Abstract:
  We introduce a novel approach, the Cosmological Trajectories Method (CTM), to
model nonlinear structure formation in the Universe by expanding
gravitationally-induced particle trajectories around the Zel'dovich
approximation. A new Beyond Zel'dovich approximation is presented, which
expands the CTM to leading second-order in the gravitational interaction and
allows for post-Born gravitational scattering. In the Beyond Zel'dovich
approximation we derive the exact expression for the matter clustering power
spectrum. This is calculated to leading order and is available in the CTM
MODULE. We compare the Beyond Zel'dovich approximation power spectrum and
correlation function to other methods including 1-loop Standard Perturbation
Theory (SPT), 1-loop Lagrangian Perturbation Theory (LPT) and Convolution
Lagrangian Perturbation Theory (CLPT). We find that the Beyond Zel'dovich
approximation power spectrum performs well, matching simulations to within
$\pm{10}\%$, on mildly non-linear scales, and at redshifts above $z=1$ it
outperforms the Zel'dovich approximation. We also find that the Beyond
Zel'dovich approximation models the BAO peak in the correlation function at
$z=0$ more accurately, to within $\pm{5}\%$ of simulations, than the Zel'dovich
approximation, SPT 1-loop and CLPT.


Abstract:
  In this study we analyze linear combinatorial optimization problems where the
cost vector is not known a priori, but is only observable through a finite data
set. In contrast to the related studies, we presume that the number of
observations with respect to particular components of the cost vector may vary.
The goal is to find a procedure that transforms the data set into an estimate
of the expected value of the objective function (which is referred to as a
prediction rule) and a procedure that retrieves a candidate decision (which is
referred to as a prescription rule). We aim at finding the least conservative
prediction and prescription rules, which satisfy some specified asymptotic
guarantees. We demonstrate that the resulting vector optimization problems
admit a weakly optimal solution, which can be obtained by solving a particular
distributionally robust optimization problem. Specifically, the decision-maker
may optimize the worst-case expected loss across all probability distributions
with given component-wise relative entropy distances from the empirical
marginal distributions. Finally, we perform numerical experiments to analyze
the out-of-sample performance of the proposed solution approach.


Abstract:
  Word Sense Disambiguation (WSD) is a long-standing task in Natural Language
Processing(NLP) that aims to automatically identify the most relevant meaning
of the words in a given context. Developing standard WSD test collections can
be mentioned as an important prerequisite for developing and evaluating
different WSD systems in the language of interest. Although many WSD test
collections have been developed for a variety of languages, no standard
All-words WSD benchmark is available for Persian. In this paper, we address
this shortage for the Persian language by introducing SBU-WSD-Corpus, as the
first standard test set for the Persian All-words WSD task. SBU-WSD-Corpus is
manually annotated with senses from the Persian WordNet (FarsNet) sense
inventory. To this end, three annotators used SAMP (a tool for sense annotation
based on FarsNet lexical graph) to perform the annotation task. SBU-WSD-Corpus
consists of 19 Persian documents in different domains such as Sports, Science,
Arts, etc. It includes 5892 content words of Persian running text and 3371
manually sense annotated words (2073 nouns, 566 verbs, 610 adjectives, and 122
adverbs). Providing baselines for future studies on the Persian All-words WSD
task, we evaluate several WSD models on SBU-WSD-Corpus. The corpus is publicly
available at https://github.com/hrouhizadeh/SBU-WSD-Corpus.


Abstract:
  In this article, we calculate the density of primes $\mathfrak{p}$ for which
the $\mathfrak{p}$-th Fourier coefficient $C^*(\mathfrak{p}, f)$ (resp.,
$C(\mathfrak{p}, f)$) of a primitive Hilbert modular form $f$ generates the
coefficient field $F_f$ (resp., $E_f$), under certain conditions on the images
of $\lambda$-adic residual Galois representations attached to $f$. Then, we
produce some examples of primitive forms $f$ satisfying these conditions. Our
work is a generalization of \cite{KSW08} to primitive Hilbert modular forms.


Abstract:
  Adding propositional quantification to the modal logics K, T or S4 is known
to lead to undecidability but CTL with propositional quantification under the
tree semantics (tQCTL) admits a non-elementary Tower-complete satisfiability
problem. We investigate the complexity of strict fragments of tQCTL as well as
of the modal logic K with propositional quantification under the tree
semantics. More specifically, we show that tQCTL restricted to the temporal
operator EX is already Tower-hard, which is unexpected as EX can only enforce
local properties. When tQCTL restricted to EX is interpreted on N-bounded trees
for some N >= 2, we prove that the satisfiability problem is AExpPol-complete;
AExpPol-hardness is established by reduction from a recently introduced tiling
problem, instrumental for studying the model-checking problem for interval
temporal logics. As consequences of our proof method, we prove Tower-hardness
of tQCTL restricted to EF or to EXEF and of the well-known modal logics such as
K, KD, GL, K4 and S4 with propositional quantification under a semantics based
on classes of trees.


Abstract:
  We are interested in the nature of the spectrum of the one-dimensional
Schr\"odinger operator $$
  - \frac{d^2}{dx^2}-Fx + \sum_{n \in \mathbb{Z}}g_n \delta(x-n)
  \qquad\text{in } L^2(\mathbb{R}) $$ with $F>0$ and two different choices of
the coupling constants $\{g_n\}_{n\in \mathbb{Z}}$. In the first model $g_n
\equiv \lambda$ and we prove that if $F\in \pi^2 \mathbb{Q}$ then the spectrum
is $\mathbb{R}$ and is furthermore absolutely continuous away from an explicit
discrete set of points. In the second model $g_n$ are independent random
variables with mean zero and variance $\lambda^2$. Under certain assumptions on
the distribution of these random variables we prove that almost surely the
spectrum is $\mathbb{R}$ and it is dense pure point if $F < \lambda^2/2$ and
purely singular continuous if $F> \lambda^2/2$.


Abstract:
  We report the discovery of a 'folded' gravitationally lensed image,
'Hamilton's Object', found in a HST image of the field near the AGN SDSS
J223010.47-081017.8 ($z=0.62$). The lensed images are sourced by a galaxy at a
spectroscopic redshift of 0.8200$\pm0.0005$ and form a fold configuration on a
caustic caused by a foreground galaxy cluster at a photometric redshift of
0.526$\pm0.018$ seen in the corresponding Pan-STARRS PS1 image and marginally
detected as a faint ROSAT All-Sky Survey X-ray source. The lensed images
exhibit properties similar to those of other folds where the source galaxy
falls very close to or straddles the caustic of a galaxy cluster. The folded
images are stretched in a direction roughly orthogonal to the critical curve,
but the configuration is that of a tangential cusp. Guided by morphological
features, published simulations and similar fold observations in the
literature, we identify a third or counter-image, confirmed by spectroscopy.
Because the fold-configuration shows highly distinctive surface brightness
features, follow-up observations of microlensing or detailed investigations of
the individual surface brightness features at higher resolution can further
shed light on kpc-scale dark matter properties. We determine the local lens
properties at the positions of the multiple images according to the
observation-based lens reconstruction of Wagner et al. (2019). The analysis is
in accordance with a mass density which hardly varies on an arc-second scale (6
kpc) over the areas covered by the multiple images.


Abstract:
  Over-the-air computation (OAC) is a promising technique to realize fast model
aggregation in the uplink of federated edge learning. OAC, however, hinges on
accurate channel-gain precoding and strict synchronization among the edge
devices, which are challenging in practice. As such, how to design the maximum
likelihood (ML) estimator in the presence of residual channel-gain mismatch and
asynchronies is an open problem. To fill this gap, this paper formulates the
problem of misaligned OAC for federated edge learning and puts forth a whitened
matched filtering and sampling scheme to obtain oversampled, but independent,
samples from the misaligned and overlapped signals. Given the whitened samples,
a sum-product ML estimator and an aligned-sample estimator are devised to
estimate the arithmetic sum of the transmitted symbols. In particular, the
computational complexity of our sum-product ML estimator is linear in the
packet length and hence is significantly lower than the conventional ML
estimator. Extensive simulations on the test accuracy versus the average
received energy per symbol to noise power spectral density ratio (EsN0) yield
two main results: 1) In the low EsN0 regime, the aligned-sample estimator can
achieve superior test accuracy provided that the phase misalignment is
non-severe. In contrast, the ML estimator does not work well due to the error
propagation and noise enhancement in the estimation process. 2) In the high
EsN0 regime, the ML estimator attains the optimal learning performance
regardless of the severity of phase misalignment. On the other hand, the
aligned-sample estimator suffers from a test-accuracy loss caused by phase
misalignment.


Abstract:
  We investigate the properties of the glass phase of a recently introduced
spin glass model of soft spins subjected to an anharmonic quartic local
potential, which serves as a model of low temperature molecular or soft
glasses. We solve the model using mean field theory and show that, at low
temperatures, it is described by full replica symmetry breaking (fullRSB). As a
consequence, at zero temperature the glass phase is marginally stable. We show
that in this case, marginal stability comes from a combination of both soft
linear excitations -- appearing in a gapless spectrum of the Hessian of linear
excitations -- and pseudogapped non-linear excitations -- corresponding to
nearly degenerate two level systems. Therefore, this model is a natural
candidate to describe what happens in soft glasses, where quasi localized soft
modes in the density of states appear together with non-linear modes triggering
avalanches and conjectured to be essential to describe the universal
low-temperature anomalies of glasses.


Abstract:
  For a rooted cluster algebra $\mathcal{A}(Q)$ over a valued quiver $Q$, a
\emph{symmetric cluster variable} is any cluster variable that belongs to a
cluster associated with a quiver $\sigma (Q)$, for some permutation $\sigma$.
The subalgebra of $\mathcal{A}(Q)$ generated by all symmetric cluster variables
is called the \emph{symmetric mutation subalgebra} and is denoted by
$\mathcal{B}(Q)$. In this paper, we identify the class of cluster algebras that
satisfy $\mathcal{B}(Q)=\mathcal{A}(Q)$, which contains almost every quiver of
finite mutation type. In the process of proving the main theorem, we provide a
classification of quivers mutation classes based on their weights. Some
properties of symmetric mutation subalgebras are given.


Abstract:
  We investigate the State-Controlled Cellular Neural Network (SC-CNN)
framework of Murali-Lakshmanan-Chua (MLC) circuit system subjected to two
logical signals. By exploiting the attractors generated by this circuit in
different regions of phase-space, we show that the nonlinear circuit is capable
of producing all the logic gates, namely OR, AND, NOR, NAND, Ex-OR and Ex-NOR
gates available in digital systems. Further the circuit system emulates
three-input gates and Set-Reset flip-flop logic as well. Moreover, all these
logical elements and flip-flop are found to be tolerant to noise. These
phenomena are also experimentally demonstrated. Thus our investigation to
realize all logic gates and memory latch in a nonlinear circuit system paves
the way to replace or complement the existing technology with a limited number
of hardware.


Abstract:
  Convolutional neural networks (CNNs) are able to attain better visual
recognition performance than fully connected neural networks despite having
much less parameters due to their parameter sharing principle. Hence, modern
architectures are designed to contain a very small number of fully-connected
layers, often at the end, after multiple layers of convolutions. It is
interesting to observe that we can replace large fully-connected layers with
relatively small groups of tiny matrices applied on the entire image. Moreover,
although this strategy already reduces the number of parameters, most of the
convolutions can be eliminated as well, without suffering any loss in
recognition performance. However, there is no solid recipe to detect this
hidden subset of convolutional neurons that is responsible for the majority of
the recognition work. Hence, in this work, we use the matrix characteristics
based on eigenvalues in addition to the classical weight-based importance
assignment approach for pruning to shed light on the internal mechanisms of a
widely used family of CNNs, namely residual neural networks (ResNets), for the
image classification problem using CIFAR-10, CIFAR-100 and Tiny ImageNet
datasets.


Abstract:
  Access to informative databases is a crucial part of notable research
developments. In the field of domestic audio classification, there have been
significant advances in recent years. Although several audio databases exist,
these can be limited in terms of the amount of information they provide, such
as the exact location of the sound sources, and the associated noise levels. In
this work, we detail our approach on generating an unbiased synthetic domestic
audio database, consisting of sound scenes and events, emulated in both quiet
and noisy environments. Data is carefully curated such that it reflects issues
commonly faced in a dementia patients environment, and recreate scenarios that
could occur in real-world settings. Similarly, the room impulse response
generated is based on a typical one-bedroom apartment at Hebrew SeniorLife
Facility. As a result, we present an 11-class database containing excerpts of
clean and noisy signals at 5-seconds duration each, uniformly sampled at 16
kHz. Using our baseline model using Continues Wavelet Transform Scalograms and
AlexNet, this yielded a weighted F1-score of 86.24 percent.


Abstract:
  Deuterium diffusion is investigated in nitrogen-doped homoepitaxial ZnO
layers. The samples were grown under slightly Zn-rich growth conditions by
plasma-assisted molecular beam epitaxy on m-plane ZnO substrates and have a
nitrogen content [N] varied up to 5x1018 at.cm-3 as measured by secondary ion
mass spectrometry (SIMS). All were exposed to a radio-frequency deuterium
plasma during 1h at room temperature. Deuterium diffusion is observed in all
epilayers while its penetration depth decreases as the nitrogen concentration
increases. This is a strong evidence of a diffusion mechanism limited by the
trapping of deuterium on a nitrogen-related trap. The SIMS profiles are
analyzed using a two-trap model including a shallow trap, associated with a
fast diffusion, and a deep trap, related to nitrogen. The capture radius of the
nitrogen-related trap is determined to be 20 times smaller than the value
expected for nitrogen-deuterium pairs formed by coulombic attraction between D+
and nitrogen-related acceptors. The (N2)O deep donor is proposed as the deep
trapping site for deuterium and accounts well for the small capture radius and
the observed photoluminescence quenching and recovery after deuteration of the
ZnO:N epilayers. It is also found that this defect is by far the N-related
defect with the highest concentration in the studied samples.


Abstract:
  This paper considers a pursuit-evasion scenario among three agents -- an
evader, a pursuer, and a defender. We design cooperative guidance laws for the
evader and the defender team to safeguard the evader from an attacking pursuer.
Unlike differential games, optimal control formulations, and other heuristic
methods, we propose a novel perspective on designing effective nonlinear
feedback control laws for the evader-defender team using a time-constrained
guidance approach. The evader lures the pursuer on the collision course by
offering itself as bait. At the same time, the defender protects the evader
from the pursuer by exercising control over the engagement duration. Depending
on the nature of the mission, the defender may choose to take an aggressive or
defensive stance. Such consideration widens the applicability of the proposed
methods in various three-agent motion planning scenarios such as aircraft
defense, asset guarding, search and rescue, surveillance, and secure
transportation. We use a fixed-time sliding mode control strategy to design the
control laws for the evader-defender team and a nonlinear finite-time
disturbance observer to estimate the pursuer's maneuver. Finally, we present
simulations to demonstrate favorable performance under various engagement
geometries, thus vindicating the efficacy of the proposed designs.


Abstract:
  We present a class of diffraction-free partially coherent beams each member
of which is comprised of a finite power, non-accelerating Airy bump residing on
a statistically homogeneous, Gaussian-correlated background. We examine
free-space propagation of soft apertured realizations of the proposed beams and
show that their evolution is governed by two spatial scales: the coherence
width of the background and aperture size. A relative magnitude of these
factors determines the practical range of propagation distances over which the
novel beams can withstand diffraction. The proposed beams can find applications
to imaging and optical communications through random media.


Abstract:
  Unsupervised person re-identification (Re-ID) aims to match pedestrian images
from different camera views in unsupervised setting. Existing methods for
unsupervised person Re-ID are usually built upon the pseudo labels from
clustering. However, the quality of clustering depends heavily on the quality
of the learned features, which are overwhelmingly dominated by the colors in
images especially in the unsupervised setting. In this paper, we propose a
Cluster-guided Asymmetric Contrastive Learning (CACL) approach for unsupervised
person Re-ID, in which cluster structure is leveraged to guide the feature
learning in a properly designed asymmetric contrastive learning framework. To
be specific, we propose a novel cluster-level contrastive loss to help the
siamese network effectively mine the invariance in feature learning with
respect to the cluster structure within and between different data augmentation
views, respectively. Extensive experiments conducted on three benchmark
datasets demonstrate superior performance of our proposal.


Abstract:
  The power conserving interconnection of port-thermodynamic systems via their
power ports results in another port-thermodynamic system, while the same holds
for the rate of entropy increasing interconnection via their entropy flow
ports. Control by interconnection of port-thermodynamic systems seeks to
control a plant port-thermodynamic system by the interconnection with a
controller port-thermodynamic system. The stability of the interconnected
port-thermodynamic system is investigated by Lyapunov functions based on
generating functions for the submanifold characterizing the state properties as
well as additional conserved quantities. Crucial tool is the use of canonical
point transformations on the symplectized thermodynamic phase space.


Abstract:
  Graph neural networks (GNN) have been proven to be mature enough for handling
graph-structured data on node-level graph representation learning tasks.
However, the graph pooling technique for learning expressive graph-level
representation is critical yet still challenging. Existing pooling methods
either struggle to capture the local substructure or fail to effectively
utilize high-order dependency, thus diminishing the expression capability. In
this paper we propose HAP, a hierarchical graph-level representation learning
framework, which is adaptively sensitive to graph structures, i.e., HAP
clusters local substructures incorporating with high-order dependencies. HAP
utilizes a novel cross-level attention mechanism MOA to naturally focus more on
close neighborhood while effectively capture higher-order dependency that may
contain crucial information. It also learns a global graph content GCont that
extracts the graph pattern properties to make the pre- and post-coarsening
graph content maintain stable, thus providing global guidance in graph
coarsening. This novel innovation also facilitates generalization across graphs
with the same form of features. Extensive experiments on fourteen datasets show
that HAP significantly outperforms twelve popular graph pooling methods on
graph classification task with an maximum accuracy improvement of 22.79%, and
exceeds the performance of state-of-the-art graph matching and graph similarity
learning algorithms by over 3.5% and 16.7%.


Abstract:
  We demonstrate the potential of dopamine modified
0.5(Ba0.7Ca0.3)TiO3-0.5Ba(Zr0.2Ti0.8)O3 filler incorporated poly-vinylidene
fluoride (PVDF) composite prepared by solution cast method as both flexible
energy storage and harvesting devices. The introduction of dopamine in filler
surface functionalization acts as bridging elements between filler and polymer
matrix and results in a better filler dispersion and an improved dielectric
loss tangent (<0.02) along with dielectric permittivity ranges from 9 to 34
which is favorable for both energy harvesting and storage. Additionally, a
significantly low DC conductivity (< 10-9 ohm-1cm-1) for all composites was
achieved leading to an improved breakdown strength and charge accumulation
capability. Maximum breakdown strength of 134 KV/mm and corresponding energy
storage density 0.72 J/cm3 were obtained from the filler content 10 weight%.
The improved energy harvesting performance was characterized by obtaining a
maximum piezoelectric charge constant (d33) = 78 pC/N, and output voltage
(Vout) = 0.84 V along with maximum power density of 3.46 microW/cm3 for the
filler content of 10 wt%. Thus, the results show
0.5(Ba0.7Ca0.3)TiO3-0.5Ba(Zr0.2Ti0.8)O3/PVDF composite has the potential for
energy storage and harvesting applications simultaneously that can
significantly suppress the excess energy loss arises while utilizing different
material.


Abstract:
  The conformal module of conjugacy classes of braids is an invariant that
appeared earlier than the entropy of conjugacy classes of braids, and is
inverse proportional to the entropy. Using the relation between the two
invariants we give a short conceptional proof of an earlier result on the
conformal module. Mainly, we consider situations, when the conformal module of
conjugacy classes of braids serves as obstruction for the existence of
homotopies (or isotopies) of smooth objects involving braids to the respective
holomorphic objects, and present theorems on the restricted validity of
Gromov's Oka principle in these situations.


Abstract:
  We study the asymptotic properties of the stochastic Cahn-Hilliard equation
with the logarithmic free energy by establishing different dimension-free
Harnack inequalities according to various kinds of noises. The main
characteristics of this equation are the singularities of the logarithmic free
energy at 1 and --1 and the conservation of the mass of the solution in its
spatial variable. Both the space-time colored noise and the space-time white
noise are considered. For the highly degenerate space-time colored noise, the
asymptotic log-Harnack inequality is established under the so-called
essentially elliptic conditions. And the Harnack inequality with power is
established for non-degenerate space-time white noise.


Abstract:
  In this article, for positive integers $n\geq m\geq 1$, the parameter spaces
for the isomorphism classes of the generic point arrangements of cardinality
$n$, and the antipodal point arrangements of cardinality $2n$ in the Eulidean
space $\mathbb{R}^m$ are described using the space of totally nonzero
Grassmannian $Gr^{tnz}_{mn}(\mathbb{R})$. A stratification
$\mathcal{S}^{tnz}_{mn}(\mathbb{R})$ of the totally nonzero Grassmannian
$Gr^{tnz}_{mn}(\mathbb{R})$ is mentioned and the parameter spaces are
respectively expressed as quotients of the space
$\mathcal{S}^{tnz}_{mn}(\mathbb{R})$ of strata under suitable actions of the
symmetric group $S_n$ and the semidirect product group $(\mathbb{R}^*)^n\rtimes
S_n$. The cardinalities of the space $\mathcal{S}^{tnz}_{mn}(\mathbb{R})$ of
strata and of the parameter spaces $S_n\backslash
\mathcal{S}^{tnz}_{mn}(\mathbb{R}), ((\mathbb{R}^*)^n\rtimes S_n)\backslash
\mathcal{S}^{tnz}_{mn}(\mathbb{R})$ are enumerated in dimension $m=2$.
Interestingly enough, the enumerated value of the isomorphism classes of the
generic point arrangements in the Euclidean plane is expressed in terms of the
number theoretic Euler-totient function. The analogous enumeration questions
are still open in higher dimensions for $m\geq 3$.


Abstract:
  There is growing interest in hydrogen (H$_2$) use for long-duration energy
storage in a future electric grid dominated by variable renewable energy (VRE)
resources. Modelling the role of H$_2$ as grid-scale energy storage, often
referred as "power-to-gas-to-power (P2G2P)" overlooks the cost-sharing and
emission benefits from using the deployed H$_2$ production and storage assets
to also supply H$_2$ for decarbonizing other end-use sectors where direct
electrification may be challenged. Here, we develop a generalized modelling
framework for co-optimizing energy infrastructure investment and operation
across power and transportation sectors and the supply chains of electricity
and H$_2$, while accounting for spatio-temporal variations in energy demand and
supply. Applying this sector-coupling framework to the U.S. Northeast under a
range of technology cost and carbon price scenarios, we find a greater value of
power-to-H$_2$ (P2G) versus P2G2P routes. P2G provides flexible demand
response, while the extra cost and efficiency penalties of P2G2P routes make
the solution less attractive for grid balancing. The effects of sector-coupling
are significant, boosting VRE generation by 12-55% with both increased
capacities and reduced curtailments and reducing the total system cost (or
levelized costs of energy) by 6-14% under 96% decarbonization scenarios. Both
the cost savings and emission reductions from sector coupling increase with
H$_2$ demand for other end-uses, more than doubling for a 96% decarbonization
scenario as H$_2$ demand quadraples. Moreover, we found that the deployment of
carbon capture and storage is more cost-effective in the H$_2$ sector because
of the lower cost and higher utilization rate. These findings highlight the
importance of using an integrated multi-sector energy system framework with
multiple energy vectors in planning energy system decarbonization pathways.


Abstract:
  Massive multiple-input multiple-output (MIMO) is a key technology for
improving the spectral and energy efficiency in 5G-and-beyond wireless
networks. For a tractable analysis, most of the previous works on Massive MIMO
have been focused on the system performance with complex Gaussian channel
impulse responses under rich-scattering environments. In contrast, this paper
investigates the uplink ergodic spectral efficiency (SE) of each user under the
double scattering channel model. We derive a closed-form expression of the
uplink ergodic SE by exploiting the maximum ratio (MR) combining technique
based on imperfect channel state information. We further study the asymptotic
SE behaviors as a function of the number of antennas at each base station (BS)
and the number of scatterers available at each radio channel. We then formulate
and solve a total energy optimization problem for the uplink data transmission
that aims at simultaneously satisfying the required SEs from all the users with
limited data power resource. Notably, our proposed algorithms can cope with the
congestion issue appearing when at least one user is served by lower SE than
requested. Numerical results illustrate the effectiveness of the closed-form
ergodic SE over Monte-Carlo simulations. Besides, the system can still provide
the required SEs to many users even under congestion.


Abstract:
  High-order implicit shock tracking is a new class of numerical methods to
approximate solutions of conservation laws with non-smooth features. These
methods align elements of the computational mesh with non-smooth features to
represent them perfectly, allowing high-order basis functions to approximate
smooth regions of the solution without the need for nonlinear stabilization,
which leads to accurate approximations on traditionally coarse meshes. The
hallmark of these methods is the underlying optimization formulation whose
solution is a feature-aligned mesh and the corresponding high-order
approximation to the flow; the key challenge is robustly solving the central
optimization problem. In this work, we develop a robust optimization solver for
high-order implicit shock tracking methods so they can be reliably used to
simulate complex, high-speed, compressible flows in multiple dimensions. The
proposed method integrates practical robustness measures into a sequential
quadratic programming method, including dimension- and order-independent
simplex element collapses, mesh smoothing, and element-wise solution
re-initialization, which prove to be necessary to reliably track complex
discontinuity surfaces, such as curved and reflecting shocks, shock formation,
and shock-shock interaction. A series of nine numerical experiments --
including two- and three-dimensional compressible flows with complex
discontinuity surfaces -- are used to demonstrate: 1) the robustness of the
solver, 2) the meshes produced are high-quality and track continuous,
non-smooth features in addition to discontinuities, 3) the method achieves the
optimal convergence rate of the underlying discretization even for flows
containing discontinuities, and 4) the method produces highly accurate
solutions on extremely coarse meshes relative to approaches based on shock
capturing.


Abstract:
  Ben Reichardt showed in a series of results that the general adversary bound
of a function characterizes its quantum query complexity. This survey seeks to
aggregate the background and definitions necessary to understand the proof.
Notable among these are the lower bound proof, span programs, witness size, and
semi-definite programs. These definitions, in addition to examples and detailed
expositions, serve to give the reader a better intuition of the graph-theoretic
nature of the upper bound. We also include an applications of this result to
lower bounds on DeMorgan formula size.


Abstract:
  In video object tracking, there exist rich temporal contexts among successive
frames, which have been largely overlooked in existing trackers. In this work,
we bridge the individual video frames and explore the temporal contexts across
them via a transformer architecture for robust object tracking. Different from
classic usage of the transformer in natural language processing tasks, we
separate its encoder and decoder into two parallel branches and carefully
design them within the Siamese-like tracking pipelines. The transformer encoder
promotes the target templates via attention-based feature reinforcement, which
benefits the high-quality tracking model generation. The transformer decoder
propagates the tracking cues from previous templates to the current frame,
which facilitates the object searching process. Our transformer-assisted
tracking framework is neat and trained in an end-to-end manner. With the
proposed transformer, a simple Siamese matching approach is able to outperform
the current top-performing trackers. By combining our transformer with the
recent discriminative tracking pipeline, our method sets several new
state-of-the-art records on prevalent tracking benchmarks.


Abstract:
  Thoracic disease detection from chest radiographs using deep learning methods
has been an active area of research in the last decade. Most previous methods
attempt to focus on the diseased organs of the image by identifying spatial
regions responsible for significant contributions to the model's prediction. In
contrast, expert radiologists first locate the prominent anatomical structures
before determining if those regions are anomalous. Therefore, integrating
anatomical knowledge within deep learning models could bring substantial
improvement in automatic disease classification. This work proposes an
anatomy-aware attention-based architecture named Anatomy X-Net, that
prioritizes the spatial features guided by the pre-identified anatomy regions.
We leverage a semi-supervised learning method using the JSRT dataset containing
organ-level annotation to obtain the anatomical segmentation masks (for lungs
and heart) for the NIH and CheXpert datasets. The proposed Anatomy X-Net uses
the pre-trained DenseNet-121 as the backbone network with two corresponding
structured modules, the Anatomy Aware Attention (AAA) and Probabilistic
Weighted Average Pooling (PWAP), in a cohesive framework for anatomical
attention learning. Our proposed method sets new state-of-the-art performance
on the official NIH test set with an AUC score of 0.8439, proving the efficacy
of utilizing the anatomy segmentation knowledge to improve the thoracic disease
classification. Furthermore, the Anatomy X-Net yields an averaged AUC of 0.9020
on the Stanford CheXpert dataset, improving on existing methods that
demonstrate the generalizability of the proposed framework.


Abstract:
  Neural architecture search (NAS) is a recent methodology for automating the
design of neural network architectures. Differentiable neural architecture
search (DARTS) is a promising NAS approach that dramatically increases search
efficiency. However, it has been shown to suffer from performance collapse,
where the search often leads to detrimental architectures. Many recent works
try to address this issue of DARTS by identifying indicators for early
stopping, regularising the search objective to reduce the dominance of some
operations, or changing the parameterisation of the search problem. In this
work, we hypothesise that performance collapses can arise from poor local
optima around typical initial architectures and weights. We address this issue
by developing a more global optimisation scheme that is able to better explore
the space without changing the DARTS problem formulation. Our experiments show
that our changes in the search algorithm allow the discovery of architectures
with both better test performance and fewer parameters.


Abstract:
  [Zhang, ICML 2018] provided the first decentralized actor-critic algorithm
for multi-agent reinforcement learning (MARL) that offers convergence
guarantees. In that work, policies are stochastic and are defined on finite
action spaces. We extend those results to offer a provably-convergent
decentralized actor-critic algorithm for learning deterministic policies on
continuous action spaces. Deterministic policies are important in real-world
settings. To handle the lack of exploration inherent in deterministic policies,
we consider both off-policy and on-policy settings. We provide the expression
of a local deterministic policy gradient, decentralized deterministic
actor-critic algorithms and convergence guarantees for linearly-approximated
value functions. This work will help enable decentralized MARL in
high-dimensional action spaces and pave the way for more widespread use of
MARL.


Abstract:
  The Force Concept Inventory (FCI) can be used as an assessment tool to
measure the gains in a cohort of students. In this study it was given to first
year mechanics students (N=256 students) pre- and post-mechanics lectures, for
students at the University of Johannesburg. From these results we examine the
effect of switching mid-semester from traditional classes to online classes, as
imposed by the COVID-19 lockdown in South Africa. Overall gains and student
perspectives indicate no appreciable difference of gain, when bench-marked
against previous studies using this assessment tool. When compared with 2019
grades, the 2020 semester grades do not appear to be greatly affected.
Furthermore, initial statistical analyses also indicate a gender difference in
mean gains in favour of females at the 95% significance level (for paired data,
N=48). A survey given to students also appeared to indicate that most students
were aware of their conceptual performance in physics, and the main constraint
to their studies was due to difficulties associated with being online. As such,
the change in pedagogy and the stresses of lockdown were found to not be
suggestive of a depreciation of FCI gains and grades.


Abstract:
  Neural Machine Translation model is a sequence-to-sequence converter based on
neural networks. Existing models use recurrent neural networks to construct
both the encoder and decoder modules. In alternative research, the recurrent
networks were substituted by convolutional neural networks for capturing the
syntactic structure in the input sentence and decreasing the processing time.
We incorporate the goodness of both approaches by proposing a
convolutional-recurrent encoder for capturing the context information as well
as the sequential information from the source sentence. Word embedding and
position embedding of the source sentence is performed prior to the
convolutional encoding layer which is basically a n-gram feature extractor
capturing phrase-level context information. The rectified output of the
convolutional encoding layer is added to the original embedding vector, and the
sum is normalized by layer normalization. The normalized output is given as a
sequential input to the recurrent encoding layer that captures the temporal
information in the sequence. For the decoder, we use the attention-based
recurrent neural network. Translation task on the German-English dataset
verifies the efficacy of the proposed approach from the higher BLEU scores
achieved as compared to the state of the art.


Abstract:
  A sample of 1.3 mm continuum cores in the Dragon infrared dark cloud (also
known as G28.37+0.07 or G28.34+0.06) is analyzed statistically. Based on their
association with molecular outflows, the sample is divided into protostellar
and starless cores. Statistical tests suggest that the protostellar cores are
more massive than the starless cores, even after temperature and opacity biases
are accounted for. We suggest that the mass difference indicates core mass
growth since their formation. The mass growth implies that massive star
formation may not have to start with massive prestellar cores, depending on the
core mass growth rate. Its impact on the relation between core mass function
and stellar initial mass function is to be further explored.


Abstract:
  In this paper, we investigate the algebraic nature of the value of a higher
Green function on an orthogonal Shimura variety at a single CM point. This is
motivated by a conjecture of Gross and Zagier in the setting of higher Green
functions on the product of two modular curves. In the process, we will study
analogue of harmonic Maass forms in the setting of Hilbert modular forms, and
obtain results concerning the arithmetic of their holomorphic part Fourier
coefficients. As a consequence, we confirm the conjecture of Gross and Zagier
under mild condition on the discriminant of the CM point.


Abstract:
  There is a strong consensus that combining the versatility of machine
learning with the assurances given by formal verification is highly desirable.
It is much less clear what verified machine learning should mean exactly. We
consider this question from the (unexpected?) perspective of computable
analysis. This allows us to define the computational tasks underlying verified
ML in a model-agnostic way, and show that they are in principle computable.


Abstract:
  Value functions are central to Dynamic Programming and Reinforcement Learning
but their exact estimation suffers from the curse of dimensionality,
challenging the development of practical value-function (VF) estimation
algorithms. Several approaches have been proposed to overcome this issue, from
non-parametric schemes that aggregate states or actions to parametric
approximations of state and action VFs via, e.g., linear estimators or deep
neural networks. Relevantly, several high-dimensional state problems can be
well-approximated by an intrinsic low-rank structure. Motivated by this and
leveraging results from low-rank optimization, this paper proposes different
stochastic algorithms to estimate a low-rank factorization of the $Q(s, a)$
matrix. This is a non-parametric alternative to VF approximation that
dramatically reduces the computational and sample complexities relative to
classical $Q$-learning methods that estimate $Q(s,a)$ separately for each
state-action pair.


Abstract:
  Low-noise frequency conversion of single photons is a critical tool in
establishing fibre-based quantum networks. We show that a single photonic
crystal fibre can achieve frequency conversion by Bragg-scattering four-wave
mixing of source photons from an ultra-broad wavelength range by engineering a
symmetric group velocity profile. Furthermore, we discuss how pump tuning can
mitigate realistic discrepancies in device fabrication. This enables a single
highly adaptable frequency conversion interface to link disparate nodes in a
quantum network via the telecoms band.


Abstract:
  In this paper, we establish the existence and uniqueness of Ricci flow that
admits an embedded closed convex surface in $\mathbb{R}^3$ as metric initial
condition. The main point is a family of smooth Ricci flows starting from
smooth convex surfaces whose metrics converge uniformly to the metric of the
initial surface in intrinsic sense.


Abstract:
  We derive the stellar-to-halo mass relation (SHMR), namely $f_\star\propto
M_\star/M_{\rm h}$ versus $M_\star$ and $M_{\rm h}$, for early-type galaxies
from their near-IR luminosities (for $M_\star$) and the position-velocity
distributions of their globular cluster systems (for $M_{\rm h}$). Our
individual estimates of $M_{\rm h}$ are based on fitting a dynamical model with
a distribution function expressed in terms of action-angle variables and
imposing a prior on $M_{\rm h}$ from the concentration-mass relation in the
standard $\Lambda$CDM cosmology. We find that the SHMR for early-type galaxies
declines with mass beyond a peak at $M_\star\sim 5\times 10^{10}M_\odot$ and
$M_{\rm h}\sim 10^{12}M_\odot$ (near the mass of the Milky Way). This result is
consistent with the standard SHMR derived by abundance matching for the general
population of galaxies, and with previous, less robust derivations of the SHMR
for early types. However, it contrasts sharply with the monotonically rising
SHMR for late types derived from extended HI rotation curves and the same
$\Lambda$CDM prior on $M_{\rm h}$ as we adopt for early types. The SHMR for
massive galaxies varies more or less continuously, from rising to falling, with
decreasing disc fraction and decreasing Hubble type. We also show that the
different SHMRs for late and early types are consistent with the similar
scaling relations between their stellar velocities and masses (Tully-Fisher and
Faber-Jackson relations). Differences in the relations between the stellar and
halo virial velocities account for the similarity of the scaling relations. We
argue that all these empirical findings are natural consequences of a picture
in which galactic discs are built mainly by smooth and gradual inflow,
regulated by feedback from young stars, while galactic spheroids are built by a
cooperation between merging, black-hole fuelling, and feedback from AGNs.


Abstract:
  Recently, the experimental discovery of high-$T_c$ superconductivity in
compressed hydrides H$_3$S and LaH$_{10}$ at megabar pressures has triggered
searches for various superconducting superhydrides. It was experimentally
observed that thorium hydrides, ThH$_{10}$ and ThH$_9$, are stabilized at much
lower pressures compared to LaH$_{10}$. Based on first-principles
density-functional theory calculations, we reveal that the isolated Th
frameworks of ThH$_{10}$ and ThH$_9$ have relatively more excess electrons in
interstitial regions than the La framework of LaH$_{10}$. Such interstitial
excess electrons easily participate in the formation of anionic H cage
surrounding metal atom. The resulting Coulomb attraction between cationic Th
atoms and anionic H cages is estimated to be stronger than the corresponding
one of LaH$_{10}$, thereby giving rise to larger chemical precompressions in
ThH$_{10}$ and ThH$_9$. Such a formation mechanism of H clathrates can also be
applied to another experimentally synthesized superhydride CeH$_9$, confirming
the experimental evidence that the chemical precompression in CeH$_9$ is larger
than that in LaH$_{10}$. Our findings demonstrate that interstitial excess
electrons in the isolated metal frameworks of high-pressure superhydrides play
an important role in generating the chemical precompression of H clathrates.


Abstract:
  The fluid flow along the Riga plate with the influence of magnetic force in a
rotating system has been investigated numerically. The governing equations have
been derived from Navier-Stokes equations. Applying the boundary layer
approximation, the appropriate boundary layer equations have been obtained. By
using usual transformation, the obtained governing equations have been
transformed into a coupled dimensionless non-linear partial differential
equation. The obtained dimensionless equations have been solved numerically by
explicit finite difference scheme. The simulated results have been obtained by
using MATLAB R2015a. Also the stability and convergence criteria have been
analyzed. The effect of several parameters on the primary velocity, secondary
velocity, temperature distributions as well as local shear stress and Nusselt
number have been shown graphically.


Abstract:
  Mobile app developers use paid advertising campaigns to acquire new users,
and they need to know the campaigns' performance to guide their spending.
Determining the campaign that led to an install requires that the app and
advertising network share an identifier that allows matching ad clicks to
installs. Ad networks use the identifier to build user profiles that help with
targeting and personalization. Modern mobile operating systems have features to
protect the privacy of the user. The privacy features of Apple's iOS 14
enforces all apps to get system permission for tracking explicitly instead of
asking the user to opt-out of tracking as before. If the user does not allow
tracking, the identifier for advertisers (IDFA) required for attributing the
installation to the campaign is not shared. The lack of an identifier for the
attribution changes profoundly how user acquisition campaigns' performance is
measured. For users who do not allow tracking, there is a new feature that
still allows following campaign performance. The app can set an integer, so
called conversion value for each user, and the developer can get the number of
installs per conversion value for each campaign. This paper investigates the
task of distributing revenue to advertising campaigns using the conversion
values. Our contributions are to formalize the problem, find the theoretically
optimal revenue attribution function for any conversion value schema, and show
empirical results on past data of a free-to-play mobile game using different
conversion value schemas.


Abstract:
  We show that for Lebesgue almost all $d$-tuples $(\theta_1,\ldots,\theta_d)$,
with $|\theta_j|>1$, any self-affine measure for a homogeneous non-degenerate
iterated function system $\{Ax+a_j\}_{j=1}^m$ in ${\mathbb R}^d$, where
$A^{-1}$ is a diagonal matrix with the entries $(\theta_1,\ldots,\theta_d)$,
has power Fourier decay at infinity.


Abstract:
  We present ALMA [C II] 158 $\mu$m line and far-infrared (FIR) continuum
emission observations toward HSC J120505.09$-$000027.9 (J1205$-$0000) at $z =
6.72$ with the beam size of $\sim 0''.8 \times 0''.5$ (or 4.1 kpc $\times$ 2.6
kpc), the most distant red quasar known to date. Red quasars are modestly
reddened by dust, and are thought to be in rapid transition from an obscured
starburst to an unobscured normal quasar, driven by powerful active galactic
nucleus (AGN) feedback which blows out a cocoon of interstellar medium (ISM).
The FIR continuum of J1205$-$0000 is bright, with an estimated luminosity of
$L_{\rm FIR} \sim 3 \times 10^{12}~L_\odot$. The [C II] line emission is
extended on scales of $r \sim 5$ kpc, greater than the FIR continuum. The line
profiles at the extended regions are complex and broad (FWHM $\sim 630-780$ km
s$^{-1}$). Although it is not practical to identify the nature of this extended
structure, possible explanations include (i) companion/merging galaxies and
(ii) massive AGN-driven outflows. For the case of (i), the companions are
modestly star-forming ($\sim 10~M_\odot$ yr$^{-1}$), but are not detected by
our Subaru optical observations ($y_{\rm AB,5\sigma} = 24.4$ mag). For the case
of (ii), our lower-limit to the cold neutral outflow rate is $\sim 100~M_\odot$
yr$^{-1}$. The outflow kinetic energy and momentum are both much smaller than
what predicted in energy-conserving wind models, suggesting that the AGN
feedback in this quasar is not capable of completely suppressing its star
formation.


Abstract:
  Baryon production is studied within the framework of quantized fragmentation
of QCD string. Baryons appear in the model in a fairly intuitive way, with help
of causally connected string breakups. A simple helical approximation of QCD
flux tube with parameters constrained by mass spectrum of light mesons is
sufficient to reproduce masses of light baryons.


Abstract:
  The minimal flavor structures for both quarks and leptons are proposed to
address fermion mass hierarchy and flavor mixings by bi-unitary decomposition
of the fermion mass matrix. The real matrix ${\bf M}_0^f$ is completely
responsive to family mass hierarchy, which is expressed by a close-to-flat
matrix structure. The left-handed unitary phase ${\bf F}_L^f$ provides the
origin of CP violation in quark and lepton mixings, which can be explained as a
quantum effect between Yukawa interaction states and weak gauge states. The
minimal flavor structure is realized by just 10 parameters without any
redundancy, corresponding to 6 fermion masses, 3 mixing angles and 1 CP
violation in the quark/lepton sector. This approach provides a general flavor
structure independent of the specific quark or lepton flavor data. We verify
the validation of the flavor structure by reproducing quark/lepton masses and
mixings. Some possible scenarios that yield the flavor structure are also
discussed.


Abstract:
  Our goal is to develop a flux limiter of the Flux-Corrected Transport method
for a nonconservative convection-diffusion equation. For this, we consider a
hybrid difference scheme that is a linear combination of a monotone scheme and
a scheme of high-order accuracy. The flux limiter is computed as an approximate
solution of a corresponding optimization problem with a linear objective
function. The constraints for this optimization problem are derived from
inequalities that are valid for the monotone scheme and apply to the hybrid
scheme. Our numerical results with the flux limiters, which are exact and
approximate solutions to the optimization problem, are in good agreement.


Abstract:
  A scalable system for real-time analysis of electron temperature and density
based on signals from the Thomson scattering diagnostic, initially developed
for and installed on the NSTX-U experiment, was recently adapted for the Large
Helical Device (LHD) and operated for the first time during plasma discharges.
During its initial operation run, it routinely recorded and processed signals
for four spatial points at the laser repetition rate of 30 Hz, well within the
system's rated capability for 60 Hz. We present examples of data collected from
this initial run and describe subsequent adaptations to the analysis code to
improve the fidelity of the temperature calculations.


Abstract:
  The DARWIN observatory is a proposed next-generation experiment to search for
particle dark matter and other rare interactions. It will operate a 50 t liquid
xenon detector, with 40 t in the time projection chamber (TPC). To inform the
final detector design and technical choices, a series of technological
questions must first be addressed. Here we describe a full-scale demonstrator
in the vertical dimension, Xenoscope, with the main goal of achieving electron
drift over a 2.6 m distance, which is the scale of the DARWIN TPC. We have
designed and constructed the facility infrastructure, including the cryostat,
cryogenic and purification systems, the xenon storage and recuperation system,
as well as the slow control system. We have also designed a xenon purity
monitor and the TPC, with the fabrication of the former nearly complete. In a
first commissioning run of the facility without an inner detector, we
demonstrated the nominal operational reach of Xenoscope and benchmarked the
components of the cryogenic and slow control systems, demonstrating reliable
and continuous operation of all subsystems over 40 days. The infrastructure is
thus ready for the integration of the purity monitor, followed by the TPC.
Further applications of the facility include R&D on the high voltage
feedthrough for DARWIN, measurements of electron cloud diffusion, as well as
measurements of optical properties of liquid xenon. In the future, Xenoscope
will be available as a test platform for the DARWIN collaboration to
characterise new detector technologies.


Abstract:
  Due to their broad application to different fields of theory and practice,
generalized Petersen graphs $GPG(n,s)$ have been extensively investigated.
Despite the regularity of generalized Petersen graphs, determining an exact
formula for the diameter is still a difficult problem. In their paper, Beenker
and Van Lint have proved that if the circulant graph $C_n(1,s)$ has diameter
$d$, then $GPG(n,s)$ has diameter at least $d+1$ and at most $d+2$. In this
paper, we provide necessary and sufficient conditions so that the diameter of
$GPG(n,s)$ is equal to $d+1,$ and sufficient conditions so that the diameter of
$GPG(n,s)$ is equal to $d+2.$ Afterwards, we give exact values for the diameter
of $GPG(n,s)$ for almost all cases of $n$ and $s.$ Furthermore, we show that
there exists an algorithm computing the diameter of generalized Petersen graphs
with running time $O$(log$n$).


Abstract:
  In many astrophysical applications, the cost of solving a chemical network
represented by a system of ordinary differential equations (ODEs) grows
significantly with the size of the network, and can often represent a
significant computational bottleneck, particularly in coupled chemo-dynamical
models. Although standard numerical techniques and complex solutions tailored
to thermochemistry can somewhat reduce the cost, more recently, machine
learning algorithms have begun to attack this challenge via data-driven
dimensional reduction techniques. In this work, we present a new class of
methods that take advantage of machine learning techniques to reduce complex
data sets (autoencoders), the optimization of multi-parameter systems (standard
backpropagation), and the robustness of well-established ODE solvers to to
explicitly incorporate time-dependence. This new method allows us to find a
compressed and simplified version of a large chemical network in a
semi-automated fashion that can be solved with a standard ODE solver, while
also enabling interpretability of the compressed, latent network. As a proof of
concept, we tested the method on an astrophysically-relevant chemical network
with 29 species and 224 reactions, obtaining a reduced but representative
network with only 5 species and 12 reactions, and a x65 speed-up.


Abstract:
  Short Read Alignment Mapping Metrics (SRAMM): is an efficient and versatile
command line tool providing additional short read mapping metrics, filtering,
and graphs. Short read aligners report MAPing Quality (MAPQ), but these methods
generally are neither standardized nor well described in literature or software
manuals. Additionally, third party mapping quality programs are typically
computationally intensive or designed for specific applications. SRAMM
efficiently generates multiple different concept-based mapping scores to
provide for an informative post alignment examination and filtering process of
aligned short reads for various downstream applications. SRAMM is compatible
with Python 2.6+ and Python 3.6+ on all operating systems. It works with any
short read aligner that generates SAM/BAM/CRAM file outputs and reports 'AS'
tags. It is freely available under the MIT license at
http://github.com/achon/sramm.


Abstract:
  We aim to give more insights on adiabatic evolution concerning the occurrence
of anti-crossings and their link to the spectral minimum gap $\Delta_{min}$. We
study in detail adiabatic quantum computation applied to a specific
combinatorial problem called weighted max $k$-clique. A clear intuition of the
parametrization introduced by V. Choi is given which explains why the
characterization isn't general enough. We show that the instantaneous vectors
involved in the anti-crossing vary brutally through it making the instantaneous
ground-state hard to follow during the evolution. This result leads to a
relaxation of the parametrization to be more general.


Abstract:
  A q-Levenberg-Marquardt method is an iterative procedure that blends a
q-steepest descent and q-Gauss-Newton methods. When the current solution is far
from the correct one the algorithm acts as the q-steepest descent method.
Otherwise the algorithm acts as the q-Gauss-Newton method. A damping parameter
is used to interpolate between these two methods. The q-parameter is used to
escape from local minima and to speed up the search process near the optimal
solution.


Abstract:
  For a complete graph $K_n$ of order $n$, an edge-labeling $c:E(K_n)\to \{
-1,1\}$ satisfying $c(E(K_n))=0$, and a spanning forest $F$ of $K_n$, we
consider the problem to minimize $|c(E(F'))|$ over all isomorphic copies $F'$
of $F$ in $K_n$. In particular, we ask under which additional conditions there
is a zero-sum copy, that is, a copy $F'$ of $F$ with $c(E(F'))=0$.
  We show that there is always a copy $F'$ of $F$ with $|c(E(F'))|\leq
\Delta(F)+1$, where $\Delta(F)$ is the maximum degree of $F$. We conjecture
that this bound can be improved to $|c(E(F'))|\leq (\Delta(F)-1)/2$ and verify
this for $F$ being the star $K_{1,n-1}$. Under some simple necessary
divisibility conditions, we show the existence of a zero-sum $P_3$-factor, and,
for sufficiently large $n$, also of a zero-sum $P_4$-factor.


Abstract:
  Deepfakes raised serious concerns on the authenticity of visual contents.
Prior works revealed the possibility to disrupt deepfakes by adding adversarial
perturbations to the source data, but we argue that the threat has not been
eliminated yet. This paper presents MagDR, a mask-guided detection and
reconstruction pipeline for defending deepfakes from adversarial attacks. MagDR
starts with a detection module that defines a few criteria to judge the
abnormality of the output of deepfakes, and then uses it to guide a learnable
reconstruction procedure. Adaptive masks are extracted to capture the change in
local facial regions. In experiments, MagDR defends three main tasks of
deepfakes, and the learned reconstruction pipeline transfers across input data,
showing promising performance in defending both black-box and white-box
attacks.


Abstract:
  We propose a variational autoencoder architecture to model both ignorable and
nonignorable missing data using pattern-set mixtures as proposed by Little
(1993). Our model explicitly learns to cluster the missing data into
missingness pattern sets based on the observed data and missingness masks.
Underpinning our approach is the assumption that the data distribution under
missingness is probabilistically semi-supervised by samples from the observed
data distribution. Our setup trades off the characteristics of ignorable and
nonignorable missingness and can thus be applied to data of both types. We
evaluate our method on a wide range of data sets with different types of
missingness and achieve state-of-the-art imputation performance. Our model
outperforms many common imputation algorithms, especially when the amount of
missing data is high and the missingness mechanism is nonignorable.


Abstract:
  In this paper, we study linear filters to process signals defined on
simplicial complexes, i.e., signals defined on nodes, edges, triangles, etc. of
a simplicial complex, thereby generalizing filtering operations for graph
signals. We propose a finite impulse response filter based on the Hodge
Laplacian, and demonstrate how this filter can be designed to amplify or
attenuate certain spectral components of simplicial signals. Specifically, we
discuss how, unlike in the case of node signals, the Fourier transform in the
context of edge signals can be understood in terms of two orthogonal subspaces
corresponding to the gradient-flow signals and curl-flow signals arising from
the Hodge decomposition. By assigning different filter coefficients to the
associated terms of the Hodge Laplacian, we develop a subspace-varying filter
which enables more nuanced control over these signal types. Numerical
experiments are conducted to show the potential of simplicial filters for
sub-component extraction, denoising and model approximation.


Abstract:
  In this paper we address the explainability of web search engines. We propose
two explainable elements on the search engine result page: a visualization of
query term weights and a visualization of passage relevance. The idea is that
search engines that indicate to the user why results are retrieved are valued
higher by users and gain user trust. We deduce the query term weights from the
term gating network in the Deep Relevance Matching Model (DRMM) and visualize
them as a doughnut chart. In addition, we train a passage-level ranker with
DRMM that selects the most relevant passage from each document and shows it as
snippet on the result page. Next to the snippet we show a document thumbnail
with this passage highlighted. We evaluate the proposed interface in an online
user study, asking users to judge the explainability and assessability of the
interface. We found that users judge our proposed interface significantly more
explainable and easier to assess than a regular search engine result page.
However, they are not significantly better in selecting the relevant documents
from the top-5. This indicates that the explainability of the search engine
result page leads to a better user experience. Thus, we conclude that the
proposed explainable elements are promising as visualization for search engine
users.


Abstract:
  Simulating time evolution of quantum systems is one of the most promising
applications of quantum computing and also appears as a subroutine in many
applications such as Green's function methods. In the current era of NISQ
machines we assess the state of algorithms for simulating time dynamics with
limited resources. We propose the Jaynes-Cummings model and extensions to it as
useful toy models to investigate time evolution algorithms on near-term quantum
computers. Using these simple models, direct Trotterisation of the time
evolution operator produces deep circuits, requiring coherence times out of
reach on current NISQ hardware. Therefore we test two alternative responses to
this problem: variational compilation of the time evolution operator, and
variational quantum simulation of the wavefunction ansatz. We demonstrate
numerically to what extent these methods are successful in time evolving this
system. The costs in terms of circuit depth and number of measurements are
compared quantitatively, along with other drawbacks and advantages of each
method. We find that computational requirements for both methods make them
suitable for performing time evolution simulations of our models on NISQ
hardware. Our results also indicate that variational quantum compilation
produces more accurate results than variational quantum simulation, at the cost
of a larger number of measurements.


Abstract:
  In this paper we give a systematic review of the theory of Gibbs measures of
Potts model on Cayley trees (developed since 2013) and discuss many
applications of the Potts model to real world situations: mainly biology,
physics, and some examples of alloy behavior, cell sorting, financial
engineering, flocking birds, flowing foams, image segmentation, medicine,
sociology etc.


Abstract:
  We introduce a new class of commutative noetherian DG-rings which generalizes
the class of regular local rings. These are defined to be local DG-rings
$(A,\bar{\mathfrak{m}})$ such that the maximal ideal $\bar{\mathfrak{m}}
\subseteq \mathrm{H}^0(A)$ can be generated by an $A$-regular sequence. We call
these DG-rings sequence-regular DG-rings, and make a detailed study of them.
Using methods of Cohen-Macaulay differential graded algebra, we prove that the
Auslander-Buchsbaum-Serre theorem about localization generalizes to this
setting. This allows us to define global sequence-regular DG-rings, and to
introduce this regularity condition to derived algebraic geometry. It is shown
that these DG-rings share many properties of classical regular local rings, and
in particular we are able to construct canonical residue DG-fields in this
context. Finally, we show that sequence-regular DG-rings are ubiquitous, and in
particular, any eventually coconnective derived algebraic variety over a
perfect field is generically sequence-regular.


Abstract:
  Tissues are characterized by layers of functional units such as cells and
extracellular matrix (ECM). Nevertheless, how dynamics at interlayer interfaces
help transmit cellular forces in tissues remains overlooked. Here, we
investigate a multi-layer system where a layer of epithelial cells is seeded
upon an elastic substrate in contact with a hard surface. Our experiments show
that, upon a cell extrusion event in the cellular layer, long-range wave
propagation emerges in the substrate only when the two substrate layers were
weakly attached to each other. We then derive a theoretical model which
quantitatively reproduces the wave dynamics and explains how frictional sliding
between substrate layers helps propagate cellular forces at a variety of
scales, depending on the stiffness, thickness, and slipperiness of the
substrate. These results highlight the importance of interfacial friction
between layers in transmitting mechanical cues in tissues in vivo.


Abstract:
  This paper proposes a differentiable robust LQR layer for reinforcement
learning and imitation learning under model uncertainty and stochastic
dynamics. The robust LQR layer can exploit the advantages of robust optimal
control and model-free learning. It provides a new type of inductive bias for
stochasticity and uncertainty modeling in control systems. In particular, we
propose an efficient way to differentiate through a robust LQR optimization
program by rewriting it as a convex program (i.e. semi-definite program) of the
worst-case cost. Based on recent work on using convex optimization inside
neural network layers, we develop a fully differentiable layer for optimizing
this worst-case cost, i.e. we compute the derivative of a performance measure
w.r.t the model's unknown parameters, model uncertainty and stochasticity
parameters. We demonstrate the proposed method on imitation learning and
approximate dynamic programming on stochastic and uncertain domains. The
experiment results show that the proposed method can optimize robust policies
under uncertain situations, and are able to achieve a significantly better
performance than existing methods that do not model uncertainty directly.


Abstract:
  Recent work on graph generative models has made remarkable progress towards
generating increasingly realistic graphs, as measured by global graph features
such as degree distribution, density, and clustering coefficients. Deep
generative models have also made significant advances through better modelling
of the local correlations in the graph topology, which have been very useful
for predicting unobserved graph components, such as the existence of a link or
the class of a node, from nearby observed graph components. A complete
scientific understanding of graph data should address both global and local
structure. In this paper, we propose a joint model for both as complementary
objectives in a graph VAE framework. Global structure is captured by
incorporating graph kernels in a probabilistic model whose loss function is
closely related to the maximum mean discrepancy(MMD) between the global
structures of the reconstructed and the input graphs. The ELBO objective
derived from the model regularizes a standard local link reconstruction term
with an MMD term. Our experiments demonstrate a significant improvement in the
realism of the generated graph structures, typically by 1-2 orders of magnitude
of graph structure metrics, compared to leading graph VAEand GAN models. Local
link reconstruction improves as well in many cases.


Abstract:
  All solid state batteries are claimed to be the next-generation battery
system, in view of their safety accompanied by high energy densities. A new
advanced, multiscale compatible, and fully three dimensional model for solid
electrolytes is presented in this note. The response of the electrolyte is
profoundly studied theoretically and numerically, analyzing the equilibrium and
steady state behaviors, the limiting factors, as well as the most relevant
constitutive parameters according to the sensitivity analysis of the model.


Abstract:
  Unmanned aerial vehicles serving as aerial base stations (UAV-BSs) can be
deployed to provide wireless connectivity to ground devices in events of
increased network demand, points-of-failure in existing infrastructure, or
disasters. However, it is challenging to conserve the energy of UAVs during
prolonged coverage tasks, considering their limited on-board battery capacity.
Reinforcement learning-based (RL) approaches have been previously used to
improve energy utilization of multiple UAVs, however, a central cloud
controller is assumed to have complete knowledge of the end-devices' locations,
i.e., the controller periodically scans and sends updates for UAV
decision-making. This assumption is impractical in dynamic network environments
with UAVs serving mobile ground devices. To address this problem, we propose a
decentralized Q-learning approach, where each UAV-BS is equipped with an
autonomous agent that maximizes the connectivity of mobile ground devices while
improving its energy utilization. Experimental results show that the proposed
design significantly outperforms the centralized approaches in jointly
maximizing the number of connected ground devices and the energy utilization of
the UAV-BSs.


Abstract:
  One-shot voice conversion (VC), which performs conversion across arbitrary
speakers with only a single target-speaker utterance for reference, can be
effectively achieved by speech representation disentanglement. Existing work
generally ignores the correlation between different speech representations
during training, which causes leakage of content information into the speaker
representation and thus degrades VC performance. To alleviate this issue, we
employ vector quantization (VQ) for content encoding and introduce mutual
information (MI) as the correlation metric during training, to achieve proper
disentanglement of content, speaker and pitch representations, by reducing
their inter-dependencies in an unsupervised manner. Experimental results
reflect the superiority of the proposed method in learning effective
disentangled speech representations for retaining source linguistic content and
intonation variations, while capturing target speaker characteristics. In doing
so, the proposed approach achieves higher speech naturalness and speaker
similarity than current state-of-the-art one-shot VC systems. Our code,
pre-trained models and demo are available at
https://github.com/Wendison/VQMIVC.


Abstract:
  One major impediment in rapidly deploying object detection models for
industrial applications is the lack of large annotated datasets. We currently
have presented the Sacked Carton Dataset(SCD) that contains carton images from
three scenarios, such as comprehensive pharmaceutical logistics company(CPLC),
e-commerce logistics company(ECLC), fruit market(FM). However, due to domain
shift, the model trained with one of the three scenarios in SCD has poor
generalization ability when applied to the rest scenarios. To solve this
problem, a novel image synthesis method is proposed to replace the foreground
texture of the source datasets with the texture of the target datasets. Our
method can keep the context relationship of foreground objects and backgrounds
unchanged and greatly augment the target datasets. We firstly propose a surface
segmentation algorithm to achieve texture decoupling of each instance.
Secondly, a contour reconstruction algorithm is proposed to keep the occlusion
and truncation relationship of the instance unchanged. Finally, the Gaussian
fusion algorithm is used to replace the foreground texture from the source
datasets with the texture from the target datasets. The novel image synthesis
method can largely boost AP by at least 4.3%~6.5% on RetinaNet and 3.4%~6.8% on
Faster R-CNN for the target domain. Code is available at
https://github.com/hustgetlijun/RCAN.


Abstract:
  The Transient High Energy Sources and Early Universe Surveyor is an ESA M5
candidate mission currently in Phase A, with Launch in $\sim$2032. The aim of
the mission is to complete a Gamma Ray Burst survey and monitor transient X-ray
events. The University of Leicester is the PI institute for the Soft X-ray
Instrument (SXI), and is responsible for both the optic and detector
development. The SXI consists of two wide field, lobster eye X-ray modules.
Each module consists of 64 Micro Pore Optics (MPO) in an 8 by 8 array and 8
CMOS detectors in each focal plane. The geometry of the MPOs comprises a square
packed array of microscopic pores with a square cross-section, arranged over a
spherical surface with a radius of curvature twice the focal length of the
optic. Working in the photon energy range 0.3-5 keV, the optimum $L/d$ ratio
(length of pore $L$ and pore width $d$) is upwards of 50 and is constant across
the whole optic aperture for the SXI. The performance goal for the SXI modules
is an angular resolution of 4.5 arcmin, localisation accuracy of $\sim$1 arcmin
and employing an $L/d$ of 60. During the Phase A study, we are investigating
methods to improve the current performance and consistency of the MPOs, in
cooperation with the manufacturer Photonis France SAS. We present the optics
design of the THESEUS SXI modules and the programme of work designed to improve
the MPOs performance and the results from the study.


Abstract:
  Recent advances in the literature have demonstrated that standard supervised
learning algorithms are ill-suited for problems with endogenous explanatory
variables. To correct for the endogeneity bias, many variants of nonparameteric
instrumental variable regression methods have been developed. In this paper, we
propose an alternative algorithm called boostIV that builds on the traditional
gradient boosting algorithm and corrects for the endogeneity bias. The
algorithm is very intuitive and resembles an iterative version of the standard
2SLS estimator. Moreover, our approach is data driven, meaning that the
researcher does not have to make a stance on neither the form of the target
function approximation nor the choice of instruments. We demonstrate that our
estimator is consistent under mild conditions. We carry out extensive Monte
Carlo simulations to demonstrate the finite sample performance of our algorithm
compared to other recently developed methods. We show that boostIV is at worst
on par with the existing methods and on average significantly outperforms them.


Abstract:
  We report the interfacing of the Exciting-Plus ("EP") FLAPW DFT code with the
SIRIUS multi-functional DFT library. Use of the SIRIUS library enhances EP with
additional task parallelism in ground state DFT calculations. Without
significant change in the EP source code, the additional eigensystem solver
method from the SIRIUS library can be exploited for performance gains in
diagonalizing the Kohn-Sham Hamiltonian. We benchmark the interfaced code
against the original EP using small bulk systems, and then demonstrate
performance on much larger molecular magnet systems that are well beyond the
capability of the original EP code.


Abstract:
  Chimera states have attracted significant attention as symmetry-broken states
exhibiting the unexpected coexistence of coherence and incoherence. Despite the
valuable insights gained from analyzing specific systems, an understanding of
the general physical mechanism underlying the emergence of chimeras is still
lacking. Here, we show that many stable chimeras arise because coherence in
part of the system is sustained by incoherence in the rest of the system. This
mechanism may be regarded as a deterministic analog of noise-induced
synchronization and is shown to underlie the emergence of strong chimeras.
These are chimera states whose coherent domain is formed by identically
synchronized oscillators. Recognizing this mechanism offers a new meaning to
the interpretation that chimeras are a natural link between coherence and
incoherence.


Abstract:
  We present a probabilistic 3D generative model, named Generative Cellular
Automata, which is able to produce diverse and high quality shapes. We
formulate the shape generation process as sampling from the transition kernel
of a Markov chain, where the sampling chain eventually evolves to the full
shape of the learned distribution. The transition kernel employs the local
update rules of cellular automata, effectively reducing the search space in a
high-resolution 3D grid space by exploiting the connectivity and sparsity of 3D
shapes. Our progressive generation only focuses on the sparse set of occupied
voxels and their neighborhood, thus enabling the utilization of an expressive
sparse convolutional network. We propose an effective training scheme to obtain
the local homogeneous rule of generative cellular automata with sequences that
are slightly different from the sampling chain but converge to the full shapes
in the training data. Extensive experiments on probabilistic shape completion
and shape generation demonstrate that our method achieves competitive
performance against recent methods.


Abstract:
  Theoretical models of a spin-polarized voltage probe (SPVP) tunnel-coupled to
the helical edge states (HES) of a quantum spin Hall system (QSHS) are studied.
Our first model of the SPVP comprises $N_{P}$ spin-polarized modes (subprobes),
each of which is locally tunnel-coupled to the HES, while the SPVP, as a whole,
is subjected to a self-consistency condition ensuring zero average current on
the probe. We carry out a numerical analysis which shows that the optimal
situation for reading off spin-resolved voltage from the HES depends on the
interplay of the probe-edge tunnel-coupling and the number of modes in the
probe ($N_P$). We further investigate the stability of our findings by
introducing Gaussian fluctuations in {\it{(i)}} the tunnel-coupling between the
subprobes and the HES about a chosen average value and {\it{(ii)}}
spin-polarization of the subprobes about a chosen direction of the net
polarization of SPVP. We also perform a numerical analysis corresponding to the
situation where four such SPVPs are implemented in a self-consistent fashion
across a ferromagnetic barrier on the HES and demonstrate that this model
facilitates the measurements of spin-resolved four-probe voltage drops across
the ferromagnetic barrier. As a second model, we employ the edge state of a
quantum anomalous Hall state (QAHS) as the SPVP which is tunnel-coupled over an
extended region with the HES. A two-dimensional lattice simulation for the
quantum transport of the proposed device setup comprising a junction of QSHS
and QAHS is considered and a feasibility study of using the edge of the QAHS as
an efficient spin-polarized voltage probe is carried out in presence of an
optimal strength of the disorder.


Abstract:
  Planck data provide precise constraints on cosmological parameters when
assuming the base $\Lambda$CDM model, including a $0.17\%$ measurement of the
age of the Universe, $t_0=13.797 \pm 0.023\,{\rm Gyr}$. However, the
persistence of the "Hubble tension" calls the base $\Lambda$CDM model's
completeness into question and has spurred interest in models such as Early
Dark Energy (EDE) that modify the assumed expansion history of the Universe. We
investigate the effect of EDE on the redshift-time relation $z \leftrightarrow
t$ and find that it differs from the base $\Lambda$CDM model by at least
${\approx} 4\%$ at all $t$ and $z$. As long as EDE remains observationally
viable, any inferred $t \leftarrow z$ or $z \leftarrow t$ quoted to a higher
level of precision do not reflect the current status of our understanding of
cosmology. This uncertainty has important astrophysical implications: the
reionization epoch - $10>z>6$ - corresponds to disjoint lookback time periods
in the base $\Lambda$CDM and EDE models, and the EDE value of $t_0=13.25 \pm
0.17~{\rm Gyr}$ is in tension with published ages of some stars, star clusters,
and ultra-faint dwarf galaxies. However, most published stellar ages do not
include an uncertainty in accuracy (due to, e.g., uncertain distances and
stellar physics) that is estimated to be $\sim7-10\%$, potentially reconciling
stellar ages with $t_{0,\rm EDE}$. We discuss how the big data era for stars is
providing extremely precise ages ($<1\%$) and how improved distances and
treatment of stellar physics such as convection could result in ages accurate
to $4-5\%$, comparable to the current accuracy of $t \leftrightarrow z$. Such
precise and accurate stellar ages can provide detailed insight into the
high-redshift Universe independent of a cosmological model.


Abstract:
  We are concerned with random ordinary differential equations (RODEs). Our
main question of interest is how uncertainties in system parameters propagate
through the possibly highly nonlinear dynamical system and affect the system's
bifurcation behavior. We come up with a methodology to determine the
probability of the occurrence of different types of bifurcations (sub- vs
super-critical) along a given bifurcation curve based on the probability
distribution of the input parameters. In a first step, we reduce the system's
behavior to the dynamics on its center manifold. We thereby still capture the
major qualitative behavior of the RODEs. In a second step, we analyze the
reduced RODEs and quantify the probability of the occurrence of different types
of bifurcations based on the (nonlinear) functional appearance of uncertain
parameters. To realize this major step, we present three approaches: an
analytical one, where the probability can be calculated explicitly based on
Mellin transformation and inversion, a semi-analytical one consisting of a
combination of the analytical approach with a moment-based numerical estimation
procedure, and a particular sampling-based approach using unscented
transformation. We complement our new methodology with various numerical
examples.


Abstract:
  The "Subset Sum problem" is a very well-known NP-complete problem. In this
work, a top-k variation of the "Subset Sum problem" is considered. This problem
has wide application in recommendation systems, where instead of k best objects
the k best subsets of objects with the lowest (or highest) overall scores are
required. Given an input set R of n real numbers and a positive integer k, our
target is to generate the k best subsets of R such that the sum of their
elements is minimized. Our solution methodology is based on constructing a
metadata structure G for a given n. Each node of G stores a bit vector of size
n from which a subset of R can be retrieved. Here it is shown that the
construction of the whole graph G is not needed. To answer a query, only
implicit traversal of the required portion of G on demand is sufficient, which
obviously gets rid of the preprocessing step, thereby reducing the overall time
and space requirement. A modified algorithm is then proposed to generate each
subset incrementally, where it is shown that it is possible to do away with the
explicit storage of the bit vector. This not only improves the space
requirement but also improves the asymptotic time complexity. Finally, a
variation of our algorithm that reports only the top-k subset sums has been
compared with an existing algorithm, which shows that our algorithm performs
better both in terms of time and space requirement by a constant factor.


Abstract:
  Attention mechanism enables the Graph Neural Networks(GNNs) to learn the
attention weights between the target node and its one-hop neighbors, the
performance is further improved. However, the most existing GNNs are oriented
to homogeneous graphs and each layer can only aggregate the information of
one-hop neighbors. Stacking multi-layer networks will introduce a lot of noise
and easily lead to over smoothing. We propose a Multi-hop Heterogeneous
Neighborhood information Fusion graph representation learning method (MHNF).
Specifically, we first propose a hybrid metapath autonomous extraction model to
efficiently extract multi-hop hybrid neighbors. Then, we propose a hop-level
heterogeneous Information aggregation model, which selectively aggregates
different-hop neighborhood information within the same hybrid metapath.
Finally, a hierarchical semantic attention fusion model (HSAF) is proposed,
which can efficiently integrate different-hop and different-path neighborhood
information respectively. This paper can solve the problem of aggregating the
multi-hop neighborhood information and can learn hybrid metapaths for target
task, reducing the limitation of manually specifying metapaths. In addition,
HSAF can extract the internal node information of the metapaths and better
integrate the semantic information of different levels. Experimental results on
real datasets show that MHNF is superior to state-of-the-art methods in node
classification and clustering tasks (10.94% - 69.09% and 11.58% - 394.93%
relative improvement on average, respectively).


Abstract:
  We present a calculation of the up, down, strange and charm quark masses
performed within the lattice QCD framework. We use the twisted mass fermion
action and carry out simulations that include in the sea two light
mass-degenerate quarks, as well as the strange and charm quarks. In the
analysis we use gauge ensembles simulated at three values of the lattice
spacing and with light quarks that correspond to pion masses in the range from
350 MeV to the physical value, while the strange and charm quark masses are
tuned approximately to their physical values. We use several quantities to set
the scale in order to check for finite lattice spacing effects and in the
continuum limit we get compatible results. The quark mass renormalization is
carried out non-perturbatively using the RI'-MOM method converted into the
$\overline{\rm MS}$ scheme. For the determination of the quark masses we use
physical observables from both the meson and the baryon sectors, obtaining
$m_{ud} = 3.636(66)(^{+60}_{-57})$~MeV and $m_s =
98.7(2.4)(^{+4.0}_{-3.2})$~MeV in the $\overline{\rm MS}(2\,{\rm GeV})$ scheme
and $m_c = 1036(17)(^{+15}_{-8})$~MeV in the $\overline{\rm MS}(3\,{\rm GeV})$
scheme, where the first errors are statistical and the second ones are
combinations of systematic errors. For the quark mass ratios we get $m_s /
m_{ud} = 27.17(32)(^{+56}_{-38})$ and $m_c / m_s = 11.48(12)(^{+25}_{-19})$.


Abstract:
  Coupled flow-induced flapping dynamics of flexible plates are governed by
three non-dimensional numbers: Reynolds number, mass-ratio, and non-dimensional
flexural rigidity. The traditional definition of these parameters is limited to
isotropic single-layered flexible plates. There is a need to define these
parameters for a more generic plate made of multiple isotropic layers placed on
top of each other. In this work, we derive the non-dimensional parameters for a
flexible plate of $n$-isotropic layers and validate the non-dimensional
parameters with the aid of numerical simulations.


Abstract:
  To help agents reason about scenes in terms of their building blocks, we wish
to extract the compositional structure of any given scene (in particular, the
configuration and characteristics of objects comprising the scene). This
problem is especially difficult when scene structure needs to be inferred while
also estimating the agent's location/viewpoint, as the two variables jointly
give rise to the agent's observations. We present an unsupervised variational
approach to this problem. Leveraging the shared structure that exists across
different scenes, our model learns to infer two sets of latent representations
from RGB video input alone: a set of "object" latents, corresponding to the
time-invariant, object-level contents of the scene, as well as a set of "frame"
latents, corresponding to global time-varying elements such as viewpoint. This
factorization of latents allows our model, SIMONe, to represent object
attributes in an allocentric manner which does not depend on viewpoint.
Moreover, it allows us to disentangle object dynamics and summarize their
trajectories as time-abstracted, view-invariant, per-object properties. We
demonstrate these capabilities, as well as the model's performance in terms of
view synthesis and instance segmentation, across three procedurally generated
video datasets.


Abstract:
  In recent years, the use of sophisticated statistical models that influence
decisions in domains of high societal relevance is on the rise. Although these
models can often bring substantial improvements in the accuracy and efficiency
of organizations, many governments, institutions, and companies are reluctant
to their adoption as their output is often difficult to explain in
human-interpretable ways. Hence, these models are often regarded as
black-boxes, in the sense that their internal mechanisms can be opaque to human
audit. In real-world applications, particularly in domains where decisions can
have a sensitive impact--e.g., criminal justice, estimating credit scores,
insurance risk, health risks, etc.--model interpretability is desired.
Recently, the academic literature has proposed a substantial amount of methods
for providing interpretable explanations to machine learning models. This
survey reviews the most relevant and novel methods that form the
state-of-the-art for addressing the particular problem of explaining individual
instances in machine learning. It seeks to provide a succinct review that can
guide data science and machine learning practitioners in the search for
appropriate methods to their problem domain.


Abstract:
  The Kolkata Paise Restaurant Problem is a challenging game, in which $n$
agents must decide where to have lunch during their lunch break. The game is
very interesting because there are exactly $n$ restaurants and each restaurant
can accommodate only one agent. If two or more agents happen to choose the same
restaurant, only one gets served and the others have to return back to work
hungry. In this paper we tackle this problem from an entirely new angle. We
abolish certain implicit assumptions, which allows us to propose a novel
strategy that results in greater utilization for the restaurants. We emphasize
the spatially distributed nature of our approach, which, for the first time,
perceives the locations of the restaurants as uniformly distributed in the
entire city area. This critical change in perspective has profound
ramifications in the topological layout of the restaurants, which now makes it
completely realistic to assume that every agent has a second chance. Every
agent now may visit, in case of failure, more than one restaurants, within the
predefined time constraints.


Abstract:
  A graph G is said to be orderenergetic, if its energy equal to its order and
it is said to be hypoenergetic if its energy less than its order. Two
non-isomorphic graphs of same order are said to be equienergetic if their
energies are equal. In this paper, we construct some new families of
orderenergetic graphs, hypoenergetic graphs, equienergetic graphs,
equiorderenergetic graphs and equihypoenergetic graphs.


Abstract:
  In order to get $\lambda$-models with a rich structure of $\infty$-groupoid,
which we call "homotopy $\lambda$-models", a general technique is described for
solving domain equations on any cartesian closed $\infty$-category (c.c.i.)
with enough points. Finally, the technique is applied in a particular c.c.i.,
where some examples of homotopy $\lambda$-models are given.


Abstract:
  The central engines of Active Galactic Nuclei (AGNs) are powered by accreting
supermassive black holes, and while AGNs are known to play an important role in
galaxy evolution, the key physical processes occur on scales that are too small
to be resolved spatially (aside from a few exceptional cases). Reverberation
mapping is a powerful technique that overcomes this limitation by using echoes
of light to determine the geometry and kinematics of the central regions.
Variable ionizing radiation from close to the black hole drives correlated
variability in surrounding gas/dust, but with a time delay due to the light
travel time between the regions, allowing reverberation mapping to effectively
replace spatial resolution with time resolution. Reverberation mapping is used
to measure black hole masses and to probe the innermost X-ray emitting region,
the UV/optical accretion disk, the broad emission line region and the dusty
torus. In this article we provide an overview of the technique and its varied
applications.


Abstract:
  In this paper, we study the Feldman-Katok metric. We give entropy formulas by
replacing Bowen metric with Feldman-Katok metric. Some relative topics are also
discussed.


Abstract:
  We propose two systematic constructions of deletion-correcting codes for
protecting quantum information. The first one works with qudits of any
dimension, but only one deletion is corrected and the constructed codes are
asymptotically bad. The second one corrects multiple deletions and can
construct asymptotically good codes. The second one also allows conversion of
stabilizer-based quantum codes to deletion-correcting codes, and entanglement
assistance.


Abstract:
  Resistive random access memories are promising for non-volatile memory and
brain-inspired computing applications. High variability and low yield of these
devices are key drawbacks hindering reliable training of physical neural
networks. In this study, we show that doping an oxide electrolyte, Al2O3, with
electronegative metals makes resistive switching significantly more
reproducible, surpassing the reproducibility requirements for obtaining
reliable hardware neuromorphic circuits. The underlying mechanism is the ease
of creating oxygen vacancies in the vicinity of electronegative dopants, due to
the capture of the associated electrons by dopant mid-gap states, and the
weakening of Al-O bonds. These oxygen vacancies and vacancy clusters also bind
significantly to the dopant, thereby serving as preferential sites and building
blocks in the formation of conducting paths. We validate this theory
experimentally by implanting multiple dopants over a range of
electronegativities, and find superior repeatability and yield with highly
electronegative metals, Au, Pt and Pd. These devices also exhibit a gradual SET
transition, enabling multibit switching that is desirable for analog computing.


Abstract:
  Characterizing the privacy degradation over compositions, i.e., privacy
accounting, is a fundamental topic in differential privacy (DP) with many
applications to differentially private machine learning and federated learning.
We propose a unification of recent advances (Renyi DP, privacy profiles, $f$-DP
and the PLD formalism) via the \emph{characteristic function} ($\phi$-function)
of a certain \emph{dominating} privacy loss random variable. We show that our
approach allows \emph{natural} adaptive composition like Renyi DP, provides
\emph{exactly tight} privacy accounting like PLD, and can be (often
\emph{losslessly}) converted to privacy profile and $f$-DP, thus providing
$(\epsilon,\delta)$-DP guarantees and interpretable tradeoff functions.
Algorithmically, we propose an \emph{analytical Fourier accountant} that
represents the \emph{complex} logarithm of $\phi$-functions symbolically and
uses Gaussian quadrature for numerical computation. On several popular DP
mechanisms and their subsampled counterparts, we demonstrate the flexibility
and tightness of our approach in theory and experiments.