Abstract:
  Symmetry-protected topological $\left(SPT\right)$ phases are gapped
short-range entangled states with symmetry $G$, which can be systematically
described by group cohomology theory. $SU(3)$ and $SU(2)\times{U(1)}$ are
considered as the basic groups of Quantum Chromodynamics and
Weak-Electromagnetic unification, respectively. In two dimension $(2D)$,
nonlinear-sigma models with a quantized topological Theta term can be used to
describe nontrivial SPT phases. By coupling the system to a probe field and
integrating out the group variables, the Theta term becomes the effective
action of Chern-Simons theory which can derive the response current density. As
a result, the current shows a spin Hall effect, and the quantized number of the
spin Hall conductance of SPT phases $SU(3)$ and $SU(2)\times{U(1)}$ are same.
In addition, relationships between $SU(3)$ and $SU(2)\times{U(1)}$ which maps
$SU(3)$ to $SU(2)$ with a rotation $U(1)$ will be given.


Abstract:
  We present a novel method for real-time pose and shape reconstruction of two
strongly interacting hands. Our approach is the first two-hand tracking
solution that combines an extensive list of favorable properties, namely it is
marker-less, uses a single consumer-level depth camera, runs in real time,
handles inter- and intra-hand collisions, and automatically adjusts to the
user's hand shape. In order to achieve this, we embed a recent parametric hand
pose and shape model and a dense correspondence predictor based on a deep
neural network into a suitable energy minimization framework. For training the
correspondence prediction network, we synthesize a two-hand dataset based on
physical simulations that includes both hand pose and shape annotations while
at the same time avoiding inter-hand penetrations. To achieve real-time rates,
we phrase the model fitting in terms of a nonlinear least-squares problem so
that the energy can be optimized based on a highly efficient GPU-based
Gauss-Newton optimizer. We show state-of-the-art results in scenes that exceed
the complexity level demonstrated by previous work, including tight two-hand
grasps, significant inter-hand occlusions, and gesture interaction.


Abstract:
  Non-target screening consists in searching a sample for all present
substances, suspected or unknown, with very little prior knowledge about the
sample. This approach has been introduced more than a decade ago in the field
of water analysis, together with dedicated compound identification tools, but
is still very scarce for indoor and atmospheric trace gas measurements, despite
the clear need for a better understanding of the atmospheric trace gas
composition.For a systematic detection of emerging trace gases in the
atmosphere, a new and powerful analytical method is gas chromatography (GC) of
preconcentrated samples, followed by electron ionisation, high resolution mass
spectrometry (EI-HRMS). In this work, we present data analysis tools to enable
automated fragment formula annotation for unknown compounds measured by
GC-EI-HRMS. Based on co-eluting mass/charge fragments, we developed an
innovative data analysis method to reliably reconstruct the chemical formulae
of the fragments, using efficient combinatorics and graph theory. The method
does not require the presence of the molecular ion, which is absent in ~40% of
EI spectra. Our method has been trained and validated on \textgreater50
halocarbons and hydrocarbons, with 3 to 20 atoms and molar masses of 30 to 330
g mol-1, measured with a mass resolution of approx.~3500. For 90% of the
compounds, more than 90% of the annotated fragment formulae are correct. Cases
of wrong identification can be attributed to the scarcity of detected fragments
per compound or the lack of isotopic constraint (no minor isotopocule
detected).Our method enables to reconstruct most probable chemical formulae
independently from spectral databases. Therefore, it demonstrates the
suitability of EI-HRMS data for non-target analysis and paves the way for the
identification of substances for which no EI mass spectrum is registered in
databases. We illustrate the performances of our method for atmospheric trace
gases and suggest that it may be well suited for many other types of samples.
The L-GPL licenced Python code is released under the name ALPINAC for
ALgorithmic Process for Identification of Non-targeted Atmospheric Compounds.


Abstract:
  Collaborative filtering has been largely used to advance modern recommender
systems to predict user preference. A key component in collaborative filtering
is representation learning, which aims to project users and items into a low
dimensional space to capture collaborative signals. However, the scene
information, which has effectively guided many recommendation tasks, is rarely
considered in existing collaborative filtering methods. To bridge this gap, we
focus on scene-based collaborative recommendation and propose a novel
representation model SceneRec. SceneRec formally defines a scene as a set of
pre-defined item categories that occur simultaneously in real-life situations
and creatively designs an item-category-scene hierarchical structure to build a
scene-based graph. In the scene-based graph, we adopt graph neural networks to
learn scene-specific representation on each item node, which is further
aggregated with latent representation learned from collaborative interactions
to make recommendations. We perform extensive experiments on real-world
E-commerce datasets and the results demonstrate the effectiveness of the
proposed method.


Abstract:
  This paper formalizes constraint-based structure learning of the "true"
causal graph from observed data when unobserved variables are also existent. We
provide conditions for a "natural" family of constraint-based
structure-learning algorithms that output graphs that are Markov equivalent to
the causal graph. Under the faithfulness assumption, this natural family
contains all exact structure-learning algorithms. More importantly, we provide
clear and testable assumptions, as an alternative to faithfulness, under which
any natural structure-learning algorithm outputs Markov equivalent graphs to
the causal graph. We provide these definitions and results for the general
class of models under the assumption that the distribution is Markovian to the
true causal graph, and we specialize the definitions and results for structural
causal models.


Abstract:
  The dynamical critical exponent $z$ of natural swarms is calculated using the
renormalization group. To order $\epsilon = 4-d$, a novel fixed point emerges,
where both off-equilibrium activity and mode-coupling inertial interactions are
relevant. In three dimensions the critical exponent at the new fixed point is
$z=1.3$, in fair agreement with experiments.


Abstract:
  We explore the benefits of adapted gauges to small mass ratio binary black
hole evolutions in the moving puncture formulation. We find expressions that
approximate the late time behavior of the lapse and shift,
$(\alpha_0,\beta_0)$, and use them as initial values for their evolutions. We
also use a position and black hole mass dependent damping term,
$\eta[\vec{x}_1(t),\vec{x}_2(t),m_1,m_2]$, in the shift evolution, rather than
a constant or conformal-factor dependent choice. We have found that this
substantially reduces noise generation at the start of the numerical
integration and keeps the numerical grid stable around both black holes,
allowing for more accuracy with lower resolutions. We test our choices for this
gauge in detail in a case study of a binary with a 7:1 mass ratio, and then use
15:1 and 32:1 binaries for a convergence study. Finally, we apply our new gauge
to a 64:1 binary and a 128:1 binary to well cover the comparable and small mass
ratio regimes.


Abstract:
  A rainbow matching in an edge-colored graph is a matching in which no two
edges have the same color. The color degree of a vertex v is the number of
different colors on edges incident to v. Kritschgau [Electron. J. Combin.
27(2020)] studied the existence of rainbow matchings in edge-colored graph G
with average color degree at least 2k, and proved some sufficient conditions
for a rainbow marching of size k in G. The sufficient conditions include that
|V(G)|>=12k^2+4k, or G is a properly edge-colored graph with |V(G)|>=8k. In
this paper, we show that every edge-colored graph G with |V(G)|>=4k-4 and
average color degree at least 2k-1 contains a rainbow matching of size k. In
addition, we also prove that every strongly edge-colored graph G with average
degree at least 2k-1 contains a rainbow matching of size at least k. The bound
is sharp for complete graphs.


Abstract:
  Modern machine learning models often employ a huge number of parameters and
are typically optimized to have zero training loss; yet surprisingly, they
possess near-optimal prediction performance, contradicting classical learning
theory. We examine how these benign overfitting phenomena occur in a two-layer
neural network setting where sample covariates are corrupted with noise. We
address the high dimensional regime, where the data dimension $d$ grows with
the number $n$ of data points. Our analysis combines an upper bound on the bias
with matching upper and lower bounds on the variance of the interpolator (an
estimator that interpolates the data). These results indicate that the excess
learning risk of the interpolator decays under mild conditions. We further show
that it is possible for the two-layer ReLU network interpolator to achieve a
near minimax-optimal learning rate, which to our knowledge is the first
generalization result for such networks. Finally, our theory predicts that the
excess learning risk starts to increase once the number of parameters $s$ grows
beyond $O(n^2)$, matching recent empirical findings.


Abstract:
  Starting from a general many-body fermionic Hamiltonian, we derive the
equations of motion (EOM) for nucleonic propagators in a superfluid system. The
resulting EOM is of the Dyson type formulated in the basis of Bogoliubov's
quasiparticles. As the leading contributions to the dynamical kernel of this
EOM in strongly-coupled regimes contain phonon degrees of freedom in various
channels, an efficient method of calculating phonon's characteristics is
required to successfully model these kernels. The traditional quasiparticle
random phase approximation (QRPA) solvers are typically used for this purpose
in nuclear structure calculations, however, they become very prohibitive in
non-spherical geometries. In this work, by linking the notion of the
quasiparticle-phonon vertex to the variation of the Bogoliubov's Hamiltonian,
we show that the recently developed finite-amplitude method (FAM) can be
efficiently employed to compute the vertices within the FAM-QRPA. To illustrate
the validity of the method, calculations based on the relativistic
density-dependent point-coupling Lagrangian are performed for the
single-nucleon states in heavy and medium-mass nuclei with axial deformations.
The cases of $^{38}$Si and $^{250}$Cf are presented and discussed.


Abstract:
  This study describes a method to detect hijacked journals based on the
analysis of the archives of clone journals. This approach is most effective in
discovering a network of hijacked journals that have the same organizer(s).
Analysis of the archives of clone journals allowed to detect 62 URLs of
hijacked journals. It also provided the possibility to predict two clone
websites before they became operational. This study shows that most detected
hijacked journals represent a network of clone journals organized by one or
several fraudulent individuals. The information and content of nine legitimate
journals were compromised in international and national scientometric
databases.


Abstract:
  The stellar mass-stellar metallicity relation (MZR) is an essential approach
to probe the chemical evolution of galaxies. It reflects the balance between
galactic feedback and gravitational potential as a function of stellar mass.
However, the current MZR of local dwarf satellite galaxies (M* <~ 10^8 Msun,
measured from resolved stellar spectroscopy) may not be reconcilable with that
of more massive galaxies (M* >~ 10^9.5 Msun, measured from integrated-light
spectroscopy). Such a discrepancy may result from a systematic difference
between the two methods, or it may indicate a break in the MZR around 10^9
Msun. To address this question, we measured the stellar metallicity of NGC 147
from integrated light using the Palomar Cosmic Web Imager (PCWI). We compared
the stellar metallicity estimates from integrated light with the measurements
from resolved stellar spectroscopy and found them to be consistent within 0.1
dex. On the other hand, the high-mass MZR overpredicts the metallicity by 0.6
dex at the mass of NGC 147. Therefore, our results tentatively suggest that the
discrepancy between the low-mass MZR and high-mass MZR should not be attributed
to a systematic difference in techniques. Instead, real physical processes
cause the transition in the MZR. In addition, we discovered a positive age
gradient in the innermost region and a negative metallicity gradient from the
resolved stars at larger radii, suggesting a possible outside-in formation of
NGC 147.


Abstract:
  The explosion of IoT and wearable devices determined a rising attention
towards energy harvesting as source for powering these systems. In this
context, many applications cannot afford the presence of a battery because of
size, weight and cost issues. Therefore, due to the intermittent nature of
ambient energy sources, these systems must be able to save and restore their
state, in order to guarantee progress across power interruptions. In this work,
we propose a specialized backup/restore controller that dynamically tracks the
memory accesses during the execution of the program. The controller then
commits the changes to a snapshot in a Non-Volatile Memory (NVM) when a power
failure is detected. Our approach does not require complex hybrid memories and
can be implemented with standard components. % and integrated in any MCU with
Results on a set of benchmarks show an average $8\times$ reduction in backup
size. Thanks to our dedicated controller, the backup time is further reduced by
more than $100\times$, with an area and power overhead of only 0.4\% and 0.8\%,
respectively, w.r.t. a low-end IoT node.


Abstract:
  Based on crystal chemistry considerations and quantum density functional
theory ground state calculations, rhombohedral rh-C3 and hexagonal h-C6 carbon
allotropes are proposed and energetically calculated as new stable ultra-hard
phases likewise lonsdaleite. Along the two kinds of carbon in linear C2-C1-C2
lattice, distorted tetrahedra C2(sp3) with an angle of 106.17{\deg} (smaller
than ideal 109.4{\deg}) and C1(sp)-like hybridizations are inferred from charge
density projections. The calculated elastic constants point to a strong
anisotropy of mechanical properties of rh-C3 and h-C6 with an exceptionally
large C33 values (1636 GPa and 1610 GPa, respectively), exceeding that of
lonsdaleite (1380 GPa), due to the presence of aligned tricarbon units along
the hexagonal c-axis. Both phases are characterized by large bulk moduli and
high hardness values that are slightly less than those of lonsdaleite and
diamond. Weak metallic behavior of both new phases is identified from
electronic band structure calculations.


Abstract:
  Voltage collapse is a type of blackout-inducing dynamic instability that
occurs when power demand exceeds the maximum power that can be transferred
through a network. The traditional (preventive) approach to avoid voltage
collapse is based on ensuring that the network never reaches its maximum
capacity. However, such an approach leads to inefficient use of network
resources and does not account for unforeseen events. To overcome this
limitation, this paper seeks to initiate the study of voltage collapse
stabilization, i.e., the design of load controllers aimed at stabilizing the
point of voltage collapse. We formulate the problem of voltage stability for a
star direct current network as a dynamic problem where each load seeks to
achieve a constant power consumption by updating its conductance as the voltage
changes. We introduce a voltage collapse stabilization controller and show that
the high-voltage equilibrium is stabilized. More importantly, we are able to
achieve proportional load shedding under extreme loading conditions. We further
highlight the key features of our controller using numerical illustrations.


Abstract:
  An algorithm is proposed, analyzed, and tested experimentally for solving
stochastic optimization problems in which the decision variables are
constrained to satisfy equations defined by deterministic, smooth, and
nonlinear functions. It is assumed that constraint function and derivative
values can be computed, but that only stochastic approximations are available
for the objective function and its derivatives. The algorithm is of the
sequential quadratic optimization variety. A distinguishing feature of the
algorithm is that it allows inexact subproblem solutions to be employed, which
is particularly useful in large-scale settings when the matrices defining the
subproblems are too large to form and/or factorize. Conditions are imposed on
the inexact subproblem solutions that account for the fact that only stochastic
objective gradient estimates are available. Convergence results in expectation
are established for the method. Numerical experiments show that it outperforms
an alternative algorithm that employs highly accurate subproblem solutions in
every iteration.


Abstract:
  The motivation of the present study is to discuss the global (in time)
existence of small data solutions to the following semi-linear structurally
damped $\sigma$-evolution models: \begin{equation*}
\partial_{tt}u+(-\Delta)^{\sigma}u+(-\Delta)^{\sigma/2}\partial_{t}u=\left|u\right|
^{p}, \ \sigma\geq 1, \ \ p>1, \end{equation*} where the Cauchy data $(u(0,x),
\partial_{t}u(0,x))$ will be chosen from energy space on the base of $L^{q}$
with different additional $L^{m}$ regularity, namely \begin{equation*}
u(0,x)\in H^{\sigma,q}(\mathbb{R}^{n})\cap L^{m_{1}}(\mathbb{R}^{n}) , \ \
\partial_{t}u(0,x)\in L^{q}(\mathbb{R}^{n})\cap L^{m_{2}}(\mathbb{R}^{n}), \ \
q\in(1,\infty),\ \ m_{1}, m_{2}\in [1,q). \end{equation*} Our new results will
show that the critical exponent which guarantees the global (in time) existence
is really affected by these different additional regularities and will take
\textit{two different values} under some restrictions on $m_{1}, m_{2}$, $q$,
$\sigma$ and the space dimension $n\geq1$. Moreover, in each case, we have no
loss of decay estimates of the unique solution with respect to the
corresponding linear models.


Abstract:
  It was conjectured by Bott-Grove-Halperin that a compact simply connected
Riemannian manifold $M$ with nonnegative sectional curvature is rationally
elliptic. We confirm this conjecture under the stronger assumption that $M$ has
entire Grauert tube,i.e.,$M$ is a real analytic Riemannian manifold that has a
unique adapted complex structure defined on the whole tangent bundle $TM$.


Abstract:
  An analytical solution of the nonlinear Richards equation is presented, for
one-dimensional infiltration into a soil of uniform initial moisture content
subject to a constant depth of surface ponded water. Adopted mathematical forms
of the soil water diffusivity and conductivity are flexible enough to model a
range of real soils. The solution takes the form of a power series in $\sqrt
t$, but is observed to converge not only for small times but also for
relatively large times at which travelling-wave-like behavior is evident. The
solution is used to tabulate exact infiltration coefficients with higher-order
corrections as the natural nonlinear limit of soil properties is approached.
Previously published approximate solutions that apply for a wide range of soil
properties are tested against the exact solution and found to be sufficiently
accurate.


Abstract:
  Normalizing flows have recently been applied to the problem of accelerating
Markov chains in lattice field theory. We propose a generalization of
normalizing flows that allows them to applied to theories with a sign problem.
These complex normalizing flows are closely related to contour deformations
(i.e. the generalized Lefschetz thimble method), which been applied to sign
problems in the past. We discuss the question of the existence of normalizing
flows: they do not exist in the most general case, but we argue that exact
normalizing flows are likely to exist for many physically interesting problems,
including cases where the Lefschetz thimble decomposition has an intractable
sign problem. Finally, normalizing flows can be constructed in perturbation
theory. We give numerical results on their effectiveness across a range of
couplings for the Schwinger-Keldysh sign problem associated to a real scalar
field in $0+1$ dimensions.


Abstract:
  Observations of binary pulsars and pulsars in globular clusters suggest that
at least some pulsars must receive weak natal kicks at birth. If all pulsars
received strong natal kicks above \unit[50]{\kms}, those born in globular
clusters would predominantly escape, while wide binaries would be disrupted. On
the other hand, observations of transverse velocities of isolated radio pulsars
indicate that only $5\pm2\%$ have velocities below \unit[50]{\kms}. We explore
this apparent tension with rapid binary population synthesis modelling. We
propose a model in which supernovae with characteristically low natal kicks
(e.g., electron-capture supernovae) only occur if the progenitor star has been
stripped via binary interaction with a companion. We show that this model
naturally reproduces the observed pulsar speed distribution and without
reducing the predicted merging double neutron star yield. We estimate that the
zero-age main sequence mass range for non-interacting progenitors of
electron-capture supernovae should be no wider than ${\approx}0.2 M_\odot$.


Abstract:
  We present a non existence result of complete, Einstein hypersurfaces tangent
to the Reeb vector field of a regular Sasakian manifold which fibers onto a
complex Stein manifold.


Abstract:
  We show that the inclusion of a recently found additional term of the spin
polarization vector at local equilibrium which is linear in the symmetrized
gradients of the velocity field, and the assumption of hadron production at
constant temperature restore the quantitative agreement between hydrodynamic
model predictions and local polarization measurements in relativistic heavy ion
collisions at $\sqrt{s_{\rm NN}} = 200$ GeV. The longitudinal component of the
spin polarization vector turns out to be very sensitive to the temperature
value, with a good fit around 155 MeV. The implications of this finding are
discussed.


Abstract:
  We present an approach for visualizing mobile robots through an Augmented
Reality headset when there is no line-of-sight visibility between the robot and
the human. Three elements are visualized in Augmented Reality: 1) Robot's 3D
model to indicate its position, 2) An arrow emanating from the robot to
indicate its planned movement direction, and 3) A 2D grid to represent the
ground plane. We conduct a user study with 18 participants, in which each
participant are asked to retrieve objects, one at a time, from stations at the
two sides of a T-junction at the end of a hallway where a mobile robot is
roaming. The results show that visualizations improved the perceived safety and
efficiency of the task and led to participants being more comfortable with the
robot within their personal spaces. Furthermore, visualizing the motion intent
in addition to the robot model was found to be more effective than visualizing
the robot model alone. The proposed system can improve the safety of automated
warehouses by increasing the visibility and predictability of robots.


Abstract:
  In this work, we represent Lex-BERT, which incorporates the lexicon
information into Chinese BERT for named entity recognition (NER) tasks in a
natural manner. Instead of using word embeddings and a newly designed
transformer layer as in FLAT, we identify the boundary of words in the
sentences using special tokens, and the modified sentence will be encoded
directly by BERT. Our model does not introduce any new parameters and are more
efficient than FLAT. In addition, we do not require any word embeddings
accompanying the lexicon collection. Experiments on Ontonotes and ZhCrossNER
show that our model outperforms FLAT and other baselines.


Abstract:
  Very recently, the Muon $g-2$ experiment at Fermilab has confirmed the E821
Brookhaven result, which hinted at a deviation of the muon anomalous magnetic
moment from the Standard Model (SM) expectation. The combined results from
Brookhaven and Fermilab show a difference with the SM prediction $\delta a_\mu
= (251 \pm 59) \times 10^{-11}$ at a significance of $4.2\sigma$, strongly
indicating the presence of new physics. Motivated by this new result we
reexamine the contributions to the muon anomalous magnetic moment from both:
(i)~the ubiquitous $U(1)$ gauge bosons of D-brane string theory constructions
and (ii)~the Regge excitations of the string. We show that, for a string scale
${\cal O} ({\rm PeV})$, the contribution from anomalous $U(1)$ gauge bosons
which couple to hadrons could help to reduce (though not fully eliminate) the
discrepancy reported by the Muon $g-2$ Collaboration. Consistency with null
results from LHC searches of new heavy vector bosons imparts the dominant
constraint. We demonstrate that the contribution from Regge excitations is
strongly suppressed as it was previously conjectured. We also comment on
contributions from Kaluza-Klein (KK) modes, which could help resolve the
$\delta a_\mu$ discrepancy. In particular, we argue that for 4-stack
intersecting D-brane models, the KK excitations of the $U(1)$ boson living on
the lepton brane would not couple to hadrons and therefore can evade the LHC
bounds while fully bridging the $\delta a_\mu$ gap observed at Brookhaven and
Fermilab.


Abstract:
  Agile and DevOps are widely adopted by the industry. Hence, integrating
security activities with industrial practices, such as continuous integration
(CI) pipelines, is necessary to detect security flaws and adhere to regulators'
demands early. In this paper, we analyze automated security activities in CI
pipelines of enterprise-driven open source software (OSS). This shall allow us,
in the long-run, to better understand the extent to which security activities
are (or should be) part of automated pipelines. In particular, we mine publicly
available OSS repositories and survey a sample of project maintainers to better
understand the role that security activities and their related tools play in
their CI pipelines. To increase transparency and allow other researchers to
replicate our study (and to take different perspectives), we further disclose
our research artefacts. Our results indicate that security activities in
enterprise-driven OSS projects are scarce and protection coverage is rather
low. Only 6.83% of the analyzed 8,243 projects apply security automation in
their CI pipelines, even though maintainers consider security to be rather
important. This alerts industry to keep the focus on vulnerabilities of 3rd
Party software and it opens space for other improvements of practice which we
outline in this manuscript.


Abstract:
  In this paper, we learn dynamics models for parametrized families of
dynamical systems with varying properties. The dynamics models are formulated
as stochastic processes conditioned on a latent context variable which is
inferred from observed transitions of the respective system. The probabilistic
formulation allows us to compute an action sequence which, for a limited number
of environment interactions, optimally explores the given system within the
parametrized family. This is achieved by steering the system through
transitions being most informative for the context variable. We demonstrate the
effectiveness of our method for exploration on a non-linear toy-problem and two
well-known reinforcement learning environments.


Abstract:
  In this paper, we propose a wrapper for feature subset selection (FSS) based
on parallel and distributed hybrid evolutionary algorithms viz., parallel
binary differential evolution and threshold accepting (PB-DETA), parallel
binary threshold accepting and differential evolution (PB-TADE) under the
Apache Spark environment. Here, the FSS is formulated as a combinatorial
optimization problem. PB-TADE comprises invoking two optimization algorithms
i.e., TA and BDE in tandem in every iteration, while in PB-DETA, BDE is invoked
first before TA takes over in tandem in every iteration. In addition to these
hybrids, parallel binary differential evolution (P-BDE), is also developed to
investigate the role played by TA and for baseline comparison. For all the
three proposed approaches, logistic regression (LR) is used to compute the
fitness function namely, the area under ROC curve (AUC) score. The
effectiveness of the parallel and distributed wrappers is assessed over five
large datasets of varying feature space dimension pertaining to the cyber
security and biology domains. It is noteworthy that the PB-TADE turned out to
be statistically significant compared to P-BDE and PB-DETA. The speed up is
reported with respect to the sequential version of the three wrappers. Average
AUC score obtained, most repeated feature subsets, feature subsets with least
cardinality having best AUC score are also reported. Further, our proposed
methods outperformed the state-of-the-art results, wherever the results were
reported.


Abstract:
  Two-particle long-range azimuthal correlations are measured in photonuclear
collisions using 1.7 nb$^{-1}$ of 5.02 TeV Pb+Pb collision data collected by
the ATLAS experiment at the LHC. Candidate events are selected using a
dedicated high-multiplicity photonuclear event trigger, a combination of
information from the zero-degree calorimeters and forward calorimeters, and
from pseudorapidity gaps constructed using calorimeter energy clusters and
charged-particle tracks. Distributions of event properties are compared between
data and Monte Carlo simulations of photonuclear processes. Two-particle
correlation functions are formed using charged-particle tracks in the selected
events, and a template-fitting method is employed to subtract the non-flow
contribution to the correlation. Significant nonzero values of the second- and
third-order flow coefficients are observed and presented as a function of
charged-particle multiplicity and transverse momentum. The results are compared
with flow coefficients obtained in proton-proton and proton-lead collisions in
similar multiplicity ranges, and with theoretical expectations. The unique
initial conditions present in this measurement provide a new way to probe the
origin of the collective signatures previously observed only in hadronic
collisions.


Abstract:
  We report direct measurements via angle-resolved photoemission spectroscopy
(ARPES) of the electronic dispersion of single-layer CoO$_2$. The Fermi contour
consists of a large hole pocket centered at the $\overline{\Gamma}$ point. To
interpret the ARPES results, we use density functional theory (DFT) in
combination with the multi-orbital Gutzwiller Approximation (DFT+GA), basing
our calculations on crystalline structure parameters derived from x-ray
photoelectron diffraction and low-energy electron diffraction. Our calculations
are in good agreement with the measured dispersion. We conclude that the
material is a moderately correlated metal. We also discuss substrate effects,
and the influence of hydroxylation on the CoO$_2$ single-layer electronic
structure.


Abstract:
  Accreting supermassive binary black holes (SMBBHs) are potential
multi-messenger sources because they emit both gravitational wave and
electromagnetic (EM) radiation. Past work has shown that their EM output may be
periodically modulated by an asymmetric density distribution in the
circumbinary disk, often called an "overdensity" or "lump;" this modulation
could possibly be used to identify a source as a binary. We explore the
sensitivity of the overdensity to SMBBH mass ratio and magnetic flux through
the accretion disk. We find that the relative amplitude of the overdensity and
its associated EM periodic signal both degrade with diminishing mass ratio,
vanishing altogether somewhere between 1:2 and 1:5. Greater magnetization also
weakens the lump and any modulation of the light output. We develop a model to
describe how lump formation results from internal stress degrading faster in
the lump region than it can be rejuvenated through accretion inflow, and
predicts a threshold value in specific internal stress below which lump
formation should occur and which all our lump-forming simulations satisfy.
Thus, detection of such a modulation would provide a constraint on both
mass-ratio and magnetic flux piercing the accretion flow.


Abstract:
  Ultra-reliable low-latency communication (URLLC) in 5G New Radio has been
originally defined only for licensed spectrum. However, due to new use cases in
the Industry 4.0 scenarios, URLLC operation is currently being extended to
unlicensed spectrum in the ongoing Release 17 of the 3rd Generation Partnership
Project. Although in such controlled environments we can guarantee the absence
of any other technology sharing the channel on a long-term basis, the
uncertainty of obtaining channel access through load based equipment (LBE) or
frame based equipment (FBE) can impede with the latency requirements of URLLC.
In FBE, the transmitters can be prioritized to support data with different
requirements and have lower energy consumption and latency compared to LBE with
a big contention window size. In this paper we analyze the performance of FBE
in an unlicensed controlled environment through a Markov chain. Based on this
analysis, we propose two schemes to improve the URLLC performance in FBE: The
first scheme allows the transmitters to use multiple fixed frame period (FFP)
configurations while the second scheme configures the FFP's starting point of
each transmitter based on its priority. The simulations show the benefits of
these schemes compared to the URLLC transmission of existing schemes.


Abstract:
  Micro-solid oxide fuel cells based on thin films have strong potential for
use in portable power devices. However, devices based on silicon substrates
typically involve thin-film metallic electrodes which are unstable at high
temperatures. Devices based on bulk metal substrates overcome these
limitations, though performance is hindered by the challenge of growing
state-of-the-art epitaxial materials on metals. Here, we demonstrate for the
first time the growth of epitaxial cathode materials on metal substrates
(stainless steel) commercially supplied with epitaxial electrolyte layers (1.5
{um (Y2O3)0.15(ZrO2)0.85 (YSZ) + 50 nm CeO2). We create epitaxial mesoporous
cathodes of (La0.60Sr0.40)0.95Co0.20Fe0.80O3 (LSCF) on the substrate by growing
LSCF/MgO vertically aligned nanocomposite films by pulsed laser deposition,
followed by selectively etching out the MgO. To enable valid comparison with
the literature, the cathodes are also grown on single-crystal substrates,
confirming state-of-the-art performance with an area specific resistance of
100ohmegacm2 at 500dC and activation energy down to 0.97 eV. The work marks an
important step toward the commercialization of high-performance micro-solid
oxide fuel cells for portable power applications.


Abstract:
  This manuscript introduces a space of functions, termed occupation kernel
Hilbert space (OKHS), that operate on collections of signals rather than real
or complex functions. To support this new definition, an explicit class of
OKHSs is given through the consideration of a reproducing kernel Hilbert space
(RKHS). This space enables the definition of nonlocal operators, such as
fractional order Liouville operators, as well as spectral decomposition methods
for corresponding fractional order dynamical systems. In this manuscript, a
fractional order DMD routine is presented, and the details of the finite rank
representations are given. Significantly, despite the added theoretical content
through the OKHS formulation, the resultant computations only differ slightly
from that of occupation kernel DMD methods for integer order systems posed over
RKHSs.


Abstract:
  We present supremum Lagrange Multiplier tests to compare a linear ARMA
specification against its threshold ARMA extension. We derive the asymptotic
distribution of the test statistics both under the null hypothesis and
contiguous local alternatives. Moreover, we prove the consistency of the tests.
The Monte Carlo study shows that the tests enjoy good finite-sample properties,
are robust against model mis-specification and their performance is not
affected if the order of the model is unknown. The tests present a low
computational burden and do not suffer from some of the drawbacks that affect
the quasi-likelihood ratio setting. Lastly, we apply our tests to a time series
of standardized tree-ring growth indexes and this can lead to new research in
climate studies.


Abstract:
  For a projective hyperplane arrangement, we study sufficient conditions in
terms of combinatorial data for ESV-calculability of the monodromy eigenspaces
of the first Milnor fiber cohomology for eigenvalues of order $m>1$. This can
be reduced to the line arrangement case by Artin's theorem. These sufficient
conditions are often unsatisfied if efficiency or complexity of the
combinatorics of arrangement is high. In order to measure these, we introduce
the notions of $m$-efficiency and $m$-complexity for $m\ge 3$. The former is
defined to be the number of points with multiplicity divisible by $m$ lying on
one line in average. In many cases, one of the above sufficient conditions is
satisfied if it is at most 2, although there are certain exceptional cases,
especially when $m=3$. The $m$-complexity is defined to be the maximal number
of edges containing one vertex of the associated $m$-graph. We can show that
one of the sufficient condition holds if it is at most $(m+1)/2$.


Abstract:
  Bayesian variable selection is a powerful tool for data analysis, as it
offers a principled method for variable selection that accounts for prior
information and uncertainty. However, wider adoption of Bayesian variable
selection has been hampered by computational challenges, especially in
difficult regimes with a large number of covariates or non-conjugate
likelihoods. Generalized linear models for count data, which are prevalent in
biology, ecology, economics, and beyond, represent an important special case.
Here we introduce an efficient MCMC scheme for variable selection in binomial
and negative binomial regression that exploits Tempered Gibbs Sampling (Zanella
and Roberts, 2019) and that includes logistic regression as a special case. In
experiments we demonstrate the effectiveness of our approach, including on
cancer data with seventeen thousand covariates.


Abstract:
  The remaining Useful Life (RUL) of equipment is defined as the duration
between the current time and its failure. An accurate and reliable prognostic
of the remaining useful life provides decision-makers with valuable information
to adopt an appropriate maintenance strategy to maximize equipment utilization
and avoid costly breakdowns. In this work, we propose an end-to-end deep
learning model based on multi-layer perceptron and long short-term memory
layers (LSTM) to predict the RUL. After normalization of all data, inputs are
fed directly to an MLP layers for feature learning, then to an LSTM layer to
capture temporal dependencies, and finally to other MLP layers for RUL
prognostic. The proposed architecture is tested on the NASA commercial modular
aero-propulsion system simulation (C-MAPSS) dataset. Despite its simplicity
with respect to other recently proposed models, the model developed outperforms
them with a significant decrease in the competition score and in the root mean
square error score between the predicted and the gold value of the RUL. In this
paper, we will discuss how the proposed end-to-end model is able to achieve
such good results and compare it to other deep learning and state-of-the-art
methods.


Abstract:
  Using the off-shell formulation for ${\cal N}=2$ conformal supergravity in
four dimensions, we describe superconformal higher-spin multiplets of conserved
currents in a curved background and present their associated unconstrained
gauge prepotentials. The latter are used to construct locally superconformal
chiral actions, which are demonstrated to be gauge invariant in arbitrary
conformally flat backgrounds. The main ${\cal N}=2$ results are then
generalised to the ${\cal N}$-extended case. We also present the
gauge-invariant field strengths for on-shell massless higher-spin ${\cal N}=2$
supermultiplets in anti-de Sitter space. These field strengths prove to furnish
representations of the ${\cal N}=2$ superconformal group.


Abstract:
  We investigate the null controllability property of the parabolic equation
associated with the Grushin operator defined by the canonical almost-Riemannian
structure on the 2-dimensional sphere $\mathbb S^2$. This is the natural
generalization of the Grushin operator $\mathcal G = \partial_x^2 +
x^2\partial_y^2$ on $\mathbb R^2$ to this curved setting, and presents a
degeneracy at the equator of $\mathbb S^2$. Namely, its expression in
latitude-longitude spherical coordinates $(x,y)\in(-\pi/2,\pi/2)\times
[0,2\pi)$ is
  $$\mathcal{L} = \partial_{x}^{2}-\tan
x\partial_{x}+\tan^2x\partial_{y}^{2}.$$
  We prove that the null controllability is verified in large time when the
control acts as a source term distributed on a subset $\omega = \{
(x_1,x_2,x_3)\in \mathbb S^2\mid \alpha<|x_3|<\beta \}$ for some
$0\le\alpha<\beta\le 1$. More precisely, we show the existence of a positive
time $T^{*}>0$ such that the system is null controllable from $\omega$ in any
time $T>T^{*}$, and that the minimal time of control from $\omega$ satisfies
$T_{\min}\ge\log(1/\sqrt{1-\alpha^2})$. Here, the lower bound corresponds to
the Agmon distance of $\omega$ from the equator.
  These results are obtained by applying the same ideas as in (Beauchard et
al., 2014), consisting in proving a suitable Carleman estimate, and then taking
advantage of a Poincar\'e-Hardy type inequality due to (Chisholm and Everitt,
1970) to show the positive null-controllability result. On the other hand, the
negative statement is proved by exploiting an appropriate family of spherical
harmonics, which concentrate at the equator, to falsify the uniform
observability inequality.


Abstract:
  Significant understanding has been achieved over the last few decades
regarding chirality-dependent properties of single-wall carbon nanotubes
(SWCNTs), primarily through single-tube studies. However, macroscopic
manifestations of chirality dependence have been limited, especially in
electronic transport, despite the fact that such distinct behaviors are needed
for many applications of SWCNT-based devices. In addition, developing reliable
transport theory is challenging since a description of localization phenomena
in an assembly of nanoobjects requires precise knowledge of disorder on
multiple spatial scales, particularly if the ensemble is heterogeneous. Here,
we report an observation of pronounced chirality-dependent electronic
localization in temperature and magnetic field dependent conductivity
measurements on macroscopic films of single-chirality SWCNTs. The samples
included large-gap semiconducting (6,5) and (10,3) films, narrow-gap
semiconducting (7,4) and (8,5) films, and armchair metallic (6,6) films.
Experimental data and theoretical calculations revealed Mott
variable-range-hopping dominated transport in all samples, while localization
lengths fall into three distinct categories depending on their band gaps.
Armchair films have the largest localization length. Our detailed analyses on
electronic transport properties of single-chirality SWCNT films provide
significant new insight into electronic transport in ensembles of nanoobjects,
offering foundations for designing and deploying macroscopic SWCNT solid-state
devices.


Abstract:
  The pythagorean fuzzy set (PFS) which is developed based on intuitionistic
fuzzy set, is more efficient in elaborating and disposing uncertainties in
indeterminate situations, which is a very reason of that PFS is applied in
various kinds of fields. How to measure the distance between two pythagorean
fuzzy sets is still an open issue. Mnay kinds of methods have been proposed to
present the of the question in former reaserches. However, not all of existing
methods can accurately manifest differences among pythagorean fuzzy sets and
satisfy the property of similarity. And some other kinds of methods neglect the
relationship among three variables of pythagorean fuzzy set. To addrees the
proplem, a new method of measuring distance is proposed which meets the
requirements of axiom of distance measurement and is able to indicate the
degree of distinction of PFSs well. Then some numerical examples are offered to
to verify that the method of measuring distances can avoid the situation that
some counter? intuitive and irrational results are produced and is more
effective, reasonable and advanced than other similar methods. Besides, the
proposed method of measuring distances between PFSs is applied in a real
environment of application which is the medical diagnosis and is compared with
other previous methods to demonstrate its superiority and efficiency. And the
feasibility of the proposed method in handling uncertainties in practice is
also proved at the same time.


Abstract:
  We consider a decision maker who must choose an action in order to maximize a
reward function that depends also on an unknown parameter {\Theta}. The
decision maker can delay taking the action in order to experiment and gather
additional information on {\Theta}. We model the decision maker's problem using
a Bayesian sequential experimentation framework and use dynamic programming and
diffusion-asymptotic analysis to solve it. For that, we scale our problem in a
way that both the average number of experiments that is conducted per unit of
time is large and the informativeness of each individual experiment is low.
Under such regime, we derive a diffusion approximation for the sequential
experimentation problem, which provides a number of important insights about
the nature of the problem and its solution. Our solution method also shows that
the complexity of the problem grows only quadratically with the cardinality of
the set of actions from which the decision maker can choose. We illustrate our
methodology and results using a concrete application in the context of
assortment selection and new product introduction. Specifically, we study the
problem of a seller who wants to select an optimal assortment of products to
launch into the marketplace and is uncertain about consumers' preferences.
Motivated by emerging practices in e-commerce, we assume that the seller is
able to use a crowdvoting system to learn these preferences before a final
assortment decision is made. In this context, we undertake an extensive
numerical analysis to assess the value of learning and demonstrate the
effectiveness and robustness of the heuristics derived from the diffusion
approximation.


Abstract:
  Prostate cancer biopsy benefits from accurate fusion of transrectal
ultrasound (TRUS) and magnetic resonance (MR) images. In the past few years,
convolutional neural networks (CNNs) have been proved powerful in extracting
image features crucial for image registration. However, challenging
applications and recent advances in computer vision suggest that CNNs are quite
limited in its ability to understand spatial correspondence between features, a
task in which the self-attention mechanism excels. This paper aims to develop a
self-attention mechanism specifically for cross-modal image registration. Our
proposed cross-modal attention block effectively maps each of the features in
one volume to all features in the corresponding volume. Our experimental
results demonstrate that a CNN network designed with the cross-modal attention
block embedded outperforms an advanced CNN network 10 times of its size. We
also incorporated visualization techniques to improve the interpretability of
our network. The source code of our work is available at
https://github.com/DIAL-RPI/Attention-Reg .


Abstract:
  This paper focuses on an important problem of detecting offensive analogy
meme on online social media where the visual content and the texts/captions of
the meme together make an analogy to convey the offensive information. Existing
offensive meme detection solutions often ignore the implicit relation between
the visual and textual contents of the meme and are insufficient to identify
the offensive analogy memes. Two important challenges exist in accurately
detecting the offensive analogy memes: i) it is not trivial to capture the
analogy that is often implicitly conveyed by a meme; ii) it is also challenging
to effectively align the complex analogy across different data modalities in a
meme. To address the above challenges, we develop a deep learning based
Analogy-aware Offensive Meme Detection (AOMD) framework to learn the implicit
analogy from the multi-modal contents of the meme and effectively detect
offensive analogy memes. We evaluate AOMD on two real-world datasets from
online social media. Evaluation results show that AOMD achieves significant
performance gains compared to state-of-the-art baselines by detecting offensive
analogy memes more accurately.


Abstract:
  Semiparametric accelerated failure time (AFT) models are a useful alternative
to Cox proportional hazards models, especially when the assumption of constant
hazard ratios is untenable. However, rank-based criteria for fitting AFT models
are often non-differentiable, which poses a computational challenge in
high-dimensional settings. In this article, we propose a new alternating
direction method of multipliers algorithm for fitting semiparametric AFT models
by minimizing a penalized rank-based loss function. Our algorithm scales well
in both the number of subjects and number of predictors; and can easily
accommodate a wide range of popular penalties. To improve the selection of
tuning parameters, we propose a new criterion which avoids some common problems
in cross-validation with censored responses. Through extensive simulation
studies, we show that our algorithm and software is much faster than existing
methods (which can only be applied to special cases), and we show that
estimators which minimize a penalized rank-based criterion often outperform
alternative estimators which minimize penalized weighted least squares
criteria. Application to nine cancer datasets further demonstrates that
rank-based estimators of semiparametric AFT models are competitive with
estimators assuming proportional hazards model in high-dimensional settings,
whereas weighted least squares estimators are often not. A software package
implementing the algorithm, along with a set of auxiliary functions, is
available for download at github.com/ajmolstad/penAFT.


Abstract:
  The Dzyaloshinskii-Moriya interaction (DMI) is at the heart of many modern
developments in the research field of spintronics. DMI is known to generate
noncollinear magnetic textures, and can take two forms in antiferromagnets:
homogeneous or inter-sublattice, leading to small, canted moments and
inhomogeneous or intra-sublattice, leading to formation of chiral structures.
In this work, we first determine the strength of the effective field created by
the DMI, using SQUID based magnetometry and transport measurements, in thin
films of the antiferromagnetic iron oxide hematite, $\alpha$-Fe$_2$O$_3$. We
demonstrate that DMI additionally introduces reconfigurability in the long
distance magnon transport in these films under different orientations of a
magnetic field. This arises as a hysteresis centred around the easy-axis
direction for an external field rotated in opposing directions whose width
decreases with increasing magnetic field as the Zeeman energy competes with the
effective field created by the DMI.


Abstract:
  The lack of specifications is a key difference between traditional software
engineering and machine learning. We discuss how it drastically impacts how we
think about divide-and-conquer approaches to system design, and how it impacts
reuse, testing and debugging activities. Traditionally, specifications provide
a cornerstone for compositional reasoning and for the divide-and-conquer
strategy of how we build large and complex systems from components, but those
are hard to come by for machine-learned components. While the lack of
specification seems like a fundamental new problem at first sight, in fact
software engineers routinely deal with iffy specifications in practice: we face
weak specifications, wrong specifications, and unanticipated interactions among
components and their specifications. Machine learning may push us further, but
the problems are not fundamentally new. Rethinking machine-learning model
composition from the perspective of the feature interaction problem, we may
even teach us a thing or two on how to move forward, including the importance
of integration testing, of requirements engineering, and of design.


Abstract:
  Gyroscopes are widely used in various field. The instability of the low-cost
gyroscopes makes them need to be calibrated on every boot. To meet the
requirement of frequency calibration, finding an efficient in-field calibration
method is essential. This paper proposes a fast calibration method that does
not require any external equipment. We use the manual rotation angle as a
calibration reference and linearize the calibration model. On the basis of this
model, a G-optimal experimental design scheme is proposed, which can get enough
calibration information with the least number of experiments. The simulations
indicate that the calibration error is relatively low, and the results are
unbiased. We empirically validate the effectiveness of the proposed method on
two commonly used low-cost gyroscope and achieve real-time calibration on a
low-energy microcontroller. We validate the proposed method by comparing the
above method with the conventional turntable method. The experiment result
shows that the error between these two methods is less than $\pm3 \times
10^{-2}$ and the calibration process takes less than 30 seconds. This method
might have a practical implication for low-cost gyroscope calibration.


Abstract:
  According to the FNAL+BNL measurements for the muon $g-2$ and the Berkeley
$^{133}$Cs measurement for the electron $g-2$, the SM prediction for the muon
(electron) $g-2$ is $4.2\sigma$ ($2.4\sigma$) below (above) the experimental
value. A joint explanation requires a positive contribution to the muon $g-2$
and a negative contribution to the electron $g-2$. In this work we explore the
possibility of such a joint explanation in the minimal supersymmetric standard
model (MSSM). Assuming no universality between smuon and selectron soft masses,
we find out a part of parameter space for a joint explanation of muon and
electron $g-2$ anomalies at $2\sigma$ level. This part of parameter space can
survive the LHC and LEP constraints, but gives an over-abundance for the dark
matter if the bino-like lightest neutralino is assumed to be the dark matter
candidate. With the assumption that the dark matter candidate is a superWIMP
(say a pseudo-goldstino in multi-sector SUSY breaking scenarios, whose mass can
be as light as GeV and produced from the late-dacay of the thermally freeze-out
lightest neutralino), the dark matter problem can be avoided. So, the MSSM may
give a joint explanation for the muon and electron $g-2$ anomalies at $2\sigma$
level (the muon $g-2$ anomaly can be ameliorated to $1\sigma$).


Abstract:
  It remains an open question how to determine the winner of an election given
incomplete or uncertain voter preferences. One solution is to assume some
probability space for the voting profile and declare the candidates having the
best chance of winning to be the (co-)winners. We refer to this as the Most
Probable Winner (MPW). In this paper, we propose an alternative winner
interpretation for positional scoring rules - the Most Expected Winner (MEW),
based on the expected performance of the candidates. This winner interpretation
enjoys some desirable properties that the MPW does not. We establish the
theoretical hardness of MEW over incomplete voter preferences, then identify a
collection of tractable cases for a variety of voting profiles. An important
contribution of this work is to separate the voter preferences into the
generation step and the observation step, which gives rise to a unified voting
profile combining both incomplete and probabilistic voting profiles.


Abstract:
  The aims of this work are to study Rees algebras of filtrations of monomial
ideals associated to covering polyhedra of rational matrices with non-negative
entries and non-zero columns using combinatorial optimization and integer
programming, and to study powers of monomial ideals and their integral closures
using irreducible decompositions and polyhedral geometry. We study the
Waldschmidt constant and the ic-resurgence of the filtration associated to a
covering polyhedron and show how to compute these constants using linear
programming. Then we show a lower bound for the ic-resurgence of the ideal of
covers of a graph and prove that the lower bound is attained when the graph is
perfect. We also show lower bounds for the ic-resurgence of the edge ideal of a
graph and give an algorithm to compute the asymptotic resurgence of squarefree
monomial ideals. A classification of when Newton's polyhedron is the
irreducible polyhedron is presented using integral closure.


Abstract:
  Polariton-based devices require materials where light-matter coupling under
ambient conditions exceeds losses, but our current selection of such materials
is limited. Here we measured the dispersion of polaritons formed by the $A$ and
$B$ excitons in thin MoS$_2$ slabs by imaging their optical near fields. We
combined fully tunable laser excitation in the visible with a scattering
near-field optical microscope to excite polaritons and image their optical near
fields. We obtained the properties of bulk MoS$_2$ from fits to the slab
dispersion. The in-plane excitons are in the strong regime of light-matter
coupling with a coupling strength ($40-100\,$meV) that exceeds their losses by
at least a factor of two. The coupling becomes comparable to the exciton
binding energy, which is known as very strong coupling. MoS$_2$ and other
transition metal dichalcogenides are excellent materials for future polariton
devices.


Abstract:
  Domain adaptation (DA) enables knowledge transfer from a labeled source
domain to an unlabeled target domain by reducing the cross-domain distribution
discrepancy. Most prior DA approaches leverage complicated and powerful deep
neural networks to improve the adaptation capacity and have shown remarkable
success. However, they may have a lack of applicability to real-world
situations such as real-time interaction, where low target inference latency is
an essential requirement under limited computational budget. In this paper, we
tackle the problem by proposing a dynamic domain adaptation (DDA) framework,
which can simultaneously achieve efficient target inference in low-resource
scenarios and inherit the favorable cross-domain generalization brought by DA.
In contrast to static models, as a simple yet generic method, DDA can integrate
various domain confusion constraints into any typical adaptive network, where
multiple intermediate classifiers can be equipped to infer "easier" and
"harder" target data dynamically. Moreover, we present two novel strategies to
further boost the adaptation performance of multiple prediction exits: 1) a
confidence score learning strategy to derive accurate target pseudo labels by
fully exploring the prediction consistency of different classifiers; 2) a
class-balanced self-training strategy to explicitly adapt multi-stage
classifiers from source to target without losing prediction diversity.
Extensive experiments on multiple benchmarks are conducted to verify that DDA
can consistently improve the adaptation performance and accelerate target
inference under domain shift and limited resources scenarios


Abstract:
  We report an ultra long range energy transfer in a layered
donor:spacer:acceptor structures, which consist of the typical organic
photovoltaic material system of P3HT as the donor and PCBM as the acceptor. By
varying the thicknesses of spacer and acceptor layers we show that the energy
transfer is to the full volume of the acceptor and not just to its nearest
interface, and we find an effective energy transfer range on the order of about
100nm. Our efforts to elucidate the origin of this process, both theoretically
and experimentally, are discussed as well. Although it was recently implied
that an exceptionally large energy transfer may take place in this material
system, this is the first time, to the best of our knowledge, that the energy
transfer mechanism is characterized in a quantitative way and compared to the
common models. Our analysis offers new prospects for the familiar photovoltaic
bi-layer configurations, which may be very efficient if the ultra long range
energy transfer is utilized through a suitable architecture.


Abstract:
  We study large-scale applications using a GPU-accelerated version of the
massively parallel J\"ulich universal quantum computer simulator (JUQCS--G).
First, we benchmark JUWELS Booster, a GPU cluster with 3744 NVIDIA A100 Tensor
Core GPUs. Then, we use JUQCS--G to study the relation between quantum
annealing (QA) and the quantum approximate optimization algorithm (QAOA). We
find that a very coarsely discretized version of QA, termed approximate quantum
annealing (AQA), performs surprisingly well in comparison to the QAOA. It can
either be used to initialize the QAOA, or to avoid the costly optimization
procedure altogether. Furthermore, we study the scaling of the success
probability when using AQA for problems with 30 to 40 qubits. We find that the
case with largest discretization error performs most favorably, surpassing the
best result obtained from the QAOA.


Abstract:
  We prove that there exists an open and dense subset $\mathcal{U}$ in the
space of $C^{2}$ expanding self-maps of the circle $\mathbb{T}$ such that the
Lyapunov minimizing measures of any $T\in{\mathcal U}$ are uniquely supported
on a periodic orbit.This answers a conjecture of Jenkinson-Morris in the $C^2$
topology.


Abstract:
  Cryptocurrencies redefined how money can be stored and transferred among
users. However, independent of the amount being sent, public blockchain-based
cryptocurrencies suffer from high transaction waiting times and fees. These
drawbacks hinder the wide use of cryptocurrencies by masses. To address these
challenges, payment channel network concept is touted as the most viable
solution to be used for micro-payments. The idea is exchanging the ownership of
money by keeping the state of the accounts locally. The users inform the
blockchain rarely, which decreases the load on the blockchain. Specifically,
payment channel networks can provide transaction approvals in seconds by
charging a nominal fee proportional to the payment amount. Such attraction on
payment channel networks inspired many recent studies which focus on how to
design them and allocate channels such that the transactions will be secure and
efficient. However, as payment channel networks are emerging and reaching large
number of users, privacy issues are becoming more relevant that raise concerns
about exposing not only individual habits but also businesses' revenues. In
this paper, we first propose a categorization of the existing payment networks
formed on top of blockchain-backed cryptocurrencies. After discussing several
emerging attacks on user/business privacy in these payment channel networks, we
qualitatively evaluate them based on a number of privacy metrics that relate to
our case. Based on the discussions on the strengths and weaknesses of the
approaches, we offer possible directions for research for the future of privacy
based payment channel networks.


Abstract:
  The accurate measurement of stellar masses over a wide range of galaxy
properties is essential for better constraining models of galaxy evolution.
Emission line galaxies (ELGs) tend to have better redshift estimates than
continuum-selected objects and have been shown to span a large range of
physical properties, including stellar mass. Using data from the 3D-HST
Treasury program, we construct a carefully vetted sample of 4350 ELGs at
redshifts 1.16<z<1.90. We combine the 3D-HST emission line fluxes with far-UV
through near-IR photometry and use the MCSED spectral energy distribution
fitting code to constrain the galaxies' physical parameters, such as their star
formation rate (SFRs) and stellar masses. Our sample is consistent with the z~2
mass-metallicity relation. More importantly, we show there is a simple but
tight correlation between stellar mass and absolute magnitude in a near-IR
filter that will be particularly useful in quickly calculating accurate stellar
masses for millions of galaxies in upcoming missions such as Euclid and the
Nancy Grace Roman Space Telescope.


Abstract:
  The impact of momentum anisotropy on the heavy quark transport coefficients
due to collisional and radiative processes in the QCD medium has been studied
within the ambit of kinetic theory. Anisotropic aspects (momentum) are
incorporated into the heavy quark dynamics through the non-equilibrium momentum
distribution function of quarks, antiquarks, and gluons. These non-equilibrium
distribution functions that encode the physics of momentum anisotropy and
turbulent chromo-fields have been obtained by solving the ensemble-averaged
diffusive Vlasov-Boltzmann equation. The momentum dependence of heavy quark
transport coefficients in the medium is seen to be sensitive to the strength of
the anisotropy for both collisional and radiative processes. In addition, the
collisional and radiative energy loss of the heavy quark in the anisotropic hot
QCD medium have been analyzed. The effects of anisotropy on the drag and
diffusion coefficients are observed to have a visible impact on the nuclear
suppression factor both at the RHIC and LHC.


Abstract:
  For years, independence has been considered as an important concept in many
disciplines. Nevertheless, we present the first research that investigates the
discovery problem of independence in data. In its arguably simplest form,
independence is a statement between two sets of columns expressing that for
every two rows in a table there is also a row in the table that coincides with
the first row on the first set of columns and with the second row on the second
set of columns. We show that the problem of deciding whether there is an
independence statement that holds on a given table is not only NP-complete but
$W[3]$-complete in its arguably most natural parameter, namely its arity. We
establish the first algorithm to discover all independence statement that hold
on a given table. We illustrate in experiments with benchmark data that our
algorithm performs well within the limits established by our hardness results.
In practice, it is often useful to determine the ratio with which independence
statements hold on a given table. For that purpose, we show that our treatment
of independence and the design of our algorithm enables us to extend our
findings to approximate independence. In our final experiments, we provide some
insight into the trade-off between run time and the approximation ratio.
Naturally, the smaller the ratio, the more approximate independence statements
hold, and the more time it takes to discover all of them. While this research
establishes first insight into the computational properties of discovering
independence from data, we hope to initiate research into more sophisticated
notions of independence, including embedded multivalued dependencies, as well
as their context-specific and probabilistic variants.


Abstract:
  Momentum-space topology of electrons under strong spin-orbit coupling
contributes to the electrically induced torques exerting on magnetic textures
insensitively to disorder or thermal fluctuation. We present a direct
connection between band topology and the torques by classifying the whole
torques phenomenologically. As well as the intrinsic anomalous Hall effect, the
torques also emerge intrinsically from the anomalous velocity of electrons
regardless of a nonequilibrium transport current. We especially point out the
intrinsic contribution arising exclusively in magnetic textures, which we call
the "topological Hall torque (THT)." The THT emerges in bulk crystals without
any interface or surface structures. We numerically demonstrate the enhancement
of the THT in comparison with the conventional spin-transfer torque in the bulk
metallic ferromagnet, which accounts for the giant current-induced torque
measured in ferromagnetic SrRuO3.


Abstract:
  Recently, the end-to-end approach has been successfully applied to
multi-speaker speech separation and recognition in both single-channel and
multichannel conditions. However, severe performance degradation is still
observed in the reverberant and noisy scenarios, and there is still a large
performance gap between anechoic and reverberant conditions. In this work, we
focus on the multichannel multi-speaker reverberant condition, and propose to
extend our previous framework for end-to-end dereverberation, beamforming, and
speech recognition with improved numerical stability and advanced frontend
subnetworks including voice activity detection like masks. The techniques
significantly stabilize the end-to-end training process. The experiments on the
spatialized wsj1-2mix corpus show that the proposed system achieves about 35%
WER relative reduction compared to our conventional multi-channel E2E ASR
system, and also obtains decent speech dereverberation and separation
performance (SDR=12.5 dB) in the reverberant multi-speaker condition while
trained only with the ASR criterion.


Abstract:
  Structure and thermodynamics in restricted primitive model electrolytes are
examined using three recently developed versions of a linear form of the
modified Poisson-Boltzmann equation. Analytical expressions for the osmotic
coefficient and the electrical part of the mean activity coefficient are
obtained and the results for the osmotic and the mean activity coefficients are
compared with that from the more established mean spherical approximation,
symmetric Poisson-Boltzmann, modified Poisson-Boltzmann theories, and available
Monte Carlo simulation results. The linear theories predict the thermodynamics
to a remarkable degree of accuracy relative to the simulations and are
consistent with the mean spherical approximation and modified Poisson-Boltzmann
results. The predicted structure in the form of the radial distribution
functions and the mean electrostatic potential also compare well with the
corresponding results from the formal theories. The excess internal energy and
the electrical part of the mean activity coefficient are shown to be identical
analytically for the mean spherical approximation and the linear modified
Poisson-Boltzmann theories.


Abstract:
  This paper describes the training process of the first Czech monolingual
language representation models based on BERT and ALBERT architectures. We
pre-train our models on more than 340K of sentences, which is 50 times more
than multilingual models that include Czech data. We outperform the
multilingual models on 9 out of 11 datasets. In addition, we establish the new
state-of-the-art results on nine datasets. At the end, we discuss properties of
monolingual and multilingual models based upon our results. We publish all the
pre-trained and fine-tuned models freely for the research community.


Abstract:
  This paper presents a novel computer vision the algorithm proposed for the
on-line range and bearing detection of the robot swarms. Results demonstrated
the reliability of the proposed vision system such that it can be used for the
robot swarms applications.


Abstract:
  The concept of Hybrid Twin (HT) has recently received a growing interest
thanks to the availability of powerful machine learning techniques. This twin
concept combines physics-based models within a model-order reduction
framework-to obtain real-time feedback rates-and data science. Thus, the main
idea of the HT is to develop on-the-fly data-driven models to correct possible
deviations between measurements and physics-based model predictions. This paper
is focused on the computation of stable, fast and accurate corrections in the
Hybrid Twin framework. Furthermore, regarding the delicate and important
problem of stability, a new approach is proposed, introducing several
sub-variants and guaranteeing a low computational cost as well as the
achievement of a stable time-integration.


Abstract:
  DeepLLL algorithm (Schnorr, 1994) is a famous variant of LLL lattice basis
reduction algorithm, and PotLLL algorithm (Fontein et al., 2014) and $S^2$LLL
algorithm (Yasuda and Yamaguchi, 2019) are recent polynomial-time variants of
DeepLLL algorithm developed from cryptographic applications. However, the known
polynomial bounds for computational complexity are shown only for parameter
$\delta < 1$; for "optimal" parameter $\delta = 1$ which ensures the best
output quality, no polynomial bounds are known, and except for LLL algorithm,
it is even not formally proved that the algorithm always halts within finitely
many steps. In this paper, we prove that these four algorithms always halt also
with optimal parameter $\delta = 1$, and furthermore give explicit upper bounds
for the numbers of loops executed during the algorithms. Unlike the known bound
(Akhavi, 2003) applicable to LLL algorithm only, our upper bounds are deduced
in a unified way for all of the four algorithms.


Abstract:
  We search for microlensing planets with signals exhibiting no
caustic-crossing features, considering the possibility that such signals may be
missed due to their weak and featureless nature. For this purpose, we reexamine
the lensing events found by the KMTNet survey before the 2019 season. From this
investigation, we find two new planetary lensing events, KMT-2018-BLG-1976 and
KMT-2018-BLG-1996. We also present the analysis of the planetary event
OGLE-2019-BLG-0954, for which the planetary signal was known, but no detailed
analysis has been presented before. We identify the genuineness of the
planetary signals by checking various interpretations that can generate
short-term anomalies in lensing light curves. From Bayesian analyses conducted
with the constraint from available observables, we find that the host and
planet masses are $(M_1, M_2)\sim (0.65~M_\odot, 2~M_{\rm J})$ for
KMT-2018-BLG-1976L, $\sim (0.69~M_\odot, 1~M_{\rm J})$ for KMT-2018-BLG-1996L,
and $\sim (0.80~M_\odot, 14~M_{\rm J})$ for OGLE-2019-BLG-0954L. The estimated
distance to OGLE-2019-BLG-0954L, $3.63^{+1.22}_{-1.64}$~kpc, indicates that it
is located in the disk, and the brightness expected from the mass and distance
matches well the brightness of the blend, indicating that the lens accounts for
most of the blended flux. The lens of OGLE-2019-BLG-0954 could be resolved from
the source by conducting high-resolution follow-up observations in and after
2024.


Abstract:
  The random q-state quantum Potts model is studied on hypercubic lattices in
dimensions 2 and 3 using the numerical implementation of the Strong Disorder
Renormalization Group introduced by Kovacs and Igl{\'o}i [Phys. Rev. B 82,
054437 (2010)]. Critical exponents $\nu$, d f and $\psi$ at the Infinite
Disorder Fixed Point are estimated by Finite-Size Scaling for several numbers
of states q between 2 and 50. When scaling corrections are not taken into
account, the estimates of both d f and $\psi$ systematically increase with q.
It is shown however that q-dependent scaling corrections are present and that
the exponents are compatible within error bars, or close to each other, when
these corrections are taking into account. This provides evidence of the
existence of a super-universality of all 2D and 3D random Potts models.


Abstract:
  Blockchain is an emerging technology with potential to address issues related
to sustainability. Literature reviews on blockchain and sustainability exist,
but there is a need to consolidate existing results, in particular, in terms of
Sustainable Development Goals (SDG). This extended abstract presents an ongoing
tertiary study based on existing literature reviews to investigate the
relationship between blockchain and sustainability in terms of SDGs. Results
from a pilot analysis of 18 reviews using thematic analysis are presented.


Abstract:
  We prove pointwise convergence for the scattering data of a Dirac system of
differential equations. Equivalently, we prove an analog of Carleson's theorem
on almost everywhere convergence of Fourier series for a version of the
non-linear Fourier transform. Our proofs are based on the study of resonances
of Dirac systems using families of meromorphic inner functions, generated by a
Ricatti equation corresponding to the system.


Abstract:
  We show that the Liouville function changes sign infinitely often for
$x^2+d$.


Abstract:
  We show that any compact surface of genus zero in Euclidean 3-space that
satisfies a quasiconformal inequality between its principal curvatures is a
round sphere. This solves an old open problem by H. Hopf, and gives a spherical
version of Simon's quasiconformal Bernstein theorem. The result generalizes,
among others, Hopf's theorem for constant mean curvature spheres, the
classification of round spheres as the only compact elliptic Weingarten
surfaces of genus zero, and the uniqueness theorem for ovaloids by Han,
Nadirashvili and Yuan. The proof relies on the Bers-Nirenberg representation of
solutions to linear elliptic equations with discontinuous coefficients.


Abstract:
  To convert a fractional solution to an instance of a constraint satisfaction
problem into a solution, a rounding scheme is needed, which can be described by
a collection of symmetric operations with one of each arity. An intriguing
possibility, raised in a recent paper by Carvalho and Krokhin, would imply that
any clone of operations on a set $D$ which contains symmetric operations of
arities $1, 2, \ldots, \lvert D \rvert$ contains symmetric operations of all
arities in the clone. If true, then it is possible to check whether any given
family of constraint satisfaction problems is solved by its linear programming
relaxation.
  We characterize all idempotent clones containing symmetric operations of
arities $1, 2, \ldots, \lvert D \rvert$ for all sets $D$ with size at most four
and prove that each one contains symmetric operations of every arity, proving
the conjecture above for $\lvert D \rvert \leq 4$.


Abstract:
  The emergence of nano-scale memristive devices encouraged many different
research areas to exploit their use in multiple applications. One of the
proposed applications was to implement synaptic connections in bio-inspired
neuromorphic systems. Large-scale neuromorphic hardware platforms are being
developed with increasing number of neurons and synapses, having a critical
bottleneck in the online learning capabilities. Spike-timing-dependent
plasticity (STDP) is a widely used learning mechanism inspired by biology which
updates the synaptic weight as a function of the temporal correlation between
pre- and post-synaptic spikes. In this work, we demonstrate experimentally that
binary stochastic STDP learning can be obtained from a memristor when the
appropriate pulses are applied at both sides of the device.


Abstract:
  In the framework of the study of helium-like atomic systems possessing the
collinear configuration, we propose a simple method for computing compact but
very accurate wave functions describing the relevant $S$ state. It is worth
noting that the considered states include the well-known states of the
electron-nucleus and electron-electron coalescences as a particular case. The
simplicity and compactness imply that the considered wave functions represent a
linear combinations of few single exponentials. We have calculated such model
wave functions for the ground state of helium and the two-electron ions with
nucleus charge $1 \leq Z \leq 5$. The parameters and the accompanying
characteristics of these functions are presented in tables for number of
exponential from 3 to 6. The accuracy of the resulting wave functions are
confirmed graphically. The specific properties of the relevant codes by Wolfram
Mathematica are discussed. An example of application of the compact wave
functions under consideration is reported.


Abstract:
  Recent work on promoting cooperation in multi-agent learning has resulted in
many methods which successfully promote cooperation at the cost of becoming
more vulnerable to exploitation by malicious actors. We show that this is an
unavoidable trade-off and propose an objective which balances these concerns,
promoting both safety and long-term cooperation. Moreover, the trade-off
between safety and cooperation is not severe, and you can receive exponentially
large returns through cooperation from a small amount of risk. We study both an
exact solution method and propose a method for training policies that targets
this objective, Accumulating Risk Capital Through Investing in Cooperation
(ARCTIC), and evaluate them in iterated Prisoner's Dilemma and Stag Hunt.


Abstract:
  Utilizing the unbiased time-dependent density-matrix renormalization group
technique, we examine the photoemission spectra in the extended Falicov-Kimball
model at zero and finite temperatures, particularly with regard to the
excitonic insulator state most likely observed in the quasi-one-dimensional
material Ta$_2$NiSe$_5$. Working with infinite boundary conditions, we are able
to simulate all dynamical correlation functions directly in the thermodynamic
limit. For model parameters best suited for Ta$_2$NiSe$_5$ the photoemission
spectra show a weak but clearly visible two-peak structure, around the Fermi
momenta $k\simeq\pm k_{\rm F}$, which suggests that Ta$_2$NiSe$_5$ develops an
excitonic insulator of BCS-like type. At higher temperatures, the leakage of
the conduction-electron band beyond the Fermi energy becomes distinct, which
provides a possible explanation for the bare non-interacting band structure
seen in time- and angle-resolved photoemission spectroscopy experiments.


Abstract:
  Quantitative measures of uptake in caudate, putamen, and globus pallidus in
dopamine transporter (DaT) brain SPECT have potential as biomarkers for the
severity of Parkinson disease. Reliable quantification of uptake requires
accurate segmentation of these regions. However, segmentation is challenging in
DaT SPECT due to partial-volume effects, system noise, physiological
variability, and the small size of these regions. To address these challenges,
we propose an estimation-based approach to segmentation. This approach
estimates the posterior mean of the fractional volume occupied by caudate,
putamen, and globus pallidus within each voxel of a 3D SPECT image. The
estimate is obtained by minimizing a cost function based on the binary
cross-entropy loss between the true and estimated fractional volumes over a
population of SPECT images, where the distribution of the true fractional
volumes is obtained from magnetic resonance images from clinical populations.
The proposed method accounts for both the sources of partial-volume effects in
SPECT, namely the limited system resolution and tissue-fraction effects. The
method was implemented using an encoder-decoder network and evaluated using
realistic clinically guided SPECT simulation studies, where the ground-truth
fractional volumes were known. The method significantly outperformed all other
considered segmentation methods and yielded accurate segmentation with dice
similarity coefficients of ~ 0.80 for all regions. The method was relatively
insensitive to changes in voxel size. Further, the method was relatively robust
up to +/- 10 degrees of patient head tilt along transaxial, sagittal, and
coronal planes. Overall, the results demonstrate the efficacy of the proposed
method to yield accurate fully automated segmentation of caudate, putamen, and
globus pallidus in 3D DaT-SPECT images.


Abstract:
  An old theorem of Ad\'amek constructs initial algebras for sufficiently
cocontinuous endofunctors via transfinite iteration over ordinals in classical
set theory. We prove a new version that works in constructive logic, using
"inflationary" iteration over a notion of size that abstracts from limit
ordinals just their transitive, directed and well-founded properties. Borrowing
from Taylor's constructive treatment of ordinals, we show that sizes exist with
upper bounds for any given signature of indexes. From this it follows that
there is a rich class of endofunctors to which the new theorem applies,
provided one admits a weak form of choice (WISC) due to Streicher, Moerdijk,
van den Berg and Palmgren, and which is known to hold in the internal
constructive logic of many kinds of elementary topos.


Abstract:
  Self-Sovereign Identity (SSI), a Blockchain-based technology for digital
identity management, is a promising concept for handling health data. It could
represent a step forward in empowering users, granting them control over their
data. This work conducts a systematic literature review to investigate
state-of-the-art measures based on SSI and Blockchain technologies for dealing
with electronic health records (EHRs), identifying gaps, and determining the
key questions for future research. As a result, this review shows a growing
interest in Blockchain methods to handle EHRs, but few works consider using the
self-sovereign identity approaches. The results obtained in this work also
suggest that: Blockchain technologies provide a viable alternative to deliver
EHR solutions such as patient monitoring, healthcare data trading, and
prescription control; consolidated Blockchain technologies are the preferred
core components of most effective strategies; keeping raw health data off-chain
helps to create scalable solutions; health data standards make searching
medical records in Blockchain structures feasible; Smart Contracts are
essential components of Blockchain-based EHR solutions; the concepts of data
ownership and Self-Sovereign Identity have been neither adequately defined nor
employed in the health context.


Abstract:
  This paper proposes a new defense approach for counteracting state-of-the-art
white and black-box adversarial attack algorithms. Our approach fits into the
implicit reactive defense algorithm category since it does not directly
manipulate the potentially malicious input signals. Instead, it reconstructs a
similar signal with a synthesized spectrogram using a cyclic generative
adversarial network. This cyclic framework helps to yield a stable generative
model. Finally, we feed the reconstructed signal into the speech-to-text model
for transcription. The conducted experiments on targeted and non-targeted
adversarial attacks developed for attacking DeepSpeech, Kaldi, and Lingvo
models demonstrate the proposed defense's effectiveness in adverse scenarios.


Abstract:
  In transportation, communication, social and other real complex networks,
some critical edges act a pivotal part in controlling the flow of information
and maintaining the integrity of the structure. Due to the importance of
critical edges in theoretical studies and practical applications, the
identification of critical edges gradually become a hot topic in current
researches. Considering the overlap of communities in the neighborhood of
edges, a novel and effective metric named subgraph overlap (SO) is proposed to
quantifying the significance of edges. The experimental results show that SO
outperforms all benchmarks in identifying critical edges which are crucial in
maintaining the integrity of the structure and functions of networks.


Abstract:
  We propose a new scenario of using the dark axion portal at one-loop level to
explain the recently observed muon anomalous magnetic moment by the Fermilab
Muon g-2 experiment. Both axion/axion-like particle (ALP) and dark photon are
involved in the same vertex with photon. Although ALP or dark photon alone
cannot explain muon $g-2$, since the former provides only negative contribution
while the latter has very much constrained parameter space, dark axion portal
can save the situation and significantly extend the allowed parameter space.
The observed muon anomalous magnetic moment provides a robust probe of the dark
axion portal scenario.


Abstract:
  Finding the appropriate words to convey concepts (i.e., lexical access) is
essential for effective communication. Reverse dictionaries fulfill this need
by helping individuals to find the word(s) which could relate to a specific
concept or idea. To the best of our knowledge, this resource has not been
available for the Persian language. In this paper, we compare four different
architectures for implementing a Persian reverse dictionary (PREDICT).
  We evaluate our models using (phrase,word) tuples extracted from the only
Persian dictionaries available online, namely Amid, Moein, and Dehkhoda where
the phrase describes the word. Given the phrase, a model suggests the most
relevant word(s) in terms of the ability to convey the concept. The model is
considered to perform well if the correct word is one of its top suggestions.
  Our experiments show that a model consisting of Long Short-Term Memory (LSTM)
units enhanced by an additive attention mechanism is enough to produce
suggestions comparable to (or in some cases better than) the word in the
original dictionary. The study also reveals that the model sometimes produces
the synonyms of the word as its output which led us to introduce a new metric
for the evaluation of reverse dictionaries called Synonym Accuracy accounting
for the percentage of times the event of producing the word or a synonym of it
occurs. The assessment of the best model using this new metric also indicates
that at least 62% of the times, it produces an accurate result within the top
100 suggestions.


Abstract:
  It is known that not only classical semantics but also intuitionistic Kripke
semantics can be generalized so that it can treat arbitrary propositional
connectives characterized by truth tables, or truth functions. In our previous
work, it has been shown that the set of Kripke-valid propositional sequents and
that of classically valid propositional sequents coincide if and only if all
available propositional connectives are monotonic. The present paper extend
this result to frst-order logic showing that, in the case of predicate logic,
the condition that all available propositional connectives are monotonic is a
necessary and sufficient condition for the set of sequents valid in all
constant domain Kripke models, not the set of Kripke-valid sequents, and the
set of classically valid sequents to coincide.


Abstract:
  Hadamard matrices are square $n\times n$ matrices whose entries are ones and
minus ones and whose rows are orthogonal to each other with respect to the
standard scalar product in $\Bbb R^n$. Each Hadamard matrix can be transformed
to a matrix whose entries are zeros and ones. This presentation of Hadamard
matrices is investigated in the paper and based on it an algorithm for
generating them is designed.


Abstract:
  Artificial intelligence (AI) is becoming increasingly more popular and can be
found in workplaces and homes around the world. However, how do we ensure trust
in these systems? Regulation changes such as the GDPR mean that users have a
right to understand how their data has been processed as well as saved.
Therefore if, for example, you are denied a loan you have the right to ask why.
This can be hard if the method for working this out uses "black box" machine
learning techniques such as neural networks. TrustyAI is a new initiative which
looks into explainable artificial intelligence (XAI) solutions to address
trustworthiness in ML as well as decision services landscapes.
  In this paper we will look at how TrustyAI can support trust in decision
services and predictive models. We investigate techniques such as LIME, SHAP
and counterfactuals, benchmarking both LIME and counterfactual techniques
against existing implementations. We also look into an extended version of
SHAP, which supports background data selection to be evaluated based on
quantitative data and allows for error bounds.


Abstract:
  We investigate multi-speaker speech recognition from ultrasound images of the
tongue and video images of the lips. We train our systems on imaging data from
modal speech, and evaluate on matched test sets of two speaking modes: silent
and modal speech. We observe that silent speech recognition from imaging data
underperforms compared to modal speech recognition, likely due to a
speaking-mode mismatch between training and testing. We improve silent speech
recognition performance using techniques that address the domain mismatch, such
as fMLLR and unsupervised model adaptation. We also analyse the properties of
silent and modal speech in terms of utterance duration and the size of the
articulatory space. To estimate the articulatory space, we compute the convex
hull of tongue splines, extracted from ultrasound tongue images. Overall, we
observe that the duration of silent speech is longer than that of modal speech,
and that silent speech covers a smaller articulatory space than modal speech.
Although these two properties are statistically significant across speaking
modes, they do not directly correlate with word error rates from speech
recognition.


Abstract:
  Clear cell renal cell carcinoma (ccRCC) is one of the most common forms of
intratumoral heterogeneity in the study of renal cancer. ccRCC originates from
the epithelial lining of proximal convoluted renal tubules. These cells undergo
abnormal mutations in the presence of Ki67 protein and create a lump-like
structure through cell proliferation. Manual counting of tumor cells in the
tissue-affected sections is one of the strongest prognostic markers for renal
cancer. However, this procedure is time-consuming and also prone to
subjectivity. These assessments are based on the physical cell appearance and
suffer wide intra-observer variations. Therefore, better cell nucleus detection
and counting techniques can be an important biomarker for the assessment of
tumor cell proliferation in routine pathological investigations. In this paper,
we introduce a deep learning-based detection model for cell classification on
IHC stained histology images. These images are classified into binary classes
to find the presence of Ki67 protein in cancer-affected nucleus regions. Our
model maps the multi-scale pyramid features and saliency information from local
bounded regions and predicts the bounding box coordinates through regression.
Our method validates the impact of Ki67 expression across a cohort of four
hundred histology images treated with localized ccRCC and compares our results
with the existing state-of-the-art nucleus detection methods. The precision and
recall scores of the proposed method are computed and compared on the clinical
data sets. The experimental results demonstrate that our model improves the F1
score up to 86.3% and an average area under the Precision-Recall curve as
85.73%.


Abstract:
  Understanding surface reactivity is crucial in many fields, going from
heterogeneous catalysis to materials oxidation and corrosion. In order to
decipher the surface reactions of ZrB2 exposed to the harsh environment of
aerospace components, the chemical activity of both Zr- and B-surfaces is
predicted and compared by using density functional theory and nudged elastic
band methods. In particular the adsorption, dissociation and diffusion of O2,
CO and H2O are extensively examined through the calculation of surface
adsorption energies and reaction pathways. We find the dissociative adsorption
of O2 dominating the reactivity of ZrB2 surfaces, while the dissociation of H2O
and CO is weakly active on Zr-surfaces, and even less activated on B-terminated
ones. Importantly, we discover that the reaction of O2 and H2O can trigger
strong surface reconstruction at B-surfaces. Our work thus provides significant
insights into the diverse adsorption and reaction mechanisms of ZrB2 surfaces.


Abstract:
  kNN-MT, recently proposed by Khandelwal et al. (2020a), successfully combines
pre-trained neural machine translation (NMT) model with token-level
k-nearest-neighbor (kNN) retrieval to improve the translation accuracy.
However, the traditional kNN algorithm used in kNN-MT simply retrieves a same
number of nearest neighbors for each target token, which may cause prediction
errors when the retrieved neighbors include noises. In this paper, we propose
Adaptive kNN-MT to dynamically determine the number of k for each target token.
We achieve this by introducing a light-weight Meta-k Network, which can be
efficiently trained with only a few training samples. On four benchmark machine
translation datasets, we demonstrate that the proposed method is able to
effectively filter out the noises in retrieval results and significantly
outperforms the vanilla kNN-MT model. Even more noteworthy is that the Meta-k
Network learned on one domain could be directly applied to other domains and
obtain consistent improvements, illustrating the generality of our method. Our
implementation is open-sourced at https://github.com/zhengxxn/adaptive-knn-mt.


Abstract:
  This paper presents VTN, a transformer-based framework for video recognition.
Inspired by recent developments in vision transformers, we ditch the standard
approach in video action recognition that relies on 3D ConvNets and introduce a
method that classifies actions by attending to the entire video sequence
information. Our approach is generic and builds on top of any given 2D spatial
network. In terms of wall runtime, it trains $16.1\times$ faster and runs
$5.1\times$ faster during inference while maintaining competitive accuracy
compared to other state-of-the-art methods. It enables whole video analysis,
via a single end-to-end pass, while requiring $1.5\times$ fewer GFLOPs. We
report competitive results on Kinetics-400 and present an ablation study of VTN
properties and the trade-off between accuracy and inference speed. We hope our
approach will serve as a new baseline and start a fresh line of research in the
video recognition domain. Code and models are available at:
https://github.com/bomri/SlowFast/blob/master/projects/vtn/README.md