWOZ 2.1.


Abstract:
  By defining the Adjacency-Matrix for the Collatz Graph it is shown that it is
possible, from the Adjacency-Matrix, to generate a 'picture' which defines the
Flow-Diagram for the Collatz Graph. In a sense the Flow-Diagram is the Collatz
Graph.
  While defining a Double 2D Matrix containing all odd numbers it is shown,
that the 3N+1-Problem is in fact a problem concerning Groups of values not only
individual Values. This implicates, that it is possible to predict the number
of Operations needed to reach the Origo/Root 1 for a Group of values, if the
number of Operations is known for a single Value in the Group.
  After the formal analysis of the 'Double 2D Matrix' a group of five
Algorithms is investigated.
  Based on the fact that two different Algorithms (with different rules) can
generate identical Odd-Series and the fact that two of the Algorithms are able
to generate identical 'Branching-codes' in both directions UP and DOWN in a
(binary) Tree, it is concluded that Collatz Conjecture is True.


Abstract:
  Based on a self-consistent $t$-matrix approximation, we explore the influence
of magnetic and nonmagnetic doping on the surface electronic states and
conductivity of topological insulators. We show that warping parameter has a
crucial impact on the density of states and dc conductivity of the doped
surfaces. As the warping strength is increased, the surface density of states
at high energies is suppressed and the resonant states induced by impurities in
the vicinity of the Dirac point gradually disappear. It is found that
nonmagnetic impurities break electron-hole symmetry at low warping strength,
while the symmetry remains unchanged when the surface is magnetically doped.
Our findings reveal that surface conductivity can be controlled by tuning the
doping, the direction of external magnetic field and that of impurity magnetic
moments. Also, the surface conductivity features in topological insulators with
warped energy dispersions are not significantly affected by the presence of
impurities compared to that of materials with circular energy contour.


Abstract:
  In the present paper we propose two new algorithms of tensor completion for
three-order tensors. The proposed methods consist in minimizing the average
rank of the underlying tensor using its approximate function namely the tensor
nuclear norm and then the recovered data will be obtained by using the total
variation regularisation technique. We will adopt the Alternating Direction
Method of Multipliers (ADM), using the tensor T-product, to solve the main
optimization problems associated to the two algorithms. In the last section, we
present some numerical experiments and comparisons with the most known image
completion methods.


Abstract:
  In this study, noise generation is investigated for a generic voluteless
centrifugal HVAC fan at an off-design operation point where tonal noise
increases. The simulations are performed by coupling IDDES with the FW-H
acoustic analogy, and the experiments are conducted in a rig consisting of a
plenum chamber and a reverberation room. In contrast to typical tonal noise
sources induced by the fan blades, we find out that another predominant source
is the turbulence stemming from the gap between the fan shroud and the inlet
duct. The turbulence evolves along with the shroud and is swept downstream to
interact with the top side of the blade leading edge. The interaction accounts
for uneven surface pressure distribution on the blades. Moreover, the pressure
is significantly unsteady near the shroud. The power spectral density (PSD) of
the noise shows obvious tones at 273Hz that is approximately equal to the
difference of the blade passing frequency (BPF0) and the fan rotation
frequency. By coarsening the mesh resolution near the inlet gap and shroud, we
artificially deactivate the gap turbulence in the numerical simulations and,
consequently, detect that the tone at 273Hz disappears completely. At this
frequency, the PSD contours of surface pressure fluctuations are found potent
at the inlet gap and the blade top side only if the gap turbulence is resolved.
These findings indicate that the tonal noise source at 273Hz is the interaction
between the gap turbulence and blades. As the gap turbulence exists near the
shroud wall upstream of the blades, the rotating wall introduces rotational
momentum into the turbulence due to the wall friction. Hence the tonal
frequency of the interaction is smaller than BPF0 with a decrement of the fan
rotation frequency. To the authors' knowledge, it is the first time that
voluteless centrifugal fans are studied for the noise generation from the gap
turbulence.


Abstract:
  Modeling thematic fit (a verb--argument compositional semantics task)
currently requires a very large burden of data. We take a high-performing
neural approach to modeling verb--argument fit, previously trained on a
linguistically machine-annotated large corpus, and replace corpus layers with
output from higher-quality taggers. Contrary to popular beliefs that, in the
deep learning era, more data is as effective as higher quality annotation, we
discover that higher annotation quality dramatically reduces our data
requirement while demonstrating better supervised predicate-argument
classification. But in applying the model to a psycholinguistic task outside
the training objective, we saw only small gains in one of two thematic fit
estimation tasks, and none in the other. We replicate previous studies while
modifying certain role representation details, and set a new state-of-the-art
in event modeling, using a fraction of the data.


Abstract:
  In quantum mechanics time usually appears as classical parameter which means
that it is treated as being essentially different from spatial coordinates that
are represented by operators. On the other hand, relativity theory demands to
treat space and time on an equal footing and there are many approaches to the
problem how to introduce time operator. In order to support these approaches we
propose the experiment that will clearly expose the equivalence of space and
time regarding the relation towards quantum interference. The proposed
experiment can show that it is possible to have superposition of being created
in two different moments in time, and by measuring standard interference
pattern, it will be shown that there is interference in time. It is similar to
the case of verification of the possibility of single quantum system to be in
the state that is superposition of two different positions in space. If so,
this would mean that there should be exactly the same formal approach to the
space coordinates and time in quantum mechanics since they are all quantum in
their nature.


Abstract:
  The tunable bandgap from 0.3 eV to 2 eV of black phosphorus (BP) makes it to
fill the gap in graphene. When studying the properties of BP more
comprehensive, scientists have discovered that many two-dimensional materials,
such as tellurene, antimonene, bismuthene, indium selenide and tin sulfide,
have similar structures and properties to black phosphorus thus called black
phosphorus analogs. In this review, we briefly introduce preparation methods of
black phosphorus and its analogs, with emphasis on the method of mechanical
exfoliation (ME), liquid phase exfoliation (LPE) and chemical vapor deposition
(CVD). And their characterization and properties according to their
classification of single-element materials and multi-element materials are
described. We focus on the performance of passively mode-locked fiber lasers
using BP and its analogs as saturable absorbers (SA) and demonstrated this part
through classification of working wavelength. Finally, we introduce the
application of BP and its analogs, and discuss their future research prospects.


Abstract:
  Tolerance estimation problems are prevailing in engineering applications. For
example, in modern robotics, it remains challenging to efficiently estimate
joint tolerance, \ie the maximal allowable deviation from a reference robot
state such that safety constraints are still satisfied. This paper presented an
efficient algorithm to estimate the joint tolerance using sum-of-squares
programming. It is theoretically proved that the algorithm provides a tight
lower bound of the joint tolerance. Extensive numerical studies demonstrate
that the proposed method is computationally efficient and near optimal. The
algorithm is implemented in the JTE toolbox and is available at
\url{https://github.com/intelligent-control-lab/Sum-of-Square-Safety-Optimization}.


Abstract:
  We revisit the pairing model of state spaces with new emergent states
introduced in J. Phys. A: Math. Theor. 51 375002, 2018.
  We facilitate our analysis by introducing a simplified pairing model
consisting of balls able to form pairs but without any internal structure.
  For both the simplified and the original model we compute exactly the
probability distribution for observing a state with $n_p$ pairs. We show this
distribution satisfies a large deviation principle with speed $n \ln(n)$. We
present closed form expressions for a variety of statistical quantities
including moments and marginal distributions.


Abstract:
  The observed emission lines of Be stars originate from a circumstellar
Keplerian disk that are generally well explained by the Viscous Decretion Disk
model. In an earlier work we performed the modeling of the full light curve of
the bright Be star $\omega$ CMa (Ghoreyshi et al. 2018) with the 1-D
time-dependent hydrodynamics code SINGLEBE and the Monte Carlo
radiative-transfer code HDUST.
  We used the V -band light curve that probes the inner disk through four disk
formation and dissipation cycles. This new study compares predictions of the
same set of model parameters with time-resolved photometry from the near UV
through the mid-infrared, comprehensive series of optical spectra, and optical
broad-band polarimetry, that overall represent a larger volume of the disk.
Qualitatively, the models reproduce the trends in the observed data due to the
growth and decay of the disk. However, quantitative differences exist, e.g., an
overprediction of the flux increasing with wavelength, too slow decreases in
Balmer emission-line strength that are too slow during disk dissipation, and
the discrepancy between the range of polarimetric data and the model. We find
that a larger value of the viscosity parameter alone, or a truncated disk by a
companion star, reduces these discrepancies by increasing the dissipation rate
in the outer regions of the disk.


Abstract:
  High-level synthesis (HLS) is a key component for the hardware acceleration
of applications, especially thanks to the diffusion of reconfigurable devices
in many domains, from data centers to edge devices. HLS reduces development
times by allowing designers to raise the abstraction level and use automated
methods for hardware generation. Since security concerns are becoming more and
more relevant for data-intensive applications, we investigate how to abstract
security properties and use HLS for their integration with the accelerator
functionality. We use the case of dynamic information flow tracking, showing
how classic software-level abstractions can be efficiently used to hide
implementation details to the designers.


Abstract:
  Many popular open source programming languages (Perl, Ruby or Python for
example) have systems for distributing packaged source code that software
developers can use when working in that particular programming language. This
paper will consider the design principals that should be followed if designing
such an open source code repository.


Abstract:
  Bottom-up design of high-entropy ceramics is a promising approach for
realizing materials with unique combination of high hardness and
fracture-resistance at elevated temperature. This work offers a simple yet
fundamental design criterion - valence electron concentration (VEC) > ~9.5
e-/f.u. to populate bonding metallic states at the Fermi level - for selecting
elemental compositions that may form rocksalt-structure (B1) high-entropy
ceramics with enhanced plasticity (reduced brittleness). Single-phase B1
(HfTaTiWZr)C and (MoNbTaVW)C, chosen as representative systems due to their
specific VEC values, are here synthesized and tested. Nanoindentation arrays at
various loads and depths statistically show that (HfTaTiWZr)C (VEC=8.6 e-/f.u.)
is hard but brittle, whilst (MoNbTaVW)C (VEC=9.4 e-/f.u.) is hard and
considerably more resistant to fracture than (HfTaTiWZr)C. Ab initio molecular
dynamics simulations and electronic-structure analysis reveal that the improved
fracture-resistance of (MoNbTaVW)C subject to tensile and shear deformation may
originate from the intrinsic material's ability to undergo local lattice
transformations beyond tensile yield points, as well as from relatively facile
activation of lattice slip. Additional simulations, carried out to follow the
evolution in mechanical properties as a function of temperature, suggest that
(MoNbTaVW)C may retain good resistance to fracture up to ~900-1200K, whereas
(HfTaTiWZr)C is predicted to remain brittle at all investigated temperatures.


Abstract:
  It is widely known that Google Trends have become one of the most popular
free tools used by forecasters both in academics and in the private and public
sectors. There are many papers, from several different fields, concluding that
Google Trends improve forecasts' accuracy. However, what seems to be widely
unknown, is that each sample of Google search data is different from the other,
even if you set the same search term, data and location. This means that it is
possible to find arbitrary conclusions merely by chance. This paper aims to
show why and when it can become a problem and how to overcome this obstacle.


Abstract:
  We present a general method to efficiently design quantum control procedures
for non-Markovian problems and illustrate it by optimizing the shape of a laser
pulse to prepare a quantum dot in a specific state. The optimization of open
quantum system dynamics with strong coupling to structured environments --
where time-local descriptions fail -- is a computationally challenging task. We
modify the numerically exact time evolving matrix product operator (TEMPO)
method, such that it allows the repeated computation of the time evolution of
the reduced system density matrix for various sets of control parameters at
very low computational cost. This method is potentially useful for studying
numerous quantum optimal control problems, in particular in solid state quantum
devices where the coupling to vibrational modes is typically strong.


Abstract:
  Let $X$ be an arbitrary set and let $T(X)$ denote the full transformation
monoid on $X$. We prove that an element of $T(X)$ is unit-regular if and only
if it is semi-balanced. For infinite $X$, we discuss regularity of the
submonoid of $T(X)$ consisting of all injective (resp. surjective)
transformations. For a partition $\mathcal{P}$ of $X$, we characterize
unit-regular elements in the monoid $T(X, \mathcal{P})$, under composition,
defined as \[T(X, \mathcal{P}) = \{f\in T(X)\mid (\forall X_i \in \mathcal{P})
(\exists X_j \in \mathcal{P})\; X_i f \subseteq X_j\}.\] We also characterize
(unit-)regular elements in various known submonoids of $T(X, \mathcal{P})$.


Abstract:
  Convolutional networks (ConvNets) have achieved promising accuracy for
various anatomical segmentation tasks. Despite the success, these methods can
be sensitive to data appearance variations. Considering the large variability
of scans caused by artifacts, pathologies, and scanning setups, robust ConvNets
are vital for clinical applications, while have not been fully explored. In
this paper, we propose to mitigate the challenge by enabling ConvNets'
awareness of the underlying anatomical invariances among imaging scans.
Specifically, we introduce a fully convolutional Constraint Adoption Module
(CAM) that incorporates probabilistic atlas priors as explicit constraints for
predictions over a locally connected Conditional Random Field (CFR), which
effectively reinforces the anatomical consistency of the labeling outputs. We
design the CAM to be flexible for boosting various ConvNet, and compact for
co-optimizing with ConvNets for fusion parameters that leads to the optimal
performance. We show the advantage of such atlas priors fusion is two-fold with
two brain parcellation tasks. First, our models achieve state-of-the-art
accuracy among ConvNet-based methods on both datasets, by significantly
reducing structural abnormalities of predictions. Second, we can largely boost
the robustness of existing ConvNets, proved by: (i) testing on scans with
synthetic pathologies, and (ii) training and evaluation on scans of different
scanning setups across datasets. Our method is proposing to be easily adopted
to existing ConvNets by fine-tuning with CAM plugged in for accuracy and
robustness boosts.


Abstract:
  We discuss recent experiments performed with an upgraded version of the main
magnetic focus ion source (MaMFIS) at the Joint Institute for Nuclear Research
(JINR) in Dubna. The device operates in the range of electron beam energies
extended up to $40$ keV. The achieved electron current densities are of the
order of $10$ kA/cm$^{2}$. This assessment is consistent both with the very
short ionization time and with the utmost ionization degree of the produced
highly charged ions. Due to its high efficiency, the MaMFIS technology is
especially promising for ionization of short-lived radionuclides and heavy
elements. A new scheme for charge breeding is proposed.


Abstract:
  Partially Observable Markov Decision Processes (POMDP) is a widely used model
to represent the interaction of an environment and an agent, under state
uncertainty. Since the agent does not observe the environment state, its
uncertainty is typically represented through a probabilistic belief. While the
set of possible beliefs is infinite, making exact planning intractable, the
belief space's complexity (and hence planning complexity) is characterized by
its covering number. Many POMDP solvers uniformly discretize the belief space
and give the planning error in terms of the (typically unknown) covering
number. We instead propose an adaptive belief discretization scheme, and give
its associated planning error. We furthermore characterize the covering number
with respect to the POMDP parameters. This allows us to specify the exact
memory requirements on the planner, needed to bound the value function error.
We then propose a novel, computationally efficient solver using this scheme. We
demonstrate that our algorithm is highly competitive with the state of the art
in a variety of scenarios.


Abstract:
  We propose and demonstrate a fast, robust method for using satellite images
to localize an Unmanned Aerial Vehicle (UAV). Previous work using satellite
images has large storage and computation costs and is unable to run in real
time. In this work, we collect Google Earth (GE) images for a desired flight
path offline and an autoencoder is trained to compress these images to a
low-dimensional vector representation while retaining the key features. This
trained autoencoder is used to compress a real UAV image, which is then
compared to the precollected, nearby, autoencoded GE images using an
inner-product kernel. This results in a distribution of weights over the
corresponding GE image poses and is used to generate a single localization and
associated covariance to represent uncertainty. Our localization is computed in
1% of the time of the current standard and is able to achieve a comparable RMSE
of less than 3m in our experiments, where we robustly matched UAV images from
six runs spanning the lighting conditions of a single day to the same map of
satellite images.


Abstract:
  Emulsion droplets trapped in an ultrasonic levitator behave in two ways that
solid spheres do not: (1) Individual droplets spin rapidly about an axis
parallel to the trapping plane, and (2) coaxially spinning droplets form long
chains aligned with their common axis of rotation. Acoustically-organized
chains interact hydrodynamically, either to merge into longer chains or to form
three-dimensional bundles of chains. Solid spheres, by contrast, form
close-packed planar crystals drawn together by the sound-mediated secondary
Bjerknes interaction. We demonstrate the chain-to-crystal transition with a
model system in which fluid emulsion droplets can be photopolymerized into
solid spheres without significantly changing other material properties. The
behavior of this experimental system is quantitatively consistent with an
acoustohydrodynamic model for spinning spheres in an acoustic levitator. This
study therefore introduces acoustically-driven spinning as a mechanism for
guiding self-organization of acoustically levitated matter.


Abstract:
  We consider the Goursat problem for linear partial differential equations
with constant coefficients in two complex variables. We find the conditions for
summable solutions of the Goursat problem in the case when the Newton polygon
has exactly one side with a positive slope.