Abstract:
  How to combine uncertain information from different sources has been a hot
topic for years. However, with respect to ordinal quantum evidences contained
in information, there is no any referable work which is able to provide a
solution to this kind of problem. Besides, the method to dispel uncertainty of
quantum information is still an open issue. Therefore, in this paper, a
specially designed method is designed to provide an excellent method which
improves the combination of ordinal quantum evidences reasonably and reduce the
effects brought by uncertainty contained in quantum information simultaneously.
Besides, some actual applications are provided to verify the correctness and
validity of the proposed method.


Abstract:
  This paper considers the integration of gap-based local navigation methods
with artificial potential field (APF) methods to derive a local planning module
for hierarchical navigation systems that has provable collision-free
properties. Given that APF theory applies to idealized robot models, the
provable properties are lost when applied to more realistic models. We describe
a set of algorithm modifications that correct for these errors and enhance
robustness to non-ideal models. Central to the construction of the local
planner is the use of sensory-derived local free-space models that detect gaps
and use them for the synthesis of the APF. Modifications are given for a
nonholonomic robot model. Integration of the local planner, called potential
gap, into a hierarchical navigation system provides the local goals and
trajectories needed for collision-free navigation through unknown environments.
Monte Carlo experiments in benchmark worlds confirm the asserted safety and
robustness properties by testing under various robot models.


Abstract:
  Quantum key distribution (QKD) networks provide an infrastructure for
establishing information-theoretic secure keys between legitimate parties via
quantum and authentic classical channels. The deployment of QKD networks in
real-world conditions faces several challenges, which are related in particular
to the high costs of QKD devices and the condition to provide reasonable secret
key rates. In this work, we present a QKD network architecture that provides a
significant reduction in the cost of deploying QKD networks by using optical
switches and reducing the number of QKD receiver devices, which use
single-photon detectors. We describe the corresponding modification of the QKD
network protocol. We also provide estimations for a network link of a total of
670 km length consisting of 8 nodes, and demonstrate that the switch-based
architecture allows achieving significant resource savings of up to 28%, while
the throughput is reduced by 8% only.


Abstract:
  Self-organized coherence-incoherence patterns, called chimera states, have
first been reported in systems of Kuramoto oscillators. For coupled excitable
units similar patterns, where coherent units are at rest, are called bump
states. Here, we study bumps in an array of active rotators coupled by
non-local attraction and global repulsion. We demonstrate how they can emerge
in a supercritical scenario from completely coherent Turing patterns: single
incoherent units appear in a homoclinic bifurcation with a subsequent
transition via quasiperiodic and chaotic behavior, eventually transforming into
extensive chaos with many incoherent units. We present different types of
transitions and explain the formation of coherence-incoherence patterns
according to the classical paradigm of short-range activation and long-range
inhibition.


Abstract:
  Although using convolutional neural networks (CNNs) as backbones achieves
great successes in computer vision, this work investigates a simple backbone
network useful for many dense prediction tasks without convolutions. Unlike the
recently-proposed Transformer model (e.g., ViT) that is specially designed for
image classification, we propose Pyramid Vision Transformer~(PVT), which
overcomes the difficulties of porting Transformer to various dense prediction
tasks. PVT has several merits compared to prior arts. (1) Different from ViT
that typically has low-resolution outputs and high computational and memory
cost, PVT can be not only trained on dense partitions of the image to achieve
high output resolution, which is important for dense predictions but also using
a progressive shrinking pyramid to reduce computations of large feature maps.
(2) PVT inherits the advantages from both CNN and Transformer, making it a
unified backbone in various vision tasks without convolutions by simply
replacing CNN backbones. (3) We validate PVT by conducting extensive
experiments, showing that it boosts the performance of many downstream tasks,
e.g., object detection, semantic, and instance segmentation. For example, with
a comparable number of parameters, RetinaNet+PVT achieves 40.4 AP on the COCO
dataset, surpassing RetinNet+ResNet50 (36.3 AP) by 4.1 absolute AP. We hope PVT
could serve as an alternative and useful backbone for pixel-level predictions
and facilitate future researches. Code is available at
https://github.com/whai362/PVT.


Abstract:
  Anomaly detection on attributed networks attracts considerable research
interests due to wide applications of attributed networks in modeling a wide
range of complex systems. Recently, the deep learning-based anomaly detection
methods have shown promising results over shallow approaches, especially on
networks with high-dimensional attributes and complex structures. However,
existing approaches, which employ graph autoencoder as their backbone, do not
fully exploit the rich information of the network, resulting in suboptimal
performance. Furthermore, these methods do not directly target anomaly
detection in their learning objective and fail to scale to large networks due
to the full graph training mechanism. To overcome these limitations, in this
paper, we present a novel contrastive self-supervised learning framework for
anomaly detection on attributed networks. Our framework fully exploits the
local information from network data by sampling a novel type of contrastive
instance pair, which can capture the relationship between each node and its
neighboring substructure in an unsupervised way. Meanwhile, a well-designed
graph neural network-based contrastive learning model is proposed to learn
informative embedding from high-dimensional attributes and local structure and
measure the agreement of each instance pairs with its outputted scores. The
multi-round predicted scores by the contrastive learning model are further used
to evaluate the abnormality of each node with statistical estimation. In this
way, the learning model is trained by a specific anomaly detection-aware
target. Furthermore, since the input of the graph neural network module is
batches of instance pairs instead of the full network, our framework can adapt
to large networks flexibly. Experimental results show that our proposed
framework outperforms the state-of-the-art baseline methods on all seven
benchmark datasets.


Abstract:
  Consider a shrinking neighborhood of a cusp of the unit tangent bundle of a
noncompact hyperbolic surface of finite area, and let the neighborhood shrink
into the cusp at a rate of $T^{-1}$ as $T \rightarrow \infty$. We show that a
closed horocycle whose length $\ell$ goes to infinity or even a segment of that
horocycle becomes equidistributed on the shrinking neighborhood when normalized
by the rate $T^{-1}$ provided that $T/\ell \rightarrow 0$ and, for any
$\delta>0$, the segment remains larger than
$\max\left\{T^{-1/6},\left(T/\ell\right)^{1/2}\right\}\left(T/\ell\right)^{-\delta}$.
We also have an effective result for a smaller range of rates of growth of $T$
and $\ell$.


Abstract:
  The COVID-19 pandemic presented itself as a challenge for separate societal
sectors. On the information technology (IT) standpoint, it does include the
maintenance of the infrastructure required to hold collaborative activities
that went to happen online; the implementation of projects in a scenario of
uncertainty; and keep the software engineering and information security best
practices in place. This article presents the context of a data science team
organized as a skunk works group composed of professionals with experience in
both the industry and academia, located in an IT department working with a team
of seasoned data engineers. At the time the pandemic started, the relatively
new data science team was positioning itself as a Center of Excellence in
Advanced Analytics. With the pandemic, it had to keep up with the expectations
from the stakeholders; manage current and upcoming data science projects within
the methodology practiced in IT; and maintain a high level in the quality of
service delivered. This article discusses how did the COVID-19 pandemic
affected the team productivity and its practices as well as the lessons learned
with it.


Abstract:
  One of the main challenges of the machine reading comprehension (MRC) models
is their fragile out-of-domain generalization, which makes these models not
properly applicable to real-world general-purpose question answering problems.
In this paper, we leverage a zero-shot weighted ensemble method for improving
the robustness of out-of-domain generalization in MRC models. In the proposed
method, a weight estimation module is used to estimate out-of-domain weights,
and an ensemble module aggregate several base models' predictions based on
their weights. The experiments indicate that the proposed method not only
improves the final accuracy, but also is robust against domain changes.


Abstract:
  We performed a Lorentz covariant quantization of the spin-1/2 fermion field
assuming the space-like energy-momentum dispersion relation. We a\-chieved the
task in the following steps: ($i$) determining the unitary realizations of the
inhomogenous Lorentz group in the preferred frame scenario by means of the
Wigner-Mackey induction procedure and constructing the Fock space; ($ii$)
formulating the theory in a manifestly covariant way by constructing the field
amplitudes according to the Weinberg method; ($iii$) obtaining the final
constraints on the amplitudes by postulating a Dirac-like free field equation.
Our theory allows to predict all chiral properties of the neutrinos, preserving
the Standard Model dynamics. We discussed the form of the fundamental
observables, energy and helicity, and show that non-observation of the
$+\tfrac{1}{2}$ helicity state of the neutrino and the $-\tfrac{1}{2}$ helicity
state of the antineutrino could be a direct consequence of the "tachyoneity" of
neutrinos at the free level. We found that the free field theory of the
space-like neutrino is not invariant under the C and P transformations
separately but is CP-invariant. We calculated and analyzed the electron energy
spectrum in tritium decay within the framework of our theory and found an
excellent agreement with the recent measurement of KATRIN. In our formalism the
questions of negative/imaginary energies and the causality problem does not
appear.


Abstract:
  Second-order methods have the capability of accelerating optimization by
using much richer curvature information than first-order methods. However, most
are impractical in a deep learning setting where the number of training
parameters is huge. In this paper, we propose KF-QN-CNN, a new
Kronecker-factored quasi-Newton method for training convolutional neural
networks (CNNs), where the Hessian is approximated by a layer-wise block
diagonal matrix and each layer's diagonal block is further approximated by a
Kronecker product corresponding to the structure of the Hessian restricted to
that layer. New damping and Hessian-action techniques for BFGS are designed to
deal with the non-convexity and the particularly large size of Kronecker
matrices in CNN models and convergence results are proved for a variant of
KF-QN-CNN under relatively mild conditions. KF-QN-CNN has memory requirements
comparable to first-order methods and much less per-iteration time complexity
than traditional second-order methods. Compared with state-of-the-art first-
and second-order methods on several CNN models, KF-QN-CNN consistently
exhibited superior performance in all of our tests.


Abstract:
  Episodic memory-based methods can rapidly latch onto past successful
strategies by a non-parametric memory and improve sample efficiency of
traditional reinforcement learning. However, little effort is put into the
continuous domain, where a state is never visited twice, and previous episodic
methods fail to efficiently aggregate experience across trajectories. To
address this problem, we propose Generalizable Episodic Memory (GEM), which
effectively organizes the state-action values of episodic memory in a
generalizable manner and supports implicit planning on memorized trajectories.
GEM utilizes a double estimator to reduce the overestimation bias induced by
value propagation in the planning process. Empirical evaluation shows that our
method significantly outperforms existing trajectory-based methods on various
MuJoCo continuous control tasks. To further show the general applicability, we
evaluate our method on Atari games with discrete action space, which also shows
a significant improvement over baseline algorithms.


Abstract:
  Potassium channels play critical roles in many physiological processes,
providing a selective permeation route for K+ ions in and out of a cell, by
employing a carefully designed selectivity filter, evolutionarily conserved
from viruses to mammals. The structure of the selectivity filter was determined
at atomic resolution by x-ray crystallography, showing a tight coordination of
desolvated K+ ions by the channel. However, the molecular mechanism of K+ ions
permeation through potassium channels remains unclear, with structural,
functional and computational studies often providing conflicting data and
interpretations. In this review, we will present the proposed mechanisms,
discuss their origins, and will critically assess them against all available
data. General properties shared by all potassium channels are introduced first,
followed by the introduction of two main mechanisms of ion permeation: soft and
direct knock-on. Then, we will discuss critical computational and experimental
studies that shaped the field. We will especially focus on molecular dynamics
(MD) simulations, that provided mechanistic and energetic aspects of K+
permeation, but at the same time created long-standing controversies. Further
challenges and possible solutions are presented as well.


Abstract:
  Braces were introduced by W. Rump in 2006 as an algebraic system related to
the quantum Yang-Baxter equation. In 2017, L. Guarnieri and L. Vendramin
defined for the same purposes a more general notion of a skew left brace.
Recently, L. Guo, H. Lang, Y. Sheng [arXiv:2009.03492] gave a definition of
what is a Rota-Baxter operator on a group. We connect these two notions as
follows. It is shown that every Rota-Baxter group gives rise to a skew left
brace. Moreover, every skew left brace can be injectively embedded into a
Rota-Baxter group. When the additive group of a skew left brace is complete,
then this brace is induced by a Rota-Baxter group. We interpret theory of skew
left braces in terms of Rota -- Baxter operators.


Abstract:
  Canonical expressions are representative of implicative propositions upto
renaming of variables. In this paper we explore, using a Monte-Carlo approach,
the model of canonical expressions in order to confirm the paradox that says
that asymptotically almost all classical theorems are intuitionistic.


Abstract:
  The increasing availability of video recordings made by multiple cameras has
offered new means for mitigatingocclusion and depth ambiguities in pose and
motion reconstruction methods. Yet, multi-view algorithms strongly depend on
camera parameters; particularly, the relativepositions between the cameras.
Such a dependency becomes a hurdle once shifting to dynamic capture in
uncontrolled settings. We introduce FLEX (Free muLti-view rEconstruXion), an
end-to-end parameter-free multi-viewmodel. FLEX is parameter-free in the sense
that it does not require any camera parameters, neither intrinsic nor
extrinsic. Our key idea is that the 3D angles between skeletal parts, as well
as bone lengths, are invariant to the camera position. Hence, learning 3D
rotations and bone lengths rather than locations allows predicting common
values for all camera views. Our network takes multiple video streams, learns
fused deep features through a novel multi-view fusion layer, and reconstructs a
single consistent skeleton with temporally coherent joint rotations. We
demonstrate quantitative and qualitative results on the Human3.6M and KTH
Multi-view Football II datasets, and on synthetic multi-person video streams
captured by dynamic cameras. We compare our model to state-of-the-art methods
that are not parameter-free and show that in the absence of camera parameters,
we outperform them by a large margin while obtaining comparable results when
camera parameters are available. Code, trained models, video examples, and more
material will be available on our project page.


Abstract:
  Context: Grey Literature (GL) recently has grown in Software Engineering (SE)
research since the increased use of online communication channels by software
engineers. However, there is still a limited understanding of how SE research
is taking advantage of GL. Objective: This research aimed to understand how SE
researchers use GL in their secondary studies. Method: We conducted a tertiary
study of studies published between 2011 and 2018 in high-quality software
engineering conferences and journals. We then applied qualitative and
quantitative analysis to investigate 446 potential studies. Results: From the
446 selected studies, 126 studies cited GL but only 95 of those used GL to
answer a specific research question representing almost 21% of all the 446
secondary studies. Interestingly, we identified that few studies employed
specific search mechanisms and used additional criteria for assessing GL.
Moreover, by the time we conducted this research, 49% of the GL URLs are not
working anymore. Based on our findings, we discuss some challenges in using GL
and potential mitigation plans. Conclusion: In this paper, we summarized the
last 10 years of software engineering research that uses GL, showing that GL
has been essential for bringing practical new perspectives that are scarce in
traditional literature. By drawing the current landscape of use, we also raise
some awareness of related challenges (and strategies to deal with them).


Abstract:
  Let $n\in\mathbb{N}$ and $\X(n) =(X_1(n), \ldots, X_{d(n)}(n))$ be a sequence
of random vectors. We prove that, under certain dependence conditions, the cdf
of the maximum of $\X_i(n)$ asymptotically equals to the cdf of the maximum of
a random vector with the same but independent marginal distributions. To prove
our result on extremal independence, we obtain new lower and upper bounds on
the probability that none of a given finite set of events occurs. Using our
result, we show that, under certain conditions, including Berman-type
condition, a sequence of Gaussian random vectors possesses the extremal
independence property. We also prove that certain extremal characterstics of
binomial random graphs and hypergraphs, after an appropriate rescaling, have
asymptotical Gumbel distribution (in particular, maximum codegrees in random
hypergraphs and the maximum number of cliques sharing a given vertex in random
graphs).


Abstract:
  We examine a pair of graph generative models for the therapeutic design of
novel drug candidates targeting SARS-CoV-2 viral proteins. Due to a sense of
urgency, we chose well-validated models with unique strengths: an autoencoder
that generates molecules with similar structures to a dataset of drugs with
anti-SARS activity and a reinforcement learning algorithm that generates highly
novel molecules. During generation, we explore optimization toward several
design targets to balance druglikeness, synthetic accessability, and anti-SARS
activity based on \icfifty. This generative
framework\footnote{https://github.com/exalearn/covid-drug-design} will
accelerate drug discovery in future pandemics through the high-throughput
generation of targeted therapeutic candidates.


Abstract:
  We show that every Kac-Moody algebra is birationally equivalent to a smash
biproduct of two copies of a Weyl algebra together with a polynomial algebra.
We also show that the same is true for quantized Kac-Moody algebras where one
replaces Weyl algebras with their quantum analogues.


Abstract:
  The large variation of datasets is a huge barrier for image classification
tasks. In this paper, we embraced this observation and introduce the finite
temperature tensor network (FTTN), which imports the thermal perturbation into
the matrix product states framework by placing all images in an environment
with constant temperature, in analog to energy-based learning. Tensor network
is chosen since it is the best platform to introduce thermal fluctuation.
Different from traditional network structure which directly takes the summation
of individual losses as its loss function, FTTN regards it as thermal average
loss computed from the entanglement with the environment. The temperature-like
parameter can be automatically optimized, which gives each database an
individual temperature. FTTN obtains improvement in both test accuracy and
convergence speed in several datasets. The non-zero temperature automatically
separates similar features, avoiding the wrong classification in previous
architecture. The thermal fluctuation may give a better improvement in other
frameworks, and we may also implement the temperature of database to improve
the training effect.


Abstract:
  We show how to learn a map that takes a content code, derived from a face
image, and a randomly chosen style code to an anime image. We derive an
adversarial loss from our simple and effective definitions of style and
content. This adversarial loss guarantees the map is diverse -- a very wide
range of anime can be produced from a single content code. Under plausible
assumptions, the map is not just diverse, but also correctly represents the
probability of an anime, conditioned on an input face. In contrast, current
multimodal generation procedures cannot capture the complex styles that appear
in anime. Extensive quantitative experiments support the idea the map is
correct. Extensive qualitative results show that the method can generate a much
more diverse range of styles than SOTA comparisons. Finally, we show that our
formalization of content and style allows us to perform video to video
translation without ever training on videos.


Abstract:
  It is known for some time that there exists an energy-minimal diffeomorphism
between two doubly-connected domains $\Omega$ and $D$ provided that
$\mathrm{Mod}(\Omega)\le \mathrm{Mod}{D}$ and that diffeomorphism is harmonic
\cite{tedi}. In this note we give a short proof of the fact that for given
annuli $\Omega$ and $D$ satisfying the condition $\mathrm{Mod}(\Omega)\le
\mathrm{Mod}(D)$ there exist a $K-$quasiconformal harmonic diffeomorphism
$f:\Omega\onto D$, where $K=K(\tau)$, $\tau=\mathrm{Mod}(\Omega)/
\mathrm{Mod}(D)$ and $\lim_{\tau\to 1}K(\tau)=1$.


Abstract:
  This paper is concerned with a compositional scheme for the construction of
control barrier certificates for interconnected discrete-time stochastic
systems. The main objective is to synthesize switching control policies against
$\omega$-regular properties that can be described by accepting languages of
deterministic Streett automata (DSA) along with providing probabilistic
guarantees for the satisfaction of such specifications. The proposed framework
leverages the interconnection topology and a notion of so-called control
sub-barrier certificates of subsystems, which are used to compositionally
construct control barrier certificates of interconnected systems by imposing
some dissipativity-type compositionality conditions. We propose a systematic
approach to decompose high-level $\omega$-regular specifications into simpler
tasks by utilizing the automata corresponding to the complement of
specifications. In addition, we formulate an alternating direction method of
multipliers (ADMM) optimization problem in order to obtain suitable control
sub-barrier certificates of subsystems while satisfying compositionality
conditions. We also provide a sum-of-squares (SOS) optimization problem for the
computation of control sub-barrier certificates and local control policies of
subsystems. Finally, we demonstrate the effectiveness of our proposed
approaches by applying them to a physical case study.


Abstract:
  We examine the question how string theory achieves a sum over bulk geometries
with fixed asymptotic boundary conditions. We discuss this problem with the
help of the tensionless string on $\mathcal{M}_3 \times \mathrm{S}^3 \times
\mathbb{T}^4$ (with one unit of NS-NS flux) that was recently understood to be
dual to the symmetric orbifold $\text{Sym}^N(\mathbb{T}^4)$. We strengthen the
analysis of arXiv:2008.07533 and show that the perturbative string partition
function around a fixed bulk background already includes a sum over
semi-classical geometries and large stringy corrections can be interpreted as
various semi-classical geometries. We argue in particular that the string
partition function on a Euclidean wormhole geometry factorizes completely into
factors associated to the two boundaries of spacetime. Central to this is the
remarkable property of the moduli space integral of string theory to localize
on covering spaces of the conformal boundary of $\mathcal{M}_3$. We also
emphasize the fact that string perturbation theory computes the grand canonical
partition function of the family of theories
$\bigoplus_N\text{Sym}^N(\mathbb{T}^4)$. The boundary partition function is
naturally expressed as a sum over winding worldsheets, each of which we
interpret as a `stringy geometry'. We argue that the semi-classical bulk
geometry can be understood as a condensate of such stringy geometries. We also
briefly discuss the effect of ensemble averaging over the Narain moduli space
of $\mathbb{T}^4$ and of deforming away from the orbifold by the marginal
deformation.


Abstract:
  Nonlinear light sources are central to a myriad of applications, driving a
quest for their miniaturisation down to the nanoscale. In this quest, nonlinear
metasurfaces hold a great promise, as they enhance nonlinear effects through
their resonant photonic environment and high refractive index, such as in
high-index dielectric metasurfaces. However, despite the sub-diffractive
operation of dielectric metasurfaces at the fundamental wave, this condition is
not fulfilled for the nonlinearly generated harmonic waves, thereby all
nonlinear metasurfaces to date emit multiple diffractive beams. Here, we
demonstrate the enhanced single-beam second- and third-harmonic generation in a
metasurface of crystalline transition-metal-dichalcogenide material, offering
the highest refractive index. We show that the interplay between the resonances
of the metasurface allows for tuning of the unidirectional second-harmonic
radiation in forward or backward direction, not possible in any bulk nonlinear
crystal. Our results open new opportunities for metasurface-based nonlinear
light-sources, including nonlinear mirrors and entangled-photon generation.


Abstract:
  Lattice QCD has reached a mature status. State of the art lattice
computations include $u,d,s$ (and even the $c$) sea quark effects, together
with an estimate of electromagnetic and isospin breaking corrections for
hadronic observables. This precise and first principles description of the
standard model at low energies allows the determination of multiple quantities
that are essential inputs for phenomenology and not accessible to perturbation
theory.
  One of the fundamental parameters that are determined from simulations of
lattice QCD is the strong coupling constant, which plays a central role in the
quest for precision at the LHC. Lattice calculations currently provide its best
determinations, and will play a central role in future phenomenological
studies. For this reason we believe that it is timely to provide a pedagogical
introduction to the lattice determinations of the strong coupling. Rather than
analysing individual studies, the emphasis will be on the methodologies and the
systematic errors that arise in these determinations. We hope that these notes
will help lattice practitioners, and QCD phenomenologists at large, by
providing a self-contained introduction to the methodology and the possible
sources of systematic error.
  The limiting factors in the determination of the strong coupling turn out to
be different from the ones that limit other lattice precision observables. We
hope to collect enough information here to allow the reader to appreciate the
challenges that arise in order to improve further our knowledge of a quantity
that is crucial for LHC phenomenology.


Abstract:
  Applications of machine learning (ML) to high-stakes policy settings -- such
as education, criminal justice, healthcare, and social service delivery -- have
grown rapidly in recent years, sparking important conversations about how to
ensure fair outcomes from these systems. The machine learning research
community has responded to this challenge with a wide array of proposed
fairness-enhancing strategies for ML models, but despite the large number of
methods that have been developed, little empirical work exists evaluating these
methods in real-world settings. Here, we seek to fill this research gap by
investigating the performance of several methods that operate at different
points in the ML pipeline across four real-world public policy and social good
problems. Across these problems, we find a wide degree of variability and
inconsistency in the ability of many of these methods to improve model
fairness, but post-processing by choosing group-specific score thresholds
consistently removes disparities, with important implications for both the ML
research community and practitioners deploying machine learning to inform
consequential policy decisions.


Abstract:
  Transformer is an attention-based neural network, which consists of two
sublayers, namely, Self-Attention Network (SAN) and Feed-Forward Network (FFN).
Existing research explores to enhance the two sublayers separately to improve
the capability of Transformer for text representation. In this paper, we
present a novel understanding of SAN and FFN as Mask Attention Networks (MANs)
and show that they are two special cases of MANs with static mask matrices.
However, their static mask matrices limit the capability for localness modeling
in text representation learning. We therefore introduce a new layer named
dynamic mask attention network (DMAN) with a learnable mask matrix which is
able to model localness adaptively. To incorporate advantages of DMAN, SAN, and
FFN, we propose a sequential layered structure to combine the three types of
layers. Extensive experiments on various tasks, including neural machine
translation and text summarization demonstrate that our model outperforms the
original Transformer.


Abstract:
  A recent construction by Amarra, Devillers and Praeger of block designs with
specific parameters depends on certain quadratic polynomials, with integer
coefficients, taking prime power values. The Bunyakovsky Conjecture, if true,
would imply that each of them takes infinitely many prime values, giving an
infinite family of block designs with the required parameters. We have found
large numbers of prime values of these polynomials, and the numbers found agree
very closely with the estimates for them provided by Li's recent modification
of the Bateman-Horn Conjecture. While this does not prove that these
polynomials take infinitely many prime values, it provides strong evidence for
this, and it also adds extra support for the validity of the Bunyakovsky and
Bateman-Horn Conjectures.


Abstract:
  Consider the normalized adjacency matrices of random $d$-regular graphs on
$N$ vertices with fixed degree $d\geq3$. We prove that, with probability
$1-N^{-1+{\varepsilon}}$ for any ${\varepsilon} >0$, the following two
properties hold as $N \to \infty$ provided that $d\geq3$: (i) The eigenvalues
are close to the classical eigenvalue locations given by the Kesten-McKay
distribution. In particular, the extremal eigenvalues are concentrated with
polynomial error bound in $N$, i.e. $\lambda_2, |\lambda_N|\leq 2+N^{-c}$. (ii)
All eigenvectors of random $d$-regular graphs are completely delocalized.


Abstract:
  The state of art of electromagnetic integral equations has seen significant
growth over the past few decades, overcoming some of the fundamental
bottlenecks: computational complexity, low frequency and dense discretization
breakdown, preconditioning, and so on. Likewise, the community has seen
extensive investment in development of methods for higher order analysis, in
both geometry and physics. Unfortunately, these standard geometric descriptors
are $C^0$ at the boundary between patches with a few exceptions; as a result,
one needs to define additional mathematical infrastructure to define physical
basis sets for vector problems. In stark contrast, the geometric representation
used for design is higher-order differentiable over the entire surface.
Geometric descriptions that have $C^{2}$-continuity almost everywhere on the
surfaces are common in computer graphics. Using these description for analysis
opens the door to several possibilities, and is the area we explore in this
paper. Our focus is on Loop subdivision based isogeometric methods. In this
paper, our goals are two fold: (i) development of computational infrastructure
necessary to effect efficient methods for isogeometric analysis of electrically
large simply connected objects, and (ii) to introduce the notion of manifold
harmonics transforms and its utility in computational electromagnetics. Several
results highlighting the efficacy of these two methods are presented.


Abstract:
  We study a model of random colliding particles interacting with an infinite
reservoir at fixed temperature and chemical potential. Interaction between the
particles is modeled via a Kac master equation \cite{kac}. Moreover, particles
can leave the system toward the reservoir or enter the system from the
reservoir. The system admits a unique steady state given by the Grand Canonical
Ensemble at temperature $T=\beta^{-1}$ and chemical potential $\chi$. We show
that any initial state converges exponentially to equilibrium by computing the
spectral gap of the generator in a suitable $L^2$ space and by showing
exponential decrease of the relative entropy with respect to the steady state.
We also show propagation of chaos and thus the validity of a Boltzmann-Kac type
equation for the particle density in the infinite system limit.


Abstract:
  We provide minimality criteria by construction of calibrations for
functionals arising in the theory of Thermal Insulation.


Abstract:
  In this paper, we investigate the impact of channel aging on the uplink
performance of a cell-free~(CF) massive multiple-input multiple-output (mMIMO)
system with a minimum mean squared error (MMSE) receiver. To this end, we
present a new model for the temporal evolution of the channel, which allows the
channel to age at different rates at different access points (APs). Under this
setting, we derive the deterministic equivalent of the per-user achievable
signal-to-interference-plus-noise ratio (SINR). In addition to validating the
theoretical expressions, our simulation results reveal that, {at low user
mobilities,} the SINR of CF-mMIMO is nearly 5 dB higher than its cellular
counterpart with the same number of antennas, and about 8 dB higher than that
of an equivalent small-cell network with the same number of APs. {On the other
hand, at very high user velocities, and when the channel between the UEs the
different APs age at same rate, the relative impact of aging is higher for
CF-mMIMO compared to cellular mMIMO. However, when the channel ages at the
different APs with different rates, the effect of aging on CF-mMIMO is
marginally mitigated, especially for larger frame durations.


Abstract:
  The prohibitive expense of automatic performance tuning at scale has largely
limited the use of autotuning to libraries for shared-memory and GPU
architectures. We introduce a framework for approximate autotuning that
achieves a desired confidence in each algorithm configuration's performance by
constructing confidence intervals to describe the performance of individual
kernels (subroutines of benchmarked programs). Once a kernel's performance is
deemed sufficiently predictable for a set of inputs, subsequent invocations are
avoided and replaced with a predictive model of the execution time. We then
leverage online execution path analysis to coordinate selective kernel
execution and propagate each kernel's statistical profile. This strategy is
effective in the presence of frequently-recurring computation and communication
kernels, which is characteristic to algorithms in numerical linear algebra. We
encapsulate this framework as part of a new profiling tool, Critter, that
automates kernel execution decisions and propagates statistical profiles along
critical paths of execution. We evaluate performance prediction accuracy
obtained by our selective execution methods using state-of-the-art
distributed-memory implementations of Cholesky and QR factorization on
Stampede2, and demonstrate speed-ups of up to 7.1x with 98% prediction
accuracy.


Abstract:
  Oversampled adaptive sensing (OAS) is a Bayesian framework recently proposed
for effective sensing of structured signals in a time-limited setting. In
contrast to the conventional blind oversampling, OAS uses the prior information
on the signal to construct posterior beliefs sequentially. These beliefs help
in constructive oversampling which iteratively evolves through a sequence of
time sub-frames.
  The initial studies of OAS consider the idealistic assumption of full control
on sensing coefficients which is not feasible in many applications. In this
work, we extend the initial investigations on OAS to more realistic settings in
which the sensing coefficients are selected from a predefined set of possible
choices, referred to as the codebook. We extend the OAS framework to these
settings and compare its performance with classical non-adaptive approaches.


Abstract:
  Let $N$ be an $n$-dimensional $C^\infty$ manifold and let
$\widetilde{\varphi}: N\to \mathbb{R}^{n+1}$, $\widetilde{\nu}: N\to S^n$ be
$C^\infty$ mappings. We first give a necessary and sufficient condition for the
hyperplane family $\mathcal{H}_{(\widetilde{\varphi}, \widetilde{\nu})}$
defined by $\mathcal{H}_{(\widetilde{\varphi}, \widetilde{\nu})} =\cup_{x\in
N}\left\{X\in \mathbb{R}^{n+1}\; |\; \left(X-\widetilde{\varphi}(x)\right)\cdot
\widetilde{\nu}(x)=0\right\}$ to create an envelope (Theorem 1). As a
by-product of the proof of Theorem 1, when the given hyperplane family
$\mathcal{H}_{(\widetilde{\varphi}, \widetilde{\nu})}$ creates an envelope
$\widetilde{f}: N\to \mathbb{R}^{n+1}$, an explicit expression of the envelope
$\widetilde{f}$ is obtained in terms of $\widetilde{\varphi}$ and
$\widetilde{\nu}$ (Corollary 2). The vector formula given in Corollary 2 holds
even at a singular point of $\widetilde{\nu}$ so long as the hyperplane family
$\mathcal{H}_{(\widetilde{\varphi}, \widetilde{\nu})}$ creates an envelope. In
this sense, Corollary 2 may be regarded as a complete generalization of the
celebrated Cahn-Hoffman vector formula. Moreover, we give a criterion when and
only when $\mathcal{H}_{\left(\widetilde{\varphi}, \widetilde{\nu}\right)}$
creates a unique envelope (Theorem 2).


Abstract:
  A summation is a shift-invariant ${\rm R}$-module homomorphism from a
submodule of ${\rm R}[[\sigma]]$ to ${\rm R}$ or another ring. [11] formalized
a method for extending a summation to a larger domain by telescoping. In this
paper, we revisit telescoping, we study multiplicative closures of summations
(such as the usual summation on convergent series) that are not themselves
multiplicatively closed, and we study rational extensions as a generalization
of telescoping.


Abstract:
  Intelligent transport systems have efficiently and effectively proved
themselves in settling up the problem of traffic congestion around the world.
The multi-agent based transportation system is one of the most important
intelligent transport systems, which represents an interaction among the
neighbouring vehicles, drivers, roads, infrastructure and vehicles. In this
paper, two traffic management models have been created to mitigate congestion
and to ensure that emergency vehicles arrive as quickly as possible. A
tool-chain SUMO-JADE is employed to create a microscopic simulation symbolizing
the interactions of traffic. The simulation model has showed a significant
reduction of at least 50% in the average time delay and thus a real improvement
in the entire journey time.


Abstract:
  We offer a theoretical characterization of off-policy evaluation (OPE) in
reinforcement learning using function approximation for marginal importance
weights and $q$-functions when these are estimated using recent minimax
methods. Under various combinations of realizability and completeness
assumptions, we show that the minimax approach enables us to achieve a fast
rate of convergence for weights and quality functions, characterized by the
critical inequality \citep{bartlett2005}. Based on this result, we analyze
convergence rates for OPE. In particular, we introduce novel alternative
completeness conditions under which OPE is feasible and we present the first
finite-sample result with first-order efficiency in non-tabular environments,
i.e., having the minimal coefficient in the leading term.


Abstract:
  Representational learning forms the backbone of most deep learning
applications, and the value of a learned representation is intimately tied to
its information content regarding different factors of variation. Finding good
representations depends on the nature of supervision and the learning
algorithm. We propose a novel algorithm that utilizes a weak form of
supervision where the data is partitioned into sets according to certain
inactive (common) factors of variation which are invariant across elements of
each set. Our key insight is that by seeking correspondence between elements of
different sets, we learn strong representations that exclude the inactive
factors of variation and isolate the active (varying) factors which vary within
all sets. As a consequence of focusing on the active factors, our method can
leverage a mix of set-supervised and wholly unsupervised data, which can even
belong to a different domain. We tackle the challenging problem of
synthetic-to-real object pose transfer, by isolating from images pose
information which generalizes to the category level and across the
synthetic/real domain gap, even without pose annotations on anything. The
method can also boost performance in supervised settings, by strengthening
intermediate representations.


Abstract:
  This paper studies a model of two-phase flow with an immersed material
viscous interface and a finite element method for numerical solution of the
resulting system of PDEs. The interaction between the bulk and surface media is
characterized by no-penetration and slip with friction interface conditions.
The system is shown to be dissipative and a model stationary problem is proved
to be well-posed. The finite element method applied in this paper belongs to a
family of unfitted discretizations. The performance of the method when model
and discretization parameters vary is assessed. Moreover, an iterative
procedure based on the splitting of the system into bulk and surface problems
is introduced and studied numerically.


Abstract:
  In previous work, we showed that an anomaly in the one point function of
marginal operators is related by the Wess-Zumino condition to the Euler density
anomaly on a two dimensional defect or boundary. Here we analyze in detail the
two point functions of marginal operators with the stress tensor and with the
displacement operator in three dimensions. We show how to get the boundary
anomaly from these bulk two point functions and find perfect agreement with our
anomaly effective action. For a higher dimensional conformal field theory with
a four dimensional defect, we describe for the first time the anomaly effective
action that relates the Euler density term to the one point function anomaly,
generalizing our result for two dimensional defects.


Abstract:
  Image denoising and artefact removal are complex inverse problems admitting
many potential solutions. Variational Autoencoders (VAEs) can be used to learn
a whole distribution of sensible solutions, from which one can sample
efficiently. However, such a generative approach to image restoration is only
studied in the context of pixel-wise noise removal (e.g. Poisson or Gaussian
noise). While important, a plethora of application domains suffer from imaging
artefacts (structured noises) that alter groups of pixels in correlated ways.
In this work we show, for the first time, that generative diversity denoising
(GDD) approaches can learn to remove structured noises without supervision. To
this end, we investigate two existing GDD architectures, introduce a new one
based on hierarchical VAEs, and compare their performances against a total of
seven state-of-the-art baseline methods on five sources of structured noise
(including tomography reconstruction artefacts and microscopy artefacts). We
find that GDD methods outperform all unsupervised baselines and in many cases
not lagging far behind supervised results (in some occasions even superseding
them). In addition to structured noise removal, we also show that our new GDD
method produces new state-of-the-art (SOTA) results on seven out of eight
benchmark datasets for pixel-noise removal. Finally, we offer insights into the
daunting question of how GDD methods distinguish structured noise, which we
like to see removed, from image signals, which we want to see retained.


Abstract:
  Source traffic prediction is one of the main challenges of enabling
predictive resource allocation in machine type communications (MTC). In this
paper, a Long Short-Term Memory (LSTM) based deep learning approach is proposed
for event-driven source traffic prediction. The source traffic prediction
problem can be formulated as a sequence generation task where the main focus is
predicting the transmission states of machine-type devices (MTDs) based on
their past transmission data. This is done by restructuring the transmission
data in a way that the LSTM network can identify the causal relationship
between the devices. Knowledge of such a causal relationship can enable
event-driven traffic prediction. The performance of the proposed approach is
studied using data regarding events from MTDs with different ranges of entropy.
Our model outperforms existing baseline solutions in saving resources and
accuracy with a margin of around 9%. Reduction in Random Access (RA) requests
by our model is also analyzed to demonstrate the low amount of signaling
required as a result of our proposed LSTM based source traffic prediction
approach.


Abstract:
  The torsion models have stood out among the proposals for an alternative
description of gravity. The simplest of them, the Teleparallel theory, is
equivalent to General Relativity and there are many studies that seek to study
its extension to more general functions of the torsion $T$. The purpose of {our
study } is to consider a family of $f(T)$ models and apply their corresponding
Tolman-Oppenheimer-Volkof equations to compact objects such as neutron stars.
Consequently, through a numerical analysis, calculate, among other things, the
maximum mass allowed by the model for a neutron star, which would also allow us
to evaluate which models are in accordance with observations. In the present
paper, the first in the series, we show explicitly the set of equations that
must be solved, and how to solve it, in order to model compact stars in $f(T)$
gravity without the need to adopt any particular form for the metric functions
or consider any perturbative approach, as has been done in some works in the
literature.


Abstract:
  Twisted bilayers of van der Waals materials have recently attracted great
attention due to their tunable strongly correlated phenomena. Here, we
investigate the chirality-specific physics in 3D moir\'e superlattices induced
by Eshelby twist. Our direct DFT calculations reveal helical rotation leads to
optical circular dichroism, and chirality-specific nonlinear Hall effect, even
though there is no magnetization or magnetic field. Both these phenomena can be
reversed by changing the structural chirality. This provides a way to
constructing chirality-specific materials.


Abstract:
  We introduce an impartial combinatorial game on Steiner triple systems called
Nofil. Players move alternately, choosing points of the triple system. If a
player is forced to fill a block on their turn, they lose. We explore the play
of Nofil on all Steiner triple systems up to order 15 and a sampling for orders
19, 21, and 25. We determine the optimal strategies by computing the nim-values
for each game and its subgames. The game Nofil can be thought of in terms of
play on a corresponding hypergraph. As game play progresses, the hypergraph
shrinks and will eventually be equivalent to playing the game Node Kayles on an
isomorphic graph. Node Kayles is well studied and understood. Motivated by
this, we study which Node Kayles positions can be reached, i.e. embedded into a
Steiner triple system. We prove necessary conditions and sufficient conditions
for the existence of such graph embeddings and conclude that the complexity of
determining the outcome of the game Nofil on Steiner triple systems is
PSPACE-complete.


Abstract:
  Federated edge learning (FEEL) has emerged as an effective approach to reduce
the large communication latency in Cloud-based machine learning solutions,
while preserving data privacy. Unfortunately, the learning performance of FEEL
may be compromised due to limited training data in a single edge cluster. In
this paper, we investigate a novel framework of FEEL, namely semi-decentralized
federated edge learning (SD-FEEL). By allowing model aggregation across
different edge clusters, SD-FEEL enjoys the benefit of FEEL in reducing the
training latency, while improving the learning performance by accessing richer
training data from multiple edge clusters. A training algorithm for SD-FEEL
with three main procedures in each round is presented, including local model
updates, intra-cluster and inter-cluster model aggregations, which is proved to
converge on non-independent and identically distributed (non-IID) data. We also
characterize the interplay between the network topology of the edge servers and
the communication overhead of inter-cluster model aggregation on the training
performance. Experiment results corroborate our analysis and demonstrate the
effectiveness of SD-FFEL in achieving faster convergence than traditional
federated learning architectures. Besides, guidelines on choosing critical
hyper-parameters of the training algorithm are also provided.


Abstract:
  Mobile manipulation tasks remain one of the critical challenges for the
widespread adoption of autonomous robots in both service and industrial
scenarios. While planning approaches are good at generating feasible whole-body
robot trajectories, they struggle with dynamic environments as well as the
incorporation of constraints given by the task and the environment. On the
other hand, dynamic motion models in the action space struggle with generating
kinematically feasible trajectories for mobile manipulation actions. We propose
a deep reinforcement learning approach to learn feasible dynamic motions for a
mobile base while the end-effector follows a trajectory in task space generated
by an arbitrary system to fulfill the task at hand. This modular formulation
has several benefits: it enables us to readily transform a broad range of
end-effector motions into mobile applications, it allows us to use the
kinematic feasibility of the end-effector trajectory as a dense reward signal
and its modular formulation allows it to generalise to unseen end-effector
motions at test time. We demonstrate the capabilities of our approach on
multiple mobile robot platforms with different kinematic abilities and
different types of wheeled platforms in extensive simulated as well as
real-world experiments.


Abstract:
  Recognizing human activity plays a significant role in the advancements of
human-interaction applications in healthcare, personal fitness, and smart
devices. Many papers presented various techniques for human activity
representation that resulted in distinguishable progress. In this study, we
conduct an extensive literature review on recent, top-performing techniques in
human activity recognition based on wearable sensors. Due to the lack of
standardized evaluation and to assess and ensure a fair comparison between the
state-of-the-art techniques, we applied a standardized evaluation benchmark on
the state-of-the-art techniques using six publicly available data-sets:
MHealth, USCHAD, UTD-MHAD, WISDM, WHARF, and OPPORTUNITY. Also, we propose an
experimental, improved approach that is a hybrid of enhanced handcrafted
features and a neural network architecture which outperformed top-performing
techniques with the same standardized evaluation benchmark applied concerning
MHealth, USCHAD, UTD-MHAD data-sets.


Abstract:
  By means the envelope function approximation, 8-band Kane model and a
finite-difference scheme with the coordinate space discretization, we
numerically performed calculations of the spin-orbit coupling parameters for 2D
electron gas confined in both symmetric and asymmetric [0 0 1] quantum wells
based on zinc-blende III-V semiconductors. Influence of the quantum well band
parameters and width as well as the magnitude of the external electric field
applied along the growth direction on the Dresselhaus and Rashba spin-orbit
coupling parameters is investigated. It has been found that in the symmetric
InGaAs/GaAs quantum wells linear-in-momentum spin-orbit coupling disappears for
the third electron subband at certain values of well width and the indium
content. It is also shown that in asymmetric InGaAs/GaAs structures the
spin-orbit coupling parameters can be equal at a certain electric field that is
the condition for the realization of the SU(2) spin symmetry and formation of
persistent spin helices. Besides, we calculated the spin-orbit coupling in the
persistent spin helix regime as a function of the well width, indium content
and external field. The proposed approach for the calculation of the spin-orbit
coupling parameters can be applied to other 2D structures with the spin-orbit
coupling.


Abstract:
  Some luminous and ultraluminous infrared galaxies (LIRGs and ULIRGs) host
extremely compact and dusty nuclei. The intense infrared radiation arising from
warm dust in these sources is prone to excite vibrational levels of molecules
such as HCN. This results in emission from the rotational transitions of
vibrationally excited HCN (HCN-vib), with the brightest emission found in
compact obscured nuclei (CONs). We aim to establish how common CONs are in the
local Universe, and whether their prevalence depends on the luminosity or other
properties of the host galaxy. We have conducted an Atacama Large
Millimeter/submillimeter Array (ALMA) survey of the rotational J=3-2 transition
of HCN-vib in a sample of 46 far-infrared luminous galaxies. Compact obscured
nuclei are identified in 38 percent of ULIRGs, 21 percent of LIRGs, and 0
percent of lower luminosity galaxies. We find no dependence on the inclination
of the host galaxy, but strong evidence of lower IRAS 25 to 60 {\mu}m flux
density ratios (f25/f60) in CONs compared to the rest of the sample.
Furthermore, we find that CONs have stronger silicate features (s9.7{\mu}m) but
similar PAH equivalent widths (EQW6.2{\mu}m) compared to other galaxies. In the
local Universe, CONs are primarily found in (U)LIRGs. High resolution continuum
observations of the individual nuclei are required to determine if the CON
phenomenon is related to the inclinations of the nuclear disks. The lower
f25/f60 ratios in CONs as well as the results for the mid-infrared diagnostics
investigated are consistent with large dust columns shifting the nuclear
radiation to longer wavelengths, making the mid- and far-infrared
"photospheres" significantly cooler than the interior regions. To assess the
importance of CONs in the context of galaxy evolution, it is necessary to
extend this study to higher redshifts where (U)LIRGs are more common.


Abstract:
  Numerous examples of ram pressure stripping in galaxy clusters are present in
literature; however, substantially less work has been focused on ram pressure
stripping in lower mass groups. In this work we use the LOFAR Two-metre Sky
Survey (LoTSS) to search for jellyfish galaxies in ~500 SDSS groups (z<0.05),
making this the most comprehensive search for ram pressure stripping in groups
to date. We identify 60 jellyfish galaxies in groups with extended, asymmetric
radio continuum tails, which are found across the entire range of group mass
from $10^{12.5} < M_\mathrm{group} < 10^{14}\,h^{-1}\,\mathrm{M_\odot}$. We
compare the group jellyfish galaxies identified in this work with the LoTSS
jellyfish galaxies in clusters presented in Roberts et al. (2021), allowing us
to compare the effects of ram pressure stripping across three decades in
group/cluster mass. We find that jellyfish galaxies are most commonly found in
clusters, with the frequency decreasing towards the lowest mass groups. Both
the orientation of observed radio continuum tails, and the positions of group
jellyfish galaxies in phase space, suggest that galaxies are stripped more
slowly in groups relative to clusters. Finally, we find that the star formation
rates of jellyfish galaxies in groups are consistent with `normal' star-forming
group galaxies, which is in contrast to cluster jellyfish galaxies that have
clearly enhanced star formation rates. On the whole, there is clear evidence
for ongoing ram pressure stripping in galaxy groups (down to very low group
masses), though the frequency of jellyfish galaxies and the strength of ram
pressure stripping appears smaller in groups than clusters. Differences in the
efficiency of ram pressure stripping in groups versus clusters likely
contributes to the positive trend between quenched fraction and host halo mass
observed in the local Universe.


Abstract:
  There are three approaches in the current social network analysis study:
Graph Representation, Content Mining, and Semantic Analysis. Graph
Representation has been used for analyzing social network topology, structural
modeling, tie-strength, community detection, group cohesion visualization, and
metrics computations. This paper provides a taxonomy of social network analysis
based on its graph representation.


Abstract:
  We propose a large, scalable engineering knowledge graph, comprising sets of
(entity, relationship, entity) triples that are real-world engineering facts
found in the patent database. We apply a set of rules based on the syntactic
and lexical properties of claims in a patent document to extract facts. We
aggregate these facts within each patent document and integrate the aggregated
sets of facts across the patent database to obtain the engineering knowledge
graph. Such a knowledge graph is expected to support inference, reasoning, and
recalling in various engineering tasks. The knowledge graph has a greater size
and coverage in comparison with the previously used knowledge graphs and
semantic networks in the engineering literature.


Abstract:
  Understanding the dynamics of complex systems is a central task in many
different areas ranging from biology via epidemics to economics and
engineering. Unexpected behaviour of dynamic systems or even system failure is
sometimes difficult to comprehend. Such a data-mismatch can be caused by
endogenous model errors including misspecified interactions and inaccurate
parameter values. These are often difficult to distinguish from unmodelled
process influencing the real system like unknown inputs or faults. Localizing
the root cause of these errors or faults and reconstructing their dynamics is
only possible if the measured outputs of the system are sufficiently
informative.
  Here, we present criteria for the measurements required to localize the
position of error sources in large dynamic networks. We assume that faults or
errors occur at a limited number of positions in the network. This invariable
sparsity differs from previous sparsity definitions for inputs to dynamic
systems. We provide an exact criterion for the recovery of invariable sparse
inputs to nonlinear systems and formulate an optimization criterion for
invariable sparse input reconstruction. For linear systems we can provide exact
error bounds for this reconstruction method.


Abstract:
  Anomia (word-finding difficulties) is the hallmark of aphasia, an acquired
language disorder most commonly caused by stroke. Assessment of speech
performance using picture naming tasks is a key method for both diagnosis and
monitoring of responses to treatment interventions by people with aphasia
(PWA). Currently, this assessment is conducted manually by speech and language
therapists (SLT). Surprisingly, despite advancements in automatic speech
recognition (ASR) and artificial intelligence with technologies like deep
learning, research on developing automated systems for this task has been
scarce. Here we present NUVA, an utterance verification system incorporating a
deep learning element that classifies 'correct' versus' incorrect' naming
attempts from aphasic stroke patients. When tested on eight native
British-English speaking PWA the system's performance accuracy ranged between
83.6% to 93.6%, with a 10-fold cross-validation mean of 89.5%. This performance
was not only significantly better than a baseline created for this study using
one of the leading commercially available ASRs (Google speech-to-text service)
but also comparable in some instances with two independent SLT ratings for the
same dataset.


Abstract:
  The advent of powerful prediction algorithms led to increased automation of
high-stake decisions regarding the allocation of scarce resources such as
government spending and welfare support. This automation bears the risk of
perpetuating unwanted discrimination against vulnerable and historically
disadvantaged groups. Research on algorithmic discrimination in computer
science and other disciplines developed a plethora of fairness metrics to
detect and correct discriminatory algorithms. Drawing on robust sociological
and philosophical discourse on distributive justice, we identify the
limitations and problematic implications of prominent fairness metrics. We show
that metrics implementing equality of opportunity only apply when resource
allocations are based on deservingness, but fail when allocations should
reflect concerns about egalitarianism, sufficiency, and priority. We argue that
by cleanly distinguishing between prediction tasks and decision tasks, research
on fair machine learning could take better advantage of the rich literature on
distributive justice.


Abstract:
  In recent years, adversarial attacks have drawn more attention for their
value on evaluating and improving the robustness of machine learning models,
especially, neural network models. However, previous attack methods have mainly
focused on applying some $l^p$ norm-bounded noise perturbations. In this paper,
we instead introduce a novel adversarial attack method based on haze, which is
a common phenomenon in real-world scenery. Our method can synthesize
potentially adversarial haze into an image based on the atmospheric scattering
model with high realisticity and mislead classifiers to predict an incorrect
class. We launch experiments on two popular datasets, i.e., ImageNet and
NIPS~2017. We demonstrate that the proposed method achieves a high success
rate, and holds better transferability across different classification models
than the baselines. We also visualize the correlation matrices, which inspire
us to jointly apply different perturbations to improve the success rate of the
attack. We hope this work can boost the development of non-noise-based
adversarial attacks and help evaluate and improve the robustness of DNNs.


Abstract:
  Sublinear circuits are generalizations of the affine circuits in matroid
theory, and they arise as the convex-combinatorial core underlying constrained
non-negativity certificates of exponential sums and of polynomials based on the
arithmetic-geometric inequality. Here, we study the polyhedral combinatorics of
sublinear circuits for polyhedral constraint sets.
  We give results on the relation between the sublinear circuits and their
supports and provide necessary as well as sufficient criteria for sublinear
circuits. Based on these characterizations, we provide some explicit results
and enumerations for two prominent polyhedral cases, namely the non-negative
orthant and the cube $[-1,1]^n$.


Abstract:
  Being able to efficiently and accurately select the top-$k$ elements with
differential privacy is an integral component of various private data analysis
tasks. In this paper, we present the oneshot Laplace mechanism, which
generalizes the well-known Report Noisy Max mechanism to reporting noisy
top-$k$ elements. We show that the oneshot Laplace mechanism with a noise level
of $\widetilde{O}(\sqrt{k}/\eps)$ is approximately differentially private.
Compared to the previous peeling approach of running Report Noisy Max $k$
times, the oneshot Laplace mechanism only adds noises and computes the top $k$
elements once, hence much more efficient for large $k$. In addition, our proof
of privacy relies on a novel coupling technique that bypasses the use of
composition theorems. Finally, we present a novel application of efficient
top-$k$ selection in the classical problem of ranking from pairwise
comparisons.


Abstract:
  Structural properties of large random maps and lambda-terms may be gleaned by
studying the limit distributions of various parameters of interest. In our work
we focus on restricted classes of maps and their counterparts in the
lambda-calculus, building on recent bijective connections between these two
domains. In such cases, parameters in maps naturally correspond to parameters
in lambda-terms and vice versa. By an interplay between lambda-terms and maps,
we obtain various combinatorial specifications which allow us to access the
distributions of pairs of related parameters such as: the number of bridges in
rooted trivalent maps and of subterms in closed linear lambda-terms, the number
of vertices of degree 1 in (1,3)-valent maps and of free variables in open
linear lambda-terms etc. To analyse asymptotically these distributions, we
introduce appropriate tools: a moment-pumping schema for differential equations
and a composition schema inspired by Bender's theorem.


Abstract:
  We present a new operator method in the Heisenberg representation to obtain
the signal of parity measurement within a lossless SU(1,1) interferometer.
Based on this method, it is convenient to derive the parity signal directly in
terms of input states, including general Gaussian or non-Gaussian state. As
applications, we revisit the signal of parity measurement within an SU(1,1)
interferometer when a coherent or thermal state and a squeezed vacuum state are
considered as input states. In addition, we also obtain the parity signal of a
Fock state when it passes through an SU(1,1) interferometer, which is also a
new result. Therefore, the operator method proposed in this work may bring
convenience to the study of quantum metrology, particularly the phase
estimation based on an SU(1,1) interferometer.


Abstract:
  We study the localization properties of the eigenmodes of the staggered Dirac
operator in finite-temperature $\mathbb{Z}_2$ pure gauge theory on the lattice
in 2+1 dimensions. We find that the low modes turn from delocalized to
localized as the system crosses over from the confined to the deconfined phase
in the "physical" sector (positive average Polyakov loop) selected by external
fermionic probes, while they remain delocalized in the "unphysical" sector
(negative average Polyakov loop). This confirms that the close connection
between deconfinement and localization of the low Dirac modes in the physical
sector, already observed in other models, holds also in the simplest gauge
theory displaying a deconfinement transition. We also observe a clear
correlation of localized modes with fluctuations of the Polyakov loop away from
the ordered value, as expected according to the "sea/islands" picture of
localization, and with clusters of negative plaquettes. A novel finding is the
presence of localized modes at the high end of the Dirac spectrum in all
phases/sectors of the theory.


Abstract:
  Identifying how a file has been created is often interesting in security. It
can be used by both attackers and defenders. Attackers can exploit this
information to tune their attacks and defenders can understand how a malicious
file has been created after an incident. In this work, we want to identify how
a PDF file has been created. This problem is important because PDF files are
extremely popular: many organizations publish PDF files online and malicious
PDF files are commonly used by attackers. Our approach to detect which software
has been used to produce a PDF file is based on coding style: given patterns
that are only created by certain PDF producers. We have analyzed the coding
style of 900 PDF files produced using 11 PDF producers on 3 different Operating
Systems. We have obtained a set of 192 rules which can be used to identify 11
PDF producers. We have tested our detection tool on 508836 PDF files published
on scientific preprints servers. Our tool is able to detect certain producers
with an accuracy of 100%. Its overall detection is still high (74%). We were
able to apply our tool to identify how online PDF services work and to spot
inconsistency.


Abstract:
  In this paper, the use of the Smith-McMillan form in decoupling
multiple-input multiple-output system dynamics is analyzed. In short, from a
transfer matrix plant model one can obtain a decoupling compensator which leads
to a decoupled plant that contains the same transmission poles and zeros of the
transfer matrix of the original system. As a result, full decoupling of the
plant transfer matrix is obtained for all frequencies, which can be
individually addressed by single-input single-output control. The aim of this
paper is to present conditions for the decoupled system that guarantee internal
stability and performance requirements for the overall control system.
Performance specifications are defined in terms of magnitude limits for the
maximum singular value of the closed-loop transfer matrices. The potential of
the decoupling procedure is shown in a simulation study of a mechanical system.


Abstract:
  We show the ferro-rotational nature of the commensurate charge density wave
(CCDW) in $1T$-$\mathrm{TaS}_2$ and track its dynamic modulations by
temperature-dependent and time-resolved electric quadrupole rotation
anisotropy-second harmonic generation (EQ RA-SHG), respectively. The ultrafast
modulations manifest as the breathing and the rotation of the EQ RA-SHG
patterns at three frequencies around the reported single CCDW amplitude mode
frequency. A sudden shift of the triplet frequencies and a dramatic increase in
the breathing and rotation magnitude further reveal a photo-induced transient
CDW phase across a critical pump fluence of ~ 0.5 mJ/cm$^2$.


Abstract:
  We consider high dimensional variants of the maximum flow and minimum cut
problems in the setting of simplicial complexes and provide both algorithmic
and hardness results. By viewing flows and cuts topologically in terms of the
simplicial (co)boundary operator we can state these problems as linear programs
and show that they are dual to one another. Unlike graphs, complexes with
integral capacity constraints may have fractional max-flows. We show that
computing a maximum integral flow is NP-hard. Moreover, we give a combinatorial
definition of a simplicial cut that seems more natural in the context of
optimization problems and show that computing such a cut is NP-hard. However,
we provide conditions on the simplicial complex for when the cut found by the
linear program is a combinatorial cut. For $d$-dimensional simplicial complexes
embedded into $\mathbb{R}^{d+1}$ we provide algorithms operating on the dual
graph: computing a maximum flow is dual to computing a shortest path and
computing a minimum cut is dual to computing a minimum cost circulation.
Finally, we investigate the Ford-Fulkerson algorithm on simplicial complexes,
prove its correctness, and provide a heuristic which guarantees it to halt.


Abstract:
  We provide several algorithms for constrained optimization of a large class
of convex problems, including softmax, $\ell_p$ regression, and logistic
regression. Central to our approach is the notion of width reduction, a
technique which has proven immensely useful in the context of maximum flow
[Christiano et al., STOC'11] and, more recently, $\ell_p$ regression [Adil et
al., SODA'19], in terms of improving the iteration complexity from $O(m^{1/2})$
to $\tilde{O}(m^{1/3})$, where $m$ is the number of rows of the design matrix,
and where each iteration amounts to a linear system solve. However, a
considerable drawback is that these methods require both problem-specific
potentials and individually tailored analyses.
  As our main contribution, we initiate a new direction of study by presenting
the first unified approach to achieving $m^{1/3}$-type rates. Notably, our
method goes beyond these previously considered problems to more broadly capture
quasi-self-concordant losses, a class which has recently generated much
interest and includes the well-studied problem of logistic regression, among
others. In order to do so, we develop a unified width reduction method for
carefully handling these losses based on a more general set of potentials.
Additionally, we directly achieve $m^{1/3}$-type rates in the constrained
setting without the need for any explicit acceleration schemes, thus naturally
complementing recent work based on a ball-oracle approach [Carmon et al.,
NeurIPS'20].


Abstract:
  We present MT-lib, an efficient message transfer library for messages gather
and scatter in benchmarks like Graph500 for Supercomputers. Our library
includes MST version as well as new-MST version. The MT-lib is deliberately
kept light-weight, efficient and friendly interfaces for massive graph
traverse. MST provides (1) a novel non-blocking communication scheme with
sending and receiving messages asynchronously to overlap calculation and
communication;(2) merging messages according to the target process for reducing
communication overhead;(3) a new communication mode of gathering intra-group
messages before forwarding between groups for reducing communication traffic.
In MT-lib, there are (1) one-sided message; (2) two-sided messages; and (3)
two-sided messages with buffer, in which dynamic buffer expansion is built for
messages delivery. We experimented with MST and then testing Graph500 with MST
on Tianhe supercomputers. Experimental results show high communication
efficiency and high throughputs for both BFS and SSSP communication operations.


Abstract:
  The space-borne missions CoRoT and Kepler opened up a new opportunity for
better understanding stellar evolution by probing stellar interiors with
unrivalled high-precision photometric data. Kepler has observed stellar
oscillation for four years, which gave access to excellent frequency resolution
that enables deciphering the oscillation spectrum of evolved red giant branch
and asymptotic giant branch stars. The internal structure of stars in the upper
parts of the red and asymptotic giant branches is poorly constrained, which
makes the distinction between red and asymptotic giants difficult. We perform a
thorough seismic analysis to address the physical conditions inside these stars
and to distinguish them. We studied the oscillation mode properties of about
2.000 evolved giants in a model described by the asymptotic pressure-mode
pattern of red giants, which includes the signature of the helium
second-ionisation zone. We extracted the mode properties up to the degree l = 3
and investigated their dependence on stellar mass, metallicity, and
evolutionary status. We identify a clear difference in the signature of the
helium second-ionisation zone between red and asymptotic giants. We also detect
a clear shortage of the energy of l = 1 modes after the core-He-burning phase.
Furthermore, we note that the mode damping observed on the asymptotic giant
branch is similar to that observed on the red giant branch. We highlight that
the signature of the helium second-ionisation zone varies with stellar
evolution. This provides us with a physical basis for distinguishing red giant
branch stars from asymptotic giants. Here, our investigation of stellar
oscillations allows us to constrain the physical processes and the key events
that occur during the advanced stages of stellar evolution, with emphasis on
the ascent along the asymptotic giant branch, including the asymptotic giant
branch bump.


Abstract:
  We point out that common evaluation practices for cross-document coreference
resolution have been unrealistically permissive in their assumed settings,
yielding inflated results. We propose addressing this issue via two evaluation
methodology principles. First, as in other tasks, models should be evaluated on
predicted mentions rather than on gold mentions. Doing this raises a subtle
issue regarding singleton coreference clusters, which we address by decoupling
the evaluation of mention detection from that of coreference linking. Second,
we argue that models should not exploit the synthetic topic structure of the
standard ECB+ dataset, forcing models to confront the lexical ambiguity
challenge, as intended by the dataset creators. We demonstrate empirically the
drastic impact of our more realistic evaluation principles on a competitive
model, yielding a score which is 33 F1 lower compared to evaluating by prior
lenient practices.


Abstract:
  In frequency-division duplexing systems, the downlink channel state
information (CSI) acquisition scheme leads to high training and feedback
overheads. In this paper, we propose an uplink-aided downlink channel
acquisition framework using deep learning to reduce these overheads. Unlike
most existing works that focus only on channel estimation or feedback modules,
to the best of our knowledge, this is the first study that considers the entire
downlink CSI acquisition process, including downlink pilot design, channel
estimation, and feedback. First, we propose an adaptive pilot design module by
exploiting the correlation in magnitude among bidirectional channels in the
angular domain to improve channel estimation. Next, to avoid the bit allocation
problem during the feedback module, we concatenate the complex channel and
embed the uplink channel magnitude to the channel reconstruction at the base
station. Lastly, we combine the above two modules and compare two popular
downlink channel acquisition frameworks. The former framework estimates and
feeds back the channel at the user equipment subsequently. The user equipment
in the latter one directly feeds back the received pilot signals to the base
station. Our results reveal that, with the help of uplink, directly feeding
back the pilot signals can save approximately 20% of feedback bits, which
provides a guideline for future research.


Abstract:
  We present a study of the Corona Borealis (CB) supercluster. We determined
the high-density cores of the CB and the richest galaxy clusters in them, and
studied their dynamical state and galaxy content. We determined filaments in
the supercluster to analyse the connectivity of clusters. We compared the mass
distribution in the CB with predictions from the spherical collapse model and
analysed the acceleration field in the CB. We found that at a radius
$R_{\mathrm{30}}$ around clusters in the CB (A2065, A2061, A2089, and Gr2064)
(corresponding to the density contrast $\Delta\rho \approx 30$), the galaxy
distribution shows a minimum. The $R_{30}$ values for individual clusters lie
in the range of $3 - 6$ $h^{-1}$ Mpc. The radii of the clusters (splashback
radii) lie in the range of $R_{\mathrm{cl}} \approx 2 - 3$ $R_{\mathrm{vir}}$.
The projected phase space diagrams and the comparison with the spherical
collapse model suggest that $R_{\mathrm{30}}$ regions have passed turnaround
and are collapsing. Galaxy content in clusters varies strongly. The cluster
A2061 has the highest fraction of galaxies with old stellar populations, and
A2065 has the highest fraction of galaxies with young stellar populations. The
number of long filaments near clusters vary from one at A2089 to five at A2061.
During the future evolution, the clusters in the main part of the CB may merge
and form one of the largest bound systems in the nearby Universe. Another part
of the CB, with the cluster Gr2064, will form a separate system. The structures
with a current density contrast $\Delta\rho \approx 30$ have passed turnaround
and started to collapse at redshifts $z \approx 0.3 - 0.4$. The comparison of
the number and properties of the most massive collapsing supercluster cores
from observations and simulations may serve as a test for cosmological models.


Abstract:
  Automatic speech recognition (ASR) systems promise to deliver objective
interpretation of human speech. Practice and recent evidence suggests that the
state-of-the-art (SotA) ASRs struggle with the large variation in speech due to
e.g., gender, age, speech impairment, race, and accents. Many factors can cause
the bias of an ASR system. Our overarching goal is to uncover bias in ASR
systems to work towards proactive bias mitigation in ASR. This paper is a first
step towards this goal and systematically quantifies the bias of a Dutch SotA
ASR system against gender, age, regional accents and non-native accents. Word
error rates are compared, and an in-depth phoneme-level error analysis is
conducted to understand where bias is occurring. We primarily focus on bias due
to articulation differences in the dataset. Based on our findings, we suggest
bias mitigation strategies for ASR development.


Abstract:
  Scenario-based stochastic optimal control problems suffer from the curse of
dimensionality as they can easily grow to six and seven figure sizes.
First-order methods are suitable as they can deal with such large-scale
problems, but may fail to achieve accurate solutions within a reasonable number
of iterations. To achieve solutions of higher accuracy and high speed, in this
paper we propose two proximal quasi-Newtonian limited-memory algorithms -
MinFBE applied to the dual problem and the Newton-type alternating minimization
algorithm (NAMA) - which can be massively parallelized on lockstep hardware
such as graphics processing units (GPUs). We demonstrate the performance of
these methods, in terms of convergence speed and parallelizability, on
large-scale problems involving millions of variables.


Abstract:
  When the input to a deep neural network (DNN) is a video signal, a sequence
of feature tensors is produced at the intermediate layers of the model. If
neighboring frames of the input video are related through motion, a natural
question is, "what is the relationship between the corresponding feature
tensors?" By analyzing the effect of common DNN operations on optical flow, we
show that the motion present in each channel of a feature tensor is
approximately equal to the scaled version of the input motion. The analysis is
validated through experiments utilizing common motion models. %These results
will be useful in collaborative intelligence applications where sequences of
feature tensors need to be compressed or further analyzed.


Abstract:
  Imitation learning aims to extract knowledge from human experts'
demonstrations or artificially created agents in order to replicate their
behaviors. Its success has been demonstrated in areas such as video games,
autonomous driving, robotic simulations and object manipulation. However, this
replicating process could be problematic, such as the performance is highly
dependent on the demonstration quality, and most trained agents are limited to
perform well in task-specific environments. In this survey, we provide a
systematic review on imitation learning. We first introduce the background
knowledge from development history and preliminaries, followed by presenting
different taxonomies within Imitation Learning and key milestones of the field.
We then detail challenges in learning strategies and present research
opportunities with learning policy from suboptimal demonstration, voice
instructions and other associated optimization schemes.


Abstract:
  In this paper, we address the problem of global-scale image geolocation,
proposing a mixed classification-retrieval scheme. Unlike other methods that
strictly tackle the problem as a classification or retrieval task, we combine
the two practices in a unified solution leveraging the advantages of each
approach with two different modules. The first leverages the EfficientNet
architecture to assign images to a specific geographic cell in a robust way.
The second introduces a new residual architecture that is trained with
contrastive learning to map input images to an embedding space that minimizes
the pairwise geodesic distance of same-location images. For the final location
estimation, the two modules are combined with a search-within-cell scheme,
where the locations of most similar images from the predicted geographic cell
are aggregated based on a spatial clustering scheme. Our approach demonstrates
very competitive performance on four public datasets, achieving new
state-of-the-art performance in fine granularity scales, i.e., 15.0% at 1km
range on Im2GPS3k.


Abstract:
  Colorectal cancer (CRC) is one of the most common types of cancer with a high
mortality rate. Colonoscopy is the preferred procedure for CRC screening and
has proven to be effective in reducing CRC mortality. Thus, a reliable
computer-aided polyp detection and classification system can significantly
increase the effectiveness of colonoscopy. In this paper, we create an
endoscopic dataset collected from various sources and annotate the ground truth
of polyp location and classification results with the help of experienced
gastroenterologists. The dataset can serve as a benchmark platform to train and
evaluate the machine learning models for polyp classification. We have also
compared the performance of eight state-of-the-art deep learning-based object
detection models. The results demonstrate that deep CNN models are promising in
CRC screening. This work can serve as a baseline for future research in polyp
detection and classification.


Abstract:
  A combination of high strength and reasonable ductility has been achieved in
a copper-1.7 at.%titanium alloy deformed by high-pressure torsion. Grain
refinement and a spinodal microstructure provided a hardness of 254 +/- 2 HV ,
yield strength of 800 MPa and elongation of 10%. The spinodal structure
persisted during isothermal ageing, further increasing the yield strength to
890MPa while retaining an elongation of 7%. This work demonstrates the
potential for spinodal microstructures to overcome the difficulties in
retaining ductility in ultra-fine grained or nanocrystalline alloys, especially
upon post-deformation heating where strain softening normally results in
brittle behavior.


Abstract:
  We present a method to over-approximate reachable tubes over compact
time-intervals, for linear continuous-time, time-varying control systems whose
initial states and inputs are subject to compact convex uncertainty. The method
uses numerical approximations of transition matrices, is convergent of first
order, and assumes the ability to compute with compact convex sets in finite
dimension. We also present a variant that applies to the case of zonotopic
uncertainties, uses only linear algebraic operations, and yields zonotopic
over-approximations. The performance of the latter variant is demonstrated on
an example.


Abstract:
  Assume ZFC. Let $\kappa$ be a cardinal. Recall that a ${<\kappa}$-ground is a
transitive proper class $W$ modelling ZFC such that $V$ is a generic extension
of $W$ via a forcing $\mathbb{P}\in W$ of cardinality ${<\kappa}$, and the
$\kappa$-mantle is the intersection of all ${<\kappa}$-grounds.
  Assume there is a Woodin cardinal and a proper class of measurables, and let
$x$ be a real of sufficiently high Turing degree. Let $\kappa$ be a limit
cardinal of $L[x]$ of uncountable cofinality in $L[x]$. Using methods from
Woodin's analysis of $\mathrm{HOD}^{L[x,G]}$, we analyze the $\kappa$-mantle of
$L[x]$, and show that it models ZFC + GCH + "There is a Woodin cardinal".
Moreover, we show that it is a fully iterable strategy mouse (analogous to
$\mathrm{HOD}^{L[x,G]}$). We also analyze another form of "local mantle",
partly assuming also a weak form of Turing determinacy. We also compute bounds
on how much iteration strategy can be added to $M_1$ before $M_1^\#$ is added.


Abstract:
  Contrastive self-supervised learning has largely narrowed the gap to
supervised pre-training on ImageNet. However, its success highly relies on the
object-centric priors of ImageNet, i.e., different augmented views of the same
image correspond to the same object. Such a heavily curated constraint becomes
immediately infeasible when pre-trained on more complex scene images with many
objects. To overcome this limitation, we introduce Object-level Representation
Learning (ORL), a new self-supervised learning framework towards scene images.
Our key insight is to leverage image-level self-supervised pre-training as the
prior to discover object-level semantic correspondence, thus realizing
object-level representation learning from scene images. Extensive experiments
on COCO show that ORL significantly improves the performance of self-supervised
learning on scene images, even surpassing supervised ImageNet pre-training on
several downstream tasks. Furthermore, ORL improves the downstream performance
when more unlabeled scene images are available, demonstrating its great
potential of harnessing unlabeled data in the wild. We hope our approach can
motivate future research on more general-purpose unsupervised representation
learning from scene data.


Abstract:
  Eta Carinae is a massive interacting binary system shrouded in a complex
circumstellar environment whose evolution is the source of the long-term
brightening observed during the last 80 years. An occulter, acting as a natural
coronagraph, impacts observations from our perspective, but not from most other
directions. Other sight-lines are visible to us through studies of the
Homunculus reflection nebula. The coronagraph appears to be vanishing,
decreasing the extinction towards the central star, and causing the star's
secular brightening. In contrast, the Homunculus remains at an almost constant
brightness. The coronagraph primarily suppresses the stellar continuum, to a
lesser extent the wind lines, and not the circumstellar emission lines. This
explains why the absolute values of equivalent widths (EWs) of the emission
lines in our direct view are larger than those seen in reflected by the
Homunculus, why the direct view absolute EWs are decreasing with time, and why
lower-excitation spectral wind lines formed at larger radii (e.g. FeII 4585A)
decrease in intensity at a faster pace than higher excitation lines that form
closer to the star (e.g. H\delta). Our main result is that the star, despite
its 10-fold brightening over two decades, is relatively stable. A vanishing
coronagraph that can explain both the large flux evolution and the much weaker
spectral evolution. This is contrary to suggestions that the long-term
variability is intrinsic to the primary star that is still recovering from the
Great Eruption with a decreasing mass-loss rate and a polar wind that is
evolving at a slower pace than at the equator.


Abstract:
  We investigate some properties and convergence theorem of
Kluv\'{a}nek-Lewis-Henstock $\m-$integrability for $\m-$measurable functions
that we introduced in \cite{ABH}. We give a $\m-$a.e. convergence version of
Dominated (resp. Bounded) Convergence Theorem for $\m.$ We introduce
Kluv\'{a}nek-Lewis-Henstock integrable of scalar-valued functions with respect
to a set valued measure in a Banach space. Finally we introduce $(KL)-$type
Dominated Convergence Theorem for the set-valued Kluv\'{a}nek-Lewis-Henstock
integral.


Abstract:
  As is known, the irreducible projective representations (Reps) of
anti-unitary groups contain three different situations, namely, the real, the
complex and quaternion types with torsion number 1,2,4 respectively. This
subtlety increases the complexity in obtaining irreducible projective Reps of
anti-unitary groups. In the present work, a physical approach is introduced to
derive the condition of irreducibility for projective Reps of anti-unitary
groups. Then a practical procedure is provided to reduce an arbitrary
projective Rep into direct sum of irreducible ones. The central idea is to
construct a hermitian Hamiltonian matrix which commutes with the representation
of every group element $g\in G$, such that each of its eigenspaces forms an
irreducible representation space of the group $G$. Thus the Rep is completely
reduced in the eigenspaces of the Hamiltonian. This approach is applied in the
$k\cdot p$ effective theory at the high symmetry points (HSPs) of the Brillouin
zone for quasi-particle excitations in magnetic materials. After giving the
criterion to judge the power of single-particle dispersion around a HSP, we
then provide a systematic procedure to construct the $k\cdot p$ effective
model.


Abstract:
  In this work, we utilize progressive growth-based Generative Adversarial
Networks (GANs) to develop the Clarkson Fingerprint Generator (CFG). We
demonstrate that the CFG is capable of generating realistic, high fidelity,
$512\times512$ pixels, full, plain impression fingerprints. Our results suggest
that the fingerprints generated by the CFG are unique, diverse, and resemble
the training dataset in terms of minutiae configuration and quality, while not
revealing the underlying identities of the training data. We make the
pre-trained CFG model and the synthetically generated dataset publicly
available at https://github.com/keivanB/Clarkson_Finger_Gen


Abstract:
  Gaussian Process Regression is a popular nonparametric regression method
based on Bayesian principles that provides uncertainty estimates for its
predictions. However, these estimates are of a Bayesian nature, whereas for
some important applications, like learning-based control with safety
guarantees, frequentist uncertainty bounds are required. Although such rigorous
bounds are available for Gaussian Processes, they are too conservative to be
useful in applications. This often leads practitioners to replacing these
bounds by heuristics, thus breaking all theoretical guarantees. To address this
problem, we introduce new uncertainty bounds that are rigorous, yet practically
useful at the same time. In particular, the bounds can be explicitly evaluated
and are much less conservative than state of the art results. Furthermore, we
show that certain model misspecifications lead to only graceful degradation. We
demonstrate these advantages and the usefulness of our results for
learning-based control with numerical examples.


Abstract:
  We established a method for obtaining set-theoretical solutions to the 3D
reflection equation by using known ones to the Zamolodchikov tetrahedron
equation, where the former equation was proposed by Isaev and Kulish as a
boundary analog of the latter. By applying our method to Sergeev's electrical
solution and a two-component solution associated with the discrete modified KP
equation, we obtain new solutions to the 3D reflection equation. Our approach
is closely related to a relation between the transition maps of Lusztig's
parametrizations of the totally positive part of $SL_3$ and $SO_5$, which is
obtained via folding the Dynkin diagram of $A_3$ into one of $B_2$.


Abstract:
  The Gaussian Process with a deep kernel is an extension of the classic GP
regression model and this extended model usually constructs a new kernel
function by deploying deep learning techniques like long short-term memory
networks. A Gaussian Process with the kernel learned by LSTM, abbreviated as
GP-LSTM, has the advantage of capturing the complex dependency of financial
sequential data, while retaining the ability of probabilistic inference.
However, the deep kernel Gaussian Process has not been applied to forecast the
conditional returns and volatility in financial market to the best of our
knowledge. In this paper, grid search algorithm, used for performing
hyper-parameter optimization, is integrated with GP-LSTM to predict both the
conditional mean and volatility of stock returns, which are then combined
together to calculate the conditional Sharpe Ratio for constructing a
long-short portfolio. The experiments are performed on a dataset covering all
constituents of Shenzhen Stock Exchange Component Index. Based on empirical
results, we find that the GP-LSTM model can provide more accurate forecasts in
stock returns and volatility, which are jointly evaluated by the performance of
constructed portfolios. Further sub-period analysis of the experiment results
indicates that the superiority of GP-LSTM model over the benchmark models stems
from better performance in highly volatile periods.