Abstract:
  Random operators constitute fundamental building blocks of models of complex
systems yet are far from fully understood. Here, we explain an asymmetry
emerging upon repeating identical isotropic (uniformly random) operations.
Specifically, in two dimensions, repeating an isotropic rotation twice maps a
given point on the two-dimensional unit sphere (the unit circle) uniformly at
random to any point on the unit sphere, reflecting a statistical symmetry as
expected. In contrast, in three and higher dimensions, a point is mapped more
often closer to the original point than a uniform distribution predicts.
Curiously, in the limit of the dimension $d \rightarrow \infty$, a symmetric
distribution is approached again. We intuitively explain the emergence of this
asymmetry and why it disappears in higher dimensions by disentangling isotropic
rotations into a sequence of partial actions. The asymmetry emerges in two
qualitatively different forms and for a wide range of general random operations
relevant in complex systems modeling, including repeated continuous and
discrete rotations, roto-reflections and general orthogonal transformations


Abstract:
  Powerful deep learning algorithms open an opportunity for solving non-image
Machine Learning (ML) problems by transforming these problems to into the image
recognition problems. The CPC-R algorithm presented in this chapter converts
non-image data into images by visualizing non-image data. Then deep learning
CNN algorithms solve the learning problems on these images. The design of the
CPC-R algorithm allows preserving all high-dimensional information in 2-D
images. The use of pair values mapping instead of single value mapping used in
the alternative approaches allows encoding each n-D point with 2 times fewer
visual elements. The attributes of an n-D point are divided into pairs of its
values and each pair is visualized as 2-D points in the same 2-D Cartesian
coordinates. Next, grey scale or color intensity values are assigned to each
pair to encode the order of pairs. This is resulted in the heatmap image. The
computational experiments with CPC-R are conducted for different CNN
architectures, and methods to optimize the CPC-R images showing that the
combined CPC-R and deep learning CNN algorithms are able to solve non-image ML
problems reaching high accuracy on the benchmark datasets. This chapter expands
our prior work by adding more experiments to test accuracy of classification,
exploring saliency and informativeness of discovered features to test their
interpretability, and generalizing the approach.


Abstract:
  We study the spectrum of certain two-particle operators in the supergravity
regime of the D1-D5 system, focussing on the four-point correlators of tensor
multiplets on $AdS_3\times S^3$ at tree level. In Mellin space, these are
nicely determined by a single amplitude, which makes manifest the large $p$
limit, the connection with the flat space S-matrix, and the six dimensional
conformal symmetry. We compute the $(1,1)\times \overline{(1,1)}$
superconformal blocks for the two-dimensional $\mathcal{N}=(4,4)$ conformal
theory at the boundary, and then we obtain a formula for the anomalous
dimensions of the operators exchanged in the symmetric and anti-symmetric
flavor channels. These anomalous dimensions solve an unmixing problem which is
analogous to the one in $AdS_5\times S^5$, with interesting modifications.
Along the way we show how the $(1,1)\times \overline{(1,1)}$ superconformal
blocks relate to those in $\mathcal{N}=4$ SYM in four dimensions, and we
provide new intuition on the known data for $AdS_5\times S^5$.


Abstract:
  Primordial black holes as a viable candidate of cold dark matter are
clustering in the large-scale structures of the Universe. The stochastic
gravitational-wave background, arising from an incoherent superposition of
unresolved gravitational waves from primordial black hole binaries, is expected
to display anisotropies across the sky. In this work, we obtain the angular
power spectrum for such anisotropies for the first time and the difference
between it and the one from astrophysical black hole binaries. When multi-band
measurements are employed, the angular power spectrum can be a smoking gun for
the existence of primordial black holes, in particular those of stellar masses
that otherwise are challenging to be distinguished from the astrophysical black
holes.


Abstract:
  Although writing code seems trivial at times, problems arise when humans
misinterpret what the code actually does. One of the potential causes are
"atoms of confusion", the smallest possible patterns of misinterpretable source
code. Previous research has investigated the impact of atoms of confusion in C
code. Results show that developers make significantly more mistakes in code
where atoms are present. In this paper, we replicate the work of Gopstein et
al. to the Java language. After deriving a set of atoms of confusion for Java,
we perform a two-phase experiment with 132 computer science students (i.e.,
novice developers). Our results show that participants are 2.7 up to 56 times
more likely to make mistakes in code snippets affected by 7 out of the 14
studied atoms of confusion, and when faced with both versions of the code
snippets, participants perceived the version affected by the atom of confusion
to be more confusing and/or less readable in 10 out of the 14 studied atoms of
confusion.


Abstract:
  Answering a question of Dan Haran and generalizing some results of
Aschbacher-Guralnick and Suzuki, we prove that given a set of primes pi, any
finite group can be generated by a pi-subgroup and a pi'-subgroup. This gives a
free product description of a countably generated free profinite group.


Abstract:
  Butt rot (BR) damages associated with Norway spruce (Picea abies [L.] Karst.)
account for considerable economic losses in timber production across the
northern hemisphere. While information on BR damages is critical for optimal
decision-making in forest management, the maps of BR damages are typically
lacking in forest information systems. We predicted timber volume damaged by BR
at the stand-level in Norway using harvester information of 186,026 stems
(clear-cuts), remotely sensed, and environmental data (e.g. climate and terrain
characteristics). We utilized random forest (RF) models with two sets of
predictor variables: (1) predictor variables available after harvest
(theoretical case) and (2) predictor variables available prior to harvest
(mapping case). We found that forest attributes characterizing the maturity of
forest, such as remote sensing-based height, harvested timber volume and
quadratic mean diameter at breast height, were among the most important
predictor variables. Remotely sensed predictor variables obtained from airborne
laser scanning data and Sentinel-2 imagery were more important than the
environmental variables. The theoretical case with a leave-stand-out
cross-validation achieved an RMSE of 11.4 $m^3ha^{-1}$ (pseudo $R^2$: 0.66)
whereas the mapping case resulted in a pseudo $R^2$ of 0.60. When the spatially
distinct k-means clusters of harvested forest stands were used as units in the
cross-validation, the RMSE value and pseudo $R^2$ associated with the mapping
case were 15.6 $m^3ha^{-1}$ and 0.37, respectively. This indicates that the
knowledge about the BR status of spatially close stands is of high importance
for obtaining satisfactory error rates in the mapping of BR damages.


Abstract:
  We report that Ly$\alpha$-emitting galaxies (LAEs) may not faithfully trace
the cosmic web of neutral hydrogen (HI), but their distribution is likely
biased depending on the viewing direction. We calculate the cross-correlation
(CCF) between galaxies and Ly$\alpha$ forest transmission fluctuations on the
near and far sides of the galaxies separately, for three galaxy samples at
$z\sim2$: LAEs, [OIII] emitters (O3Es), and continuum-selected galaxies. We
find that only LAEs have anisotropic CCFs, with the near side one showing lower
signals up to $r=3-4~h^{-1}$ comoving Mpc. This means that the average HI
density on the near side of LAEs is lower than that on the far-side by a factor
of $2.1$ under the Fluctuating Gunn-Peterson Approximation. Mock LAEs created
by assigning Ly$\alpha$ equivalent width ($EW_\text{Ly$\alpha$}^\text{obs}$)
values to O3Es with an empirical relation also show similar, anisotropic CCFs
if we use only objects with higher $EW_\text{Ly$\alpha$}^\text{obs}$ than a
certain threshold. These results indicate that galaxies on the far side of a
dense region are more difficult to be detected ("hidden") in Ly$\alpha$ because
Ly$\alpha$ emission toward us is absorbed by dense neutral hydrogen. If the
same region is viewed from a different direction, a different set of LAEs will
be selected as if galaxies are playing hide-and-seek using HI gas. Care is
needed when using LAEs to search for overdensities.


Abstract:
  For all 4d $\mathcal{N} = 4$ SYM theories with simple gauge groups $G$, we
show that the residues of the integrands in the $\mathcal{N} = 4$ Schur
indices, which are related to Gukov-Witten type surface defects in the
theories, equal the vacuum characters of rank$G$ copies of $bc \beta \gamma$
systems that provide the free field realization of associated $\mathcal{N} = 4$
VOAs. This result predicts that these residues, as module characters, are
additional solutions to the flavored modular differential equations satisfied
by the original Schur index. The prediction is verified in the $G = SU(2)$
case, where an additional logarithmic solution is constructed.


Abstract:
  We present an up-to-date complete model-independent global fit to $b\to
s\ell^+\ell^-$ observables that confirms patterns of New Physics able to
explain the data. We include the recent LHCb measurements of $R_K$, $R_{K_S}$,
$R_{K^{*+}}$, $B_s \to \phi\mu^+\mu^-$ and $B_s\to\mu\mu$ in our analysis,
which now includes 254 observables. This updates our previous analyses and
strengthens their two main outcomes. First, the presence of right-handed
couplings encoded in the Wilson coefficients $\mathcal{C}_{9'\mu}$ and
$\mathcal{C}_{10'\mu}$ remains a viable possibility. Second, a lepton flavour
universality violating (LFUV) left-handed lepton coupling
($\mathcal{C}_{9\mu}^{\rm V}=-\mathcal{C}_{10\mu}^{\rm V}$), often preferred
from the model building point of view, accommodates the data better if
lepton-flavour universal New Physics is allowed, in particular in
$\mathcal{C}_{9}^{\rm U}$. We observe that the LFUV observable $Q_5$ offers a
very interesting possibility to separate both types of scenarios.


Abstract:
  The new paradigm of human-robot collaboration has led to the creation of
shared work environments in which humans and robots work in close contact with
each other. Consequently, the safety regulations have been updated addressing
these new scenarios. The mere application of these regulations may lead to a
very inefficient behavior of the robot. In order to preserve safety for the
human operators and allow the robot to reach a desired configuration in a safe
and efficient way, a two layers architecture for trajectory planning and
scaling is proposed. The first layer calculates the nominal trajectory and
continuously adapts it based on the human behavior. The second layer, which
explicitly considers the safety regulations, scales the robot velocity and
requests for a new trajectory if the robot speed drops. The proposed
architecture is experimentally validated on a Pilz PRBT manipulator.


Abstract:
  Microarray studies, in order to identify genes associated with an outcome of
interest, usually produce noisy measurements for a large number of gene
expression features from a small number of subjects. One common approach to
analyzing such high-dimensional data is to use linear errors-in-variables
models; however, current methods for fitting such models are computationally
expensive. In this paper, we present two efficient screening procedures, namely
corrected penalized marginal screening and corrected sure independence
screening, to reduce the number of variables for final model building. Both
screening procedures are based on fitting corrected marginal regression models
relating the outcome to each contaminated covariate separately, which can be
computed efficiently even with a large number of features. Under mild
conditions, we show that these procedures achieve screening consistency and
reduce the number of features considerably, even when the number of covariates
grows exponentially with the sample size. Additionally, if the true covariates
are weakly correlated, corrected penalized marginal screening can achieve full
variable selection consistency. Through simulation studies and an analysis of
gene expression data for bone mineral density of Norwegian women, we
demonstrate that the two new screening procedures make estimation of linear
errors-in-variables models computationally scalable in high dimensional
settings, and improve finite sample estimation and selection performance
compared with estimators that do not employ a screening stage.


Abstract:
  GRB 180720B, observed by {\it Fermi}-GBM, with redshift $z=0.653$, isotropic
energy $E_{\rm iso}=5.92\times 10^{53}$ erg, and X-ray afterglow observed by
the XRT onboard the Neil Gehrels Swift satellite, is here classified as a
Binary-driven Hypernova I (BdHN I). BdHN I are long GRBs with a binary
progenitor composed of a carbon-oxygen core (CO$_{\rm core}$) and a neutron
star (NS) companion with orbital period $\sim 5$ min. The gravitational
collapse of the CO$_{\rm core}$ generates a supernova (SN) and a new NS
($\nu$NS) at its center. The SN hypercritical accretion onto the companion NS
triggers its gravitational collapse forming a black hole (BH). An
electrodynamical process near the BH horizon leads to the long-lasting GeV
emission with power-law luminosity $L_{\rm GeV}\propto t^{-1.19\pm0.04}$,
powered by the BH rotational energy. Correspondingly, we determine the BH mass
and spin. The $\nu$NS via its pulsar-like emission and fallback accretion
injects energy into the magnetized SN ejecta generating synchrotron radiation.
This explains the \textit{long-lasting} X-ray afterglow with power-law
luminosity $L_X \propto t^{-1.48\pm 0.32}$, energized by the $\nu$NS rotational
energy. We apply this to GRB 180720B determining the $\nu$NS magnetic field and
spin. We also analyze the GRB 180720B emission observed by the High-Energy
Stereoscopic System (H.E.S.S.), at $100$-$440$ GeV energies, $10.1$-$12.1$ h
after the Fermi-GBM trigger. We propose that this \textit{short-term} radiation
of energy $2.4\times 10^{50}$ erg and duration $\sim 10^3$ s, is powered by a
"glitch" event that suddenly injects relativistic electrons into the $\nu$NS
magnetosphere during its slowing down phase.


Abstract:
  It is demonstrated that a constant magnetic moment does not emit
electo-magnetic radiation while moving in an arbitrary field


Abstract:
  We present Fourier-space based methods to calculate the electronic structure
of wurtzite quantum dot systems with continuous alloy profiles. We incorporate
spatially varying elastic and dielectric constants in strain and piezoelectric
potential calculations. A method to incorporate smooth alloy profiles in all
aspects of the calculations is presented. We demonstrate our methodology for
the case of a 1D InGaN quantum dot array and show the importance of including
these spatially varying parameters in the modeling of devices. We demonstrate
that convergence of the lowest bound state energies is to good approximation
determined by the largest wave vector used in constructing the states. We also
present a novel approach of coupling strain into the
$\mathbf{k}\cdot\mathbf{p}$ Hamiltonian, greatly reducing the computational
cost of generating the Hamiltonian.


Abstract:
  Image-to-image (I2I) translation methods based on generative adversarial
networks (GANs) typically suffer from overfitting when limited training data is
available. In this work, we propose a data augmentation method (ReMix) to
tackle this issue. We interpolate training samples at the feature level and
propose a novel content loss based on the perceptual relations among samples.
The generator learns to translate the in-between samples rather than memorizing
the training set, and thereby forces the discriminator to generalize. The
proposed approach effectively reduces the ambiguity of generation and renders
content-preserving results. The ReMix method can be easily incorporated into
existing GAN models with minor modifications. Experimental results on numerous
tasks demonstrate that GAN models equipped with the ReMix method achieve
significant improvements.


Abstract:
  Artificial neural networks have achieved great success in many fields ranging
from image recognition to video understanding. However, its high requirements
for computing and memory resources have limited further development on
processing big data with high dimensions. In recent years, advances in quantum
computing show that building neural networks on quantum processors is a
potential solution to this problem. In this paper, we propose a novel neural
network model named Quantum Convolutional Neural Network (QCNN), aiming at
utilizing the computing power of quantum systems to accelerate classical
machine learning tasks. The designed QCNN is based on implementable quantum
circuits and has a similar structure as classical convolutional neural
networks. Numerical simulation results on the MNIST dataset demonstrate the
effectiveness of our model.


Abstract:
  In this paper, we demonstrate the generation of high-performance entangled
photon-pairs in different degrees of freedom from a single piece of fiber
pigtailed periodically poled LiNbO$_3$ (PPLN) waveguide. We utilize cascaded
second-order nonlinear optical processes, i.e. second-harmonic generation (SHG)
and spontaneous parametric down conversion (SPDC), to generate photon-pairs.
Previously, the performance of the photon pairs is contaminated by Raman noise
photons from the fiber pigtails. Here by integrating the PPLN waveguide with
noise rejecting filters, we obtain a coincidence-to-accidental ratio (CAR)
higher than 52,600 with photon-pair generation and detection rate of 52.3 kHz
and 3.5 kHz, respectively. Energy-time, frequency-bin and time-bin entanglement
is prepared by coherently superposing correlated two-photon states in these
degrees of freedom, respectively. The energy-time entangled two-photon states
achieve the maximum value of CHSH-Bell inequality of S=2.708$\pm$0.024 with a
two-photon interference visibility of 95.74$\pm$0.86%. The frequency-bin
entangled two-photon states achieve fidelity of 97.56$\pm$1.79% with a spatial
quantum beating visibility of 96.85$\pm$2.46%. The time-bin entangled
two-photon states achieve the maximum value of CHSH-Bell inequality of
S=2.595$\pm$0.037 and quantum tomographic fidelity of 89.07$\pm$4.35%. Our
results provide a potential candidate for quantum light source in quantum
photonics.


Abstract:
  Quantum coherence, like entanglement, is a fundamental resource in quantum
information. In recent years, remarkable progress has been made in formulating
resource theory of coherence from a broader perspective. The notions of
block-coherence and POVM-based coherence have been established. Certain
challenges, however, remain to be addressed. It is difficult to define
incoherent operations directly, without requiring incoherent states, which
proves a major obstacle in establishing the resource theory of dynamical
coherence. In this paper, we overcome this limitation by introducing an
alternate definition of incoherent operations, induced via coherence measures,
and quantify dynamical coherence based on this definition. Finally, we apply
our proposed definition to quantify POVM-based dynamical coherence.


Abstract:
  In causal bandit problems, the action set consists of interventions on
variables of a causal graph. Several researchers have recently studied such
bandit problems and pointed out their practical applications. However, all
existing works rely on a restrictive and impractical assumption that the
learner is given full knowledge of the causal graph structure upfront. In this
paper, we develop novel causal bandit algorithms without knowing the causal
graph. Our algorithms work well for causal trees, causal forests and a general
class of causal graphs. The regret guarantees of our algorithms greatly improve
upon those of standard multi-armed bandit (MAB) algorithms under mild
conditions. Lastly, we prove our mild conditions are necessary: without them
one cannot do better than standard MAB algorithms.


Abstract:
  Two-dimensional, resonant scanners have been utilized in a large variety of
imaging modules due to their compact form, low power consumption, large angular
range, and high speed. However, resonant scanners have problems with
non-optimal and inflexible scanning patterns and inherent phase uncertainty,
which limit practical applications. Here we propose methods for optimized
design and control of the scanning trajectory of two-dimensional resonant
scanners under various physical constraints, including high frame-rate and
limited actuation amplitude. First, we propose an analytical design rule for
uniform spatial sampling. We demonstrate theoretically and experimentally that
by including non-repeating scanning patterns, the proposed designs outperform
previous designs in terms of scanning range and fill factor. Second, we show
that we can create flexible scanning patterns that allow focusing on
user-defined Regions-of-Interest (RoI) by modulation of the scanning
parameters. The scanning parameters are found by an optimization algorithm. In
simulations, we demonstrate the benefits of these designs with standard metrics
and higher-level computer vision tasks (LiDAR odometry and 3D object
detection). Finally, we experimentally implement and verify both unmodulated
and modulated scanning modes using a two-dimensional, resonant MEMS scanner.
Central to the implementations is high bandwidth monitoring of the phase of the
angular scans in both dimensions. This task is carried out with a
position-sensitive photodetector combined with high-bandwidth electronics,
enabling fast spatial sampling at ~ 100Hz frame-rate.


Abstract:
  We give a simple, low resource method to produce order embeddings from
ontologies. Such embeddings map words to vectors so that order relations on the
words, such as hypernymy/hyponymy, are represented in a direct way. Our method
uses sketching techniques, in particular countsketch, for dimensionality
reduction. We also study methods to merge ontologies, in particular those in
medical domains, so that order relations are preserved. We give computational
results for medical ontologies and for wordnet, showing that our merging
techniques are effective and our embedding yields an accurate representation in
both generic and specialised domains.


Abstract:
  With information technology entering new fields and levels of deployment,
e.g., in areas of energy, mobility, and production, network security monitoring
needs to be able to cope with those environments and their evolution. However,
state-of-the-art Network Security Monitors (NSMs) typically lack the necessary
flexibility to handle the diversity of the packet-oriented layers below the
abstraction of TCP/IP connections. In this work, we advance the software
architecture of a network security monitor to facilitate the flexible
integration of lower-layer protocol dissectors while maintaining required
performance levels. We proceed in three steps: First, we identify the
challenges for modular packet-level analysis, present a refined NSM
architecture to address them and specify requirements for its implementation.
Second, we evaluate the performance of data structures to be used for protocol
dispatching, implement the proposed design into the popular open-source NSM
Zeek and assess its impact on the monitor performance. Our experiments show
that hash-based data structures for dispatching introduce a significant
overhead while array-based approaches qualify for practical application.
Finally, we demonstrate the benefits of the proposed architecture and
implementation by migrating Zeek's previously hard-coded stack of link and
internet layer protocols to the new interface. Furthermore, we implement
dissectors for non-IP based industrial communication protocols and leverage
them to realize attack detection strategies from recent applied research. We
integrate the proposed architecture into the Zeek open-source project and
publish the implementation to support the scientific community as well as
practitioners, promoting the transfer of research into practice.


Abstract:
  We present an experimental study of the magnetic structure and dynamics of
two frustrated hyperkagome compounds, Gd3Ga5O12 and Gd3Al5O12. It has
previously been shown that Gd3Ga5O12 exhibits long-range correlations of
multipolar directors, that are formed from antiferromagnetic spins on loops of
ten ions. Using neutron diffraction and Reverse Monte Carlo simulations we
prove the existence of similar magnetic correlations in Gd3Al5O12, showing the
ubiquity of these complex structures in frustrated hyperkagome materials.
  Using inelastic neutron scattering we shed further light on the director
state and the associated low lying magnetic excitations. In addition we have
measured quasielastic dynamics that show evidence of spin diffusion. Finally,
we present AC susceptibility measurements on both Gd3Ga5O12 and Gd3Al5O12,
revealing a large difference in the low frequency dynamics between the two
otherwise similar compounds.


Abstract:
  We revisit the formulation of non-relativistic (NR) string theory and its
target space geometry. We obtain a new formulation in which the geometry
contains a two-form field that couples to the tension current and that
transforms under string Galilei boosts. This parallels the Newton-Cartan
one-form that couples to the mass current of a non-relativistic point particle.
We show how this formulation of the NR string arises both from an infinite
speed of light limit and a null reduction of the relativistic closed bosonic
string. In both cases, the two-form originates from a combination of metric
quantities and the Kalb-Ramond field. The target space geometry of the NR
string is seen to arise from the gauging of a new algebra that is obtained by
an Inonu-Wigner contraction of the Poincar\'e algebra extended by the
symmetries of the Kalb-Ramond field. In this new formulation, there are no
superfluous target space fields that can be removed by fixing a St\"uckelberg
symmetry. Classically, there are no foliation/torsion constraints imposed on
the target space geometry.


Abstract:
  Application of the radial velocity (RV) technique in the near infrared is
valuable because of the diminished impact of stellar activity at longer
wavelengths, making it particularly advantageous for the study of late-type
stars but also for solar-type objects. In this paper, we present the IGRINS RV
open source python pipeline for computing infrared RV measurements from reduced
spectra taken with IGRINS, a R ~ 45,000 spectrograph with simultaneous coverage
of the H band (1.49--1.80 $\mu$m) and K band (1.96--2.46 $\mu$m). Using a
modified forward modeling technique, we construct high resolution telluric
templates from A0 standard observations on a nightly basis to provide a source
of common-path wavelength calibration while mitigating the need to mask or
correct for telluric absorption. Telluric standard observations are also used
to model the variations in instrumental resolution across the detector,
including a yearlong period when the K band was defocused. Without any
additional instrument hardware, such as a gas cell or laser frequency comb, we
are able to achieve precisions of 26.8 $\rm m\,s^{-1}$ in the K band and 31.1
$\rm m\,s^{-1}$ in the H band for narrow-line hosts. These precisions are
empirically determined by a monitoring campaign of two RV standard stars as
well as the successful retrieval of planet-induced RV signals for both HD
189733 and $\tau$ Boo A; furthermore, our results affirm the presence of the
Rossiter-McLaughlin effect for HD 189733. The IGRINS RV pipeline extends
another important science capability to IGRINS, with publicly available
software designed for widespread use.


Abstract:
  The paper introduces the concept of complex frequency. The imaginary part of
the complex frequency is the variation with respect of a synchronous reference
of the local bus frequency as commonly defined in power system studies. The
real part is defined based on the variation of the voltage magnitude. The
latter term is crucial for the correct interpretation and analysis of the
variation of the frequency at each bus of the network. The paper also develops
a set of differential equations that describe the link between complex powers
and complex frequencies at network buses in transient conditions. No
simplifications are assumed except for constant elements of the network
admittance matrix. A variety of analytical and numerical examples show the
applications and potentials of the proposed concept.


Abstract:
  Within the context of autonomous driving, safety-related metrics for deep
neural networks have been widely studied for image classification and object
detection. In this paper, we further consider safety-aware correctness and
robustness metrics specialized for semantic segmentation. The novelty of our
proposal is to move beyond pixel-level metrics: Given two images with each
having N pixels being class-flipped, the designed metrics should, depending on
the clustering of pixels being class-flipped or the location of occurrence,
reflect a different level of safety criticality. The result evaluated on an
autonomous driving dataset demonstrates the validity and practicality of our
proposed methodology.


Abstract:
  We present a statistical mechanical description of randomly packed spherical
particles, where the average coordination number is treated as a macroscopic
thermodynamic variable. The overall packing entropy is shown to have two
contributions: geometric, reflecting statistical weights of individual
configurations, and topological, which corresponds to the number of
topologically distinct states. Both of them are computed in the thermodynamic
limit for isostatic packings in 2D and 3D, and the result is further expanded
to the case of "floppy" particle clusters. The theory is directly applicable to
sticky colloids, and in addition, generalizes concepts of granular and glassy
configurational entropies for the case of non-jammed systems.


Abstract:
  Encoding textural content remains a challenge for current standardised video
codecs. It is therefore beneficial to understand video textures in terms of
both their spatio-temporal characteristics and their encoding statistics in
order to optimize encoding performance. In this paper, we analyse the
spatio-temporal features and statistics of video textures, explore the
rate-quality performance of different texture types and investigate models to
mathematically describe them. For all considered theoretical models, we employ
machine-learning regression to predict the rate-quality curves based solely on
selected spatio-temporal features extracted from uncompressed content. All
experiments were performed on homogeneous video textures to ensure validity of
the observations. The results of the regression indicate that using an
exponential model we can more accurately predict the expected rate-quality
curve (with a mean Bj{\o}ntegaard Delta rate of 0.46% over the considered
dataset) while maintaining a low relative complexity. This is expected to be
adopted by in the loop processes for faster encoding decisions such as
rate-distortion optimisation, adaptive quantization, partitioning, etc.


Abstract:
  We develop a notion of formal groups in the filtered setting and describe a
duality relating these to a specified class of filtered Hopf algebras. We then
study a deformation to the normal cone construction in the setting of derived
algebraic geometry. Applied to the unit section of a formal group
$\widehat{\mathbb{G}}$, this provides a $\mathbb{G}_m$-equivariant degeneration
of $\widehat{\mathbb{G}}$ to its tangent Lie algebra. We prove a unicity result
on complete filtrations, which, in particular, identifies the resulting
filtration on the coordinate algebra of this deformation with the adic
filtration on the coordinate algebra of $\widehat{\mathbb{G}}$. We use this in
a special case, together with the aforementioned notion of Cartier duality, to
recover the filtration on the filtered circle of [MRT19]. Finally, we
investigate some properties of $\widehat{\mathbb{G}}$-Hochschild homology set
out in loc. cit., and describe "lifts" of these invariants to the setting of
spectral algebraic geometry.


Abstract:
  The origin of the Chicxulub impactor, which is attributed as the cause of the
K/T mass extinction event, is an unsolved puzzle. The background impact rates
of main-belt asteroids and long-period comets have been previously dismissed as
being too low to explain the Chicxulub impact event. Here, we show that a
fraction of long-period comets are tidally disrupted after passing close to the
Sun, each producing a collection of smaller fragments that cross the orbit of
Earth. This population could increase the impact rate of long-period comets
capable of producing Chicxulub impact events by an order of magnitude. This new
rate would be consistent with the age of the Chicxulub impact crater, thereby
providing a satisfactory explanation for the origin of the impactor. Our
hypothesis explains the composition of the largest confirmed impact crater in
Earth's history as well as the largest one within the last million years. It
predicts a larger proportion of impactors with carbonaceous chondritic
compositions than would be expected from meteorite falls of main-belt
asteroids.


Abstract:
  Within SU(2) Yang-Mills theory with a source of the non-Abelian gauge field
in the form of a classical spinor field, we study the dependence of the mass
gap on the coupling constant between the gauge and nonlinear spinor fields. It
is shown that the total dimensionless energy of the monopole interacting with
the nonlinear spinor fields depends only on the dimensionless coupling
constant.


Abstract:
  This review summarizes recent progress in investigating polymer systems by
using Differential dynamic microscopy (DDM), a rapidly emerging approach that
transforms a commercial microscope by combining real-space information with the
powerful capabilities of conventional light scattering analysis. DDM analysis
of a single microscope movie gives access to the sample dynamics in a wide
range of scattering wave-vectors, enabling contemporary polymer science
experiments that would be difficult or impossible with standard light
scattering techniques. Examples of application include the characterization of
polymer solutions and networks, of polymer based colloidal systems, of
biopolymers, and of cellular motility in polymeric fluids. Further applications
of DDM to a variety of polymer systems are suggested to be just behind the
corner and it is thus likely that DDM will become a tool of choice of the
modern experimental polymer scientists.


Abstract:
  Charge manipulation and fabrication of stable domain patterns in
ferroelectric materials by scanning probe microscopy open up broad avenues for
the development of tunable electronics. Harnessing the polarization energy and
electrostatic forces with specific geometry of the system enables producing the
nanoscale domains by-design. Along with that, domain engineering requires
mastery of underlying physical mechanisms that govern the domain formation.
Here, we present a theoretical description of the domain formation by a
scanning probe microscopy tip in a ferroelectric film with strong in-plane
anisotropy of polarization. We demonstrate that local charge injection produces
wedge-shaped domains that propagate along the anisotropy axis, whereas the
tip-written lines of charge generate a comb-like domain structure. The results
of our calculations agree with earlier experimental observations and allow for
the optimization of the targeted domain structures.


Abstract:
  We use the Dirac operator technique to establish sharp distance estimates for
compact spin manifolds under lower bounds on the scalar curvature in the
interior and on the mean curvature of the boundary. In the situations we
consider, we thereby give refined answers to questions on metric inequalities
recently proposed by Gromov. This includes optimal estimates for Riemannian
bands and for the long neck problem. In the case of bands over manifolds of
non-vanishing $\widehat{\mathrm{A}}$-genus, we establish a rigidity result
stating that any band attaining the predicted upper bound is isometric to a
particular warped product over some spin manifold admitting a parallel spinor.
Furthermore, we establish scalar- and mean curvature extremality results for
certain log-concave warped products. The latter includes annuli in all
simply-connected space forms. On a technical level, our proofs are based on new
spectral estimates for the Dirac operator augmented by a Lipschitz potential
together with local boundary conditions.


Abstract:
  Several methods have been explored for automating parts of Systematic Mapping
(SM) and Systematic Review (SR) methodologies. Challenges typically evolve
around the gaps in semantic understanding of text, as well as lack of domain
and background knowledge necessary to bridge that gap. In this paper we
investigate possible ways of automating parts of the SM/SR process, i.e. that
of extracting keywords and key-phrases from scientific documents using
unsupervised methods, which are then used as a basis to construct the
corresponding Classification Scheme using semantic key-phrase clustering
techniques. Specifically, we explore the effect of ensemble scores measure in
key-phrase extraction, we explore semantic network based word embedding in
embedding representation of phrase semantics and finally we also explore how
clustering can be used to group related key-phrases. The evaluation is
conducted on a dataset of publications pertaining the domain of "Explainable
AI" which we constructed using standard publicly available digital libraries
and sets of indexing terms (keywords). Results shows that: ensemble ranking
score does improve the key-phrase extraction performance. Semantic-network
based word embedding based on the ConceptNet Semantic Network has similar
performance with contextualized word embedding, however the former are
computationally more efficient. Finally Semantic key-phrase clustering at
term-level can group similar terms together that can be suitable for
classification scheme.


Abstract:
  Various fonts give us various impressions, which are often represented by
words. This paper proposes Impressions2Font (Imp2Font) that generates font
images with specific impressions. Imp2Font is an extended version of
conditional generative adversarial networks (GANs). More precisely, Imp2Font
accepts an arbitrary number of impression words as the condition to generate
the font images. These impression words are converted into a soft-constraint
vector by an impression embedding module built on a word embedding technique.
Qualitative and quantitative evaluations prove that Imp2Font generates font
images with higher quality than comparative methods by providing multiple
impression words or even unlearned words.


Abstract:
  Context. The high number of super-Earth and Earth-like planets in the
habitable zone (HZ) detected around M-dwarf stars in the last years has
revealed these stellar objects to be the key for planetary radial velocity (RV)
searches. Aims. Using the HARPS-N spectrograph within The HArps-n red Dwarf
Exoplanet Survey (HADES) we reach the precision needed to detect small planets
with a few Earth masses using the RV technique. Methods. We obtained 138
HARPS-N RV measurements between 2013 May and 2020 September of GJ 720 A,
classified as an M0.5V star located at a distance of 15.56 pc. To characterize
the stellar variability and to discern the periodic variation due to the
Keplerian signals from those related to stellar activity, the HARPS-N
spectroscopic activity indicators and the simultaneous photometric observations
were analyzed. The combined analysis of HARPS-N RVs and activity indicators let
us to address the nature of the periodic signals. The final model and the
orbital planetary parameters were obtained by fitting simultaneously the
stellar variability and the Keplerian signal using a Gaussian process
regression and following a Bayesian criterion. Results. The HARPS-N RV periodic
signals around 40 d and 100 d have counterparts at the same frequencies in
HARPS-N activity indicators and photometric light curves. Then we attribute
these periodicities to stellar activity the former period being likely
associated with the stellar rotation. GJ 720 A shows the most significant
signal at 19.466$\pm$0.005 d with no counterparts in any stellar activity
indices. We hence ascribe this RV signal, having a semiamplitude of
4.72$\pm$0.27 m/s , to the presence of a sub-Neptune mass planet. The planet GJ
720 Ab has a minimum mass of 13.64$\pm$0.79 M$_{\oplus}$, it is in circular
orbit at 0.119$\pm$0.002 AU from its parent star, and lies inside the inner
boundary of the HZ around its parent star.


Abstract:
  We provide a new family of $K_k$-free pseudorandom graphs with edge density
$\Theta(n^{-1/(k-1)})$, matching a recent construction due to Bishnoi, Ihringer
and Pepe. As in the former result, the idea is to use large subgraphs of
polarity graphs, which are defined over a finite field $\mathbb{F}_q$. While
their construction required $q$ to be odd, we will give the first construction
with $q$ even.


Abstract:
  In this article, we consider a closed rank one Riemannian manifold $M$
without focal points. Let $P(t)$ be the set of free-homotopy classes containing
a closed geodesic on $M$ with length at most $t$, and $\# P(t)$ its
cardinality. We obtain the following Margulis-type asymptotic estimates:
\[\lim_{t\to \infty}\#P(t)/\frac{e^{ht}}{ht}=1\] where $h$ is the topological
entropy of the geodesic flow. In the appendix, we also show that the unique
measure of maximal entropy of the geodesic flow has the Bernoulli property.


Abstract:
  Many intellectual endeavors require mathematical problem solving, but this
skill remains beyond the capabilities of computers. To measure this ability in
machine learning models, we introduce MATH, a new dataset of 12,500 challenging
competition mathematics problems. Each problem in MATH has a full step-by-step
solution which can be used to teach models to generate answer derivations and
explanations. To facilitate future research and increase accuracy on MATH, we
also contribute a large auxiliary pretraining dataset which helps teach models
the fundamentals of mathematics. Even though we are able to increase accuracy
on MATH, our results show that accuracy remains relatively low, even with
enormous Transformer models. Moreover, we find that simply increasing budgets
and model parameter counts will be impractical for achieving strong
mathematical reasoning if scaling trends continue. While scaling Transformers
is automatically solving most other text-based tasks, scaling is not currently
solving MATH. To have more traction on mathematical problem solving we will
likely need new algorithmic advancements from the broader research community.


Abstract:
  It is well-known that machine learning models are vulnerable to small but
cleverly-designed adversarial perturbations that can cause misclassification.
While there has been major progress in designing attacks and defenses for
various adversarial settings, many fundamental and theoretical problems are yet
to be resolved. In this paper, we consider classification in the presence of
$\ell_0$-bounded adversarial perturbations, a.k.a. sparse attacks. This setting
is significantly different from other $\ell_p$-adversarial settings, with
$p\geq 1$, as the $\ell_0$-ball is non-convex and highly non-smooth. Under the
assumption that data is distributed according to the Gaussian mixture model,
our goal is to characterize the optimal robust classifier and the corresponding
robust classification error as well as a variety of trade-offs between
robustness, accuracy, and the adversary's budget. To this end, we develop a
novel classification algorithm called FilTrun that has two main modules:
Filtration and Truncation. The key idea of our method is to first filter out
the non-robust coordinates of the input and then apply a carefully-designed
truncated inner product for classification. By analyzing the performance of
FilTrun, we derive an upper bound on the optimal robust classification error.
We also find a lower bound by designing a specific adversarial strategy that
enables us to derive the corresponding robust classifier and its achieved
error. For the case that the covariance matrix of the Gaussian mixtures is
diagonal, we show that as the input's dimension gets large, the upper and lower
bounds converge; i.e. we characterize the asymptotically-optimal robust
classifier. Throughout, we discuss several examples that illustrate interesting
behaviors such as the existence of a phase transition for adversary's budget
determining whether the effect of adversarial perturbation can be fully
neutralized.


Abstract:
  Transients powered by interaction with the circumstellar medium (CSM) are
often observed in wavelengths other than optical, and multi-wavelength
modelling can be important when inferring the properties of the explosion and
CSM, or for distinguishing from other powering mechanisms. We develop a model
calculating time dependent emission spectrum of interaction-powered transients.
We solve energy equations of electron-proton plasma in the shocked SN ejecta
and CSM and a radiation transfer equation out to the outer edge of the CSM,
incorporating the collisional relaxation and the comptonization of the
bremsstrahlung radiation. We compare our model to observations of Type IIn
supernovae covering frequency ranges from optical to X-rays. For SN 2010jl the
observed optical and X-ray light curves can be consistently explained if clumpy
or asymmetric structure in the CSM is assumed, in agreement with previous
studies. For SN 2014C our model successfully reproduces the X-ray
bremsstrahlung component and the emergence of H$\alpha$ emission at 400 days
after explosion. Finally we find a parameter space where the supernova is
extremely X-ray bright, reaching $10^{43}$-$10^{44}\ {\rm erg\ s^{-1}}$ for up
to $100$ days. Such X-ray transients are likely detectable with all-sky surveys
by e.g. eROSITA.


Abstract:
  Data from NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite is essential
to many carbon management strategies. A retrieval algorithm is used to estimate
CO2 concentration using the radiance data measured by OCO-2. However, due to
factors such as cloud cover and cosmic rays, the spatial coverage of the
retrieval algorithm is limited in some areas of critical importance for carbon
cycle science. Mixed land/water pixels along the coastline are also not used in
the retrieval processing due to the lack of valid ancillary variables including
land fraction. We propose an approach to model spatial spectral data to solve
these two problems by radiance imputation and land fraction estimation. The
spectral observations are modeled as spatially indexed functional data with
footprint-specific parameters and are reduced to much lower dimensions by
functional principal component analysis. The principal component scores are
modeled as random fields to account for the spatial dependence, and the missing
spectral observations are imputed by kriging the principal component scores.
The proposed method is shown to impute spectral radiance with high accuracy for
observations over the Pacific Ocean. An unmixing approach based on this model
provides much more accurate land fraction estimates in our validation study
along Greece coastlines.


Abstract:
  Formal design of embedded and cyber-physical systems relies on mathematical
modeling. In this paper, we consider the model class of hybrid automata whose
dynamics are defined by affine differential equations. Given a set of
time-series data, we present an algorithmic approach to synthesize a hybrid
automaton exhibiting behavior that is close to the data, up to a specified
precision, and changes in synchrony with the data. A fundamental problem in our
synthesis algorithm is to check membership of a time series in a hybrid
automaton. Our solution integrates reachability and optimization techniques for
affine dynamical systems to obtain both a sufficient and a necessary condition
for membership, combined in a refinement framework. The algorithm processes one
time series at a time and hence can be interrupted, provide an intermediate
result, and be resumed. We report experimental results demonstrating the
applicability of our synthesis approach.


Abstract:
  We report on the first steady-state simulations of strong plasma shocks with
fully kinetic ions and electrons, independently confirmed by two fully kinetic
codes (an Eulerian continuum and a Lagrangian particle-in-cell). While kinetic
electrons do not fundamentally change the shock structure as compared with
fluid electrons, we find an appreciable rearrangement of the preheat layer,
associated with nonlocal electron heat transport effects. The electron heat
flux profile qualitatively agrees between kinetic and fluid electron models,
suggesting a certain level of "stiffness", though substantial nonlocality is
observed in the kinetic heat flux. We also find good agreement with nonlocal
electron heat-flux closures proposed in the literature. Finally, in contrast to
the classical hydrodynamic picture, we find a significant collapse in the
"precursor" electric-field shock at the preheat layer edge, which correlates
with the electron-temperature gradient relaxation.


Abstract:
  Discriminative analysis in neuroimaging by means of deep/machine learning
techniques is usually tested with validation techniques, whereas the associated
statistical significance remains largely under-developed due to their
computational complexity. In this work, a non-parametric framework is proposed
that estimates the statistical significance of classifications using deep
learning architectures. In particular, a combination of autoencoders (AE) and
support vector machines (SVM) is applied to: (i) a one-condition, within-group
designs often of normal controls (NC) and; (ii) a two-condition, between-group
designs which contrast, for example, Alzheimer's disease (AD) patients with NC
(the extension to multi-class analyses is also included). A random-effects
inference based on a label permutation test is proposed in both studies using
cross-validation (CV) and resubstitution with upper bound correction (RUB) as
validation methods. This allows both false positives and classifier overfitting
to be detected as well as estimating the statistical power of the test. Several
experiments were carried out using the Alzheimer's Disease Neuroimaging
Initiative (ADNI) dataset, the Dominantly Inherited Alzheimer Network (DIAN)
dataset, and a MCI prediction dataset. We found in the permutation test that CV
and RUB methods offer a false positive rate close to the significance level and
an acceptable statistical power (although lower using cross-validation). A
large separation between training and test accuracies using CV was observed,
especially in one-condition designs. This implies a low generalization ability
as the model fitted in training is not informative with respect to the test
set. We propose as solution by applying RUB, whereby similar results are
obtained to those of the CV test set, but considering the whole set and with a
lower computational cost per iteration.


Abstract:
  As an extension of an author's previous paper, we prove the Gersten's
conjecture for the $p$-torsion subgroup of the generalized Brauer group over a
henselian regular local ring of mixed characteristic $(0, p)$. We also prove
the $\mathbb{P}^{1}$-homotopy invariance for the generalized Brauer group which
is an analogue of a result due to Auel-Bigazzi-B\"{o}hning-Graf von Bothmer in
mixed characteristic cases.


Abstract:
  Structural analysis methods (e.g., probing and feature attribution) are
increasingly important tools for neural network analysis. We propose a new
structural analysis method grounded in a formal theory of causal abstraction
that provides rich characterizations of model-internal representations and
their roles in input/output behavior. In this method, neural representations
are aligned with variables in interpretable causal models, and then interchange
interventions are used to experimentally verify that the neural representations
have the causal properties of their aligned variables. We apply this method in
a case study to analyze neural models trained on Multiply Quantified Natural
Language Inference (MQNLI) corpus, a highly complex NLI dataset that was
constructed with a tree-structured natural logic causal model. We discover that
a BERT-based model with state-of-the-art performance successfully realizes
parts of the natural logic model's causal structure, whereas a simpler baseline
model fails to show any such structure, demonstrating that BERT representations
encode the compositional structure of MQNLI.


Abstract:
  We present a general scheme to approach the space - time evolution of
deformations, currents, and the electric field in charge density waves related
to appearance of intrinsic topological defects: dislocations, their loops or
pairs, and solitons. We derive general equations for the multi-fluid
hydrodynamics taking into account the collective mode, electric field, normal
electrons, and the intrinsic defects. These equations may allow to study the
transformation of injected carriers from normal electrons to new periods of the
charge density wave, the collective motion in constrained geometry, and the
plastic states and flows. As an application, we present analytical and
numerical solutions for distributions of fields around an isolated dislocation
line in the regime of nonlinear screening by the gas of phase solitons.


Abstract:
  An approach for the description of stochastic systems is derived. Some of the
variables in the system are studied forward in time, others backward in time.
The approach is based on a perturbation expansion in the strength of the
coupling between forward and backward variables, and is well suited for
situations in which initial and final conditions are imposed on different
components of the system and the coupling between those components is weak. The
form of the stochastic equations in our approach is determined by requiring
that they generate the same statistics obtained in a forward description of the
dynamics. Numerical tests are carried out on a few simple two-degree-of-freedom
systems. The merit and the difficulties of the approach are discussed and
compared to more traditional strategies based on transition path sampling and
simple shooting algorithms.


Abstract:
  Policies are authoritative assets that are present in multiple domains to
support decision-making. They describe what actions are allowed or recommended
when domain entities and their attributes satisfy certain criteria. It is
common to find policies that contain geographical rules, including distance and
containment relationships among named locations. These locations' polygons can
often be found encoded in geospatial datasets. We present an approach to
transform data from geospatial datasets into Linked Data using the OWL, PROV-O,
and GeoSPARQL standards, and to leverage this representation to support
automated ontology-based policy decisions. We applied our approach to
location-sensitive radio spectrum policies to identify relationships between
radio transmitters coordinates and policy-regulated regions in Census.gov
datasets. Using a policy evaluation pipeline that mixes OWL reasoning and
GeoSPARQL, our approach implements the relevant geospatial relationships,
according to a set of requirements elicited by radio spectrum domain experts.


Abstract:
  Natural Language Processing (NLP) models' current trend consists of using
increasingly more extra-data to build the best models as possible. It implies
more expensive computational costs and training time, difficulties for
deployment, and worries about these models' carbon footprint reveal a critical
problem in the future. Against this trend, our goal is to develop NLP models
requiring no extra-data and minimizing training time. To do so, in this paper,
we explore Markov chain models, Hidden Markov Chain (HMC) and Pairwise Markov
Chain (PMC), for NLP segmentation tasks. We apply these models for three
classic applications: POS Tagging, Named-Entity-Recognition, and Chunking. We
develop an original method to adapt these models for text segmentation's
specific challenges to obtain relevant performances with very short training
and execution times. PMC achieves equivalent results to those obtained by
Conditional Random Fields (CRF), one of the most applied models for these tasks
when no extra-data are used. Moreover, PMC has training times 30 times shorter
than the CRF ones, which validates this model given our objectives.


Abstract:
  Engineering a low singlet-triplet energy gap ({\Delta}EST) is necessary for
efficient reverse intersystem crossing (rISC) in delayed fluorescence (DF)
organic semiconductors, but results in a small radiative rate that limits
performance in LEDs. Here, we study a model DF material, BF2, that exhibits a
strong optical absorption (absorption coefficient =3.8x10^5 cm^-1) and a
relatively large {\Delta}EST of 0.2 eV. In isolated BF2 molecules,
intramolecular rISC is slow (260 {\mu}s), but in aggregated films, BF2
generates intermolecular CT (inter-CT) states on picosecond timescales. In
contrast to the microsecond intramolecular rISC that is promoted by spin-orbit
interactions in most isolated DF molecules, photoluminescence-detected magnetic
resonance shows that these inter-CT states undergo rISC mediated by hyperfine
interactions on a ~24 ns timescale and have an average electron-hole separation
of >1.5 nm. Transfer back to the emissive singlet exciton then enables
efficient DF and LED operation. Thus, access to these inter-CT states resolves
the conflicting requirements of fast radiative emission and low {\Delta}EST.


Abstract:
  The recent physical model-free dehazing methods have achieved
state-of-the-art performances. However, without the guidance of physical
models, the performances degrade rapidly when applied to real scenarios due to
the unavailable or insufficient data problems. On the other hand, the physical
model-based methods have better interpretability but suffer from
multi-objective optimizations of parameters, which may lead to sub-optimal
dehazing results. In this paper, a progressive residual learning strategy has
been proposed to combine the physical model-free dehazing process with
reformulated scattering model-based dehazing operations, which enjoys the
merits of dehazing methods in both categories. Specifically, the global
atmosphere light and transmission maps are interactively optimized with the aid
of accurate residual information and preliminary dehazed restorations from the
initial physical model-free dehazing process. The proposed method performs
favorably against the state-of-the-art methods on public dehazing benchmarks
with better model interpretability and adaptivity for complex hazy data.


Abstract:
  Node.js is one of the most popular frameworks for building web applications.
As software systems mature, the cost of running their entire regression test
suite can become significant. Selective Regression Testing (SRT) is a technique
that executes only a subset of tests the regression test suite can detect
software failures more efficiently. Previous SRT studies mainly focused on
standard desktop applications. Node.js applications are considered hard to
perform test reduction because of Node's asynchronous, event-driven programming
model and because JavaScript is a dynamic programming language. In this paper,
we present NodeSRT, a Selective Regression Testing framework for Node.js
applications. By performing static and dynamic analysis, NodeSRT identifies the
relationship between changed methods and tests, then reduces the regression
test suite to only tests that are affected by the change to improve the
execution time of the regression test suite. To evaluate our selection
technique, we applied NodeSRT to two open-source projects: Uppy and Simorgh,
then compared our approach with the retest-all strategy and current
industry-standard SRT technique: Jest OnlyChange. The results demonstrate that
NodeSRT correctly selects affected tests based on changes and is 250% faster,
450% more precise than the Jest OnlyChange.


Abstract:
  In the present work we prove that given any two unicycle graphs
(pseudoforests)
  that share the same degree sequence there is a finite sequence of 2-switches
  transforming one into the other such that all the graphs in the sequence
  are also unicyclic graphs (pseudoforests).


Abstract:
  In this paper, we propose a minimax model predictive control (MPC)-based
energy management system that is robust with respect to uncertainties in
renewable infeed and load. The MPC formulation includes a model of low-level
droop control with saturation at the power and energy limits of the units.
Robust MPC-based energy management systems tend to under-utilize the renewable
energy sources to guarantee safe operation. In order to mitigate this effect,
we further consider droop control of renewable energy sources. For a microgrid
with droop-controlled units, we show that enhancing droop feedback with
saturation enlarges the space of feasible control actions. However, the
resulting controller requires to solve a mixed-integer problem with additional
variables and equations representing saturation. We derive a computationally
tractable formulation for this problem. Furthermore, we investigate the
performance gained by using droop with saturation, renewable droop and
combination of both in a case study.


Abstract:
  Sensor configuration, including the sensor selections and their installation
locations, serves a crucial role in autonomous driving. A well-designed sensor
configuration significantly improves the performance upper bound of the
perception system. However, as leveraging multiple sensors is becoming the
mainstream setting, existing methods mainly focusing on single-sensor
configuration problems are hardly utilized in practice. To tackle these issues,
we propose a novel method based on conditional entropy in Bayesian theory to
evaluate the sensor configurations containing both cameras and LiDARs.
Correspondingly, an evaluation metric, perception entropy, is introduced to
measure the difference between two configurations, which considers both the
perception algorithm performance and the selections of the sensors. To the best
of our knowledge, this is the first method to tackle the multi-sensor
configuration problem for autonomous vehicles. The simulation results,
extensive comparisons, and analysis all demonstrate the superior performance of
our proposed approach.


Abstract:
  This note formally defines the concept of coinductive validity of judgements,
and contrasts it with inductive validity. For both notions it shows how a
judgement is valid iff it has a formal proof. Finally, it defines and
illustrates the notion of a proof by coinduction.


Abstract:
  Self-supervised contrastive learning has demonstrated great potential in
learning visual representations. Despite their success on various downstream
tasks such as image classification and object detection, self-supervised
pre-training for fine-grained scenarios is not fully explored. In this paper,
we first point out that current contrastive methods are prone to memorizing
background/foreground texture and therefore have a limitation in localizing the
foreground object. Analysis suggests that learning to extract discriminative
texture information and localization are equally crucial for self-supervised
pre-training in fine-grained scenarios. Based on our findings, we introduce
cross-view saliency alignment (CVSA), a contrastive learning framework that
first crops and swaps saliency regions of images as a novel view generation and
then guides the model to localize on the foreground object via a cross-view
alignment loss. Extensive experiments on four popular fine-grained
classification benchmarks show that CVSA significantly improves the learned
representation.


Abstract:
  Cooling and heating faster a system is a crucial problem in science,
technology and industry. Indeed, choosing the best thermal protocol to reach a
desired temperature or energy is not a trivial task. Noticeably, we find that
the phase transitions may speed up thermalization in systems where there are no
conserved quantities. In particular, we show that the slow growth of magnetic
domains shortens the overall time that the system takes to reach a final
desired state. To prove that statement, we use intensive numerical simulations
of a prototypical many-body system, namely the 2D Ising model.


Abstract:
  We present the first detailed dissection of the circumgalactic medium (CGM)
of massive starburst galaxies at z > 2. Our target is a submillimeter galaxy
(SMG) at z = 2.674 that has a star formation rate of 1200 $M_\odot$/yr and a
molecular gas reservoir of $1.3\times10^{11} M_\odot$. We characterize its CGM
with two background QSOs at impact parameters of 93 kpc and 176 kpc. We detect
strong HI and metal-line absorption near the redshift of the SMG towards both
QSOs, each consisting of three main subsystems spanning over 1500 km/s. The
absorbers show remarkable kinematic and metallicity coherence across a
separation of 86 kpc. In particular, the cool gas in the CGM of the SMG
exhibits high HI column densities ($\log N_{\rm HI}/{\rm cm}^{-2} = 20.2,
18.6$), low metallicities ([M/H] $\approx$ -2.0), and similar radial velocities
($\approx$ -300 km/s). While the HI column densities match previous results on
the CGM around QSOs at z > 2, the metallicities are lower by more than an order
of magnitude, making it an outlier in the line width$-$metallicity relation of
damped Ly$\alpha$ absorbers. The large physical extent, the velocity coherence,
the high surface density, and the low metallicity are all consistent with the
cool, inflowing, and near-pristine gas streams predicted to penetrate hot
massive halos at z > 1.5. We estimate a total gas accretion rate of ~100
$M_\odot$/yr from three such streams, which falls short of the star formation
rate but is consistent with simulations. At this rate, it takes about a
gigayear to acquire the molecular gas reservoir of the central starburst.


Abstract:
  We investigate the connection between galactic outflows and star formation
using two independent data sets covering a sample of 22 galaxies between $1
\lesssim z \lesssim 1.5$. The HST WFC3/G141 grism provides low spectral
resolution, high spatial resolution spectroscopy yielding H$\alpha$ emission
line maps from which we measure the spatial extent and strength of star
formation. In the rest-frame near-UV, Keck/DEIMOS observes Fe II and Mg II
interstellar absorption lines, which provide constraints on the intensity and
velocity of the outflows. We compare outflow properties from individual and
composite spectra with the star formation rate (SFR) and SFR surface density
(SigmaSFR), as well as the stellar mass and specific star formation rate
(sSFR). The Fe II and Mg II equivalent widths (EWs) increase with both SFR and
SigmaSFR at $\gtrsim 3\sigma$ significance, while the composite spectra show
larger Fe II EWs and outflow velocities in galaxies with higher SFR, SigmaSFR,
and sSFR. Absorption line profiles of the composite spectra further indicate
that the differences between subsamples are driven by outflows rather than the
ISM. While these results are consistent with those of previous studies, the use
of H$\alpha$ images makes them the most direct test of the relationship between
star formation and outflows at $z>1$ to date. Future facilities such as the
James Webb Space Telescope and the upcoming Extremely Large Telescopes will
extend these direct, H$\alpha$-based studies to lower masses and star formation
rates, probing galactic feedback across orders of magnitude in galaxy
properties and augmenting the correlations we find here.


Abstract:
  We show that the rate for dark matter-electron scattering in an arbitrary
material is determined by an experimentally measurable quantity, the complex
dielectric function, for any dark matter interaction that couples to electron
density. This formulation automatically includes many-body effects, eliminates
all systematic theoretical uncertainties on the electronic wavefunctions, and
allows a direct calibration of the spectrum by electromagnetic probes such as
infrared spectroscopy, X-ray scattering, and electron energy-loss spectroscopy
(EELS). Our formalism applies for several common benchmark models, including
spin-independent interactions through scalar and vector mediators of arbitrary
mass. We discuss the consequences for standard semiconductor and superconductor
targets, and find that the true reach of superconductor detectors for light
mediators exceeds previous estimates by several orders of magnitude, with
further enhancements possible due to the low-energy tail of the plasmon. Using
a heavy-fermion superconductor as an example, we show how our formulation
allows a rapid and systematic investigation of novel electron scattering
targets.


Abstract:
  Level density $\rho(E,A)$ is derived for a one-component nucleon system with
a given energy $E$ and particle number $A$ within the mean-field semiclassical
periodic-orbit theory beyond the saddle-point method of the Fermi gas model. We
obtain $~~\rho \propto I_\nu(S)/S^\nu$, with $I_\nu(S)$ being the modified
Bessel function of the entropy $S$. Within the micro-macro-canonical
approximation (MMA), for a small thermal excitation energy, $U$, with respect
to rotational excitations, $E_{\rm rot}$, one obtains $\nu=3/2$ for
$\rho(E,A)$. In the case of excitation energy $U$ larger than $E_{\rm rot}$ but
smaller than the neutron separation energy, one finds a larger value of
$\nu=5/2$. A role of the fixed spin variables for rotating nuclei is discussed.
The MMA level density $\rho$ reaches the well-known grand-canonical ensemble
limit (Fermi gas asymptotic) for large $S$ related to large excitation
energies, and also reaches the finite micro-canonical limit for small
combinatorial entropy $S$ at low excitation energies (the constant
"temperature" model). Fitting the $\rho(E,A)$ of the MMA to the experimental
data for low excitation energies, taking into account shell and, qualitatively,
pairing effects, one obtains for the inverse level density parameter $K$ a
value which differs essentially from that parameter derived from data on
neutron resonances.


Abstract:
  Several studies with brain signals suggested that bottom-up and top-down
influences are exerted through distinct frequency bands among visual cortical
areas. It has been recently shown that theta and gamma rhythms subserve
feedforward, whereas the feedback influence is dominated by the alpha-beta
rhythm in primates. A few theoretical models for reproducing these effects have
been proposed so far. Here we show that a simple but biophysically plausible
two-network motif composed of spiking-neuron models and chemical synapses can
exhibit feedforward and feedback influences through distinct frequency bands.
Differently from previous studies, this kind of model allows us to study
directed influences not only at the population level, by using a proxy for the
local field potential, but also at the cellular level, by using the neuronal
spiking series.


Abstract:
  Context: Given the acknowledged need to understand the people processes
enacted during software development, software repositories and mailing lists
have become a focus for many studies. However, researchers have tended to use
mostly mathematical and frequency-based techniques to examine the software
artifacts contained within them. Objective: There is growing recognition that
these approaches uncover only a partial picture of what happens during software
projects, and deeper contextual approaches may provide further understanding of
the intricate nature of software teams' dynamics. We demonstrate the relevance
and utility of such approaches in this study. Method: We use psycholinguistics
and directed content analysis (CA) to study the way project tasks drive teams'
attitudes and knowledge sharing. We compare the outcomes of these two
approaches and offer methodological advice for researchers using similar forms
of repository data. Results: Our analysis reveals significant differences in
the way teams work given their portfolio of tasks and the distribution of
roles. Conclusion: We overcome the limitations associated with employing purely
quantitative approaches, while avoiding the time-intensive and potentially
invasive nature of field work required in full case studies.


Abstract:
  A discrete multidimensional system is the set of solutions to a system of
linear partial difference equations defined on the lattice $\Z^n$. This paper
shows that it is determined by a unique coarsest sublattice, in the sense that
the solutions of the system on this sublattice determine the solutions on
$\Z^n$; it is therefore the correct domain of definition of the discrete
system. In turn, the defining sublattice is determined by a Galois group of
symmetries that leave invariant the equations defining the system. These
results find application in understanding properties of the system such as
controllability and autonomy, and in its order reduction.


Abstract:
  Deep models trained in supervised mode have achieved remarkable success on a
variety of tasks. When labeled samples are limited, self-supervised learning
(SSL) is emerging as a new paradigm for making use of large amounts of
unlabeled samples. SSL has achieved promising performance on natural language
and image learning tasks. Recently, there is a trend to extend such success to
graph data using graph neural networks (GNNs). In this survey, we provide a
unified review of different ways of training GNNs using SSL. Specifically, we
categorize SSL methods into contrastive and predictive models. In either
category, we provide a unified framework for methods as well as how these
methods differ in each component under the framework. Our unified treatment of
SSL methods for GNNs sheds light on the similarities and differences of various
methods, setting the stage for developing new methods and algorithms. We also
summarize different SSL settings and the corresponding datasets used in each
setting. To facilitate methodological development and empirical comparison, we
develop a standardized testbed for SSL in GNNs, including implementations of
common baseline methods, datasets, and evaluation metrics.


Abstract:
  We investigate a recombination-drift-diffusion model coupled to Poisson's
equation modelling the transport of charge within certain types of
semiconductors. In more detail, we study a two-level system for electrons and
holes endowed with an intermediate energy level for electrons occupying trapped
states. As our main result, we establish an explicit functional inequality
between relative entropy and entropy production, which leads to exponential
convergence to equilibrium. We stress that our approach is applied uniformly in
the lifetime of electrons on the trap level assuming that this lifetime is
sufficiently small.


Abstract:
  A recurrent structure is a popular framework choice for the task of video
super-resolution. The state-of-the-art method BasicVSR adopts bidirectional
propagation with feature alignment to effectively exploit information from the
entire input video. In this study, we redesign BasicVSR by proposing
second-order grid propagation and flow-guided deformable alignment. We show
that by empowering the recurrent framework with the enhanced propagation and
alignment, one can exploit spatiotemporal information across misaligned video
frames more effectively. The new components lead to an improved performance
under a similar computational constraint. In particular, our model BasicVSR++
surpasses BasicVSR by 0.82 dB in PSNR with similar number of parameters. In
addition to video super-resolution, BasicVSR++ generalizes well to other video
restoration tasks such as compressed video enhancement. In NTIRE 2021,
BasicVSR++ obtains three champions and one runner-up in the Video
Super-Resolution and Compressed Video Enhancement Challenges. Codes and models
will be released to MMEditing.


Abstract:
  We consider (Enc, Dec) schemes which are used to encode a classical/quantum
message $m$ and derive an $n$-qubit quantum codeword $\psi_m$. The quantum
codeword $\psi_m$ can adversarially tamper via a unitary $U \in \mathcal{U}$
from some known tampering unitary family $\mathcal{U}$, resulting in $U \psi_m
U^\dagger$.
  Firstly, we initiate the general study of quantum tamper detection codes,
which must detect that tampering occurred with high probability. In case there
was no tampering, we would like to output the message $m$ with a probability of
$1$. We show that quantum tamper detection codes exist for both classical
messages and quantum messages for any family of unitaries $\mathcal{U}$, such
that $|\mathcal{U}| < 2^{2^{\alpha n}}$ for some known constant $\alpha \in
(0,1)$ and all the unitaries satisfy one additional condition :
  \begin{itemize}
  \item Far from Identity : For each $U \in \mathcal{U}$, we require that its
modulus of trace value isn't too much i.e. $ |Trace(U)| \leq \phi N$, where
$N=2^n.$
  \end{itemize}
  Quantum tamper-detection codes are quantum generalizations of classical
tamper detection codes studied by Jafargholi et al. \cite{JW15}.
  Additionally for classical message $m$, if we must either output message $m$
or detect that tampering occurred and output $\perp$ with high probability, we
show that it is possible without the restriction of Far from Identity condition
for any family of unitaries $\mathcal{U}$, such that $|\mathcal{U} | <
2^{2^{\alpha n}}$. We also provide efficient (Enc, Dec) schemes when the family
of tampering unitaries are from Pauli group $\mathcal{P}_n$, which can be
thought of as a quantum version of the algebraic manipulation detection (AMD)
codes of Cramer et al. \cite{CDFPW08}.


Abstract:
  We give new and efficient black-box reconstruction algorithms for some
classes of depth-$3$ arithmetic circuits. As a consequence, we obtain the first
efficient algorithm for computing the tensor rank and for finding the optimal
tensor decomposition as a sum of rank-one tensors when then input is a
constant-rank tensor. More specifically, we provide efficient learning
algorithms that run in randomized polynomial time over general fields and in
deterministic polynomial time over the reals and the complex numbers for the
following classes:
  (1) Set-multilinear depth-$3$ circuits of constant top fan-in
$\Sigma\Pi\Sigma\{\sqcup_j X_j\}(k)$ circuits). As a consequence of our
algorithm, we obtain the first polynomial time algorithm for tensor rank
computation and optimal tensor decomposition of constant-rank tensors. This
result holds for $d$ dimensional tensors for any $d$, but is interesting even
for $d=3$.
  (2) Sums of powers of constantly many linear forms ($\Sigma\wedge\Sigma$
circuits). As a consequence we obtain the first polynomial-time algorithm for
tensor rank computation and optimal tensor decomposition of constant-rank
symmetric tensors.
  (3) Multilinear depth-3 circuits of constant top fan-in (multilinear
$\Sigma\Pi\Sigma(k)$ circuits). Our algorithm works over all fields of
characteristic 0 or large enough characteristic. Prior to our work the only
efficient algorithms known were over polynomially-sized finite fields (see.
Karnin-Shpilka 09').
  Prior to our work, the only polynomial-time or even subexponential-time
algorithms known (deterministic or randomized) for subclasses of
$\Sigma\Pi\Sigma(k)$ circuits that also work over large/infinite fields were
for the setting when the top fan-in $k$ is at most $2$ (see Sinha 16' and Sinha
20').


Abstract:
  In many real-world applications, the mismatch between distributions of
training data (source) and test data (target) significantly degrades the
performance of machine learning algorithms. In speech data, causes of this
mismatch include different acoustic environments or speaker characteristics. In
this paper, we address this issue in the challenging context of dysarthric
speech, by multi-source domain/speaker adaptation (MSDA/MSSA). Specifically, we
propose the use of an optimal-transport based approach, called MSDA via
Weighted Joint Optimal Transport (MSDA-WDJOT). We confront the mismatch problem
in dysarthria detection for which the proposed approach outperforms both the
Baseline and the state-of-the-art MSDA models, improving the detection accuracy
of 0.9% over the best competitor method. We then employ MSDA-WJDOT for
dysarthric speaker adaptation in command speech recognition. This provides a
Command Error Rate relative reduction of 16% and 7% over the baseline and the
best competitor model, respectively. Interestingly, MSDA-WJDOT provides a
similarity score between the source and the target, i.e. between speakers in
this case. We leverage this similarity measure to define a Dysarthric and
Healthy score of the target speaker and diagnose the dysarthria with an
accuracy of 95%.


Abstract:
  Rapidly changing business environments expose companies to high levels of
uncertainty. This uncertainty manifests itself in significant changes that tend
to occur over the lifetime of a process and possibly affect its performance. It
is important to understand the root causes of such changes since this allows us
to react to change or anticipate future changes. Research in process mining has
so far only focused on detecting, locating and characterizing significant
changes in a process and not on finding root causes of such changes. In this
paper, we aim to close this gap. We propose a framework that adds an
explainability level onto concept drift detection in process mining and
provides insights into the cause-effect relationships behind significant
changes. We define different perspectives of a process, detect concept drifts
in these perspectives and plug the perspectives into a causality check that
determines whether these concept drifts can be causal to each other. We
showcase the effectiveness of our framework by evaluating it on both synthetic
and real event data. Our experiments show that our approach unravels
cause-effect relationships and provides novel insights into executed processes.


Abstract:
  We demonstrate a computational scheme which drastically decreases the
required time to get theoretical predictions based on chiral two- and
three-nucleon forces for observables in three-nucleon continuum. For a
three-nucleon force containing N short-range terms all workload is reduced to
solving N+1 Faddeev-type integral equations. That done, computation of
observables for any combination of strengths of the contact terms is done in a
flash. We demonstrate on example of the elastic nucleon-deuteron scattering
observables the high precision of the proposed emulator and its capability to
reproduce exact results.


Abstract:
  This study investigates how the location-routing decisions of the electric
vehicle (EV) DCFC charging stations are impacted by the ambient temperature. We
formulated this problem as a mixed-integer linear programming model that
captures the realistic charging behavior of the DCFC in association with the
ambient temperature and their subsequent impact on the EV charging station
location and routing decisions. Two innovative heuristics are proposed to solve
this challenging model in a realistic test setting, namely, the two-phase Tabu
Search-modified Clarke and Wright algorithm and the Sweep-based Iterative
Greedy Adaptive Large Neighborhood algorithm. We use Fargo city in North Dakota
as a testbed to visualize and validate the algorithm performances. The results
clearly indicate that the EV DCFC charging station location decisions are
highly sensitive to the ambient temperature, the charging time, and the initial
state of charge.


Abstract:
  The detrimental impacts of climate change include stronger and more
destructive hurricanes happening all over the world. Identifying different
damaged structures of an area including buildings and roads are vital since it
helps the rescue team to plan their efforts to minimize the damage caused by a
natural disaster. Semantic segmentation helps to identify different parts of an
image. We implement a novel self-attention based semantic segmentation model on
a high resolution UAV dataset and attain Mean IoU score of around 88% on the
test set. The result inspires to use self-attention schemes in natural disaster
damage assessment which will save human lives and reduce economic losses.


Abstract:
  It is demonstrated in this paper that the propagation of the electric wave
field in a heterogeneous medium in 3D can sometimes be governed well by a
single PDE, which is derived from the Maxwell's equations. The corresponding
component of the electric field dominates two other components. This justifies
some past results of the second author with coauthors about numerical solutions
of coefficient inverse problems with experimental electromagnetic data. In
addition, since it is simpler to work in applications with a single PDE rather
than with the complete Maxwell's system, then the result of this paper might be
useful to researchers working on applied issues of the propagation of
electromagnetic waves in inhomogeneous media.


Abstract:
  In this paper, we define `simplicial GKM orbifold complexes' and `simplicial
GKM graph complexes'. We study some of their topological and combinatorial
properties. We discuss some necessary conditions to confirm an invariant
$q$-cell structure on a simplicial GKM orbifold complex. We prove Thom
isomorphism theorem for orbifold $G$-vector bundles for equivariant cohomology
and equivariant K-theory with rational coefficients. We use this to extend the
main result of Harada-Henriques-Holm (2005) to the category of $G$-spaces
equipped with `singular invariant stratification'. Then we introduce an
equivalent definition of weighted Grassmann orbifolds which were introduced by
Corti and Reid (2002) and explicitly studied by Abe and Matsumura (2015) to
compute their equivariant cohomology with rational coefficients. These
orbifolds are a generalization of Grassmann manifolds. We study their different
$q$-cell structures and discuss some necessary conditions to determine if they
have invariant CW-structures. We employ the techniques from the first part to
study some generalized equivariant cohomology theories of weighted Grassmann
orbifolds. We introduce `divisive' weighted Grassmann orbifolds and compute
their equivariant cohomology, equivariant K-theory and equivariant cobordism
ring with integer coefficients. We discuss Schubert calculus for the
equivariant cohomology ring and show how to compute the corresponding structure
constants with integer coefficients.


Abstract:
  We prove that, for an undirected graph with $n$ vertices and $m$ edges, each
labeled with a linear function of a parameter $\lambda$, the number of
different minimum spanning trees obtained as the parameter varies can be
$\Omega(m\log n)$.


Abstract:
  We investigate the homogenization through Gamma-convergence for the
L^2(\Omega)-weak topology of the conductivity functional with a zero-order term
where the matrix-valued conductivity is assumed to be non strongly elliptic.
Under proper assumptions, we show that the homogenized matrix A^\ast is
provided by the classical homogenization formula. We also give algebraic
conditions for two and three dimensional 1-periodic rank-one laminates such
that the homogenization result holds. For this class of laminates, an explicit
expression of A^\ast is provided which is a generalization of the classical
laminate formula. We construct a two-dimensional counter-example which shows an
anomalous asymptotic behaviour of the conductivity functional.


Abstract:
  Neural methods have been shown to achieve high performance in Named Entity
Recognition (NER), but rely on costly high-quality labeled data for training,
which is not always available across languages. While previous works have shown
that unlabeled data in a target language can be used to improve cross-lingual
model performance, we propose a novel adversarial approach (AdvPicker) to
better leverage such data and further improve results. We design an adversarial
learning framework in which an encoder learns entity domain knowledge from
labeled source-language data and better shared features are captured via
adversarial training - where a discriminator selects less language-dependent
target-language data via similarity to the source language. Experimental
results on standard benchmark datasets well demonstrate that the proposed
method benefits strongly from this data selection process and outperforms
existing state-of-the-art methods; without requiring any additional external
resources (e.g., gazetteers or via machine translation). The code is available
at https://aka.ms/AdvPicker


Abstract:
  Dynamical friction is typically regarded a secular process, in which the
subject ('perturber') evolves very slowly (secular approximation), and has been
introduced to the host over a long time (adiabatic approximation). These
assumptions imply that dynamical friction arises from the LBK torque with
non-zero contribution only from pure resonance orbits. However, dynamical
friction is only of astrophysical interest if its timescale is shorter than the
age of the Universe. In this paper we therefore relax the adiabatic and secular
approximations. We first derive a generalized LBK torque, which reduces to the
LBK torque in the adiabatic limit, and show that it gives rise to transient
oscillations due to non-resonant orbits that slowly damp out, giving way to the
LBK torque. This is analogous to how a forced, damped oscillator undergoes
transients before settling to a steady state, except that here the damping is
due to phase mixing rather than dissipation. Next, we present a self-consistent
treatment, that properly accounts for time-dependence of the perturber
potential and circular frequency (memory effect), which we use to examine
orbital decay in a cored galaxy. We find that the memory effect results in a
phase of accelerated, super-Chandrasekhar friction before the perturber stalls
at a critical radius, $R_{\mathrm{crit}}$, in the core (core-stalling). Inside
of $R_{\mathrm{crit}}$ the torque flips sign, giving rise to dynamical
buoyancy, which counteracts friction and causes the perturber to stall. This
phenomenology is consistent with $N$-body simulations, but has thus far eluded
proper explanation.


Abstract:
  We study a family of algorithms, which we refer to as local update methods,
generalizing many federated and meta-learning algorithms. We prove that for
quadratic models, local update methods are equivalent to first-order
optimization on a surrogate loss we exactly characterize. Moreover, fundamental
algorithmic choices (such as learning rates) explicitly govern a trade-off
between the condition number of the surrogate loss and its alignment with the
true loss. We derive novel convergence rates showcasing these trade-offs and
highlight their importance in communication-limited settings. Using these
insights, we are able to compare local update methods based on their
convergence/accuracy trade-off, not just their convergence to critical points
of the empirical loss. Our results shed new light on a broad range of
phenomena, including the efficacy of server momentum in federated learning and
the impact of proximal client updates.


Abstract:
  Symmetry plays a fundamental role in the security analysis of quantum key
distribution (QKD). Here we review how symmetry is exploited in
continuous-variable (CV) QKD to prove the optimality of Gaussian attacks in the
finite-size regime. We then apply these results to improve the efficiency, and
thus the key rate, of these protocols. First we show how to improve the
efficiency of the energy test, which is one important routine in a CV QKD
protocol aimed at establishing an upper bound on the effective dimensions of
the otherwise infinite-dimensional Hilbert space of CV systems. Second, we show
how the routine of parameter estimation can be made resource efficient in
measurement-device independent (MDI) QKD. These results show that all the raw
data can be used both for key extraction and for the routines of energy test
and parameter estimation.


Abstract:
  It is commonly believed that Bayesian optimization (BO) algorithms are highly
efficient for optimizing numerically costly functions. However, BO is not often
compared to widely different alternatives, and is mostly tested on narrow sets
of problems (multimodal, low-dimensional functions), which makes it difficult
to assess where (or if) they actually achieve state-of-the-art performance.
Moreover, several aspects in the design of these algorithms vary across
implementations without a clear recommendation emerging from current practices,
and many of these design choices are not substantiated by authoritative test
campaigns. This article reports a large investigation about the effects on the
performance of (Gaussian process based) BO of common and less common design
choices. The experiments are carried out with the established COCO (COmparing
Continuous Optimizers) software. It is found that a small initial budget, a
quadratic trend, high-quality optimization of the acquisition criterion bring
consistent progress. Using the GP mean as an occasional acquisition contributes
to a negligible additional improvement. Warping degrades performance. The
Mat\'ern 5/2 kernel is a good default but it may be surpassed by the
exponential kernel on irregular functions. Overall, the best EGO variants are
competitive or improve over state-of-the-art algorithms in dimensions less or
equal to 5 for multimodal functions. The code developed for this study makes
the new version (v2.1.1) of the R package DiceOptim available on CRAN. The
structure of the experiments by function groups allows to define priorities for
future research on Bayesian optimization.


Abstract:
  We consider the Dirichlet problem for second-order linear elliptic equations
in divergence form \begin{equation*} -\mathrm{div }(A\nabla u)+\mathbf{b} \cdot
\nabla u+\lambda u=f+\mathrm{div } \mathbf{F}\quad \text{in }
\Omega\quad\text{and}\quad u=0\quad \text{on } \partial\Omega, \end{equation*}
in bounded Lipschitz domain $\Omega$ in $\mathbb{R}^2$, where
$A:\mathbb{R}^2\rightarrow \mathbb{R}^{2^2}$, $\mathbf{b} : \Omega\rightarrow
\mathbb{R}^2$, and $\lambda \geq 0$ are given. If $2<p<\infty$ and $A$ has a
small mean oscillation in small balls, $\Omega$ has small Lipschitz constant,
and $\mathrm{div } A,\,\mathbf{b} \in L^{2}(\Omega;\mathbb{R}^2)$, then we
prove existence and uniqueness of weak solutions in $W^{1,p}_0(\Omega)$ of the
problem. Similar result also holds for the dual problem.


Abstract:
  We present Doppler-corrected position and velocity measurement with a
fiber-coupled COFDR system based on the FMCW radar principle for high precision
localization applications. A high measurement accuracy and the ability to track
targets are demonstrated.


Abstract:
  In this paper, we present a survey on the utility of machine learning (ML)
algorithms for applications in cognitive radio networks (CRN). We start with a
high-level overview of some of the major challenges in CRNs, and mention the ML
architectures and algorithms that can be used to alleviate them. In particular,
our focus is on two fundamental applications in CRNs, namely spectrum sensing
-- with non-cooperative and cooperative scenarios, and dynamic spectrum access
-- with spectrum auction and prediction. We present a detailed study of recent
advancements in the field of ML in CRNs for these applications, and briefly
discuss the set of challenges in real-time implementation of ML techniques for
CRNs.


Abstract:
  Positron Emission Particle Tracking (PEPT) is an imaging method that tracks
individual radioactive particles. PEPT relies on the detection of back-to-back
photon pairs emitted by positron annihilation. It requires an algorithm to
locate the radioactive particles based on the set of lines defined by
successive photon-pair detections. We propose and test a new algorithm for this
task. The algorithm relies on the maximization of a likelihood arising from a
simple Gaussian-mixture model defined in the space of lines. The model includes
a component that accounts for spurious lines caused by scattering and random
coincidence, and treats the relative activity of particles as well as their
positions as parameters to be inferred. Values of these parameters that
approximately maximize the likelihood are computed by application of an
expectation-maximization algorithm. A generalization of the model that includes
the particle velocities and accelerations as additional parameters takes
advantage of the information contained in the exact timing of positron
annihilations to reconstruct pieces of trajectories rather than fixed
positions, with clear benefits.
  We test the algorithm on both simulated and experimental data. The results
show the algorithm to be highly effective for the simultaneous tracking of many
particles (up to 80 in one test). It provides estimates of particle positions
that are easily mapped to entire trajectories and handles a variable number of
particles in the field of view. The ability to track a large number of
particles robustly offers the possibility of a dramatic expansion of the scope
of PEPT


Abstract:
  In this paper we propose and investigate a general approach to constructing
local energy-preserving algorithms which can be of arbitrarily high order in
time for solving Hamiltonian PDEs. This approach is based on the temporal
discretization using continuous Runge-Kutta-type methods, and the spatial
discretization using pseudospectral methods or Gauss--Legendre collocation
methods. The local energy conservation law of our new schemes is analyzed in
detail. The effectiveness of the novel local energy-preserving integrators is
demonstrated by coupled nonlinear Schr\"odinger equations and 2D nonlinear
Schr\"odinger equations with external fields. Our new schemes are compared with
some classical multi-symplectic and symplectic schemes in numerical
experiments. The numerical results show the remarkable \emph{long-term}
behaviour of our new schemes.