Abstract:
  Optical implementations of neural networks (ONNs) herald the next-generation
high-speed and energy-efficient deep learning computing by harnessing the
technical advantages of large bandwidth and high parallelism of optics.
However, due to the problems of incomplete numerical domain, limited hardware
scale, or inadequate numerical accuracy, the majority of existing ONNs were
studied for basic classification tasks. Given that regression is a fundamental
form of deep learning and accounts for a large part of current artificial
intelligence applications, it is necessary to master deep learning regression
for further development and deployment of ONNs. Here, we demonstrate a
silicon-based optical coherent dot-product chip (OCDC) capable of completing
deep learning regression tasks. The OCDC adopts optical fields to carry out
operations in complete real-value domain instead of in only positive domain.
Via reusing, a single chip conducts matrix multiplications and convolutions in
neural networks of any complexity. Also, hardware deviations are compensated
via in-situ backpropagation control provided the simplicity of chip
architecture. Therefore, the OCDC meets the requirements for sophisticated
regression tasks and we successfully demonstrate a representative neural
network, the AUTOMAP (a cutting-edge neural network model for image
reconstruction). The quality of reconstructed images by the OCDC and a 32-bit
digital computer is comparable. To the best of our knowledge, there is no
precedent of performing such state-of-the-art regression tasks on ONN chip. It
is anticipated that the OCDC can promote novel accomplishment of ONNs in modern
AI applications including autonomous driving, natural language processing, and
scientific study.


Abstract:
  The paper describes a number of simple but quite effective methods for
constructing exact solutions of PDEs, that involve a relatively small amount of
intermediate calculations. The methods employ two main ideas: (i) simple exact
solutions can serve to construct more complex solutions of the equations under
consideration and (ii) exact solutions of some equations can serve to construct
solutions of other, more complex equations. In particular, we propose a method
for constructing complex solutions from simple solutions using translation and
scaling. We show that in some cases, rather complex solutions can be obtained
by adding one or more terms to simpler solutions. There are situations where
nonlinear superposition allows us to construct a complex composite solution
using similar simple solutions. We also propose a few methods for constructing
complex exact solutions to linear and nonlinear PDEs by introducing
complex-valued parameters into simpler solutions. The effectiveness of the
methods is illustrated by a large number of specific examples (over 30 in
total). These include nonlinear heat/diffusion equations, wave type equations,
Klein--Gordon type equations, hydrodynamic boundary layer equations,
Navier--Stokes equations, and some other PDEs. Apart from exact solutions to
`ordinary' PDEs, we also describe some exact solutions to more complex
nonlinear delay PDEs. Along with the unknown function at the current time,
$u=u(x,t)$, these equations contain the same function at a past time,
$w=u(x,t-\tau)$, where $\tau>0$ is the delay time. Furthermore, we look at
nonlinear partial functional-differential equations of the pantograph type,
which in addition to the unknown $u=u(x,t)$, also contain the same functions
with dilated or contracted arguments, $w=u(px,qt)$, where $p$ and $q$ are
scaling parameters.


Abstract:
  Light's internal reflectivity near a critical angle is very sensitive to the
angle of incidence and the optical properties of the external medium near the
interface. Novel applications in biology and medicine of subcritical internal
reflection are being pursued. In many practical situations the refractive index
of the external medium may vary with respect to its bulk value due to different
physical phenomena at surfaces. Thus, there is a pressing need to understand
the effects of a refractive-index gradient at a surface for near-critical-angle
reflection. In this work we investigate theoretically the reflectivity near the
critical angle at an interface with glass assuming the external medium has a
continuous depth-dependent refractive index. We present graphs of the internal
reflectivity as a function of the angle of incidence, which exhibit the effects
of a refractive-index gradient at the interface. We analyse the behaviour of
the reflectivity curves before total internal reflection is achieved. Our
results provide insight into how one can recognise the existence of a
refractive-index gradient at the interface and shed light on the viability of
characterising it.


Abstract:
  In this paper, we present difference of convex algorithms for solving bilevel
programs in which the upper level objective functions are difference of convex
functions, and the lower level programs are fully convex. This nontrivial class
of bilevel programs provides a powerful modelling framework for dealing with
applications arising from hyperparameter selection in machine learning. Thanks
to the full convexity of the lower level program, the value function of the
lower level program turns out to be convex and hence the bilevel program can be
reformulated as a difference of convex bilevel program. We propose two
algorithms for solving the reformulated difference of convex program and show
their convergence under very mild assumptions. Finally we conduct numerical
experiments to a bilevel model of support vector machine classification.


Abstract:
  We propose a new method for unsupervised continual knowledge consolidation in
generative models that relies on the partitioning of Variational Autoencoder's
latent space. Acquiring knowledge about new data samples without forgetting
previous ones is a critical problem of continual learning. Currently proposed
methods achieve this goal by extending the existing model while constraining
its behavior not to degrade on the past data, which does not exploit the full
potential of relations within the entire training dataset. In this work, we
identify this limitation and posit the goal of continual learning as a
knowledge accumulation task. We solve it by continuously re-aligning latent
space partitions that we call bands which are representations of samples seen
in different tasks, driven by the similarity of the information they contain.
In addition, we introduce a simple yet effective method for controlled
forgetting of past data that improves the quality of reconstructions encoded in
latent bands and a latent space disentanglement technique that improves
knowledge consolidation. On top of the standard continual learning evaluation
benchmarks, we evaluate our method on a new knowledge consolidation scenario
and show that the proposed approach outperforms state-of-the-art by up to
twofold across all testing scenarios.


Abstract:
  Registration is a transformation estimation problem between two point clouds,
which has a unique and critical role in numerous computer vision applications.
The developments of optimization-based methods and deep learning methods have
improved registration robustness and efficiency. Recently, the combinations of
optimization-based and deep learning methods have further improved performance.
However, the connections between optimization-based and deep learning methods
are still unclear. Moreover, with the recent development of 3D sensors and 3D
reconstruction techniques, a new research direction emerges to align
cross-source point clouds. This survey conducts a comprehensive survey,
including both same-source and cross-source registration methods, and summarize
the connections between optimization-based and deep learning methods, to
provide further research insight. This survey also builds a new benchmark to
evaluate the state-of-the-art registration algorithms in solving cross-source
challenges. Besides, this survey summarizes the benchmark data sets and
discusses point cloud registration applications across various domains.
Finally, this survey proposes potential research directions in this rapidly
growing field.


Abstract:
  We perform asymptotic analysis for the Euler--Riesz system posed in either
$\mathbb{T}^d$ or $\mathbb{R}^d$ in the high-force regime and establish a
quantified relaxation limit result from the Euler--Riesz system to the
fractional porous medium equation. We provide a unified approach for asymptotic
analysis regardless of the presence of pressure, based on the modulated energy
estimates, the Wasserstein distance of order $2$, and the bounded Lipschitz
distance.


Abstract:
  Resistive random-access memory is one of the most promising candidates for
the next generation of non-volatile memory technology. However, its crossbar
structure causes severe "sneak-path" interference, which also leads to strong
inter-cell correlation. Recent works have mainly focused on sub-optimal data
detection schemes by ignoring inter-cell correlation and treating sneak-path
interference as independent noise. We propose a near-optimal data detection
scheme that can approach the performance bound of the optimal detection scheme.
Our detection scheme leverages a joint data and sneak-path interference
recovery and can use all inter-cell correlations. The scheme is appropriate for
data detection of large memory arrays with only linear operation complexity.


Abstract:
  We present a robust version of the life-cycle optimal portfolio choice
problem in the presence of labor income, as introduced in Biffis, Gozzi and
Prosdocimi ("Optimal portfolio choice with path dependent labor income: the
infinite horizon case", SIAM Journal on Control and Optimization, 58(4),
1906-1938.) and Dybvig and Liu ("Lifetime consumption and investment:
retirement and constrained borrowing", Journal of Economic Theory, 145, pp.
885-907). In particular, in Biffis, Gozzi and Prosdocimi the influence of past
wages on the future ones is modelled linearly in the evolution equation of
labor income, through a given weight function. The optimization relies on the
resolution of an infinite dimensional HJB equation. We improve the state of art
in three ways. First, we allow the weight to be a Radon measure. This
accommodates for more realistic weighting of the sticky wages, like e.g. on a
discrete temporal grid according to some periodic income. Second, there is a
general correlation structure between labor income and stocks market. This
naturally affects the optimal hedging demand, which may increase or decrease
according to the correlation sign. Third, we allow the weight to change with
time, possibly lacking perfect identification. The uncertainty is specified by
a given set of Radon measures $K$, in which the weight process takes values.
This renders the inevitable uncertainty on how the past affects the future, and
includes the standard case of error bounds on a specific estimate for the
weight. Under uncertainty averse preferences, the decision maker takes a maxmin
approach to the problem. Our analysis confirms the intuition: in the infinite
dimensional setting, the optimal policy remains the best investment strategy
under the worst case weight.


Abstract:
  Conventional multi-agent path planners typically determine a path that
optimizes a single objective, such as path length. Many applications, however,
may require multiple objectives, say time-to-completion and fuel use, to be
simultaneously optimized in the planning process. Often, these criteria may not
be readily compared and sometimes lie in competition with each other. Simply
applying standard multi-objective search algorithms to multi-agent path finding
may prove to be inefficient because the size of the space of possible
solutions, i.e., the Pareto-optimal set, can grow exponentially with the number
of agents (the dimension of the search space). This paper presents an approach
that bypasses this so-called curse of dimensionality by leveraging our prior
multi-agent work with a framework called subdimensional expansion. One example
of subdimensional expansion, when applied to A*, is called M* and M* was
limited to a single objective function. We combine principles of dominance and
subdimensional expansion to create a new algorithm named multi-objective M*
(MOM*), which dynamically couples agents for planning only when those agents
have to "interact" with each other. MOM* computes the complete Pareto-optimal
set for multiple agents efficiently and naturally trades off sub-optimal
approximations of the Pareto-optimal set and computational efficiency. Our
approach is able to find the complete Pareto-optimal set for problem instances
with hundreds of solutions which the standard multi-objective A* algorithms
could not find within a bounded time.


Abstract:
  The chromatic index of a cubic graph is either 3 or 4. Edge-Kempe switching,
which can be used to transform edge-colorings, is here considered for
3-edge-colorings of cubic graphs. Computational results for edge-Kempe
switching of cubic graphs up to order 30 and bipartite cubic graphs up to order
36 are tabulated. Families of cubic graphs of orders $4n+2$ and $4n+4$ with
$2^n$ edge-Kempe equivalence classes are presented; it is conjectured that
there are no cubic graphs with more edge-Kempe equivalence classes. New
families of nonplanar bipartite cubic graphs with exactly one edge-Kempe
equivalence class are also obtained. Edge-Kempe switching is further connected
to cycle switching of Steiner triple systems, for which an improvement of the
established classification algorithm is presented.


Abstract:
  Type-B permutation tableaux are combinatorial objects introduced by Lam and
Williams that have an interesting connection with the partially asymmetric
simple exclusion process (PASEP). In this paper, we compute the expected value
of several statistics on these tableaux. Some of these computations are
motivated by a similar paper on permutation tableaux. Others are motivated by
the PASEP. In particular, we compute the expected number of rows, unrestricted
rows, diagonal ones, adjacent south steps, and adjacent west steps.


Abstract:
  Adversarial training (AT) is currently one of the most successful methods to
obtain the adversarial robustness of deep neural networks. However, the
phenomenon of robust overfitting, i.e., the robustness starts to decrease
significantly during AT, has been problematic, not only making practitioners
consider a bag of tricks for a successful training, e.g., early stopping, but
also incurring a significant generalization gap in the robustness. In this
paper, we propose an effective regularization technique that prevents robust
overfitting by optimizing an auxiliary `consistency' regularization loss during
AT. Specifically, we discover that data augmentation is a quite effective tool
to mitigate the overfitting in AT, and develop a regularization that forces the
predictive distributions after attacking from two different augmentations of
the same instance to be similar with each other. Our experimental results
demonstrate that such a simple regularization technique brings significant
improvements in the test robust accuracy of a wide range of AT methods. More
remarkably, we also show that our method could significantly help the model to
generalize its robustness against unseen adversaries, e.g., other types or
larger perturbations compared to those used during training. Code is available
at https://github.com/alinlab/consistency-adversarial.


Abstract:
  Deep learning techniques are increasingly being adopted for classification
tasks over the past decade, yet explaining how deep learning architectures can
achieve state-of-the-art performance is still an elusive goal. While all the
training information is embedded deeply in a trained model, we still do not
understand much about its performance by only analyzing the model. This paper
examines the neuron activation patterns of deep learning-based classification
models and explores whether the models' performances can be explained through
neurons' activation behavior. We propose two approaches: one that models
neurons' activation behavior as a graph and examines whether the neurons form
meaningful communities, and the other examines the predictability of neurons'
behavior using entropy. Our comprehensive experimental study reveals that both
the community quality and entropy can provide new insights into the deep
learning models' performances, thus paves a novel way of explaining deep
learning models directly from the neurons' activation pattern.


Abstract:
  Cavity ring-down spectroscopy is a ubiquitous optical method used to study
light-matter interactions with high resolution, sensitivity and accuracy.
However, it has never been performed with the multiplexing advantages of direct
frequency comb spectroscopy without sacrificing orders of magnitude of
resolution. We present dual-comb cavity ring-down spectroscopy (DC-CRDS) based
on the parallel heterodyne detection of ring-down signals with a local
oscillator comb to yield absorption and dispersion spectra. These spectra are
obtained from widths and positions of cavity modes. We present two approaches
which leverage the dynamic cavity response to coherently or randomly driven
changes in the amplitude or frequency of the probe field. Both techniques yield
accurate spectra of methane - an important greenhouse gas and breath biomarker.
The high sensitivity and accuracy of broadband DC-CRDS, shows promise for
applications like studies of the structure and dynamics of large molecules,
multispecies trace gas detection and isotopic composition.


Abstract:
  Using direct numerical simulations of rotating Rayleigh-B\'enard convection,
we explore the transitions between turbulent states from a 3D flow state
towards a quasi-2D condensate known as the large-scale vortex (LSV). We vary
the Rayleigh number $Ra$ as control parameter and study the system response
(strength of the LSV) in terms of order parameters assessing the energetic
content in the flow and the upscale energy flux. By sensitively probing the
boundaries of the domain of existence of the LSV, we find discontinuous
transitions and we identify the presence of a hysteresis loop as well as
nucleation & growth type of dynamics, manifesting a remarkable correspondence
with first-order phase transitions in equilibrium statistical mechanics. We
show furthermore that the creation of the condensate state coincides with a
discontinuous transition of the energy transport into the largest mode of the
system.


Abstract:
  Radial imaging techniques, such as projection-reconstruction (PR), are used
in magnetic resonance imaging (MRI) for dynamic imaging, angiography, and
short-imaging. They are robust to flow and motion, have diffuse aliasing
patterns, and support short readouts and echo times. One drawback is that
standard implementations do not support anisotropic field-of-view (FOV) shapes,
which are used to match the imaging parameters to the object or
region-of-interest. A set of fast, simple algorithms for 2-D and 3-D PR, and
3-D cones acquisitions are introduced that match the sampling density in
frequency space to the desired FOV shape. Tailoring the acquisitions allows for
reduction of aliasing artifacts in undersampled applications or scan time
reductions without introducing aliasing in fully-sampled applications. It also
makes possible new radial imaging applications that were previously unsuitable,
such as imaging elongated regions or thin slabs. 2-D PR longitudinal leg images
and thin-slab, single breath-hold 3-D PR abdomen images, both with isotropic
resolution, demonstrate these new possibilities. No scan time to volume
efficiency is lost by using anisotropic FOVs. The acquisition trajectories can
be computed on a scan by scan basis.


Abstract:
  We develop a visual analytics system, NewsKaleidoscope, to investigate the
how news reporting of events varies. NewsKaleidoscope combines several backend
text language processing techniques with a coordinated visualization interface
tailored for visualization non-expert users. To robustly evaluate
NewsKaleidoscope, we conduct a trio of user studies. (1) A usability study with
news novices assesses the overall system and the specific insights promoted for
journalism-agnostic users. (2) A follow-up study with news experts assesses the
insights promoted for journalism-savvy users. (3) Based on identified system
limitations in these two studies, we amend NewsKaleidoscope design and conduct
a third study to validate these improvements. Results indicate that, for both
news novice and experts, NewsKaleidoscope supports an effective, task-driven
workflow for analyzing the diversity of news coverage about events, though
journalism expertise has a significant influence on the user insights and
takeaways. Our insights while developing and evaluating NewsKaleidoscope can
aid future interface designs that combine visualization with natural language
processing to analyze coverage diversity in news event reporting.


Abstract:
  We investigate the possibility of simultaneously explaining inflation, the
neutrino masses and the baryon asymmetry through extending the Standard Model
by a triplet Higgs. The neutrino masses are generated by the vacuum expectation
value of the triplet Higgs, while a combination of the triplet and doublet
Higgs' plays the role of the inflaton. Additionally, the dynamics of the
triplet, and its inherent lepton number violating interactions, lead to the
generation of a lepton asymmetry during inflation. The resultant baryon
asymmetry, inflationary predictions and neutrino masses are consistent with
current observational and experimental results.


Abstract:
  Prosody plays an important role in characterizing the style of a speaker or
an emotion, but most non-parallel voice or emotion style transfer algorithms do
not convert any prosody information. Two major components of prosody are pitch
and rhythm. Disentangling the prosody information, particularly the rhythm
component, from the speech is challenging because it involves breaking the
synchrony between the input speech and the disentangled speech representation.
As a result, most existing prosody style transfer algorithms would need to rely
on some form of text transcriptions to identify the content information, which
confines their application to high-resource languages only. Recently,
SpeechSplit has made sizeable progress towards unsupervised prosody style
transfer, but it is unable to extract high-level global prosody style in an
unsupervised manner. In this paper, we propose AutoPST, which can disentangle
global prosody style from speech without relying on any text transcriptions.
AutoPST is an Autoencoder-based Prosody Style Transfer framework with a
thorough rhythm removal module guided by the self-expressive representation
learning. Experiments on different style transfer tasks show that AutoPST can
effectively convert prosody that correctly reflects the styles of the target
domains.


Abstract:
  Virtual Reality (VR) head-mounted displays (HMDs) have been studied widely as
tools for the most diverse kinds of training activities. One special kind that
is the basis for many real-world applications is spatial knowledge acquisition
and navigation. For example, knowing well by heart escape routes can be an
important factor for firefighters and soldiers. Prior research on how well
knowledge acquired in virtual worlds translates to real, physical one has had
mixed results, with some suggesting spatial learning in VR is akin to using a
regular 2D display. However, VR HMDs have evolved drastically in the last
decade, and little is known about how spatial training skills in a simulated
environment using up-to-date VR HMDs compares to training in the real world. In
this paper, we aim to investigate how people trained in a VR maze compare
against those trained in a physical maze in terms of recall of the position of
items inside the environment. While our results did not find significant
differences in time performance for people who experienced the physical and
those who trained in VR, other behavioural factors were different.


Abstract:
  We trace what happens with asymptotically free behavior of the running
coupling in $\phi^{3}$ theory in six-dimensional space-time if to compactify
two spatial dimensions on a 2D closed manifold. The result can be considered as
an effective 4D theory of infinitely many KK-type scalar fields with triple
interactions. The effective \emph{dimensional} coupling inherits running to
zero at high mass scales in a modified form depending on the size of the
compact manifold. Some physical implications are discussed.


Abstract:
  Let $\beta=\frac{1+\sqrt{5}}{2}$, $(a_n)_{n \in \mathbb{N}^+}$ be a
non-uniform morphic sequence involving the infinite Fibonacci word and
$(\delta(n))_{n \in \mathbb{N}^+}$ be a positive sequence such that for all
positive integers $n$, $\delta(n)=\frac{1}{\sqrt{5}}\sum_{j \geq
0}\epsilon_j\beta^{j+2}$ if the unique Zeckendorf expansion of $n$ is
$n=\sum_{j \geq 0}\epsilon_jF_{j+2}$ with Fibonacci numbers $F_0,F_1,F_2...$.
We define and study some Dirichlet series in the form of $\sum_{n\geq
1}\frac{a_n}{(\delta(n))^s}$ and relations between them. Moreover, we compute
the values of some infinite series involving the infinite Fibonacci word.


Abstract:
  Learning representations of stochastic processes is an emerging problem in
machine learning with applications from meta-learning to physical object models
to time series. Typical methods rely on exact reconstruction of observations,
but this approach breaks down as observations become high-dimensional or noise
distributions become complex. To address this, we propose a unifying framework
for learning contrastive representations of stochastic processes (CReSP) that
does away with exact reconstruction. We dissect potential use cases for
stochastic process representations, and propose methods that accommodate each.
Empirically, we show that our methods are effective for learning
representations of periodic functions, 3D objects and dynamical processes. Our
methods tolerate noisy high-dimensional observations better than traditional
approaches, and the learned representations transfer to a range of downstream
tasks.


Abstract:
  Among numerous applications of muon tomography, deployment in civil
structures has caught attraction of many recently. In this work, the
appropriateness of muon scattering tomography to detect defects in concrete
structures has been studied numerically. A few basic concrete structures that
are frequently used in civil construction, have been considered as test cases.
A simulation has been performed on Geant4 platform where an imaging setup built
with several gaseous ionization detectors, having a specific spatial resolution
for tracking the muons, have been modeled. The images of the test cases with
and without the defect have been simulated for a month-long exposure of cosmic
muons on the basis of their scattering from the composite concrete structures.
The images have been compared using t-test to evaluate the performance of the
imaging setup in identifying the defects. Further processing of the images has
been done with a pattern recognition method proposed in our earlier work to
improve defect identification. The efficacy of the said method has been
evaluated in terms of the PRM-score devised in this work. The limitation and
advantages of the present application of the muon scattering tomography
encompassing the imaging and image processing technique in non-destructive
evaluation of concrete structures have been discussed.


Abstract:
  This paper studies network resilience against structured additive
perturbations to its topology. We consider dynamic networks modeled as linear
time-invariant systems subject to perturbations of bounded energy satisfying
specific sparsity and entry-wise constraints. Given an energy level, the
structured pseudospectral abscissa captures the worst-possible perturbation an
adversary could employ to de-stabilize the network, and the structured
stability radius is the maximum energy in the structured perturbation that the
network can withstand without becoming unstable. Building on a novel
characterization of the worst-case structured perturbation, we propose
iterative algorithms that efficiently compute the structured pseudospectral
abscissa and structured stability radius. We provide theoretical guarantees of
the local convergence of the algorithms and illustrate their efficacy and
accuracy on several network examples.


Abstract:
  Due to the zero nontrivial aperiodic correlation of complete complementary
code (CCCs), it is used in asynchronous multi carrier code division multiple
access (MC-CDMA) communication to provide zero interference performance.
However, there is no CCC for some lengths. In this case, if the system pays
more attention to the correlation, it can use mutually orthogonal complementary
sequence sets (MOCSSs), and if the system pays more attention to the set size,
it can use Z-complementary code sets (ZCCSs). In this paper, we propose a
direct construction of $q$-ary $(q^{v+1},q,q^m,q^{m-v})$-ZCCS. In addition,
based on the known CCCs, we propose a new construction of MOCSSs and CCCs
respectively.


Abstract:
  We show that, in almost every $n$-vertex random directed graph process, a
copy of every possible $n$-vertex oriented cycle will appear strictly before a
directed Hamilton cycle does, except of course for the directed cycle itself.
Furthermore, given an arbitrary $n$-vertex oriented cycle, we determine the
sharp threshold for its appearance in the binomial random directed graph. These
results confirm, in a strong form, a conjecture of Ferber and Long.


Abstract:
  The paper addresses a joint sequential changepoint detection and
identification/isolation problem for a general stochastic model, assuming that
the observed data may be dependent and non-identically distributed, the prior
distribution of the change point is arbitrary, and the post-change hypotheses
are composite. The developed detection-identification theory generalizes the
changepoint detection theory developed by Tartakovsky (2019) to the case of
multiple composite post-change hypotheses when one has not only to detect a
change as quickly as possible but also to identify (or isolate) the true
post-change distribution. We propose a multi-hypothesis change
detection-identification rule and show that it is nearly optimal, minimizing
moments of the delay to detection as the probability of a false alarm and the
probabilities of misidentification go to zero.


Abstract:
  Machine learning models are poised to make a transformative impact on
chemical sciences by dramatically accelerating computational algorithms and
amplifying insights available from computational chemistry methods. However,
achieving this requires a confluence and coaction of expertise in computer
science and physical sciences. This review is written for new and experienced
researchers working at the intersection of both fields. We first provide
concise tutorials of computational chemistry and machine learning methods,
showing how insights involving both can be achieved. We then follow with a
critical review of noteworthy applications that demonstrate how computational
chemistry and machine learning can be used together to provide insightful (and
useful) predictions in molecular and materials modeling, retrosyntheses,
catalysis, and drug design.


Abstract:
  It is known that the numerical invariants Betti numbers and Bass numbers are
worthwhile tools for decoding a large amount of information about modules over
commutative rings. We highlight this fact, further, by establishing some
criteria for certain semidualizing complexes via their Betti and Bass numbers.
Two distinguished types of semidualizing complexes are the shifts of the
underlying rings and dualizing complexes. Let $C$ be a semidualizing complex
for an analytically irreducible local ring $R$ and set $n:=\sup C$ and
$d:=\dim_RC$. We show that $C$ is quasi-isomorphic to a shift of $R$ if and
only if the $n$th Betti number of $C$ is one. Also, we show that $C$ is a
dualizing complex for $R$ if and only if the $d$th Bass number of $C$ is one.


Abstract:
  We construct families of fundamental, dipole, and tripole solitons in the
fractional Schr\"{o}dinger equation (FSE)\ incorporating self-focusing cubic
and defocusing quintic terms modulated by factors $\cos ^{2}x$ and $\sin^{2}x$,
respectively. While the fundamental solitons are similar to those in the model
with the uniform nonlinearity, the multipole complexes exist only in the
presence of the nonlinear lattice. The shapes and stability of all the solitons
strongly depend on the L\'{e}vy index (LI)\ that determines the FSE
fractionality. Stability areas are identified in the plane of LI and
propagation constant by means of numerical methods, and some results are
explained with the help of an analytical approximation. The stability areas are
broadest for the fundamental solitons and narrowest for the tripoles.


Abstract:
  Kerala, the south-west coastal state of India, was ravaged by a series of
floods during the South-West Monsoon of 2018. The season was marked by severely
anomalous rainfall trends, with upto 150 mm of departures from the mean daily
precipitation in the northern districts of the State. Although, there were many
studies about the hydrogeological factors which aggravated the floods in
Kerala, no attempt was made to delve into the physics which actually resulted
in anomalous precipitation during the year. This study intends to document the
dynamical phenomenon which caused the Kerala Floods of 2018. The westward
propagating convectively-coupled Mixed Rossby-Gravity (MRG) waves were excited
by the synoptic disturbances of the tropical Pacific at the pressure level of
700 hPa, during the Indian Monsoon of 2018. They travelled across the Indian
Ocean in two significant modes -- a predominant slow moving wave of 20-40 days
period (as Madden-Julian Oscillation) and a secondary faster wave of 5-8 days
period. They are characterised by vertical phase propagation to the upper
troposphere, a precursor to deep convection and intense precipitation. Further,
the propagation of these waves through a medium enhances its relative vorticity
and the gyres or circulations thus formed are symmetrical about the equator.
Consequently, the meanders in the wind field and widening of the Intertropical
Convergence Zone were observed. The MRG waves, especially the slow mode induced
divergence in the wind field, which fueled convection in tropics and brought
very heavy rainfall to the State of Kerala in 2018.


Abstract:
  Many promising optoelectronic devices, such as broadband photodetectors,
nonlinear frequency converters, and building blocks for data communication
systems, exploit photoexcited charge carriers in graphene. For these systems,
it is essential to understand, and eventually control, the cooling dynamics of
the photoinduced hot-carrier distribution. There is, however, still an active
debate on the different mechanisms that contribute to hot-carrier cooling. In
particular, the intrinsic cooling mechanism that ultimately limits the cooling
dynamics remains an open question. Here, we address this question by studying
two technologically relevant systems, consisting of high-quality graphene with
a mobility >10,000 cm$^2$V$^{-1}$s$^{-1}$ and environments that do not
efficiently take up electronic heat from graphene: WSe$_2$-encapsulated
graphene and suspended graphene. We study the cooling dynamics of these two
high-quality graphene systems using ultrafast pump-probe spectroscopy at room
temperature. Cooling via disorder-assisted acoustic phonon scattering and
out-of-plane heat transfer to the environment is relatively inefficient in
these systems, predicting a cooling time of tens of picoseconds. However, we
observe much faster cooling, on a timescale of a few picoseconds. We attribute
this to an intrinsic cooling mechanism, where carriers in the hot-carrier
distribution with enough kinetic energy emit optical phonons. During phonon
emission, the electronic system continuously re-thermalizes, re-creating
carriers with enough energy to emit optical phonons. We develop an analytical
model that explains the observed dynamics, where cooling is eventually limited
by optical-to-acoustic phonon coupling. These fundamental insights into the
intrinsic cooling mechanism of hot carriers in graphene will play a key role in
guiding the development of graphene-based optoelectronic devices.


Abstract:
  We derive universal thermodynamic inequalities that bound from below the
moments of first-passage times of stochastic currents in nonequilibrium
stationary states and in the limit where the thresholds that define the
first-passage problem are large. These inequalities describe a tradeoff between
speed, uncertainty, and dissipation in nonequilibrium processes, which are
quantified, respectively, with the moments of the first-passage times of
stochastic currents, the splitting probability, and the mean entropy production
rate. Near equilibrium, the inequalities imply that mean-first passage times
are lower bounded by the Van't Hoff-Arrhenius law, whereas far from thermal
equilibrium the bounds describe a universal speed limit for rate processes.
When the current is the stochastic entropy production, then the bounds are
equalities, a remarkable property that follows from the fact that the
exponentiated negative entropy production is a martingale.


Abstract:
  We revisit the dissipative approach to producing and stabilizing
spin-squeezed states of an ensemble of $N$ two-level systems, providing a
detailed analysis of two surprising yet generic features of such protocols. The
first is a macroscopic sensitivity of the steady state to whether $N$ is even
or odd. We discuss how this effect can be avoided (if the goal is
parity-insensitive squeezing), or could be exploited as a new kind of sensing
modality with single-spin sensitivity. The second effect is an anomalous
emergent long timescale and a "prethermalized" regime that occurs for even weak
single-spin dephasing. We also discuss a general hybrid-systems approach for
implementing dissipative spin squeezing that does not require squeezed input
light or complex multi-level atoms, but instead makes use of bosonic
reservoir-engineering ideas. Our protocol is compatible with a variety of
platforms, including trapped ions, NV defect spins coupled to diamond
optomechanical crystals, and spin ensembles coupled to superconducting
microwave circuits.


Abstract:
  We first show the realization of exceptional points in a non-Hermitian
superconducting system based on a conventional superconductor and then
demonstrate that, surprisingly, the system hosts odd-frequency pairing, solely
generated by the non-Hermiticity. While there is a coexistence of even- and
odd-frequency pairs under general conditions, we find that the even-frequency
term vanishes at the exceptional degeneracies, leaving only odd-frequency
pairing. This exceptional odd-frequency pairing is directly given by the
imaginary part of the eigenvalues at the exceptional points and can be measured
from the spectral function. Our results thus put forward non-Hermitian systems
as a powerful platform to realize odd-frequency superconducting pairing.


Abstract:
  The recent advances in power plants and energy resources have extended the
applications of DC-DC converters in the power systems (especially in the
context of DC micro-grids). Parameter identification can extract the parameters
of the converters and generate accurate discrete simulation models. In this
paper, we propose a measurement-based converter parameter calibration method by
an adaptive Approximate Bayesian Computation with sequential Monte Carlo
sampler (ABC SMC), which estimates the parameters related to passive and
parasitic components. At first, we propose to find suitable prior distribution
for the parameter which we don't know the prior information about them. With
having prior distributions, we can use the ABC SMC to find the exact values of
the parameters of the converter. We chose the distance function carefully and
based on the simulations we assigned the best method for the threshold
sequencing. For improving the computationally of the algorithm, we propose an
adaptive weight that helps the algorithm to find the optimal values with fewer
simulations. The effectiveness of the proposed method is validated for a DC-DC
buck converter. The results show that the proposed approach can accurately and
efficiently estimate the posterior distributions of the buck parameters subject
to gross errors in the prior distributions of the parameters. The proposed
algorithm can also be applied to other parameter identifications and
optimization applications such as rectifiers, filters, or power supplies, among
others.


Abstract:
  We develop a new physical model for the rain effect and show that the
well-known atmosphere scattering model (ASM) for the haze effect naturally
emerges as its homogeneous continuous limit. Via depth-aware fusion of
multi-layer rain streaks according to the camera imaging mechanism, the new
model can better capture the sophisticated non-deterministic degradation
patterns commonly seen in real rainy images. We also propose a Densely
Scale-Connected Attentive Network (DSCAN) that is suitable for both deraining
and dehazing tasks. Our design alleviates the bottleneck issue existent in
conventional multi-scale networks and enables more effective information
exchange and aggregation. Extensive experimental results demonstrate that the
proposed DSCAN is able to deliver superior derained/dehazed results on both
synthetic and real images as compared to the state-of-the-art. Moreover, it is
shown that for our DSCAN, the synthetic dataset built using the new physical
model yields better generalization performance on real images in comparison
with the existing datasets based on over-simplified models.


Abstract:
  The comparison of the Hamiltonians of the noncommutative isotropic harmonic
oscillator and Landau problem are analysed to study the specific conditions
under which these two models are indistinguishable. The energy eigenvalues and
eigenstates of Landau problem in symmetric and two Landau gauges are evaluated
analytically. The Hamiltonian of a noncommutative isotropic harmonic oscillator
is found by using Bopp's shift in commutative coordinate space. The result
shows that the two systems are isomorphic up to the similar values of $n_{r}$
and $m_{l}$ and $qB = eB > 0$ for both gauge choices. However, there is an
additional requirement for Landau gauge where the noncommutative oscillator has
to lose one spatial degree of freedom. It also needs to be parametrized by a
factor $\zeta$ for their Hamiltonians to be consistent with each other. The
wavefunctions and probability density functions are then plotted and the
behaviour that emerges is explained. Finally, the effects of noncommutativity
or magnetic field on the eigenstates and their probability distribution of the
isomorphic system are shown.


Abstract:
  For robot touch to converge with the human sense of touch, artificial
transduction should involve biologically-plausible population codes analogous
to those of natural afferents. Using a biomimetic tactile sensor with
3d-printed skin based on the dermal-epidermal boundary, we propose two novel
feature sets to mimic slowly-adapting and rapidly-adapting type-I tactile
mechanoreceptor function. Their plausibility is tested with three classic
experiments from the study of natural touch: impingement on a flat plate to
probe adaptation and spatial modulation; stimulation by spatially-complex
ridged stimuli to probe single afferent responses; and perception of grating
orientation to probe the population response. Our results show a match between
artificial and natural afferent responses in their sensitivity to edges and
gaps; likewise, the human and robot psychometric functions match for grating
orientation. These findings could benefit robot manipulation, prosthetics and
the neurophysiology of touch.


Abstract:
  Gaussian mixture models are a popular tool for model-based clustering, and
mixtures of factor analyzers are Gaussian mixture models having parsimonious
factor covariance structure for mixture components. There are several recent
extensions of mixture of factor analyzers to deep mixtures, where the Gaussian
model for the latent factors is replaced by a mixture of factor analyzers. This
construction can be iterated to obtain a model with many layers. These deep
models are challenging to fit, and we consider Bayesian inference using
sparsity priors to further regularize the estimation. A scalable natural
gradient variational inference algorithm is developed for fitting the model,
and we suggest computationally efficient approaches to the architecture choice
using overfitted mixtures where unnecessary components drop out in the
estimation. In a number of simulated and two real examples, we demonstrate the
versatility of our approach for high-dimensional problems, and demonstrate that
the use of sparsity inducing priors can be helpful for obtaining improved
clustering results.


Abstract:
  Despite the constant advances in computer vision, integrating modern
single-image detectors in real-time handgun alarm systems in video-surveillance
is still debatable. Using such detectors still implies a high number of false
alarms and false negatives. In this context, most existent studies select one
of the latest single-image detectors and train it on a better dataset or use
some pre-processing, post-processing or data-fusion approach to further reduce
false alarms. However, none of these works tried to exploit the temporal
information present in the videos to mitigate false detections. This paper
presents a new system, called MULTI Confirmation-level Alarm SysTem based on
Convolutional Neural Networks (CNN) and Long Short Term Memory networks (LSTM)
(MULTICAST), that leverages not only the spacial information but also the
temporal information existent in the videos for a more reliable handgun
detection. MULTICAST consists of three stages, i) a handgun detection stage,
ii) a CNN-based spacial confirmation stage and iii) LSTM-based temporal
confirmation stage. The temporal confirmation stage uses the positions of the
detected handgun in previous instants to predict its trajectory in the next
frame. Our experiments show that MULTICAST reduces by 80% the number of false
alarms with respect to Faster R-CNN based-single-image detector, which makes it
more useful in providing more effective and rapid security responses.


Abstract:
  We present an analysis of the intracluster light in the Frontier Field
Cluster MACS J1149.5+2223 (z=0.544), which combines new and archival Hubble
WFC3/IR imaging to provide continuous radial coverage out to 2.8 Mpc from the
brightest cluster galaxy. Employing careful treatment of potential systematic
biases and using data at the largest radii to determine the background sky
level, we reconstruct the surface brightness profile out to a radius of 2 Mpc.
This radius is the largest to which the intracluster light (ICL) has been
measured for an individual cluster. Within this radius, we measure a total
luminosity of 1.5e13 Lsun for the brightest cluster galaxy plus ICL light. From
the profile and its logarithmic slope, we identify the transition from the
brightest cluster galaxy to ICL at r~70 kpc. Remarkably, we also detect an
inflection in the profile centered in the 1.2-1.7 Mpc (0.37-0.52 r200m) radial
bin, a signature of an infall caustic in the stellar distribution. Based upon
the shape and strength of the feature, we interpret it as potentially being at
the splashback radius, although the radius is smaller than theoretical
predictions. If this is the splashback radius, then it is the first such
detection in the ICL and the first detection of the splashback radius for an
individual cluster. Similar analyses should be possible with the other Frontier
Field clusters, and eventually with clusters from the Euclid and Roman
missions.


Abstract:
  When manipulating a novel object with complex dynamics, a state
representation is not always available, for example for deformable objects.
Learning both a representation and dynamics from observations requires large
amounts of data. We propose Learned Visual Similarity Predictive Control
(LVSPC), a novel method for data-efficient learning to control systems with
complex dynamics and high-dimensional state spaces from images. LVSPC leverages
a given simple model approximation from which image observations can be
generated. We use these images to train a perception model that estimates the
simple model state from observations of the complex system online. We then use
data from the complex system to fit the parameters of the simple model and
learn where this model is inaccurate, also online. Finally, we use Model
Predictive Control and bias the controller away from regions where the simple
model is inaccurate and thus where the controller is less reliable. We evaluate
LVSPC on two tasks; manipulating a tethered mass and a rope. We find that our
method performs comparably to state-of-the-art reinforcement learning methods
with an order of magnitude less data. LVSPC also completes the rope
manipulation task on a real robot with 80% success rate after only 10 trials,
despite using a perception system trained only on images from simulation.


Abstract:
  In this note we confirm the guess of Calegari, Garoufalidis and Zagier in
Arxiv 1712.04887 that $R_\zeta=c_\zeta^2$, where $R_\zeta$ is their map on
$K_3$ defined using the cyclic quantum dilogarithm and $c_\zeta$ is the Chern
class map on $K_3$.


Abstract:
  For a positive trace-class operator $C$ and a bounded operator $A$, we
provide an explicit description of the closure of the orbit-closed
$C$-numerical range of $A$ in terms of those operators submajorized by $C$ and
the essential numerical range of $A$. This generalizes and subsumes recent work
of Chan, Li and Poon for the $k$-numerical range, as well as some of our own
previous work on the orbit-closed $C$-numerical range.


Abstract:
  We study target space theory on a torus for the states with $N_L+N_R=2$
through Double Field Theory. The spin-two Fierz-Pauli fields are not allowed
when all spatial dimensions are non-compact. The massive states provide both
non-vanishing momentum and winding numbers in the target space theory. To
derive the cubic action, we provide the unique constraint for $N_L\neq N_R$
compatible with the integration by part. We first make a correspondence of
massive and massless fields. The quadratic action is gauge invariant by
introducing the mass term. We then proceed to the cubic order. The cubic action
is also gauge invariant by introducing the coupling between the one-form field
and other fields. The massive states do not follow the consistent truncation.
One should expect the self-consistent theory by summing over infinite modes.
Hence the naive expectation is wrong up to the cubic order. In the end, we show
that the momentum and winding modes cannot both appear for only one compact
doubled space.


Abstract:
  Pharmaceutical companies are relying more often on external sources of
innovation to boost their discovery research productivity. However, more
in-depth knowledge about how external innovation may translate to successful
product launches is still required in order to better understand how to best
leverage the innovation ecosystem. We analyzed the pre-approval publication
histories for FDA-approved new molecular entities (NMEs) and new biologic
entities (NBEs) launched by 13 top research pharma companies during the last
decade (2006-2016). We found that academic institutions contributed the
majority of pre-approval publications and that publication subject matter is
closely aligned with the strengths of the respective innovator. We found this
to also be true for candidate drugs terminated in Phase 3, but the volume of
literature on these molecules is substantially less than for approved drugs.
This may suggest that approved drugs are often associated with a more robust
dataset provided by a large number of institutes. Collectively, the results of
our analysis support the hypothesis that a collaborative research innovation
environment spanning across academia, industry and government is highly
conducive to successful drug approvals.


Abstract:
  The mechanical responses of single crystalline Body-Centered Cubic (BCC)
metals, such as molybdenum (Mo), outperform other metals at high temperatures,
so much so that they are considered as excellent candidates for applications
under extreme conditions, such as the divertor of fusion reactors. The
excellent thermomechanical stability of molybdenum at high temperatures
(400-1000$^{\rm o}$C) has also been detected through nanoindentation, pointing
towards connections to emergent local dislocation mechanisms related to defect
nucleation. In this work, we carry out a computational study of the effects of
high temperature on the mechanical deformation properties of single crystalline
Mo under nanoindentation. Molecular dynamics (MD) simulations of spherical
nanoindentation are performed at two indenter tip diameters and crystalline
sample orientations [100], [110], and [111], for the temperature range of
10-1000K. We investigate how the increase of temperature influences the
nanoindentation process, modifying dislocation densities, mechanisms, atomic
displacements and also, hardness, in agreement with reported experimental
measurements. Our results suggest that the characteristic formation and
high-temperature stability of [001] dislocation junctions in Mo during
nanoindentation, in contrast to other BCC metals, may be the cause of the
persistent thermomechanical stability of Mo.


Abstract:
  Higher-order topological insulators in two dimensions have states that
localize at their corners, called corner states. In this paper, we reveal
characteristics of the penetration depth of their corner states by using the
Benalcazar-Bernevig-Hughes model. First, we show that when we change the energy
of the corner states toward the end of the edge gap by adding an on-site
potential to the corner site, the penetration depth along the edge diverges
toward infinity while the penetration depth into the bulk remaining finite. We
analytically derive the corner-state wavefunction in a form of elliptic
integrals, which reproduces this anisotropic behavior of corner states. This
means that corner states have two kinds of penetration depths, and they behave
differently. At last, we show that hybridizations between corner states are
governed by the penetration depth through interference between the corner
states. It is because the corner states almost do not interfere with edge
states or bulk states.


Abstract:
  Motivated by thermal conductivity experiments in spin chain compounds, we
propose a phenomenological model to account for a ballistic magnetic transport
coupled to a diffusive phononic one, along the line of the seminal
two-temperature diffusive transport Sanders-Walton model. Although the
expression for the effective thermal conductivity is identical to that of
Sanders-Walton, the interpretation is entirely different, as the "magnetic
conductivity" is replaced by an "effective transfer conductivity" between the
magnetic and phononic component. This model also reveals the fascinating
possibility of visualizing the ballistic character of magnetic transport, for
appropriately chosen material parameters, as a two peak counter-propagating
feature in the phononic temperature. It is also appropriate for the analysis of
any thermal transport experiment involving a diffusive component coupled to a
ballistic one.


Abstract:
  Given a Lipschitz map $f$ from a cube into a metric space, we find several
equivalent conditions for $f$ to have a Lipschitz factorization through a
metric tree. As an application we prove a recent conjecture of David and Schul.
The techniques developed for the proof of the factorization result yield
several other new and seemingly unrelated results. We prove that if $f$ is a
Lipschitz mapping from an open set in $\mathbb{R}^n$ onto a metric space $X$,
then the topological dimension of $X$ equals $n$ if and only if $X$ has
positive $n$-dimensional Hausdorff measure. We also prove an area formula for
length-preserving maps between metric spaces, which gives, in particular, a new
formula for integration on countably rectifiable sets in the Heisenberg group.


Abstract:
  For a probability measure $\mu$ on SL d (R), we consider the Furstenberg
stationary measure on the space of flags. Under general non-degeneracy
conditions, if $\mu$ is discrete and if g log g d$\mu$(g) < +$\infty$, then the
measure $\nu$ is exact-dimensional.


Abstract:
  Tactical selection of experiments to estimate an underlying model is an
innate task across various fields. Since each experiment has costs associated
with it, selecting statistically significant experiments becomes necessary.
Classic linear experimental design deals with experiment selection so as to
minimize (functions of) variance in estimation of regression parameter.
Typically, standard algorithms for solving this problem assume that data
associated with each experiment is fully known. This isn't often true since
missing data is a common problem. For instance, remote sensors often miss data
due to poor connection. Hence experiment selection under such scenarios is a
widespread but challenging task. Though decoupling the tasks and using standard
data imputation methods like matrix completion followed by experiment selection
might seem a way forward, they perform sub-optimally since the tasks are
naturally interdependent. Standard design of experiments is an NP hard problem,
and the additional objective of imputing for missing data amplifies the
computational complexity. In this paper, we propose a maximum-entropy-principle
based framework that simultaneously addresses the problem of design of
experiments as well as the imputation of missing data. Our algorithm exploits
homotopy from a suitably chosen convex function to the non-convex cost
function; hence avoiding poor local minima. Further, our proposed framework is
flexible to incorporate additional application specific constraints.
Simulations on various datasets show improvement in the cost value by over 60%
in comparison to benchmark algorithms applied sequentially to the imputation
and experiment selection problems.


Abstract:
  In this paper, we study wave transmission in a rotating fluid with multiple
alternating convectively stable and unstable layers. We have discussed wave
transmissions in two different circumstances: cases where the wave is
propagative in each layer and cases where wave tunneling occurs. We find that
efficient wave transmission can be achieved by `resonant propagation' or
`resonant tunneling', even when stable layers are strongly stratified, and we
call this phenomenon `enhanced wave transmission'. Enhanced wave transmission
only occurs when the total number of layers is odd (embedding stable layers are
alternatingly embedded within clamping convective layers, or vise versa). For
wave propagation, the occurrence of enhanced wave transmission requires that
clamping layers have similar properties, the thickness of each clamping layer
is close to a multiple of the half wavelength of the corresponding propagative
wave, and the total thickness of embedded layers is close to a multiple of the
half wavelength of the corresponding propagating wave (resonant propagation).
For wave tunneling, we have considered two cases: tunneling of gravity waves
and tunneling of inertial waves. In both cases, efficient tunneling requires
that clamping layers have similar properties, the thickness of each embedded
layer is much smaller than the corresponding e-folding decay distance, and the
thickness of each clamping layer is close to a multiple-and-a-half of half
wavelength (resonant tunneling).


Abstract:
  Digital contact tracing is a public health intervention. It should be
integrated with local health policy, provide rapid and accurate notifications
to exposed individuals, and encourage high app uptake and adherence to
quarantine. Real-time monitoring and evaluation of effectiveness of app-based
contact tracing is key for improvement and public trust.


Abstract:
  In the field of multimodal segmentation, the correlation between different
modalities can be considered for improving the segmentation results. In this
paper, we propose a multi-modality segmentation network with a correlation
constraint. Our network includes N model-independent encoding paths with N
image sources, a correlation constraint block, a feature fusion block, and a
decoding path. The model independent encoding path can capture
modality-specific features from the N modalities. Since there exists a strong
correlation between different modalities, we first propose a linear correlation
block to learn the correlation between modalities, then a loss function is used
to guide the network to learn the correlated features based on the linear
correlation block. This block forces the network to learn the latent correlated
features which are more relevant for segmentation. Considering that not all the
features extracted from the encoders are useful for segmentation, we propose to
use dual attention based fusion block to recalibrate the features along the
modality and spatial paths, which can suppress less informative features and
emphasize the useful ones. The fused feature representation is finally
projected by the decoder to obtain the segmentation result. Our experiment
results tested on BraTS-2018 dataset for brain tumor segmentation demonstrate
the effectiveness of our proposed method.


Abstract:
  The operation of Terahertz (THz) communication can be significantly impacted
by the interaction between the transmitted wave and the molecules in the
atmosphere. In particular, it has been observed experimentally that the signal
undergoes not only molecular absorption, but also molecular re-radiation. Two
extreme modeling assumptions are prevalent in the literature, where the
re-radiated energy is modeled in the first as additive Gaussian noise and in
the second as a scattered component strongly correlated to the actual signal.
Since the exact characterization is still an open problem, we provide in this
paper the first comparative study of the performance of a reconfigurable
intelligent surface (RIS) assisted THz system under these two extreme models of
re-radiation. In particular, we employ an RIS to overcome the large pathloss by
creating a virtual line-of-sight (LOS) path. We then develop an optimization
framework for this setup and utilize the block-coordinate descent (BCD) method
to iteratively optimize both RIS configuration vector and receive beamforming
weight resulting in significant throughput gains for the user of interest
compared to random RIS configurations. As expected, our results reveal that
better throughput is achieved under the scattering assumption for the molecular
re-radiation than the noise assumption.


Abstract:
  Contrastive learning (CL) has recently emerged as an effective approach to
learning representation in a range of downstream tasks. Central to this
approach is the selection of positive (similar) and negative (dissimilar) sets
to provide the model the opportunity to `contrast' between data and class
representation in the latent space. In this paper, we investigate CL for
improving model robustness using adversarial samples. We first designed and
performed a comprehensive study to understand how adversarial vulnerability
behaves in the latent space. Based on this empirical evidence, we propose an
effective and efficient supervised contrastive learning to achieve model
robustness against adversarial attacks. Moreover, we propose a new sample
selection strategy that optimizes the positive/negative sets by removing
redundancy and improving correlation with the anchor. Extensive experiments
show that our Adversarial Supervised Contrastive Learning (ASCL) approach
achieves comparable performance with the state-of-the-art defenses while
significantly outperforms other CL-based defense methods by using only $42.8\%$
positives and $6.3\%$ negatives.


Abstract:
  Air traffic management and specifically air-traffic control (ATC) rely mostly
on voice communications between Air Traffic Controllers (ATCos) and pilots. In
most cases, these voice communications follow a well-defined grammar that could
be leveraged in Automatic Speech Recognition (ASR) technologies. The callsign
used to address an airplane is an essential part of all ATCo-pilot
communications. We propose a two-steps approach to add contextual knowledge
during semi-supervised training to reduce the ASR system error rates at
recognizing the part of the utterance that contains the callsign. Initially, we
represent in a WFST the contextual knowledge (i.e. air-surveillance data) of an
ATCo-pilot communication. Then, during Semi-Supervised Learning (SSL) the
contextual knowledge is added by second-pass decoding (i.e. lattice
re-scoring). Results show that `unseen domains' (e.g. data from airports not
present in the supervised training data) are further aided by contextual SSL
when compared to standalone SSL. For this task, we introduce the Callsign Word
Error Rate (CA-WER) as an evaluation metric, which only assesses ASR
performance of the spoken callsign in an utterance. We obtained a 32.1% CA-WER
relative improvement applying SSL with an additional 17.5% CA-WER improvement
by adding contextual knowledge during SSL on a challenging ATC-based test set
gathered from LiveATC.


Abstract:
  Communal pairing in superconductors introduces variational freedom for Cooper
pairs to share fermions. Temporal oscillations of the superconducting gap
entropically drive communal pairing through the order by disorder
phenomenology, stabilising a finite momentum space width of the superconducting
gap that increases with interaction strength, creating a smooth evolution from
the weakly interacting BCS state to the strongly interacting BEC state.


Abstract:
  In this paper, we study various aspects of the ODE's flow $X$ solution to the
equation $\partial_t X(t,x)=b(X(t,x))$, $X(0,x)=x$ in the $d$-dimensional torus
$Y_d$, where $b$ is a regular $\mathbb{Z}^d$-periodic vector field from
$\mathbb{R}^d$ in $\mathbb{R}^d$.We present an original and complete picture in
any dimension of all logical connections between the following seven conditions
involving the field $b$: (i) the everywhere asymptotics of the flow $X$, (ii)
the almost-everywhere asymptotics of the flow $X$, (iii) the global
rectification of the vector field $b$ in $Y_d$, (iv) the ergodicity of the flow
related to an invariant probability measure which is absolutely continuous with
respect to Lebesgue's measure, (v) the unit set condition for Herman's rotation
set $C_b$ composed of the means of $b$ related to the invariant probability
measures, (vi) the unit set condition for the subset $D_b$ of $C_b$ composed of
the means of $b$ related to the invariant probability measures which are
absolutely continuous with respect to Lebesgue's measure, (vii) the
homogenization of the linear transport equation with oscillating data and the
oscillating velocity $b(x/\varepsilon)$ when $b$ is divergence free. The main
and surprising result of the paper is that the almost-everywhere asymptotics of
the flow $X$ and the unit set condition for $D_b$ are equivalent when $D_b$ is
assumed to be non empty, and that the two conditions turn to be equivalent to
the homogenization of the transport equation when $b$ is divergence free. In
contrast, using an elementary approach based on classical tools of PDE's
analysis, we extend the two-dimensional results of Oxtoby and Marchetto to any
$d$-dimensional Stepanoff flow: this shows that the ergodicity of the flow may
hold without satisfying the everywhere asymptotics of the flow.


Abstract:
  Online misogyny has become an increasing worry for Arab women who experience
gender-based online abuse on a daily basis. Misogyny automatic detection
systems can assist in the prohibition of anti-women Arabic toxic content.
Developing such systems is hindered by the lack of the Arabic misogyny
benchmark datasets. In this paper, we introduce an Arabic Levantine Twitter
dataset for Misogynistic language (LeT-Mi) to be the first benchmark dataset
for Arabic misogyny. We further provide a detailed review of the dataset
creation and annotation phases. The consistency of the annotations for the
proposed dataset was emphasized through inter-rater agreement evaluation
measures. Moreover, Let-Mi was used as an evaluation dataset through
binary/multi-/target classification tasks conducted by several state-of-the-art
machine learning systems along with Multi-Task Learning (MTL) configuration.
The obtained results indicated that the performances achieved by the used
systems are consistent with state-of-the-art results for languages other than
Arabic, while employing MTL improved the performance of the misogyny/target
classification tasks.


Abstract:
  In critical infrastructures like airports, much care has to be devoted in
protecting radio communication networks from external electromagnetic
interference. Protection of such mission-critical radio communication networks
is usually tackled by exploiting radiogoniometers: at least three suitably
deployed radiogoniometers, and a gateway gathering information from them,
permit to monitor and localise sources of electromagnetic emissions that are
not supposed to be present in the monitored area. Typically, radiogoniometers
are connected to the gateway through relay nodes. As a result, some degree of
fault-tolerance for the network of relay nodes is essential in order to offer a
reliable monitoring. On the other hand, deployment of relay nodes is typically
quite expensive. As a result, we have two conflicting requirements: minimise
costs while guaranteeing a given fault-tolerance. In this paper, we address the
problem of computing a deployment for relay nodes that minimises the relay node
network cost while at the same time guaranteeing proper working of the network
even when some of the relay nodes (up to a given maximum number) become faulty
(fault-tolerance). We show that, by means of a computation-intensive
pre-processing on a HPC infrastructure, the above optimisation problem can be
encoded as a 0/1 Linear Program, becoming suitable to be approached with
standard Artificial Intelligence reasoners like MILP, PB-SAT, and SMT/OMT
solvers. Our problem formulation enables us to present experimental results
comparing the performance of these three solving technologies on a real case
study of a relay node network deployment in areas of the Leonardo da Vinci
Airport in Rome, Italy.


Abstract:
  Single image dehazing is a challenging ill-posed problem that has drawn
significant attention in the last few years. Recently, convolutional neural
networks have achieved great success in image dehazing. However, it is still
difficult for these increasingly complex models to recover accurate details
from the hazy image. In this paper, we pay attention to the feature extraction
and utilization of the input image itself. To achieve this, we propose a
Multi-scale Topological Network (MSTN) to fully explore the features at
different scales. Meanwhile, we design a Multi-scale Feature Fusion Module
(MFFM) and an Adaptive Feature Selection Module (AFSM) to achieve the selection
and fusion of features at different scales, so as to achieve progressive image
dehazing. This topological network provides a large number of search paths that
enable the network to extract abundant image features as well as strong fault
tolerance and robustness. In addition, ASFM and MFFM can adaptively select
important features and ignore interference information when fusing different
scale representations. Extensive experiments are conducted to demonstrate the
superiority of our method compared with state-of-the-art methods.


Abstract:
  In this paper we consider $m$ ($m \geq 1$)conjunctions of Max-atoms that is
atoms of the form $\max(z,y) + r \geq x$, where the offset $r$ is a real
constant and $x,y,z$ are variables. We show that the Max-atom problem (MAP)
belongs to $\textsf{P}$. Indeed, we provide an algorithm which solves the MAP
in $O(n^{6} m^{2} + n^{4} m^{3} + n^{2} m^{4})$ operations, where $n$ is the
number of variables which compose the max-atoms. As a by-product other problems
also known to be in $\textsf{NP} \cap \textsf{co-NP}$ are in $\textsf{P}$. P1:
the problem to know if a tropical cone is trivial or not. P2: problem of
tropical rank of a tropical matrix. P3: parity game problem. P4: scheduling
problem with AND/OR precedence constraints. P5: problem on hypergraph (shortest
path). P6: problem in model checking and $\mu$-calculus.


Abstract:
  Galactic winds constitute a primary feedback process in the ecology and
evolution of galaxies. They are ubiquitously observed and exhibit a rich
phenomenology, whose origin is actively investigated both theoretically and
observationally. Cosmic rays have been widely recognized as a possible driving
agent of galactic winds, especially in Milky-Way like galaxies. The formation
of cosmic ray-driven winds is intimately connected with the microphysics of the
cosmic ray transport in galaxies, making it an intrinsically non-linear and
multiscale phenomenon. In this complex interplay the cosmic ray distribution
affects the wind launching and, in turns, is shaped by the presence of winds.
In this review we summarize the present knowledge of the physics of cosmic rays
involved in the wind formation and of the wind hydrodynamics. We also discuss
the theoretical difficulties connected with the study of cosmic ray-driven
winds and possible future improvements and directions.


Abstract:
  The magnetic, electronic, and optical properties of rare earth-oxides are
directly influenced by the valency of the metallic cation. With the development
of next generation electron energy-loss spectrometers, high-energy lanthanide
fine structure can be studied with improved signal-to-noise for quantitative
analysis. Unfortunately, the behavior of rare-earth $4f$ orbital electrons is
not well understood. To establish best practices for analysis of energy-loss
spectra from lanthanide oxides, we have performed a comparative study of the
four traditional white line analysis methods extended to lanthanide $M_{4,5}$
edges resulting from $3d \rightarrow 4f$ orbital transitions using data from
Gatan's EELS Atlas. The ${M_4}/{M_5}$ spectral feature ratios were examined as
a function of $4f$ occupancy. The ${M_4}/{M_5}$ spectral feature ratio
decreases exponentially as $4f$ occupancy increases, except for a plateau
between S$\text{m}^{3+}$ and D$\text{y}^{3+}$. The full-width at half the
maximum intensity of the $M_4$ edges shows increased broadening for
S$\text{m}^{3+}$ through D$\text{y}^{3+}$. We suggest that the plateau results
from $4f$ orbital half-filling and is explained through the relationship
between electron transition probability and transition lifetime as expressed
through Fermi's Golden Rule. Of the four spectral analysis methods described,
only the integrated area method can be ascribed a quantitative physical
interpretation.


Abstract:
  Synapses change on multiple timescales, ranging from milliseconds to minutes,
due to a combination of both short- and long-term plasticity. Here we develop
an extension of the common Generalized Linear Model to infer both short- and
long-term changes in the coupling between a pre- and post-synaptic neuron based
on observed spiking activity. We model short-term synaptic plasticity using
additive effects that depend on the presynaptic spike timing, and we model
long-term changes in both synaptic weight and baseline firing rate using point
process adaptive smoothing. Using simulations, we first show that this model
can accurately recover time-varying synaptic weights 1) for both depressing and
facilitating synapses, 2) with a variety of long-term changes (including
realistic changes, such as due to STDP), 3) with a range of pre- and
post-synaptic firing rates, and 4) for both excitatory and inhibitory synapses.
We then apply our model to two experimentally recorded putative synaptic
connections. We find that simultaneously tracking fast changes in synaptic
weights, slow changes in synaptic weights, and unexplained variations in
baseline firing is essential. Omitting any one of these factors can lead to
spurious inferences for the others. Altogether, this model provides a flexible
framework for tracking short- and long-term variation in spike transmission.


Abstract:
  Factorization models express a statistical object of interest in terms of a
collection of simpler objects. For example, a matrix or tensor can be expressed
as a sum of rank-one components. However, in practice, it can be challenging to
infer the relative impact of the different components as well as the number of
components. A popular idea is to include infinitely many components having
impact decreasing with the component index. This article is motivated by two
limitations of existing methods: (1) lack of careful consideration of the
within component sparsity structure; and (2) no accommodation for grouped
variables and other non-exchangeable structures. We propose a general class of
infinite factorization models that address these limitations. Theoretical
support is provided, practical gains are shown in simulation studies, and an
ecology application focusing on modelling bird species occurrence is discussed.


Abstract:
  Liquid polyamorphism is the intriguing possibility for a single component
substance to exist in multiple liquid phases. We propose a minimal model for
this phenomenon. Starting with a binary lattice model with critical azeotropy
and liquid-liquid demixing, we allow interconversion of the two species,
turning the system into a single-component fluid with two states differing in
energy and entropy. Unveiling the phase diagram of the non-interconverting
binary mixture gives unprecedented insight on the phase behaviors accessible to
the interconverting fluid, such as a liquid-liquid transition with a critical
point, or a singularity-free scenario, exhibiting thermodynamic anomalies
without polyamorphism. The model provides a unified theoretical framework to
describe supercooled water and a variety of polyamorphic liquids with
water-like anomalies.


Abstract:
  Deep Neural Networks (DNN) are typically tested for accuracy relying on a set
of unlabelled real world data (operational dataset), from which a subset is
selected, manually labelled and used as test suite. This subset is required to
be small (due to manual labelling cost) yet to faithfully represent the
operational context, with the resulting test suite containing roughly the same
proportion of examples causing misprediction (i.e., failing test cases) as the
operational dataset. However, while testing to estimate accuracy, it is
desirable to also learn as much as possible from the failing tests in the
operational dataset, since they inform about possible bugs of the DNN. A smart
sampling strategy may allow to intentionally include in the test suite many
examples causing misprediction, thus providing this way more valuable inputs
for DNN improvement while preserving the ability to get trustworthy unbiased
estimates. This paper presents a test selection technique (DeepEST) that
actively looks for failing test cases in the operational dataset of a DNN, with
the goal of assessing the DNN expected accuracy by a small and ''informative''
test suite (namely with a high number of mispredictions) for subsequent DNN
improvement. Experiments with five subjects, combining four DNN models and
three datasets, are described. The results show that DeepEST provides DNN
accuracy estimates with precision close to (and often better than) those of
existing sampling-based DNN testing techniques, while detecting from 5 to 30
times more mispredictions, with the same test suite size.


Abstract:
  We consider the sequential decision optimization on the periodic environment,
that occurs in a wide variety of real-world applications when the data involves
seasonality, such as the daily demand of drivers in ride-sharing and dynamic
traffic patterns in transportation. In this work, we focus on learning the
stochastic periodic world by leveraging this seasonal law. To deal with the
general action space, we use the bandit based on Gaussian process (GP) as the
base model due to its flexibility and generality, and propose the Periodic-GP
method with a temporal periodic kernel based on the upper confidence bound.
Theoretically, we provide a new regret bound of the proposed method, by
explicitly characterizing the periodic kernel in the periodic stationary model.
Empirically, the proposed algorithm significantly outperforms the existing
methods in both synthetic data experiments and a real data application on
Madrid traffic pollution.


Abstract:
  Corner-shared ABX$_3$ perovskites have long featured prominently in
solid-state chemistry and condensed matter physics. Still, the joint
understanding of their two main subgroups-halides and oxides-has not been fully
developed. Indeed, unlike the case that compounds having a single repeated
motif ("monomorphous"), certain cubic perovskites can manifest a non-thermal
distribution of local motifs ("polymorphous networks"). Such intrinsic
deformations can include positional degrees of freedom. Unlike thermal motion,
such intrinsic distortions do not time-average to zero. The present study
compares electronic structure features of oxide and halide perovskites starting
from the intrinsic polymorphous network described by DFT minimization of the
internal energy, continuing to finite temperature thermal disorder using AIMD.
We find that (i) different oxide vs. halide ABX$_3$ compounds adopt different
energy-lowering distortion modes. The DFT calculated pair distribution function
(PDF) of SrTiO$_3$ agrees with the recently measured PDF. (ii) In both oxides
and halides, such intrinsic distortions lead to bandgap blueshifts with respect
to monomorphous structure. (iii) For oxide perovskites, high-temperature AIMD
simulations initiated from the polymorphous structures reveal that the
thermally-induced distortions can lead to a bandgap redshift. (iv) In contrast,
for cubic CsPbI$_3$, both the intrinsic distortions and the thermal distortions
contribute in tandem to bandgap blueshift, the former, intrinsic effect being
dominant. (v) In the oxide SrTiO$_3$ and CaTiO$_3$ (but not in halide),
octahedral tilting leads to the emergence of a distinct $\Gamma$-$\Gamma$
direct bandgap component as a secondary valley minimum to the well-known
indirect R-$\Gamma$ gap. Understanding such intrinsic vs. thermal effects on
oxide vs. halide perovskites holds the potential for designing target
electronic properties.


Abstract:
  Recently, a novel relativistic polyatomic BGK model was suggested by Pennisi
and Ruggeri [J. of Phys. Conf. Series, 1035, (2018)] to overcome drawbacks of
the Anderson-Witting model and Marle model.In this paper, we prove the unique
existence and asymptotic behavior of classical solutions to the relativistic
polyatomic BGK model when the initial data is sufficiently close to a global
equilibrium.


Abstract:
  We investigate the Glauber divergences in nonfactorizable annihilation
amplitudes for two-body hadronic heavy meson decays in the $k_T$ factorization
theorem at one-loop level. These divergences can be absorbed into the Glauber
factors in the dominant kinematic regions of small parton momenta, which modify
the interference between a nonfactorizable annihilation amplitude and other
amplitudes by rotating it with a phase. We postulate that only the Glauber
effect associated with a pion is significant, due to its special role as a
$q\bar q$ bound state and as a pseudo Nambu-Goldstone boson simultaneously. It
is elaborated that the data of the $D\to\pi\pi$ and $\pi K$ branching ratios
have revealed prominent Glauber effects. This work provides a solid theoretical
ground for the factorization-assisted topological-amplitude parametrization of
two-body hadronic $D$ meson decays.


Abstract:
  The three pentaquark states, $P_{c}(4312)$, $P_{c}(4440)$, and $P_{c}(4457)$,
discovered by the LHCb Collaboration in 2019, can be nicely arranged into a
multiplet of $\bar{D}^{(\ast)}\Sigma_{c}^{(\ast)}$ of seven molecules dictated
by heavy quark spin symmetry. In this work we employ the effective Lagrangian
approach to investigate the two decay modes of $P_{c}(4457)$, $P_{c}(4457) \to
P_{c}(4312) \pi$ and $P_{c}(4457) \to P_{c}(4312) \gamma$, via the triangle
mechanism, assuming that $P_{c}(4457)$ and $P_{c}(4312)$ are
$\bar{D}^{\ast}\Sigma_{c}$ and $\bar{D}\Sigma_{c}$ bound states but the spin of
$P_{c}(4457)$ can be either 1/2 or 3/2. Our results show that the spin of
$P_{c}(4457)$ can not be discriminated through these two decay modes. The decay
widths of $P_{c}(4457) \to P_{c}(4312) \pi$ and $P_{c}(4457) \to P_{c}(4312)
\gamma$ are estimated to be of order of 100 keV and 1 keV, respectively. The
ratio of the partial decay widths of $P_{c}(4457) \to P_{c}(4312) \pi$ to
$P_{c}(4457) \to P_{c}(4312) \gamma$ is similar to the ratio of $D^{\ast}\to
D\pi$ to $D^{\ast}\to D\gamma$, which could be used to check the molecular
nature of $P_{c}(4457)$ and $P_{c}(4312)$ if they can be observed in the
future.


Abstract:
  We show that the Vietoris-Rips complex $\mathcal R(n,r)$ built over $n$
points sampled at random from a uniformly positive probability measure on a
convex body $K\subseteq \mathbb R^d$ is a.a.s. contractible when $r \geq c
\left(\frac{\ln n}{n}\right)^{1/d}$ for a certain constant that depends on $K$
and the probability measure used. This answers a question of Kahle [Discrete
Comput. Geom. 45 (2011), 553-573]. We also extend the proof to show that if $K$
is a compact, smooth $d$-manifold with boundary - but not necessarily convex -
then $\mathcal R(n,r)$ is a.a.s. homotopy equivalent to $K$ when $c_1
\left(\frac{\ln n}{n}\right)^{1/d} \leq r \leq c_2$ for constants $c_1=c_1(K),
c_2=c_2(K)$. Our proofs expose a connection with the game of cops and robbers.


Abstract:
  Deep learning based methods have been used successfully in recommender system
problems. Approaches using recurrent neural networks, transformers, and
attention mechanisms are useful to model users' long- and short-term
preferences in sequential interactions. To explore different session-based
recommendation solutions, Booking.com recently organized the WSDM WebTour 2021
Challenge, which aims to benchmark models to recommend the final city in a
trip. This study presents our approach to this challenge. We conducted several
experiments to test different state-of-the-art deep learning architectures for
recommender systems. Further, we proposed some changes to Neural Attentive
Recommendation Machine (NARM), adapted its architecture for the challenge
objective, and implemented training approaches that can be used in any
session-based model to improve accuracy. Our experimental result shows that the
improved NARM outperforms all other state-of-the-art benchmark methods.


Abstract:
  The availability of open-source projects facilitates developers to contribute
and collaborate on a wide range of projects. As a result, the developer
community contributing to such open-source projects is also increasing. Many of
the projects involve frequent updates and extensive reuses. A well-updated
documentation helps in a better understanding of the software project and also
facilitates efficient contribution and reuse. Though software documentation
plays an important role in the development and maintenance of software, it also
suffers from various issues that include insufficiency, inconsistency,
ill-maintainability, and so on. Exploring the perception of developers towards
documentation could help in understanding the reasons behind prevalent issues
in software documentation. It could further aid in deciding on training that
could be given to the developer community towards building more sustainable
projects for society. Analyzing sentiments of contributors to a project could
provide insights on understanding developer perceptions. Hence, as the first
step towards this direction, we analyze sentiments of commit messages specific
to the documentation of a software project. To this end, we considered the
commit history of 998 GitHub projects from the GHTorrent dataset and identified
10,996 commits that correspond to the documentation of repositories. Further,
we apply sentiment analysis techniques to obtain insights on the type of
sentiment being expressed in commit messages of the selected commits. We
observe that around 45% of the identified commit messages express trust
emotion.


Abstract:
  In many modern applications, a dependent functional response is observed for
each subject over repeated time, leading to longitudinal functional data. In
this paper, we propose a novel statistical procedure to test whether the mean
function varies over time. Our approach relies on reducing the dimension of the
response using data-driven orthogonal projections and it employs a
likelihood-based hypothesis testing. We investigate the methodology
theoretically and discuss a computationally efficient implementation. The
proposed test maintains the type I error rate, and shows excellent power to
detect departures from the null hypothesis in finite sample simulation studies.
We apply our method to the longitudinal diffusion tensor imaging study of
multiple sclerosis (MS) patients to formally assess whether the brain's health
tissue, as summarized by fractional anisotropy (FA) profile, degrades over time
during the study period.


Abstract:
  The helimagnets Cr$_{1/3}M$S$_2$ ($M$ = Nb or Ta) have attracted renewed
attention due to the discovery of a chiral soltion lattice (CSL) stabilized in
Cr$_{1/3}$NbS$_2$ in an applied magnetic field, but reports of unusual
low-temperature transport and magnetic properties in this system lack a
unifying explanation. Here we present electronic structure calculations
demonstrating that Cr$_{1/3}M$S$_2$ ($M$ = Nb or Ta) are half-metals whose
low-temperature electronic and magnetic behavior can be explained by the
presence of a gap-like feature (width in range 40-100 meV) in the density of
states of one spin channel. Our magnetometry measurements confirm the existence
of this gap. Dynamic spin fluctuations driven by excitations across this gap
are seen over a wide range of frequencies (0.1 Hz to MHz) with AC
susceptibility and muon-spin relaxation ($\mu^+$SR) measurements. We show
further how effects due to the CSL in Cr$_{1/3}$NbS$_2$, as detected with
$\mu^+$SR, dominate over the gap-driven magnetism when the CSL is stabilized as
the majority phase.


Abstract:
  We investigate possible reasons for the absence of historical records of the
supernova of 1054 in Europe. At the same time, we search for the new evidences
as well. We establish that the previously acclaimed 'Arabic' records from ibn
Butlan originate from Europe. As one of the most prominent scientists of the
era, he was in Constantinople at the time of the supernova and actively
participated in the medieval Church feud known as the Great Schism. Next, we
reconstruct the European sky at the time of the event and find that the 'new
star' (SN 1054) was in the west while the planet Venus was on the opposite side
of the sky (in the east) with the Sun sited directly between these two equally
bright objects, as documented in East-Asian records.


Abstract:
  Recent advances in deep generative models have led to impressive results in a
variety of application domains. Motivated by the possibility that deep learning
models might memorize part of the input data, there have been increased efforts
to understand how memorization arises. In this work, we extend a recently
proposed measure of memorization for supervised learning (Feldman, 2019) to the
unsupervised density estimation problem and adapt it to be more computationally
efficient. Next, we present a study that demonstrates how memorization can
occur in probabilistic deep generative models such as variational autoencoders.
This reveals that the form of memorization to which these models are
susceptible differs fundamentally from mode collapse and overfitting.
Furthermore, we show that the proposed memorization score measures a phenomenon
that is not captured by commonly-used nearest neighbor tests. Finally, we
discuss several strategies that can be used to limit memorization in practice.
Our work thus provides a framework for understanding problematic memorization
in probabilistic generative models.


Abstract:
  Sound event localization aims at estimating the positions of sound sources in
the environment with respect to an acoustic receiver (e.g. a microphone array).
Recent advances in this domain most prominently focused on utilizing deep
recurrent neural networks. Inspired by the success of transformer architectures
as a suitable alternative to classical recurrent neural networks, this paper
introduces a novel transformer-based sound event localization framework, where
temporal dependencies in the received multi-channel audio signals are captured
via self-attention mechanisms. Additionally, the estimated sound event
positions are represented as multivariate Gaussian variables, yielding an
additional notion of uncertainty, which many previously proposed deep
learning-based systems designed for this application do not provide. The
framework is evaluated on three publicly available multi-source sound event
localization datasets and compared against state-of-the-art methods in terms of
localization error and event detection accuracy. It outperforms all competing
systems on all datasets with statistical significant differences in
performance.


Abstract:
  We formulate and prove an analogue of Beurling's theorem for the Fourier
transform on the Heisenberg group. As a consequence we deduce Hardy and
Cowling-Price theorems.


Abstract:
  Cheung and Piliouras (2020) recently showed that two variants of the
Multiplicative Weights Update method - OMWU and MWU - display opposite
convergence properties depending on whether the game is zero-sum or
cooperative. Inspired by this work and the recent literature on learning to
optimize for single functions, we introduce a new framework for learning
last-iterate convergence to Nash Equilibria in games, where the update rule's
coefficients (learning rates) along a trajectory are learnt by a reinforcement
learning policy that is conditioned on the nature of the game: \textit{the game
signature}. We construct the latter using a new decomposition of two-player
games into eight components corresponding to commutative projection operators,
generalizing and unifying recent game concepts studied in the literature. We
compare the performance of various update rules when their coefficients are
learnt, and show that the RL policy is able to exploit the game signature
across a wide range of game types. In doing so, we introduce CMWU, a new
algorithm that extends consensus optimization to the constrained case, has
local convergence guarantees for zero-sum bimatrix games, and show that it
enjoys competitive performance on both zero-sum games with constant
coefficients and across a spectrum of games when its coefficients are learnt.


Abstract:
  How will the coronavirus disease 2019 (COVID-19) pandemic develop in the
coming months and years? Based on an expert survey, we examine key aspects that
are likely to influence COVID-19 in Europe. The future challenges and
developments will strongly depend on the progress of national and global
vaccination programs, the emergence and spread of variants of concern, and
public responses to nonpharmaceutical interventions (NPIs). In the short term,
many people are still unvaccinated, VOCs continue to emerge and spread, and
mobility and population mixing is expected to increase over the summer.
Therefore, policies that lift restrictions too much and too early risk another
damaging wave. This challenge remains despite the reduced opportunities for
transmission due to vaccination progress and reduced indoor mixing in the
summer. In autumn 2021, increased indoor activity might accelerate the spread
again, but a necessary reintroduction of NPIs might be too slow. The incidence
may strongly rise again, possibly filling intensive care units, if vaccination
levels are not high enough. A moderate, adaptive level of NPIs will thus remain
necessary. These epidemiological aspects are put into perspective with the
economic, social, and health-related consequences and thereby provide a
holistic perspective on the future of COVID-19.


Abstract:
  Assisting end users to identify desired results from a large dataset is an
important problem for multi-criteria decision making. To address this problem,
top-k and skyline queries have been widely adopted, but they both have inherent
drawbacks, i.e., the user either has to provide a specific utility function or
faces many results. The k-regret minimization query is proposed, which
integrates the merits of top-k and skyline queries. Due to the NP-hardness of
the problem, the k-regret minimization query is time consuming and the greedy
framework is widely adopted. However, formal theoretical analysis of the greedy
approaches for the quality of the returned results is still lacking. In this
paper, we first fill this gap by conducting a nontrivial theoretical analysis
of the approximation ratio of the returned results. To speed up query
processing, a sampling-based method, StocPreGreed,, is developed to reduce the
evaluation cost. In addition, a theoretical analysis of the required sample
size is conducted to bound the quality of the returned results. Finally,
comprehensive experiments are conducted on both real and synthetic datasets to
demonstrate the efficiency and effectiveness of the proposed methods.