Abstract:
  We determine the fundamental limit of the microresonator field uniformity. It
can be achieved in a specially designed microresonator, called a bat
microresonator, fabricated at the optical fiber surface. We show that the
relative nonuniformity of an eigenmode amplitude along the axial length $L$ of
an ideal bat microresonator cannot be smaller than ${{\frac{1}{3}}}{\pi
}^2n^4_r{\lambda }^{-4}Q^{-2}L^4$, where $n_r,\ \lambda $ and $Q$ are its
refractive index, the eigenmode wavelength and Q-factor. In the absence of
losses ($Q=\infty $), this eigenmode has the amplitude independent of axial
coordinate and zero axial speed (i.e., is stopped) within the length $L$. For a
silica microresonator with $Q={10}^8$ this eigenmode has the axial speed
$\mathrm{\sim}$ 10${}^{-4}$c, where c is the speed of light in vacuum, and its
nonuniformity along the length 100 micron at wavelength $\lambda =1.5$ micron
is $\mathrm{\sim}$ 10${}^{-7}$. For a realistic fiber with diameter 100 micron
and surface roughness 0.2 nm, the smallest eigenmode nonuniformity is
$\mathrm{\sim}$ 0.0003. As an application, we consider a bat microresonator
evanescently coupled to high Q-factor silica microspheres which serves as a
reference supporting the angstrom-precise straight-line translation over the
distance $L$ exceeding a hundred microns.


Abstract:
  The increasing value of data held in enterprises makes it an attractive
target to attackers. The increasing likelihood and impact of a cyber attack
have highlighted the importance of effective cyber risk estimation. We propose
two methods for modelling Value-at-Risk (VaR) which can be used for any
time-series data. The first approach is based on Quantile Autoregression (QAR),
which can estimate VaR for different quantiles, i.e. confidence levels. The
second method, we term Competitive Quantile Autoregression (CQAR), dynamically
re-estimates cyber risk as soon as new data becomes available. This method
provides a theoretical guarantee that it asymptotically performs as well as any
QAR at any time point in the future. We show that these methods can predict the
size and inter-arrival time of cyber hacking breaches by running coverage
tests. The proposed approaches allow to model a separate stochastic process for
each significance level and therefore provide more flexibility compared to
previously proposed techniques. We provide a fully reproducible code used for
conducting the experiments.


Abstract:
  The formulation of the hazy image is mainly dominated by the reflected lights
and ambient airlight. Existing dehazing methods often ignore the depth cues and
fail in distant areas where heavier haze disturbs the visibility. However, we
note that the guidance of the depth information for transmission estimation
could remedy the decreased visibility as distances increase. In turn, the good
transmission estimation could facilitate the depth estimation for hazy images.
In this paper, a deep end-to-end model that iteratively estimates image depths
and transmission maps is proposed to perform an effective depth prediction for
hazy images and improve the dehazing performance with the guidance of depth
information. The image depth and transmission map are progressively refined to
better restore the dehazed image. Our approach benefits from explicitly
modeling the inner relationship of image depth and transmission map, which is
especially effective for distant hazy areas. Extensive results on the
benchmarks demonstrate that our proposed network performs favorably against the
state-of-the-art dehazing methods in terms of depth estimation and haze
removal.


Abstract:
  One of the most commonly performed manipulation in a human's daily life is
pouring. Many factors have an effect on target accuracy, including pouring
velocity, rotation angle, geometric of the source, and the receiving
containers. This paper presents an approach to increase the repeatability and
accuracy of the robotic manipulator by estimating the change in the amount of
water of the pouring cup to a sequence of pouring actions using multiple layers
of the deep recurrent neural network, especially gated recurrent units (GRU).
The proposed GRU model achieved a validation mean squared error as low as 1e-4
(lbf) for the predicted value of weight f(t). This paper contains a
comprehensive evaluation and analysis of numerous experiments with various
designs of recurrent neural networks and hyperparameters fine-tuning.


Abstract:
  S. Ramanujan introduced a technique in 1913 for providing analytic
expressions for certain Mellin-type integrals which is now known as Ramanujan's
Master Theorem. This technique was communicated through his "Quarterly Reports"
and has a wide range of applicability in calculating the values of certain
definite integrals. In this paper, we have presented some extensions of
Ramanujan's Master Theorem that arise from the k-gamma function and the p-k
gamma function. Furthermore, we have established a Mellin type double integral
along with its corollaries. Some applications are established through the
evaluation of a variety of definite integrals.


Abstract:
  Recent experimental discoveries show that hydrogen-rich compounds can reach
room temperature superconductivity, at least at high pressures. Also that there
exist metallic hydrogen-abundant systems with critical temperatures of few
Kelvin, or even with no trace of superconductivity at all. By analyzing through
first-principles calculations the structural and electronic properties of more
than one hundred compounds predicted to be superconductors in the literature,
we determine that the capacity of creating a bonding network of connected
localized units is the key to enhance the critical temperature in
hydrogen-based superconductors, explaining the large variety of critical
temperatures of superconducting hydrogen-rich materials. We define a magnitude
named as the {\it networking value}, which correlates well with the predicted
critical temperature, much better than any other descriptor analyzed thus far.
This magnitude can be easily calculated for any compound by analyzing
isosurfaces of the electron localization function. By classifying the studied
compounds according to their bonding nature, we observe that the {\it
networking value} correlates with the critical temperature for all bonding
types. Our analysis also highlights that systems with weakened covalent bonds
are the most promising candidates for reaching high critical temperatures. The
discovery of the positive correlation between superconductivity and the bonding
network offers the possibility of screening easily hydrogen-based compounds
and, at the same time, sets clear paths for chemically engineering better
superconductors.


Abstract:
  This paper studies high-dimensional regression with two-way structured data.
To estimate the high-dimensional coefficient vector, we propose the generalized
matrix decomposition regression (GMDR) to efficiently leverage any auxiliary
information on row and column structures. The GMDR extends the principal
component regression (PCR) to two-way structured data, but unlike PCR, the GMDR
selects the components that are most predictive of the outcome, leading to more
accurate prediction. For inference on regression coefficients of individual
variables, we propose the generalized matrix decomposition inference (GMDI), a
general high-dimensional inferential framework for a large family of estimators
that include the proposed GMDR estimator. GMDI provides more flexibility for
modeling relevant auxiliary row and column structures. As a result, GMDI does
not require the true regression coefficients to be sparse; it also allows
dependent and heteroscedastic observations. We study the theoretical properties
of GMDI in terms of both the type-I error rate and power and demonstrate the
effectiveness of GMDR and GMDI on simulation studies and an application to
human microbiome data.


Abstract:
  COVID-19 infection caused by SARS-CoV-2 pathogen is a catastrophic pandemic
outbreak all over the world with exponential increasing of confirmed cases and,
unfortunately, deaths. In this work we propose an AI-powered pipeline, based on
the deep-learning paradigm, for automated COVID-19 detection and lesion
categorization from CT scans. We first propose a new segmentation module aimed
at identifying automatically lung parenchyma and lobes. Next, we combined such
segmentation network with classification networks for COVID-19 identification
and lesion categorization. We compare the obtained classification results with
those obtained by three expert radiologists on a dataset consisting of 162 CT
scans. Results showed a sensitivity of 90\% and a specificity of 93.5% for
COVID-19 detection, outperforming those yielded by the expert radiologists, and
an average lesion categorization accuracy of over 84%. Results also show that a
significant role is played by prior lung and lobe segmentation that allowed us
to enhance performance by over 20 percent points. The interpretation of the
trained AI models, moreover, reveals that the most significant areas for
supporting the decision on COVID-19 identification are consistent with the
lesions clinically associated to the virus, i.e., crazy paving, consolidation
and ground glass. This means that the artificial models are able to
discriminate a positive patient from a negative one (both controls and patients
with interstitial pneumonia tested negative to COVID) by evaluating the
presence of those lesions into CT scans. Finally, the AI models are integrated
into a user-friendly GUI to support AI explainability for radiologists, which
is publicly available at http://perceivelab.com/covid-ai.


Abstract:
  For spin systems, such as the $q$-colorings and independent-set models,
approximating the partition function in the so-called non-uniqueness region,
where the model exhibits long-range correlations, is typically computationally
hard for bounded-degree graphs. We present new algorithmic results for
approximating the partition function and sampling from the Gibbs distribution
for spin systems in the non-uniqueness region on random regular bipartite
graphs. We give an $\mathsf{FPRAS}$ for counting $q$-colorings for even
$q=O\big(\tfrac{\Delta}{\log{\Delta}}\big)$ on almost every $\Delta$-regular
bipartite graph. This is within a factor $O(\log{\Delta})$ of the sampling
algorithm for general graphs in the uniqueness region and improves
significantly upon the previous best bound of
$q=O\big(\tfrac{\sqrt{\Delta}}{(\log\Delta)^2}\big)$ by Jenssen, Keevash, and
Perkins (SODA'19). Analogously, for the hard-core model on independent sets
weighted by $\lambda>0$, we present an $\mathsf{FPRAS}$ for estimating the
partition function when $\lambda=\Omega\big(\tfrac{\log{\Delta}}{\Delta}\big)$,
which improves upon previous results by an $\Omega(\log \Delta)$ factor. Our
results for the colorings and hard-core models follow from a general result
that applies to arbitrary spin systems. Our main contribution is to show how to
elevate probabilistic/analytic bounds on the marginal probabilities for the
typical structure of phases on random bipartite regular graphs into efficient
algorithms, using the polymer method. We further show evidence that our result
for colorings is within a constant factor of best possible using current
polymer-method approaches.


Abstract:
  The COVID-19 pandemic forced almost all professional and amateur sports to be
played without attending crowds. Thus, it induced a large-scale natural
experiment on the impact of social pressure on decision making and behavior in
sports fields. Using a data set of 1027 rugby union matches from 11 tournaments
in 10 countries, we find that home teams have won less matches and their points
difference decreased during the pandemics, shedding light on the impact of
crowd attendance on the {\em home advantage} of sports teams.


Abstract:
  We study online convex optimization in the random order model, recently
proposed by \citet{garber2020online}, where the loss functions may be chosen by
an adversary, but are then presented to the online algorithm in a uniformly
random order. Focusing on the scenario where the cumulative loss function is
(strongly) convex, yet individual loss functions are smooth but might be
non-convex, we give algorithms that achieve the optimal bounds and
significantly outperform the results of \citet{garber2020online}, completely
removing the dimension dependence and improving their scaling with respect to
the strong convexity parameter. Our analysis relies on novel connections
between algorithmic stability and generalization for sampling
without-replacement analogous to those studied in the with-replacement
i.i.d.~setting, as well as on a refined average stability analysis of
stochastic gradient descent.


Abstract:
  Using kinetic Monte Carlo simulations, we develop a framework to relate
morphological properties and microscopic dynamics during island growth,
coalescence, and initial formation of continuous heteroepitaxial films. The
average island width is controlled by adatom mobility on the substrate.
Subsequent evolution strongly depends on the Ehrlich-Schw\"{o}ebel energy
barrier $E_{ES}$ of the deposited material. As $E_{ES}$ decreases, islands
becomes taller and their coalescence is delayed. For small islands and large
$E_{ES}$, the global roughness increases as $W\sim{\text{thickness}}^{1/2}$ and
the local roughness increases at short scales (apparent anomalous scaling)
before and after island coalescence. If the islands are wide, $W$ may have a
plateau for large $E_{ES}$ and has a maximum for small $E_{ES}$ when the
islands coalesce. This framework is applied to atomic-force microscopy data of
the initial stages of CdTe deposition on Kapton: a maximum of $W$ during island
coalescence indicates negligible ES barrier, consistently with scaling
properties of much thicker films, and the diffusion coefficient
${10}^{-7}{\text{--}}{10}^{-5}{\text{cm}}^2/{\text s}$ on the Kapton surface at
$150\,^{\circ}\mathrm{C}$ is estimated. Applications of the framework to other
materials are suggested, in which the expected roles of ES barriers are
highlighted.


Abstract:
  At any epoch, particle physics must be open to completely unexpected
discoveries, and that is reason enough to extend the reach of searches for
ultra-high energy (UHE) photons. The observation of a population of photons
with energies $E \gtrsim 100$ EeV would for example imply the existence of
either a completely new physical phenomena, or particle acceleration mechanisms
heretofore never seen or imagined. But as we outline in this Letter of
Interest, there are also good arguments for super-heavy dark matter (SHDM) in a
parameter range such that it could be discovered via its decays to, in
particular, UHE photons. Only ultra-high energy cosmic ray observatories have
capabilities to detect UHE photons. We first investigate how current and future
observations can probe and constrain SHDM models in important directions, and
then outline some of the scenarios that motivate such searches. We also discuss
connections between constraints on SHDM and on the parameter values of
cosmological models.


Abstract:
  The discovery of the moderate differential rotation between the core and the
envelope of evolved solar-like stars could be the signature of a strong
magnetic field trapped inside the radiative interior. The population of
intermediate-mass red giants presenting a surprisingly low-amplitude of their
mixed modes could also arise from the effect of an internal magnetic field.
Indeed, stars more massive than about 1.1Ms are known to develop a convective
core during their main sequence, which could relax into a strong fossil
magnetic field trapped inside the core of the star for the rest of its
evolution. The observations of mixed modes can constitute an excellent probe of
the deepest layers of evolved solar-like stars. The magnetic perturbation on
mixed modes may thus be visible in asteroseismic data. To unravel which
constraints can be obtained from observations, we theoretically investigate the
effects of a plausible mixed axisymmetric magnetic field with various
amplitudes on the mixed-mode frequencies of evolved solar-like stars. The
first-order frequency perturbations are computed for dipolar and quadrupolar
mixed modes. These computations are carried out for a range of stellar ages,
masses, and metallicities. We show that typical fossil-field strengths of 0.1-1
MG, consistent with the presence of a dynamo in the convective core during the
main sequence, provoke significant asymmetries on mixed-mode frequency
multiplets during the red-giant branch. We show that these signatures may be
detectable in asteroseismic data for field amplitudes small enough for the
amplitude of the modes not to be affected by the conversion of gravity into
Alfven waves inside the magnetised interior. Finally, we infer an upper limit
for the strength of the field, and the associated lower limit for the timescale
of its action, to redistribute angular momentum in stellar interiors.


Abstract:
  In this paper, we establish some important results for the impulsive wave
equation. We begin by proving the existence of a solution. Then, we study the
impulse approximate controllability where the control function acts on a
subdomain $\omega$ and at one instant of time $\tau \in (0, T)$. Afterward, we
study impulse observability


Abstract:
  We discuss the towers of finite \'etale covers which were essentially
introduced by A.Tamagawa. The statement about correspondence between sections
and cofinal towers is a folklore but perhaps not in a very explicit form. The
last section explains how the "injectivity statement" of Grothendieck section
conjecture fails for abelian varieties, which is also known in some form.
  The paper is based on an earlier article which was aimed to reinterpret
anabelian setting in model theory terms.


Abstract:
  Cervical cancer is a malignant tumor that seriously threatens women's health,
and is one of the most common that affects women worldwide. For its early
detection, colposcopic images of the cervix are used for searching for possible
injuries or abnormalities. An inherent characteristic of these images is the
presence of specular reflections (brightness) that make it difficult to observe
some regions, which might imply misdiagnosis. In this paper, a new strategy
based on neural networks is introduced for eliminating specular reflections and
estimating the unobserved anatomical cervix portion under the bright zones. For
overcoming the fact that the ground truth corresponding to the specular
reflection regions is always unknown, the new strategy proposes the supervised
training of a neural network to learn how to restore any hidden regions of
colposcopic images. Once the specular reflections are identified, they are
removed from the image, and the previously trained network is used to fulfill
these deleted areas. The quality of the processed images was evaluated
quantitatively and qualitatively. In 21 of the 22 evaluated images, the
detected specular reflections were eliminated, whereas, in the remaining one,
these reflections were almost completely eliminated. The distribution of the
colors and the content of the restored images are similar to those of the
originals. The evaluation carried out by a specialist in Cervix Pathology
concluded that, after eliminating the specular reflections, the anatomical and
physiological elements of the cervix are observable in the restored images,
which facilitates the medical diagnosis of cervical pathologies. Our method has
the potential to improve the early detection of cervical cancer.


Abstract:
  In spirit of Gan-Ichino's work on the Arthur's multiplicity formula for
metaplectic groups, we have established the Arthur's multiplicity formula for
even orthogonal or unitary groups with Witt index less than or equal to one. In
that multiplicity formula, some local packets defined using the stable range
theta lifts are involved. In this paper, we prove that at non-Archimedean
places, the definition of the local packets involved in that multiplicity
formula is independent of the choice of the dual-pairs used in their
construction. Moreover, at those places where the groups are quasi-split, we
prove that the local packets involved are the same as the local A-packets
defined by Arthur/ Mok.


Abstract:
  We explore the relationships between two elliptic functions constructed by
Shen in the signature four Ramanujan theory.


Abstract:
  For classical discrete systems, we establish basic formulations that can
treat evolution of nonlinearity in canonical ensemble, in analogy with
stochastic thermodynamics. We reveal that through the transition from one to
another state, information gain about the nonlinearity is identical to negative
sum of entropy change for thermal bath and fluctuation of initial entropy
change for system from deterministic transition: Evolution of nonlinearity in
thermodynamics on configuration space is bridged to nonlinearity on information
geometry. We derive lower bound of entropy production and conservation for
entropy production in exponential form, respectively corresponding to the
second law of thermodynamics and integral flutuation theorem for evolution of
the nonlinearity. We find that system average for nonlinearity information gain
is bounded by entropy production for linear system.


Abstract:
  In this paper we describe the history of the LHCb experiment over the last
three decades, and its remarkable successes and achievements. LHCb was
conceived primarily as a b-physics experiment, dedicated to CP violation
studies and measurements of very rare b decays, however the tremendous
potential for c-physics was also clear. At first data taking, the versatility
of the experiment as a general-purpose detector in the forward region also
became evident, with measurements achievable such as electroweak physics, jets
and new particle searches in open states. These were facilitated by the
excellent capability of the detector to identify muons and to reconstruct decay
vertices close to the primary pp interaction region. By the end of the LHC Run
2 in 2018, before the accelerator paused for its second long shut down, LHCb
had measured the CKM quark mixing matrix elements and CP violation parameters
to world-leading precision in the heavy-quark systems. The experiment had also
measured many rare decays of b and c quark mesons and baryons to below their
Standard Model expectations, some down to branching ratios of order 10-9. In
addition, world knowledge of b and c spectroscopy had improved significantly
through discoveries of many new resonances already anticipated in the quark
model, and also adding new exotic four and five quark states.


Abstract:
  We construct free resolutions for quotient rings $R/\langle \mathcal{I}',
\mathcal{I}\mathcal{J}, \mathcal{J}'\rangle$, give conditions for the quotient
to be realized as a fiber product, and give criteria for the construction to be
minimal. We then specialize this result to fiber products over a field $k$ and
recover explicit formulas for Betti numbers, graded Betti numbers, and
Poincar\'{e} series.


Abstract:
  We present two methods for constructing a flat band (FB) system having a flat
energy dispersion over the entire Brillouin zone within tight-binding model,
where the resulting Hamiltonian may not be easily obtained by existing methods
based on a bipartite graph and line graph techniques. In the first method, we
derive a set of conditions equivalent to the appearance of FBs for a given
graph structure. This method allows parameter to be tuned so that systems with
a small number of sites per unit cell has a FB. In the second method, we show
that FB systems can be obtained by removing or adding sites to an existing FB
system under specific rules. In particular, the site addition method enables us
to construct multiple FB systems stemming from a single FB system. The FB
system obtained by the second method has the characteristics that the component
ratios in the FB eigenstate are partially common to the original system. We
illustrate how lattices having a FB can be constructed by applying the latter
method starting from an existing lattice such as a kagome lattice,
demonstrating that a wide variety of lattices can possess a FB in the band
structure.


Abstract:
  This paper presents a mobile application for vote-casting and
vote-verification based on the Selene e-voting protocol and explains how it was
developed and implemented using the User Experience Design process. The
resulting interface was tested with 38 participants, and user experience data
was collected via questionnaires and semi-structured interviews on user
experience and perceived security. Results concerning the impact of displaying
security mechanisms on UX were presented in a complementary paper. Here we
expand on this analysis by studying the mental models revealed during the
interviews and compare them with theoretical security notions. Finally, we
propose a list of improvements for designs of future voting protocols.


Abstract:
  Big progress has been achieved in domain adaptation in decades. Existing
works are always based on an ideal assumption that testing target domain are
i.i.d. with training target domains. However, due to unpredictable corruptions
(e.g., noise and blur) in real data like web images, domain adaptation methods
are increasingly required to be corruption robust on target domains. In this
paper, we investigate a new task, Corruption-agnostic Robust Domain Adaptation
(CRDA): to be accurate on original data and robust against
unavailable-for-training corruptions on target domains. This task is
non-trivial due to large domain discrepancy and unsupervised target domains. We
observe that simple combinations of popular methods of domain adaptation and
corruption robustness have sub-optimal CRDA results. We propose a new approach
based on two technical insights into CRDA: 1) an easy-to-plug module called
Domain Discrepancy Generator (DDG) that generates samples that enlarge domain
discrepancy to mimic unpredictable corruptions; 2) a simple but effective
teacher-student scheme with contrastive loss to enhance the constraints on
target domains. Experiments verify that DDG keeps or even improves performance
on original data and achieves better corruption robustness that baselines.


Abstract:
  Spin-orbit torques (SOTs) from transition metal dichalcogenides systems
(TMDs) in conjunction with ferromagnetic materials are recently attractive in
spintronics for their versatile features. However, most of the previously
studied crystalline TMDs are prepared by mechanical exfoliation, which limits
their potentials for industrial applications. Here we show that amorphous WTe2
heterostructures deposited by magnetron sputtering possess a sizable
damping-like SOT efficiency {\xi}_DL^WTe2 ~ 0.20 and low damping constant
{\alpha} = 0.009/pm0.001. Only an extremely low critical switching current
density J_c ~ 7.05\times10^9 A/m^2 is required to achieve SOT-driven
magnetization switching. The SOT efficiency is further proved to depend on the
W and Te relative compositions in the co-sputtered W_100-xTe_x samples, from
which a sign change of {\xi}_DL^WTe2 is observed. Besides, the electronic
transport in amorphous WTe2 is found to be semiconducting and is governed by a
hopping mechanism. With the above advantages and rich tunability, amorphous and
semiconducting WTe2 serves as a unique SOT source for future spintronics
applications.


Abstract:
  This paper introduces a dual-critic reinforcement learning (RL) framework to
address the problem of frame-level bit allocation in HEVC/H.265. The objective
is to minimize the distortion of a group of pictures (GOP) under a rate
constraint. Previous RL-based methods tackle such a constrained optimization
problem by maximizing a single reward function that often combines a distortion
and a rate reward. However, the way how these rewards are combined is usually
ad hoc and may not generalize well to various coding conditions and video
sequences. To overcome this issue, we adapt the deep deterministic policy
gradient (DDPG) reinforcement learning algorithm for use with two critics, with
one learning to predict the distortion reward and the other the rate reward. In
particular, the distortion critic works to update the agent when the rate
constraint is satisfied. By contrast, the rate critic makes the rate constraint
a priority when the agent goes over the bit budget. Experimental results on
commonly used datasets show that our method outperforms the bit allocation
scheme in x265 and the single-critic baseline by a significant margin in terms
of rate-distortion performance while offering fairly precise rate control.


Abstract:
  The phase field fracture method is attracting significant interest. Phase
field approaches have enabled predicting - on arbitrary geometries and
dimensions - complex fracture phenomena such as crack branching, coalescence,
deflection and nucleation. In this work, we present a simple and robust
implementation of the phase field fracture method in the commercial finite
element package Abaqus. The implementation exploits the analogy between the
phase field evolution law and the heat transfer equation, enabling the use of
Abaqus' in-built features and circumventing the need for defining user
elements. The framework is general, and is shown to accommodate different
solution schemes (staggered and monolithic), as well as various constitutive
choices for preventing damage under compression. The robustness and
applicability of the numerical framework presented is demonstrated by
addressing several 2D and 3D boundary value problems of particular interest.
Focus is on the solution of paradigmatic case studies that are known to be
particularly demanding from a convergence perspective. The results reveal that
our phase field fracture implementation can be readily combined with other
advanced computational features, such as contact, and deliver robust and
precise solutions. The code developed can be downloaded from
www.empaneda.com/codes.


Abstract:
  We experimentally investigate internal coastal Kelvin waves in a two-layer
fluid system on a rotating table. Waves in our system propagate in the prograde
direction and are exponentially localized near the boundary. Our experiments
verify the theoretical dispersion relation of the wave and show that the wave
amplitude decays exponentially along the propagation direction. We further
demonstrate that the waves can robustly propagate along boundaries of complex
geometries without being scattered and that adding obstacles to the wave
propagation path does not cause additional attenuation.


Abstract:
  This work investigates the well-known problem of morphing attacks, which has
drawn considerable attention in the biometrics community. Morphed images have
exposed face recognition systems' susceptibility to false acceptance, resulting
in dire consequences, especially for national security applications. To detect
morphing attacks, we propose a method which is based on a discriminative 2D
Discrete Wavelet Transform (2D-DWT). A discriminative wavelet sub-band can
highlight inconsistencies between a real and a morphed image. We observe that
there is a salient discrepancy between the entropy of a given sub-band in a
bona fide image, and the same sub-band's entropy in a morphed sample.
Considering this dissimilarity between these two entropy values, we find the
Kullback-Leibler divergence between the two distributions, namely the entropy
of the bona fide and the corresponding morphed images. The most discriminative
wavelet sub-bands are those with the highest corresponding KL-divergence
values. Accordingly, 22 sub-bands are selected as the most discriminative ones
in terms of morph detection. We show that a Deep Neural Network (DNN) trained
on the 22 discriminative sub-bands can detect morphed samples precisely. Most
importantly, the effectiveness of our algorithm is validated through
experiments on three datasets: VISAPP17, LMA, and MorGAN. We also performed an
ablation study on the sub-band selection.


Abstract:
  Hawkes Processes have undergone increasing popularity as default tools for
modeling self- and mutually exciting interactions of discrete events in
continuous-time event streams. A Maximum Likelihood Estimation (MLE)
unconstrained optimization procedure over parametrically assumed forms of the
triggering kernels of the corresponding intensity function are a widespread
cost-effective modeling strategy, particularly suitable for data with few
and/or short sequences. However, the MLE optimization lacks guarantees, except
for strong assumptions on the parameters of the triggering kernels, and may
lead to instability of the resulting parameters .In the present work, we show
how a simple stabilization procedure improves the performance of the MLE
optimization without these overly restrictive assumptions.This stabilized
version of the MLE is shown to outperform traditional methods over sequences of
several different lengths.


Abstract:
  Measuring human capabilities to synchronize in time, adapt to perturbations
to timing sequences or reproduce time intervals often require experimental
setups that allow recording response times with millisecond precision. Most
setups present auditory stimuli using either MIDI devices or specialized
hardware such as Arduino and are often expensive or require calibration and
advanced programming skills. Here, we present in detail an experimental setup
that only requires an external sound card and minor electronic skills, works on
a conventional PC, is cheaper than alternatives and requires almost no
programming skills. It is intended for presenting any auditory stimuli and
recording tapping response times with within 2 milliseconds precision (up to
-2ms lag). This paper shows why desired accuracy in recording response times
against auditory stimuli is difficult to achieve in conventional computer
setups, presents an experimental setup to overcome this and explains in detail
how to set it up and use the provided code. Finally, the code for analyzing the
recorded tapping responses was evaluated, showing that no spurious or missing
events were found in 94% of the analyzed recordings.


Abstract:
  We introduce the interactive tool pandemonium to cluster model predictions
that depend on a set of parameters. The model predictions are used to define
the coordinates in observable space which go into the clustering. The results
of this partitioning are then visualized in both observable and parameter space
to study correlations between them. The tool offers multiple choices for
coordinates, distance functions and linkage methods within hierarchical
clustering. It provides a set of diagnostic statistics and visualization
methods to study the clustering results in order to interpret the outcome. The
methods are most useful in an interactive environment that enables exploration,
and we have implemented them with a graphical user interface in R. We
demonstrate the concepts with an application to phenomenological studies in
flavor physics in the context of the so-called B anomalies.


Abstract:
  Given a generic time-dependent many-body quantum state, we determine the
associated parent Hamiltonian. This procedure may require, in general,
interactions of any sort. Enforcing the requirement of a fixed set of
engineerable Hamiltonians, we find the optimal Hamiltonian once a set of
realistic elementary interactions is defined. We provide three examples of this
approach. We first apply the optimization protocol to the ground states of the
one-dimensional Ising model and a ferromagnetic $p$-spin model but with
time-dependent coefficients. We also consider a time-dependent state that
interpolates between a product state and the ground state of a $p$-spin model.
We determine the time-dependent optimal parent Hamiltonian for these states and
analyze the capability of this Hamiltonian of generating the state evolution.
Finally, we discuss the connections of our approach to shortcuts to
adiabaticity.


Abstract:
  Food resources face severe damages under extraordinary situations of
catastrophes such as earthquakes, cyclones, and tsunamis. Under such scenarios,
speedy assessment of food resources from agricultural land is critical as it
supports aid activity in the disaster hit areas. In this article, a deep
learning approach is presented for the detection and segmentation of coconut
tress in aerial imagery provided through the AI competition organized by the
World Bank in collaboration with OpenAerialMap and WeRobotics. Maked
Region-based Convolutional Neural Network approach was used identification and
segmentation of coconut trees. For the segmentation task, Mask R-CNN model with
ResNet50 and ResNet1010 based architectures was used. Several experiments with
different configuration parameters were performed and the best configuration
for the detection of coconut trees with more than 90% confidence factor was
reported. For the purpose of evaluation, Microsoft COCO dataset evaluation
metric namely mean average precision (mAP) was used. An overall 91% mean
average precision for coconut trees detection was achieved.


Abstract:
  We present an epidemiological model for the effectiveness of CoronaMelder,
the Dutch digital contact tracing app developed on top of the Google/Apple
Exposure Notification framework. We compare the effectiveness of CoronaMelder
with manual contract tracing on a number of metrics. CoronaMelder turns out to
have a small but noticeable positive influence in slowing down the COVID-19
pandemic, an effect that will become more pronounced in an opened-up society
where adoption of CoronaMelder is increased.


Abstract:
  Physical unclonable functions (PUFs) are widely considered in secret key
generation for resource constrained devices. However, PUFs require additional
hardware overhead. In this paper, we focus on developing a PUF-efficient,
robust, and secure key generation scheme. First, a novel method for extracting
quaternary PUF responses is proposed to increase the entropy of a PUF response,
in which a 2-bit response is extracted from evaluating a single PUF cell
multiple times. The probability masses of the responses can be adjusted by
setting parameters appropriately. Then, a chosen secret model based fuzzy
extractor (FE) is designed to extract secret keys from the quaternary PUF
responses. To improve the security of this FE, it is modeled as a wiretap
channel system, and wiretap polar coding is adopted to reduce secrecy leakage.
An upper bound of secrecy leakage is also given in this paper, and it suggests
that an arbitrarily small (even zero) leakage can be achieved by properly
choosing parameters of the quaternary PUF responses generation. Comparison
results show that the required number of PUF cells to achieve the same level of
secrecy in our scheme is as low as half that of the state-of-the-art schemes.


Abstract:
  We derive sufficient conditions for strict dissipativity for optimal control
of linear evolution equations on Hilbert spaces with a cost functional
including linear and quadratic terms. We show that strict dissipativity with a
particular storage function is equivalent to ellipticity of a Lyapunov-like
operator. Further we prove under a spectral decomposition assumption of the
underlying generator and an orthogonality condition of the resulting subspaces
that this ellipticity property holds under a detectability assumption. We
illustrate our result by means of an example involving a heat equation on a
one-dimensional domain.


Abstract:
  Digital health applications that leverage multiple sources of patient data
for insights to patients' behaviours or disease symptoms as well as remote
patient monitoring, nudging and treatments are becoming increasingly popular in
various medical practices and research. One common issue among these
applications is that they are generally based on project-specific solutions and
developed from scratch. Such application development fashion results in large
amounts of repetitive effort, for example, in building study specific websites
and mobile frontends, deploying customised infrastructures, and collecting data
that may have already been collected in other studies and projects. What is
worse, the data collected, and functions built cannot be easily reused by other
applications. In this paper, we present an event-driven microservice platform,
namely DHLink, to address this issue. DHLink securely links existing digital
health applications of different projects, facilitates real-time data sharing,
and supports rapid application development by reusing data and functions of
existing digital health applications. In addition, comes with DHLink, a set of
highly generic and reusable microservices is provided, which allows developers
to rapidly create a typical above-mentioned digital health application by only
developing the core algorithms. Two use cases outlined in this paper have shown
the use of DHLink and the set of microservices for application collaboration
and new application development to be efficient and practical.


Abstract:
  Supercuspidal representations are usually infinite-dimensional, so the size
of such a representation cannot be measured by its dimension; the formal degree
is a better alternative. Hiraga, Ichino, and Ikeda conjectured a formula for
the formal degree of a supercuspidal in terms of its $L$-parameter only. Our
first main result is to compute the formal degrees of the supercuspidal
representations constructed by Yu. Our second, using the first, is to verify
that Kaletha's regular supercuspidal $L$-packets satisfy the conjecture.


Abstract:
  We prove a general alternate circular summation formula of theta functions,
which implies a great deal of theta-function identities. In particular, we
recover several identities in Ramanujan's Notebook from this identity. We also
obtain two formulaes for $(q;q)_\infty^{2n}$.


Abstract:
  Geometric predicates are a basic ingredient to implement a vast range of
algorithms in computational geometry. Modern implementations employ floating
point filtering techniques to combine efficiency and robustness, and
state-of-the-art predicates are guaranteed to be always exact while being only
slightly slower than corresponding (inexact) floating point implementations.
Unfortunately, if the input to these predicates is an intermediate construction
of an algorithm, its floating point representation may be affected by an
approximation error, and correctness is no longer guaranteed. This paper
introduces the concept of indirect geometric predicate: instead of taking the
intermediate construction as an explicit input, an indirect predicate considers
the primitive geometric elements which are combined to produce such a
construction. This makes it possible to keep track of the floating point
approximation, and thus to exploit efficient filters and expansion arithmetic
to exactly resolve the predicate with minimal overhead with respect to a naive
floating point implementation. As a representative example, we show how to
extend standard predicates to the case of points of intersection of linear
elements (i.e. lines and planes) and show that, on classical problems, this
approach outperforms state-of-the-art solutions based on lazy exact
intermediate representations.


Abstract:
  Much recent work in NLP has documented dataset artifacts, bias, and spurious
correlations between input features and output labels. However, how to tell
which features have "spurious" instead of legitimate correlations is typically
left unspecified. In this work we argue that for complex language understanding
tasks, all simple feature correlations are spurious, and we formalize this
notion into a class of problems which we call competency problems. For example,
the word "amazing" on its own should not give information about a sentiment
label independent of the context in which it appears, which could include
negation, metaphor, sarcasm, etc. We theoretically analyze the difficulty of
creating data for competency problems when human bias is taken into account,
showing that realistic datasets will increasingly deviate from competency
problems as dataset size increases. This analysis gives us a simple statistical
test for dataset artifacts, which we use to show more subtle biases than were
described in prior work, including demonstrating that models are
inappropriately affected by these less extreme biases. Our theoretical
treatment of this problem also allows us to analyze proposed solutions, such as
making local edits to dataset instances, and to give recommendations for future
data collection and model design efforts that target competency problems.


Abstract:
  Finite element approximation to a decoupled formulation for the quad--curl
problem is studied in this paper. The difficulty of constructing elements with
certain conformity to the quad--curl problems has been greatly reduced. For
convex domains, where the regularity assumption holds for Stokes equation, the
approximation to the curl of the true solution has quadratic order of
convergence and first order for the energy norm. If the solution shows
singularity, an a posterior error estimator is developed and a separate marking
adaptive finite element procedure is proposed, together with its convergence
proved. Both the a priori and a posteriori error analysis are supported by the
numerical examples.


Abstract:
  We consider the problem of estimating the parameters a Gaussian Mixture Model
with K components of known weights, all with an identity covariance matrix. We
make two contributions. First, at the population level, we present a sharper
analysis of the local convergence of EM and gradient EM, compared to previous
works. Assuming a separation of $\Omega(\sqrt{\log K})$, we prove convergence
of both methods to the global optima from an initialization region larger than
those of previous works. Specifically, the initial guess of each component can
be as far as (almost) half its distance to the nearest Gaussian. This is
essentially the largest possible contraction region. Our second contribution
are improved sample size requirements for accurate estimation by EM and
gradient EM. In previous works, the required number of samples had a quadratic
dependence on the maximal separation between the K components, and the
resulting error estimate increased linearly with this maximal separation. In
this manuscript we show that both quantities depend only logarithmically on the
maximal separation.


Abstract:
  Integral field unit (IFU) spectra of face-on star-forming galaxies from the
MaNGA survey are stacked in radial bins so as to reach a S/N high enough to
measure emission lines and Lick indices out to 2.5-3 R_e. Two thirds of
galaxies have stellar populations in the outer disks that are older, more metal
poor and less dusty than in the inner disks. Recent bursts of star formation
have occurred more frequently in the outer disk, but extinction-corrected
Halpha equivalent widths are significantly lower at fixed D_n(4000) in these
regions. I examine the properties of a subset of galaxies with the the most
H$\alpha$ deficient outer disks. These regions contain young stellar
populations that must have formed within the last 0.5 Gyr, but
extinction-corrected Halpha values well below the values predicted for a
standard Kroupa IMF. The Halpha deficient galaxies have flat D_n(4000) and
Hdelta_A profiles with little radial fluctuation, indicating that star
formation has occurred extremely uniformly across the entire disk. The
H$\alpha$ line profiles indicate that the ionized gas kinematics is also very
regular across the disk. The main clue to the origin of the Halpha deficiency
is that it sets in at the same radius where the dust extinction abruptly
decreases, suggesting a mode of star formation deficient in massive stars in
quiescent, HI-dominated gas. Finally, I have carried out a search for galaxies
with signatures of unusual Halpha kinematics and find that 15% of the sample
exhibit evidence for significant ionized gas that is displaced from the
systemic velocity of the disk.


Abstract:
  Non-autoregressive translation (NAT) significantly accelerates the inference
process via predicting the entire target sequence. However, recent studies show
that NAT is weak at learning high-mode of knowledge such as one-to-many
translations. We argue that modes can be divided into various granularities
which can be learned from easy to hard. In this study, we empirically show that
NAT models are prone to learn fine-grained lower-mode knowledge, such as words
and phrases, compared with sentences. Based on this observation, we propose
progressive multi-granularity training for NAT. More specifically, to make the
most of the training data, we break down the sentence-level examples into three
types, i.e. words, phrases, sentences, and with the training goes, we
progressively increase the granularities. Experiments on Romanian-English,
English-German, Chinese-English, and Japanese-English demonstrate that our
approach improves the phrase translation accuracy and model reordering ability,
therefore resulting in better translation quality against strong NAT baselines.
Also, we show that more deterministic fine-grained knowledge can further
enhance performance.


Abstract:
  Recent developments in autonomous driving technology have proven that map
data may be used, not only for general routing purposes, but also for to
enhance and complement common sensor data. This document reviews the most
commonly used interfaces and formats at each step of a selfhealing map data
chain.


Abstract:
  The regular (Bardeen)-AdS (BAdS) black hole (BH) in the extended phase space
is taken as an example for investigating the BH phase transition grade from
both macroscopic and microscopic points of view. The equation of state and
thermodynamic quantities of this BH are obtained. It is found that the BAdS BH
phase space in the extended phase space should be a second-order phase
transition near the critical point by verifying the Ehrenfest's equation, and
the possibility of its first-order phase transition can be ruled out by the
entropy continuity and the heat capacity mutation. The critical exponents from
the microscopic structure are analytically and numerically presented with the
Landau continuous phase transition theory by introducing a microscopic
order-parameter.


Abstract:
  Quantum computers are becoming more mainstream. As more programmers are
starting to look at writing quantum programs, they need to test and debug their
code. In this paper, we discuss various use-cases for quantum computers, either
standalone or as part of a System of Systems. Based on these use-cases, we
discuss some testing and debugging tactics that one can leverage to ensure the
quality of the quantum software. We also highlight quantum-computer-specific
issues and list novel techniques that are needed to address these issues. The
practitioners can readily apply some of these tactics to their process of
writing quantum programs, while researchers can learn about opportunities for
future work.


Abstract:
  SPIRAL 2 is a high intensity heavy ions beams accelerator project that has
been going on for more than 10 years now. Countless efforts in different
disciplines made it what it is today. One of the most important steps after the
set up of the different equipments has been the very first full cool down of
the superconducting cavities in an accelerator operation type configuration.
While this has been a major achievement for the SPIRAL 2 teams, it also
hi-lighted new challenges and constraints that would have to be addressed in
order to have a high availability rate of the beam from the cryogenics side.
This paper retraces this particular episode.


Abstract:
  In project scheduling under processing times uncertainty, the Anchor-Robust
Project Scheduling Problem is to find a baseline schedule of bounded makespan
and a max-weight subset of jobs whose starting times are guaranteed. The
problem was proven NP-hard even for budgeted uncertainty. In the present work
we design mixed-integer programming (MIP) formulations that are valid for a
variety of uncertainty sets encompassing budgeted uncertainty. A new dominance
among solutions is proposed, resulting into an MIP formulation. We further
study the combinatorial structure of the problem. Non-trivial polynomial cases
under budgeted uncertainty are exhibited, where the dominance-based formulation
yields a polyhedral characterization of integer solutions. In more general
cases, the dominance-based formulation is shown to be tighter than all
previously known formulations. In numerical experiments we investigate how the
formulation performs on instances around the polynomial cases, for both
budgeted uncertainty sets and more elaborate uncertainty sets involving several
budgets.


Abstract:
  Cognitive Diagnosis Models (CDMs) are a special family of discrete latent
variable models that are widely used in modern educational, psychological,
social and biological sciences. A key component of CDMs is a binary $Q$-matrix
characterizing the dependence structure between the items and the latent
attributes. Additionally, researchers also assume in many applications certain
hierarchical structures among the latent attributes to characterize their
dependence. In most CDM applications, the attribute-attribute hierarchical
structures, the item-attribute $Q$-matrix, the item-level diagnostic model, as
well as the number of latent attributes, need to be fully or partially
pre-specified, which however may be subjective and misspecified as noted by
many recent studies. This paper considers the problem of jointly learning these
latent and hierarchical structures in CDMs from observed data with minimal
model assumptions. Specifically, a penalized likelihood approach is proposed to
select the number of attributes and estimate the latent and hierarchical
structures simultaneously. An efficient expectation-maximization (EM) algorithm
and a latent structure recovery algorithm are developed, and statistical
consistency theory is also established under mild conditions. The good
performance of the proposed method is illustrated by simulation studies and a
real data application in educational assessment.


Abstract:
  We give communication-based semantics and reasoning techniques for Polarized
SILL, a rich session-typed programming language with general recursion. Its
features include channel and code transmission, synchronous and asynchronous
communication, and functional programming. Our contributions are distinguished
by their faithfulness to the process abstraction, i.e., to the premise that
communication is the only observable phenomenon of processes. We give the first
observed communication semantics that supports general recursion and code
transmission. Observed communication semantics define the meaning of processes
in terms of their observed communications. We use this observational semantics
to define experiments on processes, and we give a communication-based testing
equivalences framework for defining observational simulations and equivalences
on processes. This framework captures several natural equivalences, and we show
that one of these coincides with barbed congruence, the canonical notion of
process equivalence.
  Polarized SILL is defined using a substructural operational semantics based
on multiset rewriting. To ensure that our contributions are well-defined in the
presence of non-termination, we introduce fairness for multiset rewriting
systems. We construct a fair scheduler, we give sufficient conditions for
traces to be fair, and we study the effects of permutation on fair traces.


Abstract:
  Recently, a variety of deformed $T^{1,1}$ manifolds, with which 2D non-linear
sigma models (NLSMs) are classically integrable, have been presented by
Arutyunov, Bassi and Lacroix (ABL) [arXiv:2010.05573]. We refer to the NLSMs
with the integrable deformed $T^{1,1}$ as the ABL model for brevity. Motivated
by this progress, we consider deriving the ABL model from a 4D Chern-Simons
(CS) theory with a meromorphic one-form with four double poles and six simple
zeros. We specify boundary conditions in the CS theory that give rise to the
ABL model and derive the sigma-model background with target-space metric and
anti-symmetric two-form. Finally, we present two simple examples 1) an
anisotropic $T^{1,1}$ model and 2) a $G/H$ $\lambda$-model. The latter one can
be seen as a one-parameter deformation of the Guadagnini-Martellini-Mintchev
model.


Abstract:
  With the establishment and maturation of the experimental programs searching
for new physics with sizeable couplings at the LHC, there is an increasing
interest in the broader particle and astrophysics community for exploring the
physics of light and feebly-interacting particles as a paradigm complementary
to a New Physics sector at the TeV scale and beyond. FIPs 2020 has been the
first workshop fully dedicated to the physics of feebly-interacting particles
and was held virtually from 31 August to 4 September 2020. The workshop has
gathered together experts from collider, beam dump, fixed target experiments,
as well as from astrophysics, axions/ALPs searches, current/future neutrino
experiments, and dark matter direct detection communities to discuss progress
in experimental searches and underlying theory models for FIPs physics, and to
enhance the cross-fertilisation across different fields. FIPs 2020 has been
complemented by the topical workshop "Physics Beyond Colliders meets theory",
held at CERN from 7 June to 9 June 2020. This document presents the summary of
the talks presented at the workshops and the outcome of the subsequent
discussions held immediately after. It aims to provide a clear picture of this
blooming field and proposes a few recommendations for the next round of
experimental results.


Abstract:
  In this study, we will discuss recent developments in risk management of the
global financial and insurance business with respect to sustainable
development. So far climate change aspects have been the dominant aspect in
managing sustainability risks and opportunities, accompanied by the development
of several legislative initiatives triggered by supervisory authorities.
However, a sole concentration on these aspects misses out other important
economic and social facets of sustainable development goals formulated by the
UN. Such aspects have very recently come into the focus of the European
Committee concerning the Solvency II project for the European insurance
industry. Clearly the new legislative expectations can be better handled by
larger insurance companies and holdings than by small- and medium-sized mutual
insurance companies which are numerous in central Europe, due to their historic
development starting in the late medieval ages and early modern times. We
therefore also concentrate on strategies within the risk management of such
small- and medium-sized enterprises that can be achieved without much effort,
in particular those that are not directly related to climate change.


Abstract:
  Graph neural networks (GNNs) are a class of powerful machine learning tools
that model node relations for making predictions of nodes or links. GNN
developers rely on quantitative metrics of the predictions to evaluate a GNN,
but similar to many other neural networks, it is difficult for them to
understand if the GNN truly learns characteristics of a graph as expected. We
propose an approach to corresponding an input graph to its node embedding (aka
latent space), a common component of GNNs that is later used for prediction. We
abstract the data and tasks, and develop an interactive multi-view interface
called CorGIE to instantiate the abstraction. As the key function in CorGIE, we
propose the K-hop graph layout to show topological neighbors in hops and their
clustering structure. To evaluate the functionality and usability of CorGIE, we
present how to use CorGIE in two usage scenarios, and conduct a case study with
five GNN experts.


Abstract:
  When human cognition is modeled in Philosophy and Cognitive Science, there is
a pervasive idea that humans employ mental representations in order to navigate
the world and make predictions about outcomes of future actions. By
understanding how these representational structures work, we not only
understand more about human cognition but also gain a better understanding for
how humans rationalise and explain decisions. This has an influencing effect on
explainable AI, where the goal is to provide explanations of computer
decision-making for a human audience. We show that the Contextual Importance
and Utility method for XAI share an overlap with the current new wave of
action-oriented predictive representational structures, in ways that makes CIU
a reliable tool for creating explanations that humans can relate to and trust.


Abstract:
  We herein propose a variant of the projected inexact Levenberg--Marquardt
method (ILMM) for solving constrained nonsmooth equations. Since the orthogonal
projection onto the feasible set may be computationally expensive, we propose a
local ILMM with feasible inexact projections. By using assumption of
semi-smoothness and an error bound condition which is weaker than the standard
full rank assumption, we establish its convergence results, including results
on its rate. Finally, some computacional results are reported in order to
illustrate the advantages of the new schemes.


Abstract:
  The coherent deflection of elliptic bunch with the Gaussian charge
distribution in the field of an opposite bunch with the Gaussian bunch
distribution is considered. The generalization of Bassetti-Erskine formulae is
derived taking into account the angle between momenta of bunches.


Abstract:
  Atomic clauses are fundamental text units for understanding complex
sentences. Identifying the atomic sentences within complex sentences is
important for applications such as summarization, argument mining, discourse
analysis, discourse parsing, and question answering. Previous work mainly
relies on rule-based methods dependent on parsing. We propose a new task to
decompose each complex sentence into simple sentences derived from the tensed
clauses in the source, and a novel problem formulation as a graph edit task.
Our neural model learns to Accept, Break, Copy or Drop elements of a graph that
combines word adjacency and grammatical dependencies. The full processing
pipeline includes modules for graph construction, graph editing, and sentence
generation from the output graph. We introduce DeSSE, a new dataset designed to
train and evaluate complex sentence decomposition, and MinWiki, a subset of
MinWikiSplit. ABCD achieves comparable performance as two parsing baselines on
MinWiki. On DeSSE, which has a more even balance of complex sentence types, our
model achieves higher accuracy on the number of atomic sentences than an
encoder-decoder baseline. Results include a detailed error analysis.


Abstract:
  We propose a novel possibility to detect a very distinctive signal with more
than four muons originating from pair-produced vector-like leptons decaying to
a muon-philic $Z^\prime$ boson. These new particles are good candidates to
explain the anomalies in the muon anomalous magnetic moment and the $b\to
s\ell\ell$ processes. The doublet (singlet) vector-like leptons lighter than
1.3 (1.0) TeV are excluded by the latest data at the LHC if $\mathrm{BR}(E\to
Z^\prime \mu) = 1$. We also show that the excess in the signal region with more
than five leptons can be explained by this scenario if the vector-like lepton
is a weak singlet, with mass about 400 GeV and $\mathrm{BR}(E\to Z^\prime \mu)
= 0.25$. The future prospects at the HL-LHC are discussed.


Abstract:
  Combining the intrinsic and extrinsic geometry, we generalize Einstein
manifolds to Integral-Einstein (IE) submanifolds. A Takahashi-type theorem is
established to characterize minimal hypersurfaces with constant scalar
curvature (CSC) in unit spheres, which is the main object of the Chern
conjecture: such hypersurfaces are isoparametric. For these hypersurfaces, we
obtain some integral inequalities with the bounds characterizing exactly the
totally geodesic hypersphere, the non-IE minimal Clifford torus
$S^{1}(\sqrt{\frac{1}{n}})\times S^{n-1}(\sqrt{\frac{n-1}{n}})$ and the IE
minimal CSC hypersurfaces. Moreover, if further the third mean curvature is
constant, then it is an IE hypersurface or an isoparametric hypersurface with
$g\leq2$ principal curvatures. In particular, all the minimal isoparametric
hypersurfaces with $g\geq3$ principal curvatures are IE hypersurfaces. As
applications, we also obtain some spherical Bernstein theorems.


Abstract:
  Detecting security vulnerabilities in software before they are exploited has
been a challenging problem for decades. Traditional code analysis methods have
been proposed, but are often ineffective and inefficient. In this work, we
model software vulnerability detection as a natural language processing (NLP)
problem with source code treated as texts, and address the automated software
venerability detection with recent advanced deep learning NLP models assisted
by transfer learning on written English. For training and testing, we have
preprocessed the NIST NVD/SARD databases and built a dataset of over 100,000
files in $C$ programming language with 123 types of vulnerabilities. The
extensive experiments generate the best performance of over 93\% accuracy in
detecting security vulnerabilities.


Abstract:
  Author Name Disambiguation (AND) is the task of resolving which author
mentions in a bibliographic database refer to the same real-world person, and
is a critical ingredient of digital library applications such as search and
citation analysis. While many AND algorithms have been proposed, comparing them
is difficult because they often employ distinct features and are evaluated on
different datasets. In response to this challenge, we present S2AND, a unified
benchmark dataset for AND on scholarly papers, as well as an open-source
reference model implementation. Our dataset harmonizes eight disparate AND
datasets into a uniform format, with a single rich feature set drawn from the
Semantic Scholar (S2) database. Our evaluation suite for S2AND reports
performance split by facets like publication year and number of papers,
allowing researchers to track both global performance and measures of fairness
across facet values. Our experiments show that because previous datasets tend
to cover idiosyncratic and biased slices of the literature, algorithms trained
to perform well on one on them may generalize poorly to others. By contrast, we
show how training on a union of datasets in S2AND results in more robust models
that perform well even on datasets unseen in training. The resulting AND model
also substantially improves over the production algorithm in S2, reducing error
by over 50% in terms of $B^3$ F1. We release our unified dataset, model code,
trained models, and evaluation suite to the research community.
https://github.com/allenai/S2AND/


Abstract:
  At present emotion extraction from speech is a very important issue due to
its diverse applications. Hence, it becomes absolutely necessary to obtain
models that take into consideration the speaking styles of a person, vocal
tract information, timbral qualities and other congenital information regarding
his voice. Our speech production system is a nonlinear system like most other
real world systems. Hence the need arises for modelling our speech information
using nonlinear techniques. In this work we have modelled our articulation
system using nonlinear multifractal analysis. The multifractal spectral width
and scaling exponents reveals essentially the complexity associated with the
speech signals taken. The multifractal spectrums are well distinguishable the
in low fluctuation region in case of different emotions. The source
characteristics have been quantified with the help of different non-linear
models like Multi-Fractal Detrended Fluctuation Analysis, Wavelet Transform
Modulus Maxima. The Results obtained from this study gives a very good result
in emotion clustering.


Abstract:
  We investigate the optical conductivity and far-infrared magneto-optical
response of BaNiS$_2$, a simple square-lattice semimetal characterized by Dirac
nodal lines that disperse exclusively along the out-of-plane direction. With
the magnetic field aligned along the nodal line the in-plane Landau level
spectra show a nearly $\sqrt{B}$ behavior, the hallmark of a conical-band
dispersion with a small spin-orbit coupling gap. The optical conductivity
exhibits an unusual temperature-independent isosbestic line, ending at a Van
Hove singularity. First-principles calculations unambiguously assign the
isosbestic line to transitions across Dirac nodal states. Our work suggests a
universal topology of the electronic structure of Dirac nodal lines.


Abstract:
  Channel capacity plays a crucial role in the development of modern
communication systems as it represents the maximum rate at which information
can be reliably transmitted over a communication channel. Nevertheless, for the
majority of channels, finding a closed-form capacity expression remains an open
challenge. This is because it requires to carry out two formidable tasks a) the
computation of the mutual information between the channel input and output, and
b) its maximization with respect to the signal distribution at the channel
input. In this paper, we address both tasks. Inspired by implicit generative
models, we propose a novel cooperative framework to automatically learn the
channel capacity, for any type of memory-less channel. In particular, we
firstly develop a new methodology to estimate the mutual information directly
from a discriminator typically deployed to train adversarial networks, referred
to as discriminative mutual information estimator (DIME). Secondly, we include
the discriminator in a cooperative channel capacity learning framework,
referred to as CORTICAL, where a discriminator learns to distinguish between
dependent and independent channel input-output samples while a generator learns
to produce the optimal channel input distribution for which the discriminator
exhibits the best performance. Lastly, we prove that a particular choice of the
cooperative value function solves the channel capacity estimation problem.
Simulation results demonstrate that the proposed method offers high accuracy.


Abstract:
  While transition metal dichalcogenide (TMD) based moire materials have been
shown to host various correlated electronic phenomena, topological states have
not been experimentally observed until now. In this work, using first
principles calculations and continuum modeling, we reveal the displacement
field induced topological moire bands in AB-stacked TMD heterobilayer
MoTe2/WSe2. Valley contrasting Chern bands with non-trivial spin texture are
formed from interlayer hybridization between MoTe2 and WSe2 bands of nominally
opposite spins. Our study establishes a recipe for creating topological bands
in AB stacked TMD bilayers in general, which provides a highly tunable platform
for realizing quantum spin Hall and interaction induced quantum anomalous Hall
effects.


Abstract:
  Formation control algorithms for multi-agent systems have gained much
attention in the recent years due to the increasing amount of mobile and aerial
robotic swarms. The design of safe controllers for these vehicles is a
substantial aspect for an increasing range of application domains. However,
parts of the vehicle's dynamics and external disturbances are often unknown or
very time-consuming to model. To overcome this issue, we present a safe
formation control law for multiagent systems based on double integrator
dynamics by using Gaussian Processes for an online learning of the unknown
dynamics. The presented approach guarantees a bounded error to desired
formations with high probability, where the bound is explicitly given. A
numerical example highlights the effectiveness of the learning-based formation
control law.


Abstract:
  This paper develops a Bayesian computational platform at the interface
between posterior sampling and optimization in models whose marginal
likelihoods are difficult to evaluate. Inspired by adversarial optimization,
namely Generative Adversarial Networks (GAN), we reframe the likelihood
function estimation problem as a classification problem. Pitting a Generator,
who simulates fake data, against a Classifier, who tries to distinguish them
from the real data, one obtains likelihood (ratio) estimators which can be
plugged into the Metropolis-Hastings algorithm. The resulting Markov chains
generate, at a steady state, samples from an approximate posterior whose
asymptotic properties we characterize. Drawing upon connections with empirical
Bayes and Bayesian mis-specification, we quantify the convergence rate in terms
of the contraction speed of the actual posterior and the convergence rate of
the Classifier. Asymptotic normality results are also provided which justify
inferential potential of our approach. We illustrate the usefulness of our
approach on examples which have posed a challenge for existing Bayesian
likelihood-free approaches.


Abstract:
  The practical application of sodium-ion hybrid capacitors is limited by their
low energy densities resulted from the kinetics mismatch between cathodes and
anodes, and the fire safety related to the flammable electrolyte-separator
system. Hence, we report a rational design of metal-organic frameworks (MOFs,
UiO-66) modified PVDF-HFP separator. High tensile strength and dimensional
thermal stability of the separator reduce the risk of electrode short circuit
caused by the separator deformation. MCC test demonstrates a reduction of 75%
in peak heat release rate (pHRR), indicating an enhanced fire-resistant
property of the separator. This is due to the transformation of UiO-66 into
ZrO2 accompanied by the consumption of oxygen and the formation of the barrier
char that suppresses further heat release. Quasi-solid-state electrolyte
prepared based on this separator presents an enhanced ionic conductivity of
2.44 mS*cm-1 and Na-ion transference number of 0.55, which are related to the
high porosity ( >70%) and electrolyte uptake (~ 320%) of the separator.
Moreover, the open metal sites of UiO-66 can capture PF6- and consequently
liberate the Na+ for faster migration, thus reducing the kinetics mismatch
between cathodes and anodes. Such multifunctional separator enables the
quasi-solid-state Na-ion hybrid capacitor to achieve high energy density (182
Wh*kg-1 @31 W*kg-1) and power density (5280 W*kg-1 @22 Wh*kg-1), as well as
excellent cyclic stability (10000 cycles @1000 mA*g-1).
  Keywords: Quasi-solid-state; PVDF-HFP; Metal-organic frameworks; Dimensional
thermal stability; Fire safety; Selective charge transfer


Abstract:
  Magnitude-limited samples have shown that 20-25 per cent of cataclysmic
variables contain white dwarfs with magnetic fields of Mega Gauss strength, in
stark contrast to the approximately 5 per cent of single white dwarfs with
similar magnetic field strengths. Moreover, the lack of identifiable progenitor
systems for magnetic cataclysmic variables leads to considerable challenges
when trying to understand how these systems form and evolve. Here we present a
sample of six magnetic white dwarfs in detached binaries with low-mass stellar
companions where we have constrained the stellar and binary parameters
including, for the first time, reliable mass estimates for these magnetic white
dwarfs. We find that they are systematically more massive than non-magnetic
white dwarfs in detached binaries. These magnetic white dwarfs generally have
cooling ages of more than 1 Gyr and reside in systems that are very close to
Roche-lobe filling. Our findings are more consistent with these systems being
temporarily detached cataclysmic variables, rather than pre-cataclysmic
binaries, but we cannot rule out the latter possibility. We find that these
systems can display unusual asymmetric light curves that may offer a way to
identify them in larger numbers in future. Seven new candidate magnetic white
dwarf systems are also presented, three of which have asymmetric light curves.
Finally, we note that several newly identified magnetic systems have archival
spectra where there is no clear evidence of magnetism, meaning that these
binaries have been previously missed. Nevertheless, there remains a clear lack
of younger detached magnetic white dwarf systems.


Abstract:
  We study two polynomial counting questions in arithmetic statistics via a
combination of Fourier analytic and arithmetic methods. First, we obtain new
quantitative forms of Hilbert's Irreducibility Theorem for degree $n$
polynomials $f$ with $\mathrm{Gal}(f) \subseteq A_n$. We study this both for
monic polynomials and non-monic polynomials. Second, we study lower bounds on
the number of degree $n$ monic polynomials with almost prime discriminants, as
well as the closely related problem of lower bounds on the number of degree $n$
number fields with almost prime discriminants.


Abstract:
  We propose a deterministic Kaczmarz algorithm for solving linear systems
$A\x=\b$. Different from previous Kaczmarz algorithms, we use reflections in
each step of the iteration. This generates a series of points distributed with
patterns on a sphere centered at a solution. Firstly, we prove that taking the
average of $O(\eta/\epsilon)$ points leads to an effective approximation of the
solution up to relative error $\epsilon$, where $\eta$ is a parameter depending
on $A$ and can be bounded above by the square of the condition number. We also
show how to select these points efficiently. From the numerical tests, our
Kaczmarz algorithm usually converges more quickly than the (block) randomized
Kaczmarz algorithms. Secondly, when the linear system is consistent, the
Kaczmarz algorithm returns the solution that has the minimal distance to the
initial vector. This gives a method to solve the least-norm problem. Finally,
we prove that our Kaczmarz algorithm indeed solves the linear system
$A^TW^{-1}A \x = A^TW^{-1} \b$, where $W$ is the low-triangular matrix such
that $W+W^T=2AA^T$. The relationship between this linear system and the
original one is studied.


Abstract:
  As the data size in Machine Learning fields grows exponentially, it is
inevitable to accelerate the computation by utilizing the ever-growing large
number of available cores provided by high-performance computing hardware.
However, existing parallel methods for clustering or regression often suffer
from problems of low accuracy, slow convergence, and complex
hyperparameter-tuning. Furthermore, the parallel efficiency is usually
difficult to improve while striking a balance between preserving model
properties and partitioning computing workloads on distributed systems. In this
paper, we propose a novel and simple data structure capturing the most
important information among data samples. It has several advantageous
properties supporting a hierarchical clustering strategy that is irrelevant to
the hardware parallelism, well-defined metrics for determining optimal
clustering, balanced partition for maintaining the compactness property, and
efficient parallelization for accelerating computation phases. Then we combine
the clustering with regression techniques as a parallel library and utilize a
hybrid structure of data and model parallelism to make predictions. Experiments
illustrate that our library obtains remarkable performance on convergence,
accuracy, and scalability.


Abstract:
  MLOps is about taking experimental ML models to production, i.e., serving the
models to actual users. Unfortunately, existing ML serving systems do not
adequately handle the dynamic environments in which online data diverges from
offline training data, resulting in tedious model updating and deployment
works. This paper implements a lightweight MLOps plugin, termed ModelCI-e
(continuous integration and evolution), to address the issue. Specifically, it
embraces continual learning (CL) and ML deployment techniques, providing
end-to-end supports for model updating and validation without serving engine
customization. ModelCI-e includes 1) a model factory that allows CL researchers
to prototype and benchmark CL models with ease, 2) a CL backend to automate and
orchestrate the model updating efficiently, and 3) a web interface for an ML
team to manage CL service collaboratively. Our preliminary results demonstrate
the usability of ModelCI-e, and indicate that eliminating the interference
between model updating and inference workloads is crucial for higher system
efficiency.


Abstract:
  Implicit expressability was introduced by A.V. Kuznetsov in 1979 as
generalization of functional expressability. Set of functions is called
implicitly complete if any function has an implicit representation over this
set. The system of all implicitly maximal classes in three-valued logic is
described. The implicit completeness criterion is stated.


Abstract:
  Kinase-targeted drug design is challenging. It requires designing inhibitors
that can bind to specific kinases when all kinase catalytic domains share a
common folding scaffold that binds ATP. Thus, obtaining the desired
selectivity, given the whole human kinome, is a fundamental task during
early-stage drug discovery. This begins with deciphering the kinase-ligand
characteristics, analyzing the structure-activity relationships, and
prioritizing the desired drug molecules across the whole kinome. Currently,
there are more than 300 kinases with released PDB structures, which provides a
substantial structural basis to gain these necessary insights. Here, we review
in silico structure-based methods - notably, a function-site interaction
fingerprint approach used in exploring the complete human kinome. In silico
methods can be explored synergistically with multiple cell-based or
protein-based assay platforms such as KINOMEscan. We conclude with new drug
discovery opportunities associated with kinase signaling networks and using
machine/deep learning techniques broadly referred to as structural biomedical
data science.


Abstract:
  The $p$-Laplacian evolution equation in metric measure spaces has been
studied as the gradient flow in $L^2$ of the $p$-Cheeger energy (for $1 < p <
\infty$). In this paper, using the first-order differential structure on a
metric measure space introduced by Gigli, we characterize the subdifferential
in $L^2$ of the $p$-Cheeger energy. This gives rise to a new definition of the
$p$-Laplacian operator in metric measure spaces, which allows us to work with
this operator in more detail. In this way, we introduce a new notion of
solutions to the $p$-Laplacian evolution equation in metric measure spaces. For
$p = 1$, we obtain a Green-Gauss formula similar to the one by Anzellotti for
Euclidean spaces, and use it to characterise the $1$-Laplacian operator and
study the total variation flow. We also study the asymptotic behaviour of the
solutions of the $p$-Laplacian evolution equation, showing that for $1 \leq p <
2$ we have finite extinction time.


Abstract:
  Airlines and other industries have been making use of sophisticated Revenue
Management Systems to maximize revenue for decades. While improving the
different components of these systems has been the focus of numerous studies,
estimating the impact of such improvements on the revenue has been overlooked
in the literature despite its practical importance. Indeed, quantifying the
benefit of a change in a system serves as support for investment decisions.
This is a challenging problem as it corresponds to the difference between the
generated value and the value that would have been generated keeping the system
as before. The latter is not observable. Moreover, the expected impact can be
small in relative value. In this paper, we cast the problem as counterfactual
prediction of unobserved revenue. The impact on revenue is then the difference
between the observed and the estimated revenue. The originality of this work
lies in the innovative application of econometric methods proposed for
macroeconomic applications to a new problem setting. Broadly applicable, the
approach benefits from only requiring revenue data observed for
origin-destination pairs in the network of the airline at each day, before and
after a change in the system is applied. We report results using real
large-scale data from Air Canada. We compare a deep neural network
counterfactual predictions model with econometric models. They achieve
respectively 1% and 1.1% of error on the counterfactual revenue predictions,
and allow to accurately estimate small impacts (in the order of 2%).


Abstract:
  We study a competitive facility location problem (CFLP), in which two firms
sequentially select locations of new facilities, in order to maximize their
market shares of customer demand that follows a probabilistic choice model.
This process is a Stackelberg game and admits a bilevel mixed-integer nonlinear
program (MINLP) formulation. Through integer programming methods, we derive an
equivalent, single-level MINLP reformulation. In addition, we exploit the
problem structures and derive two classes of valid inequalities, one based on
submodularity and the other based on concave overestimation. We apply these
inequalities in a branch-and-cut algorithm to find a globally optimal solution
to CFLP. Furthermore, we propose an approximation algorithm for solving CFLP
that is computationally more effective. Notably, this algorithm admits a
constant approximation guarantee. Extensive numerical studies demonstrate that
the exact algorithm can significantly accelerate the solving of CFLP on problem
instances that have not been solved to optimality by existing methods. The
approximation algorithm can find near-optimal solutions even more quickly.


Abstract:
  In this paper we research a model of multilayer circuits with a single
logical layer. We consider $\lambda$-separable graphs as a support for
circuits. We establish the Shannon function lower bound $\max
\bigl(\frac{2^n}{n}, \frac{2^n (1 - \lambda)}{\log k} \bigr)$ for this type of
circuits where $k$ is the number of layers. For $d$-dimensional graphs, which
are $\lambda$-separable for $\lambda = \frac{d - 1}{d}$, this gives the Shannon
function lower bound $\frac{2^n}{\min(n, d \log k)}$. For multidimensional
rectangular circuits the proved lower bound asymptotically matches to the upper
bound.


Abstract:
  For the sake of recognizing and classifying textile defects, deep
learning-based methods have been proposed and achieved remarkable success in
single-label textile images. However, detecting multi-label defects in a
textile image remains challenging due to the coexistence of multiple defects
and small-size defects. To address these challenges, a multi-level,
multi-attentional deep learning network (MLMA-Net) is proposed and built to 1)
increase the feature representation ability to detect small-size defects; 2)
generate a discriminative representation that maximizes the capability of
attending the defect status, which leverages higher-resolution feature maps for
multiple defects. Moreover, a multi-label object detection dataset (DHU-ML1000)
in textile defect images is built to verify the performance of the proposed
model. The results demonstrate that the network extracts more distinctive
features and has better performance than the state-of-the-art approaches on the
real-world industrial dataset.


Abstract:
  Simulation-based Inference (SBI) is a widely used set of algorithms to learn
the parameters of complex scientific simulation models. While primarily run on
CPUs in HPC clusters, these algorithms have been shown to scale in performance
when developed to be run on massively parallel architectures such as GPUs.
While parallelizing existing SBI algorithms provides us with performance gains,
this might not be the most efficient way to utilize the achieved parallelism.
This work proposes a new algorithm, that builds on an existing SBI method -
Approximate Bayesian Computation with Sequential Monte Carlo(ABC-SMC). This new
algorithm is designed to utilize the parallelism not only for performance gain,
but also toward qualitative benefits in the learnt parameters. The key idea is
to replace the notion of a single 'step-size' hyperparameter, which governs how
the state space of parameters is explored during learning, with step-sizes
sampled from a tuned Beta distribution. This allows this new ABC-SMC algorithm
to more efficiently explore the state-space of the parameters being learnt. We
test the effectiveness of the proposed algorithm to learn parameters for an
epidemiology model running on a Tesla T4 GPU. Compared to the parallelized
state-of-the-art SBI algorithm, we get similar quality results in $\sim 100$x
fewer simulations and observe ~80x lower run-to-run variance across 10
independent trials.


Abstract:
  One-bit analog-to-digital converter (ADC), performing signal sampling as an
extreme simple comparator, is an overwhelming technology for spectrum sensing
due to its low-cost, low-power consumptions and high sampling rate. In this
letter, we propose a novel one-bit sensing approach based on the eigenvalue
moment ratio (EMR), which has been proved to be highly efficient for
conventional multi-antenna spectrum sensing in $\infty$-bit situation.
Particularly, we determine the asymptotic distribution of one-bit EMR under
null hypothesis via the central limited theorem (CLT), allowing us to perform
spectrum sensing with one-bit samples directly. Theoretical and simulation
analysis show the new approach can provide reasonably good sensing performance
at a low hardware cost.


Abstract:
  Compositional Zero-Shot learning (CZSL) requires to recognize state-object
compositions unseen during training. In this work, instead of assuming prior
knowledge about the unseen compositions, we operate in the open world setting,
where the search space includes a large number of unseen compositions some of
which might be unfeasible. In this setting, we start from the cosine similarity
between visual features and compositional embeddings. After estimating the
feasibility score of each composition, we use these scores to either directly
mask the output space or as a margin for the cosine similarity between visual
features and compositional embeddings during training. Our experiments on two
standard CZSL benchmarks show that all the methods suffer severe performance
degradation when applied in the open world setting. While our simple CZSL model
achieves state-of-the-art performances in the closed world scenario, our
feasibility scores boost the performance of our approach in the open world
setting, clearly outperforming the previous state of the art.


Abstract:
  Ensuring the safety and efficacy of Cyber-Physical Systems (CPSs) is
challenging due to the large variability of their operating environment. Model
checking has been proposed for validation of CPSs, but the models of the
environment are either too specific to capture the variability of the
environment, or too abstract to provide counter-examples interpretable by
experts in the application domain. Domain-specific solutions to this problem
require expertise in both formal methods and the application domain, which
prevents effective application of model checking in CPSs validation. A
domain-independent framework based on timed-automata is proposed for
abstraction and refinement of environment models during model checking. The
framework maintains an abstraction tree of environment models, which provides
interpretable counter-examples while ensuring coverage of environment
behaviors. With the framework, experts in the application domain can
effectively use model checking without expertise in formal methods.


Abstract:
  The hidden symmetry of the asymmetric quantum Rabi model (AQRM) with a
half-integral bias (ibQRM$_{\ell}$) was uncovered in recent studies by the
explicit construction of operators $J_\ell$ commuting with the Hamiltonian. The
existence of such symmetry has been widely believed to cause the degeneration
of the spectrum, that is, the crossings on the energy curves. In this paper we
propose a conjectural relation between the symmetry and degeneracy for the
ibQRM$_{\ell}$ given explicitly in terms of two polynomials appearing
independently in the respective investigations. Concretely, one of the
polynomials appears as the quotient of the constraint polynomials that assure
the existence of degenerate solutions while the other determines a quadratic
relation (in general, it defines a curve of hyperelliptic type) between the
ibQRM$_{\ell}$ Hamiltonian and its basic commuting operator $J_\ell$. Following
this conjecture, we derive several interesting structural insights of the whole
spectrum. For instance, the energy curves are naturally shown to lie on a
surface determined by the family of hyperelliptic curves by considering the
coupling constant as a variable. This geometric picture contains the
generalization of the parity decomposition of the symmetric quantum Rabi model.
Moreover, it allows us to describe a remarkable approximation of the first
$\ell$ energy curves by the zero-section of the corresponding hyperelliptic
curve. These investigations naturally lead to a geometric picture of the
(hyper-)elliptic surfaces given by the Kodaira-N\'eron type model for a family
of curves over the projective line in connection with the energy curves, which
may be expected to provide a complex analytic proof of the conjecture.


Abstract:
  Scheduling the transmission of time-sensitive information from a source node
to multiple users over error-prone communication channels is studied with the
goal of minimizing the long-term average age of information (AoI) at the users.
A long-term average resource constraint is imposed on the source, which limits
the average number of transmissions. The source can transmit only to a single
user at each time slot, and after each transmission, it receives an
instantaneous ACK/NACK feedback from the intended receiver, and decides when
and to which user to transmit the next update. Assuming the channel statistics
are known, the optimal scheduling policy is studied for both the standard
automatic repeat request (ARQ) and hybrid ARQ (HARQ) protocols. Then, a
reinforcement learning(RL) approach is introduced to find a near-optimal
policy, which does not assume any a priori information on the random processes
governing the channel states. Different RL methods including average-cost
SARSAwith linear function approximation (LFA), upper confidence reinforcement
learning (UCRL2), and deep Q-network (DQN) are applied and compared through
numerical simulations


Abstract:
  Several phenomena are known to attenuate waves in the marginal ice zone, such
as wave scattering due to ice floes, viscous dissipation inside the ice and
energy dissipation caused by ice floe interactions. Our aim of this study is to
investigate colliding ice floes and to quantify the Turbulent Kinetic Energy
(TKE) dissipation in the surrounding water through direct observations. The
field work was carried out in Van Mijen Fjord on Svalbard, where an artificial
3x4 m^2 ice floe was sawed out in the 1 m thick ice. Wave motion was simulated
by pulling the ice floe back and forth in an oscillatory manner with two
electrical winches in a 4x6 m^2 pool. Ice floe motion was measured with a range
meter, and the water turbulence was measured with acoustic velocimeters. TKE
frequency spectra were found to contain an inertial subrange where energy was
cascading at a rate proportional to f^(-5/3). From the spectra estimated at
several vertical positions, the TKE dissipation rate was found to decrease
exponentially with depth. The total TKE dissipation rate was estimated by
assuming that turbulence was induced over an area spanning over the ice floe
width and the pool length. The preliminary energy budget suggests that
approximately 60% of the input power from the winches was dissipated in
turbulence, which experimentally confirms that energy dissipation by induced
turbulent water motion is an important mechanism for colliding ice floe fields.


Abstract:
  We consider the problem of bridging the gap between geometric tracking
control theory and implementation of model predictive control (MPC) for robotic
systems operating on manifolds. We propose a generic on-manifold MPC
formulation based on a canonical representation of the system evolving on
manifolds. Then, we present a method that solves the on-manifold MPC
formulation by linearizing the system along the trajectory under tracking.
There are two main advantages of the proposed scheme. The first is that the
linearized system leads to an equivalent error system represented by a set of
minimal parameters without any singularity. Secondly, the process of system
modeling, error-system derivation, linearization and control has the manifold
constraints completely decoupled from the system descriptions, enabling the
development of a symbolic MPC framework that naturally encapsulates the
manifold constraints. In this framework, users need only to supply
system-specific descriptions without dealing with the manifold constraints. We
implement this framework and test it on a quadrotor unmanned aerial vehicle
(UAV) operating on $SO(3) \times \mathbb{R}^n$ and an unmanned ground vehicle
(UGV) moving on a curved surface. Real-world experiments show that the proposed
framework and implementation achieve high tracking performance and
computational efficiency even in highly aggressive aerobatic quadrotor
maneuvers.


Abstract:
  Communication of model updates between client nodes and the central
aggregating server is a major bottleneck in federated learning, especially in
bandwidth-limited settings and high-dimensional models. Gradient quantization
is an effective way of reducing the number of bits required to communicate each
model update, albeit at the cost of having a higher error floor due to the
higher variance of the stochastic gradients. In this work, we propose an
adaptive quantization strategy called AdaQuantFL that aims to achieve
communication efficiency as well as a low error floor by changing the number of
quantization levels during the course of training. Experiments on training deep
neural networks show that our method can converge in much fewer communicated
bits as compared to fixed quantization level setups, with little or no impact
on training and test accuracy.