Abstract:
  Security issues in shipped code can lead to unforeseen device malfunction,
system crashes or malicious exploitation by crackers, post-deployment. These
vulnerabilities incur a cost of repair and foremost risk the credibility of the
company. It is rewarding when these issues are detected and fixed well ahead of
time, before release. Common Weakness Estimation (CWE) is a nomenclature
describing general vulnerability patterns observed in C code. In this work, we
propose a deep learning model that learns to detect some of the common
categories of security vulnerabilities in source code efficiently. The AI
architecture is an Attention Fusion model, that combines the effectiveness of
recurrent, convolutional and self-attention networks towards decoding the
vulnerability hotspots in code. Utilizing the code AST structure, our model
builds an accurate understanding of code semantics with a lot less learnable
parameters. Besides a novel way of efficiently detecting code vulnerability, an
additional novelty in this model is to exactly point to the code sections,
which were deemed vulnerable by the model. Thus helping a developer to quickly
focus on the vulnerable code sections; and this becomes the "explainable" part
of the vulnerability detection. The proposed AI achieves 98.40% F1-score on
specific CWEs from the benchmarked NIST SARD dataset and compares well with
state of the art.


Abstract:
  Off-policy multi-step reinforcement learning algorithms consist of
conservative and non-conservative algorithms: the former actively cut traces,
whereas the latter do not. Recently, Munos et al. (2016) proved the convergence
of conservative algorithms to an optimal Q-function. In contrast,
non-conservative algorithms are thought to be unsafe and have a limited or no
theoretical guarantee. Nonetheless, recent studies have shown that
non-conservative algorithms empirically outperform conservative ones. Motivated
by the empirical results and the lack of theory, we carry out theoretical
analyses of Peng's Q($\lambda$), a representative example of non-conservative
algorithms. We prove that it also converges to an optimal policy provided that
the behavior policy slowly tracks a greedy policy in a way similar to
conservative policy iteration. Such a result has been conjectured to be true
but has not been proven. We also experiment with Peng's Q($\lambda$) in complex
continuous control tasks, confirming that Peng's Q($\lambda$) often outperforms
conservative algorithms despite its simplicity. These results indicate that
Peng's Q($\lambda$), which was thought to be unsafe, is a theoretically-sound
and practically effective algorithm.


Abstract:
  As the use of algorithmic systems in high-stakes decision-making increases,
the ability to contest algorithmic decisions is being recognised as an
important safeguard for individuals. Yet, there is little guidance on what
`contestability'--the ability to contest decisions--in relation to algorithmic
decision-making requires. Recent research presents different conceptualisations
of contestability in algorithmic decision-making. We contribute to this growing
body of work by describing and analysing the perspectives of people and
organisations who made submissions in response to Australia's proposed `AI
Ethics Framework', the first framework of its kind to include `contestability'
as a core ethical principle. Our findings reveal that while the nature of
contestability is disputed, it is seen as a way to protect individuals, and it
resembles contestability in relation to human decision-making. We reflect on
and discuss the implications of these findings.


Abstract:
  It is well known that entropy production is a proxy to the detection of
non-equilibrium, i.e. of the absence of detailed balance; however, due to the
global character of this quantity, its knowledge does not allow to identify
spatial currents or fluxes of information among specific elements of the system
under study. In this respect, much more insight can be gained by studying
transfer entropy and response, which allow quantifying the relative influence
of parts of the system and the asymmetry of the fluxes. In order to understand
the relation between the above-mentioned quantities, we investigate spatially
asymmetric extended systems. First, we consider a simplified linear stochastic
model, which can be studied analytically; then, we include nonlinear terms in
the dynamics. Extensive numerical investigation shows the relation between
entropy production and the above-introduced degrees of asymmetry. Finally, we
apply our approach to the highly nontrivial dynamics generated by the Lorenz
'96 model for Earth oceanic circulation.


Abstract:
  Recurrent neural networks are a powerful means in diverse applications. We
show that, together with so-called conceptors, they also allow fast learning,
in contrast to other deep learning methods. In addition, a relatively small
number of examples suffices to train neural networks with high accuracy. We
demonstrate this with two applications, namely speech recognition and detecting
car driving maneuvers. We improve the state of the art by application-specific
preparation techniques: For speech recognition, we use mel frequency cepstral
coefficients leading to a compact representation of the frequency spectra, and
detecting car driving maneuvers can be done without the commonly used
polynomial interpolation, as our evaluation suggests.


Abstract:
  Network filtering is an important form of dimension reduction to isolate the
core constituents of large and interconnected complex systems. We introduce a
new technique to filter large dimensional networks arising out of dynamical
behavior of the constituent nodes, exploiting their spectral properties. As
opposed to the well known network filters that rely on preserving key
topological properties of the realized network, our method treats the spectrum
as the fundamental object and preserves spectral properties. Applying
asymptotic theory for high dimensional data for the filter, we show that it can
be tuned to interpolate between zero filtering to maximal filtering that
induces sparsity and consistency while having the least spectral distance from
a linear shrinkage estimator. We apply our proposed filter to covariance
networks constructed from financial data, to extract the key subnetwork
embedded in the full sample network.


Abstract:
  Intent Classification (IC) and Slot Labeling (SL) models, which form the
basis of dialogue systems, often encounter noisy data in real-word
environments. In this work, we investigate how robust IC/SL models are to noisy
data. We collect and publicly release a test-suite for seven common noise types
found in production human-to-bot conversations (abbreviations, casing,
misspellings, morphological variants, paraphrases, punctuation and synonyms).
On this test-suite, we show that common noise types substantially degrade the
IC accuracy and SL F1 performance of state-of-the-art BERT-based IC/SL models.
By leveraging cross-noise robustness transfer -- training on one noise type to
improve robustness on another noise type -- we design aggregate
data-augmentation approaches that increase the model performance across all
seven noise types by +10.8% for IC accuracy and +15 points for SL F1 on
average. To the best of our knowledge, this is the first work to present a
single IC/SL model that is robust to a wide range of noise phenomena.


Abstract:
  The Malmquist-Takenaka system is a perturbation of the classical
trigonometric system, where powers of $z$ are replaced by products of other
M\"obius transforms of the disc. The system is also inherently connected to the
so-called nonlinear phase unwinding decomposition which has been in the center
of some recent activity. We prove $L^p$ bounds for the maximal partial sum
operator of the Malmquist-Takenaka series under additional assumptions on the
zeros of the M\"obius transforms. We locate the problem in the time-frequency
setting and, in particular, we connect it to the polynomial Carleson theorem.


Abstract:
  We study the properties of the Nakajima-Zwanzig memory kernel for a qubit
immersed in a many-body localized (i.e.\ disordered and interacting) bath. We
argue that the memory kernel decays as a power law in both the localized and
ergodic regimes, and show how this can be leveraged to extract $t\to\infty$
populations for the qubit from finite time ($J t \leq 10^2$) data in the
thermalizing phase. This allows us to quantify how the long-time values of the
populations approach the expected thermalized state as the bath approaches the
thermodynamic limit. This approach should provide a good complement to
state-of-the-art numerical methods, for which the long-time dynamics with large
baths are impossible to simulate in this phase. Additionally, our numerics on
finite baths reveal the possibility for unbounded exponential growth in the
memory kernel, a phenomenon rooted in the appearance of exceptional points in
the projected Liouvillian governing the reduced dynamics. In small systems
amenable to exact numerics, we find that these pathologies may have some
correlation with delocalization.


Abstract:
  Human evaluation of modern high-quality machine translation systems is a
difficult problem, and there is increasing evidence that inadequate evaluation
procedures can lead to erroneous conclusions. While there has been considerable
research on human evaluation, the field still lacks a commonly-accepted
standard procedure. As a step toward this goal, we propose an evaluation
methodology grounded in explicit error analysis, based on the Multidimensional
Quality Metrics (MQM) framework. We carry out the largest MQM research study to
date, scoring the outputs of top systems from the WMT 2020 shared task in two
language pairs using annotations provided by professional translators with
access to full document context. We analyze the resulting data extensively,
finding among other results a substantially different ranking of evaluated
systems from the one established by the WMT crowd workers, exhibiting a clear
preference for human over machine output. Surprisingly, we also find that
automatic metrics based on pre-trained embeddings can outperform human crowd
workers. We make our corpus publicly available for further research.


Abstract:
  We define a multi-group version of the mean-field or Curie-Weiss spin model.
For this model, we show how, analogously to the classical (single-group) model,
the three temperature regimes are defined. Then we use the method of moments to
determine for each regime how the vector of the group magnetisations behaves
asymptotically. Some possible applications to social or political sciences are
discussed.


Abstract:
  Short pulse lasers are used to characterize the nonlinear response of
amplified photodetectors. Two widely used balanced detectors are characterized
in terms of amplitude, area, broadening, and balancing the mismatch of their
impulse response. The dynamic impact of pulses on the detector is also
discussed. It is demonstrated that using photodetectors with short pulses
triggers nonlinearities even when the source average power is well below the
detector continuous power saturation threshold.


Abstract:
  To enable a deep learning-based system to be used in the medical domain as a
computer-aided diagnosis system, it is essential to not only classify diseases
but also present the locations of the diseases. However, collecting
instance-level annotations for various thoracic diseases is expensive.
Therefore, weakly supervised localization methods have been proposed that use
only image-level annotation. While the previous methods presented the disease
location as the most discriminative part for classification, this causes a deep
network to localize wrong areas for indistinguishable X-ray images. To solve
this issue, we propose a spatial attention method using disease masks that
describe the areas where diseases mainly occur. We then apply the spatial
attention to find the precise disease area by highlighting the highest
probability of disease occurrence. Meanwhile, the various sizes, rotations and
noise in chest X-ray images make generating the disease masks challenging. To
reduce the variation among images, we employ an alignment module to transform
an input X-ray image into a generalized image. Through extensive experiments on
the NIH-Chest X-ray dataset with eight kinds of diseases, we show that the
proposed method results in superior localization performances compared to
state-of-the-art methods.


Abstract:
  Probability is an important question in the ontological interpretation of
quantum mechanics. It has been discussed in some trajectory interpretations
such as Bohmian mechanics and stochastic mechanics. New questions arise when
the probability domain extends to the complex space, including the generation
of complex trajectory, the definition of the complex probability, and the
relation of the complex probability to the quantum probability. The complex
treatment proposed in this article applies the optimal quantum guidance law to
derive the stochastic differential equation governing a particle random motion
in the complex plane. The probability distribution of the particle position
over the complex plane is formed by an ensemble of the complex quantum random
trajectories, which are solved from the complex stochastic differential
equation. Meanwhile, this probability distribution is verified by the solution
of the complex Fokker Planck equation. It is shown that quantum probability and
classical probability can be integrated under the framework of complex
probability, such that they can both be derived from the same probability
distribution by different statistical ways of collecting spatial points.


Abstract:
  We consider a family of Schr\"odinger equations with unbounded Hamiltonian
quadratic nonlinearities on a generic tori of dimension $d\geq1$. We study the
behaviour of high Sobolev norms $H^{s}$, $s\gg1$, of solutions with initial
conditions in $H^{s}$ whose
  $H^{\rho}$-Sobolev norm, $1\ll\rho\ll s$, is smaller than $\e\ll1$. We
provide a control of the $H^{s}$-norm over a time interval of order
$O(\e^{-2})$. %where $\e\ll1$ is the size of the initial condition in
$H^{\rho}$.
  Due to the lack of conserved quantities controlling high Sobolev norms, the
key ingredient of the proof is the construction of a modified energy equivalent
to the "low norm" $H^{\rho}$ (when $\rho$ is sufficiently high) over a
nontrivial time interval $O(\e^{-2})$. This is achieved by means of normal form
techniques for quasi-linear equations involving para-differential calculus. The
main difficulty is to control the possible loss of derivatives due to the small
divisors arising form three waves interactions. By performing "tame" energy
estimates we obtain upper bounds for higher Sobolev norms $H^{s}$.


Abstract:
  Deepfakes are the result of digital manipulation to obtain credible videos in
order to deceive the viewer. This is done through deep learning techniques
based on autoencoders or GANs that become more accessible and accurate year
after year, resulting in fake videos that are very difficult to distinguish
from real ones. Traditionally, CNN networks have been used to perform deepfake
detection, with the best results obtained using methods based on EfficientNet
B7. In this study, we combine various types of Vision Transformers with a
convolutional EfficientNet B0 used as a feature extractor, obtaining comparable
results with some very recent methods that use Vision Transformers. Differently
from the state-of-the-art approaches, we use neither distillation nor ensemble
methods. The best model achieved an AUC of 0.951 and an F1 score of 88.0%, very
close to the state-of-the-art on the DeepFake Detection Challenge (DFDC).


Abstract:
  The focus of this research is sensor applications including radar and sonar.
Optimal sensing means achieving the best signal quality with the least time and
energy cost, which allows processing more data. This paper presents novel work
by using an integer linear programming "algorithm" to achieve optimal sensing
by selecting the best possible number of signals of a type or a combination of
multiple types of signals to ensure the best sensing quality considering all
given constraints. A solution based on a heuristic algorithm is implemented to
improve the computing time performance. What is novel in this solution is
synthesis of an optimized signal mix using information such as but not limited
to signal quality, energy and computing time.


Abstract:
  Mahi-mahi (Coryphaena hippurus) are a highly migratory pelagic fish, but
little is known about what environmental factors drive their broad
distribution. This study examined how temperature influences aerobic scope and
swimming performance in mahi. Mahi were acclimated to four temperatures
spanning their natural range (20, 24, 28, and 32{\deg}C; 5-27 days) and
critical swimming speed (Ucrit), metabolic rates, aerobic scope, and optimal
swim speed were measured. Aerobic scope and Ucrit were highest in
28{\deg}C-acclimated fish. 20{\deg}C-acclimated mahi experienced significantly
decreased aerobic scope and Ucrit relative to 28{\deg}C-acclimated fish (57 and
28% declines, respectively). 32{\deg}C-acclimated mahi experienced increased
mortality and a significant 23% decline in Ucrit, and a trend for a 26% decline
in factorial aerobic scope relative to 28{\deg}C-acclimated fish. Absolute
aerobic scope showed a similar pattern to factorial aerobic scope. Our results
are generally in agreement with previously observed distribution patterns for
wild fish. Although thermal performance can vary across life stages, the
highest tested swim performance and aerobic scope found in the present study
(28{\deg}C), aligns with recently observed habitat utilization patterns for
wild mahi and could be relevant for climate change predictions.


Abstract:
  The mapped bases or Fake Nodes Approach (FNA), introduced in [10], allows to
change the set of nodes without the need of resampling the function. Such
scheme has been successfully applied in preventing the appearance of the Gibbs
phenomenon when interpolating discontinuous functions. However, the originally
proposed S-Gibbs map suffers of a subtle instability when the interpolant is
constructed at equidistant nodes, due to the Runge's phenomenon. Here, we
propose a novel approach, termed Gibbs-Runge-Avoiding Stable Polynomial
Approximation (GRASPA), where both Runge's and Gibbs phenomena are mitigated.
After providing a theoretical analysis of the Lebesgue constant associated to
the mapped nodes, we test the new approach by performing different numerical
experiments which confirm the theoretical findings.


Abstract:
  We prove that the torsion points of an abelian variety are equidistributed
over the corresponding berkovich space with respect to the canonical measure.


Abstract:
  We give explicit examples of quaternion-K\"ahler and hypercomplex structures
on bundles over hyperK\"ahler manifolds. We study the infinitesimal symmetries
of these examples and the associated Galicki-Lawson quaternion-K\"ahler moment
map. In particular, this leads us to a new proof a
hyperK\"ahler/quaternion-K\"ahler type correspondence. We also give examples of
other Einstein metrics and balanced Hermitian structures on these bundles.


Abstract:
  Code review plays an important role in software quality control. A typical
review process would involve a careful check of a piece of code in an attempt
to find defects and other quality issues/violations. One type of issues that
may impact the quality of the software is code smells - i.e., bad programming
practices that may lead to defects or maintenance issues. Yet, little is known
about the extent to which code smells are identified during code reviews. To
investigate the concept behind code smells identified in code reviews and what
actions reviewers suggest and developers take in response to the identified
smells, we conducted an empirical study of code smells in code reviews using
the two most active OpenStack projects (Nova and Neutron). We manually checked
19,146 review comments obtained by keywords search and random selection, and
got 1,190 smell-related reviews to study the causes of code smells and actions
taken against the identified smells. Our analysis found that 1) code smells
were not commonly identified in code reviews, 2) smells were usually caused by
violation of coding conventions, 3) reviewers usually provided constructive
feedback, including fixing (refactoring) recommendations to help developers
remove smells, and 4) developers generally followed those recommendations and
actioned the changes. Our results suggest that 1) developers should closely
follow coding conventions in their projects to avoid introducing code smells,
and 2) review-based detection of code smells is perceived to be a trustworthy
approach by developers, mainly because reviews are context-sensitive (as
reviewers are more aware of the context of the code given that they are part of
the project's development team).


Abstract:
  With the popularity of Machine Learning (ML) solutions, algorithms and data
have been released faster than the capacity of processing them. In this
context, the problem of Algorithm Recommendation (AR) is receiving a
significant deal of attention recently. This problem has been addressed in the
literature as a learning task, often as a Meta-Learning problem where the aim
is to recommend the best alternative for a specific dataset. For such, datasets
encoded by meta-features are explored by ML algorithms that try to learn the
mapping between meta-representations and the best technique to be used. One of
the challenges for the successful use of ML is to define which features are the
most valuable for a specific dataset since several meta-features can be used,
which increases the meta-feature dimension. This paper presents an empirical
analysis of Feature Selection and Feature Extraction in the meta-level for the
AR problem. The present study was focused on three criteria: predictive
performance, dimensionality reduction, and pipeline runtime. As we verified,
applying Dimensionality Reduction (DR) methods did not improve predictive
performances in general. However, DR solutions reduced about 80% of the
meta-features, obtaining pretty much the same performance as the original setup
but with lower runtimes. The only exception was PCA, which presented about the
same runtime as the original meta-features. Experimental results also showed
that various datasets have many non-informative meta-features and that it is
possible to obtain high predictive performance using around 20% of the original
meta-features. Therefore, due to their natural trend for high dimensionality,
DR methods should be used for Meta-Feature Selection and Meta-Feature
Extraction.


Abstract:
  Whereas electron-phonon scattering typically relaxes the electron's momentum
in metals, a perpetual exchange of momentum between phonons and electrons
conserves total momentum and can lead to a coupled electron-phonon liquid with
unique transport properties. This theoretical idea was proposed decades ago and
has been revisited recently, but the experimental signatures of an
electron-phonon liquid have been rarely reported. We present evidence of such a
behavior in a transition metal ditetrelide, NbGe$_2$, from three different
experiments. First, quantum oscillations reveal an enhanced quasiparticle mass,
which is unexpected in NbGe$_2$ due to weak electron-electron correlations,
hence pointing at electron-phonon interactions. Second, resistivity
measurements exhibit a discrepancy between the experimental data and calculated
curves within a standard Fermi liquid theory. Third, Raman scattering shows
anomalous temperature dependence of the phonon linewidths which fits an
empirical model based on phonon-electron coupling. We discuss structural
factors, such as chiral symmetry, short metallic bonds, and a low-symmetry
coordination environment as potential sources of coupled electron-phonon
liquids.


Abstract:
  A variety of techniques have been developed for the approximation of
non-periodic functions. In particular, there are approximation techniques based
on rank-$1$ lattices and transformed rank-$1$ lattices, including methods that
use sampling sets consisting of Chebyshev- and tent-transformed nodes. We
compare these methods with a parameterized transformed Fourier system that
yields similar $\ell_2$-approximation errors.


Abstract:
  We show that the cobordism class of a polarization of Hodge module defines a
natural transformation from the Grothendieck group of Hodge modules to the
cobordism group of self-dual bounded complexes with real coefficients and
constructible cohomology sheaves in a compatible way with pushforward by proper
morphisms. This implies a new proof of the well-definedness of the natural
transformation from the Grothendieck group of varieties over a given variety to
the above cobordism group (with real coefficients). As a corollary, we get a
slight extension of a conjecture of Brasselet, Sch\"urmann and Yokura, showing
that in the $\bf Q$-homologically isolated singularity case, the homology
$L$-class which is the specialization of the Hirzebruch class coincides with
the intersection complex $L$-class defined by Goresky, MacPherson, and others
if and only if the sum of the reduced Euler-Hodge signatures of the stalks of
the shifted intersection complex vanishes. Here Hodge signature uses a
polarization of Hodge structure, and it does not seem easy to define it by a
purely topological method.


Abstract:
  Stellar flares are an important aspect of magnetic activity -- both for
stellar evolution and circumstellar habitability viewpoints - but automatically
and accurately finding them is still a challenge to researchers in the Big Data
era of astronomy. We present an experiment to detect flares in space-borne
photometric data using deep neural networks. Using a set of artificial data and
real photometric data we trained a set of neural networks, and found that the
best performing architectures were the recurrent neural networks (RNNs) using
Long Short-Term Memory (LSTM) layers. The best trained network detected flares
over {$5\sigma$ with $\gtrsim80$\% recall and precision} and was also capable
to distinguish typical false signals (e.g. maxima of RR Lyr stars) from real
flares. Testing the network trained on Kepler data on TESS light curves showed
that the neural net is able to generalize and find flares - with similar
effectiveness - in completely new data having previously unseen sampling and
characteristics.


Abstract:
  This paper studies Imitation Learning from Observations alone (ILFO) where
the learner is presented with expert demonstrations that consist only of states
visited by an expert (without access to actions taken by the expert). We
present a provably efficient model-based framework MobILE to solve the ILFO
problem. MobILE involves carefully trading off strategic exploration against
imitation - this is achieved by integrating the idea of optimism in the face of
uncertainty into the distribution matching imitation learning (IL) framework.
We provide a unified analysis for MobILE, and demonstrate that MobILE enjoys
strong performance guarantees for classes of MDP dynamics that satisfy certain
well studied notions of structural complexity. We also show that the ILFO
problem is strictly harder than the standard IL problem by presenting an
exponential sample complexity separation between IL and ILFO. We complement
these theoretical results with experimental simulations on benchmark OpenAI Gym
tasks that indicate the efficacy of MobILE.


Abstract:
  The seven basic facial expression classifications are a basic way to express
complex human emotions and are an important part of artificial intelligence
research. Based on the traditional Bayesian neural network framework, the
ResNet18_BNN network constructed in this paper has been improved in the
following three aspects: (1) A new objective function is proposed, which is
composed of the KL loss of uncertain parameters and the intersection of
specific parameters. Entropy loss composition. (2) Aiming at a special
objective function, a training scheme for alternately updating these two
parameters is proposed. (3) Only model the parameters of the last convolution
group. Through testing on the FER2013 test set, we achieved 71.5% and 73.1%
accuracy in PublicTestSet and PrivateTestSet, respectively. Compared with
traditional Bayesian neural networks, our method brings the highest
classification accuracy gain.


Abstract:
  Motivated by emerging applications such as live-streaming e-commerce,
promotions and recommendations, we introduce and solve a general class of
non-stationary multi-armed bandit problems that have the following two
features: (i) the decision maker can pull and collect rewards from up to
$K\,(\ge 1)$ out of $N$ different arms in each time period; (ii) the expected
reward of an arm immediately drops after it is pulled, and then
non-parametrically recovers as the arm's idle time increases. With the
objective of maximizing the expected cumulative reward over $T$ time periods,
we design a class of ``Purely Periodic Policies'' that jointly set a period to
pull each arm. For the proposed policies, we prove performance guarantees for
both the offline problem and the online problems. For the offline problem when
all model parameters are known, the proposed periodic policy obtains an
approximation ratio that is at the order of $1-\mathcal O(1/\sqrt{K})$, which
is asymptotically optimal when $K$ grows to infinity. For the online problem
when the model parameters are unknown and need to be dynamically learned, we
integrate the offline periodic policy with the upper confidence bound procedure
to construct on online policy. The proposed online policy is proved to
approximately have $\widetilde{\mathcal O}(N\sqrt{T})$ regret against the
offline benchmark. Our framework and policy design may shed light on broader
offline planning and online learning applications with non-stationary and
recovering rewards.


Abstract:
  We associate to a sufficiently generic oriented matroid program and choice of
linear system of parameters a finite dimensional algebra, whose representation
theory is analogous to blocks of Bernstein--Gelfand--Gelfand category
$\mathcal{O}$. When the data above comes from a generic linear program for a
hyperplane arrangement, we recover the algebra defined by
Braden--Licata--Proudfoot--Webster.


Abstract:
  This paper presents a framework to enable a team of heterogeneous mobile
robots to model and sense a multiscale system. We propose a coupled strategy,
where robots of one type collect high-fidelity measurements at a slow time
scale and robots of another type collect low-fidelity measurements at a fast
time scale, for the purpose of fusing measurements together. The multiscale
measurements are fused to create a model of a complex, nonlinear spatiotemporal
process. The model helps determine optimal sensing locations and predict the
evolution of the process. Key contributions are: i) consolidation of multiple
types of data into one cohesive model, ii) fast determination of optimal
sensing locations for mobile robots, and iii) adaptation of models online for
various monitoring scenarios. We illustrate the proposed framework by modeling
and predicting the evolution of an artificial plasma cloud. We test our
approach using physical marine robots adaptively sampling a process in a water
tank.


Abstract:
  We develop a distributed framework for the physics-informed neural networks
(PINNs) based on two recent extensions, namely conservative PINNs (cPINNs) and
extended PINNs (XPINNs), which employ domain decomposition in space and in
time-space, respectively. This domain decomposition endows cPINNs and XPINNs
with several advantages over the vanilla PINNs, such as parallelization
capacity, large representation capacity, efficient hyperparameter tuning, and
is particularly effective for multi-scale and multi-physics problems. Here, we
present a parallel algorithm for cPINNs and XPINNs constructed with a hybrid
programming model described by MPI $+$ X, where X $\in
\{\text{CPUs},~\text{GPUs}\}$. The main advantage of cPINN and XPINN over the
more classical data and model parallel approaches is the flexibility of
optimizing all hyperparameters of each neural network separately in each
subdomain. We compare the performance of distributed cPINNs and XPINNs for
various forward problems, using both weak and strong scalings. Our results
indicate that for space domain decomposition, cPINNs are more efficient in
terms of communication cost but XPINNs provide greater flexibility as they can
also handle time-domain decomposition for any differential equations, and can
deal with any arbitrarily shaped complex subdomains. To this end, we also
present an application of the parallel XPINN method for solving an inverse
diffusion problem with variable conductivity on the United States map, using
ten regions as subdomains.


Abstract:
  We analyse the impact of the selective pressure for the global optimisation
capabilities of steady-state EAs. For the standard bimodal benchmark function
\twomax we rigorously prove that using uniform parent selection leads to
exponential runtimes with high probability to locate both optima for the
standard ($\mu$+1)~EA and ($\mu$+1)~RLS with any polynomial population sizes.
On the other hand, we prove that selecting the worst individual as parent leads
to efficient global optimisation with overwhelming probability for reasonable
population sizes. Since always selecting the worst individual may have
detrimental effects for escaping from local optima, we consider the performance
of stochastic parent selection operators with low selective pressure for a
function class called \textsc{TruncatedTwoMax} where one slope is shorter than
the other. An experimental analysis shows that the EAs equipped with inverse
tournament selection, where the loser is selected for reproduction and small
tournament sizes, globally optimise \textsc{TwoMax} efficiently and effectively
escape from local optima of \textsc{TruncatedTwoMax} with high probability.
Thus they identify both optima efficiently while uniform (or stronger)
selection fails in theory and in practice. We then show the power of inverse
selection on function classes from the literature where populations are
essential by providing rigorous proofs or experimental evidence that it
outperforms uniform selection equipped with or without a restart strategy. We
conclude the paper by confirming our theoretical insights with an empirical
analysis of the different selective pressures on standard benchmarks of the
classical MaxSat and Multidimensional Knapsack Problems.


Abstract:
  We study the spectral dimensions and spectral asymptotics of Krein-Feller
operators for arbitrary finite Borel measures on $\left(0,1\right).$
Connections between the spectral dimension, the $L^{q}$-spectrum, the partition
entropy and the optimised coarse multifractal dimension are established. In
particular, we show that the upper spectral dimension always corresponds to the
fixed point of the $L^{q}$-spectrum of the corresponding measure. Natural
bounds reveal intrinsic connections to the Minkowski dimension of the support
of the associated Borel measure. Further, we give a sufficient condition on the
$L^{q}$-spectrum to guarantee the existence of the spectral dimension. As an
application, we confirm the existence of the spectral dimension of
self-conformal measures with and without overlap as well as of certain measures
of pure point type. We construct a simple example for which the spectral
dimension does not exist and determine explicitly its upper and lower spectral
dimension.


Abstract:
  We propose a novel approach to the statistical analysis of simulation models
and, especially, agent-based models (ABMs). Our main goal is to provide a fully
automated and model-independent tool-kit to inspect simulations and perform
counterfactual analysis. Our approach: (i) is easy-to-use by the modeller, (ii)
improves reproducibility of results, (iii) optimizes running time given the
modeller's machine, (iv) automatically chooses the number of required
simulations and simulation steps to reach user-specified statistical
confidence, and (v) automatically performs a variety of statistical tests. In
particular, our framework is designed to distinguish the transient dynamics of
the model from its steady-state behaviour (if any), estimate properties of the
model in both "phases", and provide indications on the ergodic (or non-ergodic)
nature of the simulated processes -- which, in turns allows one to gauge the
reliability of a steady-state analysis. Estimates are equipped with statistical
guarantees, allowing for robust comparisons across computational experiments.
To demonstrate the effectiveness of our approach, we apply it to two models
from the literature: a large scale macro-financial ABM and a small scale
prediction market model. Compared to prior analyses of these models, we obtain
new insights and we are able to identify and fix some erroneous conclusions.


Abstract:
  We investigate a Maclaurin inequality for vectors and its connection to an
Aleksandrov-type inequality for parallelepipeds.


Abstract:
  A quantum effect at ambient conditions is presented. A benzene dithiol (BDT)
molecule or a tetrahydrofuran (THF) molecule is used as a barrier molecule
bridging the gold electrodes from a Mechanically Controllable Break junction.
It has been known for a long time that the environment of a junction couples to
the conduction. With a microscopic layer of fluid it is actually possible to
influence this coupling. The electrode-molecule-electrode configuration is
lined with a microscopic layer of THF fluid, also known as the partially wet
phase. This is effectively decoupling the electrode-molecule-electrode system
from environmental modes outside of this system. The effects on coherence and
quantum interference are exceptional.


Abstract:
  Photodynamically active fibres (PAFs) are a novel class of stimulus-sensitive
systems capable of triggering antibiotic-free antibacterial effect on-demand
when exposed to light. Despite their relevance in infection control, however,
the broad clinical applicability of PAFs has not yet been fully realised due to
the limited control in fibrous microstructure, cell tolerance and antibacterial
activity in the physiologic environment. We addressed this challenge by
creating semicrystalline electrospun fibres with varying content of
poly[(L-lactide)-co-(glycolide)] (PLGA), poly(epsilon-caprolactone) (PCL) and
methylene blue (MB), whereby the effect of polymer morphology, fibre
composition and photosensitiser (PS) uptake on wet state fibre behaviour and
functions was studied. The presence of crystalline domains and PS-polymer
secondary interactions proved key to accomplishing long-lasting fibrous
microstructure, controlled mass loss and controlled MB release profiles (37
degC, pH 7.4, 8 weeks). PAFs with equivalent PLGA:PCL weight ratio successfully
promoted attachment and proliferation of L929 cells over a 7-day culture with
and without light activation, while triggering up to 2.5 and 4 log reduction in
E. coli and S. mutans viability, respectively. These results support the
therapeutic applicability of PAFs for frequently encountered bacterial
infections, opening up new opportunities in photodynamic fibrous systems with
integrated wound healing and infection control capabilities.


Abstract:
  In this work, we investigate the recovery of a parameter in a diffusion
process given by the order of derivation in time for a class of diffusion type
equations, including both classical and time-fractional diffusion equations,
from the flux measurement observed at one point on the boundary. The
mathematical model for time-fractional diffusion equations involves a
Djrbashian-Caputo fractional derivative in time. We prove a uniqueness result
in an unknown medium (e.g., diffusion coefficients, obstacle, initial condition
and source), i.e., the recovery of the order of derivation in a diffusion
process having several pieces of unknown information. The proof relies on the
analyticity of the solution at large time, asymptotic decay behavior, strong
maximum principle of the elliptic problem and suitable application of the Hopf
lemma. Further we provide an easy-to-implement reconstruction algorithm based
on a nonlinear least-squares formulation, and several numerical experiments are
presented to complement the theoretical analysis.


Abstract:
  The system of stability equations for galactic halos is under-determined in
most of the models of dark matter (DM). Conventionally, the issue is resolved
by taking the temperature as a constant, and the chemical potential and the
mass density as position-dependent variables. In this paper, to close the
under-determined set of equations, we remove the mass density using
observations and leave the temperature and the chemical potential as
position-dependent variables. We analyze observations of the mass profiles of
175 late-type galaxies in the Spitzer Photometry \& Accurate Rotation Curves
(SPARC) database as well as 26 late-type dwarfs in the Little Things database,
to construct the temperature profile of their DM halos by assuming that (1) DM
in the halos obeys either the Fermi-Dirac or the Maxwell-Boltzmann
distribution, and (2) the halos are in the virial state. We derive the
dispersion velocity of DM at the center of the halos and show that its
correlation with the halo's total mass is consistent with the direct
observations of visible matter. Taking the latter agreement as a validation of
our analysis, we derive the mass to the temperature of DM at the edge of the
halos and show that it is galaxy independent and is equal to
$m/T_{R_{200}}\simeq 10^{10}$ in natural units. In the thermal models of DM,
such universal temperature is inherently assumed. In this paper, we derive the
universal temperature from observations without imposing it by assumptions.
Therefore, $T_{R_{200}}$ in the above ratio can be expressed in terms of the
temperature of the cosmic microwave background (CMB) at the time of DM
decoupling. This result is used to study possible cosmological scenarios. We
show that observations are at odds with (1) non-thermal DM, (2) hot DM, and (3)
collision-less cold DM. If DM is warm, we estimate its mass to be in the range
of keV--MeV.


Abstract:
  Phonon polaritons (PhPs),light coupled to lattice vibrations,with in-plane
hyperbolic dispersion exhibit ray-like propagation with large wavevectors and
enhanced density of optical states along certain directions on a surface. As
such, they have raised a surge of interest as they promise unprecedented
possibilities for the manipulation of infrared light with planar circuitry and
at the nanoscale. Here, we demonstrate, for the first time, the focusing of
in-plane hyperbolic PhPs propagating along thin slabs of MoO3. To that end, we
developed metallic nanoantennas of convex geometries for both the efficient
launching and focusing of the polaritons. Remarkably, the foci obtained exhibit
enhanced near-field confinement and absorption compared to foci produced by
in-plane isotropic PhPs. More intriguingly, foci sizes as small as lamdap/5
=lamda0/50 were achieved (lamdap is the polariton wavelength and lamda0 the
photon wavelength). Focusing of in-plane hyperbolic polaritons introduces a
first and most basic building block developing planar polariton optics
utilizing in-plane anisotropic van der Waals materials and metasurfaces.


Abstract:
  Sequential matching using hand-crafted heuristics has been standard practice
in route-based place recognition for enhancing pairwise similarity results for
nearly a decade. However, precision-recall performance of these algorithms
dramatically degrades when searching on short temporal window (TW) lengths,
while demanding high compute and storage costs on large robotic datasets for
autonomous navigation research. Here, influenced by biological systems that
robustly navigate spacetime scales even without vision, we develop a joint
visual and positional representation learning technique, via a sequential
process, and design a learning-based CNN+LSTM architecture, trainable via
backpropagation through time, for viewpoint- and appearance-invariant place
recognition. Our approach, Sequential Place Learning (SPL), is based on a CNN
function that visually encodes an environment from a single traversal, thus
reducing storage capacity, while an LSTM temporally fuses each visual embedding
with corresponding positional data -- obtained from any source of motion
estimation -- for direct sequential inference. Contrary to classical two-stage
pipelines, e.g., match-then-temporally-filter, our network directly eliminates
false-positive rates while jointly learning sequence matching from a single
monocular image sequence, even using short TWs. Hence, we demonstrate that our
model outperforms 15 classical methods while setting new state-of-the-art
performance standards on 4 challenging benchmark datasets, where one of them
can be considered solved with recall rates of 100% at 100% precision, correctly
matching all places under extreme sunlight-darkness changes. In addition, we
show that SPL can be up to 70x faster to deploy than classical methods on a 729
km route comprising 35,768 consecutive frames. Extensive experiments
demonstrate the... Baseline code available at
https://github.com/mchancan/deepseqslam


Abstract:
  Policy gradient methods are an attractive approach to multi-agent
reinforcement learning problems due to their convergence properties and
robustness in partially observable scenarios. However, there is a significant
performance gap between state-of-the-art policy gradient and value-based
methods on the popular StarCraft Multi-Agent Challenge (SMAC) benchmark. In
this paper, we introduce semi-on-policy (SOP) training as an effective and
computationally efficient way to address the sample inefficiency of on-policy
policy gradient methods. We enhance two state-of-the-art policy gradient
algorithms with SOP training, demonstrating significant performance
improvements. Furthermore, we show that our methods perform as well or better
than state-of-the-art value-based methods on a variety of SMAC tasks.


Abstract:
  Deep neural networks are powerful machines for visual pattern recognition,
but reasoning tasks that are easy for humans may still be difficult for neural
models. Humans possess the ability to extrapolate reasoning strategies learned
on simple problems to solve harder examples, often by thinking for longer. For
example, a person who has learned to solve small mazes can easily extend the
very same search techniques to solve much larger mazes by spending more time.
In computers, this behavior is often achieved through the use of algorithms,
which scale to arbitrarily hard problem instances at the cost of more
computation. In contrast, the sequential computing budget of feed-forward
neural networks is limited by their depth, and networks trained on simple
problems have no way of extending their reasoning to accommodate harder
problems. In this work, we show that recurrent networks trained to solve simple
problems with few recurrent steps can indeed solve much more complex problems
simply by performing additional recurrences during inference. We demonstrate
this algorithmic behavior of recurrent networks on prefix sum computation,
mazes, and chess. In all three domains, networks trained on simple problem
instances are able to extend their reasoning abilities at test time simply by
"thinking for longer."


Abstract:
  We propose a new end-to-end neural diarization (EEND) system that is based on
Conformer, a recently proposed neural architecture that combines convolutional
mappings and Transformer to model both local and global dependencies in speech.
We first show that data augmentation and convolutional subsampling layers
enhance the original self-attentive EEND in the Transformer-based EEND, and
then Conformer gives an additional gain over the Transformer-based EEND.
However, we notice that the Conformer-based EEND does not generalize as well
from simulated to real conversation data as the Transformer-based model. This
leads us to quantify the mismatch between simulated data and real speaker
behavior in terms of temporal statistics reflecting turn-taking between
speakers, and investigate its correlation with diarization error. By mixing
simulated and real data in EEND training, we mitigate the mismatch further,
with Conformer-based EEND achieving 24% error reduction over the baseline
SA-EEND system, and 10% improvement over the best augmented Transformer-based
system, on two-speaker CALLHOME data.


Abstract:
  Low-Rank Representation (LRR) highly suffers from discarding the locality
information of data points in subspace clustering, as it may not incorporate
the data structure nonlinearity and the non-uniform distribution of
observations over the ambient space. Thus, the information of the observational
density is lost by the state-of-art LRR models, as they take a constant number
of adjacent neighbors into account. This, as a result, degrades the subspace
clustering accuracy in such situations. To cope with deficiency, in this paper,
we propose to consider a hypergraph model to facilitate having a variable
number of adjacent nodes and incorporating the locality information of the
data. The sparsity of the number of subspaces is also taken into account. To do
so, an optimization problem is defined based on a set of regularization terms
and is solved by developing a tensor Laplacian-based algorithm. Extensive
experiments on artificial and real datasets demonstrate the higher accuracy and
precision of the proposed method in subspace clustering compared to the
state-of-the-art methods. The outperformance of this method is more revealed in
presence of inherent structure of the data such as nonlinearity, geometrical
overlapping, and outliers.


Abstract:
  In light of increasing recent attention to political polarization,
understanding how polarization can arise poses an important theoretical
question. While more classical models of opinion dynamics seem poorly equipped
to study this phenomenon, a recent novel approach by H\k{a}z{\l}a, Jin, Mossel,
and Ramnarayan (HJMR) proposes a simple geometric model of opinion evolution
that provably exhibits strong polarization in specialized cases. Moreover,
polarization arises quite organically in their model: in each time step, each
agent updates opinions according to their correlation/response with an issue
drawn at random. However, their techniques do not seem to extend beyond a set
of special cases they identify, which benefit from fragile symmetry or
contractiveness assumptions, leaving open how general this phenomenon really
is.
  In this paper, we further the study of polarization in related geometric
models. We show that the exact form of polarization in such models is quite
nuanced: even when strong polarization does not hold, it is possible for weaker
notions of polarization to nonetheless attain. We provide a concrete example
where weak polarization holds, but strong polarization provably fails. However,
we show that strong polarization provably holds in many variants of the HJMR
model, which are also robust to a wider array of distributions of random issues
-- this indicates that the form of polarization introduced by HJMR is more
universal than suggested by their special cases. We also show that the weaker
notions connect more readily to the theory of Markov chains on general state
spaces.


Abstract:
  Finite mixtures of regressions with fixed covariates are a commonly used
model-based clustering methodology to deal with regression data. However, they
assume assignment independence, i.e. the allocation of data points to the
clusters is made independently of the distribution of the covariates. In order
to take into account the latter aspect, finite mixtures of regressions with
random covariates, also known as cluster-weighted models (CWMs), have been
proposed in the univariate and multivariate literature. In this paper, the CWM
is extended to matrix data, e.g. those data where a set of variables are
simultaneously observed at different time points or locations. Specifically,
the cluster-specific marginal distribution of the covariates, and the
cluster-specific conditional distribution of the responses given the
covariates, are assumed to be matrix normal. Maximum likelihood parameter
estimates are derived using an ECM algorithm. Parameter recovery,
classification assessment and the capability of the BIC to detect the
underlying groups are analyzed on simulated data. Finally, two real data
applications concerning educational indicators and the Italian non-life
insurance market are presented.


Abstract:
  We present a novel direction-aware feature-level frequency decomposition
network for single image deraining. Compared with existing solutions, the
proposed network has three compelling characteristics. First, unlike previous
algorithms, we propose to perform frequency decomposition at feature-level
instead of image-level, allowing both low-frequency maps containing structures
and high-frequency maps containing details to be continuously refined during
the training procedure. Second, we further establish communication channels
between low-frequency maps and high-frequency maps to interactively capture
structures from high-frequency maps and add them back to low-frequency maps
and, simultaneously, extract details from low-frequency maps and send them back
to high-frequency maps, thereby removing rain streaks while preserving more
delicate features in the input image. Third, different from existing algorithms
using convolutional filters consistent in all directions, we propose a
direction-aware filter to capture the direction of rain streaks in order to
more effectively and thoroughly purge the input images of rain streaks. We
extensively evaluate the proposed approach in three representative datasets and
experimental results corroborate our approach consistently outperforms
state-of-the-art deraining algorithms.


Abstract:
  Energy landscapes are high-dimensional surfaces representing the dependence
of system energy on variable configurations, which determine crucially the
system's emergent behavior but are difficult to be analyzed due to their
high-dimensional nature. In this article, we introduce an approach to reveal
the complete energy landscapes of small spin glasses and Boolean satisfiability
problems, which also unravels their non-equilibrium dynamics at an arbitrary
temperature for an arbitrarily long time. In contrary to our common belief, our
results show that it can be less likely to identify the ground states when
temperature decreases, due to trapping in individual local minima, which ceases
at different time, leading to multiple abrupt jumps with time in the
ground-state probability. Simulations agree well with theoretical predictions
on these remarkable phenomena. Finally, for large systems, we introduce a
variant approach to extract partially the energy landscapes and observe both
analytically and in simulations similar phenomena. This work introduces new
methodology to unravel the non-equilibrium dynamics of glassy systems, and
provides us with a clear, complete and new physical picture on their long-time
behaviors inaccessible by modern numerics.


Abstract:
  From a theoretical view, this paper addresses the general problem of
designing ordinal classification methods based on comparing actions with subset
of actions, which are representative of their classes (categories). The basic
demand of the proposal consists in setting a relational system (D, S), where S
is a reflexive relation compatible with the preferential order of the set of
classes, and D is a transitive relation such that D is a subset of S. Different
ordinal classification methods can be derived from diverse model of preferences
fulfilling the basic conditions on S and D. Two complementary assignment
procedures compose each method, which correspond through the transposition
operation and should be used complementarily. The methods work under relatively
slight conditions on the representative actions and satisfy several fundamental
properties. ELECTRE TRI-nC, INTERCLASS-nC, and the hierarchical ELECTRE TRI-nC
with interacting criteria, can be considered as particular cases of this
general framework.


Abstract:
  NuSTAR observed the black hole candidate XTE J1908$+$094 during its 2013 and
2019 outbursts. We use relativistic reflection to measure the spin of the black
hole through 19 different assumptions of relxill flavors and parameter
combinations. The most favored model in terms of Deviance Information Criterion
(DIC) measures the spin of the black hole to be $a = 0.55^{+0.29}_{-0.45}$, and
an inclination of $\theta=27^{+2}_{-3}$ degrees ($1\sigma$ statistical errors).
We look at the effects of coronal geometry assumptions and density of the
accretion disk on the spin prediction. All 19 tested models provide consistent
spin estimates. We discuss the evolution of spin measurement techniques using
relativistic reflection in X-ray binaries and discuss the implications of this
spin measurement in reconciling the distributions of stellar mass black hole
spin measurements made through X-ray and gravitational wave observations.


Abstract:
  The search for exoplanets is pushing adaptive optics systems on ground-based
telescopes to their limits. One of the major limitations at small angular
separations, exactly where exoplanets are predicted to be, is the servo-lag of
the adaptive optics systems. The servo-lag error can be reduced with predictive
control where the control is based on the future state of the atmospheric
disturbance. We propose to use a linear data-driven integral predictive
controller based on subspace methods that is updated in real time. The new
controller only uses the measured wavefront errors and the changes in the
deformable mirror commands, which allows for closed-loop operation without
requiring pseudo-open loop reconstruction. This enables operation with
non-linear wavefront sensors such as the pyramid wavefront sensor. We show that
the proposed controller performs near-optimal control in simulations for both
stationary and non-stationary disturbances and that we are able to gain several
orders of magnitude in raw contrast. The algorithm has been demonstrated in the
lab with MagAO-X, where we gain more than two orders of magnitude in contrast.


Abstract:
  The behavior of Bayesian model averaging (BMA) for the normal linear
regression model in the presence of influential observations that contribute to
model misfit is investigated. Remedies to attenuate the potential negative
impacts of such observations on inference and prediction are proposed. The
methodology is motivated by the view that well-behaved residuals and good
predictive performance often go hand-in-hand. Focus is placed on regression
models that use variants on Zellner's g prior. Studying the impact of various
forms of model misfit on BMA predictions in simple situations points to
prescriptive guidelines for "tuning" Zellner's g prior to obtain optimal
predictions. The tuning of the prior distribution is obtained by considering
theoretical properties that should be enjoyed by the optimal fits of the
various models in the BMA ensemble. The methodology can be thought of as an
"empirical Bayes" approach to modeling, as the data help to inform the
specification of the prior in an attempt to attenuate the negative impact of
influential cases.


Abstract:
  This paper considers the information bottleneck (IB) problem of a Rayleigh
fading multiple-input multiple-out (MIMO) channel with an oblivious relay. The
relay is constrained to operate without knowledge of the codebooks, i.e., it
performs oblivious processing. Moreover, due to the bottleneck constraint, it
is impossible for the relay to inform the destination node of the perfect
channel state information (CSI) in each channel realization. To evaluate the
bottleneck rate, we first provide an upper bound by assuming that the
destination node can get the perfect CSI at no cost. Then, we provide four
achievable schemes where each scheme satisfies the bottleneck constraint and
gives a lower bound to the bottleneck rate. In the first and second schemes,
the relay splits the capacity of the relay-destination link into two parts, and
conveys both the CSI and its observation to the destination node. Due to CSI
transmission, the performance of these two schemes is sensitive to the MIMO
channel dimension, especially the channel input dimension. To ensure that it
still performs well when the channel dimension grows large, in the third and
fourth achievable schemes, the relay only transmits compressed observation to
the destination node. Numerical results show that with simple symbol-by-symbol
oblivious relay processing and compression, the proposed achievable schemes
work well and can demonstrate lower bounds coming quite close to the upper
bound on a wide range of relevant system parameters.


Abstract:
  In this paper, we present a system for remote health-monitoring of first
responders over TETRA radio links. The system features a smart garment that
periodically records and sends physiological parameters of first responders to
a remote agent, which processes the recordings and feeds back the health-status
notifications and warnings in the form of electrotactile stimuli. The choice of
TETRA as the connectivity solution is driven by its routine use by first
responders all over the world, thus representing a convenient and
economically-effective connectivity basis. Although the support for data
communications in TETRA is rather limited and in practice reduced to the Short
Data Service, we show that TETRA can serve the intended purpose in the
considered scenario, achieving tolerable delay and message-loss performance.
Moreover, when the system is examined and optimized in terms of the peak
Age-of-Information, a metric suitable to characterize the quasi-periodic nature
of the monitoring process, its performance becomes rather favorable, enabling
timely monitoring of the first responders' health status.


Abstract:
  Every integral current in a locally compact metric space $X$ can be
approximated by a Lipschitz chain with respect to the normal mass, provided
that Lipschitz maps into $X$ can be extended slightly.


Abstract:
  Graphics Processing Units (GPUs) are currently the dominating programmable
architecture for Deep Learning (DL) accelerators. The adoption of Field
Programmable Gate Arrays (FPGAs) in DL accelerators is however getting
momentum. In this paper, we demonstrate that Direct Hardware Mapping (DHM) of a
Convolutional Neural Network (CNN) on an embedded FPGA substantially
outperforms a GPU implementation in terms of energy efficiency and execution
time. However, DHM is highly resource intensive and cannot fully substitute the
GPU when implementing a state-of-the-art CNN. We thus propose a hybrid FPGA-GPU
DL acceleration method and demonstrate that heterogeneous acceleration
outperforms GPU acceleration even including communication overheads.
  Experimental results are conducted on a heterogeneous multi-platform setup
embedding an Nvidia(R) Jetson TX2 CPU-GPU board and an Intel(R) Cyclone10GX
FPGA board. The SqueezeNet, MobileNetv2, and ShuffleNetv2 mobile-oriented CNNs
are experimented. We show that heterogeneous FPG-AGPU acceleration outperforms
GPU acceleration for classification inference task over MobileNetv2 (12%-30%
energy reduction, 4% to 26% latency reduction), SqueezeNet (21%-28% energy
reduction, same latency), and ShuffleNetv2 (25% energy reduction, 21% latency
reduction).


Abstract:
  We address fundamental limitations of quantum teleportation -- the process of
transferring quantum information using classical communication and preshared
entanglement. We develop a new teleportation protocol based upon the idea of
using ancillary entanglement catalytically, i.e. without depleting it. This
protocol is then used to show that catalytic entanglement allows for a
noiseless quantum channel to be simulated with a quality that could never be
achieved using only entanglement from the shared state, even for catalysts with
a small dimension. On the one hand, this allows for a more faithful
transmission of quantum information using generic states and a fixed amount of
consumed entanglement. On the other hand, this shows, for the first time, that
entanglement catalysis provides a genuine advantage in a generic
quantum-information processing task. Finally, we show that similar ideas can be
directly applied to study quantum catalysis for more general problems in
quantum mechanics. As an application, we show that catalysts can activate
so-called passive states, a concept that finds widespread application e.g. in
quantum thermodynamics.


Abstract:
  Cavity optomechanical systems enable fine manipulation of nanomechanical
degrees of freedom with light, adding operational functionality and impacting
their appeal in photonic technologies. We show that distinct mechanical modes
can be exploited with a temporally modulated laser drive to steer between
bistable steady states induced by changes of cavity radiation pressure. We
investigate the influence of thermo-optic nonlinearity on these Floquet
dynamics and find that it can inhibit or enhance the performance of the
coupling mechanism in contrast to their often performance limiting character.
Our results provide new techniques for the characterization of thermal
properties and the control of optomechanical systems in sensing and
computational applications


Abstract:
  We present a new particle filtering algorithm for nonlinear systems in the
discrete-time setting. Our algorithm is based on the Stein variational gradient
descent (SVGD) framework, which is a general approach to sample from a target
distribution. We merge the standard two-step paradigm in particle filtering
into one step so that SVGD can be used. A distinguishing feature of the
proposed algorithm is that, unlike most particle filtering methods, all the
particles at any time step are equally weighted and thus no update on the
weights is needed. We further extended our algorithm to allow for updating
previous particles within a sliding window. This strategy may improve the
reliability of the algorithm with respect to unexpected disturbance in the
dynamics or outlier-measurements. The efficacy of the proposed algorithms is
illustrated through several numerical examples in comparison with a standard
particle filtering method.


Abstract:
  Fomalhaut C (LP 876-10) is a low mass M4V star in the intriguing Fomalhaut
triple system and, like Fomalhaut A, possesses a debris disc. It is one of very
few nearby M-dwarfs known to host a debris disc and of these has by far the
lowest stellar mass. We present new resolved observations of the debris disc
around Fomalhaut C with the Atacama Large Millimetre Array which allow us to
model its properties and investigate the system's unique history. The ring has
a radius of 26 au and a narrow full width at half maximum of at most 4.2 au. We
find a 3$\sigma$ upper limit on the eccentricity of 0.14, neither confirming
nor ruling out previous dynamic interactions with Fomalhaut A that could have
affected Fomalhaut C's disc. We detect no $^{12}$CO J=3-2 emission in the
system and do not detect the disc in scattered light with HST/STIS or
VLT/SPHERE. We find the original Herschel detection to be consistent with our
ALMA model's radial size. We place the disc in the context of the wider debris
disc population and find that its radius is as expected from previous disc
radius-host luminosity trends. Higher signal-to-noise observations of the
system would be required to further constrain the disc properties and provide
further insight to the history of the Fomalhaut triple system as a whole.


Abstract:
  The phenomenon of charge pumping and the modern theory of electric
polarization are reconsidered by analytically taking into account emergent
non-Hermitian contributions. These are accounted for through the use of an
extended definition of the velocity operator and are determined by means of a
dynamic Hellmann-Feynman theorem (DHFT) that we derive here for the first time.
The DHFT introduces generalized Berry curvatures and it is valid for
calculating observables nonperturbatively, hence with results valid to all
orders of the external fields. By using the extended velocity operator we
rigorously show how the charge pumping is linked up with the boundaries of the
material (with the non-Hermiticity being essential for this connection), and by
means of the DHFT we show that the well-known topological quantization of the
pumped charge breaks down due to a nonintegrable Aharonov-Anandan phase in
driven non-equilibrium processes whenever the periodic gauge cannot be applied
to the Floquet-Bloch states. Likewise, we show that the electronic polarization
change has an additional non-Hermitian contribution, which is overlooked in the
modern theory of electric polarization. The non-Hermitian contribution is by
definition a bulk quantity that may equally be evaluated as a boundary quantity
due to a symmetric structure that allows the bulk integration to be transformed
into a boundary one. This non-Hermitian contribution is very sensitive to the
realistic boundary conditions imposed on the wavefunctions and it is therefore
expected to be significant in biased insulators where charge accumulation over
their boundaries is present during the process that causes the polarization
change. Finally, we show how a well-defined surface-charge theorem can be
formulated in terms of the boundary non-Hermitian contribution.


Abstract:
  In this paper, we propose a Bayesian framework to create two dimensional
ionospheric images of high spatio-temporal resolution to monitor ionospheric
irregularities as measured by the S4 index. Here, we recast the standard
Bayesian recursive filtering for a linear Gaussian state-space model, also
referred to as the Kalman filter, first by augmenting the (pierce point)
observation model with connectivity information stemming from the insight and
assumptions/standard modeling about the spatial distribution of the
scintillation activity on the ionospheric shell at 350 km altitude. Thus, we
achieve to handle the limited spatio-temporal observations. Then, by
introducing a set of Kalman filters running in parallel, we mitigate the
uncertainty related to a tuning parameter of the proposed augmented model. The
output images are a weighted average of the state estimates of the individual
filters. We demonstrate our approach by rendering two dimensional real-time
ionospheric images of S4 amplitude scintillation at 350 km over South America
with temporal resolution of one minute. Furthermore, we employ extra S4 data
that was not used in producing these ionospheric images, to check and verify
the ability of our images to predict this extra data in particular ionospheric
pierce points. Our results show that in areas with a network of ground
receivers with a relatively good coverage (e.g. within a couple of kilometers
distance) the produced images can provide reliable real-time results. Our
proposed algorithmic framework can be readily used to visualize real-time
ionospheric images taking as inputs the available scintillation data provided
from freely available web-servers.


Abstract:
  The Sunyaev-Zel'dovich (SZ) effect introduces a specific distortion of the
blackbody spectrum of the cosmic microwave background (CMB) radiation when it
scatters off hot gas in clusters of galaxies. The frequency dependence of the
distortion is only independent of the cluster redshift when the evolution of
the CMB radiation is adiabatic. Using 370 clusters within the redshift range
$0.07\lesssim z\lesssim1.4$ from the largest SZ-selected cluster sample to date
from the Atacama Cosmology Telescope, we provide new constraints on the
deviation of CMB temperature evolution from the standard model
$\alpha=0.017^{+0.029}_{-0.032}$, where $T(z)=T_0(1+z)^{1-\alpha}$. This result
is consistent with no deviation from the standard adiabatic model. Combining it
with previous, independent datasets we obtain a joint constraint of
$\alpha=-0.001\pm0.012$. Attributing deviation from adiabaticity to the decay
of dark energy, this result constrains its effective equation of state
$w_\mathrm{eff}=-0.998^{+0.008}_{-0.010}$.


Abstract:
  We perform on-the-fly non-adiabatic molecular dynamics simulations using the
symmetrical quasi-classical (SQC) approach with the recently suggested
molecular Tully models: ethylene and fulvene. We attempt to provide benchmarks
of the SQC methods using both the square and the triangle windowing schemes as
well as the recently proposed electronic zero-point-energy correction scheme
(so-called the gamma correction). We use the quasi-diabatic propagation scheme
to directly interface the diabatic SQC methods with adiabatic electronic
structure calculations. Our results showcase the drastic improvement of the
accuracy by using the trajectory-adjusted gamma-corrections, which outperform
the widely used trajectory surface hopping method with decoherence corrections.
These calculations provide useful and non-trivial tests to systematically
investigate the numerical performance of various diabatic quantum dynamics
approaches, going beyond simple diabatic model systems that have been used as
the major workhorse in the quantum dynamics field. At the same time, these
available benchmark studies will also likely foster the development of new
quantum dynamics approaches based on these techniques.


Abstract:
  Minimal algebraic surfaces of general type $X$ such that
$K^2_X=2\chi(\mathcal{O}_X)-6$ or $K^2_X=2\chi(\mathcal{O}_X)-5$ are called
Horikawa surfaces. In this note we study $\mathbb{Z}^2_2$-actions on Horikawa
surfaces. The main result is that all the connected components of Gieseker's
moduli space of canonical models of surfaces of general type with invariants
satisfying these relations contain surfaces with $\mathbb{Z}^2_2$-actions.


Abstract:
  We investigate the electromagnetic form factor $F(q^2)$ of the meson by using
the solvable $\phi^{3}$ scalar field model in $(1+1)$ dimensions. As the
transverse rotations are absent in $(1+1)$ dimensions, the advantage of the
light-front dynamics (LFD) with the light-front time $x^+ = x^0 + x^3$ as the
evolution parameter is maximized in contrast to the usual instant form dynamics
(IFD) with the ordinary time $x^0$ as the evolution parameter. In LFD, the
individual $x^+$-ordered amplitudes contributing to $F(q^2)$ are invariant
under the boost, i.e., frame-independent, while the individual $x^0$-ordered
amplitudes in IFD are not invariant under the boost but dependent on the
reference frame. The LFD allows to get the analytic result for the one-loop
triangle diagram which covers not only the spacelike ($q^{2}<0$) but also
timelike region ($q^{2}>0$). Using the analytic results, we verify that the
real and imaginary parts of the form factor satisfy the dispersion relations in
the entire $q^{2}$ space. Comparing with the results in $(3+1)$ dimensions, we
discuss the transverse momentum effects on $F(q^2)$ . We also discuss the
longitudinal charge density in terms of the boost invariant variable $\tilde z
= p^+ x^-$ in LFD.


Abstract:
  We report a comprehensive de Haas--van Alphen (dHvA) study of the
heavy-fermion material CeRhIn$_5$ in magnetic fields up to 70~T. Several dHvA
frequencies gradually emerge at high fields as a result of magnetic breakdown.
Among them is the thermodynamically important $\beta_1$ branch, which has not
been observed so far. Comparison of our angule-dependent dHvA spectra with
those of the non-$4f$ compound LaRhIn$_5$ and with band-structure calculations
evidences that the Ce $4f$ electrons in CeRhIn$_5$ remain localized over the
whole field range. This rules out any significant Fermi-surface reconstruction,
either at the suggested nematic phase transition at $B^{*}\approx$ 30~T or at
the putative quantum critical point at $B_c \simeq$ 50~T. Our results rather
demonstrate the robustness of the Fermi surface and the localized nature of the
4$f$ electrons inside and outside of the antiferromagnetic phase.


Abstract:
  We study turbulent flows in a smooth straight pipe of circular cross--section
up to $Re_{\tau} \approx 6000$ using direct--numerical-simulation (DNS) of the
Navier--Stokes equations. The DNS results highlight systematic deviations from
Prandtl friction law, amounting to about $2\%$, which would extrapolate to
about $4\%$ at extreme Reynolds numbers. Data fitting of the DNS friction
coefficient yields an estimated von K\'arm\'an constant $k \approx 0.387$,
which nicely fits the mean velocity profile, and which supports universality of
canonical wall-bounded flows. The same constant also applies to the pipe
centerline velocity, thus providing support for the claim that the asymptotic
state of pipe flow at extreme Reynolds numbers should be plug flow. At the
Reynolds numbers under scrutiny, no evidence for saturation of the logarithmic
growth of the inner peak of the axial velocity variance is found. Although no
outer peak of the velocity variance directly emerges in our DNS, we provide
strong evidence that it should appear at $Re_{\tau} \gtrsim 10^4$, as a result
of turbulence production exceeding dissipation over a large part of the outer
wall layer, thus invalidating the classical equilibrium hypothesis.


Abstract:
  The growth of online consumer health questions has led to the necessity for
reliable and accurate question answering systems. A recent study showed that
manual summarization of consumer health questions brings significant
improvement in retrieving relevant answers. However, the automatic
summarization of long questions is a challenging task due to the lack of
training data and the complexity of the related subtasks, such as the question
focus and type recognition. In this paper, we introduce a reinforcement
learning-based framework for abstractive question summarization. We propose two
novel rewards obtained from the downstream tasks of (i) question-type
identification and (ii) question-focus recognition to regularize the question
generation model. These rewards ensure the generation of semantically valid
questions and encourage the inclusion of key medical entities/foci in the
question summary. We evaluated our proposed method on two benchmark datasets
and achieved higher performance over state-of-the-art models. The manual
evaluation of the summaries reveals that the generated questions are more
diverse and have fewer factual inconsistencies than the baseline summaries


Abstract:
  How to estimate heterogeneity, e.g. the effect of some variable differing
across observations, is a key question in political science. Methods for doing
so make simplifying assumptions about the underlying nature of the
heterogeneity to draw reliable inferences. This paper allows a common way of
simplifying complex phenomenon (placing observations with similar effects into
discrete groups) to be integrated into regression analysis. The framework
allows researchers to (i) use their prior knowledge to guide which groups are
permissible and (ii) appropriately quantify uncertainty. The paper does this by
extending work on "structured sparsity" from a traditional penalized likelihood
approach to a Bayesian one by deriving new theoretical results and inferential
techniques. It shows that this method outperforms state-of-the-art methods for
estimating heterogeneous effects when the underlying heterogeneity is grouped
and more effectively identifies groups of observations with different effects
in observational data.


Abstract:
  We study the Minkowski formula of conformal Killing-Yano two-forms in a
spacetime of constant curvature. We establish the spacetime Alexandrov theorem
with a free boundary.


Abstract:
  State-of-the-art transformer models use pairwise dot-product based
self-attention, which comes at a computational cost quadratic in the input
sequence length. In this paper, we investigate the global structure of
attention scores computed using this dot product mechanism on a typical
distribution of inputs, and study the principal components of their variation.
Through eigen analysis of full attention score matrices, as well as of their
individual rows, we find that most of the variation among attention scores lie
in a low-dimensional eigenspace. Moreover, we find significant overlap between
these eigenspaces for different layers and even different transformer models.
Based on this, we propose to compute scores only for a partial subset of token
pairs, and use them to estimate scores for the remaining pairs. Beyond
investigating the accuracy of reconstructing attention scores themselves, we
investigate training transformer models that employ these approximations, and
analyze the effect on overall accuracy. Our analysis and the proposed method
provide insights into how to balance the benefits of exact pair-wise attention
and its significant computational expense.


Abstract:
  It is necessary to improve the performance of some special classes or to
particularly protect them from attacks in adversarial learning. This paper
proposes a framework combining cost-sensitive classification and adversarial
learning together to train a model that can distinguish between protected and
unprotected classes, such that the protected classes are less vulnerable to
adversarial examples. We find in this framework an interesting phenomenon
during the training of deep neural networks, called Min-Max property, that is,
the absolute values of most parameters in the convolutional layer approach zero
while the absolute values of a few parameters are significantly larger becoming
bigger. Based on this Min-Max property which is formulated and analyzed in a
view of random distribution, we further build a new defense model against
adversarial examples for adversarial robustness improvement. An advantage of
the built model is that it does no longer need adversarial training, and thus,
has a higher computational efficiency than most existing models of needing
adversarial training. It is experimentally confirmed that, regarding the
average accuracy of all classes, our model is almost as same as the existing
models when an attack does not occur and is better than the existing models
when an attack occurs. Specifically, regarding the accuracy of protected
classes, the proposed model is much better than the existing models when an
attack occurs.


Abstract:
  We continue the study of Confined Vortex Surfaces (\CVS{}) that we introduced
in the previous paper.
  We classify the solutions of the \CVS{} equation and find the analytical
formula for the velocity field for arbitrary background strain eigenvalues in
the stable region.
  The vortex surface cross-section has the form of four symmetric hyperbolic
sheets with a simple equation $|y| |x|^\mu =1$ in each quadrant of the tube
cross-section ($x y $ plane).
  We use the dilute gas approximation for the vorticity structures in a
turbulent flow, assuming their size is much smaller than the mean distance
between them.
  We introduce the Gaussian random background strain for each vortex surface as
an accumulation of a large number of small random contributions coming from
other surfaces far away. We compute this self-consistent background strain,
relating the variance of the strain to the energy dissipation rate.
  We find a universal asymmetric distribution for energy dissipation.
  A new phenomenon is a probability distribution of the shape of the profile of
the vortex tube in the $x y$ plane.
  This phenomenon naturally leads to imitation of the "multi-fractal" scaling
of the moments of velocity difference $v(\vec r_1) - \vec v(\vec r_2)$.
  These moments have a nontrivial dependence of $n, \log |r_1 - r_2|$,
approximating power laws with nonlinear index $\zeta(n)$. The rough estimate we
provide here is not matching the observed DNS data, which may indicate
necessity of the full 3D solution of the \CVS{} equations.
  We argue that the approximate relations for these moments suggested in a
recent paper by Sreenivasan and Yakhot are consistent with the \CVS{} theory.
We reinterpret their renormalization parameter $\alpha\approx 0.95$ in the
Bernoulli law $ p = - \frac{1}{2}\alpha \vec v^2$ as a probability to find no
vortex surface at a random point in space.


Abstract:
  Cooperation in multi-agent and multi-robot systems can help agents build
various formations, shapes, and patterns presenting corresponding functions and
purposes adapting to different situations. Relationships between agents such as
their spatial proximity and functional similarities could play a crucial role
in cooperation between agents. Trust level between agents is an essential
factor in evaluating their relationships' reliability and stability, much as
people do. This paper proposes a new model called Relative Needs Entropy (RNE)
to assess trust between robotic agents. RNE measures the distance of needs
distribution between individual agents or groups of agents. To exemplify its
utility, we implement and demonstrate our trust model through experiments
simulating a heterogeneous multi-robot grouping task in a persistent urban
search and rescue mission consisting of tasks at two levels of difficulty. The
results suggest that RNE trust-Based grouping of robots can achieve better
performance and adaptability for diverse task execution compared to the
state-of-the-art energy-based or distance-based grouping models.


Abstract:
  Electropolymerization is a bottom-up materials engineering process of micro
and nano-scale that utilizes electrical signals to deposit conducting
dendrites' morphologies by a redox reaction in the liquid phase. It resembles
synaptogenesis in the brain, in which electrical stimulation in the brain
causes the formation of synapses from the cellular neural composites. The
strategy has been recently explored for neuromorphic engineering by
establishing link between the electrical signals and the dendrites' shapes.
Since the geometry of these structures determines their electrochemical
properties, understanding the mechanisms that regulate the polymer assembly
under electrically programmed conditions is an important aspect. In this
manuscript, we simulate this phenomenon using mesoscale simulations, taking
into account the important features of spatial-temporal potential mapping based
on the time-varying signal, the motion of charged particles in the liquid due
to the electric field, and the attachment of particles on the electrode. The
study helps in visualizing the motion of particles in different electrical
conditions, which is not possible to probe experimentally. Consistent with the
experiments, the higher AC frequency of electrical activities favors linear
wire-like growth, while lower frequency leads to more dense and fractal
dendrites growth, and voltage offset leads to asymmetrical growth. We find that
dendrites' shape and growth process systematically depend on particle
concentration and random scattering. We discover that the different dendrites'
architectures are associated with different Laplace and diffusion fields, which
govern the monomers trajectory and subsequent dendrites' growth. Such
unconventional engineering routes could have a variety of applications from
neuromorphic engineering to bottom-up computing strategies.


Abstract:
  In Meta-Reinforcement Learning (meta-RL) an agent is trained on a set of
tasks to prepare for and learn faster in new, unseen, but related tasks. The
training tasks are usually hand-crafted to be representative of the expected
distribution of test tasks and hence all used in training. We show that given a
set of training tasks, learning can be both faster and more effective (leading
to better performance in the test tasks), if the training tasks are
appropriately selected. We propose a task selection algorithm,
Information-Theoretic Task Selection (ITTS), based on information theory, which
optimizes the set of tasks used for training in meta-RL, irrespectively of how
they are generated. The algorithm establishes which training tasks are both
sufficiently relevant for the test tasks, and different enough from one
another. We reproduce different meta-RL experiments from the literature and
show that ITTS improves the final performance in all of them.


Abstract:
  We propose the convergent graph solver (CGS), a deep learning method that
learns iterative mappings to predict the properties of a graph system at its
stationary state (fixed point) with guaranteed convergence. CGS systematically
computes the fixed points of a target graph system and decodes them to estimate
the stationary properties of the system without the prior knowledge of existing
solvers or intermediate solutions. The forward propagation of CGS proceeds in
three steps: (1) constructing the input dependent linear contracting iterative
maps, (2) computing the fixed-points of the linear maps, and (3) decoding the
fixed-points to estimate the properties. The contractivity of the constructed
linear maps guarantees the existence and uniqueness of the fixed points
following the Banach fixed point theorem. To train CGS efficiently, we also
derive a tractable analytical expression for its gradient by leveraging the
implicit function theorem. We evaluate the performance of CGS by applying it to
various network-analytic and graph benchmark problems. The results indicate
that CGS has competitive capabilities for predicting the stationary properties
of graph systems, irrespective of whether the target systems are linear or
non-linear. CGS also shows high performance for graph classification problems
where the existence or the meaning of a fixed point is hard to be clearly
defined, which highlights the potential of CGS as a general graph neural
network architecture.


Abstract:
  Partial subtle mirroring of nonverbal behaviors during conversations (also
known as mimicking or parallel empathy), is essential for rapport building,
which in turn is essential for optimal human-human communication outcomes.
Mirroring has been studied in interactions between robots and humans, and in
interactions between Embodied Conversational Agents (ECAs) and humans. However,
very few studies examine interactions between humans and ECAs that are
integrated with robots, and none of them examine the effect of mirroring
nonverbal behaviors in such interactions. Our research question is whether
integrating an ECA able to mirror its interlocutor's facial expressions and
head movements (continuously or intermittently) with a human-service robot will
improve the user's experience with the support robot that is able to perform
useful mobile manipulative tasks (e.g. at home). Our contribution is the
complex integration of an expressive ECA, able to track its interlocutor's
face, and to mirror his/her facial expressions and head movements in real time,
integrated with a human support robot such that the robot and the agent are
fully aware of each others', and of the users', nonverbals cues. We also
describe a pilot study we conducted towards answering our research question,
which shows promising results for our forthcoming larger user study.


Abstract:
  We perform the shadow calculation of the loop quantum gravity motivated
regular black hole recently proposed by Ashtekar, Olmedo and Singh (will be
termed AOS black hole hereafter). In the process, we also construct the
rotating loop quantum gravity inspired solution of the originally proposed
static spherically symmetric AOS black hole by applying the modified
Newman-Janis algorithm. We study the quantum effects on the shadows of both the
non-rotating and rotating loop quantum black hole solutions. It is observed
that the general shape of the shadow for non-rotating AOS black hole is
circular in shape as is expected for its classical counter part too, but the
presence of loop quantum gravity inspired modification contracts the shadow
radius and the effect reduces with the increase in the mass of the black hole.
On a similar note, in the rotating situation, we find contraction in shadow
radius due to quantum effects and the tapered nature of the shadow as expected
from the classical Kerr case. However, instead of the symmetrical contraction,
like non-rotating one, we found more contraction on one side relative to the
other when we compare our result with the shadow of the Kerr black hole. We
finally studied super-radiance in rotating AOS background and observed that the
super-radiance condition for massless scalar field is identical to that of the
Kerr case with the rotation of the BH being more compared to Kerr in the low
mass regime. With an increase in mass of the rotating AOS black hole, the
difference from Kerr starts to become insignificant.


Abstract:
  We report the discovery of a transiting, temperate, Neptune-sized exoplanet
orbiting the nearby ($d$ = 27.5 pc), M3V star TOI-1231 (NLTT 24399, L 248-27,
2MASS J10265947-5228099). The planet was detected using photometric data from
the Transiting Exoplanet Survey Satellite and followed up with observations
from the Las Cumbres Observatory and the Antarctica Search for Transiting
ExoPlanets program. Combining the photometric data sets, we find that the newly
discovered planet has a radius of 3.65$^{+0.16}_{-0.15}$ R$_{\oplus}$, and an
orbital period of 24.246 days. Radial velocity measurements obtained with the
Planet Finder Spectrograph on the Magellan Clay telescope confirm the existence
of the planet and lead to a mass measurement of 15.5$\pm$3.3 M$_{\oplus}$. With
an equilibrium temperature of just 330K TOI-1231 b is one of the coolest small
planets accessible for atmospheric studies thus far, and its host star's bright
NIR brightness (J=8.88, K$_{s}$=8.07) make it an exciting target for HST and
JWST. Future atmospheric observations would enable the first comparative
planetology efforts in the 250-350 K temperature regime via comparisons with
K2-18 b. Furthermore, TOI-1231's high systemic radial velocity (70.5 k\ms) may
allow for the detection of low-velocity hydrogen atoms escaping the planet by
Doppler shifting the H I Ly-alpha stellar emission away from the geocoronal and
ISM absorption features.


Abstract:
  Massive bosonic fields in the background of a Kerr black hole can either
trigger superradiant instabilities (black-hole bombs) or form equilibrium
configurations corresponding to pure bound states, known as stationary scalar
clouds. Here, similar phenomena are shown to emerge in the fluctuation dynamics
of a rotating photon-fluid model. In the presence of suitable vortex flows, the
density fluctuations are governed by the massive Klein-Gordon equation on a
(2+1) curved spacetime, possessing an ergoregion and an event horizon. We
report on superradiant instabilities originating from quasi-bound phonon states
trapped by the vortex background and, remarkably, on the existence of
stationary modes in synchronous rotation with the horizon. These represent the
acoustic counterpart of astrophysical scalar clouds. Our system offers a
promising platform for analogue gravity experiments on superradiant
instabilities of massive bosons and black-hole-field equilibrium
configurations.


Abstract:
  The Fermi National Accelerator Laboratory has measured the anomalous
precession frequency $a^{}_\mu = (g^{}_\mu-2)/2$ of the muon to a combined
precision of 0.46 parts per million with data collected during its first
physics run in 2018. This paper documents the measurement of the magnetic field
in the muon storage ring. The magnetic field is monitored by nuclear magnetic
resonance systems and calibrated in terms of the equivalent proton spin
precession frequency in a spherical water sample at 34.7$^\circ$C. The measured
field is weighted by the muon distribution resulting in $\tilde{\omega}'^{}_p$,
the denominator in the ratio $\omega^{}_a$/$\tilde{\omega}'^{}_p$ that together
with known fundamental constants yields $a^{}_\mu$. The reported uncertainty on
$\tilde{\omega}'^{}_p$ for the Run-1 data set is 114 ppb consisting of
uncertainty contributions from frequency extraction, calibration, mapping,
tracking, and averaging of 56 ppb, and contributions from fast transient fields
of 99 ppb.


Abstract:
  Targeted syntactic evaluations have demonstrated the ability of language
models to perform subject-verb agreement given difficult contexts. To elucidate
the mechanisms by which the models accomplish this behavior, this study applies
causal mediation analysis to pre-trained neural language models. We investigate
the magnitude of models' preferences for grammatical inflections, as well as
whether neurons process subject-verb agreement similarly across sentences with
different syntactic structures. We uncover similarities and differences across
architectures and model sizes -- notably, that larger models do not necessarily
learn stronger preferences. We also observe two distinct mechanisms for
producing subject-verb agreement depending on the syntactic structure of the
input sentence. Finally, we find that language models rely on similar sets of
neurons when given sentences with similar syntactic structure.


Abstract:
  Accurate quantification of model uncertainty has long been recognized as a
fundamental requirement for trusted AI. In regression tasks, uncertainty is
typically quantified using prediction intervals calibrated to a specific
operating point, making evaluation and comparison across different studies
difficult. Our work leverages: (1) the concept of operating characteristics
curves and (2) the notion of a gain over a simple reference, to derive a novel
operating point agnostic assessment methodology for prediction intervals. The
paper describes the corresponding algorithm, provides a theoretical analysis,
and demonstrates its utility in multiple scenarios. We argue that the proposed
method addresses the current need for comprehensive assessment of prediction
intervals and thus represents a valuable addition to the uncertainty
quantification toolbox.


Abstract:
  Contrastive learning has recently seen tremendous success in self-supervised
learning. So far, however, it is largely unclear why the learned
representations generalize so effectively to a large variety of downstream
tasks. We here prove that feedforward models trained with objectives belonging
to the commonly used InfoNCE family learn to implicitly invert the underlying
generative model of the observed data. While the proofs make certain
statistical assumptions about the generative model, we observe empirically that
our findings hold even if these assumptions are severely violated. Our theory
highlights a fundamental connection between contrastive learning, generative
modeling, and nonlinear independent component analysis, thereby furthering our
understanding of the learned representations as well as providing a theoretical
foundation to derive more effective contrastive losses.