Abstract:
  The microquasar MAXI J\(1820+070\) went into outburst from mid-March until
mid-July 2018 with several faint rebrightenings afterwards. With a peak flux of
approximately 4 Crab in the \(20-50\) keV, energy range the source was
monitored across the electromagnetic spectrum with detections from radio to
hard X-ray frequencies. Using these multi-wavelength observations, we analyzed
quasi-simultaneous observations from 12 April, near the peak of the outburst
(\(\sim 23\) March). Spectral analysis of the hard X-rays found a \(kT_e \sim
30 \) keV and \( \tau \sim 2\) with a \texttt{CompTT} model, indicative of an
accreting black hole binary in the hard state. The flat/inverted radio spectrum
and the accretion disk winds seen at optical wavelengths are also consistent
with the hard state. Then we constructed a spectral energy distribution
spanning \(\sim 12\) orders of magnitude using modelling in \texttt{JetSeT}.
The model is composed of an irradiated disk with a Compton hump and a leptonic
jet with an acceleration region and a synchrotron-dominated cooling region.
\texttt{JetSeT} finds the spectrum is dominated by jet emission up to
approximately \(10^{14}\) Hz after which disk and coronal emission dominate.
The acceleration region has a magnetic field of \( B \sim 1.6 \times 10^4 \) G,
a cross section of \(R \sim 2.8 \times 10^{9} \) cm, and a flat radio spectral
shape naturally obtained from the synchroton cooling of the accelerated
electrons. The jet luminosity of \(> 8 \times 10^{37} \) erg/s (\(>
0.15L_{Edd}\)) compared to an accretion luminosity of \( \sim 6 \times
10^{37}\) erg/s, assuming a distance of 3 kpc. Because these two values are
comparable, it is possible the jet is powered predominately via accretion with
only a small contribution needed from the Blanford-Znajek mechanism from the
reportedly slowly spinning black hole.


Abstract:
  We demonstrate a hierarchy of various classes of quantum correlations on
experimentally prepared two-qubit Werner-like states with controllable white
noise. Werner states, which are white-noise-affected Bell states, are
prototypal examples for studying such a hierarchy as a function of the amount
of white noise. We experimentally generated Werner states and their
generalizations, i.e., partially entangled pure states affected by white noise.
These states enabled us to study the hierarchy of the following classes of
correlations: separability, entanglement, steering in three- and
two-measurement scenarios, and Bell nonlocality. We show that the generalized
Werner states (GWSs) reveal fundamentally different aspects of the hierarchy
compared to the Werner states. In particular, we find five different parameter
regimes of the GWSs, including those steerable in a two-measurement scenario
but not violating Bell inequalities. This regime cannot be observed for the
usual Werner states. Furthermore, we find threshold curves separating different
regimes of the quantum correlations and find the optimal states which allow for
the largest amount of white noise, which does not destroy their specific
quantum correlations (e.g., unsteerable entanglement). Thus, we could identify
the optimal Bell-nondiagonal GWSs, which are, for this specific meaning, more
robust against white noise compared to the Bell-diagonal GWSs (i.e., Werner
states).


Abstract:
  We show that the maximal number of planes in a complex smooth cubic fourfold
in ${\mathbb P}^5$ is $405$, realized by the Fermat cubic only; the maximal
number of real planes in a real smooth cubic fourfold is $357$, realized by the
so-called Clebsch--Segre cubic. Altogether, there are but three (up to
projective equivalence) cubics with more than $350$ planes.


Abstract:
  The study of exoplanet atmospheres showed large diversity compared to the
planets in our solar system. Especially Jupiter type exoplanets orbiting their
host star in close orbits, the so-called hot and ultra-hot Jupiters, have been
studied in detail due to their enhanced atmospheric signature. Due to their
tidally locked status, the temperature difference between the day- and
nightside triggers atmospheric winds which can lead to various fingerprints in
the observations. Spatially resolved absorption lines during transit such as
sodium (Na) could be a good tracer for such winds. Different works resolved the
Na$^-$ absorption lines on different exoplanets which show different line
widths. Assuming that this could be attributed to such zonal jet streams, this
work models the effect of such winds on synthetic absorption lines. For this,
transiting Jupiter type planets with rotational velocities similar to hot and
ultra-hot Jupiter are considered. The investigation shows that high wind
velocities could reproduce the broadening of Na-line profiles inferred in
different high-resolution transit observations. There is a tendency that the
broadening values decrease for planets with lower equilibrium temperature. This
could be explained by atmospheric drag induced by the ionization of alkali
lines which slow down the zonal jet streams, favoring their existence on hot
Jupiter rather than ultra-hot Jupiter.


Abstract:
  In this work we compute for the first time the so-called Complete-NLO
predictions for top-quark pair hadroproduction in association with at least one
isolated photon ($ t \bar t \gamma$). We also compute NLO QCD+EW predictions
for the similar case with at least two isolated photons ($ t \bar t
\gamma\gamma$) and for single-top hadroproduction in association with at least
one isolated photon. In addition, we complement our results with NLO QCD+EW
predictions of the hadronic and leptonic decays of top-quark including an
isolated photon. All these results have been obtained in a completely automated
approach, by extending the capabilities of the MadGraph5_aMC@NLO framework and
enabling the Complete-NLO predictions for processes with isolated photons in
the final state. We discuss the technical details of the implementation, which
involves a mixed EW renormalisation scheme for such processes.


Abstract:
  We establish multiplicity results for the following class of quasilinear
problems $$ \left\{ \begin{array}{l} -\Delta_{\Phi}u=f(x,u) \quad \mbox{in}
\quad \Omega, \\ u=0 \quad \mbox{on} \quad \partial \Omega, \end{array} \right.
\leqno{(P)} $$ where $\Delta_{\Phi}u=\text{div}(\varphi(x,|\nabla u|)\nabla u)$
for a generalized N-function $\Phi(x,t)=\int_{0}^{|t|}\varphi(x,s)s\,ds$. We
consider $\Omega\subset\mathbb{R}^N$ to be a smooth bounded domain that
contains two disjoint open regions $\Omega_N$ and $\Omega_p$ such that
$\overline{\Omega_N}\cap\overline{\Omega_p}=\emptyset$. The main feature of the
problem $(P)$ is that the operator $-\Delta_{\Phi}$ behaves like $-\Delta_N$ on
$\Omega_N$ and $-\Delta_p$ on $\Omega_p$. We assume the nonlinearity
$f:\Omega\times\mathbb{R}\to\mathbb{R}$ of two different types, but both
behaves like $e^{\alpha|t|^\frac{N}{N-1}}$ on $\Omega_N$ and $|t|^{p^*-2}t$ on
$\Omega_p$ as $|t|$ is large enough, for some $\alpha>0$ and
$p^*=\frac{Np}{N-p}$ being the critical Sobolev exponent for $1<p<N$. In this
context, for one type of nonlinearity $f$, we provide multiplicity of solutions
in a general smooth bounded domain and for another type of nonlinearity $f$, in
an annular domain $\Omega$, we establish existence of multiple solutions for
the problem $(P)$ that are nonradial and rotationally nonequivalent.


Abstract:
  The representation of functions by artificial neural networks depends on a
large number of parameters in a non-linear fashion. Suitable parameters of
these are found by minimizing a 'loss functional', typically by stochastic
gradient descent (SGD) or an advanced SGD-based algorithm.
  In a continuous time model for SGD with noise that follows the 'machine
learning scaling', we show that in a certain noise regime, the optimization
algorithm prefers 'flat' minima of the objective function in a sense which is
different from the flat minimum selection of continuous time SGD with
homogeneous noise.


Abstract:
  This paper establishes the equivalence between Bayesian revealed preference
(Caplin and Dean, 2015) and classical revealed preference with non-linear
budget constraints (Forges and Minelli, 2009). Classical revealed preference
tests for utility maximization given known budget constraints. Bayesian
revealed preference tests for costly information acquisition given a utility
function. Our main result shows that the key theorem in Caplin and Dean (2015)
on Bayesian revealed preference is equivalent to Afriat-type feasibility
inequalities (Afriat, 1967) for general (non-linear) budget sets. Our second
result exploits this connection between classical and Bayesian revealed
preference to construct a monotone convex information acquisition cost from
decision maker's utilities and decisions in Bayesian revealed preference.
Keywords: Afriat's Theorem, Revealed preference, Bayesian revealed preference,
Costly Information acquisition, Rational Inattention, Blackwell ordering,
Utility Theory.


Abstract:
  We consider estimation and control of the cylinder wake at low Reynolds
numbers. A particular focus is on the development of efficient numerical
algorithms to design optimal linear feedback controllers when there are many
inputs (disturbances applied everywhere) and many outputs (perturbations
measured everywhere). We propose a resolvent-based iterative algorithm to
perform i) optimal estimation of the flow using a limited number of sensors;
and ii) optimal control of the flow when the entire flow is known but only a
limited number of actuators are available for control. The method uses
resolvent analysis to take advantage of the low-rank characteristics of the
cylinder wake and solutions are obtained without any model-order reduction.
Optimal feedback controllers are also obtained by combining the solutions of
the estimation and control problems. We show that the performance of the
estimators and controllers converges to the true global optima, indicating that
the important physical mechanisms for estimation and control are of low rank.


Abstract:
  Understanding the escape of ionizing (Lyman continuum) photons from galaxies
is vital for determining how galaxies contributed to reionization in the early
universe. While directly detecting Lyman continuum from high redshift galaxies
is impossible due to the intergalactic medium, low redshift galaxies in
principle offer this possibility, but requirie observations from space. The
first local galaxy for which Lyman continuum escape was found is Haro11 , a
luminous blue compact galaxy at z=0.02, where observations with the FUSE
satellite revealed an escape fraction of 3.3 %. However the FUSE aperture
covers the entire galaxy, and it is not clear from where the Lyman continuum is
leaking out. Here we utilize HST/COS spectroscopy in the wavelength range
1100-1700 A of the three knots (A, B, and C) of Haro11 to study the presence of
Ly-$\alpha$ emission and the properties of intervening gas. We find that all
knots have bright Ly-$\alpha$ emission. UV absorption lines, originating in the
neutral interstellar medium, as well as lines probing the ionized medium, are
seen extending to blue shifted velocities of 500 km/s in all three knots,
demonstrating the presence of an outflowing multiphase medium. We find that
knots A and B have large covering fractions of neutral gas, making LyC escape
along these sightlines improbable, while knot C has a much lower covering
fraction ($\lesssim50$%). Knot C also has the the highest Ly-$\alpha$ escape
fraction and we conclude that it is the most likely source of the escaping
Lyman continuum detected in Haro11.


Abstract:
  The performance of state-of-the-art neural rankers can deteriorate
substantially when exposed to noisy inputs or applied to a new domain. In this
paper, we present a novel method for fine-tuning neural rankers that can
significantly improve their robustness to out-of-domain data and query
perturbations. Specifically, a contrastive loss that compares data points in
the representation space is combined with the standard ranking loss during
fine-tuning. We use relevance labels to denote similar/dissimilar pairs, which
allows the model to learn the underlying matching semantics across different
query-document pairs and leads to improved robustness. In experiments with four
passage ranking datasets, the proposed contrastive fine-tuning method obtains
improvements on robustness to query reformulations, noise perturbations, and
zero-shot transfer for both BERT and BART based rankers. Additionally, our
experiments show that contrastive fine-tuning outperforms data augmentation for
robustifying neural rankers.


Abstract:
  We study locally conformally balanced metrics on almost abelian Lie algebras,
namely solvable Lie algebras admitting an abelian ideal of codimension one,
providing characterizations in every dimension. Moreover, we classify
six-dimensional almost abelian Lie algebras admitting locally conformally
balanced metrics and study some compatibility results between different types
of special Hermitian metrics on almost abelian Lie groups and their compact
quotients. We end by classifying almost abelian Lie algebras admitting locally
conformally hyperk\"ahler structures.


Abstract:
  We show that the homotopy groups of a Moore space $P^n(p^r)$, where $p^r \neq
2$, are $\mathbb{Z}/p^s$-hyperbolic for $s \leq r$. Combined with work of
Huang-Wu, Neisendorfer, and Theriault, this completely resolves the question of
when such a Moore space is $\mathbb{Z}/p^s$-hyperbolic for $p \geq 5$, or when
$p=2$ and $r \geq 6$. We also give a criterion in ordinary homology for a space
to be $\mathbb{Z}/p^r$-hyperbolic, and deduce some examples.


Abstract:
  The excessive use of images in social networks, government databases, and
industrial applications has posed great privacy risks and raised serious
concerns from the public. Even though differential privacy (DP) is a widely
accepted criterion that can provide a provable privacy guarantee, the
application of DP on unstructured data such as images is not trivial due to the
lack of a clear qualification on the meaningful difference between any two
images. In this paper, for the first time, we introduce a novel notion of
image-aware differential privacy, referred to as DP-image, that can protect
user's personal information in images, from both human and AI adversaries. The
DP-Image definition is formulated as an extended version of traditional
differential privacy, considering the distance measurements between feature
space vectors of images. Then we propose a mechanism to achieve DP-Image by
adding noise to an image feature vector. Finally, we conduct experiments with a
case study on face image privacy. Our results show that the proposed DP-Image
method provides excellent DP protection on images, with a controllable
distortion to faces.


Abstract:
  We propose a new approach to constant-roll warm inflation as a generalization
of constant-roll inflation. Based on the $\beta$-function formalism, it is
shown that constant-roll warm inflation models with a natural end fall into
universality classes defined by three different types of $\beta$-functions,
under the assumption that radiation energy density is quasi-stable. Given that
warm inflation is completely specified by the $\beta$-function and dissipation
coefficient ratio, we investigate whether or not the inflation can physically
be realized for enough number of e-foldings at the background level for some
combinations of the $\beta$-functions and non-trivial dissipation coefficient
ratios.


Abstract:
  Understanding individuals' behavior during hurricane evacuation is of
paramount importance for local, state, and government agencies hoping to be
prepared for natural disasters. Complexities involved with human
decision-making procedures and lack of data for such disasters are the main
reasons that make hurricane evacuation studies challenging. In this paper, we
utilized a large mobile phone Location-Based Services (LBS) data to construct
the evacuation pattern during the landfall of Hurricane Irma. By employing our
proposed framework on more than 11 billion mobile phone location sightings, we
were able to capture the evacuation decision of 807,623 smartphone users who
were living within the state of Florida. We studied users' evacuation
decisions, departure and reentry date distribution, and destination choice. In
addition to these decisions, we empirically examined the influence of
evacuation order and low-lying residential areas on individuals' evacuation
decisions. Our analysis revealed that 57.92% of people living in mandatory
evacuation zones evacuated their residences while this ratio was 32.98% and
33.68% for people living in areas with no evacuation order and voluntary
evacuation order, respectively. Moreover, our analysis revealed the importance
of the individuals' mobility behavior in modeling the evacuation decision
choice. Historical mobility behavior information such as number of trips taken
by each individual and the spatial area covered by individuals' location
trajectory estimated significant in our choice model and improve the overall
accuracy of the model significantly.


Abstract:
  We examine the question of the integrability of the recently defined
$\mathbb{Z}_2\times \mathbb{Z}_2$-graded sine-Gordon model, which is a natural
generalisation of the supersymmetric sine-Gordon equation. We do this via
appropriate auto-B\"{a}cklund transformations, construction of conserved
spinor-valued currents and a pair of infinite sets of conservation laws.


Abstract:
  DNS over TLS (DoT) and DNS over HTTPS (DoH) promise to improve privacy and
security of DNS by encrypting DNS messages, especially when messages are padded
to a uniform size. Firstly, to demonstrate the limitations of recommended
padding approaches, we present Segram, a novel app fingerprinting attack that
allows adversaries to infer which mobile apps are executed on a device.
Secondly, we record traffic traces of 118 Android apps using 10 different
DoT/DoH resolvers to study the effectiveness of Segram under different
conditions. According to our results, Segram identifies apps with accuracies of
up to 72% with padding in a controlled closed world setting. The effectiveness
of Segram is comparable with state-of-the-art techniques but Segram requires
less computational effort. We release our datasets and code. Thirdly, we study
the prevalence of padding among privacy-focused DoT/DoH resolvers, finding that
up to 81% of our sample fail to enable padding. Our results suggest that
recommended padding approaches are less effective than expected and that
resolver operators are not sufficiently aware about this feature.


Abstract:
  BG Ind is a well studied, bright, nearby binary consisting of a pair of F
stars in a 1.46-day orbit. We have discovered in the TESS lightcurve for TIC
229804573 (aka BG Ind) a second eclipsing binary in the system with a 0.53-day.
Our subsequent analyses of the recent TESS and archival ground-based
photometric and radial velocity data, reveal that the two binaries are
gravitationally bound in a 721-day period, moderately eccentric orbit. We
present the results of a joint spectro-photodynamical analysis of the eclipse
timing variation curves of both binaries based on TESS and ground-based
archival data, the TESS lightcurve, archival radial velocity data and the
spectral energy distribution, coupled with the use of PARSEC stellar
isochrones. We confirm prior studies of BG Ind which found that the brighter
binary A consists of slightly evolved F-type stars with refined masses of 1.32
and 1.43 $M_\odot$, and radii of 1.59 and 2.34 $R_\odot$. The previously
unknown binary B has two less massive stars of 0.69 and 0.64 $M_\odot$ and
radii of 0.64 and 0.61 $R_\odot$. Based on a number of different arguments
which we discuss, we conclude that the three orbital planes are likely aligned
to within 17$^\circ$.


Abstract:
  We propose an unsupervised approach for learning end-to-end reconstruction
operators for ill-posed inverse problems. The proposed method combines the
classical variational framework with iterative unrolling, which essentially
seeks to minimize a weighted combination of the expected distortion in the
measurement space and the Wasserstein-1 distance between the distributions of
the reconstruction and ground-truth. More specifically, the regularizer in the
variational setting is parametrized by a deep neural network and learned
simultaneously with the unrolled reconstruction operator. The variational
problem is then initialized with the reconstruction of the unrolled operator
and solved iteratively till convergence. Notably, it takes significantly fewer
iterations to converge, thanks to the excellent initialization obtained via the
unrolled operator. The resulting approach combines the computational efficiency
of end-to-end unrolled reconstruction with the well-posedness and
noise-stability guarantees of the variational setting. Moreover, we demonstrate
with the example of X-ray computed tomography (CT) that our approach
outperforms state-of-the-art unsupervised methods, and that it outperforms or
is on par with state-of-the-art supervised learned reconstruction approaches.


Abstract:
  This paper investigates performance attribution measures as a basis for
constraining portfolio optimization. We employ optimizations that minimize
expected tail loss and investigate both asset allocation (AA) and the selection
effect (SE) as hard constraints on asset weights. The test portfolio consists
of stocks from the Dow Jones Industrial Average index; the benchmark is an
equi-weighted portfolio of the same stocks. Performance of the optimized
portfolios is judged using comparisons of cumulative price and the
risk-measures maximum drawdown, Sharpe ratio, and Rachev ratio. The results
suggest a positive role in price and risk-measure performance for the
imposition of constraints on AA and SE, with SE constraints producing the
larger performance enhancement.


Abstract:
  We consider smooth solutions of the Burgers-Hilbert equation that are a small
perturbation $\delta$ from a global periodic traveling wave with small
amplitude $\epsilon$. We use a modified energy method to prove the existence
time of smooth solutions on a time scale of $\frac{1}{\epsilon\delta}$ with
$0<\delta\ll\epsilon\ll1$ and on a time scale of $\frac{\epsilon}{\delta^2}$
with $0<\delta\ll\epsilon^2\ll1$. Moreover, we show that the traveling wave
exists for an amplitude $\epsilon$ in the range $(0,\epsilon^*)$ with
$\epsilon^*\sim 0.29$ and fails to exist for $\epsilon>\frac{2}{e}$.


Abstract:
  Recently, denoising diffusion probabilistic models and generative score
matching have shown high potential in modelling complex data distributions
while stochastic calculus has provided a unified point of view on these
techniques allowing for flexible inference schemes. In this paper we introduce
Grad-TTS, a novel text-to-speech model with score-based decoder producing
mel-spectrograms by gradually transforming noise predicted by encoder and
aligned with text input by means of Monotonic Alignment Search. The framework
of stochastic differential equations helps us to generalize conventional
diffusion probabilistic models to the case of reconstructing data from noise
with different parameters and allows to make this reconstruction flexible by
explicitly controlling trade-off between sound quality and inference speed.
Subjective human evaluation shows that Grad-TTS is competitive with
state-of-the-art text-to-speech approaches in terms of Mean Opinion Score. We
will make the code publicly available shortly.


Abstract:
  COVID-19 patient triaging with predictive outcome of the patients upon first
present to emergency department (ED) is crucial for improving patient
prognosis, as well as better hospital resources management and cross-infection
control. We trained a deep feature fusion model to predict patient outcomes,
where the model inputs were EHR data including demographic information,
co-morbidities, vital signs and laboratory measurements, plus patient's CXR
images. The model output was patient outcomes defined as the most insensitive
oxygen therapy required. For patients without CXR images, we employed Random
Forest method for the prediction. Predictive risk scores for COVID-19 severe
outcomes ("CO-RISK" score) were derived from model output and evaluated on the
testing dataset, as well as compared to human performance. The study's dataset
(the "MGB COVID Cohort") was constructed from all patients presenting to the
Mass General Brigham (MGB) healthcare system from March 1st to June 1st, 2020.
ED visits with incomplete or erroneous data were excluded. Patients with no
test order for COVID or confirmed negative test results were excluded. Patients
under the age of 15 were also excluded. Finally, electronic health record (EHR)
data from a total of 11060 COVID-19 confirmed or suspected patients were used
in this study. Chest X-ray (CXR) images were also collected from each patient
if available. Results show that CO-RISK score achieved area under the Curve
(AUC) of predicting MV/death (i.e. severe outcomes) in 24 hours of 0.95, and
0.92 in 72 hours on the testing dataset. The model shows superior performance
to the commonly used risk scores in ED (CURB-65 and MEWS). Comparing with
physician's decisions, CO-RISK score has demonstrated superior performance to
human in making ICU/floor decisions.


Abstract:
  We present a new approach ARLPCG: Adversarial Reinforcement Learning for
Procedural Content Generation, which procedurally generates and tests
previously unseen environments with an auxiliary input as a control variable.
Training RL agents over novel environments is a notoriously difficult task. One
popular approach is to procedurally generate different environments to increase
the generalizability of the trained agents. ARLPCG instead deploys an
adversarial model with one PCG RL agent (called Generator) and one solving RL
agent (called Solver). The Generator receives a reward signal based on the
Solver's performance, which encourages the environment design to be challenging
but not impossible. To further drive diversity and control of the environment
generation, we propose using auxiliary inputs for the Generator. The benefit is
two-fold: Firstly, the Solver achieves better generalization through the
Generator's generated challenges. Secondly, the trained Generator can be used
as a creator of novel environments that, together with the Solver, can be shown
to be solvable. We create two types of 3D environments to validate our model,
representing two popular game genres: a third-person platformer and a racing
game. In these cases, we shows that ARLPCG has a significantly better solve
ratio, and that the auxiliary inputs renders the levels creation controllable
to a certain degree. For a video compilation of the results please visit
https://youtu.be/z7q2PtVsT0I.


Abstract:
  In this paper, we study the regulation of the Ground Contact Forces (GRF) in
thruster-assisted legged locomotion. We will employ Reference Governors (RGs)
for enforcing GRF constraints in Harpy model which is a bipedal robot that is
being developed at Northeastern University. Optimization-based methods and
whole body control are widely used for enforcing the no-slip constraints in
legged locomotion which can be very computationally expensive. In contrast, RGs
can enforce these constraints by manipulating joint reference trajectories
using Lyapunov stability arguments which can be computed much faster. The
addition of the thrusters in our model allows to manipulate the gait parameters
and the GRF without sacrificing the locomotion stability.


Abstract:
  In many real-world applications such as business planning and sensor data
monitoring, one important, yet challenging, the task is to rank objects(e.g.,
products, documents, or spatial objects) based on their ranking scores and
efficiently return those objects with the highest scores. In practice, due to
the unreliability of data sources, many real-world objects often contain noises
and are thus imprecise and uncertain. In this paper, we study the problem of
probabilistic top-k dominating(PTD) query on such large-scale uncertain data in
a distributed environment, which retrieves k uncertain objects from distributed
uncertain databases(on multiple distributed servers), having the largest
ranking scores with high confidences. In order to efficiently tackle the
distributed PTD problem, we propose a MapReduce framework for processing
distributed PTD queries over distributed uncertain databases. In this MapReduce
framework, we design effective pruning strategies to filter out false alarms in
the distributed setting, propose cost-model-based index distribution mechanisms
over servers, and develop efficient distributed PTD query processing
algorithms. Extensive experiments have demonstrated the efficiency and
effectiveness of our proposed distributed PTD approach on both real and
synthetic data sets through various experimental settings.


Abstract:
  Devising domain- and model-agnostic evaluation metrics for generative models
is an important and as yet unresolved problem. Most existing metrics, which
were tailored solely to the image synthesis setup, exhibit a limited capacity
for diagnosing the different modes of failure of generative models across
broader application domains. In this paper, we introduce a 3-dimensional
evaluation metric, ($\alpha$-Precision, $\beta$-Recall, Authenticity), that
characterizes the fidelity, diversity and generalization performance of any
generative model in a domain-agnostic fashion. Our metric unifies statistical
divergence measures with precision-recall analysis, enabling sample- and
distribution-level diagnoses of model fidelity and diversity. We introduce
generalization as an additional, independent dimension (to the
fidelity-diversity trade-off) that quantifies the extent to which a model
copies training data -- a crucial performance indicator when modeling sensitive
data with requirements on privacy. The three metric components correspond to
(interpretable) probabilistic quantities, and are estimated via sample-level
binary classification. The sample-level nature of our metric inspires a novel
use case which we call model auditing, wherein we judge the quality of
individual samples generated by a (black-box) model, discarding low-quality
samples and hence improving the overall model performance in a post-hoc manner.


Abstract:
  Summary: R and Matlab are two high-level scientific programming languages
which are frequently applied in computational biology. To extend the wide
variety of available and approved implementations, we present the Rcall
interface which runs in MATLAB and provides direct access to methods and
software packages implemented in R. Rcall involves passing the relevant data to
R, executing the specified R commands and forwarding the results to MATLAB for
further use. The evaluation and conversion of the basic data types in R and
MATLAB are provided. Due to the easy embedding of R facilities, Rcall greatly
extends the functionality of the MATLAB programming language.
  Availability: Source code is freely available at
https://github.com/kreutz-lab/Rcall, implemented in MATLAB and supported on
Linux, MS Windows and Mac OS X.


Abstract:
  Random projections reduce the dimension of a set of vectors while preserving
structural information, such as distances between vectors in the set. This
paper proposes a novel use of row-product random matrices in random projection,
where we call it Tensor Random Projection (TRP). It requires substantially less
memory than existing dimension reduction maps. The TRP map is formed as the
Khatri-Rao product of several smaller random projections, and is compatible
with any base random projection including sparse maps, which enable dimension
reduction with very low query cost and no floating point operations. We also
develop a reduced variance extension. We provide a theoretical analysis of the
bias and variance of the TRP, and a non-asymptotic error analysis for a TRP
composed of two smaller maps. Experiments on both synthetic and MNIST data show
that our method performs as well as conventional methods with substantially
less storage.


Abstract:
  Motivated by the issue of inaccurate channel state information (CSI) at the
base station (BS), which is commonly due to feedback/processing delays and
compression problems, in this paper, we introduce a scalable idea of adopting
artificial intelligence (AI) aided CSI acquisition. The proposed scheme
enhances the CSI compression, which is done at the mobile terminal (MT), along
with accurate recovery of estimated CSI at the BS. Simulation-based results
corroborate the validity of the proposed scheme. Numerically, nearly 100\%
recovery of the estimated CSI is observed with relatively lower overhead than
the benchmark scheme. The proposed idea can bring potential benefits in the
wireless communication environment, e.g., ultra-reliable and low latency
communication (URLLC), where imperfect CSI and overhead is intolerable.


Abstract:
  This paper aims at proposing a sufficient matrix inequality condition to
carry out the global exponential stability of the wave equation under an
event-triggering mechanism that updates a damping source term. The damping is
distributed in the whole space but sampled in time. The wellposedness of the
closed-loop event-triggered control system is shown. Furthermore, the avoidance
of Zeno behavior is ensured provided that the initial data are more regular.
The interest of the results is drawn through some numerical simulations.


Abstract:
  Discovery of high-temperature superconductivity in hydrogen-rich compounds
has fuelled the enthusiasm for finding materials with more promising
superconducting properties among hydrides. However, the ultrahigh pressure
needed to synthesize and maintain high-temperature hydrogen-rich
superconductors hinders the experimental investigation of these materials. For
practical applications, it is also highly desired to find more hydrogen-rich
materials that superconduct at high temperatures but under relatively lower
pressures. Based on first-principles density functional theory, we calculate
the electronic and phonon band structures for a ternary borohydride formed by
intercalating BH$_4$ tetrahedrons into a face-centered-cubic potassium lattice,
KB$_2$H$_8$. Remarkably, we find that this material is dynamically stable and
one of its $sp^3$-hybridized $\sigma$-bonding bands is metallized (i.e.
partially filled) above a moderate high pressure. This metallized
$\sigma$-bonding band couples strongly with phonons, giving rise to a strong
superconducting pairing potential. By solving the anisotropic Eliashberg
equations, we predict that the superconducting transition temperature of this
compound is 134-146 K around 12 GPa.


Abstract:
  Deep neural networks are playing an important role in many real-life
applications. After being trained with abundant data and computing resources, a
deep neural network model providing service is endowed with economic value. An
important prerequisite in commercializing and protecting deep neural networks
is the reliable identification of their genuine author. To meet this goal,
watermarking schemes that embed the author's identity information into the
networks have been proposed. However, current schemes can hardly meet all the
necessary requirements for securely proving the authorship and mostly focus on
models for classification. To explicitly meet the formal definitions of the
security requirements and increase the applicability of deep neural network
watermarking schemes, we propose a new framework based on multi-task learning.
By treating the watermark embedding as an extra task, most of the security
requirements are explicitly formulated and met with well-designed regularizers,
the rest is guaranteed by using components from cryptography. Moreover, a
decentralized verification protocol is proposed to standardize the ownership
verification. The experiment results show that the proposed scheme is flexible,
secure, and robust, hence a promising candidate in deep learning model
protection.


Abstract:
  The effects of Poissonian resetting at a constant rate $r$ on the reaction
time between a Brownian particle and a stochastically gated target are studied.
The target switches between a reactive state and a non-reactive one. We
calculate the mean time at which the particle subject to resetting hits the
target for the first time, while the latter is in the reactive state. The
search time is minimum at a resetting rate that depends on the target
transition rates. When the target relaxation rate is much larger than both the
resetting rate and the inverse diffusion time, the system becomes equivalent to
a partially absorbing boundary problem. In other cases, however, the optimal
resetting rate can be a non-monotonic function of the target rates, a feature
not observed in partial absorption.
  We compute the relative fluctuations of the first hitting time around its
mean and compare our results with the ungated case. The usual universal
behavior of these fluctuations for resetting processes at their optimum breaks
down due to the target internal dynamics.


Abstract:
  There is always a natural embedding of $S_s\wr S_k$ into the linear
programming (LP) relaxation permutation symmetry group of an orthogonal array
integer linear programming (ILP) formulation with equality constraints.
  The point of this paper is to prove that in the $2$ level, strength $1$ case
the LP relaxation permutation symmetry group of this formulation is isomorphic
to $S_2\wr S_k$ for all $k$, and in the $2$ level, strength $2$ case it is
isomorphic to $S_2^k\rtimes S_{k+1}$ for $k\geq 4$. The strength $2$ result
reveals previously unknown permutation symmetries that can not be captured by
the natural embedding of $S_2\wr S_k$. We also conjecture a complete
characterization of the LP relaxation permutation symmetry group of the ILP
formulation.


Abstract:
  Phase-change memory devices have found applications in in-memory computing
where the physical attributes of these devices are exploited to compute in
place without the need to shuttle data between memory and processing units.
However, non-idealities such as temporal variations in the electrical
resistance have a detrimental impact on the achievable computational precision.
To address this, a promising approach is projecting the phase configuration of
phase change material onto some stable element within the device. Here we
investigate the projection mechanism in a prominent phase-change memory device
architecture, namely mushroom-type phase-change memory. Using nanoscale
projected Ge2Sb2Te5 devices we study the key attributes of state-dependent
resistance, drift coefficients, and phase configurations, and using them reveal
how these devices fundamentally work.


Abstract:
  The widespread application of modern machine learning has increased the need
for robust statistical algorithms. This work studies one such fundamental
statistical measure known as the Tukey depth. We study the problem in the
continuum (population) limit. In particular, we derive the associated necessary
conditions, which take the form of a first-order partial differential equation.
We discuss the classical interpretation of this necessary condition as the
viscosity solution of a Hamilton-Jacobi equation, but with a non-classical
Hamiltonian with discontinuous dependence on the gradient at zero. We prove
that this equation possesses a unique viscosity solution and that this solution
always bounds the Tukey depth from below. In certain cases, we prove that the
Tukey depth is equal to the viscosity solution, and we give some illustrations
of standard numerical methods from the optimal control community which deal
directly with the partial differential equation. We conclude by outlining
several promising research directions both in terms of new numerical algorithms
and theoretical challenges.


Abstract:
  Code reuse is an important part of software development. The adoption of code
reuse practices is especially noticeable in Node.js. The Node.js package
manager, NPM, contains over 1 Million packages and developers often seek out
packages to solve programming tasks. Due to the vast number of packages,
selecting the right package is difficult and time consuming. With the goal of
improving productivity of developers that heavily reuse code through
third-party packages, we present Node Code Query(NCQ), a custom Read-Eval-Print
Loop environment that allows developers to 1) search for NPM packages using
natural language queries, 2) search for code snippets related to those
packages, 3) quickly setup new environments for testing those snippets, and 4)
transition between search and editing modes. Our user study shows that
participants began programming faster and concluded tasks faster with NCQ than
with a baseline approach, and that they liked, among other features, the search
for code snippets and packages. Our results suggest that NCQ makes Node.js
developers more efficient in reusing code.


Abstract:
  Unmanned Aerial Vehicles (UAVs) have recently attracted significant attention
due to their outstanding ability to be used in different sectors and serve in
difficult and dangerous areas. Moreover, the advancements in computer vision
and artificial intelligence have increased the use of UAVs in various
applications and solutions, such as forest fires detection and borders
monitoring. However, using deep neural networks (DNNs) with UAVs introduces
several challenges of processing deeper networks and complex models, which
restricts their on-board computation. In this work, we present a strategy
aiming at distributing inference requests to a swarm of resource-constrained
UAVs that classifies captured images on-board and finds the minimum
decision-making latency. We formulate the model as an optimization problem that
minimizes the latency between acquiring images and making the final decisions.
The formulated optimization solution is an NP-hard problem. Hence it is not
adequate for online resource allocation. Therefore, we introduce an online
heuristic solution, namely DistInference, to find the layers placement strategy
that gives the best latency among the available UAVs. The proposed approach is
general enough to be used for different low decision-latency applications as
well as for all CNN types organized into the pipeline of layers (e.g., VGG) or
based on residual blocks (e.g., ResNet).


Abstract:
  We show that if a measure of dimension $s$ on $\mathbb{R}^d$ admits $(p,q)$
Fourier restriction for some endpoint exponents allowed by its dimension,
namely $q=\tfrac{s}{d}p'$ for some $p>1$, then it is either absolutely
continuous or $1$-purely unrectifiable.


Abstract:
  Semantic segmentation models only perform well on the domain they are trained
on and datasets for training are scarce and often have a small label-spaces,
because the pixel level annotations required are expensive to make. Thus
training models on multiple existing domains is desired to increase the output
label-space. Current research shows that there is potential to improve accuracy
across datasets by using multi-domain training, but this has not yet been
successfully extended to datasets of three different non-overlapping domains
without manual labelling. In this paper a method for this is proposed for the
datasets Cityscapes, SUIM and SUN RGB-D, by creating a label-space that spans
all classes of the datasets. Duplicate classes are merged and discrepant
granularity is solved by keeping classes separate. Results show that accuracy
of the multi-domain model has higher accuracy than all baseline models
together, if hardware performance is equalized, as resources are not limitless,
showing that models benefit from additional data even from domains that have
nothing in common.


Abstract:
  Effective communication is an important skill for enabling information
exchange in multi-agent settings and emergent communication is now a vibrant
field of research, with common settings involving discrete cheap-talk channels.
Since, by definition, these settings involve arbitrary encoding of information,
typically they do not allow for the learned protocols to generalize beyond
training partners. In contrast, in this work, we present a novel problem
setting and the Quasi-Equivalence Discovery (QED) algorithm that allows for
zero-shot coordination (ZSC), i.e., discovering protocols that can generalize
to independently trained agents. Real world problem settings often contain
costly communication channels, e.g., robots have to physically move their
limbs, and a non-uniform distribution over intents. We show that these two
factors lead to unique optimal ZSC policies in referential games, where agents
use the energy cost of the messages to communicate intent. Other-Play was
recently introduced for learning optimal ZSC policies, but requires prior
access to the symmetries of the problem. Instead, QED can iteratively discovers
the symmetries in this setting and converges to the optimal ZSC policy.


Abstract:
  When to initiate treatment on patients is an important problem in many
medical studies such as AIDS and cancer. In this article, we formulate the
treatment initiation time problem for time-to-event data and propose an optimal
individualized regime that determines the best treatment initiation time for
individual patients based on their characteristics. Different from existing
optimal treatment regimes where treatments are undertaken at a pre-specified
time, here new challenges arise from the complicated missing mechanisms in
treatment initiation time data and the continuous treatment rule in terms of
initiation time. To tackle these challenges, we propose to use restricted mean
residual lifetime as a value function to evaluate the performance of different
treatment initiation regimes, and develop a nonparametric estimator for the
value function, which is consistent even when treatment initiation times are
not completely observable and their distribution is unknown. We also establish
the asymptotic properties of the resulting estimator in the decision rule and
its associated value function estimator. In particular, the asymptotic
distribution of the estimated value function is nonstandard, which follows a
weighted chi-squared distribution. The finite-sample performance of the
proposed method is evaluated by simulation studies and is further illustrated
with an application to a breast cancer data.


Abstract:
  We propose various methods for combining or amalgamating propositional
languages and deductive systems. We make heavy use of quantales and quantale
modules in the wake of previous works by the present and other authors. We also
describe quite extensively the relationships among the algebraic and
order-theoretic constructions and the corresponding ones based on a purely
logical approach.


Abstract:
  Inspired by the latest measurement of muon g-2 by the Fermilab Experiment, we
revisit a class of supersymmetric models in which non-universality at M_GUT
allows us to realize relatively light sleptons in the few hundred GeV range.
These sleptons provide additional contributions to muon g-2 that can be
arranged to reconcile theory and experiment. The solutions compatible with the
muon g-2 resolution typically predict light sleptons, charginos and
neutralinos. We show how these solutions can be probed during LHC-Run3. A
direct impact on the chargino mass is observed such that the chargino can be
probed up to about 800 GeV during the Run3 experiments. Despite such a direct
impact, it is still possible to realize lighter chargino masses which can
escape detection due to the chirality mixture of the lighter slepton states.
The colored squarks as well as the gluino turn out to be heavier than about 3-4
TeV if the LSP neutralino satisfies the Planck bound on the dark matter relic
abundance. We highlight a variety of benchmark points and, in particular,
coannihilation scenarios with dark matter candidates that will be tested in the
ongoing and planned direct and indirect detection experiments. By relaxing the
requirement that the LSP neutralino saturates the relic dark matter abundance,
we are able to find solutions with gluino and squark masses in a range that may
be accessible at LHC-Run3.


Abstract:
  The goal of the present paper is to study the viscoelastic wave equation with
the time delay \[ |u_t|^\rho u_{tt}-\Delta u-\Delta u_{tt}+\int_0^tg(t-s)\Delta
u(s)ds+\mu_1u_t(x,t)+\mu_2 u_t(x,t-\tau)=b|u|^{p-2}u\] under initial boundary
value conditions, where $\rho,~b,~\mu_1$ are positive constants, $\mu_2$ is a
real number, $\tau>0$ represents the time delay. By using the multiplier method
together with some properties of the convex functions, the explicit and general
stability results of energy are proved under the general assumption on the
relaxation function $g$. This work generalizes and improves earlier results on
the stability of the viscoelastic equations with the time delay in the
literature.


Abstract:
  We show that in large enough rank, the Gromov boundary of the free factor
complex is path connected and locally path connected.


Abstract:
  We study numerically the nonlinear stability of {\it excited} fermion-boson
stars in spherical symmetry. Such compound hypothetical stars, composed by
fermions and bosons, are gravitationally bound, regular, and static
configurations described within the coupled Einstein-Klein-Gordon-Euler
theoretical framework. The excited configurations are characterized by the
presence in the radial profile of the (complex, massive) scalar field -- the
bosonic piece -- of at least one node across the star. The dynamical emergence
of one such configuration from the accretion of a cloud of scalar field onto an
already-formed neutron star, was numerically revealed in our previous
investigation. Prompted by that finding we construct here equilibrium
configurations of excited fermion-boson stars and study their stability
properties using numerical-relativity simulations. In addition, we also analyze
their dynamical formation from generic, constraint-satisfying initial data.
Contrary to purely boson stars in the excited state, which are known to be
generically unstable, our study reveals the appearance of a cooperative
stabilization mechanism between the fermionic and bosonic constituents of those
excited-state mixed stars. While similar examples of stabilization mechanisms
have been recently discussed in the context of $\ell-$boson stars and
multi-field, multi-frequency boson stars, our results seem to indicate that the
stabilization mechanism is a purely gravitational effect and does not depend on
the type of matter of the companion star.


Abstract:
  The Labastida-Marin\~o-Ooguri-Vafa (LMOV) invariants are the open string BPS
invariants which are expected to be integers based on the string duality
conjecture from M-theory. Several explicit formulae of LMOV invariants for
framed unknot have been obtained in the literature. In this paper, we present a
unified method to deal with the integrality of such explicit formulae.
Furthermore, we also prove the integrality of certain LMOV invariants for
framed unknot in higher genera.


Abstract:
  For reliable environment perception, the use of temporal information is
essential in some situations. Especially for object detection, sometimes a
situation can only be understood in the right perspective through temporal
information. Since image-based object detectors are currently based almost
exclusively on CNN architectures, an extension of their feature extraction with
temporal features seems promising.
  Within this work we investigate different architectural components for a
CNN-based temporal information extraction. We present a Temporal Feature
Network which is based on the insights gained from our architectural
investigations. This network is trained from scratch without any ImageNet
information based pre-training as these images are not available with temporal
information. The object detector based on this network is evaluated against the
non-temporal counterpart as baseline and achieves competitive results in an
evaluation on the KITTI object detection dataset.


Abstract:
  This work investigates the structure of rank-metric codes in connection with
concepts from finite geometry, most notably the $q$-analogues of projective
systems and blocking sets. We also illustrate how to associate a classical
Hamming-metric code to a rank-metric one, in such a way that various
rank-metric properties naturally translate into the homonymous Hamming-metric
notions under this correspondence. The most interesting applications of our
results lie in the theory of minimal rank-metric codes, which we introduce and
study from several angles. Our main contributions are bounds for the parameters
of a minimal rank-metric codes, a general existence result based on a
combinatorial argument, and an explicit code construction for some parameter
sets that uses the notion of a scattered linear set. Throughout the paper we
also show and comment on curious analogies/divergences between the theories of
error-correcting codes in the rank and in the Hamming metric.


Abstract:
  This paper is concerned with the regularization of large-scale discrete
inverse problems by means of inexact Krylov methods. Specifically, we derive
two new inexact Krylov methods that can be efficiently applied to unregularized
or Tikhonov-regularized least squares problems, and we study their theoretical
properties, including links with their exact counterparts and strategies to
monitor the amount of inexactness. We then apply the new methods to separable
nonlinear inverse problems arising in blind deblurring. In this setting
inexactness stems from the uncertainty in the parameters defining the blur,
which may be recovered using a variable projection method leading to an
inner-outer iteration scheme (i.e., one cycle of inner iterations is performed
to solve one linear deblurring subproblem for any intermediate values of the
blurring parameters computed by a nonlinear least squares solver). The new
inexact solvers can naturally handle varying inexact blurring parameters while
solving the linear deblurring subproblems, allowing for a much reduced number
of total iterations and substantial computational savings with respect to their
exact counterparts.


Abstract:
  We investigate the Josephson current through a suspended carbon nanotube
double quantum dot which, at sufficiently low temperatures, is characterized by
the ground state of the electronic subsystem. Depending on parameters like a
magnetic field or the inter-dot coupling, the ground state can either be a
current-carrying singlet or doublet, or a blockaded triplet state. Since the
electron-vibration interaction has been demonstrated to be electrostatically
tuneable, we study in particular its effect on the current-phase relation. We
show that the coupling to the vibration mode can lift the current-suppressing
triplet blockade by inducing a quantum phase transition to a ground state of a
different total spin. Our key finding is the development of a triple point in
the Josephson current parameterized by the resonator coupling and the Josephson
phase. The quantum phase transitions around the triple point are directly
accessible through the critical current and resilient to moderately finite
temperatures. The proposed setup makes the mechanical degree of freedom part of
a superconducting hybrid device which is interesting for ultra-sensitive
displacement detectors.


Abstract:
  Distinguishing classes within substellar objects and understanding their
formation and evolution need larger samples of substellar companions such as
exoplanets, brown dwarfs, and low-mass stars. In this paper, we look for
substellar companions using radial velocity surveys of FGK stars with the
SOPHIE spectrograph at the Observatoire de Haute-Provence. We assign here the
radial velocity variations of 27 stars to their orbital motion induced by
low-mass companions. We also constrained their plane-of-the-sky motion using
HIPPARCOS and Gaia Data Release 1 measurements, which constrain the true masses
of some of these companions. We report the detection and characterization of
six cool Jupiters, three brown dwarf candidates, and 16 low-mass stellar
companions. We additionally update the orbital parameters of the low-mass star
HD 8291 B, and we conclude that the radial velocity variations of HD 204277 are
likely due to stellar activity despite resembling the signal of a giant planet.
One of the new giant planets, BD+631405 b, adds to the population of highly
eccentric cool Jupiters, and it is presently the most massive member. Two of
the cool Jupiter systems also exhibit signatures of an additional outer
companion. The orbital periods of the new companions span 30 days to 11.5
years, their masses 0.72 Jupiter mass to 0.61 Solar mass, and their
eccentricities 0.04 to 0.88. These discoveries probe the diversity of
substellar objects and low-mass stars, which will help constrain the models of
their formation and evolution.


Abstract:
  The goal of Multi-task Bayesian Optimization (MBO) is to minimize the number
of queries required to accurately optimize a target black-box function, given
access to offline evaluations of other auxiliary functions. When offline
datasets are large, the scalability of prior approaches comes at the expense of
expressivity and inference quality. We propose JUMBO, an MBO algorithm that
sidesteps these limitations by querying additional data based on a combination
of acquisition signals derived from training two Gaussian Processes (GP): a
cold-GP operating directly in the input domain and a warm-GP that operates in
the feature space of a deep neural network pretrained using the offline data.
Such a decomposition can dynamically control the reliability of information
derived from the online and offline data and the use of pretrained neural
networks permits scalability to large offline datasets. Theoretically, we
derive regret bounds for JUMBO and show that it achieves no-regret under
conditions analogous to GP-UCB (Srinivas et. al. 2010). Empirically, we
demonstrate significant performance improvements over existing approaches on
two real-world optimization problems: hyper-parameter optimization and
automated circuit design.


Abstract:
  Liquid-liquid phase separation is emerging as a crucial phenomenon in several
fundamental cell processes. A range of eukaryotic systems exhibit liquid
condensates. However, their function in bacteria, which in general lack
membrane-bound compartments, remains less clear. Here, we used high-resolution
optical microscopy to observe single bacterial aggresomes, nanostructured
intracellular assemblies of proteins, to undercover their role in cell stress.
We find that proteins inside aggresomes are mobile and undergo dynamic
turnover, consistent with a liquid state. Our observations are in quantitative
agreement with phase-separated liquid droplet formation driven by interacting
proteins under thermal equilibrium that nucleate following diffusive collisions
in the cytoplasm. We have discovered aggresomes in multiple species of
bacteria, and show that these emergent, metastable liquid-structured protein
assemblies increase bacterial fitness by enabling cells to tolerate
environmental stresses.


Abstract:
  In this paper we compare the notion of stochastic two-scale convergence in
the mean (by Bourgeat, Mikeli\'c and Wright), the notion of stochastic
unfolding (recently introduced by the authors), and the quenched notion of
stochastic two-scale convergence (by Zhikov and Pyatnitskii). In particular, we
introduce stochastic two-scale Young measures as a tool to compare mean and
quenched limits. Moreover, we discuss two examples, which can be naturally
analyzed via stochastic unfolding, but which cannot be treated via quenched
stochastic two-scale convergence.


Abstract:
  Let $n$ and $a$ be positive integers such that $2\leq a\leq \frac{n}{2}$. In
this short note, we compute for the exact value of the distance spectral
radius, vertex-forwarding index, and some distance-based topological indices of
the complement of circulant networks $C_n(1,a)$ and
$C_{m^h}(1,m,m^2,\ldots,m^{h-1})$. For $a\neq \frac{n}{2}$, the circulant
$C_n(1,a)$ is called a double loop network while the circulant
$C_{m^h}(1,m,m^2,\ldots,m^{h-1})$ is called the multiplicative circulant
network on $m^h$ vertices.


Abstract:
  Effective postprandial glucose control is important to glucose management for
subjects with diabetes mellitus. In this work, a data-driven meal bolus
decision method is proposed without the need of subject-specific glucose
management parameters. The postprandial glucose dynamics is learnt using
Gaussian process regression. Considering the asymmetric risks of hyper- and
hypoglycemia and the uncertainties in the predicted glucose trajectories, an
asymmetric risk-sensitive cost function is designed. Bayesian optimization is
utilized to solve the optimization problem, since the gradient of the cost
function is unavailable. The proposed approach is evaluated using the 10-adult
cohort of the FDA-accepted UVA/Padova T1DM simulator and compared with the
standard insulin bolus calculator. For the case of announced meals, the
proposed method achieves satisfactory and similar performance in terms of mean
glucose and percentage time in [70, 180] mg/dL without increasing the risk of
hypoglycemia. Similar results are observed for the case without the meal
information (assuming that the patient follows a consistent diet) and the case
of basal rate mismatches. In addition, advisory-mode analysis is performed
based on clinical data, which indicates that the method can determine safe and
reasonable meal boluses in real clinical settings. The results verify the
effectiveness and robustness of the proposed method and indicate the
feasibility of achieving improved postprandial glucose regulation through a
data-driven optimal control method.


Abstract:
  In an interactive belief model, are the players "commonly meta-certain" of
the model itself? This paper formalizes such implicit "common meta-certainty"
assumption. To that end, the paper expands the objects of players' beliefs from
events to functions defined on the underlying states. Then, the paper defines a
player's belief-generating map: it associates, with each state, whether a
player believes each event at that state. The paper formalizes what it means
by: "a player is (meta-)certain of her own belief-generating map" or "the
players are (meta-)certain of the profile of belief-generating maps (i.e., the
model)." The paper shows: a player is (meta-)certain of her own
belief-generating map if and only if her beliefs are introspective. The players
are commonly (meta-)certain of the model if and only if, for any event which
some player i believes at some state, it is common belief at the state that
player i believes the event. This paper then asks whether the "common
meta-certainty" assumption is needed for an epistemic characterization of
game-theoretic solution concepts. The paper shows: if each player is logical
and (meta-)certain of her own strategy and belief-generating map, then each
player correctly believes her own rationality. Consequently, common belief in
rationality alone leads to actions that survive iterated elimination of
strictly dominated actions.


Abstract:
  Can a computer determine a piano player's skill level? Is it preferable to
base this assessment on visual analysis of the player's performance or should
we trust our ears over our eyes? Since current CNNs have difficulty processing
long video videos, how can shorter clips be sampled to best reflect the players
skill level? In this work, we collect and release a first-of-its-kind dataset
for multimodal skill assessment focusing on assessing piano player's skill
level, answer the asked questions, initiate work in automated evaluation of
piano playing skills and provide baselines for future work. Dataset is
available from: https://github.com/ParitoshParmar/Piano-Skills-Assessment.


Abstract:
  Performance modeling of parallel applications on multicore processors remains
a challenge in computational co-design due to multicore processors' complex
design. Multicores include complex private and shared memory hierarchies. We
present a Scalable Analytical Shared Memory Model (SASMM). SASMM can predict
the performance of parallel applications running on a multicore. SASMM uses a
probabilistic and computationally-efficient method to predict the reuse
distance profiles of caches in multicores. SASMM relies on a stochastic, static
basic block-level analysis of reuse profiles. The profiles are calculated from
the memory traces of applications that run sequentially rather than using
multi-threaded traces. The experiments show that our model can predict private
L1 cache hit rates with 2.12% and shared L2 cache hit rates with about 1.50%
error rate.


Abstract:
  As machine learning models are increasingly used in critical decision-making
settings (e.g., healthcare, finance), there has been a growing emphasis on
developing methods to explain model predictions. Such \textit{explanations} are
used to understand and establish trust in models and are vital components in
machine learning pipelines. Though explanations are a critical piece in these
systems, there is little understanding about how they are vulnerable to
manipulation by adversaries. In this paper, we discuss how two broad classes of
explanations are vulnerable to manipulation. We demonstrate how adversaries can
design biased models that manipulate model agnostic feature attribution methods
(e.g., LIME \& SHAP) and counterfactual explanations that hill-climb during the
counterfactual search (e.g., Wachter's Algorithm \& DiCE) into
\textit{concealing} the model's biases. These vulnerabilities allow an
adversary to deploy a biased model, yet explanations will not reveal this bias,
thereby deceiving stakeholders into trusting the model. We evaluate the
manipulations on real world data sets, including COMPAS and Communities \&
Crime, and find explanations can be manipulated in practice.


Abstract:
  Recent studies of silicon spin qubits at temperatures above 1 K are
encouraging demonstrations that the cooling requirements for solid-state
quantum computing can be considerably relaxed. However, qubit readout
mechanisms that rely on charge sensing with a single-island single-electron
transistor (SISET) quickly lose sensitivity due to thermal broadening of the
electron distribution in the reservoirs. Here we exploit the tunneling between
two quantised states in a double-island SET (DISET) to demonstrate a charge
sensor with an improvement in signal-to-noise by an order of magnitude compared
to a standard SISET, and a single-shot charge readout fidelity above 99 % up to
8 K at a bandwidth > 100 kHz. These improvements are consistent with our
theoretical modelling of the temperature-dependent current transport for both
types of SETs. With minor additional hardware overheads, these sensors can be
integrated into existing qubit architectures for high fidelity charge readout
at few-kelvin temperatures.


Abstract:
  We propose a convolutional-recurrent channel equalizer and experimentally
demonstrate 1dB Q-factor improvement both in single-channel and 96 x WDM,
DP-16QAM transmission over 450km of TWC fiber. The new equalizer outperforms
previous NN-based approaches and a 3-steps-per-span DBP.


Abstract:
  Image inpainting is one of the important tasks in computer vision which
focuses on the reconstruction of missing regions in an image. The aim of this
paper is to introduce an image inpainting model based on Wasserstein Generative
Adversarial Imputation Network. The generator network of the model uses
building blocks of convolutional layers with different dilation rates, together
with skip connections that help the model reproduce fine details of the output.
This combination yields a universal imputation model that is able to handle
various scenarios of missingness with sufficient quality. To show this
experimentally, the model is simultaneously trained to deal with three
scenarios given by missing pixels at random, missing various smaller square
regions, and one missing square placed in the center of the image. It turns out
that our model achieves high-quality inpainting results on all scenarios.
Performance is evaluated using peak signal-to-noise ratio and structural
similarity index on two real-world benchmark datasets, CelebA faces and Paris
StreetView. The results of our model are compared to biharmonic imputation and
to some of the other state-of-the-art image inpainting methods.


Abstract:
  Prior studies have shown that, training machine learning models via empirical
loss minimization to maximize a utility metric (e.g., accuracy), might yield
models that make discriminatory predictions. To alleviate this issue, we
develop a new training algorithm, named BiFair, which jointly minimizes for a
utility, and a fairness loss of interest. Crucially, we do so without directly
modifying the training objective, e.g., by adding regularization terms. Rather,
we learn a set of weights on the training dataset, such that, training on the
weighted dataset ensures both good utility, and fairness. The dataset weights
are learned in concurrence to the model training, which is done by solving a
bilevel optimization problem using a held-out validation dataset. Overall, this
approach yields models with better fairness-utility trade-offs. Particularly,
we compare our algorithm with three other state-of-the-art fair training
algorithms over three real-world datasets, and demonstrate that, BiFair
consistently performs better, i.e., we reach to better values of a given
fairness metric under same, or higher accuracy. Further, our algorithm is
scalable. It is applicable both to simple models, such as logistic regression,
as well as more complex models, such as deep neural networks, as evidenced by
our experimental analysis.


Abstract:
  Extracting robust feature representation is one of the key challenges in
object re-identification (ReID). Although convolution neural network
(CNN)-based methods have achieved great success, they only process one local
neighborhood at a time and suffer from information loss on details caused by
convolution and downsampling operators (e.g. pooling and strided convolution).
To overcome these limitations, we propose a pure transformer-based object ReID
framework named TransReID. Specifically, we first encode an image as a sequence
of patches and build a transformer-based strong baseline with a few critical
improvements, which achieves competitive results on several ReID benchmarks
with CNN-based methods. To further enhance the robust feature learning in the
context of transformers, two novel modules are carefully designed. (i) The
jigsaw patch module (JPM) is proposed to rearrange the patch embeddings via
shift and patch shuffle operations which generates robust features with
improved discrimination ability and more diversified coverage. (ii) The side
information embeddings (SIE) is introduced to mitigate feature bias towards
camera/view variations by plugging in learnable embeddings to incorporate these
non-visual clues. To the best of our knowledge, this is the first work to adopt
a pure transformer for ReID research. Experimental results of TransReID are
superior promising, which achieve state-of-the-art performance on both person
and vehicle ReID benchmarks.


Abstract:
  There have been many successful implementations of neural style transfer in
recent years. In most of these works, the stylization process is confined to
the pixel domain. However, we argue that this representation is unnatural
because paintings usually consist of brushstrokes rather than pixels. We
propose a method to stylize images by optimizing parameterized brushstrokes
instead of pixels and further introduce a simple differentiable rendering
mechanism. Our approach significantly improves visual quality and enables
additional control over the stylization process such as controlling the flow of
brushstrokes through user input. We provide qualitative and quantitative
evaluations that show the efficacy of the proposed parameterized
representation.


Abstract:
  We use Higgs bundles to study the 3d $\mathcal{N} = 1$ vacua obtained from
M-theory compactified on a local $Spin(7)$ space given as a four-manifold $M_4$
of ADE singularities with further generic enhancements in the singularity type
along one-dimensional subspaces. There can be strong quantum corrections to the
massless degrees of freedom in the low energy effective field theory, but
topologically robust quantities such as "parity" anomalies are still
calculable. We show how geometric reflections of the compactification space
descend to 3d reflections and discrete symmetries. The "parity" anomalies of
the effective field theory descend from topological data of the
compactification. The geometric perspective also allows us to track various
perturbative and non-perturbative corrections to the 3d effective field theory.
We also provide some explicit constructions of well-known 3d theories,
including those which arise as edge modes of 4d topological insulators, and 3d
$\mathcal{N} = 1$ analogs of grand unified theories. An additional result of
our analysis is that we are able to track the spectrum of extended objects and
their transformations under higher-form symmetries.


Abstract:
  We study lattice two-color QCD (QC$_2$D) with two flavors of staggered
fermions at imaginary and real quark chemical potential $\mu_q$ and $T>T_c$. We
employ various methods of analytic continuation of the quark number density
from imaginary to real quark chemical potentials $\mu_q$, including series
expansions as well as based on phenomenological models, and study their
accuracy by comparing the results to the lattice data. Below the Roberge-Weiss
temperature, $T<T_{RW}$, we find that the cluster expansion model provides an
accurate analytic continuation of the baryon number density in the studied
range of chemical potentials. On the other hand, the behavior of the
reconstructed canonical partition functions indicate that the available models
may require corrections at high quark densities. At $T>T_{RW}$ we show that the
analytic continuation to the real values of $\mu_q$ based on trigonometric
functions works equally well with the conventional method based on the Taylor
expansion in powers of $\mu_q$.


Abstract:
  The fusion orbit category $\overline{\mathcal F _{\mathcal C}} (G)$ of a
discrete group $G$ over a collection $\mathcal C$ is the category whose objects
are the subgroups $H$ in $\mathcal C$, and whose morphisms $H \to K$ are given
by the $G$-maps $G/H \to G/K$ modulo the action of the centralizer group
$C_G(H)$. We show that the higher limits over $\overline{\mathcal F _{\mathcal
C}} (G)$ can be computed using the hypercohomology spectral sequences coming
from the Dwyer $G$-spaces for centralizer and normalizer decompositions for
$G$.
  If $G$ is the discrete group realizing a saturated fusion system $\mathcal
F$, then these hypercohomology spectral sequences give two spectral sequences
that converge to the cohomology of the centric orbit category ${\mathcal O}^c
(\mathcal F)$. This allows us to apply our results to the sharpness problem for
the subgroup decomposition of a $p$-local finite group. We prove that the
subgroup decomposition for every $p$-local finite group is sharp (over
$\mathcal F$-centric subgroups) if it is sharp for every $p$-local finite group
with nontrivial center. We also show that for every $p$-local finite group $(S,
\mathcal F, \mathcal L)$, the subgroup decomposition is sharp if and only if
the normalizer decomposition is sharp.


Abstract:
  Dynamical multiferroicity features entangled dynamic orders: fluctuating
electric dipoles induce magnetization. Hence, the material with paraelectric
fluctuations can develop magnetic signatures if dynamically driven. We identify
the paraelectric KTaO$_3$ (KTO) as a prime candidate for the observation of the
dynamical multiferroicity. We show that when a KTO sample is exposed to a
circularly polarized laser pulse, the dynamically induced ionic magnetic
moments are of the order of 5\% of the nuclear magneton per unit cell. We
determine the phonon spectrum using ab initio methods and identify T$_{1u}$ as
relevant soft phonon modes that couple to the external field and induce
magnetic polarization. We also predict a corresponding electron effect for the
dynamically induced magnetic moment which is enhanced by several orders of
magnitude due to the significant mass difference between electron and ionic
nucleus.


Abstract:
  The paper presents the derivation of the asymptotic behavior of $\nu$-zeros
of the modified Bessel function of imaginary order $K_{{\rm i}\nu}(z)$. This
derivation is based on the quasiclassical treatment of the exponential
potential on the positive half axis. The asymptotic expression for the
$\nu$-zeros (zeros with respect to order) contains the Lambert $W$ function,
which is readily available in most computer algebra systems and numerical
software packages. The use of this function provides much higher accuracy of
the estimation comparing to known relations containing the logarithm, which is
just the leading term of $W(x)$ at large $x$. Our result ensures accuracies
sufficient for practical applications.


Abstract:
  Current domain-independent, classical planners require symbolic models of the
problem domain and instance as input, resulting in a knowledge acquisition
bottleneck. Meanwhile, although deep learning has achieved significant success
in many fields, the knowledge is encoded in a subsymbolic representation which
is incompatible with symbolic systems such as planners. We propose Latplan, an
unsupervised architecture combining deep learning and classical planning. Given
only an unlabeled set of image pairs showing a subset of transitions allowed in
the environment (training inputs), Latplan learns a complete propositional PDDL
action model of the environment. Later, when a pair of images representing the
initial and the goal states (planning inputs) is given, Latplan finds a plan to
the goal state in a symbolic latent space and returns a visualized plan
execution. We evaluate Latplan using image-based versions of 6 planning
domains: 8-puzzle, 15-Puzzle, Blocksworld, Sokoban and Two variations of
LightsOut.


Abstract:
  Recently, there has been an increased interest in the study of the generation
of low-energy cosmic rays (CRs; < 1 TeV) in shocks situated on the surface of a
protostar or along protostellar jets. These locally accelerated CRs offer an
attractive explanation for the high levels of non-thermal emission and
ionisation rate, $\zeta$, observed close to these sources. The high $\zeta$
observed in some protostellar sources is generally attributed to
shock-generated UV photons. The aim of this article is to show that when
synchrotron emission and a high $\zeta$ are measured in the same spatial
region, a locally shock-accelerated CR flux is sufficient to explain both
phenomena. We assume that relativistic particles are accelerated according to
the first-order Fermi acceleration mechanism and compute $\zeta$ and the
non-thermal emission at cm wavelengths. We then apply our model to the
star-forming region OMC-2 FIR 3/FIR 4. Using a Bayesian analysis, we constrain
the parameters of the model and estimate the spectral indices of the
non-thermal radio emission. We demonstrate that the local CR acceleration model
makes it possible to simultaneously explain the synchrotron emission along the
HOPS 370 jet within the FIR 3 region and $\zeta$ observed near the FIR 4
protocluster. Our model constrains the magnetic field strength
(~250-450$~\mu$G), its turbulent component (~20-40$~\mu$G), and the jet
velocity in the shock reference frame for the three non-thermal sources of the
HOPS 370 jet (~350-1000 km s$^{-1}$). Beyond the modelling of the OMC-2 FIR
3/FIR 4 system, we show how the combination of continuum observations at cm
wavelengths and molecular transitions is a powerful new tool for the analysis
of star-forming regions: these two types of observations can be simultaneously
interpreted by invoking only the presence of locally accelerated CRs, without
having to resort to shock-generated UV photons.


Abstract:
  A number of approximations have been proposed to estimate basic hydrodynamic
quantities, in particular the frequency of a limit cycle. One of these, RZIF
(for Real Zero Imaginary Frequency), calls for linearizing the governing
equations about the mean flow and estimating the frequency as the imaginary
part of the leading eigenvalue. A further reduction, the SCM (for
Self-Consistent Model), approximates the mean flow as well, as resulting only
from the nonlinear interaction of the leading eigenmode with itself. Both RZIF
and SCM have proven dramatically successful for the archetypal case of the wake
of a circular cylinder.
  Here, the SCM is applied to thermosolutal convection, for which a
supercritical Hopf bifurcation gives rise to branches of standing waves and
traveling waves. The SCM is solved by means of a full Newton method coupling
the approximate mean flow and leading eigenmode. Although the RZIF property is
verified for the traveling waves, the SCM reproduces the nonlinear frequency
only very near the onset of the bifurcation and for another isolated parameter
value. Thus, the nonlinear interaction arising from the leading mode is
insufficient to reproduce the nonlinear mean field and frequency.


Abstract:
  Empirical Dynamic Modeling (EDM) is a state-of-the-art non-linear time-series
analysis framework. Despite its wide applicability, EDM was not scalable to
large datasets due to its expensive computational cost. To overcome this
obstacle, researchers have attempted and succeeded in accelerating EDM from
both algorithmic and implementational aspects. In previous work, we developed a
massively parallel implementation of EDM targeting HPC systems (mpEDM).
However, mpEDM maintains different backends for different architectures. This
design becomes a burden in the increasingly diversifying HPC systems, when
porting to new hardware. In this paper, we design and develop a
performance-portable implementation of EDM based on the Kokkos performance
portability framework (kEDM), which runs on both CPUs and GPUs while based on a
single codebase. Furthermore, we optimize individual kernels specifically for
EDM computation, and use real-world datasets to demonstrate up to $5.5\times$
speedup compared to mpEDM in convergent cross mapping computation.


Abstract:
  In this work, we only use data on the unstable manifold to locate the
partition boundaries by checking folding points at different levels, which
practically coincide with homoclinic tangencies (HTs). The method is then
applied to the classic two-dimensional Henon map and a well-known
three-dimensional map. Comparison with previous results is made in the Henon
case and Lyapunov exponents are computed through the metric entropy based on
the partition, to show the validity of the current scheme.


Abstract:
  We study the problem of content delivery in two-user interference channels
with altering topology, random available cache at the receivers, and delayed
channel knowledge at the transmitter. We establish a new set of outer-bounds on
the achievable rates when each receiver has access to a random fraction of the
message intended for the other receiver, and when each transmitter is aware of
which part of its own message is known to the unintended receiver. The
outer-bounds reveal the significant potential rate boost associated with even a
small amount of side-information at each receiver. The key in deriving the
bounds is to quantify the baseline entropy that will always become available to
the unintended receiver given the altering topology and the already available
side-information. We will also present matching achievable rates in certain
scenarios and outline the challenges in more general settings.


Abstract:
  We present a unified and hardware efficient architecture for two stage voice
trigger detection (VTD) and false trigger mitigation (FTM) tasks. Two stage VTD
systems of voice assistants can get falsely activated to audio segments
acoustically similar to the trigger phrase of interest. FTM systems cancel such
activations by using post trigger audio context. Traditional FTM systems rely
on automatic speech recognition lattices which are computationally expensive to
obtain on device. We propose a streaming transformer (TF) encoder architecture,
which progressively processes incoming audio chunks and maintains audio context
to perform both VTD and FTM tasks using only acoustic features. The proposed
joint model yields an average 18% relative reduction in false reject rate (FRR)
for the VTD task at a given false alarm rate. Moreover, our model suppresses
95% of the false triggers with an additional one second of post-trigger audio.
Finally, on-device measurements show 32% reduction in runtime memory and 56%
reduction in inference time compared to non-streaming version of the model.


Abstract:
  Brockett's necessary condition yields a test to determine whether a system
can be made to stabilize about some operating point via continuous, purely
state-dependent feedback. For many real-world systems, however, one wants to
stabilize sets which are more general than a single point. One also wants to
control such systems to operate safely by making obstacles and other
"dangerous" sets repelling.
  We generalize Brockett's necessary condition to the case of stabilizing
general compact subsets having a nonzero Euler characteristic. Using this
generalization, we also formulate a necessary condition for the existence of
"safe" control laws. We illustrate the theory in concrete examples and for some
general classes of systems including a broad class of nonholonomically
constrained Lagrangian systems. We also show that, for the special case of
stabilizing a point, the specialization of our general stabilizability test is
stronger than Brockett's.


Abstract:
  Fault Tree analysis is a widely used failure analysis methodology to assess a
system in terms of safety or reliability in many industrial application
domains. However, with Fault Tree methodology there is no possibility to
express a temporal sequence of events or state-dependent behavior of
software-controlled systems. In contrast to this, Markov Chains are a
state-based analysis technique based on a stochastic model. But the use of
Markov Chains for failure analysis of complex safety-critical systems is
limited due to exponential explosion of the size of the model. In this paper,
we present a concept to integrate Markov Chains in Component Fault Tree models.
Based on a component concept for Markov Chains, which enables the association
of Markov Chains to system development elements such as components, complex or
software-controlled systems can be analyzed w.r.t. safety or reliability in a
modular and compositional way. We illustrate this approach using a case study
from the automotive domain.


Abstract:
  Multi-user capabilities in Cisco Packet Tracer provide an incentive for
immersive realistic learning to increase the efficiency of teaching and
learning in the networking class. It is difficult to evaluate the configuration
skills of the students in the classroom, particularly during laboratory class,
while teaching networking course, the most problems for lecturers. The
multi-user functionality that uses the one-to-many remote relay server concept
allows lecturers to remotely control and test many students at one time. The
key purpose of this paper is to incorporate creativity in teaching by using
realistic multi-user practises in the networking class. In this study, the
multiuser operation was developed and used via an existing LAN link in the
classroom. The focus of the operation is the simple configuration of network
equipment such as routers and switches. This practise was used during the
laboratory session, encouraging lecturers to establish relationships based on a
large audience. The professor is able to evaluate the success of the students
during the execution of the activity and to ensure that each student engages in
the activity. As a result of introducing this approach, the curiosity of
students in technical subjects can be improved and student success can be
conveniently tracked on the part of the lecturer. Lecturers are able to monitor
the configuration capabilities of students closely to ensure that each student
engages in the laboratory class. The lecturer will train students not only to
develop their configuration and troubleshooting abilities by using this
teaching form, but also to accelerate the pace at which students need to fix
network problems.


Abstract:
  Plastic yielding in solids strongly depends on various conditions, such as
temperature and loading rate and indeed, sample-dependent knowledge of yield
points in structural materials promotes reliability in mechanical behavior.
Commonly, yielding is measured through controlled mechanical testing at small
or large scales, in ways that either distinguish elastic (stress) from total
deformation measurements, or by identifying plastic slip contributions. In this
paper we argue that instead of separate elastic/plastic measurements, yielding
can be unraveled through statistical analysis of total strain fluctuations
during the evolution sequence of profiles measured in-situ, through digital
image correlation. We demonstrate two distinct ways of precisely quantifying
yield locations in widely applicable crystal plasticity models, that apply in
polycrystalline solids, either by using principal component analysis or
discrete wavelet transforms. We test and compare these approaches in synthetic
data of polycrystal simulations and a variety of yielding responses, through
changes of the applied loading rates and the strain-rate sensitivity exponents.


Abstract:
  Semi-algebraic proof systems such as sum-of-squares (SoS) have attracted a
lot of attention recently due to their relation to approximation algorithms:
constant degree semi-algebraic proofs lead to conjecturally optimal
polynomial-time approximation algorithms for important NP-hard optimization
problems. Motivated by the need to allow a more streamlined and uniform
framework for working with SoS proofs than the restrictive propositional level,
we initiate a systematic first-order logical investigation into the kinds of
reasoning possible in algebraic and semi-algebraic proof systems. Specifically,
we develop first-order theories that capture in a precise manner constant
degree algebraic and semi-algebraic proof systems: every statement of a certain
form that is provable in our theories translates into a family of constant
degree polynomial calculus or SoS refutations, respectively; and using a
reflection principle, the converse also holds.
  This places algebraic and semi-algebraic proof systems in the established
framework of bounded arithmetic, while providing theories corresponding to
systems that vary quite substantially from the usual propositional-logic ones.
  We give examples of how our semi-algebraic theory proves statements such as
the pigeonhole principle, we provide a separation between algebraic and
semi-algebraic theories, and we describe initial attempts to go beyond these
theories by introducing extensions that use the inequality symbol, identifying
along the way which extensions lead outside the scope of constant degree SoS.
Moreover, we prove new results for propositional proofs, and specifically
extend Berkholz's dynamic-by-static simulation of polynomial calculus (PC) by
SoS to PC with the radical rule.


Abstract:
  Recent advancements in machine learning and signal processing domains have
resulted in an extensive surge of interest in Deep Neural Networks (DNNs) due
to their unprecedented performance and high accuracy for different and
challenging problems of significant engineering importance. However, when such
deep learning architectures are utilized for making critical decisions such as
the ones that involve human lives (e.g., in control systems and medical
applications), it is of paramount importance to understand, trust, and in one
word "explain" the argument behind deep models' decisions. In many
applications, artificial neural networks (including DNNs) are considered as
black-box systems, which do not provide sufficient clue on their internal
processing actions. Although some recent efforts have been initiated to explain
the behaviors and decisions of deep networks, explainable artificial
intelligence (XAI) domain, which aims at reasoning about the behavior and
decisions of DNNs, is still in its infancy. The aim of this paper is to provide
a comprehensive overview on Understanding, Visualization, and Explanation of
the internal and overall behavior of DNNs.


Abstract:
  Reinforcement Learning (RL) algorithms have led to recent successes in
solving complex games, such as Atari or Starcraft, and to a huge impact in
real-world applications, such as cybersecurity or autonomous driving. In the
side of the drawbacks, recent works have shown how the performance of RL
algorithms decreases under the influence of soft changes in the reward
function. However, little work has been done about how sensitive these
disturbances are depending on the aggressiveness of the attack and the learning
exploration strategy. In this paper, we propose to fill this gap in the
literature analyzing the effects of different attack strategies based on reward
perturbations, and studying the effect in the learner depending on its
exploration strategy. In order to explain all the behaviors, we choose a
sub-class of MDPs: episodic, stochastic goal-only-rewards MDPs, and in
particular, an intelligible grid domain as a benchmark. In this domain, we
demonstrate that smoothly crafting adversarial rewards are able to mislead the
learner, and that using low exploration probability values, the policy learned
is more robust to corrupt rewards. Finally, in the proposed learning scenario,
a counterintuitive result arises: attacking at each learning episode is the
lowest cost attack strategy.


Abstract:
  State of the art architectures for untrimmed video Temporal Action
Localization (TAL) have only considered RGB and Flow modalities, leaving the
information-rich audio modality totally unexploited. Audio fusion has been
explored for the related but arguably easier problem of trimmed (clip-level)
action recognition. However, TAL poses a unique set of challenges. In this
paper, we propose simple but effective fusion-based approaches for TAL. To the
best of our knowledge, our work is the first to jointly consider audio and
video modalities for supervised TAL. We experimentally show that our schemes
consistently improve performance for state of the art video-only TAL
approaches. Specifically, they help achieve new state of the art performance on
large-scale benchmark datasets - ActivityNet-1.3 (54.34 mAP@0.5) and THUMOS14
(57.18 mAP@0.5). Our experiments include ablations involving multiple fusion
schemes, modality combinations and TAL architectures. Our code, models and
associated data are available at https://github.com/skelemoa/tal-hmo.


Abstract:
  This paper is concerned with quasi-linear parabolic equations driven by an
additive forcing $\xi \in C^{\alpha-2}$, in the full sub-critical regime
$\alpha \in (0,1)$. We are inspired by Hairer's regularity structures, however
we work with a more parsimonious model indexed by multi-indices rather than
trees. This allows us to capture additional symmetries which play a crucial
role in our analysis. Assuming bounds on this model, which is modified in
agreement with the concept of algebraic renormalization, we prove local a
priori estimates on solutions to the quasi-linear equations modified by the
corresponding counter terms.


Abstract:
  There is a great diversity of formal models to understand the dynamics of
transport and vehicular flow on a road. Many of these models are inspired by
the dynamics of flows governed by partial differential equations. However, it
is possible to simplify these models to ordinary equations by considering
constant variations in some of the input variables in this type of models.
However, given that these types of systems present discrete changes when the
vehicle density is altered in some sections of the lane, it seems reasonable to
make use of hybrid systems to better understand the evolution of these
dynamics. In this work we are interested in making use of dynamic differential
logic to formally verify one of these models proposed in ordinary equations.
This verification will be done through a proof assistant specially designed for
hybrid systems called KeYmaera. Once we adapt the model to a hybrid system
representation we proceed to use KeYmaera to verify that the proposed model is
formally correct.


Abstract:
  We compute explicitly the Khovanov polynomials (using the computer program
from katlas.org) for the two simplest families of the satellite knots, which
are the twisted Whitehead doubles and the two-strand cables. We find that a
quantum group decomposition for the HOMFLY polynomial of a satellite knot can
be extended to the Khovanov polynomial, whose quantum group properties are not
manifest. Namely, the Khovanov polynomial of a twisted Whitehead double or
two-strand cable (the two simplest satellite families) can be presented as a
naively deformed linear combination of the pattern and companion invariants.
For a given companion, the satellite polynomial "smoothly" depends on the
pattern but for the "jump" at one critical point defined by the s-invariant of
the companion knot. A similar phenomenon is known for the knot Floer homology
and tau-invariant for the same kind of satellites.


Abstract:
  This paper is an introduction to our ongoing more comprehensive work on a
critically important period in the history of Russian mathematics education; it
provides a glimpse into the socio-political environment in which the famous
Soviet tradition of mathematics education was born. The authors are
practitioners of mathematics education in two very different countries, England
and Russia. We have a chance to see that too many trends and debates in current
education policy resemble battles around mathematics education in the 1920s and
1930s Soviet Russia. This is why this period should be revisited and
re-analysed, despite quite a considerable amount of previous research. Our main
conclusion: mathematicians, first of all, were fighting for control over
selection, education, and career development, of young mathematicians. In the
harshest possible political environment, they were taking potentially lethal
risks.