Abstract:
  Although single-image super-resolution (SISR) methods have achieved great
success on single degradation, they still suffer performance drop with multiple
degrading effects in real scenarios. Recently, some blind and non-blind models
for multiple degradations have been explored. However, those methods usually
degrade significantly for distribution shifts between the training and test
data. Towards this end, we propose a conditional meta-network framework (named
CMDSR) for the first time, which helps SR framework learn how to adapt to
changes in input distribution. We extract degradation prior at task-level with
the proposed ConditionNet, which will be used to adapt the parameters of the
basic SR network (BaseNet). Specifically, the ConditionNet of our framework
first learns the degradation prior from a support set, which is composed of a
series of degraded image patches from the same task. Then the adaptive BaseNet
rapidly shifts its parameters according to the conditional features. Moreover,
in order to better extract degradation prior, we propose a task contrastive
loss to decrease the inner-task distance and increase the cross-task distance
between task-level features. Without predefining degradation maps, our blind
framework can conduct one single parameter update to yield considerable SR
results. Extensive experiments demonstrate the effectiveness of CMDSR over
various blind, even non-blind methods. The flexible BaseNet structure also
reveals that CMDSR can be a general framework for large series of SISR models.


Abstract:
  Deep neural networks (DNNs) could be very useful in blockchain applications
such as DeFi and NFT trading. However, training / running large-scale DNNs as
part of a smart contract is infeasible on today's blockchain platforms, due to
two fundamental design issues of these platforms. First, blockchains nowadays
typically require that each node maintain the complete world state at any time,
meaning that the node must execute all transactions in every block. This is
prohibitively expensive for computationally intensive smart contracts involving
DNNs. Second, existing blockchain platforms expect smart contract transactions
to have deterministic, reproducible results and effects. In contrast, DNNs are
usually trained / run lock-free on massively parallel computing devices such as
GPUs, TPUs and / or computing clusters, which often do not yield deterministic
results.
  This paper proposes novel platform designs, collectively called A New Hope
(ANH), that address the above issues. The main ideas are (i)
computing-intensive smart contract transactions are only executed by nodes who
need their results, or by specialized serviced providers, and (ii) a
non-deterministic smart contract transaction leads to uncertain results, which
can still be validated, though at a relatively high cost; specifically for
DNNs, the validation cost can often be reduced by verifying properties of the
results instead of their exact values. In addition, we discuss various
implications of ANH, including its effects on token fungibility, sharding,
private transactions, and the fundamental meaning of a smart contract.


Abstract:
  Let $P$ be a crossing-free polygon and $\mathcal C$ a set of shortcuts, where
each shortcut is a directed straight-line segment connecting two vertices of
$P$. A shortcut hull of $P$ is another crossing-free polygon that encloses $P$
and whose oriented boundary is composed of elements from $\mathcal C$. Shortcut
hulls find their application in geo-related problems such as the simplification
of contour lines. We aim at a shortcut hull that linearly balances the enclosed
area and perimeter. If no holes in the shortcut hull are allowed, the problem
admits a straight-forward solution via shortest paths. For the more challenging
case that the shortcut hull may contain holes, we present a polynomial-time
algorithm that is based on computing a constrained, weighted triangulation of
the input polygon's exterior. We use this problem as a starting point for
investigating further variants, e.g., restricting the number of edges or bends.
We demonstrate that shortcut hulls can be used for drawing the rough extent of
point sets as well as for the schematization of polygons.


Abstract:
  Low-precision arithmetic trains deep learning models using less energy, less
memory and less time. However, we pay a price for the savings: lower precision
may yield larger round-off error and hence larger prediction error. As
applications proliferate, users must choose which precision to use to train a
new model, and chip manufacturers must decide which precisions to manufacture.
We view these precision choices as a hyperparameter tuning problem, and borrow
ideas from meta-learning to learn the tradeoff between memory and error. In
this paper, we introduce Pareto Estimation to Pick the Perfect Precision
(PEPPP). We use matrix factorization to find non-dominated configurations (the
Pareto frontier) with a limited number of network evaluations. For any given
memory budget, the precision that minimizes error is a point on this frontier.
Practitioners can use the frontier to trade memory for error and choose the
best precision for their goals.


Abstract:
  The asymmetric dark matter (ADM) scenario solves the baryon-dark matter
coincidence problem when the dark matter (DM) mass is of $\mathcal{O}(1)$GeV.
Composite ADM models based on QCD-like strong dynamics are particularly
motivated since the strong dynamics naturally provides the DM mass of
$\mathcal{O}(1)$GeV and the large annihilation cross-section simultaneously. In
those models, the sub-GeV dark photon often plays an essential role in
transferring the excessive entropy in the dark sector into the visible sector,
i.e., the Standard Model sector. This paper constructs a chiral composite ADM
model where the $U(1)_D$ gauge symmetry is embedded into the chiral flavor
symmetry. Due to the dynamical breaking of the chiral flavor symmetry, the
model naturally provides the masses of the dark photon and the dark pions in
the sub-GeV range, both of which play crucial roles for a successful ADM model.


Abstract:
  In the present paper we present the potential of Explainable Artificial
Intelligence methods for decision-support in medical image analysis scenarios.
With three types of explainable methods applied to the same medical image data
set our aim was to improve the comprehensibility of the decisions provided by
the Convolutional Neural Network (CNN). The visual explanations were provided
on in-vivo gastral images obtained from a Video capsule endoscopy (VCE), with
the goal of increasing the health professionals' trust in the black box
predictions. We implemented two post-hoc interpretable machine learning methods
LIME and SHAP and the alternative explanation approach CIU, centered on the
Contextual Value and Utility (CIU). The produced explanations were evaluated
using human evaluation. We conducted three user studies based on the
explanations provided by LIME, SHAP and CIU. Users from different non-medical
backgrounds carried out a series of tests in the web-based survey setting and
stated their experience and understanding of the given explanations. Three user
groups (n=20, 20, 20) with three distinct forms of explanations were
quantitatively analyzed. We have found that, as hypothesized, the CIU
explainable method performed better than both LIME and SHAP methods in terms of
increasing support for human decision-making as well as being more transparent
and thus understandable to users. Additionally, CIU outperformed LIME and SHAP
by generating explanations more rapidly. Our findings suggest that there are
notable differences in human decision-making between various explanation
support settings. In line with that, we present three potential explainable
methods that can with future improvements in implementation be generalized on
different medical data sets and can provide great decision-support for medical
experts.


Abstract:
  Astronomers use to derive MOdified Newtonian Dynamics (MOND) rotation curves
using the simple algebraic rule of calculating the acceleration as equal to the
Newtonian acceleration ($a$) divided by some factor $\mu (a)$. However, there
are velocity differences between this simple rule and the calculation derived
from more sophisticated MOND versions such as AQUAL or QMOND, created to expand
MOND heuristic law and preserve the conservation of momentum, angular momentum,
and energy, and follow the weak equivalence principle. Here we provide recipes
based on Milgrom's proposal to calculate semianalytically (without numerical
simulations) MOND rotation curves for any density distribution based on AQUAL,
applying it to different models of thin disks. The application of this
formalism is equivalent to the creation of a fictitious phantom mass whose
field may be used in a Newtonian way to calculate iteratively the MOND
accelerations. In most cases, the differences between the application of the
simple algebraic rule and the AQUAL-MOND calculations are small, $\lesssim 5$%.
However, the error of the algebraic solution is larger than 5% when more than
half of the mass is in the MONDian regime (where Newtonian and MOND rotation
speeds differ by more than 10%), reaching in some cases $>70$% discrepance,
such as in Maclaurin disks, representative of galaxies for which the rotational
velocity rises to the edge of the disk as is seen in irregular galaxies. The
slope of the rotation speed in the dependence with the radius or the vertical
distance of the plane is also significantly changed.


Abstract:
  Spin-orbit effects, inherent to electrons confined in quantum dots at a
silicon heterointerface, provide a means to control electron spin qubits
without the added complexity of on-chip, nanofabricated micromagnets or nearby
coplanar striplines. Here, we demonstrate a novel singlet-triplet qubit
operating mode that can drive qubit evolution at frequencies in excess of 200
MHz. This approach offers a means to electrically turn on and off fast control,
while providing high logic gate orthogonality and long qubit dephasing times.
We utilize this operational mode for dynamical decoupling experiments to probe
the charge noise power spectrum in a silicon metal-oxide-semiconductor double
quantum dot. In addition, we assess qubit frequency drift over longer
timescales to capture low-frequency noise. We present the charge noise power
spectral density up to 3 MHz, which exhibits a $1/f^{\alpha}$ dependence
consistent with $\alpha \sim 0.7$, over 9 orders of magnitude in noise
frequency.


Abstract:
  Piezoelectric fluoropolymers convert mechanical energy to electricity and are
ideal for sustainably providing power to electronic devices. To convert
mechanical energy, a net polarization must be induced in the fluoropolymer,
which is currently achieved via an energy intensive electrical poling process.
Eliminating this process will enable the low-energy production of efficient
energy harvesters. Here, by combining molecular dynamics simulations,
piezoresponse force microscopy, and electrodynamic measurements, we reveal a
hitherto unseen polarization locking phenomena of poly(vinylidene
fluoride-$\mathit{co}$-trifluoroethylene) (PVDF-TrFE) perpendicular to the
basal plane of two-dimensional (2D) Ti$_{3}$C$_{2}$T$_{\mathit{x}}$ MXene
nanosheets. This polarization locking, driven by strong electrostatic
interactions enabled exceptional energy harvesting performance, with a measured
piezoelectric charge coefficient, $\mathit{d_{33}}$, of -52.0 picocoulombs per
newton, significantly higher than electrically poled PVDF-TrFE (approximately
-38 picocoulombs per newton). This study provides a new fundamental and low
energy input mechanism of poling fluoropolymers, which enables new levels of
performance in electromechanical technologies.


Abstract:
  We analyze the effect of interphase boundary anisotropy on the dynamics of
lamellar eutectic solidification fronts, in the limit that the lamellar spacing
varies slowly along the envelope of the front. In the isotropic case, it is
known that the spacing obeys a diffusion equation, which can be obtained
theoretically by making two assumptions: (i) the lamellae always grow normal to
the large-scale envelope of the front, and (ii) the Jackson-Hunt law that links
lamellar spacing and front temperature remains locally valid. For anisotropic
boundaries, we replace hypothesis (i) by the symmetric pattern approximation,
which has recently been found to yield good predictions for lamellar growth
direction in presence of interphase anisotropy. We obtain a generalized
Jackson-Hunt law for tilted lamellae, and an evolution equation for the
envelope of the front. The latter contains a propagative term if the initial
lamellar array is tilted with respect to the direction of the temperature
gradient. However, the propagation velocity of the propagative wave modes are
found to be small, so that the dynamics of the front can be reasonably
described by a diffusion equation with a diffusion coefficient that is modified
with respect to the isotropic case.


Abstract:
  We investigate the feasibility of sample average approximation (SAA) for
general stochastic optimization problems, including two-stage stochastic
programming without the relatively complete recourse assumption. Instead of
analyzing problems with specific structures, we utilize results from the
Vapnik-Chervonenkis (VC) dimension and Probably Approximately Correct learning
to provide a general framework that offers explicit feasibility bounds for SAA
solutions under minimal structural or distributional assumption. We show that,
as long as the hypothesis class formed by the feasbible region has a finite VC
dimension, the infeasibility of SAA solutions decreases exponentially with
computable rates and explicitly identifiable accompanying constants. We
demonstrate how our bounds apply more generally and competitively compared to
existing results.


Abstract:
  In this paper, explicit stable integrators based on symplectic and contact
geometries are proposed for a non-autonomous ordinarily differential equation
(ODE) found in improving convergence rate of Nesterov's accelerated gradient
method. Symplectic geometry is known to be suitable for describing Hamiltonian
mechanics, and contact geometry is known as an odd-dimensional counterpart of
symplectic geometry. Moreover, a procedure, called symplectization, is a known
way to construct a symplectic manifold from a contact manifold, yielding
Hamiltonian systems from contact ones. It is found in this paper that a
previously investigated non-autonomous ODE can be written as a contact
Hamiltonian system. Then, by symplectization of a non-autonomous contact
Hamiltonian vector field expressing the non-autonomous ODE, novel symplectic
integrators are derived. Because the proposed symplectic integrators preserve
hidden symplectic and contact structures in the ODE, they should be more stable
than the Runge-Kutta method. Numerical experiments demonstrate that, as
expected, the second-order symplectic integrator is stable and high convergence
rates are achieved.


Abstract:
  In this paper we study quasilinear elliptic equations driven by the double
phase operator along with a reaction that has a singular and a parametric
superlinear term and with a nonlinear Neumann boundary condition of critical
growth. Based on a new equivalent norm for Musielak-Orlicz Sobolev spaces and
the Nehari manifold along with the fibering method we prove the existence of at
least two weak solutions provided the parameter is sufficiently small.


Abstract:
  We propose a novel method for drift estimation of multiscale diffusion
processes when a sequence of discrete observations is given. For the Langevin
dynamics in a two-scale potential, our approach relies on the eigenvalues and
the eigenfunctions of the homogenized dynamics. Our first estimator is derived
from a martingale estimating function of the generator of the homogenized
diffusion process. However, the unbiasedness of the estimator depends on the
rate with which the observations are sampled. We therefore introduce a second
estimator which relies also on filtering the data and we prove that it is
asymptotically unbiased independently of the sampling rate. A series of
numerical experiments illustrate the reliability and efficiency of our
different estimators.


Abstract:
  Photochemically induced dynamic nuclear polarization (photo-CIDNP) is a
method to hyperpolarize nuclear spins using light. In most cases, CIDNP
experiments are performed at a high magnetic field and the sample is irradiated
by the light inside a nuclear magnetic resonance (NMR) spectrometer. Here we
demonstrate photo-CIDNP hyperpolarization generated in the Earth magnetic field
and under zero- to ultralow field (ZULF) conditions. Irradiating a sample for
several seconds with inexpensive light-emitting diodes produces a strong
hyperpolarization of 1H and 13C nuclear spins enhancing the NMR signals several
hundred times. The hyperpolarized spin states at the Earth field and in ZULF
are different. In the latter case, the state corresponds to the singlet order
between scalar-coupled 1H-13C nuclear spins. This state has a longer lifetime
than the state hyperpolarized at Earth field. The method is simple and
cost-efficient and should be applicable to many molecular systems known to
exhibit photo-CIDNP, including amino acids and nucleotides.


Abstract:
  In this letter, we propose a joint active device detection and channel
estimation framework based on factor graphs for asynchronous uplink grant-free
massive multiple-antenna systems. We then develop the message-scheduling GAMP
(MSGAMP) algorithm to perform joint active device detection and channel
estimation. In MSGAMP we apply scheduling techniques based on the residual
belief propagation (RBP) and the activity user detection (AUD) in which
messages are generated using the latest available information. MSGAMP-type
schemes show a good performance in terms of activity error rate and normalized
mean squared error, requiring a smaller number of iterations for convergence
and lower complexity than state-of-the-art techniques.


Abstract:
  This work investigates the use of pulse stretching inverters for monitoring
the variation of flux and Linear Energy Transfer (LET) of energetic particles.
The basic particle detector consists of two cascaded pulse stretching
(skew-sized) inverters designed in CMOS technology, and the required sensing
area is obtained by connecting multiple two-inverter pulse stretching cells in
parallel, and employing the required number of parallel arrays. The particle
strikes are detected in terms of the Single Event Transients (SETs), and the
detector provides the information on the SET count rate and SET pulse width
variation, from which the particle flux and LET can be determined. The main
advantage of the proposed solution is the possibility to sense the LET
variations using purely digital processing logic. The SPICE simulations done on
IHP 130 nm bulk CMOS technology have shown that the SET pulse width at the
output of detector changes by 550 ps over the LET range from 1 to 100
MeVcm2mg-1. The proposed solution is intended to operate as an on-chip particle
detector within the self-adaptive multiprocessing systems.


Abstract:
  In this paper, we study the $k$-th moment of central values of the family of
quadratic Dirichlet $L$-functions of moduli $8p$, with $p$ ranging over odd
primes. Assuming the truth of the generlized Riemann hypothesis, we establish
sharp upper and lower bounds for the $k$-th power moment of these $L$-values
for all real $k \geq 0$.


Abstract:
  The effect of the interaction of mesons in the final state is additionally
considered within the description of $\tau \to \pi \eta(\eta') \nu_{\tau}$
decays. This interaction is taken into account at the level of production of
intermediate pions. One of them, in turn, might be transited into $\eta$ or
$\eta'$ mesons. Our results do not exceed the experimentally established
branching fractions, and they are in agreement with the results of other
theoretical studies.


Abstract:
  Two polarization modes of gravitational wave are derived from the general
relativity which are plus and cross modes. However, the alternative theories of
gravity can yield the gravitational wave with up to six polarizations.
Searching for the polarizations beyond plus and cross is an important test of
general relativity. In principle, one space-borne detector, like LISA, could
measure the gravitational wave polarizations from a long time observation with
its orbital motion. With the comparable sensitivities, the joint LISA and TAIJI
missions will improve the observations on the polarization predictions of
theories beyond general relativity. In this work, a class of parameterized
post-Einsteinian waveform is employed to describe the alternative
polarizations, and six parameterized post-Einsteinian parameters quantifying
from general relativity waveform are examined by using the LISA-TAIJI network.
Our results show that the measurements on amplitudes of alternative
polarizations from joint LISA-TAIJI observation could be improved by more than
10 times compared to LISA single mission in an optimal scenario.


Abstract:
  We analyze the decay of spin waves into Stoner excitations in magnetic Weyl
semimetals. The lifetime of a mode is found to have a universal dependence on
its frequency and momentum, and on a few parameters that characterize the
relativistic Weyl spectrum. At the same time, Gilbert damping by Weyl electrons
is absent. The decay rate of spin waves is calculated perturbatively using the
s-d model of itinerant Weyl or Dirac electrons coupled to local moments. We
show that many details of the Weyl spectrum, such as the momentum-space
locations, dispersions and sizes of the Weyl Fermi pockets, can be deduced
indirectly by probing the spin waves of local moments using inelastic neutron
scattering.


Abstract:
  This letter is a proof of concept for quantum power flow (QPF) algorithms
which underpin various unprecedentedly efficient power system analytics
exploiting quantum computing. Our contributions are three-fold: 1) Establish a
quantum-state-based fast decoupled model empowered by Hermitian and constant
Jacobian matrices; 2) Devise an enhanced Harrow-Hassidim-Lloyd (HHL) algorithm
to solve the fast decoupled QPF; 3) Further improve the HHL efficiency by
parameterizing quantum phase estimation and reciprocal rotation only at the
beginning stage. Promising test results validate the accuracy and efficacy of
QPF and demonstrate QPF's enormous potential in the era of quantum computing.


Abstract:
  This paper deals with the design of resilient Cooperative Adaptive Cruise
Control (CACC) for homogeneous vehicle platoons in which communication is
vulnerable to Denial-of-Service (DOS) attacks. We consider DOS attacks as
consecutive packet dropouts. We present a controller tuning procedure based on
linear matrix inequalities (LMI) that maximizes the resiliency to DOS attacks,
while guaranteeing performance and string stability. The design procedure
returns controller gains and gives a lower bound on the maximum allowable
number of successive packet dropouts. A numerical example is employed to
illustrate the effectiveness of the proposed approach.


Abstract:
  We present on-going work of evaluating the, to our knowledge, first large
generative language model trained to converse in Swedish, using data from the
online discussion forum Flashback. We conduct a human evaluation pilot study
that indicates the model is often able to respond to conversations in both a
human-like and informative manner, on a diverse set of topics. While data from
online forums can be useful to build conversational systems, we reflect on the
negative consequences that incautious application might have, and the need for
taking active measures to safeguard against them.


Abstract:
  The physical and mathematical properties of the non-linearly coupled
black-hole-orbiting-ring system are studied analytically to second order in the
dimensionless angular velocity $M_{\text{ir}}\omega_{\text{H}}$ of the
black-hole horizon (here $M_{\text{ir}}$ is the irreducible mass of the slowly
rotating central black hole). In particular, we determine analytically, to
first order in the dimensionless ring-to-black-hole mass ratio
$m/M_{\text{ir}}$, the shift $\Delta\Omega_{\text{mb}}/\Omega_{\text{mb}}$ in
the orbital frequency of the {\it marginally bound} circular geodesic that
characterizes the composed curved spacetime. Interestingly, our analytical
results for the frequency shift $\Delta\Omega_{\text{mb}}$ in the composed
black-hole-orbiting-ring toy model agree qualitatively with the recently
published numerical results for the corresponding frequency shift in the
physically related (and mathematically much more complex)
black-hole-orbiting-particle system. In particular, the present analysis
provides evidence that, at order $O(m/M_{\text{ir}})$, the recently observed
positive shift in the angular frequency of the marginally bound circular orbit
is directly related to the physically intriguing phenomenon of dragging of
inertial frames by orbiting masses in general relativity.


Abstract:
  We discuss gluon-gluon mechanisms for production of mesons with hidden
strangeness, such as $\eta'$ and $\phi$ meson, in proton-proton collisions at
large energies. The $g^* g^* \to \eta'$ and $g^* g^* \to \phi g$ mechanisms are
considered only and the corresponding cross sections are calculated in the
$k_t$-factorization approach. The $F_{\gamma^* \gamma^* \to \eta'}$ and $F_{g^*
g^* \to \eta'}$ form factors are calculated from quark-antiquark $\eta'$
light-cone wave function including quark/antiquark transverse momenta. The
result for two-photon transition form factor demonstrates that higher twists
may survive even to large photon virtualities. The result is compared with the
result of a recent leading-twist NLO analysis which uses phenomenological
distribution amplitudes fitted to exclusive production of $\eta'$ in $e^+ e^-
\to e^+ e^- \eta'$ reaction. We calculate transverse momentum distributions of
both $\eta'$ and $\phi$ mesons in proton-proton collisions for RHIC and LHC
energies. The results are compared to experimental data whenever available. The
results of the Lund string model are shown for comparison for $\eta'$
production at LHC energies. It seems that the two-gluon fusion is not the
dominant mechanism for both $\phi$ and $\eta'$ production, although the
situation for $\eta'$, especially at larger energies, is less clear due to lack
of experimental data.


Abstract:
  Most subdwarf B (sdB) + Helium white dwarf (He WD) binaries are believed to
be formed from a particular channel. In this channel, the He WDs are produced
first from red giants (RGs) with degenerate cores via stable mass transfer and
sdB stars are produced from RGs with degenerate cores via common envelope (CE)
ejection. They are important for the studies of CE evolution, binary evolution,
and binary population synthesis. However, the relation between WD mass and
orbital period of sdB + He WD binaries has not been specifically studied. In
this paper, we first use a semi-analytic method to follow their formation and
find a WD mass and orbital period relation. Then we use a detailed stellar
evolution code to model their formation from main-sequence binaries. We find a
similar relation between the WD mass and orbital period, which is in broad
agreement with observations. For most sdB + He WD systems, if the WD mass
(orbital period) can be determined, the orbital period (WD mass) can be
inferred with this relation and then the inclination angle can be constrained
with the binary mass function. In addition, we can also use this relation to
constrain the CE ejection efficiency and find that a relative large CE ejection
efficiency is favoured. If both the WD and sdB star masses can be determined,
the critical mass ratios of dynamically unstable mass transfer for RG binaries
can also be constrained.


Abstract:
  Recurrent meta reinforcement learning (meta-RL) agents are agents that employ
a recurrent neural network (RNN) for the purpose of "learning a learning
algorithm". After being trained on a pre-specified task distribution, the
learned weights of the agent's RNN are said to implement an efficient learning
algorithm through their activity dynamics, which allows the agent to quickly
solve new tasks sampled from the same distribution. However, due to the
black-box nature of these agents, the way in which they work is not yet fully
understood. In this study, we shed light on the internal working mechanisms of
these agents by reformulating the meta-RL problem using the Partially
Observable Markov Decision Process (POMDP) framework. We hypothesize that the
learned activity dynamics is acting as belief states for such agents. Several
illustrative experiments suggest that this hypothesis is true, and that
recurrent meta-RL agents can be viewed as agents that learn to act optimally in
partially observable environments consisting of multiple related tasks. This
view helps in understanding their failure cases and some interesting
model-based results reported in the literature.


Abstract:
  This work presents proximally optimal predictive control algorithm, which is
essentially a model-based lateral controller for steered autonomous vehicles
that selects an optimal steering command within the neighborhood of previous
steering angle based on the predicted vehicle location. The proposed algorithm
was formulated with an aim of overcoming the limitations associated with the
existing control laws for autonomous steering - namely PID, Pure-Pursuit and
Stanley controllers. Particularly, our approach was aimed at bridging the gap
between tracking efficiency and computational cost, thereby ensuring effective
path tracking in real-time. The effectiveness of our approach was investigated
through a series of dynamic simulation experiments pertaining to autonomous
path tracking, employing an adaptive control law for longitudinal motion
control of the vehicle. We measured the latency of the proposed algorithm in
order to comment on its real-time factor and validated our approach by
comparing it against the established control laws in terms of both crosstrack
and heading errors recorded throughout the respective path tracking
simulations.


Abstract:
  Marginalized importance sampling (MIS), which measures the density ratio
between the state-action occupancy of a target policy and that of a sampling
distribution, is a promising approach for off-policy evaluation. However,
current state-of-the-art MIS methods rely on complex optimization tricks and
succeed mostly on simple toy problems. We bridge the gap between MIS and deep
reinforcement learning by observing that the density ratio can be computed from
the successor representation of the target policy. The successor representation
can be trained through deep reinforcement learning methodology and decouples
the reward optimization from the dynamics of the environment, making the
resulting algorithm stable and applicable to high-dimensional domains. We
evaluate the empirical performance of our approach on a variety of challenging
Atari and MuJoCo environments.


Abstract:
  An important issue in dealing with Deep Convolutional Neural Networks (DCNN)
is the 'black box problem', which represents the unknowns about internal
information representation and processing, especially in the middle and higher
layers. In this study, we adopted a systems neuroscience methodology to measure
the visual feature selectivity and visualize the spatial receptive field of the
units in VGG16. Orientation and spatial frequency tunings of each unit were
measured using sinusoidal grating stimuli. The image category selectivity of
each unit was also measured using natural image stimuli. The spatial structures
of the receptive fields of all convolutional units were estimated by
activation-weighted average (AWA) and activation-weighted covariance (AWC)
analyses. In the middle layers (convolutional layers in block3 and block4), AWC
analysis successfully reconstructed the receptive field that predicted the
visual feature selectivity of the unit. Those results suggested the possibility
that analyzing the reconstructed receptive field structure can be used to
interpret the functional significance of the units and layers of a DCNN.


Abstract:
  Safety assurance is a critical yet challenging aspect when developing
self-driving technologies. Hamilton-Jacobi backward-reachability analysis is a
formal verification tool for verifying the safety of dynamic systems in the
presence of disturbances. However, the standard approach is too conservative to
be applied to self-driving applications due to its worst-case assumption on
humans' behaviors (i.e., guard against worst-case outcomes). In this work, we
integrate a learning-based prediction algorithm and a game-theoretic human
behavioral model to online update the conservativeness of backward-reachability
analysis. We evaluate our approach using real driving data. The results show
that, with reasonable assumptions on human behaviors, our approach can
effectively reduce the conservativeness of the standard approach without
sacrificing its safety verification ability.


Abstract:
  This paper describes an approach to solving the next destination city
recommendation problem for a travel reservation system. We propose a two stages
approach: a heuristic approach for candidates selection and an attention neural
network model for candidates re-ranking. Our method was inspired by listwise
learning-to-rank methods and recent developments in natural language processing
and the transformer architecture in particular. We used this approach to solve
the Booking.com recommendations challenge Our team achieved 5th place on the
challenge using this method, with 0.555 accuracy@4 value on the closed part of
the dataset.


Abstract:
  The simulation of time evolution of large quantum systems is a classically
challenging and in general intractable task, making it a promising application
for quantum computation. A Trotter-Suzuki approximation yields an
implementation thereof, where a higher approximation accuracy can be traded for
an increased gate count. In this work, we introduce a variational algorithm
which uses solutions of classical optimizations to predict efficient quantum
circuits for time evolution of translationally invariant quantum systems. Our
strategy can improve upon the Trotter-Suzuki accuracy by several orders of
magnitude. It translates into a reduction in gate count and hence gain in
overall fidelity at the same algorithmic accuracy. This is important in
NISQ-applications where the fidelity of the output state decays exponentially
with the number of gates. The performance advantage of our classical assisted
strategy can be extended to open boundaries with translational symmetry in the
bulk. We can extrapolate our method to beyond classically simulatable system
sizes, maintaining its total fidelity advantage over a Trotter-Suzuki
approximation making it an interesting candidate for beyond classical time
evolution.


Abstract:
  Robust tube-based model predictive control (MPC) methods address constraint
satisfaction by leveraging an a priori determined tube controller in the
prediction to tighten the constraints. This paper presents a system level
tube-MPC (SLTMPC) method derived from the system level parameterization (SLP),
which allows optimization over the tube controller online when solving the MPC
problem, which can significantly reduce conservativeness. We derive the SLTMPC
method by establishing an equivalence relation between a class of robust MPC
methods and the SLP. Finally, we show that the SLTMPC formulation naturally
arises from an extended SLP formulation and show its merits in a numerical
example.


Abstract:
  Generating metaphors is a challenging task as it requires a proper
understanding of abstract concepts, making connections between unrelated
concepts, and deviating from the literal meaning. In this paper, we aim to
generate a metaphoric sentence given a literal expression by replacing relevant
verbs. Based on a theoretically-grounded connection between metaphors and
symbols, we propose a method to automatically construct a parallel corpus by
transforming a large number of metaphorical sentences from the Gutenberg Poetry
corpus (Jacobs, 2018) to their literal counterpart using recent advances in
masked language modeling coupled with commonsense inference. For the generation
task, we incorporate a metaphor discriminator to guide the decoding of a
sequence to sequence model fine-tuned on our parallel data to generate
high-quality metaphors. Human evaluation on an independent test set of literal
statements shows that our best model generates metaphors better than three
well-crafted baselines 66% of the time on average. A task-based evaluation
shows that human-written poems enhanced with metaphors proposed by our model
are preferred 68% of the time compared to poems without metaphors.


Abstract:
  There is a longstanding discrepancy between the observed Galactic classical
nova rate of $\sim 10$ yr$^{-1}$ and the predicted rate from Galactic models of
$\sim 30$--50 yr$^{-1}$. One explanation for this discrepancy is that many
novae are hidden by interstellar extinction, but the degree to which dust can
obscure novae is poorly constrained. We use newly available all-sky
three-dimensional dust maps to compare the brightness and spatial distribution
of known novae to that predicted from relatively simple models in which novae
trace Galactic stellar mass. We find that only half ($\sim 48$\%) of novae are
expected to be easily detectable ($g \lesssim 15$) with current all-sky optical
surveys such as the All-Sky Automated Survey for Supernovae (ASAS-SN). This
fraction is much lower than previously estimated, showing that dust does
substantially affect nova detection in the optical. By comparing complementary
survey results from ASAS-SN, OGLE-IV, and the Palomar Gattini IR-survey in the
context of our modeling, we find a tentative Galactic nova rate of $\sim 40$
yr$^{-1}$, though this could decrease to as low as $\sim 30$ yr$^{-1}$
depending on the assumed distribution of novae within the Galaxy. These
preliminary estimates will be improved in future work through more
sophisticated modeling of nova detection in ASAS-SN and other surveys.


Abstract:
  We propose a generalized CUR (GCUR) decomposition for matrix pairs $(A, B)$.
Given matrices $A$ and $B$ with the same number of columns, such a
decomposition provides low-rank approximations of both matrices simultaneously,
in terms of some of their rows and columns. We obtain the indices for selecting
the subset of rows and columns of the original matrices using the discrete
empirical interpolation method (DEIM) on the generalized singular vectors. When
$B$ is square and nonsingular, there are close connections between the GCUR of
$(A, B)$ and the DEIM-induced CUR of $AB^{-1}$. When $B$ is the identity, the
GCUR decomposition of $A$ coincides with the DEIM-induced CUR decomposition of
$A$. We also show a similar connection between the GCUR of $(A, B)$ and the CUR
of $AB^+$ for a nonsquare but full-rank matrix $B$, where $B^+$ denotes the
Moore--Penrose pseudoinverse of $B$. While a CUR decomposition acts on one data
set, a GCUR factorization jointly decomposes two data sets. The algorithm may
be suitable for applications where one is interested in extracting the most
discriminative features from one data set relative to another data set. In
numerical experiments, we demonstrate the advantages of the new method over the
standard CUR approximation; for recovering data perturbed with colored noise
and subgroup discovery.


Abstract:
  Automatic medical image segmentation has made great progress benefit from the
development of deep learning. However, most existing methods are based on
convolutional neural networks (CNNs), which fail to build long-range
dependencies and global context connections due to the limitation of receptive
field in convolution operation. Inspired by the success of Transformer in
modeling the long-range contextual information, some researchers have expended
considerable efforts in designing the robust variants of Transformer-based
U-Net. Moreover, the patch division used in vision transformers usually ignores
the pixel-level intrinsic structural features inside each patch. To alleviate
these problems, we propose a novel deep medical image segmentation framework
called Dual Swin Transformer U-Net (DS-TransUNet), which might be the first
attempt to concurrently incorporate the advantages of hierarchical Swin
Transformer into both encoder and decoder of the standard U-shaped architecture
to enhance the semantic segmentation quality of varying medical images. Unlike
many prior Transformer-based solutions, the proposed DS-TransUNet first adopts
dual-scale encoder subnetworks based on Swin Transformer to extract the coarse
and fine-grained feature representations of different semantic scales. As the
core component for our DS-TransUNet, a well-designed Transformer Interactive
Fusion (TIF) module is proposed to effectively establish global dependencies
between features of different scales through the self-attention mechanism.
Furthermore, we also introduce the Swin Transformer block into decoder to
further explore the long-range contextual information during the up-sampling
process. Extensive experiments across four typical tasks for medical image
segmentation demonstrate the effectiveness of DS-TransUNet, and show that our
approach significantly outperforms the state-of-the-art methods.


Abstract:
  Accurate constraints on curvature provide a powerful probe of inflation.
However, curvature constraints based on specific assumptions of dark energy may
lead to unreliable conclusions when used to test inflation models. To avoid
this, it is important to obtain constraints that are independent on assumptions
for dark energy. In this paper, we investigate such constraints on curvature
from the geometrical probe constructed from galaxy-lensing cross-correlations.
We study comprehensively the cross-correlations of galaxy with magnification,
measured from type Ia supernovae's brightnesses ("$g\kappa^{\rm SN}$"), with
shear ("$g\kappa^{\rm g}$"), and with CMB lensing ("$g\kappa^{\rm CMB}$"). We
find for the LSST and Stage IV CMB surveys, "$g\kappa^{\rm SN}$" ,
"$g\kappa^{\rm g}$" and "$g\kappa^{\rm CMB}$" can be detected with
signal-to-noise ratio $S/N=104,\ 2291,\ 1842$ respectively. When combined with
supernovae Hubble diagram ("SN") to constrain curvature, we find galaxy-lensing
cross-correlation becomes increasingly important with more degrees of freedom
allowed in dark energy. Without any priors, we obtain error on $\Omega_K$ of
$0.723$ from "SN + $g\kappa^{\rm SN}$", $0.0417$ from "SN + $g\kappa^{\rm g}$",
and $0.04$ from "SN + $g\kappa^{\rm g}$ + $g\kappa^{\rm CMB}$" for the LSST and
Stage IV CMB surveys. The last one is more competitive than a Stage IV BAO
survey ("BAO"). When galaxy-lensing cross-correlations are added to the
combined probe of "SN + BAO + CMB", where "CMB" stands for Planck measurement
for the CMB acoustic scale, we obtain constraint on $\Omega_K$ of $0.0013$,
which is a factor of 7 improvement from "SN + BAO + CMB". We study improvements
in these results from increasing the high redshift extension of supernovae.


Abstract:
  Improving the use of Type Ia supernovae (SNIa) as standard candles requires a
better approach to incorporate the relationship between SNIa and the properties
of their host galaxies. Using a spectroscopically-confirmed sample of
$\sim$1600 SNIa, we develop the first empirical model of underlying populations
for SNIa light-curve properties that includes their dependence on host-galaxy
stellar mass. These populations are important inputs to simulations that are
used to model selection effects and correct distance biases within the BEAMS
with Bias Correction (BBC) framework. Here we improve BBC to also account for
SNIa-host correlations, and we validate this technique on simulated data
samples. We recover the input relationship between SNIa luminosity and
host-galaxy stellar mass (the mass step, $\gamma$) to within 0.004 mags, which
is a factor of 5 improvement over the previous method that results in a
$\gamma$-bias of ${\sim}0.02$. We adapt BBC for a novel dust-based model of
intrinsic brightness variations, which results in a greatly reduced mass step
for data ($\gamma = 0.017 \pm 0.008$), and for simulations ($\gamma =0.006 \pm
0.007$). Analysing simulated SNIa, the biases on the dark energy
equation-of-state, $w$, vary from $\Delta w = 0.006(5)$ to $0.010(5)$ with our
new BBC method; these biases are significantly smaller than the $0.02(5)$
$w$-bias using previous BBC methods that ignore SNIa-host correlations.


Abstract:
  Sagittarius A* (Sgr A*) -- the supermassive black hole (SMBH) in the center
of our galaxy -- has been observed in most of the electromagnetic spectrum,
from radio to X-rays. Diffuse gamma-ray emission has been observed around Sgr
A* and a gamma-ray point source has been detected coinciding with the SMBH's
position, although there is no definitive association between the two. In this
work, we have used ~11 years of Fermi Large Area Telescope (LAT) observations
of the point source 4FGL J1745.6-2859 and performed a detailed imaging analysis
across four energy bands. Our goal is to elucidate the nature of the gamma-ray
emission at the Galactic Center (GC) and whether it is associated with the
SMBH. We find that the centroid of the emission approaches Sgr A*'s location as
the energy increases. Assuming that the gamma-ray point source is located at
the GC, we estimate a luminosity of 2.61E36 erg/s in the 100 MeV to 500 GeV
energy range. This is consistent with Sgr A*'s bolometric luminosity. Based on
the point source properties, we ruled out several potential candidates for its
nature and favor a cosmic ray origin either from protons, electrons or both,
accelerated by -- or in the vicinity of -- the SMBH. Our results indicate that
the point source at the GC is indeed the gamma-ray counterpart of Sgr A* in the
GeV range.


Abstract:
  We study high energy neutrino emission from relativistic jets launched by a
black hole (BH) spiraling-in inside the envelope of a red supergiant (RSG), and
find that such common envelope jets supernovae (CEJSNe) are a potential source
for the ~10^15 eV neutrinos detected by IceCube. We first use the stellar
evolution code MESA to mimic the effect of the jets on the RSG envelope, and
find that the jets substantially inflate the envelope. We then study the
propagation of jets inside the extended RSG envelope and find that in most
cases the jets do not penetrate the envelope but are rather stalled. We show
that such jets can accelerate cosmic rays to high enough energies to produce
high energy neutrinos. While the neutrinos stream out freely, the gamma-rays
that accompany the neutrino production remain trapped inside the optically
thick envelope. This explains the lack of observational association between
high energy neutrinos and gamma-rays. We crudely estimate the diffuse neutrino
spectrum from a CEJSN and find that CEJSNe with BH companions might have a
substantial contribution to the high energy neutrinos flux detected by IceCube.


Abstract:
  In this paper we introduce and develop the concept of Interval-Weighted
Networks (IWN), a novel approach in Social Network Analysis, where the edge
weights are represented by closed intervals composed with precise information,
comprehending intrinsic variability. We extend IWN for both Newman's modularity
and modularity gain and the Louvain algorithm (LA), considering a tabular
representation of networks by contingency tables. We apply our methodology in a
real-world commuter network in mainland Portugal between the twenty three NUTS
3 regions. The optimal partition of regions is developed and compared using two
new different approaches, designated as ``Classic Louvain'' (CL) and ``Hybrid
Louvain'' (HL), which allow taking into account the variability observed in the
original network, thereby minimizing the loss of information present in the raw
data. Our findings suggest the division of the twenty three Portuguese regions
in three main communities. However, we find different geographical partitions
according to the community detection methodology used. This analysis can be
useful in many real-world applications, since it takes into account that the
weights may vary within the ranges, rather than being constant.


Abstract:
  Let $g(z)=\int_0^zp(t)\exp(q(t))\,dt+c$ where $p,q$ are polynomials and
$c\in\mathbb{C}$, and let $f$ be the function from Newton's method for $g$. We
show that under suitable assumptions the Julia set of $f$ has Lebesgue measure
zero. Together with a theorem by Bergweiler, our result implies that $f^n(z)$
converges to zeros of $g$ almost everywhere in $\mathbb{C}$ if this is the case
for each zero of $g''$. In order to prove our result, we establish general
conditions ensuring that Julia sets have Lebesgue measure zero.


Abstract:
  Left-wing authoritarianism remains far less understood than right-wing
authoritarianism. We contribute to the literature on the former, which
typically relies on surveys, using a new social media analytics approach. We
use a list of 60 terms to provide an exploratory sketch of the outlines of a
political ideology (tribal equalitarianism) with origins in 19th and 20th
century social philosophy. We then use analyses of the English Corpus of Google
Books (over 8 million books) and scraped unique tweets from Twitter (n =
202,852) to conduct a series of investigations to discern the extent to which
this ideology is cohesive amongst the public, reveals signatures of
authoritarianism and has been growing in popularity. Though exploratory, our
results provide some evidence of left-wing authoritarianism in two forms (1) a
uniquely conservative moral signature amongst ostensible liberals using
measures from Moral Foundations Theory and (2) a substantial prevalence of
anger, relative to anxiety or sadness. In general, results indicate that this
worldview is growing in popularity, is increasingly cohesive, and shows
signatures of authoritarianism.


Abstract:
  The phenomenon of adversarial examples in deep learning models has caused
substantial concern over their reliability. While many deep neural networks
have shown impressive performance in terms of predictive accuracy, it has been
shown that in many instances an imperceptible perturbation can falsely flip the
network's prediction. Most research has then focused on developing defenses
against adversarial attacks or learning under a worst-case adversarial loss. In
this work, we take a step back and aim to provide a framework for determining
whether a model's label change under small perturbation is justified (and when
it is not). We carefully argue that adversarial robustness should be defined as
a locally adaptive measure complying with the underlying distribution. We then
suggest a definition for an adaptive robust loss, derive an empirical version
of it, and develop a resulting data-augmentation framework. We prove that our
adaptive data-augmentation maintains consistency of 1-nearest neighbor
classification under deterministic labels and provide illustrative empirical
evaluations.


Abstract:
  Sleep disorders are very widespread in the world population and suffer from a
generalized underdiagnosis, given the complexity of their diagnostic methods.
Therefore, there is an increasing interest in developing simpler screening
methods. A pulse oximeter is an ideal device for sleep disorder screenings
since it is a portable, low-cost and accessible technology. This device can
provide an estimation of the heart rate (HR), which can be useful to obtain
information regarding the sleep stage. In this work, we developed a network
architecture with the aim of classifying the sleep stage in awake or asleep
using only HR signals from a pulse oximeter. The proposed architecture has two
fundamental parts. The first part has the objective of obtaining a
representation of the HR by using temporal convolutional networks. Then, the
obtained representation is used to feed the second part, which is based on
transformers, a model built solely with attention mechanisms. Transformers are
able to model the sequence, learning the transition rules between sleep stages.
The performance of the proposed method was evaluated on Sleep Heart Health
Study dataset, composed of 5000 healthy and pathological subjects. The dataset
was split into three subsets: 2500 for training, $1250$ for validating, and
1250 for testing. The overall accuracy, specificity, sensibility, and Cohen's
Kappa coefficient were 90.0%, 94.9%, 78.1%, and 0.73.


Abstract:
  Subshifts of deterministic substitutions are ubiquitous objects in dynamical
systems. Two of their most appealing features are that they have low complexity
(zero topological entropy) and are uniquely ergodic. Random substitutions are a
generalisation of deterministic substitutions where the substituted image of a
letter is determined by a Markov process. In stark contrast to their
deterministic counterparts, subshifts of random substitution often have
positive topological entropy, and support uncountably many ergodic measures,
known as frequency measures. Here, we develop techniques for computing and
studying the entropy of these frequency measures. As an application of our
results, we obtain closed form formulas for the entropy of frequency measures
for a wide range of random substitution subshifts and show that in many cases
there exists a frequency measure of maximal entropy. Further, for a class of
random substitution subshifts, we prove that this measure is the unique measure
of maximal entropy.


Abstract:
  Assuming positive entropy we prove a measure rigidity theorem for higher rank
actions on tori and solenoids by commuting automorphisms. We also apply this
result to obtain a complete classification of disjointness and measurable
factors for these actions.


Abstract:
  Recently, collaborative learning proposed by Song and Chai has achieved
remarkable improvements in image classification tasks by simultaneously
training multiple classifier heads. However, huge memory footprints required by
such multi-head structures may hinder the training of large-capacity baseline
models. The natural question is how to achieve collaborative learning within a
single network without duplicating any modules. In this paper, we propose four
ways of collaborative learning among different parts of a single network with
negligible engineering efforts. To improve the robustness of the network, we
leverage the consistency of the output layer and intermediate layers for
training under the collaborative learning framework. Besides, the similarity of
intermediate representation and convolution kernel is also introduced to reduce
the reduce redundant in a neural network. Compared to the method of Song and
Chai, our framework further considers the collaboration inside a single model
and takes smaller overhead. Extensive experiments on Cifar-10, Cifar-100,
ImageNet32 and STL-10 corroborate the effectiveness of these four ways
separately while combining them leads to further improvements. In particular,
test errors on the STL-10 dataset are decreased by $9.28\%$ and $5.45\%$ for
ResNet-18 and VGG-16 respectively. Moreover, our method is proven to be robust
to label noise with experiments on Cifar-10 dataset. For example, our method
has $3.53\%$ higher performance under $50\%$ noise ratio setting.


Abstract:
  We present a systematic investigation of the role and importance of excitonic
effects on the optical properties of transitions metal oxide perovskites. A
representative set of fourteen compounds has been selected, including 3$d$
(SrTiO$_3$, LaScO$_3$, LaTiO$_3$, LaVO$_3$, LaCrO$_3$, LaMnO$_3$, LaFeO$_3$ and
SrMnO$_3$), 4$d$ (SrZrO$_3$, SrTcO$_3$ and Ca$_2$RuO$_4$) and 5$d$ (SrHfO$_3$,
KTaO$_3$ and NaOsO$_3$) perovskites, covering a band gap ranging from 0.1 eV to
6.1 eV and exhibiting different electronic, structural and magnetic properties.
Optical conductivities and optical transitions including electron-hole
interactions are calculated through the solution of the Bethe-Salpeter equation
(BSE) with quasi-particle energies evaluated by single-shot $G_0W_0$
approximation. The exciton binding energies are computed by means of a
model-BSE (mBSE), carefully benchmarked against the full BSE method, in order
to obtain well-converged results in terms of k-point sampling. The predicted
results are compared with available measured data, with an overall satisfactory
agreement between theory and experiment.


Abstract:
  Data efficiency is a key challenge for deep reinforcement learning. We
address this problem by using unlabeled data to pretrain an encoder which is
then finetuned on a small amount of task-specific data. To encourage learning
representations which capture diverse aspects of the underlying MDP, we employ
a combination of latent dynamics modelling and unsupervised goal-conditioned
RL. When limited to 100k steps of interaction on Atari games (equivalent to two
hours of human experience), our approach significantly surpasses prior work
combining offline representation pretraining with task-specific finetuning, and
compares favourably with other pretraining methods that require orders of
magnitude more data. Our approach shows particular promise when combined with
larger models as well as more diverse, task-aligned observational data --
approaching human-level performance and data-efficiency on Atari in our best
setting. We provide code associated with this work at
https://github.com/mila-iqia/SGI.


Abstract:
  Standard losses for training deep segmentation networks could be seen as
individual classifications of pixels, instead of supervising the global shape
of the predicted segmentations. While effective, they require exact knowledge
of the label of each pixel in an image.
  This study investigates how effective global geometric shape descriptors
could be, when used on their own as segmentation losses for training deep
networks. Not only interesting theoretically, there exist deeper motivations to
posing segmentation problems as a reconstruction of shape descriptors:
Annotations to obtain approximations of low-order shape moments could be much
less cumbersome than their full-mask counterparts, and anatomical priors could
be readily encoded into invariant shape descriptions, which might alleviate the
annotation burden. Also, and most importantly, we hypothesize that, given a
task, certain shape descriptions might be invariant across image acquisition
protocols/modalities and subject populations, which might open interesting
research avenues for generalization in medical image segmentation.
  We introduce and formulate a few shape descriptors in the context of deep
segmentation, and evaluate their potential as standalone losses on two
different challenging tasks. Inspired by recent works in constrained
optimization for deep networks, we propose a way to use those descriptors to
supervise segmentation, without any pixel-level label. Very surprisingly, as
little as 4 descriptors values per class can approach the performance of a
segmentation mask with 65k individual discrete labels. We also found that shape
descriptors can be a valid way to encode anatomical priors about the task,
enabling to leverage expert knowledge without additional annotations. Our
implementation is publicly available and can be easily extended to other tasks
and descriptors: https://github.com/hkervadec/shape_descriptors


Abstract:
  This letter analyzes a class of information freshness metrics for large IoT
systems in which terminals employ slotted ALOHA to access a common channel.
Considering a Gilbert- Elliot channel model, information freshness is evaluated
through a penalty function that follows a power law of the time elapsed since
the last received update, in contrast with the linear growth of age of
information. By means of a signal flow graph analysis of Markov processes, we
provide exact closed form expressions for the average penalty and for the peak
penalty violation probability.


Abstract:
  We review the current status of the research on effective nonlocal NJL-like
chiral quark models with separable interactions, focusing on the application of
this approach to the description of the properties of hadronic and quark matter
under extreme conditions. The analysis includes the predictions for various
hadron properties in vacuum, as well as the study of the features of
deconfinement and chiral restoration phase transitions for systems at finite
temperature and/or density. We also address other related subjects, such as the
study of phase transitions for imaginary chemical potentials, the possible
existence of inhomogeneous phase regions, the presence of color
superconductivity, the effects produced by strong external magnetic fields, and
the application to the description of compact stellar objects.


Abstract:
  We propose the concept of a spontaneous many-body Floquet state. This is a
state that, in the absence of external periodic driving, still self-oscillates
like in the presence of a Floquet Hamiltonian, this behavior being
spontaneously induced by many-body interactions. Furthermore, we prove that it
is also a time crystal, presenting long-range time-periodic order. However, its
time crystalline behavior is very different to that of conventional Floquet
discrete time crystals: here, there is no external periodic driving, and the
nature of the spontaneous symmetry breaking is continuous instead of discrete.
We also demonstrate that a spontaneous many-body Floquet state can be
implemented in a one-dimensional flowing atom condensate, resulting from a
dynamical phase transition and stable against quantum fluctuations, and propose
realistic experimental scenarios for its observation. The realization of a
spontaneous many-body Floquet state would then not only provide a novel form of
ordered quantum matter, but also a continuous time crystal.


Abstract:
  Recent studies of warp kinematics using Gaia DR2 data have produced
detections of warp precession for the first time, which greatly exceeds
theoretical predictions of models. However, this detection assumes a warp model
derived for a young population (few tens of megayears) to fit velocities of an
average older stellar population of the thin disk (several gigayears) in
Gaia-DR2 observations, which may lead to unaccounted systematic errors. Here,
we recalculate the warp precession with the same approach and Gaia DR2
kinematic data, but using different warp parameters based on the fit of star
counts of the Gaia DR2 sample, which has a much lower warp amplitude than the
young population. When we take into account this variation of the warp
amplitude with the age of the population, we find that there is no need for
precession. We find the value of warp precession $\beta = 4^{+6}_{-4}$ km
s$^{-1}$ kpc$^{-1}$, which does not exclude nonprecessing warp.


Abstract:
  In this letter, we use the exactly solvable Sachdev-Ye-Kitaev model to
address the issue of entropy dynamics when an interacting quantum system is
coupled to a non-Markovian environment. We find that at the initial stage, the
entropy always increases linearly matching the Markovian result. When the
system thermalizes with the environment at a sufficiently long time, if the
environment temperature is low and the coupling between system and environment
is weak, then the total thermal entropy is low and the entanglement between
system and environment is also weak, which yields a small system entropy in the
long-time steady state. This manifestation of non-Markovian effects of the
environment forces the entropy to decrease in the later stage, which yields the
Page curve for the entropy dynamics. We argue that this physical scenario
revealed by the exact solution of the Sachdev-Ye-Kitaev model is universally
applicable for general chaotic quantum many-body systems and can be verified
experimentally in near future.


Abstract:
  Digital payment innovation is currently increasingly needed by the community,
especially in making non-cash payment transactions. The purpose of this
research is to know and measure the ease and usefulness of e-wallet digital
wallet services, especially in the GoPay application. The population in this
study are users of the Go-Pay service on the GO-JEK platform. The sample of
this study consisted of 124 respondents from distributing questionnaires in
Depok, West Java using a modified Technology Acceptance Model (TAM) based on
existing references. The data processing in this research uses Importance
Performance Analysis (IPA) analysis. The results show that based on the gap
analysis, it is found that in general Go-Pay users are not satisfied with the
current service quality. Based on the IPA analysis, the priority scale of
E-Wallet Go-Pay quality improvement can be mapped, where quadrant I is the
highest priority scale according to the user's perspective: [1], [4], [5], and
[6]. These three items must be upgraded immediately by the manager to meet user
expectations. Areas that become the achievements or advantages of the GoPay
E-Wallet that must be maintained are in quadrant II, namely: [2] and [3]. From
this explanation, it can be concluded that in general the E-Wallet GoPay
Service must be improved to improve its service performance.


Abstract:
  We recursively extend the Lov\'asz theta number to geometric hypergraphs on
the unit sphere and on Euclidean space, obtaining an upper bound for the
independence ratio of these hypergraphs. As an application we reprove a result
in Euclidean Ramsey theory in the measurable setting, namely that every
$k$-simplex is exponentially Ramsey, and we improve existing bounds for the
base of the exponential.


Abstract:
  We interpret the correlation between local star-forming galaxy positions and
ultra-high-energy cosmic ray (UHECR) directions, recently detected by the
Pierre Auger Observatory (PAO), in terms of physical parameters: the local
density of sources and the magnetic fields governing the UHECR propagation. We
include a Galactic magnetic field model on top of a random extragalactic
magnetic field description to determine the level of UHECR deflections expected
from an ensemble of source positions. Besides deflections in magnetic fields,
we also take into account energy losses with background photon fields as well
as spectrum and composition measurements by the PAO. We find consistency
between the PAO anisotropy measurement and the local star-forming galaxy
density for large extragalactic magnetic field strengths with $B > 0.2 \ \rm
nG$ (for a coherence length of $1 \ \rm Mpc$) at the $5\sigma$ confidence
level. Larger source densities lead to more isotropic background and
consequently allow for weaker extragalactic magnetic fields. However, the
acceleration of UHECR by such abundant sources is more challenging to motivate.
Too large source densities and extragalactic magnetic field strengths, on the
other hand, are also disfavored as that decreases the expected level of
anisotropy. This leads to upper limits of $B < 22 \ \rm nG$ and $\rho_0 < 8.4
\cdot 10^{-2} \ \rm Mpc^{-3}$ at the 90\% confidence level.


Abstract:
  The penalization method is a popular technique to provide particle swarm
optimizers with the ability to handle constraints. The downside is the need of
penalization coefficients whose settings are problem-specific. While adaptive
coefficients can be found in the literature, a different adaptive scheme is
proposed in this paper, where coefficients are kept constant. A pseudo-adaptive
relaxation of the tolerances for constraint violations while penalizing only
violations beyond such tolerances results in a pseudo-adaptive penalization. A
particle swarm optimizer is tested on a suite of benchmark problems for three
types of tolerance relaxation: no relaxation; self-tuned initial relaxation
with deterministic decrease; and self-tuned initial relaxation with
pseudo-adaptive decrease. Other authors' results are offered as frames of
reference.


Abstract:
  There is an ongoing debate in the NLP community whether modern language
models contain linguistic knowledge, recovered through so-called probes. In
this paper, we study whether linguistic knowledge is a necessary condition for
the good performance of modern language models, which we call the
\textit{rediscovery hypothesis}. In the first place, we show that language
models that are significantly compressed but perform well on their pretraining
objectives retain good scores when probed for linguistic structures. This
result supports the rediscovery hypothesis and leads to the second contribution
of our paper: an information-theoretic framework that relates language modeling
objectives with linguistic information. This framework also provides a metric
to measure the impact of linguistic information on the word prediction task. We
reinforce our analytical results with various experiments, both on synthetic
and on real NLP tasks in English.


Abstract:
  Digital privacy notices aim to provide users with information to make
informed decisions. They are, however, fraught with difficulties. Instead, I
propose that data privacy decisions can be understood as an expression of user
values. To optimize this value expression, I further propose the creation of a
value-centered privacy assistant (VcPA). Here, I preliminary explore how a VcPA
could enhance user value expression by utilizing three user scenarios in the
context of considering whether or not to download an environmental application,
the OpenLitterMap app. These scenarios are conceptually constructed from
established privacy user groups - the privacy fundamentalists; the privacy
pragmatists; and the privacy unconcerned. I conclude that the VcPA best
facilitates user value expression of the privacy fundamentalists. In contrast,
the value expression of the privacy pragmatists and the privacy unconcerned
could be enhanced or hindered depending on the context and their internal
states. Possible implications for optimal VcPA design are also discussed.
Following this initial conceptual exploration of VcPAs, further empirical
research will be required to demonstrate the effectiveness of the VcPA system
in real-world settings.


Abstract:
  The mass media play at least five basic functions which include news
dissemination, surveillance of the environment, correlation of the components
of the society, entertainment and transmission of social heritage. Sometimes,
disruptions and impairments do occur in the performance of these roles and some
of these basic functions become dysfunctions, which turn the media into
purveyor of negative values. The present study investigates how popular the
Nigerian TV reality show, Big Brother Naija BBN, is perceived by its viewers.
Three hundred heavy viewers of the program were surveyed from Lagos and Ede,
South-West Nigeria, and their opinions and attitudes were sought regarding, why
they like or dislike the program; the gratifications that those who like the
program derive and whether the BBN, as media content, is generally functional
or dysfunctional to the society. Sixty six per cent 66 33.7 of respondents like
the program because it entertains. Half of the respondents, 99 50.5 dislike
immoral aspects of the program. The viewers affirm that the eviction part of
the program was their highest form of gratification. Most respondents, despite
public outcry against the program, consider the program to be functional.
Findings reinforce the postulation that TV viewers are not passive consumers of
media contents.


Abstract:
  The initial majority identification task is a fundamental test problem in
cellular automaton research. To pass the test, an automaton must evolve to a
uniform configuration consisting of the state that was in the majority for any
initial configuration, employing only its internal, local dynamics. It is known
that no two-state automaton can perform the majority task perfectly. Thus, it
is a matter of continuing interest to identify and analyze new automata with
improved majority identification capability. Here, we show that a coarsened
version of one of the best majority identifiers can out-perform its parent
automaton while simultaneously reducing the associated computational costs.


Abstract:
  We present a novel method for relation extraction (RE) from a single
sentence, mapping the sentence and two given entities to a canonical fact in a
knowledge graph (KG). Especially in this presumed sentential RE setting, the
context of a single sentence is often sparse. This paper introduces the KGPool
method to address this sparsity, dynamically expanding the context with
additional facts from the KG. It learns the representation of these facts
(entity alias, entity descriptions, etc.) using neural methods, supplementing
the sentential context. Unlike existing methods that statically use all
expanded facts, KGPool conditions this expansion on the sentence. We study the
efficacy of KGPool by evaluating it with different neural models and KGs
(Wikidata and NYT Freebase). Our experimental evaluation on standard datasets
shows that by feeding the KGPool representation into a Graph Neural Network,
the overall method is significantly more accurate than state-of-the-art
methods.


Abstract:
  Glasses show vibrational properties that are markedly different to those of
crystals which are known as phonons. For example, excess low-frequency modes
(the so-called boson peak), vibrational localization, and strong scattering of
phonons have been the most discussed topics, and a theoretical understanding of
these phenomena is challenging. To address this problem, computational
simulations are a powerful tool, which have been employed by many previous
works. In this chapter, we describe simulation methods for studying the
vibrational properties of glasses (and any solid-state materials). We first
present a method for studying vibrational eigenmodes. Since vibrational motions
of particles are excited along eigenmodes, the eigenmodes are fundamental to
descriptions of vibrational properties. The eigenmodes in glasses are
non-phonon modes in general, and some of them are even localized in space. We
next present a method of analysing phonon transport, which is also crucial for
understanding vibrational properties. Since phonons are not eigenmodes in
glasses, they are decomposed into several different, non-phonon eigenmodes. As
a result, phonons in glasses are strongly scattered. In addition, we describe
how to analyse the elastic response. The elastic response of glasses is also
anomalous with respect to that of crystals. Finally, we briefly introduce
recent advances that have been achieved by means of large-scale computational
simulations.


Abstract:
  Target speech separation is the process of filtering a certain speaker's
voice out of speech mixtures according to the additional speaker identity
information provided. Recent works have made considerable improvement by
processing signals in the time domain directly. The majority of them take fully
overlapped speech mixtures for training. However, since most real-life
conversations occur randomly and are sparsely overlapped, we argue that
training with different overlap ratio data benefits. To do so, an unavoidable
problem is that the popularly used SI-SNR loss has no definition for silent
sources. This paper proposes the weighted SI-SNR loss, together with the joint
learning of target speech separation and personal VAD. The weighted SI-SNR loss
imposes a weight factor that is proportional to the target speaker's duration
and returns zero when the target speaker is absent. Meanwhile, the personal VAD
generates masks and sets non-target speech to silence. Experiments show that
our proposed method outperforms the baseline by 1.73 dB in terms of SDR on
fully overlapped speech, as well as by 4.17 dB and 0.9 dB on sparsely
overlapped speech of clean and noisy conditions. Besides, with slight
degradation in performance, our model could reduce the time costs in inference.


Abstract:
  We consider the sensing of scalar valued fields with specific spatial
dependence using a network of sensors, e.g. multiple atoms located at different
positions within a trap. We show how to harness the spatial correlations to
sense only a specific signal, and be insensitive to others at different
positions or with unequal spatial dependence by constructing a decoherence-free
subspace for noise sources at fixed, known positions. This can be extended to
noise sources lying on certain surfaces, where we encounter a connection to
mirror charges and equipotential surfaces in classical electrostatics. For
general situations, we introduce the notion of an approximate decoherence-free
subspace, where noise for all sources within some volume is significantly
suppressed, at the cost of reducing the signal strength in a controlled way. We
show that one can use this approach to maintain Heisenberg-scaling over long
times and for a large number of sensors, despite the presence of multiple noise
sources in large volumes. We introduce an efficient formalism to construct
internal states and sensor configurations, and apply it to several examples to
demonstrate the usefulness and wide applicability of our approach.


Abstract:
  This paper explores the challenge of teaching a machine how to
reverse-engineer the grid-marked surfaces used to represent data in 3D surface
plots of two-variable functions. These are common in scientific and economic
publications; and humans can often interpret them with ease, quickly gleaning
general shape and curvature information from the simple collection of curves.
While machines have no such visual intuition, they do have the potential to
accurately extract the more detailed quantitative data that guided the
surface's construction. We approach this problem by synthesizing a new dataset
of 3D grid-marked surfaces (SurfaceGrid) and training a deep neural net to
estimate their shape. Our algorithm successfully recovers shape information
from synthetic 3D surface plots that have had axes and shading information
removed, been rendered with a variety of grid types, and viewed from a range of
viewpoints.


Abstract:
  The light field (LF) reconstruction is mainly confronted with two challenges,
large disparity and the non-Lambertian effect. Typical approaches either
address the large disparity challenge using depth estimation followed by view
synthesis or eschew explicit depth information to enable non-Lambertian
rendering, but rarely solve both challenges in a unified framework. In this
paper, we revisit the classic LF rendering framework to address both challenges
by incorporating it with advanced deep learning techniques. First, we
analytically show that the essential issue behind the large disparity and
non-Lambertian challenges is the aliasing problem. Classic LF rendering
approaches typically mitigate the aliasing with a reconstruction filter in the
Fourier domain, which is, however, intractable to implement within a deep
learning pipeline. Instead, we introduce an alternative framework to perform
anti-aliasing reconstruction in the image domain and analytically show
comparable efficacy on the aliasing issue. To explore the full potential, we
then embed the anti-aliasing framework into a deep neural network through the
design of an integrated architecture and trainable parameters. The network is
trained through end-to-end optimization using a peculiar training set,
including regular LFs and unstructured LFs. The proposed deep learning pipeline
shows a substantial superiority in solving both the large disparity and the
non-Lambertian challenges compared with other state-of-the-art approaches. In
addition to the view interpolation for an LF, we also show that the proposed
pipeline also benefits light field view extrapolation.


Abstract:
  Municipal solid waste management is a major challenge for nowadays urban
societies, because it accounts for a large proportion of public budget and,
when mishandled, it can lead to environmental and social problems. This work
focuses on the problem of locating waste bins in an urban area, which is
considered to have a strong influence in the overall efficiency of the reverse
logistic chain. This article contributes with an exact multiobjective approach
to solve the waste bin location in which the optimization criteria that are
considered are: the accessibility to the system (as quality of service
measure), the investment cost, and the required frequency of waste removal from
the bins (as a proxy of the posterior routing costs). In this approach,
different methods to obtain the objectives ideal and nadir values over the
Pareto front are proposed and compared. Then, a family of heuristic methods
based on the PageRank algorithm is proposed which aims to optimize the
accessibility to the system, the amount of collected waste and the installation
cost. The experimental evaluation was performed on real-world scenarios of the
cities of Montevideo, Uruguay, and Bah\'ia Blanca, Argentina. The obtained
results show the competitiveness of the proposed approaches for constructing a
set of candidate solutions that considers the different trade-offs between the
optimization criteria.


Abstract:
  Social dynamics models may present discontinuities in the right-hand side of
the dynamics for multiple reasons, including topology changes and quantization.
Several concepts of generalized solutions for discontinuous equations are
available in the literature and are useful to analyze these models. In this
chapter, we study Caratheodory and Krasovsky generalized solutions for
discontinuous models of opinion dynamics with state dependent interactions. We
consider two definitions of "bounded confidence" interactions, which we
respectively call metric and topological: in the former, individuals interact
if their opinions are closer than a threshold; in the latter, individuals
interact with a fixed number of nearest neighbors. We compare the dynamics
produced by the two kinds of interactions, in terms of existence, uniqueness
and asymptotic behavior of different types of solutions.


Abstract:
  In this paper, we introduce the first Challenge on Multi-modal Aerial View
Object Classification (MAVOC) in conjunction with the NTIRE 2021 workshop at
CVPR. This challenge is composed of two different tracks using EO andSAR
imagery. Both EO and SAR sensors possess different advantages and drawbacks.
The purpose of this competition is to analyze how to use both sets of sensory
information in complementary ways. We discuss the top methods submitted for
this competition and evaluate their results on our blind test set. Our
challenge results show significant improvement of more than 15% accuracy from
our current baselines for each track of the competition


Abstract:
  Road intersections are widely recognized as a lead cause for accidents and
traffic delays. In a future scenario with a significant adoption of Cooperative
Autonomous Vehicles, solutions based on fully automatic, signage-less
Intersection Control would become viable. Such a solution, however, requires
communication between vehicles and, possibly, the infrastructure over wireless
networks. This increases the attack surface available to a malicious actor,
which could lead to dangerous situations. In this paper, we address the safety
of Intersection Control algorithms, and design a Sliding-Mode-Observer based
solution capable of detecting and estimating false data injection attacks
affecting vehicles' communication. With respect to previous literature, a novel
detection logic with improved detection performances is presented. Simulation
results are provided to show the effectiveness of the proposed approach.


Abstract:
  Getting machines to generate text perceived as creative is a long-pursued
goal. A growing body of research directs this goal towards augmenting the
creative writing abilities of human authors. In this paper, we pursue this
objective by analyzing how observing examples of automatically generated text
influences writing. In particular, we examine a task referred to as sentence
infilling, which involves transforming a list of words into a complete
sentence. We emphasize "storiability" as a desirable feature of the resulting
sentences, where "storiable" sentences are those that suggest a story a reader
would be curious to hear about. Both humans and an automated system (based on a
neural language model) performed this sentence infilling task. In one setting,
people wrote sentences on their own; in a different setting, people observed
the sentences produced by the model while writing their own sentences. Readers
then assigned storiability preferences to the resulting sentences in a
subsequent evaluation. We find that human-authored sentences were judged as
more storiable when authors observed the generated examples, and that
storiability increased as authors derived more semantic content from the
examples. This result gives evidence of an "inspiration through observation"
paradigm for human-computer collaborative writing, through which human writing
can be enhanced by text generation models without directly copying their
output.


Abstract:
  Fire stations are among the most crucial emergency facilities in urban
emergency control system in terms of their quick response to fires and other
emergencies. Location plannings for fire stations have a significant influence
on their effectiveness and capability of emergency responses trading off with
the cost of constructions. To obtain efficient and practical siting plans for
fire stations, various major requirements including effectiveness maximization,
distance constraint and workload limitation are required to be considered in
location models, especially for multi-level fire facility systems with macro
and micro fire stations having specific aims and construction requirements.
This paper proposes a novel hierarchical optimization approach taking all the
major requirements for location planning into consideration and bonds
functional connections between different levels of fire stations at the same
time. A single-objective and a multi-objective optimization model are
established to solve the location siting problems for macro and micro fire
stations simultaneously. Genetic algorithm with elitist reservation and
Pareto-based multi-objective evolutionary algorithm are adopted to solve the
problem with NP-hard nature. The proposed hierarchical location model is
further performed in a case study of Futian District in Shenzhen, and the
siting result justifies the effectiveness and practicality of our novel
approach.


Abstract:
  While the utility of well-chosen abstractions for understanding and
predicting the behaviour of complex systems is well appreciated, precisely what
an abstraction $\textit{is}$ has so far has largely eluded mathematical
formalization. In this paper, we aim to set out a mathematical theory of
abstraction. We provide a precise characterisation of what an abstraction is
and, perhaps more importantly, suggest how abstractions can be learnt directly
from data both for static datasets and for dynamical systems. We define an
abstraction to be a small set of `summaries' of a system which can be used to
answer a set of queries about the system or its behaviour. The difference
between the ground truth behaviour of the system on the queries and the
behaviour of the system predicted only by the abstraction provides a measure of
the `leakiness' of the abstraction which can be used as a loss function to
directly learn abstractions from data. Our approach can be considered a
generalization of classical statistics where we are not interested in
reconstructing `the data' in full, but are instead only concerned with
answering a set of arbitrary queries about the data. While highly theoretical,
our results have deep implications for statistical inference and machine
learning and could be used to develop explicit methods for learning precise
kinds of abstractions directly from data.


Abstract:
  Gradient descent optimization algorithms are the standard ingredients that
are used to train artificial neural networks (ANNs). Even though a huge number
of numerical simulations indicate that gradient descent optimization methods do
indeed convergence in the training of ANNs, until today there is no rigorous
theoretical analysis which proves (or disproves) this conjecture. In
particular, even in the case of the most basic variant of gradient descent
optimization algorithms, the plain vanilla gradient descent method, it remains
an open problem to prove or disprove the conjecture that gradient descent
converges in the training of ANNs. In this article we solve this problem in the
special situation where the target function under consideration is a constant
function. More specifically, in the case of constant target functions we prove
in the training of rectified fully-connected feedforward ANNs with one-hidden
layer that the risk function of the gradient descent method does indeed
converge to zero. Our mathematical analysis strongly exploits the property that
the rectifier function is the activation function used in the considered ANNs.
A key contribution of this work is to explicitly specify a Lyapunov function
for the gradient flow system of the ANN parameters. This Lyapunov function is
the central tool in our convergence proof of the gradient descent method.


Abstract:
  Recent work has demonstrated real-time mapping and reconstruction from dense
perception, while motion planning based on distance fields has been shown to
achieve fast, collision-free motion synthesis with good convergence properties.
However, demonstration of a fully integrated system that can safely re-plan in
unknown environments, in the presence of static and dynamic obstacles, has
remained an open challenge. In this work, we first study the impact that signed
and unsigned distance fields have on optimisation convergence, and the
resultant error cost in trajectory optimisation problems in 2D path planning,
arm manipulator motion planning, and whole-body loco-manipulation planning. We
further analyse the performance of three state-of-the-art approaches to
generating distance fields (Voxblox, Fiesta, and GPU-Voxels) for use in
real-time environment reconstruction. Finally, we use our findings to construct
a practical hybrid mapping and motion planning system which uses GPU-Voxels and
GPMP2 to perform receding-horizon whole-body motion planning that can smoothly
avoid moving obstacles in 3D space using live sensor data. Our results are
validated in simulation and on a real-world Toyota Human Support Robot (HSR).


Abstract:
  Cluster states are useful in many quantum information processing
applications. In particular, universal measurement-based quantum computation
(MBQC) utilizes 2D cluster states, and topologically fault-tolerant MBQC
requires cluster states with three or higher dimensions. This work proposes a
protocol to deterministically generate multidimensional photonic cluster states
using a single atom-cavity system and time-delay feedback. The dimensionality
of the cluster state increases linearly with the number of time-delay feedback.
We firstly give a diagrammatic derivation of the tensor network states, which
is valuable in simulating matrix product states and projected entangled pair
states generated from sequential photons. Our method also provides a simple way
to bridge and analyze the experimental imperfections and the logical errors of
the generated states. In this method, we analyze the generated cluster states
under realistic experimental conditions and address both one-qubit and
two-qubit errors. Through numerical simulation, we observe an optimal
atom-cavity cooperativity for the fidelity of the generated states, which is
surprising given the prevailing assumption that higher cooperativity systems
are inherently better for photonic applications.


Abstract:
  In this work, we use the framework of effective field theory to couple
Einstein's gravity to quantum electrodynamics (QED) and determine the
gravitational corrections to the two-loop beta function of the electric charge
in arbitrary electrodynamics (Lorentz-like) and arbitrary (de Donder like)
gravitational gauges. Our results indicate that gravitational corrections do
not alter the running behavior of the electric charge; on the contrary, we
observe that it gives a positive contribution to the beta function, making the
electric charge grow even faster.


Abstract:
  Stochastic thermodynamics allows us to define heat and work for microscopic
systems far from thermodynamic equilibrium, based on observations of their
stochastic dynamics. However, a complete account of the energetics necessitates
that all relevant nonequilibrium degrees of freedom are resolved, which is not
feasible in many experimental situations. A simple approach is to map the
visible dynamics onto a Markov model, which produces a lower-bound estimate of
the entropy production. The bound, however, can be quite loose, especially when
the visible dynamics only have small or vanishing observable currents. An
alternative approach is presented that uses all observable data to find an
underlying hidden Markov model responsible for generating the observed
non-Markovian dynamics. For masked Markovian kinetic networks, one obtains the
tightest possible lower bound on entropy production of the full dynamics that
is compatible with the observable data. The formalism is illustrated with a
simple example system.


Abstract:
  We review recent progress in elucidating the relationship between high-energy
radiation and the interstellar medium (ISM) in young supernova remnants (SNRs)
with ages of $\sim$2000 yr, focusing in particular on RX J1713.7$-$3946 and RCW
86. Both SNRs emit strong nonthermal X-rays and TeV $\gamma$-rays, and they
contain clumpy distributions of interstellar gas that includes both atomic and
molecular hydrogen. We find that shock-cloud interactions provide a viable
explanation for the spatial correlation between the X-rays and ISM. In these
interactions, the supernova shocks hit the typically pc-scale dense cores,
generating a highly turbulent velocity field that amplifies the magnetic field
up to 0.1-1 mG. This amplification leads to enhanced nonthermal synchrotron
emission around the clumps, whereas the cosmic-ray electrons do not penetrate
the clumps. Accordingly, the nonthermal X-rays exhibit a spatial distribution
similar to that of the ISM on the pc scale, while they are anticorrelated at
sub-pc scales. These results predict that hadronic $\gamma$-rays can be emitted
from the dense cores, resulting in a spatial correspondence between the
$\gamma$-rays and the ISM. The current pc-scale resolution of $\gamma$-ray
observations is too low to resolve this correspondence. Future $\gamma$-ray
observations with the Cherenkov Telescope Array will be able to resolve the
sub-pc-scale $\gamma$-ray distribution and provide clues to the origin of these
cosmic $\gamma$-rays.


Abstract:
  There is a lack of scalable quantitative measures of reactivity for
functional groups in organic chemistry. Measuring reactivity experimentally is
costly and time-consuming and does not scale to the astronomical size of
chemical space. In previous quantum chemistry studies, we have introduced
Methyl Cation Affinities (MCA*) and Methyl Anion Affinities (MAA*), using a
solvation model, as quantitative measures of reactivity for organic functional
groups over the broadest range. Although MCA* and MAA* offer good estimates of
reactivity parameters, their calculation through Density Functional Theory
(DFT) simulations is time-consuming. To circumvent this problem, we first use
DFT to calculate MCA* and MAA* for more than 2,400 organic molecules thereby
establishing a large dataset of chemical reactivity scores. We then design deep
learning methods to predict the reactivity of molecular structures and train
them using this curated dataset in combination with different representations
of molecular structures. Using ten-fold cross-validation, we show that graph
attention neural networks applied to informative input fingerprints produce the
most accurate estimates of reactivity, achieving over 91% test accuracy for
predicting the MCA* plus-minus 3.0 or MAA* plus-minus 3.0, over 50 orders of
magnitude. Finally, we demonstrate the application of these reactivity scores
to two tasks: (1) chemical reaction prediction; (2) combinatorial generation of
reaction mechanisms. The curated dataset of MCA* and MAA* scores is available
through the ChemDB chemoinformatics web portal at www.cdb.ics.uci.edu.


Abstract:
  Twisted homogeneous coordinate rings are natural invariants associated to a
projective variety X with an automorphism f. We study the Gelfand-Kirillov
dimensions of these noncommutative algebras from the perspective of complex
dynamics, by noticing that when X is a smooth complex projective variety, they
essentially coincide with the polynomial logarithmic volume growth Plov(f) of
(X,f). We formulate some basic dynamical properties about these invariants and
study explicit examples. Our main results are new upper bounds and lower bounds
of these invariants, in terms of the dimension of X (as well as other
refinements). As an application, we completely determine Plov(f) when dim X =
3.


Abstract:
  In the present article, we develop a general framework for the description of
an $N$-sequential state discrimination, where each of $N$ receivers always
obtains a conclusive result. For this new state discrimination scenario, we
derive two mutually equivalent general representations of the success
probability and prove that if one of two states, pure or mixed, is prepared by
a sender, then the optimal success probability is given by the Helstrom bound
for any number $N$ of sequential receivers. Furthermore, we specify receivers'
indirect measurements resulting in the optimal $N$-sequential conclusive state
discrimination protocol. The developed framework is true for any number $N$ of
sequential receivers, any number of arbitrary quantum states, pure or mixed, to
be discriminated, and all types of receivers' quantum measurements. The new
general results derived within the developed framework are important both from
the theoretical point of view and for a successful multipartite quantum
communication even in the presence of a quantum noise.


Abstract:
  We discuss soft-gluon resummation for the associated production of a single
top quark and a Higgs boson ($tqH$ production) in single-particle-inclusive
(1PI) kinematics. We present analytical results for the higher-order
corrections and numerical results for the cross sections at LHC energies. We
calculate approximate NNLO total rates, including scale dependence and
uncertainties from parton distributions, as well as top-quark
transverse-momentum and rapidity distributions. In all cases we find that the
soft-gluon corrections are dominant and provide important contributions to the
total and differential cross sections.