Abstract:
  We present high-precision photometry and polarimetry for the protoplanetary
disk around HD142527, with a focus on determining the light scattering
parameters of the dust. We re-reduced polarimetric differential imaging data of
HD142527 in the VBB (735 nm) and H-band (1625 nm) from the ZIMPOL and IRDIS
subinstruments of SPHERE/VLT. With polarimetry and photometry based on
reference star differential imaging, we were able to measure the linearly
polarized intensity and the total intensity of the light scattered by the
circumstellar disk with high precision. We used simple Monte Carlo simulations
of multiple light scattering by the disk surface to derive constraints for
three scattering parameters of the dust: the maximum polarization of $P_{\rm
max}$, the asymmetry parameter $g$, and the single-scattering albedo $\omega$.
We measure a reflected total intensity of $51.4\pm1.5$ mJy and $206\pm12$ mJy
and a polarized intensity of $11.3\pm0.3$ mJy and $55.1\pm3.3$ mJy in the VBB
and H-band, respectively. We also find in the visual range a degree of
polarization that varies between $28\%$ on the far side of the disk and $17\%$
on the near side. The disk shows a red color for the scattered light intensity
and the polarized intensity, which are about twice as high in the near-infrared
when compared to the visual. We determine with model calculations the
scattering properties of the dust particles and find evidence for strong
forward scattering ($g\approx 0.5-0.75$), relatively low single-scattering
albedo ($\omega \approx 0.2-0.5$), and high maximum polarization ($P_{\rm max}
\approx 0.5-0.75$) at the surface on the far side of the disk for both observed
wavelengths. The optical parameters indicate the presence of large aggregate
dust particles, which are necessary to explain the high maximum polarization,
the strong forward-scattering nature of the dust, and the observed red disk
color.


Abstract:
  Interpretability techniques aim to provide the rationale behind a model's
decision, typically by explaining either an individual prediction (local
explanation, e.g. `why is this patient diagnosed with this condition') or a
class of predictions (global explanation, e.g. `why are patients diagnosed with
this condition in general'). While there are many methods focused on either
one, few frameworks can provide both local and global explanations in a
consistent manner. In this work, we combine two powerful existing techniques,
one local (Integrated Gradients, IG) and one global (Testing with Concept
Activation Vectors), to provide local, and global concept-based explanations.
We first validate our idea using two synthetic datasets with a known ground
truth, and further demonstrate with a benchmark natural image dataset. We test
our method with various concepts, target classes, model architectures and IG
baselines. We show that our method improves global explanations over TCAV when
compared to ground truth, and provides useful insights. We hope our work
provides a step towards building bridges between many existing local and global
methods to get the best of both worlds.


Abstract:
  B\'ezier curves provide the basic building blocks of graphic design in 2D. In
this paper, we port B\'ezier curves to manifolds. We support the interactive
drawing and editing of B\'ezier splines on manifold meshes with millions of
triangles, by relying on just repeated manifold averages. We show that direct
extensions of the De Casteljau and Bernstein evaluation algorithms to the
manifold setting are fragile, and prone to discontinuities when control
polygons become large. Conversely, approaches based on subdivision are robust
and can be implemented efficiently. We define B\'ezier curves on manifolds, by
extending both the recursive De Casteljau bisection and a new open-uniform
Lane-Riesenfeld subdivision scheme, which provide curves with different degrees
of smoothness. For both schemes, we present algorithms for curve tracing, point
evaluation, and point insertion. We test our algorithms for robustness and
performance on all watertight, manifold, models from the Thingi10k repository,
without any pre-processing and with random control points. For interactive
editing, we port all the basic user interface interactions found in 2D tools
directly to the mesh. We also support mapping complex SVG drawings to the mesh
and their interactive editing.


Abstract:
  Personalized news recommendation techniques are widely adopted by many online
news feed platforms to target user interests. Learning accurate user interest
models is important for news recommendation. Most existing methods for news
recommendation rely on implicit feedbacks like click behaviors for inferring
user interests and model training. However, click behaviors are implicit
feedbacks and usually contain heavy noise. In addition, they cannot help infer
complicated user interest such as dislike. Besides, the feed recommendation
models trained solely on click behaviors cannot optimize other objectives such
as user engagement. In this paper, we present a news feed recommendation method
that can exploit various kinds of user feedbacks to enhance both user interest
modeling and recommendation model training. In our method we propose a unified
user modeling framework to incorporate various explicit and implicit user
feedbacks to infer both positive and negative user interests. In addition, we
propose a strong-to-weak attention network that uses the representations of
stronger feedbacks to distill positive and negative user interests from
implicit weak feedbacks for accurate user interest modeling. Besides, we
propose a multi-feedback model training framework by jointly training the model
in the click, finish and dwell time prediction tasks to learn an
engagement-aware feed recommendation model. Extensive experiments on real-world
dataset show that our approach can effectively improve the model performance in
terms of both news clicks and user engagement.


Abstract:
  The evaluation of rare but high-stakes events remains one of the main
difficulties in obtaining reliable policies from intelligent agents, especially
in large or continuous state/action spaces where limited scalability enforces
the use of a prohibitively large number of testing iterations. On the other
hand, a biased or inaccurate policy evaluation in a safety-critical system
could potentially cause unexpected catastrophic failures during deployment. In
this paper, we propose the Accelerated Policy Evaluation (APE) method, which
simultaneously uncovers rare events and estimates the rare event probability in
Markov decision processes. The APE method treats the environment nature as an
adversarial agent and learns towards, through adaptive importance sampling, the
zero-variance sampling distribution for the policy evaluation. Moreover, APE is
scalable to large discrete or continuous spaces by incorporating function
approximators. We investigate the convergence properties of proposed algorithms
under suitable regularity conditions. Our empirical studies show that APE
estimates rare event probability with a smaller variance while only using
orders of magnitude fewer samples compared to baseline methods in both
multi-agent and single-agent environments.


Abstract:
  This work is motivated by the sign problem in a logarithmic parameter of
black hole entropy and the existing more massive white dwarfs than the
Chandrasekhar mass limit. We examine the quadratic, linear, and
linear-quadratic generalized uncertainty principle (GUP) models within the
virtue of recent masses and radii of white dwarfs. We consider the modification
generated by introducing the minimal length proposal on the degenerate Fermi
gas equation of state (EOS) and on the hydrostatic equation. For the latter, we
applied Verlinde's proposal regarding entropic gravity to derived the quantum
corrected Newtonian gravity which in turn responsible for modifying the
hydrostatic equation. Through the chi-square analysis of the models, we have
found that the observation data favor the quadratic dan linear GUP models
without mass limit. However, for the quadratic-linear GUP model, we can obtain
the positive value of the free parameter $\gamma_0$ as well as we can get mass
limit more massive than the Chandrasekhar mass limit. In the linear-quadratic
GUP model, the formation of stable massive white dwarfs than the Chandrasekhar
limit is possible only if both parameters are not equal.


Abstract:
  Tidal disruption events (TDEs) can uncover the quiescent black holes (BHs) at
the center of galaxies and also offer a promising method to study them. In a
partial TDE (PTDE), the BH's tidal force cannot fully disrupt the star, so the
stellar core survives and only a varied portion of the stellar mass is bound to
the BH and feeds it. We calculate the event rate of PTDEs and full TDEs
(FTDEs). In general, the event rate of PTDEs is higher than that of FTDEs,
especially for the larger BHs. And the detection rate of PTDEs is about dozens
per year by Zwicky Transient Factory (ZTF). During the circularization process
of the debris stream in PTDEs, no outflow can be launched due to the efficient
radiative diffusion. The circularized debris ring then experiences viscous
evolution and forms an accretion disk. We calculate the light curves of PTDEs
contributed by these two processes, along with their radiation temperature
evolution. The light curves have double peaks and the spectra peak in UV.
Without obscuration or reprocessing of the radiation by an outflow, PTDEs
provide a clean environment to study the circularization and transient disk
formation in TDEs.


Abstract:
  Generative adversarial networks (GANs) have shown promising results when
applied on partial differential equations and financial time series generation.
We investigate if GANs can also be used to approximate one-dimensional Ito
stochastic differential equations (SDEs). We propose a scheme that approximates
the path-wise conditional distribution of SDEs for large time steps. Standard
GANs are only able to approximate processes in distribution, yielding a weak
approximation to the SDE. A conditional GAN architecture is proposed that
enables strong approximation. We inform the discriminator of this GAN with the
map between the prior input to the generator and the corresponding output
samples, i.e. we introduce a `supervised GAN'. We compare the input-output map
obtained with the standard GAN and supervised GAN and show experimentally that
the standard GAN may fail to provide a path-wise approximation. The GAN is
trained on a dataset obtained with exact simulation. The architecture was
tested on geometric Brownian motion (GBM) and the Cox-Ingersoll-Ross (CIR)
process. The supervised GAN outperformed the Euler and Milstein schemes in
strong error on a discretisation with large time steps. It also outperformed
the standard conditional GAN when approximating the conditional distribution.
We also demonstrate how standard GANs may give rise to non-parsimonious
input-output maps that are sensitive to perturbations, which motivates the need
for constraints and regularisation on GAN generators.


Abstract:
  The majority of planetary nebulae (PNe) show axisymmetric morphologies, whose
causes are not well understood. In this work, we present spatially resolved
kinematic observations of 14 Galactic PNe surrounding Wolf-Rayet ([WR]) and
weak emission-line stars ($wels$) based on the H$\alpha$ and [N II] emission
taken with the Wide Field Spectrograph on the ANU 2.3-m telescope.
Velocity-resolved channel maps and position--velocity diagrams, together with
archival Hubble Space Telescope ($HST$) and ground-based images, are employed
to construct three-dimensional morpho-kinematic models of 12 objects using the
program SHAPE. Our results indicate that these 12 PNe have elliptical
morphologies with either open or closed outer ends. Kinematic maps also
illustrate on-sky orientations of elliptically symmetric morphologies of the
interior shells in NGC 6578 and NGC 6629, and the compact ($\leq 6$ arcsec) PNe
Pe1-1, M3-15, M1-25, Hen2-142, and NGC 6567, in agreement with the
high-resolution $HST$ images containing morphological details. Point-symmetric
knots in Hb4 exhibit deceleration with distance from the nebular center that
could be due to shock collisions with the ambient medium. Velocity dispersion
maps of Pe1-1 disclose point-symmetric knots similar to those in Hb4.
Collimated outflows are also visible in the position--velocity diagrams of
M3-30, M1-32, M3-15, and K2-16, which are reconstructed by tenuous prolate
ellipsoids extending upwardly from thick toroidal shells in our models.


Abstract:
  Artificial intelligence systems, which are designed with a capability to
learn from the data presented to them, are used throughout society. These
systems are used to screen loan applicants, make sentencing recommendations for
criminal defendants, scan social media posts for disallowed content and more.
Because these systems don't assign meaning to their complex learned correlation
network, they can learn associations that don't equate to causality, resulting
in non-optimal and indefensible decisions being made. In addition to making
decisions that are sub-optimal, these systems may create legal liability for
their designers and operators by learning correlations that violate
anti-discrimination and other laws regarding what factors can be used in
different types of decision making. This paper presents the use of a machine
learning expert system, which is developed with meaning-assigned nodes (facts)
and correlations (rules). Multiple potential implementations are considered and
evaluated under different conditions, including different network error and
augmentation levels and different training levels. The performance of these
systems is compared to random and fully connected networks.


Abstract:
  Learning from implicit feedback is challenging because of the difficult
nature of the one-class problem: we can observe only positive examples. Most
conventional methods use a pairwise ranking approach and negative samplers to
cope with the one-class problem. However, such methods have two main drawbacks
particularly in large-scale applications; (1) the pairwise approach is severely
inefficient due to the quadratic computational cost; and (2) even recent
model-based samplers (e.g. IRGAN) cannot achieve practical efficiency due to
the training of an extra model.
  In this paper, we propose a learning-to-rank approach, which achieves
convergence speed comparable to the pointwise counterpart while performing
similarly to the pairwise counterpart in terms of ranking effectiveness. Our
approach estimates the probability densities of positive items for each user
within a rich class of distributions, viz. \emph{exponential family}. In our
formulation, we derive a loss function and the appropriate negative sampling
distribution based on maximum likelihood estimation. We also develop a
practical technique for risk approximation and a regularisation scheme. We then
discuss that our single-model approach is equivalent to an IRGAN variant under
a certain condition. Through experiments on real-world datasets, our approach
outperforms the pointwise and pairwise counterparts in terms of effectiveness
and efficiency.


Abstract:
  Recent Juno observations have greatly extended the temporal and spatial
coverage of lightning detection on Jupiter. We use these data to constrain a
model of moist convection and lightning generation in Jupiter's atmosphere, and
derive a roughly solar abundance of water at the base of the water cloud.
Shallow lightning, observed by Juno (Becker et al., 2020, Nature, 584, 55-58)
and defined as flashes originating at altitudes corresponding to pressure less
than 2 bars, is reproduced, as is lightning at a deeper range of pressures,
including those below the water cloud base. It is found that the generation of
lightning requires ammonia to stabilize liquid water at altitudes corresponding
to sub-freezing temperatures. We find a range of local water abundances in
which lightning is possible, including subsolar values of water--consistent
with other determinations of deep oxygen abundance.


Abstract:
  Auditory perception involves cues in the monaural auditory pathways as well
as binaural cues based on differences between the ears. So far auditory models
have often focused on either monaural or binaural experiments in isolation.
Although binaural models typically build upon stages of (existing) monaural
models, only a few attempts have been made to extend a monaural model by a
binaural stage using a unified decision stage for monaural and binaural cues.
In such approaches, a typical prototype of binaural processing has been the
classical equalization-cancelation mechanism, which either involves
signal-adaptive delays and provides a single channel output or can be
implemented with tapped delays providing a high-dimensional multichannel
output. This contribution extends the (monaural) generalized envelope power
spectrum model by a non-adaptive binaural stage with only a few, fixed output
channels. The binaural stage resembles features of physiologically motivated
hemispheric binaural processing, as simplified signal processing stages,
yielding a 5-channel monaural and binaural matrix feature "decoder" (BMFD). The
back end of the existing monaural model is applied to the 5-channel BMFD output
and calculates short-time envelope power and power features. The model is
evaluated and discussed for a baseline database of monaural and binaural
psychoacoustic experiments from the literature.


Abstract:
  We report on the first demonstration of fluorescence detection using
single-photon avalanche photodiodes (SPADs) monolithically integrated with a
microfabricated surface ion trap. The SPADs are positioned below the trapping
positions of the ions, and designed to detect 370 nm photons emitted from
single $^{174}$Yb$^+$ and $^{171}$Yb$^+$ ions. We achieve an ion/no-ion
detection fidelity for $^{174}$Yb$^+$ of 0.99 with an average detection window
of 7.7(1) ms. We report a dark count rate as low as 1.2 kHz at room temperature
operation. The fidelity is limited by laser scatter, dark counts, and heating
that prevents holding the ion directly above the SPAD. We measure count rates
from each of the contributing sources and fluorescence as a function of ion
position. Based on the active detector area and using the ion as a calibrated
light source we estimate a SPAD quantum efficiency of 24$\pm$1%.


Abstract:
  We study inflationary universes with an SU(3) gauge field coupled to an
inflaton through a gauge kinetic function. Although the SU(3) gauge field grows
at the initial stage of inflation due to the interaction with the inflaton,
nonlinear self-couplings in the kinetic term of the gauge field become
significant and cause nontrivial dynamics after sufficient growth. We
investigate the evolution of the SU(3) gauge field numerically and reveal
attractor solutions in the Bianchi type I spacetime. In general cases where all
the components of the SU(3) gauge field have the same magnitude initially, they
all tend to decay eventually because of the nonlinear self-couplings.
Therefore, the cosmic no-hair conjecture generically holds in a mathematical
sense. Practically, however, the anisotropy can be generated transiently in the
early universe, even for an isotropic initial condition. Moreover, we find
particular cases for which several components of the SU(3) gauge field survive
against the nonlinear self-couplings. It occurs due to flat directions in the
potential of a gauge field for Lie groups whose rank is higher than one. Thus,
an SU(2) gauge field has a specialty among general non-Abelian gauge fields.


Abstract:
  Network Design problems typically ask for a minimum cost sub-network from a
given host network. This classical point-of-view assumes a central authority
enforcing the optimum solution. But how should networks be designed to cope
with selfish agents that own parts of the network? In this setting, agents will
deviate from a minimum cost network if this decreases their individual cost.
Hence, designed networks should be both efficient in terms of total cost and
stable in terms of the agents' willingness to accept the network.
  We study this novel type of Network Design problem by investigating the
creation of $(\beta,\gamma)$-networks, that are in $\beta$-approximate Nash
equilibrium and have a total cost of at most $\gamma$ times the optimal cost,
for the recently proposed Euclidean Generalized Network Creation Game by Bil\`o
et al. [SPAA 2019]. There, $n$ agents corresponding to points in Euclidean
space create costly edges among themselves to optimize their centrality in the
created network. Our main result is a simple $\mathcal{O}(n^2)$-time algorithm
that computes a $(\beta,\beta)$-network with low $\beta$ for any given set of
points. Moreover, on integer grid point sets or random point sets our algorithm
achieves a low constant $\beta$. Besides these results, we discuss a
generalization of our algorithm to instances with arbitrary, even non-metric,
edge lengths. Moreover, we show that no such positive results are possible when
focusing on either optimal networks or perfectly stable networks as in both
cases NP-hard problems arise, there exist instances with very unstable optimal
networks, and there are instances for perfectly stable networks with high total
cost. Along the way, we significantly improve several results from Bil\`o et
al. and we asymptotically resolve their conjecture about the Price of Anarchy
by providing a tight bound.


Abstract:
  Background: Despite lacking scientific support, vaccine hesitancy is
widespread. While vaccine damage as a scientific fact is statistically highly
uncommon, emerging social and technological forces have converted probable
vaccine damage into an alternative fact. Methods: Using the Eurobarometer 91.2
survey on a statistically representative EU27-UK sample interviewed in March
2019, we documented perceptions of vaccine risks and identified three belief
configurations regarding vaccine effectiveness, safety, and usefulness, through
exploratory cluster analysis. Results: The public beliefs in vaccine risks are
frequent. Approximatively one-tenth of the EU27-UK population consider vaccines
are not rigorously tested before authorization, one-third believe vaccines can
overload or weaken the immune system and that they can cause the disease
against which they protect, and almost one-half believe vaccines can cause
serious side effects. We identified three belief configurations: the skeptical,
the confident, and the trade-off clusters. The skeptical type (approx. 11
percent of EU27-UK respondents) is defined by the belief that vaccines are
rather ineffective, affected by risks of probable vaccine damage, not
well-tested, and useless; the confident type (approx. 59 percent) is defined by
beliefs that vaccines are effective, safe, well-tested, and useful; and the
trade-off type (approx. 29 percent) combines beliefs that vaccines are
effective, well-tested and useful, with beliefs of probable vaccine damage.
Conclusions: Probable vaccine damage presently exists as an alternative fact in
the public imagination, perceptively available for wide segments of the public,
including those who trust medical science.


Abstract:
  The $b\to s(d)$ quark-level transitions are flavor-changing neutral current
processes, which are not allowed at tree level in the standard model. These
processes are very rare and constitute a potential probe for new physics. Belle
II at SuperKEKB is a substantial upgrade of the Belle experiment. It aims to
collect 50 ab$^{-1}$ of data with a design peak luminosity of $8\times 10^{35}$
cm$^{-2}$s$^{-1}$ that is 40 times more than its predecessor. It has been
recording data since 2019 and during these early days of the experiment,
efforts are being made to detect early signals of the above decays. We report
the first reconstrution in Belle II data of a $B\to K^{*}\gamma$ signal as well
as future prospects for radiative and electroweak decays at Belle II.


Abstract:
  Deep learning has provided new ways of manipulating, processing and analyzing
data. It sometimes may achieve results comparable to, or surpassing human
expert performance, and has become a source of inspiration in the era of
artificial intelligence. Another subfield of machine learning named
reinforcement learning, tries to find an optimal behavior strategy through
interactions with the environment. Combining deep learning and reinforcement
learning permits resolving critical issues relative to the dimensionality and
scalability of data in tasks with sparse reward signals, such as robotic
manipulation and control tasks, that neither method permits resolving when
applied on its own. In this paper, we present recent significant progress of
deep reinforcement learning algorithms, which try to tackle the problems for
the application in the domain of robotic manipulation control, such as sample
efficiency and generalization. Despite these continuous improvements,
currently, the challenges of learning robust and versatile manipulation skills
for robots with deep reinforcement learning are still far from being resolved
for real world applications.


Abstract:
  We present a systematic study of the productions and decays of light axial
vector mesons with $J^{PC}=1^{+\pm}$ in charmonium decays. In the quark model
scenario, the two axial vector nonets are connected with each other by the
Gell-Mann-Okubo mass relation through the mixing between two $K_1$ states (i.e.
$K_1(1270)$ and $K_1(1400)$) with configurations of $^3P_1$ and $^1P_1$. The
mixing angles between $f_1$ and $f_1'$ (i.e. $f_1(1285)$ and $f_1(1420)$), and
between $h_1$ and $h_1'$ (i.e. $h_1(1170)$ and $h_1(1415)$) can be reliably
constrained. We then introduce the intermediate $K^*\bar{K}+c.c.$ meson loop
transitions in the description of the productions and decays of these axial
vector mesons. The presence of the nearby $S$-wave $K^*\bar{K}+c.c.$ to which
these axial vector mesons have strong coupling strengths, turns out to be
crucial for understanding many puzzling questions related to their productions
and decays. This is because that the $S$-wave $K^*\bar{K}+c.c.$ rescatterings
by the kaon exchange satisfy the triangle singularity (TS) condition in some of
these cases and the TS mechanism can introduce special interference effects in
the exclusive decays of these light axial vector mesons.


Abstract:
  Math expressions are important parts of scientific and educational documents,
but some of them may be challenging for junior scholars or students to
understand. Nevertheless, constructing textual descriptions for math
expressions is nontrivial. In this paper, we explore the feasibility to
automatically construct descriptions for math expressions. But there are two
challenges that need to be addressed: 1) finding relevant documents since a
math equation understanding usually requires several topics, but these topics
are often explained in different documents. 2) the sparsity of the collected
relevant documents making it difficult to extract reasonable descriptions.
Different documents mainly focus on different topics which makes model hard to
extract salient information and organize them to form a description of math
expressions. To address these issues, we propose a hybrid model (MathDes) which
contains two important modules: Selector and Summarizer. In the Selector, a
Topic Relation Graph (TRG) is proposed to obtain the relevant documents which
contain the comprehensive information of math expressions. TRG is a graph built
according to the citations between expressions. In the Summarizer, a
summarization model under the Integer Linear Programming (ILP) framework is
proposed. This module constructs the final description with the help of a
timeline that is extracted from TRG. The experimental results demonstrate that
our methods are promising for this task and outperform the baselines in all
aspects.


Abstract:
  In this work, based on the Fredkin spin chain, we introduce a family of
spin-$1/2$ many-body Hamiltonians with a three-site interaction featuring a
fragmented Hilbert space with coexisting quantum many-body scars. The
fragmentation results from an emergent kinetic constraint resembling the
conserved spin configuration in the 1D Fermi-Hubbard model in the infinite
onsite repulsion limit. To demonstrate the many-body scars, we construct an
exact eigenstate that is in the middle of the spectrum within each fractured
sub-sector displaying either logarithmic or area-law entanglement entropy. The
interplay between fragmentation and scarring leads to rich tunable non-ergodic
dynamics by quenching different initial states that is shown through large
scale matrix product state simulations. In addition, we provide a Floquet
quantum circuit that displays non-ergodic dynamics as a result of sharing the
same fragmentation structure and scarring as the Hamiltonian.


Abstract:
  The rich literature on online Bayesian selection problems has long focused on
so-called prophet inequalities, which compare the gain of an online algorithm
to that of a "prophet" who knows the future. An equally-natural, though
significantly less well-studied benchmark is the optimum online algorithm,
which may be omnipotent (i.e., computationally-unbounded), but not omniscient.
What is the computational complexity of the optimum online? How well can a
polynomial-time algorithm approximate it?
  We study the above questions for the online stochastic maximum-weight
matching problem under vertex arrivals. For this problem, a number of
$1/2$-competitive algorithms are known against optimum offline. This is the
best possible ratio for this problem, as it generalizes the original
single-item prophet inequality problem.
  We present a polynomial-time algorithm which approximates the optimal online
algorithm within a factor of $0.51$ -- beating the best-possible prophet
inequality. In contrast, we show that it is PSPACE-hard to approximate this
problem within some constant $\alpha < 1$.


Abstract:
  Motivated by the industrial processing of chocolate, we study experimentally
the fluidisation of a sessile drop of yield-stress fluid on a pre-existing
layer of the same fluid under vertical sinusoidal oscillations. We compare the
behaviours of molten chocolate and Carbopol which are both shear-thinning with
a similar yield stress but exhibit very different elastic properties. We find
that these materials spread when the forcing acceleration exceeds a threshold
which is determined by the initial deposition process. However, they exhibit
very different spreading behaviours: whereas chocolate exhibits slow long-term
spreading, the Carbopol drop rapidly relaxes its stress by spreading to a new
equilibrium shape with an enlarged footprint. This spreading is insensitive to
the history of the forcing. In addition, the Carbopol drop performs
large-amplitude oscillations with the forcing frequency, both above and below
the threshold. We investigate these viscoelastic oscillations and provide
evidence of complex nonlinear viscoelastic behaviour in the vicinity of the
spreading threshold. In fact, for forcing accelerations greater than the
spreading threshold, our drop automatically adjusts its shape to remain at the
yield stress. We discuss how our vibrated-drop experiment offers a new and
powerful approach to probing the yield transition in elastoviscoplastic fluids.


Abstract:
  In this paper, a video service enhancement strategy is investigated under an
edge-cloud collaboration framework, where video caching and delivery decisions
are made in the cloud and edge respectively. We aim to guarantee the user
fairness in terms of video coding rate under statistical delay constraint and
edge caching capacity constraint. A hybrid human-artificial intelligence
approach is developed to improve the user hit rate for video caching.
Specifically, individual user interest is first characterized by merging
factorization machine (FM) model and multi-layer perceptron (MLP) model, where
both low-order and high-order features can be well learned simultaneously.
Thereafter, a social aware similarity model is constructed to transferred
individual user interest to group interest, based on which, videos can be
selected to cache. Furthermore, a double bisection exploration scheme is
proposed to optimize wireless resource allocation and video coding rate. The
effectiveness of the proposed video caching scheme and video delivery scheme is
finally validated by extensive experiments with a real-world data set.


Abstract:
  Semi-supervised learning has received a lot of recent attention as it
alleviates the need for large amounts of labelled data which can often be
expensive, requires expert knowledge and be time consuming to collect. Recent
developments in deep semi-supervised classification have reached unprecedented
performance and the gap between supervised and semi-supervised learning is
ever-decreasing. This improvement in performance has been based on the
inclusion of numerous technical tricks, strong augmentation techniques and
costly optimisation schemes with multi-term loss functions. We propose a new
framework, LaplaceNet, for deep semi-supervised classification that has a
greatly reduced model complexity. We utilise a hybrid energy-neural network
where graph based pseudo-labels, generated by minimising the graphical
Laplacian, are used to iteratively improve a neural-network backbone. Our model
outperforms state-of-the-art methods for deep semi-supervised classification,
over several benchmark datasets. Furthermore, we consider the application of
strong-augmentations to neural networks theoretically and justify the use of a
multi-sampling approach for semi-supervised learning. We demonstrate, through
rigorous experimentation, that a multi-sampling augmentation approach improves
generalisation and reduces the sensitivity of the network to augmentation.


Abstract:
  Measuring the information leakage is critical for evaluating practical
security of cryptographic devices against side-channel analysis. More
straightforwardly, it is interesting to have an upper bound on success rate of
any attack given a (fixed) number of side-channel measurements. Or conversely,
we wish to derive a lower bound on the number of queries for a given success
rate of optimal attacks. In this paper, we derive several bounds in both
directions by using information-theoretic tools, particularly for cryptographic
implementations protected by masking schemes. We show that a generic upper
bound on the probability of success, irrespective to specific attacks, is
linked to mutual information between side-channel measurements and the secret.
Moreover, our numerical evaluation confirms that, the success rate of optimal
maximum likelihood distinguishers is tightly bounded given a fixed number of
measurements.


Abstract:
  Sparse optimization problems are ubiquitous in many fields such as
statistics, signal/image processing and machine learning. This has led to the
birth of many iterative algorithms to solve them. A powerful strategy to boost
the performance of these algorithms is known as safe screening: it allows the
early identification of zero coordinates in the solution, which can then be
eliminated to reduce the problem's size and accelerate convergence. In this
work, we extend the existing Gap Safe screening framework by relaxing the
global strong-concavity assumption on the dual cost function. Instead, we
exploit local regularity properties, that is, strong concavity on well-chosen
subsets of the domain. The non-negativity constraint is also integrated to the
existing framework. Besides making safe screening possible to a broader class
of functions that includes beta-divergences (e.g., the Kullback-Leibler
divergence), the proposed approach also improves upon the existing Gap Safe
screening rules on previously applicable cases (e.g., logistic regression). The
proposed general framework is exemplified by some notable particular cases:
logistic function, beta = 1.5 and Kullback-Leibler divergences. Finally, we
showcase the effectiveness of the proposed screening rules with different
solvers (coordinate descent, multiplicative-update and proximal gradient
algorithms) and different data sets (binary classification, hyperspectral and
count data).


Abstract:
  We study colored coverage and clustering problems. Here, we are given a
colored point set where the points are covered by (unknown) $k$ clusters, which
are monochromatic (i.e., all the points covered by the same cluster, have the
same color). The access to the colors of the points (or even the points
themselves) is provided indirectly via various queries (such as nearest
neighbor, or separation queries). We show that if the number of clusters is a
constant, then one can correctly deduce the color of all the points (i.e.,
compute a monochromatic clustering of the points) using a polylogarithmic
number of queries.
  We investigate several variants of this problem, including Undecided Linear
Programming, covering of points by $k$ monochromatic balls, covering by $k$
triangles/simplices, and terrain simplification. For the later problem, we
present the first near linear time approximation algorithm. While our
approximation is slightly worse than previous work, this is the first algorithm
to have subquadratic complexity if the terrain has "small" complexity.


Abstract:
  Demographic changes increase the necessity to base the pension system more
and more on the second and the third pillar, namely the occupational and
private pension plans; this paper deals with Target Date Funds (TDFs), which
are a typical investment opportunity for occupational pension planners. TDFs
are usually identified with a decreasing fraction of wealth invested in equity
(a so-called glide path) as retirement comes closer, i.e., wealth is invested
more risky the younger the saver is. We investigate whether this is actually
optimal in the presence of non-tradable income risk in a stochastic volatility
environment. The retirement planning procedure is formulated as a stochastic
optimization problem. We find it is the (random) contributions that induce the
optimal path exhibiting a glide path structure, both in the constant and
stochastic volatility environment. Moreover, the initial wealth and the initial
contribution made to a retirement account strongly influence the fractional
amount of wealth to be invested in risky assets. The risk aversion of an
individual mainly determines the steepness of the glide path.


Abstract:
  The number $\frac{\pi ^{2}}{6}$ is involved in the variance of several
distributions in statistics. At the same time it holds
$\sum\nolimits_{k=1}^{\infty }k^{-2}= \frac{\pi ^{2}}{6}$, which solves the
famous Basel problem. We first provide a historical perspective on the Basel
problem, and second show how to generate further proofs building on stochastic
processes.


Abstract:
  This paper considers the problem of asynchronous distributed multi-agent
optimization on server-based system architecture. In this problem, each agent
has a local cost, and the goal for the agents is to collectively find a minimum
of their aggregate cost. A standard algorithm to solve this problem is the
iterative distributed gradient-descent (DGD) method being implemented
collaboratively by the server and the agents. In the synchronous setting, the
algorithm proceeds from one iteration to the next only after all the agents
complete their expected communication with the server. However, such synchrony
can be expensive and even infeasible in real-world applications. We show that
waiting for all the agents is unnecessary in many applications of distributed
optimization, including distributed machine learning, due to redundancy in the
cost functions (or {\em data}). Specifically, we consider a generic notion of
redundancy named $(r,\epsilon)$-redundancy implying solvability of the original
multi-agent optimization problem with $\epsilon$ accuracy, despite the removal
of up to $r$ (out of total $n$) agents from the system. We present an
asynchronous DGD algorithm where in each iteration the server only waits for
(any) $n-r$ agents, instead of all the $n$ agents. Assuming
$(r,\epsilon)$-redundancy, we show that our asynchronous algorithm converges to
an approximate solution with error that is linear in $\epsilon$ and $r$.
Moreover, we also present a generalization of our algorithm to tolerate some
Byzantine faulty agents in the system. Finally, we demonstrate the improved
communication efficiency of our algorithm through experiments on MNIST and
Fashion-MNIST using the benchmark neural network LeNet.


Abstract:
  We develop techniques that lay out a basis for generalizations of the famous
Thurston's Topological Characterization of Rational Functions for an infinite
set of marked points and branched coverings of infinite degree. Analogously to
the classical theorem we consider the Thurston's $\sigma$-map acting on a
Teichm\"uller space which is this time infinite-dimensional -- and this leads
to a completely different theory comparing to the classical setting.
  We demonstrate our techniques by giving an alternative proof of the result by
Markus F\"orster about the classification of exponential functions with the
escaping singular value.


Abstract:
  The study of the visual system of the brain has attracted the attention and
interest of many neuro-scientists, that derived computational models of some
types of neuron that compose it. These findings inspired researchers in image
processing and computer vision to deploy such models to solve problems of
visual data processing. In this paper, we review approaches for image
processing and computer vision, the design of which is based on
neuro-scientific findings about the functions of some neurons in the visual
cortex. Furthermore, we analyze the connection between the hierarchical
organization of the visual system of the brain and the structure of
Convolutional Networks (ConvNets). We pay particular attention to the
mechanisms of inhibition of the responses of some neurons, which provide the
visual system with improved stability to changing input stimuli, and discuss
their implementation in image processing operators and in ConvNets.


Abstract:
  Small, highly absorbing points are randomly present on the surfaces of the
main interferometer optics in Advanced LIGO. The resulting nano-meter scale
thermo-elastic deformations and substrate lenses from these micron-scale
absorbers significantly reduces the sensitivity of the interferometer directly
though a reduction in the power-recycling gain and indirect interactions with
the feedback control system. We review the expected surface deformation from
point absorbers and provide a pedagogical description of the impact on power
build-up in second generation gravitational wave detectors (dual-recycled
Fabry-Perot Michelson interferometers). This analysis predicts that the
power-dependent reduction in interferometer performance will significantly
degrade maximum stored power by up to 50% and hence, limit GW sensitivity, but
suggests system wide corrections that can be implemented in current and future
GW detectors. This is particularly pressing given that future GW detectors call
for an order of magnitude more stored power than currently used in Advanced
LIGO in Observing Run 3. We briefly review strategies to mitigate the effects
of point absorbers in current and future GW wave detectors to maximize the
success of these enterprises.


Abstract:
  Interpolation and internal painting are one of the basic approaches in image
internal painting, which is used to eliminate undesirable parts that occur in
digital images or to enhance faulty parts. This study was designed to compare
the interpolation algorithms used in image in-painting in the literature.
Errors and noise generated on the colour and grayscale formats of some of the
commonly used standard images in the literature were corrected by using Cubic,
Kriging, Radial based function and High dimensional model representation
approaches and the results were compared using standard image comparison
criteria, namely, PSNR (peak signal-to-noise ratio), SSIM (Structural
SIMilarity), Mean Square Error (MSE). According to the results obtained from
the study, the absolute superiority of the methods against each other was not
observed. However, Kriging and RBF interpolation give better results both for
numerical data and visual evaluation for image in-painting problems with large
area losses.


Abstract:
  We develop a mean-field theory of the growth, exchange and distribution (GED)
model introduced by Kang et al. (preceding paper) that accurately describes the
phase transition in the limit that the number of agents $N$ approaches
infinity. The GED model is a generalization of the Yard-Sale model in which the
additional wealth added by economic growth is nonuniformly distributed to the
agents according to their wealth in a way determined by the parameter
$\lambda$. The model was shown numerically to have a phase transition at
$\lambda=1$ and be characterized by critical exponents and critical slowing
down. Our mean-field treatment of the GED model correctly predicts the
existence of the phase transition, critical slowing down, the values of the
critical exponents, and introduces an energy whose probability satisfies the
Boltzmann distribution for $\lambda < 1$, implying that the system is in
thermodynamic equilibrium in the limit that $N \to \infty$. We show that the
values of the critical exponents obtained by varying $\lambda$ for a fixed
value of $N$ do not satisfy the usual scaling laws, but do satisfy scaling if a
combination of parameters, which we refer to as the Ginzburg parameter, is much
greater than one and is held constant. We discuss possible implications of our
results for understanding economic systems and the subtle nature of the
mean-field limit in systems with both additive and multiplicative noise.


Abstract:
  We study the emergence of de Sitter space in Euclidean dynamical
triangulations (EDT). Working within the semi-classical approximation, it is
possible to relate the lattice parameters entering the simulations to the
partition function of Euclidean quantum gravity. We verify that the EDT
geometries behave semi-classically, and by making contact with the Hawking-Moss
instanton solution for the Euclidean partition function, we show how to extract
a value of the renormalized Newton's constant from the simulations. This value
is consistent with that of our previous determination coming from the
interaction of scalar particles. That the same universal constant appears in
these two different sectors of the theory is a strong indication that EDT
provides a viable formulation of quantum gravity.


Abstract:
  Pre-trained language models of the BERT family have defined the
state-of-the-arts in a wide range of NLP tasks. However, the performance of
BERT-based models is mainly driven by the enormous amount of parameters, which
hinders their application to resource-limited scenarios. Faced with this
problem, recent studies have been attempting to compress BERT into a
small-scale model. However, most previous work primarily focuses on a single
kind of compression technique, and few attention has been paid to the
combination of different methods. When BERT is compressed with integrated
techniques, a critical question is how to design the entire compression
framework to obtain the optimal performance. In response to this question, we
integrate three kinds of compression methods (weight pruning, low-rank
factorization and knowledge distillation (KD)) and explore a range of designs
concerning model architecture, KD strategy, pruning frequency and learning rate
schedule. We find that a careful choice of the designs is crucial to the
performance of the compressed model. Based on the empirical findings, our best
compressed model, dubbed Refined BERT cOmpreSsion with InTegrAted techniques
(ROSITA), is $7.5 \times$ smaller than BERT while maintains $98.5\%$ of the
performance on five tasks of the GLUE benchmark, outperforming the previous
BERT compression methods with similar parameter budget. The code is available
at https://github.com/llyx97/Rosita.


Abstract:
  We consider the scattering of $n$ classical particles interacting via pair
potentials, under the assumption that each pair potential is "long-range", i.e.
being of order ${\cal O}(r^{-\alpha})$ for some $\alpha >0$. We define and
focus on the "free region", the set of states leading to well-defined and
well-separated final states at infinity. As a first step, we prove the
existence of an explicit, global surface of section for the free region. This
surface of section is key to proving the smoothness of the map sending a point
to its final state and to establishing a forward conjugacy between the $n$-body
dynamics and free dynamics.


Abstract:
  From global pandemics to geopolitical turmoil, leaders in logistics, product
allocation, procurement and operations are facing increasing difficulty with
safeguarding their organizations against supply chain vulnerabilities. It is
recommended to opt for forecasting against trending based benchmark because
auditing a future forecast puts more focus on seasonality. The forecasting
models provide with end-to-end, real time oversight of the entire supply chain,
while utilizing predictive analytics and artificial intelligence to identify
potential disruptions before they occur. By combining internal and external
data points, coming up with an AI-enabled modelling engine can greatly reduce
risk by helping retail companies proactively respond to supply and demand
variability. This research paper puts focus on creating an ingenious way to
tackle the impact of COVID19 on Supply chain, product allocation, trending and
seasonality.
  Key words: Supply chain, covid-19, forecasting, coronavirus, manufacturing,
seasonality, trending, retail.


Abstract:
  Dimensionality reduction (DR) techniques have been consistently supporting
high-dimensional data analysis in various applications. Besides the patterns
uncovered by these techniques, the interpretation of DR results based on each
feature's contribution to the low-dimensional representation supports new finds
through exploratory analysis. Current literature approaches designed to
interpret DR techniques do not explain the features' contributions well since
they focus only on the low-dimensional representation or do not consider the
relationship among features. This paper presents ClusterShapley to address
these problems, using Shapley values to generate explanations of dimensionality
reduction techniques and interpret these algorithms using a cluster-oriented
analysis. ClusterShapley explains the formation of clusters and the meaning of
their relationship, which is useful for exploratory data analysis in various
domains. We propose novel visualization techniques to guide the interpretation
of features' contributions on clustering formation and validate our methodology
through case studies of publicly available datasets. The results demonstrate
our approach's interpretability and analysis power to generate insights about
pathologies and patients in different conditions using DR results.


Abstract:
  The convergence speed of machine learning models trained with Federated
Learning is significantly affected by heterogeneous data partitions, even more
so in a fully decentralized setting without a central server. In this paper, we
show that the impact of label distribution skew, an important type of data
heterogeneity, can be significantly reduced by carefully designing the
underlying communication topology. We present D-Cliques, a novel topology that
reduces gradient bias by grouping nodes in sparsely interconnected cliques such
that the label distribution in a clique is representative of the global label
distribution. We also show how to adapt the updates of decentralized SGD to
obtain unbiased gradients and implement an effective momentum with D-Cliques.
Our extensive empirical evaluation on MNIST and CIFAR10 demonstrates that our
approach provides similar convergence speed as a fully-connected topology,
which provides the best convergence in a data heterogeneous setting, with a
significant reduction in the number of edges and messages. In a 1000-node
topology, D-Cliques require 98% less edges and 96% less total messages, with
further possible gains using a small-world topology across cliques.


Abstract:
  The overall predictive uncertainty of a trained predictor can be decomposed
into separate contributions due to epistemic and aleatoric uncertainty. Under a
Bayesian formulation, assuming a well-specified model, the two contributions
can be exactly expressed (for the log-loss) or bounded (for more general
losses) in terms of information-theoretic quantities (Xu and Raginsky, 2020).
This paper addresses the study of epistemic uncertainty within an
information-theoretic framework in the broader setting of Bayesian
meta-learning. A general hierarchical Bayesian model is assumed in which
hyperparameters determine the per-task priors of the model parameters. Exact
characterizations (for the log-loss) and bounds (for more general losses) are
derived for the epistemic uncertainty -quantified by the minimum excess
meta-risk (MEMR)- of optimal meta-learning rules. This characterization is
leveraged to bring insights into the dependence of the epistemic uncertainty on
the number of tasks and on the amount of per-task training data. Experiments
are presented that use the proposed information-theoretic bounds, evaluated via
neural mutual information estimators, to compare the performance of
conventional learning and meta-learning as the number of meta-learning tasks
increases.


Abstract:
  Neural network (NN) interatomic potentials provide fast prediction of
potential energy surfaces, closely matching the accuracy of the electronic
structure methods used to produce the training data. However, NN predictions
are only reliable within well-learned training domains, and show volatile
behavior when extrapolating. Uncertainty quantification approaches can flag
atomic configurations for which prediction confidence is low, but arriving at
such uncertain regions requires expensive sampling of the NN phase space, often
using atomistic simulations. Here, we exploit automatic differentiation to
drive atomistic systems towards high-likelihood, high-uncertainty
configurations without the need for molecular dynamics simulations. By
performing adversarial attacks on an uncertainty metric, informative geometries
that expand the training domain of NNs are sampled. When combined to an active
learning loop, this approach bootstraps and improves NN potentials while
decreasing the number of calls to the ground truth method. This efficiency is
demonstrated on sampling of kinetic barriers and collective variables in
molecules, and can be extended to any NN potential architecture and materials
system.


Abstract:
  Normalizing flows are a class of flexible deep generative models that offer
easy likelihood computation. Despite their empirical success, there is little
theoretical understanding of their expressiveness. In this work, we study
residual flows, a class of normalizing flows composed of Lipschitz residual
blocks. We prove residual flows are universal approximators in maximum mean
discrepancy. We provide upper bounds on the number of residual blocks to
achieve approximation under different assumptions.


Abstract:
  The use of conversational assistants to search for information is becoming
increasingly more popular among the general public, pushing the research
towards more advanced and sophisticated techniques. In the last few years, in
particular, the interest in conversational search is increasing, not only
because of the generalization of conversational assistants but also because
conversational search is a step forward in allowing a more natural interaction
with the system.
  In this work, the focus is on exploring the context present of the
conversation via the historical utterances and respective embeddings with the
aim of developing a conversational search system that helps people search for
information in a natural way. In particular, this system must be able to
understand the context where the question is posed, tracking the current state
of the conversation and detecting mentions to previous questions and answers.
We achieve this by using a context-tracking component based on neural
query-rewriting models. Another crucial aspect of the system is to provide the
most relevant answers given the question and the conversational history. To
achieve this objective, we used a Transformer-based re-ranking method and
expanded this architecture to use the conversational context.
  The results obtained with the system developed showed the advantages of using
the context present in the natural language utterances and in the neural
embeddings generated throughout the conversation.


Abstract:
  Hand-eye calibration of laser profile sensors is the process of extracting
the homogeneous transformation between the laser profile sensor frame and the
end-effector frame of a robot in order to express the data extracted by the
sensor in the robot's global coordinate system. For laser profile scanners this
is a challenging procedure, as they provide data only in two dimensions and
state-of-the-art calibration procedures require the use of specialised
calibration targets. This paper presents a novel method to extract the
translation-part of the hand-eye calibration matrix with rotation-part known a
priori in a target-agnostic way. Our methodology is applicable to any 2D image
or 3D object as a calibration target and can also be performed in situ in the
final application. The method is experimentally validated on a real
robot-sensor setup with 2D and 3D targets.


Abstract:
  Many machine learning models have been built to tackle information overload
issues on Massive Open Online Courses (MOOC) platforms. These models rely on
learning powerful representations of MOOC entities. However, they suffer from
the problem of scarce expert label data. To overcome this problem, we propose
to learn pre-trained representations of MOOC entities using abundant unlabeled
data from the structure of MOOCs which can directly be applied to the
downstream tasks. While existing pre-training methods have been successful in
NLP areas as they learn powerful textual representation, their models do not
leverage the richer information about MOOC entities. This richer information
includes the graph relationship between the lectures, concepts, and courses
along with the domain knowledge about the complexity of a concept. We develop
MOOCRep, a novel method based on Transformer language model trained with two
pre-training objectives : 1) graph-based objective to capture the powerful
signal of entities and relations that exist in the graph, and 2)
domain-oriented objective to effectively incorporate the complexity level of
concepts. Our experiments reveal that MOOCRep's embeddings outperform
state-of-the-art representation learning methods on two tasks important for
education community, concept pre-requisite prediction and lecture
recommendation.


Abstract:
  To reduce the resource demand of neural network (NN) inference systems, it
has been proposed to use approximate memory, in which the supply voltage and
the timing parameters are tuned trading accuracy with energy consumption and
performance. Tuning these parameters aggressively leads to bit errors, which
can be tolerated by NNs when bit flips are injected during training. However,
bit flip training, which is the state of the art for achieving bit error
tolerance, does not scale well; it leads to massive overheads and cannot be
applied for high bit error rates (BERs). Alternative methods to achieve bit
error tolerance in NNs are needed, but the underlying principles behind the bit
error tolerance of NNs have not been reported yet. With this lack of
understanding, further progress in the research on NN bit error tolerance will
be restrained.
  In this study, our objective is to investigate the internal changes in the
NNs that bit flip training causes, with a focus on binarized NNs (BNNs). To
this end, we quantify the properties of bit error tolerant BNNs with two
metrics. First, we propose a neuron-level bit error tolerance metric, which
calculates the margin between the pre-activation values and batch normalization
thresholds. Secondly, to capture the effects of bit error tolerance on the
interplay of neurons, we propose an inter-neuron bit error tolerance metric,
which measures the importance of each neuron and computes the variance over all
importance values. Our experimental results support that these two metrics are
strongly related to bit error tolerance.


Abstract:
  It is a commonly held belief that enforcing invariance improves
generalisation. Although this approach enjoys widespread popularity, it is only
very recently that a rigorous theoretical demonstration of this benefit has
been established. In this work we build on the function space perspective of
Elesedy and Zaidi arXiv:2102.10333 to derive a strictly non-zero generalisation
benefit of incorporating invariance in kernel ridge regression when the target
is invariant to the action of a compact group. We study invariance enforced by
feature averaging and find that generalisation is governed by a notion of
effective dimension that arises from the interplay between the kernel and the
group. In building towards this result, we find that the action of the group
induces an orthogonal decomposition of both the reproducing kernel Hilbert
space and its kernel, which may be of interest in its own right.


Abstract:
  Deeper and wider CNNs are known to provide improved performance for deep
learning tasks. However, most such networks have poor performance gain per
parameter increase. In this paper, we investigate whether the gain observed in
deeper models is purely due to the addition of more optimization parameters or
whether the physical size of the network as well plays a role. Further, we
present a novel rescaling strategy for CNNs based on learnable repetition of
its parameters. Based on this strategy, we rescale CNNs without changing their
parameter count, and show that learnable sharing of weights itself can provide
significant boost in the performance of any given model without changing its
parameter count. We show that small base networks when rescaled, can provide
performance comparable to deeper networks with as low as 6% of optimization
parameters of the deeper one.
  The relevance of weight sharing is further highlighted through the example of
group-equivariant CNNs. We show that the significant improvements obtained with
group-equivariant CNNs over the regular CNNs on classification problems are
only partly due to the added equivariance property, and part of it comes from
the learnable repetition of network weights. For rot-MNIST dataset, we show
that up to 40% of the relative gain reported by state-of-the-art methods for
rotation equivariance could actually be due to just the learnt repetition of
weights.


Abstract:
  We investigate self-gravitating equilibria of halos constituted by dark
matter (DM) non-minimally coupled to gravity. In particular, we consider a
theoretically motivated non-minimal coupling which may arise when the
averaging/coherence length $L$ associated to the fluid description of the DM
collective behavior is comparable to the local curvature scale. In the
Newtonian limit, such a non-minimal coupling amounts to a modification of the
Poisson equation by a term $L^2\,\nabla^2\rho$ proportional to the Laplacian of
the DM density $\rho$ itself. We further adopt a general power-law equation of
state $p\propto \rho^{\Gamma}\, r^\alpha$ relating the DM dynamical pressure
$p$ to density $\rho$ and radius $r$, as expected by phase-space density
stratification during the gravitational assembly of halos in a cosmological
context. We confirm previous findings that, in absence of the non-minimal
coupling, the resulting density $\rho(r)$ features a steep central cusp and an
overall shape mirroring the outcomes of $N-$body simulations in the standard
$\Lambda$CDM cosmology, as described by the classic NFW or Einasto profiles.
Most importantly, we find that the non-minimal coupling causes the density
distribution to develop an inner core and a shape closely following, out to
several core scale radii, the Burkert profile. In fact, we highlight that the
resulting mass distributions can fit, with an accuracy comparable to the
Burkert's one, the co-added rotation curves of dwarf, DM-dominated galaxies.
Finally, we show that non-minimally coupled DM halos are consistent with the
observed scaling relation between the core radius $r_0$ and core density
$\rho_0$, in terms of an universal core surface density $\rho_0\times r_0$
among different galaxies.


Abstract:
  This paper studies the application of the Quantum Approximate Optimization
Algorithm (QAOA) to spin-glass models with random multi-body couplings in the
limit of a large number of spins. We show that for such mixed-spin models the
performance of depth $1$ QAOA is independent of the specific instance in the
limit of infinite sized systems and we give an explicit formula for the
expected performance. We also give explicit expressions for the higher moments
of the expected energy, thereby proving that the expected performance of QAOA
concentrates.


Abstract:
  Code cloning plays a very important role in open-source software engineering.
The presence of clones within a project may indicate a need for refactoring,
and clones between projects are even more interesting, since code migration
takes place and violations are possible. But how is code being copied? How
prevalent is the process and on what level does it happen?
  In this general study, we attempt to shed some light on these questions by
searching for clones in a large dataset of over 23 thousand Java projects on
the level of both files and methods, and by studying the code fragments
themselves and their clone pairs. We study the size and the age of code
fragments, the prevalence of their clones, relationships between exact and
non-exact clones, as well as between method-level and file-level clones. We
also discover and describe various anomalies in the code clones that were
detected in the dataset.
  Our research shows that the copying occurs all through the years of the Java
code existence and that method-level copying is much more prevalent than
file-level copying, with only 35.4% of methods having no clones at all.
Additionally, some of the discovered anomalies can be useful for future
large-scale cloning research as they can be used for removing auto-generated
code.


Abstract:
  Driver drowsiness is one of main factors leading to road fatalities and
hazards in the transportation industry. Electroencephalography (EEG) has been
considered as one of the best physiological signals to detect drivers drowsy
states, since it directly measures neurophysiological activities in the brain.
However, designing a calibration-free system for driver drowsiness detection
with EEG is still a challenging task, as EEG suffers from serious mental and
physical drifts across different subjects. In this paper, we propose a compact
and interpretable Convolutional Neural Network (CNN) to discover shared EEG
features across different subjects for driver drowsiness detection. We
incorporate the Global Average Pooling (GAP) layer in the model structure,
allowing the Class Activation Map (CAM) method to be used for localizing
regions of the input signal that contribute most for classification. Results
show that the proposed model can achieve an average accuracy of 73.22% on 11
subjects for 2-class cross-subject EEG signal classification, which is higher
than conventional machine learning methods and other state-of-art deep learning
methods. It is revealed by the visualization technique that the model has
learned biologically explainable features, e.g., Alpha spindles and Theta
burst, as evidence for the drowsy state. It is also interesting to see that the
model uses artifacts that usually dominate the wakeful EEG, e.g., muscle
artifacts and sensor drifts, to recognize the alert state. The proposed model
illustrates a potential direction to use CNN models as a powerful tool to
discover shared features related to different mental states across different
subjects from EEG signals.


Abstract:
  Existing conversational recommendation (CR) systems usually suffer from
insufficient item information when conducted on short dialogue history and
unfamiliar items. Incorporating external information (e.g., reviews) is a
potential solution to alleviate this problem. Given that reviews often provide
a rich and detailed user experience on different interests, they are potential
ideal resources for providing high-quality recommendations within an
informative conversation. In this paper, we design a novel end-to-end
framework, namely, Review-augmented Conversational Recommender (RevCore), where
reviews are seamlessly incorporated to enrich item information and assist in
generating both coherent and informative responses. In detail, we extract
sentiment-consistent reviews, perform review-enriched and entity-based
recommendations for item suggestions, as well as use a review-attentive
encoder-decoder for response generation. Experimental results demonstrate the
superiority of our approach in yielding better performance on both
recommendation and conversation responding.


Abstract:
  The evolution of chiral spin structures is studied in ferrimagnet
Ta/Ir/Fe/GdFeCo/Pt multilayers as a function of temperature using scanning
electron microscopy with polarization analysis (SEMPA). The GdFeCo ferrimagnet
exhibits pure right-hand N\'eel-type domain wall (DW) spin textures over a
large temperature range. This indicates the presence of a negative
Dzyaloshinskii-Moriya interaction (DMI) that can originate from both the top
Fe/Pt and the Co/Pt interfaces. From measurements of the DW width, as well as
complementary magnetic characterization, the exchange stiffness as a function
of temperature is ascertained. The exchange stiffness is surprisingly mostly
constant, which is explained by theoretical predictions. Beyond single
skyrmions, we find by direct imaging a pure N\'eel-type skyrmionium, which due
to the absence of a skyrmion Hall angle is a promising topological spin
structure to enable high impact potential applications in the next generation
of spintronic devices.


Abstract:
  We present a novel deep learning-based framework to generate embedding
representations of fine-grained emotions that can be used to computationally
describe psychological models of emotions. Our framework integrates a
contextualized embedding encoder with a multi-head probing model that enables
to interpret dynamically learned representations optimized for an emotion
classification task. Our model is evaluated on the Empathetic Dialogue dataset
and shows the state-of-the-art result for classifying 32 emotions. Our layer
analysis can derive an emotion graph to depict hierarchical relations among the
emotions. Our emotion representations can be used to generate an emotion wheel
directly comparable to the one from Plutchik's\LN model, and also augment the
values of missing emotions in the PAD emotional state model.


Abstract:
  Traditional Cauchy-Stokes decomposition of velocity gradient tensor gives a
symmetric and an anti-symmetric subtensors which are called the strain-rate and
vorticity tensors. There are two problems with Cauchy-Stokes decomposition. The
first one is that the anti-symmetric or vorticity tensor cannot represent the
fluid rotation or vortex. The second one is that the symmetric (strain-rate)
tensor cannot distinguish the stretching (compression) and shear. The
stretching and shear are dependent on the coordinate or are not Galilean
invariant. Since vorticity cannot distinguish between the non-rotational shear
and the rigid rotation, vorticity has been decomposed to a rigid rotation
called Liutex and anti-symmetric shear in our previous work. A Liutex-based
principal coordinate was developed and the velocity gradient tensor was
decomposed in the principal coordinate as a rigid rotation (Liutex tensor), a
pure shear tensor and a stretching (compression) tensor, which is called the
principal decomposition. However, the principal decomposition is made at each
point which has own principal coordinate different from other points. This
paper derives the principal decomposition in the original xyz coordinate
system, and, therefore, provides a new tool for fluid kinematics to conduct the
velocity gradient tensor decomposition to rigid rotation, pure shear, and
stretching (compression) which is Galilean invariant and has clear physical
meanings. The new velocity gradient tensor decomposition could become a
foundation for new fluid kinematics.


Abstract:
  This study aims to assess the properties and classification of 62 variable
stars in Cygnus, little studied since their discovery and originally reported
in the Information Bulletin on Variable Stars (IBVS) 1302. Using data from
previous studies and several astronomical databases, we performed our analysis
mainly utilizing a period analysis software and comparing the photometric
characteristics of the variables in a Colour-Absolute Magnitude Diagram. For
all stars, the variability is confirmed. We discovered new significant results
for the period and/or type of 17 variables and highlighted incorrect
cross-reference names on astronomical databases for 23 stars. For 3 stars,
whose original type was unknown, we propose a new type. We calculated an epoch
of a minimum or a maximum for 24 stars; for 3 of them, the epoch has been
defined for the first time. This assessment also identifies some cases for
which results from the ASAS-SN Catalog of Variable Stars are not consistent
with results from Gaia DR2 and/or our analysis.


Abstract:
  The classification of glomerular lesions is a routine and essential task in
renal pathology. Recently, machine learning approaches, especially deep
learning algorithms, have been used to perform computer-aided lesion
characterization of glomeruli. However, one major challenge of developing such
methods is the naturally imbalanced distribution of different lesions. In this
paper, we propose CircleMix, a novel data augmentation technique, to improve
the accuracy of classifying globally sclerotic glomeruli with a hierarchical
learning strategy. Different from the recently proposed CutMix method, the
CircleMix augmentation is optimized for the ball-shaped biomedical objects,
such as glomeruli. 6,861 glomeruli with five classes (normal, periglomerular
fibrosis, obsolescent glomerulosclerosis, solidified glomerulosclerosis, and
disappearing glomerulosclerosis) were employed to develop and evaluate the
proposed methods. From five-fold cross-validation, the proposed CircleMix
augmentation achieved superior performance (Balanced Accuracy=73.0%) compared
with the EfficientNet-B0 baseline (Balanced Accuracy=69.4%)


Abstract:
  Trapped ions are excellent candidates for quantum computing and quantum
networks because of their long coherence times, ability to generate entangled
photons as well as high fidelity single- and two-qubit gates. To scale up
trapped ion quantum computing, we need a Bell-state analyzer on a
reconfigurable platform that can herald high fidelity entanglement between
ions. In this work, we design a photonic Bell-state analyzer on a
reconfigurable thin film lithium niobate platform for polarization-encoded
qubits. We optimize the device to achieve high fidelity entanglement between
two trapped ions and find >99% fidelity. The proposed device can scale up
trapped ion quantum computing as well as other optically active spin qubits,
such as color centers in diamond, quantum dots, and rare-earth ions.


Abstract:
  We present the results of imaging photometric and long-slit spectroscopic
observations of comet 2P/Encke performed at the heliocentric distance 0.56 au,
geocentric distance 0.65 au, and phase angle 109.2 deg on November 4, 2013 and
at 1.05 au, 1.34 au, and 46.8 deg on January 23, 2017. Observations were
carried out at the 6-m BTA telescope of the Special Astrophysical Observatory
(Russia) with the multimode focal reducer SCORPIO-2. In 2013, the direct images
of comet Encke were obtained with the broad-band V filters, whereas in 2017 the
narrow-band cometary BC, RC, and NH2 filters as well as the medium-band SED500
and broad-band r-sdss filters were used for observations. About 60 emissions
belonging to the CN, C2, C3, NH2, CH, and CO+ molecules were identified within
the range 3750-7100 {\AA}. The ratios of the production rates C2/CN and C3/CN
correspond to the typical comets, not depleted in the carbon-chain. A complex
structure of the coma was detected in both observational periods. In January
2017, the dust was in general concentrated near the nucleus, the dust/gas ratio
was 2.9 in the r-sdss filter, however, this ratio was larger than 1 at
distances 3000-40000 km from the nucleus. We found that about 75% of the flux
of the reflected light in the central pixel was due to the nucleus, whereas the
nucleus's flux contributed 48% in the total intensity of the 2000 km area of
the coma. We found that after correction for the dust coma contamination the
nucleus magnitude is 18.8m+/-0.2m.


Abstract:
  An effective recipe for building seq2seq, non-autoregressive, task-oriented
parsers to map utterances to semantic frames proceeds in three steps: encoding
an utterance $x$, predicting a frame's length |y|, and decoding a |y|-sized
frame with utterance and ontology tokens. Though empirically strong, these
models are typically bottlenecked by length prediction, as even small
inaccuracies change the syntactic and semantic characteristics of resulting
frames. In our work, we propose span pointer networks, non-autoregressive
parsers which shift the decoding task from text generation to span prediction;
that is, when imputing utterance spans into frame slots, our model produces
endpoints (e.g., [i, j]) as opposed to text (e.g., "6pm"). This natural
quantization of the output space reduces the variability of gold frames,
therefore improving length prediction and, ultimately, exact match.
Furthermore, length prediction is now responsible for frame syntax and the
decoder is responsible for frame semantics, resulting in a coarse-to-fine
model. We evaluate our approach on several task-oriented semantic parsing
datasets. Notably, we bridge the quality gap between non-autogressive and
autoregressive parsers, achieving 87 EM on TOPv2 (Chen et al. 2020).
Furthermore, due to our more consistent gold frames, we show strong
improvements in model generalization in both cross-domain and cross-lingual
transfer in low-resource settings. Finally, due to our diminished output
vocabulary, we observe 70% reduction in latency and 83% reduction in memory at
beam size 5 compared to prior non-autoregressive parsers.


Abstract:
  Purpose: We seek to explore the realm of literature about digital libraries.
We specifically seek to ascertain how interest in this subject has evolved, its
impact, the most productive journals and countries, the number of occurrences
of digital libraries, the relationships and dynamics of the main concepts
mentioned, and the dynamics of metadata formats.Methods: We extracted corpora
from the Google Scholar and Microsoft Academic Search bibliographic databases.
We analyzed the named entities and concepts contained within these corpora with
the help of text mining technologies, CorTexT in particular.Results: While the
number of publications on the subject of digital libraries is increasing, their
average number of citations is decreasing. China, the United States and India
are the most productive countries on the subject. Literature about conservation
and national libraries has gradually been replaced by literature about open
access, university libraries and the relationship with users. Internet Archive
is the most cited digital library in literature and continues to grow. Dublin
Core is the most talked about metadata format, however the subject of metadata
formats is declining in the corpus today.Conclusion: Digital libraries now seem
to be reaching the age of maturity.


Abstract:
  Finding parsimonious models through variable selection is a fundamental
problem in many areas of statistical inference. Here, we focus on Bayesian
regression models, where variable selection can be implemented through a
regularizing prior imposed on the distribution of the regression coefficients.
In the Bayesian literature, there are two main types of priors used to
accomplish this goal: the spike-and-slab and the continuous scale mixtures of
Gaussians. The former is a discrete mixture of two distributions characterized
by low and high variance. In the latter, a continuous prior is elicited on the
scale of a zero-mean Gaussian distribution. In contrast to these existing
methods, we propose a new class of priors based on discrete mixture of
continuous scale mixtures providing a more general framework for Bayesian
variable selection. To this end, we substitute the observation-specific local
shrinkage parameters (typical of continuous mixtures) with mixture component
shrinkage parameters. Our approach drastically reduces the number of parameters
needed and allows sharing information across the coefficients, improving the
shrinkage effect. By using half-Cauchy distributions, this approach leads to a
cluster-shrinkage version of the Horseshoe prior. We present the properties of
our model and showcase its estimation and prediction performance in a
simulation study. We then recast the model in a multiple hypothesis testing
framework and apply it to a neurological dataset obtained using a novel
whole-brain imaging technique.


Abstract:
  LiteBIRD has been selected as JAXA's strategic large mission in the 2020s, to
observe the cosmic microwave background (CMB) $B$-mode polarization over the
full sky at large angular scales. The challenges of LiteBIRD are the wide
field-of-view (FoV) and broadband capabilities of millimeter-wave polarization
measurements, which are derived from the system requirements. The possible
paths of stray light increase with a wider FoV and the far sidelobe knowledge
of $-56$ dB is a challenging optical requirement. A crossed-Dragone
configuration was chosen for the low frequency telescope (LFT : 34--161 GHz),
one of LiteBIRD's onboard telescopes. It has a wide field-of-view ($18^\circ
\times 9^\circ$) with an aperture of 400 mm in diameter, corresponding to an
angular resolution of about 30 arcminutes around 100 GHz. The focal ratio f/3.0
and the crossing angle of the optical axes of 90$^\circ$ are chosen after an
extensive study of the stray light. The primary and secondary reflectors have
rectangular shapes with serrations to reduce the diffraction pattern from the
edges of the mirrors. The reflectors and structure are made of aluminum to
proportionally contract from warm down to the operating temperature at $5\,$K.
A 1/4 scaled model of the LFT has been developed to validate the wide
field-of-view design and to demonstrate the reduced far sidelobes. A
polarization modulation unit (PMU), realized with a half-wave plate (HWP) is
placed in front of the aperture stop, the entrance pupil of this system. A
large focal plane with approximately 1000 AlMn TES detectors and frequency
multiplexing SQUID amplifiers is cooled to 100 mK. The lens and sinuous
antennas have broadband capability. Performance specifications of the LFT and
an outline of the proposed verification plan are presented.


Abstract:
  In programming education, teachers need to monitor and assess the progress of
their students by investigating the code they write. Code quality of programs
written in traditional programming languages can be automatically assessed with
automated tests, verification tools, or linters. In many cases these approaches
rely on some form of manually written formal specification to analyze the given
programs. Writing such specifications, however, is hard for teachers, who are
often not adequately trained for this task. Furthermore, automated tool support
for popular block-based introductory programming languages like Scratch is
lacking. Anomaly detection is an approach to automatically identify deviations
of common behavior in datasets without any need for writing a specification. In
this paper, we use anomaly detection to automatically find deviations of
Scratch code in a classroom setting, where anomalies can represent erroneous
code, alternative solutions, or distinguished work. Evaluation on solutions of
different programming tasks demonstrates that anomaly detection can
successfully be applied to tightly specified as well as open-ended programming
tasks.


Abstract:
  The incorporation of Wilson lines leads to an extension of the modular
symmetries of string compactification beyond $\mathrm{SL}(2,\mathbb Z)$. In the
simplest case with one Wilson line $Z$, K\"ahler modulus $T$ and complex
structure modulus $U$, we are led to the Siegel modular group
$\mathrm{Sp}(4,\mathbb Z)$. It includes $\mathrm{SL}(2,\mathbb
Z)_T\times\mathrm{SL}(2,\mathbb Z)_U$ as well as $\mathbb Z_2$ mirror symmetry,
which interchanges $T$ and $U$. Possible applications to flavor physics of the
Standard Model require the study of orbifolds of $\mathrm{Sp}(4,\mathbb Z)$ to
obtain chiral fermions. We identify the 13 possible orbifolds and determine
their modular flavor symmetries as subgroups of $\mathrm{Sp}(4,\mathbb Z)$.
Some cases correspond to symmetric orbifolds that extend previously discussed
cases of $\mathrm{SL}(2,\mathbb Z)$. Others are based on asymmetric orbifold
twists (including mirror symmetry) that do no longer allow for a simple
intuitive geometrical interpretation and require further study. Sometimes they
can be mapped back to symmetric orbifolds with quantized Wilson lines. The
symmetries of $\mathrm{Sp}(4,\mathbb Z)$ reveal exciting new aspects of modular
symmetries with promising applications to flavor model building.


Abstract:
  We study the top quark pair events production in $pp$ collisions in the
$\ell$+jets channel at the LHC energy of $\sqrt{s} = 14$ TeV for Standard Model
as well as new physics processes. We explore the usage of semi-boosted
topologies where the top quark decays into a boosted hadronic $W$-jet and an
isolated $b$-jet and study their performance in the $t\bar{t}$ events kinematic
reconstruction. An efficiency increase is observed and the correlation of
selected kinematic variables between the detector and particle level is
studied. Quality of the reconstructed mass line shape of a $Z'$ model with
applications in current LHC experiments is evaluated. The results of the
unfolding procedure for chosen spectra and an explicit proof of a slight
degradation in the signal significance after the unfolding procedure are
presented using the Fully Bayesian unfolding technique.


Abstract:
  It is shown for causal fermion systems describing Minkowski-type spacetimes
that an interacting causal fermion system at time $t$ gives rise to a
distinguished state on the algebra generated by fermionic and bosonic field
operators. The proof of positivity of the state is given and representations
are constructed.


Abstract:
  We show how a complete mathematical description of a complicated physical
phenomenon can be constructed from observational data via a hybrid approach
combining three simple and general ingredients: physical assumptions of
smoothness, locality, and symmetry, a weak formulation of differential
equations, and sparse regression. To illustrate this, we extract a complete
system of governing equations describing flows of incompressible Newtonian
fluids -- the Navier-Stokes equation, the continuity equation, and the boundary
conditions -- from numerical data describing a highly turbulent channel flow in
three dimensions. The hybrid approach is remarkably robust, yielding accurate
results for very high noise levels, and should thus work equally well for a
wide range of experimental data. In addition, this approach provides easily
interpretable information about the relative importance of different physical
effects (such as viscosity) as well as useful insight into the quality of the
data, making it a useful diagnostic tool.


Abstract:
  An optical circuit-switched network core has the potential to overcome the
inherent challenges of a conventional electrical packet-switched core of
today's compute clusters. As optical circuit switches (OCS) directly handle the
photon beams without any optical-electrical-optical (O/E/O) conversion and
packet processing, OCS-based network cores have the following desirable
properties: a) agnostic to data-rate, b) negligible/zero power consumption, c)
no need of transceivers, d) negligible forwarding latency, and e) no need for
frequent upgrade. Unfortunately, OCS can only provide point-to-point (unicast)
circuits. They do not have built-in support for one-to-many (multicast)
communication, yet multicast is fundamental to a plethora of data-intensive
applications running on compute clusters nowadays. In this paper, we propose
Shufflecast, a novel optical network architecture for next-generation compute
clusters that can support high-performance multicast satisfying all the
properties of an OCS-based network core. Shufflecast leverages small fanout,
inexpensive, passive optical splitters to connect the Top-of-rack (ToR) switch
ports, ensuring data-rate agnostic, low-power, physical-layer multicast. We
thoroughly analyze Shufflecast's highly scalable data plane, light-weight
control plane, and graceful failure handling. Further, we implement a complete
prototype of Shufflecast in our testbed and extensively evaluate the network.
Shufflecast is more power-efficient than the state-of-the-art multicast
mechanisms. Also, Shufflecast is more cost-efficient than a conventional
packet-switched network. By adding Shufflecast alongside an OCS-based unicast
network, an all-optical network core with the aforementioned desirable
properties supporting both unicast and multicast can be realized.


Abstract:
  A picture-hanging puzzle is the task of hanging a framed picture with a wire
around a set of nails in such a way that it can remain hanging on certain
specified sets of nails, but will fall if any more are removed. The classical
brain teaser asks us to hang a picture on two nails in such a way that it falls
when any one is detached. Demaine et al (2012) proved that all reasonable
puzzles of this kind are solvable, and that for the $k$-out-of-$n$ problem, the
size of a solution can be bounded by a polynomial in $n$. We give simplified
proofs of these facts, for the latter leading to a reasonable exponent in the
polynomial bound.


Abstract:
  Detecting and classifying instances of hate in social media text has been a
problem of interest in Natural Language Processing in the recent years. Our
work leverages state of the art Transformer language models to identify hate
speech in a multilingual setting. Capturing the intent of a post or a comment
on social media involves careful evaluation of the language style, semantic
content and additional pointers such as hashtags and emojis. In this paper, we
look at the problem of identifying whether a Twitter post is hateful and
offensive or not. We further discriminate the detected toxic content into one
of the following three classes: (a) Hate Speech (HATE), (b) Offensive (OFFN)
and (c) Profane (PRFN). With a pre-trained multilingual Transformer-based text
encoder at the base, we are able to successfully identify and classify hate
speech from multiple languages. On the provided testing corpora, we achieve
Macro F1 scores of 90.29, 81.87 and 75.40 for English, German and Hindi
respectively while performing hate speech detection and of 60.70, 53.28 and
49.74 during fine-grained classification. In our experiments, we show the
efficacy of Perspective API features for hate speech classification and the
effects of exploiting a multilingual training scheme. A feature selection study
is provided to illustrate impacts of specific features upon the architecture's
classification head.


Abstract:
  In this work, we study tracker phantom dark energy models with a general
parameterization of the scalar potentials. Our analysis also considers the
scenario of having both phantom field and the cosmological constant as the dark
energy components. A detailed statistical analysis with current cosmological
observations shows an increase in the value of the Hubble parameter due to the
presence of phantom dark energy but it can not alleviate the Hubble tension
completely. Our results using Bayesian methods suggests a decisive evidence in
favor of a phantom field over a positive cosmological constant, although the
possibility of a negative cosmological constant cannot be ruled out hidden in
the dark sector.


Abstract:
  The predictive power of the deformed relativistic Hartree-Bogoliubov theory
in continuum (DRHBc) for nuclear mass is examined in the superheavy region,
$102 \le Z \le 120$. The accuracy of predicting the 10 (56) measured (measured
and empirical) masses is $0.635$ ($0.642$) MeV, in comparison with $0.515$
($1.360$) MeV by WS4 and $0.910$ ($2.831$) MeV by FRDM. Possible stability
against multineutron emission beyond the two-neutron drip line is explored by
the DRHBc theory, which takes into account simultaneously the deformation
effects, the pairing correlations, and the continuum effects. Nuclei stable
against two- and multineutron emissions beyond the two-neutron drip line are
predicted in $_{106}$Sg, $_{108}$Hs, $_{110}$Ds, and $_{112}$Cn isotopic
chains, forming a peninsula of stability adjacent to the nuclear mainland. This
stability is mainly due to the deformation which significantly affects the
shell structure around the Fermi surface. The pairing correlations and
continuum influence the stability peninsula in a self-consistent way.


Abstract:
  Two-dimensional metal particle focusing is an essential task for various
fabrication processes. While acoustofluidic devices can manipulate particles in
two dimensions, the production of these devices often demands a cleanroom
environment. Therefore, acoustically excited glass capillaries present a cheap
alternative to labour-intensive cleanroom production. Here, we present 2D metal
micro-particle focusing in a round glass capillary using bulk acoustic waves.
Excitation of the piezoelectric transducer at specific frequencies leads to
mode shapes in the round capillary, concentrating particles towards the
capillary centre. We experimentally investigate the particle linewidth for
different particle materials and concentrations. We demonstrate the focus of
copper particles with 1 $\mu$m in diameter down to a line of width 60.8 $\pm$
7.0 $\mu$m and height 45.2 $\pm$ 9.3 $\mu$m, corresponding to a local
concentration of 4.5 % v/v, which is 90 times higher than the concentration of
the initial solution. Through numerical analysis, we could obtain further
insights into the particle manipulation mechanism inside the capillary and
predict the particle trajectories. We found that a transition of the acoustic
streaming pattern enables us to manipulate particles close to the critical
particle radius. Finally, we used our method to eject copper particles through
a tapered round capillary with an opening of 25 $\mu$m in diameter, which would
not be possible without particle focusing. Our novel setup can be utilized for
various applications, that otherwise might suffer from abrasion, clogging and
limited resolution.


Abstract:
  In this paper we study the problem of classifying pencils of curves of degree
$d$ in $\mathbb{P}^2$ using geometric invariant theory. We consider the action
of $SL(3)$ and we relate the stability of a pencil to the stability of its
generators, to the log canonical threshold of its members, and to the
multiplicities of its base points, thus obtaining explicit stability criteria.


Abstract:
  It is widely assumed that long-wavelength infrared (IR) emission from cold
dust (T~20-40K) is a reliable tracer of star formation even in the presence of
a bright active galactic nucleus (AGN). Based on radiative transfer (RT) models
of clumpy AGN tori, hot dust emission from the torus contributes negligibly to
the galaxy spectral energy distribution (SED) at $\lambda\ga100$ \micron.
However, these models do not include AGN heating of host-galaxy-scale diffuse
dust, which may have far-IR (FIR) colors comparable to cold diffuse dust heated
by stars. To quantify the contribution of AGN heating to host-galaxy-scale cold
dust emission at $\lambda\ga100$ \micron, we perform dust RT calculations on a
simulated galaxy merger both including and excluding the bright AGN that it
hosts. By differencing the SEDs yielded by RT calculations with and without AGN
that are otherwise identical, we quantify the FIR cold dust emission arising
solely from re-processed AGN photons. In extreme cases, AGN-heated
host-galaxy-scale dust can increase galaxy-integrated FIR flux densities by
factors of 2-4; star formation rates calculated from the FIR luminosity
assuming no AGN contribution can overestimate the true value by comparable
factors. Because the FIR colors of such systems are similar to those of purely
star-forming galaxies and redder than torus models, broadband SED decomposition
may be insufficient for disentangling the contributions of stars and heavily
dust-enshrouded AGN in the most IR-luminous galaxies. We demonstrate how
kpc-scale resolved observations can be used to identify deeply dust-enshrouded
AGN with cool FIR colors when spectroscopic and/or X-ray detection methods are
unavailable.


Abstract:
  We resolve the long-standing "impossible tuning" issue for the classic expert
problem and show that, it is in fact possible to achieve regret
$O\left(\sqrt{(\ln d)\sum_t \ell_{t,i}^2}\right)$ simultaneously for all expert
$i$ in a $T$-round $d$-expert problem where $\ell_{t,i}$ is the loss for expert
$i$ in round $t$. Our algorithm is based on the Mirror Descent framework with a
correction term and a weighted entropy regularizer. While natural, the
algorithm has not been studied before and requires a careful analysis. We also
generalize the bound to $O\left(\sqrt{(\ln d)\sum_t
(\ell_{t,i}-m_{t,i})^2}\right)$ for any prediction vector $m_t$ that the
learner receives, and recover or improve many existing results by choosing
different $m_t$. Furthermore, we use the same framework to create a master
algorithm that combines a set of base algorithms and learns the best one with
little overhead. The new guarantee of our master allows us to derive many new
results for both the expert problem and more generally Online Linear
Optimization.