Abstract:
  Let $\mathfrak g$ be a finite-dimensional Lie algebra. The symmetric algebra
$\mathcal S(\mathfrak g)$ is equipped with the standard Lie-Poisson bracket. In
this paper, we elaborate on a surprising observation that one naturally
associates the second compatible Poisson bracket on $\mathcal S(\mathfrak g)$
to any finite order automorphism $\theta$ of $\mathfrak g$. We study related
Poisson-commutative subalgebras $\mathcal C$ of $\mathcal S(\mathfrak g)$ and
associated Lie algebra contractions of $\mathfrak g$. To obtain substantial
results, we have to assume that $\mathfrak g$ is semisimple. Then we can use
Vinberg's theory of $\theta$-groups and the machinery of Invariant Theory.
  If $\mathfrak g=\mathfrak h\oplus\dots \oplus \mathfrak h$ (sum of $k$
copies), where $\mathfrak h$ is simple, and $\theta$ is the cyclic permutation,
then we prove that the corresponding Poisson-commutative subalgebra $\mathcal
C$ is polynomial and maximal. Furthermore, we quantise this $\mathcal C$ using
a Gaudin subalgebra in the enveloping algebra $\mathcal U(\mathfrak g)$.


Abstract:
  Understanding and controlling the rheology of polymeric fluids that are
out-of-equilibrium is a fundamental problem in biology and industry. For
example, to package, repair, and replicate DNA, cells use enzymes to constantly
manipulate DNA topology, length, and structure. Inspired by this impressive
feat, we combine experiments with theory and simulations to show that complex
fluids of entangled DNA display a rich range of non-equilibrium material
properties when undergoing enzymatic reactions that alter their topology and
size. We reveal that while enzymatically-active fluids of linear DNA display
universal viscous thinning, circular DNA fluids - undergoing the same
non-equilibrium process - display thickening with a rate and degree that can be
tuned by the DNA and enzyme concentrations. Our results open the way for the
topological functionalization of DNA-based materials via naturally occurring
enzymes to create a new class of "topologically-active" materials that can
autonomously alter their rheological properties in a programmable manner.


Abstract:
  Using EUV images and line-of-sight magnetograms from Solar Dynamics
Observatory, we examine eight emerging bipolar magnetic regions (BMRs) in
central-disk coronal holes for whether the emerging magnetic arch made any
noticeable coronal jets directly, via reconnection with ambient open field as
modeled by Yokoyama and Shibata (1995). During emergence, each BMR produced no
obvious EUV coronal jet of normal brightness, but each produced one or more
faint EUV coronal jets that are discernible in AIA 193 {\AA} images. The spires
of these jets are much fainter and usually narrower than for typical EUV jets
that have been observed to be produced by minifilament eruptions in quiet
regions and coronal holes. For each of 26 faint jets from the eight emerging
BMRs, we examine whether the faint spire was evidently made a la Yokoyama and
Shibata (1995). We find: (1) 16 of these faint spires evidently originate from
sites of converging opposite-polarity magnetic flux and show base brightenings
like those in minifilament-eruption-driven coronal jets, (2) the 10 other faint
spires maybe were made by a burst of the external-magnetic-arcade-building
reconnection of the emerging magnetic arch with the ambient open field,
reconnection directly driven by the arch's emergence, but (3) none were
unambiguously made by such emergence-driven reconnection. Thus, for these eight
emerging BMRs the observations indicate that emergence-driven external
reconnection of the emerging magnetic arch with ambient open field at most
produces a jet spire that is much fainter than in previously-reported, much
more obvious coronal jets driven by minifilament eruptions.


Abstract:
  The task of detecting morphed face images has become highly relevant in
recent years to ensure the security of automatic verification systems based on
facial images, e.g. automated border control gates. Detection methods based on
Deep Neural Networks (DNN) have been shown to be very suitable to this end.
However, they do not provide transparency in the decision making and it is not
clear how they distinguish between genuine and morphed face images. This is
particularly relevant for systems intended to assist a human operator, who
should be able to understand the reasoning. In this paper, we tackle this
problem and present Focused Layer-wise Relevance Propagation (FLRP). This
framework explains to a human inspector on a precise pixel level, which image
regions are used by a Deep Neural Network to distinguish between a genuine and
a morphed face image. Additionally, we propose another framework to objectively
analyze the quality of our method and compare FLRP to other DNN
interpretability methods. This evaluation framework is based on removing
detected artifacts and analyzing the influence of these changes on the decision
of the DNN. Especially, if the DNN is uncertain in its decision or even
incorrect, FLRP performs much better in highlighting visible artifacts compared
to other methods.


Abstract:
  We aim to investigate the potential impacts of smart homes on human behavior.
To this end, we simulate a series of human models capable of performing various
activities inside a reinforcement learning-based smart home. We then
investigate the possibility of human behavior being altered as a result of the
smart home and the human model adapting to one-another. We design a semi-Markov
decision process human task interleaving model based on hierarchical
reinforcement learning that learns to make decisions to either pursue or leave
an activity. We then integrate our human model in the smart home which is based
on Q-learning. We show that a smart home trained on a generic human model is
able to anticipate and learn the thermal preferences of human models with
intrinsic rewards similar to the generic model. The hierarchical human model
learns to complete each activity and set optimal thermal settings for maximum
comfort. With the smart home, the number of time steps required to change the
thermal settings are reduced for the human models. Interestingly, we observe
that small variations in the human model reward structures can lead to the
opposite behavior in the form of unexpected switching between activities which
signals changes in human behavior due to the presence of the smart home.


Abstract:
  By harnessing quantum superposition and entanglement, remarkable progress has
sprouted over the past three decades from different areas of research in
communication computation and simulation. To further improve the processing
ability of microwave pho-tonics, here, we have demonstrated a quantum microwave
photonic processing system using a low jitter superconducting nanowire single
photon detector (SNSPD) and a time-correlated single-photon counting (TCSPC)
module. This method uniquely combines extreme optical sensitivity, down to a
single-photon level (below -100 dBm), and wide processing bandwidth, twice
higher than the transmission bandwidth of the cable. Moreover, benefitted from
the trigger, the system can selectively process the desired RF signal and
attenuates the other in-tense noise and undesired RF components even the power
is 15dB greater than the desired signal power. Using this method we show
microwave phase shifting and frequency filtering for the desired RF signal on
the single-photon level. Besides its applications in space and under-water
communications and testing and qualification of pre-packaged photonic
modulators and detectors. This RF signal processing capability at the
single-photon level can lead to significant development in the high-speed
quantum processing method.


Abstract:
  Context: In the context of the solar coronal heating problem, one possible
explanation for the high coronal temperature is the release of energy by
magnetohydrodynamic (MHD) waves. The energy transfer is believed to be
possible, among others, by the development of the Kelvin-Helmholtz instability
(KHI) in coronal loops. Aims: Our aim is to determine if standing slow waves in
solar atmospheric structures such as coronal loops, and also prominence
threads, sunspots, and pores, can trigger the KHI due to the oscillating shear
flow at the structure's boundary. Methods: We used linearized nonstationary MHD
to work out an analytical model in a cartesian reference frame. The model
describes a compressible plasma near a discontinuous interface separating two
regions of homogeneous plasma, each harboring an oscillating velocity field
with a constant amplitude which is parallel to the background magnetic field
and aligned with the interface. The obtained analytical results were then used
to determine the stability of said interface, both in coronal and photospheric
conditions. Results: We find that the stability of the interface is determined
by a Mathieu equation. In function of the parameters of this equation, the
interface can either be stable or unstable. For coronal as well as photospheric
conditions, we find that the interface is stable with respect to the KHI.
Theoretically, it can, however, be unstable with respect to a parametric
resonance instability, although it seems physically unlikely. We conclude that,
in this simplified setup, a standing slow wave does not trigger the KHI without
the involvement of additional physical processes.


Abstract:
  If $\psi:M^n\to \mathbb{R}^{n+1}$ is a smooth immersed closed hypersurface,
we consider the functional $\mathcal{F}_m(\psi) = \int_M 1 + |\nabla^m \nu |^2
\, d\mu$, where $\nu$ is a local unit normal vector along $\psi$, $\nabla$ is
the Levi-Civita connection of the Riemannian manifold $(M,g)$, with $g$ the
pull-back metric induced by the immersion and $\mu$ the associated volume
measure. We prove that if $m>\lfloor n/2 \rfloor$ then the unique globally
defined smooth solution to the $L^2$-gradient flow of $\mathcal{F}_m$, for
every initial hypersurface, smoothly converges asymptotically to a critical
point of $\mathcal{F}_m$, up to diffeomorphisms. The proof is based on the
application of a Lojasiewicz-Simon gradient inequality for the functional
$\mathcal{F}_m$.


Abstract:
  By design, average precision (AP) for object detection aims to treat all
classes independently: AP is computed independently per category and averaged.
On the one hand, this is desirable as it treats all classes, rare to frequent,
equally. On the other hand, it ignores cross-category confidence calibration, a
key property in real-world use cases. Unfortunately, we find that on
imbalanced, large-vocabulary datasets, the default implementation of AP is
neither category independent, nor does it directly reward properly calibrated
detectors. In fact, we show that the default implementation produces a gameable
metric, where a simple, nonsensical re-ranking policy can improve AP by a large
margin. To address these limitations, we introduce two complementary metrics.
First, we present a simple fix to the default AP implementation, ensuring that
it is truly independent across categories as originally intended. We benchmark
recent advances in large-vocabulary detection and find that many reported gains
do not translate to improvements under our new per-class independent
evaluation, suggesting recent improvements may arise from difficult to
interpret changes to cross-category rankings. Given the importance of reliably
benchmarking cross-category rankings, we consider a pooled version of AP
(AP-pool) that rewards properly calibrated detectors by directly comparing
cross-category rankings. Finally, we revisit classical approaches for
calibration and find that explicitly calibrating detectors improves
state-of-the-art on AP-pool by 1.7 points.


Abstract:
  In this paper, we are concerned with the uniqueness result for non-negative
solutions of the higher-order Lane-Emden equations involving the GJMS operators
on $\mathbb{S}^n$. Since the classical moving-plane method based on the Kelvin
transform and maximum principle fails in dealing with the high-order elliptic
equations in $\mathbb{S}^n$, we first employ the Mobius transform between
$\mathbb{S}^n$ and $\mathbb{R}^n$, poly-harmonic average and iteration
arguments to show that the higher-order Lane-Emden equation on $\mathbb{S}^n$
is equivalent to some integral equation in $\mathbb{R}^n$. Then we apply the
method of moving plane in integral forms and the symmetry of sphere to obtain
the uniqueness of nonnegative solutions to the higher-order Lane-Emden
equations with subcritical polynomial growth on $\mathbb{S}^n$. As an
application, we also identify the best constants and classify the extremals of
the sharp subcritical high-order Sobolev inequalities involving the GJMS
operators on $\mathbb{S}^n$. Our results do not seem to be in the literature
even for the Lane-Emden equation and sharp subcritical Sobolev inequalities for
first order derivatives on $\mathbb{S}^n$.


Abstract:
  We consider reduction cohomology of modular functions defined on complex
curves via generalizations of holomorphic connections. The cohomology is
explicitly found in terms of higher genus counterparts of elliptic functions as
analytic continuations of solutions for functional equations. Examples of
modular functions on various genera are provided.


Abstract:
  Intelligent reflecting surface (IRS) is a promising technology to improve the
performance of backscatter communication systems by smartly reconfiguring the
multi-reflection channel. To fully exploit the passive beamforming gain of IRS
in backscatter communication, channel state information (CSI) is indispensable
but more practically challenging to acquire than conventional IRS-assisted
systems, since IRS passively reflects signals over both the forward and
backward (backscattering) links between the reader and tag. To address this
issue, we propose in this letter a new and efficient channel estimation scheme
for the IRS-assisted backscatter communication system. To minimize the
mean-square error (MSE) of channel estimation, we formulate and solve an
optimization problem by designing the IRS training reflection matrix for
channel estimation under the constraints of unit-modulus elements and full
rank. Simulation results verify the effectiveness of the proposed channel
estimation scheme as compared to other baseline schemes.


Abstract:
  Multilevel techniques are efficient approaches for solving the large linear
systems that arise from discretized partial differential equations and other
problems. While geometric multigrid requires detailed knowledge about the
underlying problem and its discretization, algebraic multigrid aims to be less
intrusive, requiring less knowledge about the origin of the linear system. A
key step in algebraic multigrid is the choice of the coarse/fine partitioning,
aiming to balance the convergence of the iteration with its cost. In work by
MacLachlan and Saad, a constrained combinatorial optimization problem is used
to define the "best" coarse grid within the setting of a two-level
reduction-based algebraic multigrid method and is shown to be NP-complete.
Here, we develop a new coarsening algorithm based on simulated annealing to
approximate solutions to this problem, which yields improved results over the
greedy algorithm developed previously. We present numerical results for test
problems on both structured and unstructured meshes, demonstrating the ability
to exploit knowledge about the underlying grid structure if it is available.


Abstract:
  We propose and analyze the convergence of a novel stochastic algorithm for
solving monotone inclusions that are the sum of a maximal monotone operator and
a monotone, Lipschitzian operator. The propose algorithm requires only unbiased
estimations of the Lipschitzian operator. We obtain the rate
$\mathcal{O}(log(n)/n)$ in expectation for the strongly monotone case, as well
as almost sure convergence for the general case. Furthermore, in the context of
application to convex-concave saddle point problems, we derive the rate of the
primal-dual gap. In particular, we also obtain $\mathcal{O}(1/n)$ rate
convergence of the primal-dual gap in the deterministic setting.


Abstract:
  We investigate pressure-induced structural changes to the Peierls-type
distorted low-temperature phase of the low-dimensional Sc$_3$CoC$_4$ as a
possible origin of its pressure-enhanced superconductivity. By means of
cryogenic high-pressure x-ray diffraction experiments we could reveal subtle,
but significant structural differences between the low-temperature phase at
ambient and elevated pressures. We could thus establish the structure of the
superconducting phase of the title compound which interestingly still shows the
main features of the Peierls-type distorted low-temperature phase. This
indicates that in contrast to other low-dimensional materials a suppression of
periodic structural distortions is no prerequisite for superconducitivity in
the transition metal carbide.


Abstract:
  Objective: In some situations that exist both scalar and functional data,
called mixed and hybrid data, the hybrid PCA (HPCA) was introduced. Among the
regression models for the hybrid data, we can count covariate-adjusted HPCA,
the Semi-functional partial linear regression, function-on-function (FOF)
regression with signal compression, and functional additive regression, models.
In this article, we study the effects of HPCA decomposition of hybrid data on
the prediction accuracy of the FOF regression with signal compressions. Method:
We stated a two-step procedure for incorporating the HPCA in the functional
regressions. The first step is reconstructing the data based on the HPCAs and
the second step is merging data on the other dimensions and calculate the
point-wise average of the desired functional dimension. We also choose the
number of HPCA based on Mean Squared Perdition Error (MSPE). Result: In the two
simulations, we show that the regression models with the first HPCA have the
best accuracy prediction and model fit summaries among no HPCA and all HPCAs
with a training/testing approach. Finally, we applied our methodology to the
EEG-fMRI dataset. Conclusions: We conclude that our methodology improves the
prediction of the experiments with the EEG datasets. And we recommend that
instead of using the functional PCA on the desired dimension, reconstruct the
data with HPCA and average it on the other two dimensions for functional
regression models.


Abstract:
  A corresponding comment, raised by Kao and Hwang, claims that the
reconstructor Bob1 is unable to obtain the expected secret information in (t,
n) Threshold d-level Quantum Secret Sharing (TDQSS)[Scientific Reports, Vol. 7,
No. 1 (2017), pp.6366] . In this reply, we show the TDQSS scheme can obtain the
dealer's secret information in the condition of adding a step on
disentanglement.


Abstract:
  We investigate the existence of infinitely many radially symmetric solutions
to the following problem $$(-\Delta_p)^s u=g(u) \ \ \textrm{ in } \ \
\mathbb{R}^N, \ \ u\in W^{s,p}(\mathbb{R}^N),$$ where $s\in (0,1)$, $2 \leq p <
\infty$, $sp \leq N $, $2 \leq N \in \mathbb{N}$ and $(-\Delta_p)^s$ is the
fractional $p$-Laplacian operator. We treat both of cases $sp=N$ and $sp<N.$
The nonlinearity $g$ is a function of Berestycki-Lions type with critical
exponential growth if $sp=N$ and critical polynomial growth if $sp<N$. We also
prove the existence of a ground state solution for the same problem.


Abstract:
  The recently discovered superconductor, UTe$_2$, has attracted immense
scientific interest due to the experimental observations that suggest
odd-parity superconductivity. It is believed that the material becomes a
heavy-fermion metal at low temperatures although details of the normal state
are unclear. Using Fourier transform infrared spectroscopy (FTIR), the normal
state electronic structure of UTe$_2$ was investigated at zero applied magnetic
field. Combining the measured reflectivity with the dc resistivity, the complex
optical conductivity was obtained over a large frequency range. The frequency
dependence of the real part of the optical conductivity exhibits a MIR peak
around 4000 cm$^{-1}$ and a narrow Drude peak that develops below 40 K. A
combination of density functional and dynamic mean field theory (DFT + DMFT)
gives spectra in close correspondence to the experiment. Via this comparison we
attribute the prominent MIR peak to inter-band transitions involving a narrow U
5$f$ feature that develops near the Fermi level. In this regard, our data gives
spectroscopic evidence for the existence of a low energy Kondo resonance at
temperatures just above the onset of superconductivity and implicates heavy
electrons in the formation of the superconducting state. We find that the
coherent Kondo resonance is primarily associated with a collapse of scattering
and less with a transfer of spectral weight.


Abstract:
  The partition function of the Symmetric Matrix Ensemble is identified with
the $\tau-$function of a particular solution of the Pfaff Lattice. We show that
in the case of even power interactions, in the thermodynamic limit, the
$\tau-$function corresponds to the solution of an integrable chain of
hydrodynamic type. We prove that the hydrodynamic chain so obtained is
diagonalisable and admits hydrodynamic reductions in Riemann invariants in an
arbitrary number of components.


Abstract:
  Since the outbreak of the novel coronavirus, COVID-19, there has been
increased interest in the use of digital contact tracing as a means of stopping
chains of viral transmission, provoking alarm from privacy advocates.
Concerning the ethics of this technology, recent studies have predominantly
focused on (1) the formation of guidelines for ethical contact tracing, (2) the
analysis of specific implementations, or (3) the review of a select number of
contact tracing applications and their relevant privacy or ethical
implications. In this study, we provide a comprehensive survey of the evolving
ecosystem of COVID-19 tracing applications, examining 152 contact tracing
applications and assessing the extent to which they comply with existing
guidelines for ethical contact tracing. The assessed criteria cover areas
including data collection and storage, transparency and consent, and whether
the implementation is open source. We find that although many apps released
early in the pandemic fell short of best practices, apps released more
recently, following the publication of the Apple/Google exposure notification
protocol, have tended to be more closely aligned with ethical contact tracing
principles. This dataset will be publicly available and may be updated as the
pandemic continues.


Abstract:
  Player modeling attempts to create a computational model which accurately
approximates a player's behavior in a game. Most player modeling techniques
rely on domain knowledge and are not transferable across games. Additionally,
player models do not currently yield any explanatory insight about a player's
cognitive processes, such as the creation and refinement of mental models. In
this paper, we present our findings with using action model learning (AML), in
which an action model is learned given data in the form of a play trace, to
learn a player model in a domain-agnostic manner. We demonstrate the utility of
this model by introducing a technique to quantitatively estimate how well a
player understands the mechanics of a game. We evaluate an existing AML
algorithm (FAMA) for player modeling and develop a novel algorithm called
Blackout that is inspired by player cognition. We compare Blackout with FAMA
using the puzzle game Sokoban and show that Blackout generates better player
models.


Abstract:
  Like many predictive models, random forests provide a point prediction for a
new observation. Besides the point prediction, it is important to quantify the
uncertainty in the prediction. Prediction intervals provide information about
the reliability of the point predictions. We have developed a comprehensive R
package, RFpredInterval, that integrates 16 methods to build prediction
intervals with random forests and boosted forests. The methods implemented in
the package are a new method to build prediction intervals with boosted forests
(PIBF) and 15 different variants to produce prediction intervals with random
forests proposed by Roy and Larocque (2020). We perform an extensive simulation
study and apply real data analyses to compare the performance of the proposed
method to ten existing methods to build prediction intervals with random
forests. The results show that the proposed method is very competitive and,
globally, it outperforms the competing methods.


Abstract:
  The influence of oscillatory perturbations on autonomous strongly nonlinear
systems in the plane is investigated. It is assumed that the intensity of
perturbations decays with time, and their frequency increases according to a
power law. The long-term behaviour of perturbed trajectories is discussed. It
is shown that, depending on the structure and the parameters of perturbations,
there are at least two different asymptotic regimes: a phase locking and a
phase drifting. In the case of phase locking, resonant solutions with an
unlimitedly growing energy occur. The stability and asymptotics at infinity of
such solutions are investigated. The proposed analysis is based on a
combination of the averaging technique and the method of Lyapunov functions.


Abstract:
  Using $1310.6\times10^{6}$ $J/\psi$ and $448.1\times10^{6}$ $\psi(3686)$
events collected with the BESIII detector, the branching fractions of $J/\psi$
and $\psi(3686)$ decays to $\Sigma^{+}\overline{\Sigma}^{-}$ are measured to be
$(10.61 \pm 0.04 \pm 0.36) \times 10^{-4}$ and $(2.52 \pm 0.04 \pm 0.09) \times
10^{-4}$, respectively. In addition, the ratio of $\mathcal{B}(\psi(3686)
\rightarrow \Sigma^{+}\overline{\Sigma}^{-})/\mathcal{B}(J/\psi \rightarrow
\Sigma^{+}\overline{\Sigma}^{-})$ is determined to be $(23.8 \pm 1.1)\%$ which
violates the "$12\%$ rule".


Abstract:
  Heavy quarkonium is one of the key candidates to study the fundamental
properties of Quark-Gluon Plasma (QGP) created in heavy-ion collisions.
Comparing the production of the $J/\psi$ meson in proton-proton and heavy-ion
collisions, namely the nuclear modification factor ($R_{\rm AA}$), can provide
some quantitative understanding of the QGP. Traditionally, the $R_{\rm AA}$ is
measured under the assumption that the quarkonium is unpolarized in both
proton-proton and heavy-ion collisions. However, the recent measurements on the
$J/\psi$ polarization in heavy-ion collisions from the ALICE Collaboration
suggest that the $J/\psi$ meson has small but non-negligible transverse
polarization in both helicity and Collins-Soper frames. In this paper, we show
the effects from the $J/\psi$ polarization to the measurement of $R_{\rm AA}$
in the forward rapidity region using the available data from the ALICE and LHCb
experiments and demonstrate the possible maximum effects from polarization in
the central barrel region at the RHIC and LHC energies. We would like to draw
people's attention that having precise measurements on the quarkonium
polarization in heavy-ion collisions is important to obtain the full picture of
how heavy quarkonium interacting with the QGP.


Abstract:
  We describe limits of line bundles on nodal curves in terms of toric
arrangements associated to Voronoi tilings of Euclidean spaces. These tilings
encode information on the relationship between the possibly infinitely many
limits, and ultimately give rise to a new definition of limit linear series.
This article and the first two that preceded it are the first in a series aimed
to explore this new approach.
  In Part I, we set up the combinatorial framework and showed how graphs
weighted with integer lengths associated to the edges provide tilings of
Euclidean spaces by certain polytopes associated to the graph itself and to its
subgraphs.
  In Part II, we described the arrangements of toric varieties associated to
the tilings of Part I in several ways: using normal fans, as unions of orbits,
by equations and as degenerations of tori.
  In the present Part III, we show how these combinatorial and toric frameworks
allow us to describe all stable limits of a family of line bundles along a
degenerating family of curves. Our main result asserts that the collection of
all these limits is parametrized by a connected 0-dimensional closed substack
of the Artin stack of all torsion-free rank-one sheaves on the limit curve.
Moreover, we thoroughly describe this closed substack and all the closed
substacks that arise in this way as certain torus quotients of the arrangements
of toric varieties of Part II determined by the Voronoi tilings of Euclidean
spaces studied in Part I.


Abstract:
  In this article, we study activity recognition in the context of sensor-rich
environments. We address, in particular, the problem of inductive biases and
their impact on the data collection process. To be effective and robust,
activity recognition systems must take these biases into account at all levels
and model them as hyperparameters by which they can be controlled. Whether it
is a bias related to sensor measurement, transmission protocol, sensor
deployment topology, heterogeneity, dynamicity, or stochastic effects, it is
important to understand their substantial impact on the quality of activity
recognition models. This study highlights the need to separate the different
types of biases arising in real situations so that machine learning models,
e.g., adapt to the dynamicity of these environments, resist to sensor failures,
and follow the evolution of the sensors topology. We propose a metamodeling
process in which the sensor data is structured in layers. The lower layers
encode the various biases linked to transformations, transmissions, and
topology of data. The upper layers encode biases related to the data itself.
This way, it becomes easier to model hyperparameters and follow changes in the
data acquisition infrastructure. We illustrate our approach on the SHL dataset
which provides motion sensor data for a list of human activities collected
under real conditions. The trade-offs exposed and the broader implications of
our approach are discussed with alternative techniques to encode and
incorporate knowledge into activity recognition models.


Abstract:
  Attention has been proved to be an efficient mechanism to capture long-range
dependencies. However, so far it has not been deployed in invertible networks.
This is due to the fact that in order to make a network invertible, every
component within the network needs to be a bijective transformation, but a
normal attention block is not. In this paper, we propose invertible attention
that can be plugged into existing invertible models. We mathematically and
experimentally prove that the invertibility of an attention model can be
achieved by carefully constraining its Lipschitz constant. We validate the
invertibility of our invertible attention on image reconstruction task with 3
popular datasets: CIFAR-10, SVHN, and CelebA. We also show that our invertible
attention achieves similar performance in comparison with normal non-invertible
attention on dense prediction tasks. The code is available at
https://github.com/Schwartz-Zha/InvertibleAttention


Abstract:
  We perform energy dependent calculations of independent and cumulative
fission product yields for $^{235}$U, $^{238}$U, and $^{239}$Pu in the first
chance fission region. Starting with the primary fission fragment distributions
taken from available experimental data and analytical functions based on
assumptions for the excitation energy and spin-parity distributions, the
Hauser-Feshbach statistical decay treatment for fission fragment de-excitation
is applied to more than 1,000 fission fragments for the incident neutron
energies up to 5 MeV. The calculated independent fission product yields are
then used as an input of $\beta$-decay to produce the cumulative yield, and
summation calculations are performed. Model parameters in these procedures are
adjusted by applying the Bayesian technique at the thermal energy for $^{235}$U
and $^{239}$Pu and in the fast energy range for $^{238}$U. The calculated
fission observable quantities, such as the energy-dependent fission yields, and
prompt and delayed neutron yields, are compared with available experimental
data. We also study a possible impact of the second chance fission opening on
the energy dependence of the delayed neutron yield by extrapolating the
calculation.


Abstract:
  Hierarchy and centrality are two popular notions used to characterize the
importance of entities in complex systems. Indeed, many complex systems exhibit
a natural hierarchical structure, and centrality is a fundamental
characteristic allowing to identify key constituents. Several measures based on
various aspects of network topology have been proposed in order to quantify
these concepts. While numerous studies have investigated whether centrality
measures convey redundant information, how centrality and hierarchy measures
are related is still an open issue. In this paper, we investigate the
interaction between centrality and hierarchy using several correlation and
similarity evaluation measures. A series of experiments is performed in order
to evaluate the combinations of 6 centrality measures with 4 hierarchy measures
across 28 diverse real-world networks with varying topological characteristics.
Results show that network density and transitivity play a key role in shaping
the pattern of relations between centrality and hierarchy measures.


Abstract:
  The long wavelength modes lost to bright foregrounds in the interferometric
21-cm surveys can partially be recovered using a forward modeling approach that
exploits the non-linear coupling between small and large scales induced by
gravitational evolution. In this work, we build upon this approach by
considering how adding external galaxy distribution data can help to fill in
these modes. We consider supplementing the 21-cm data at two different
redshifts with a spectroscopic sample (good radial resolution but low number
density) loosely modeled on DESI-ELG at $z=1$ and a photometric sample (high
number density but poor radial resolution) similar to LSST sample at $z=1$ and
$z=4$ respectively. We find that both the galaxy samples are able to
reconstruct the largest modes better than only using 21-cm data, with the
spectroscopic sample performing significantly better than the photometric
sample despite much lower number density. We demonstrate the synergies between
surveys by showing that the primordial initial density field is reconstructed
better with the combination of surveys than using either of them individually.
Methodologically, we also explore the importance of smoothing the density field
when using bias models to forward model these tracers for reconstruction.


Abstract:
  Let $R$ be a Noetherian local ring. We prove that $R$ is regular of dimension
at most four if, and only if, every prime ideal, defining a Gorenstein quotient
ring, is syzygetic. We deduce a characterization of these rings in terms of the
Andr\'e-Quillen homology.


Abstract:
  In the United States, from the start of the COVID-19 pandemic to December 31,
2020, 341,199 deaths and more than 19,663,976 infections were recorded. Recent
literature establishes that communities with poverty-related health problems,
such as obesity, cardiovascular disease, diabetes, and hypertension, are more
susceptible to mortality from SARS-CoV-2 infection. Additionally, controversial
public health policies implemented by the nation's political leaders have
highlighted the socioeconomic inequalities of minorities. Therefore, through
multivariate correlational analysis using machine learning techniques and
structural equations, we measure whether social determinants are associated
with increased infection and death from COVID-19 disease. The PLS least squares
regression analysis allowed identifying a significant impact between social
determinants and COVID-19 disease through a predictive value of R2 = .916,
\b{eta} = .836, p =. 000 (t-value = 66,137) shows that for each unit of
increase in social determinants, COVID-19 disease increases by 91.6%. The
clustering index used for correlational analysis generated a new data set
comprising three groups: C1 Republicans, C2 and C3 Democrats from California,
New York, Texas, and Florida. This analysis made it possible to identify the
poverty variable as the main risk factor related to the high rates of infection
in Republican states and a high positive correlation between the population not
insured with a medical plan and high levels of virus contagion in the states of
group C3. These findings explain the argument that poverty and lack of economic
security put the public or private health system at risk and calamity.


Abstract:
  The loop equations for the $\beta$-ensembles are conventionally solved in
terms of a $1/N$ expansion. We observe that it is also possible to fix $N$ and
expand in inverse powers of $\beta$. At leading order, for the one-point
function $W_1(x)$ corresponding to the average of the linear statistic $A =
\sum_{j=1}^N 1/(x - \lambda_j)$, and specialising the classical weights, this
reclaims well known results of Stieltjes relating the zeros of the classical
polynomials to the minimum energy configuration of certain log-gas potential
energies. Moreover, it is observed that the differential equations satisfied by
$W_1(x)$ in the case of classical weights -- which are particular Riccati
equations -- are simply related to the differential equations satisfied by
$W_1(x)$ in the high temperature scaled limit $\beta = 2\alpha/N$ ($\alpha$
fixed, $N \to \infty$), implying a certain high-low temperature duality. A
generalisation of this duality, valid without any limiting procedure, is shown
to hold for $W_1(x)$ and all its higher point analogues in the classical
$\beta$-ensembles.


Abstract:
  Disease classification relying solely on imaging data attracts great interest
in medical image analysis. Current models could be further improved, however,
by also employing Electronic Health Records (EHRs), which contain rich
information on patients and findings from clinicians. It is challenging to
incorporate this information into disease classification due to the high
reliance on clinician input in EHRs, limiting the possibility for automated
diagnosis. In this paper, we propose \textit{variational knowledge
distillation} (VKD), which is a new probabilistic inference framework for
disease classification based on X-rays that leverages knowledge from EHRs.
Specifically, we introduce a conditional latent variable model, where we infer
the latent representation of the X-ray image with the variational posterior
conditioning on the associated EHR text. By doing so, the model acquires the
ability to extract the visual features relevant to the disease during learning
and can therefore perform more accurate classification for unseen patients at
inference based solely on their X-ray scans. We demonstrate the effectiveness
of our method on three public benchmark datasets with paired X-ray images and
EHRs. The results show that the proposed variational knowledge distillation can
consistently improve the performance of medical image classification and
significantly surpasses current methods.


Abstract:
  Existing grasp synthesis methods are either analytical or data-driven. The
former one is oftentimes limited to specific application scope. The latter one
depends heavily on demonstrations, thus suffers from generalization issues;
\eg, models trained with human grasp data would be difficult to transfer to
3-finger grippers. To tackle these deficiencies, we formulate a fast and
differentiable force closure estimation method, capable of producing diverse
and physically stable grasps with arbitrary hand structures, without any
training data. Although force closure has commonly served as a measure of grasp
quality, it has not been widely adopted as an optimization objective for grasp
synthesis primarily due to its high computational complexity; in comparison,
the proposed differentiable method can test a force closure within
milliseconds. In experiments, we validate the proposed method's efficacy in 6
different settings.


Abstract:
  In this paper, we present the spectroscopy of 6P$_{1/2}$ state in $^{87}$Rb
using double resonance technique at $780~\text{nm}$ and $421~\text{nm}$. The
double resonance technique is implemented using electromagnetically induced
transparency (EIT) and optical pumping methods. Using these spectroscopy
methods, we have measured the hyperfine splitting of 6P$_{1/2}$ state with
precision of $<$400~kHz which agrees well with other spectroscopy methods such
as electrical discharge and saturated absorption spectroscopy at
$421~\text{nm}$.


Abstract:
  In this paper, we study the dynamics of temporal difference learning with
neural network-based value function approximation over a general state space,
namely, \emph{Neural TD learning}. We consider two practically used algorithms,
projection-free and max-norm regularized Neural TD learning, and establish the
first convergence bounds for these algorithms. An interesting observation from
our results is that max-norm regularization can dramatically improve the
performance of TD learning algorithms, both in terms of sample complexity and
overparameterization. In particular, we prove that max-norm regularization
improves state-of-the-art sample complexity and overparameterization bounds.
The results in this work rely on a novel Lyapunov drift analysis of the network
parameters as a stopped and controlled random process.


Abstract:
  Vocoders received renewed attention as main components in statistical
parametric text-to-speech (TTS) synthesis and speech transformation systems.
Even though there are vocoding techniques give almost accepted synthesized
speech, their high computational complexity and irregular structures are still
considered challenging concerns, which yield a variety of voice quality
degradation. Therefore, this paper presents new techniques in a continuous
vocoder, that is all features are continuous and presents a flexible speech
synthesis system. First, a new continuous noise masking based on the phase
distortion is proposed to eliminate the perceptual impact of the residual noise
and letting an accurate reconstruction of noise characteristics. Second, we
addressed the need of neural sequence to sequence modeling approach for the
task of TTS based on recurrent networks. Bidirectional long short-term memory
(LSTM) and gated recurrent unit (GRU) are studied and applied to model
continuous parameters for more natural-sounding like a human. The evaluation
results proved that the proposed model achieves the state-of-the-art
performance of the speech synthesis compared with the other traditional
methods.


Abstract:
  ECG is an attractive option to assess stress in serious Virtual Reality (VR)
applications due to its non-invasive nature. However, the existing Machine
Learning (ML) models perform poorly. Moreover, existing studies only perform a
binary stress assessment, while to develop a more engaging biofeedback-based
application, multi-level assessment is necessary. Existing studies annotate and
classify a single experience (e.g. watching a VR video) to a single stress
level, which again prevents design of dynamic experiences where real-time
in-game stress assessment can be utilized. In this paper, we report our
findings on a new study on VR stress assessment, where three stress levels are
assessed. ECG data was collected from 9 users experiencing a VR roller coaster.
The VR experience was then manually labeled in 10-seconds segments to three
stress levels by three raters. We then propose a novel multimodal deep fusion
model utilizing spectrogram and 1D ECG that can provide a stress prediction
from just a 1-second window. Experimental results demonstrate that the proposed
model outperforms the classical HRV-based ML models (9% increase in accuracy)
and baseline deep learning models (2.5% increase in accuracy). We also report
results on the benchmark WESAD dataset to show the supremacy of the model.


Abstract:
  Deployment of machine learning models in real high-risk settings (e.g.
healthcare) often depends not only on model's accuracy but also on its
fairness, robustness and interpretability. Generalized Additive Models (GAMs)
are a class of interpretable models with a long history of use in these
high-risk domains, but they lack desirable features of deep learning such as
differentiability and scalability. In this work, we propose a neural GAM
(NODE-GAM) and neural GA$^2$M (NODE-GA$^2$M) that scale well and perform better
than other GAMs on large datasets, while remaining interpretable compared to
other ensemble and deep learning models. We demonstrate that our models find
interesting patterns in the data. Lastly, we show that we improve model
accuracy via self-supervised pre-training, an improvement that is not possible
for non-differentiable GAMs.


Abstract:
  For the first time, we report a theoretical methodology to predict charge
state distribution of projectile ions inside a solid-target. The method
utilizes either a simple Fermi gas model or an ab initio theoretical method and
a certain parameterization of width for the Lorentzian charge state
distributions. Results obtained from the two approaches are comparable, but the
former has a certain edge over the latter. The projectile charge state
distribution inside a solid-target plays a significant role in estimating
electron capture cross-sections and then to describe the observed K-shell
ionization dynamics. The electron capture process plays a certain role in
L-shell ionization dynamics too, but in a test case of Si on Au target the
subshell charge sharing contributes a more vital role than the electron
capture. Thus, we have validated the present model as a reliable as well as
useful for many solid-target based applications viz. tumour therapy,
biophysics, accelerators, material science etc.


Abstract:
  We present experimental results using a single-electron resolution
Skipper-CCD running above ground level to demonstrate the potential of this
technology for its use in reactor neutrino observations and other low-energy
particle interaction experiments. Operating conditions and event-selection
criteria are provided to decouple most of the background rate at low energies.
Our final results for events with energies as low as $5$ ionized electron-hole
pairs show that the exponentially increasing rate of events seen in other
technologies is not present in our data. This demonstrates that the Skipper CCD
proves to be among the best options to measure low energy and weakly
interacting particles at ground level.


Abstract:
  The minimization of electronics makes heat dissipation of related devices an
increasing challenge. When the size of materials is smaller than the phonon
mean free paths, phonons transport without internal scatterings and laws of
diffusive thermal conduction fail, resulting in significant reduction in the
effective thermal conductivity. This work reports, for the first time, the
temperature dependent thermal conductivity of doped epitaxial 6H-SiC and
monocrystalline porous 6H-SiC below room temperature probed by time-domain
thermoreflectance. Strong quasi-ballistic thermal transport was observed in
these samples, especially at low temperatures. Doping and structural boundaries
were applied to tune the quasi-ballistic thermal transport since dopants
selectively scatter high-frequency phonons while boundaries scatter phonons
with long mean free paths. Exceptionally strong phonon scattering by boron
dopants are observed, compared to nitrogen dopants. Furthermore, orders of
magnitude reduction in the measured thermal conductivity was observed at low
temperatures for the porous 6H-SiC compared to the epitaxial 6H-SiC. Finally,
first principles calculations and a simple Callaway model are built to
understand the measured thermal conductivities. Our work sheds light on the
fundamental understanding of thermal conduction in technologically-important
wide bandgap semiconductors such as 6H-SiC and will impact applications such as
thermal management of 6H-SiC-related electronics and devices.


Abstract:
  We consider probe-based quantum thermometry and show that machine
classification can provide model-independent estimation with quantifiable error
assessment. Our approach is based on the k-nearest-neighbor algorithm. The
machine is trained using data from either computer simulations or a calibration
experiment. This yields a predictor which can be used to estimate the
temperature from new observations. The algorithm is highly flexible and works
with any kind of probe observable. It also allows to incorporate experimental
errors, as well as uncertainties about experimental parameters. We illustrate
our method with an impurity thermometer in a Bose-gas, as well as in the
estimation of the thermal phonon number in the Rabi model.


Abstract:
  We present a second-order recursive Fermi-operator expansion scheme using
mixed precision floating point operations to perform electronic structure
calculations using tensor core units. A performance of over 100 teraFLOPs is
achieved for half-precision floating point operations on Nvidia's A100 tensor
core units. The second-order recursive Fermi-operator scheme is formulated in
terms of a generalized, differentiable deep neural network structure, which
solves the quantum mechanical electronic structure problem. We demonstrate how
this network can be accelerated by optimizing the weight and bias values to
substantially reduce the number of layers required for convergence. We also
show how this machine learning approach can be used to optimize the
coefficients of the recursive Fermi-operator expansion to accurately represent
fractional occupation numbers of the electronic states at finite temperatures.


Abstract:
  Existing end-to-end-encrypted (E2EE) email systems, mainly PGP, have long
been evaluated in controlled lab settings. While these studies have exposed
usability obstacles for the average user and offer design improvements, there
exist users with an immediate need for private communication, who must cope
with existing software and its limitations. We seek to understand whether
individuals motivated by concrete privacy threats, such as those vulnerable to
state surveillance, can overcome usability issues to adopt complex E2EE tools
for long-term use. We surveyed regional activists, as surveillance of social
movements is well-documented. Our study group includes individuals from 9
social movement groups in the US who had elected to participate in a workshop
on using Thunderbird+Enigmail for email encryption. These workshops tool place
prior to mid-2017, via a partnership with a non-profit which supports social
movement groups. Six to 40 months after their PGP email encryption training,
more than half of the study participants were continuing to use PGP email
encryption despite intervening widespread deployment of simple E2EE messaging
apps such as Signal. We study the interplay of usability with social factors
such as motivation and the risks that individuals undertake through their
activism. We find that while usability is an important factor, it is not enough
to explain long term use. For example, we find that riskiness of one's activism
is negatively correlated with long-term PGP use. This study represents the
first long-term study, and the first in-the-wild study, of PGP email encryption
adoption.


Abstract:
  3D delineation of anatomical structures is a cardinal goal in medical imaging
analysis. Prior to deep learning, statistical shape models that imposed
anatomical constraints and produced high quality surfaces were a core
technology. Prior to deep learning, statistical shape models that imposed
anatomical constraints and produced high quality surfaces were a core
technology. Today fully-convolutional networks (FCNs), while dominant, do not
offer these capabilities. We present deep implicit statistical shape models
(DISSMs), a new approach to delineation that marries the representation power
of convolutional neural networks (CNNs) with the robustness of SSMs. DISSMs use
a deep implicit surface representation to produce a compact and descriptive
shape latent space that permits statistical models of anatomical variance. To
reliably fit anatomically plausible shapes to an image, we introduce a novel
rigid and non-rigid pose estimation pipeline that is modelled as a Markov
decision process(MDP). We outline a training regime that includes inverted
episodic training and a deep realization of marginal space learning (MSL).
Intra-dataset experiments on the task of pathological liver segmentation
demonstrate that DISSMs can perform more robustly than three leading FCN
models, including nnU-Net: reducing the mean Hausdorff distance (HD) by
7.7-14.3mm and improving the worst case Dice-Sorensen coefficient (DSC) by
1.2-2.3%. More critically, cross-dataset experiments on a dataset directly
reflecting clinical deployment scenarios demonstrate that DISSMs improve the
mean DSC and HD by 3.5-5.9% and 12.3-24.5mm, respectively, and the worst-case
DSC by 5.4-7.3%. These improvements are over and above any benefits from
representing delineations with high-quality surface.


Abstract:
  Classification and identification of amino acids in aqueous solutions is
important in the study of biomacromolecules. Laser Induced Breakdown
Spectroscopy (LIBS) uses high energy laser-pulses for ablation of chemical
compounds whose radiated spectra are captured and recorded to reveal molecular
structure. Spectral peaks and noise from LIBS are impacted by experimental
protocols. Current methods for LIBS spectral analysis achieves promising
results using PCA, a linear method. It is well-known that the underlying
physical processes behind LIBS are highly nonlinear. Our work set out to
understand the impact of LIBS spectra on suitable neighborhood size over which
to consider pattern phenomena, if nonlinear methods capture pattern phenomena
with increased efficacy, and how they improve classification and identification
of compounds. We analyzed four amino acids, polysaccharide, and a control
group, water. We developed an information theoretic method for measurement of
LIBS energy spectra, implemented manifold methods for nonlinear dimensionality
reduction, and found while clustering results were not statistically
significantly different, nonlinear methods lead to increased classification
accuracy. Moreover, our approach uncovered the contribution of micro-wells
(experimental protocol) in LIBS spectra. To the best of our knowledge, ours is
the first application of Manifold methods to LIBS amino-acid analysis in the
research literature.


Abstract:
  Cross-modal retrieval is generally performed by projecting and aligning the
data from two different modalities onto a shared representation space. This
shared space often also acts as a bridge for translating the modalities. We
address the problem of learning such representation space by proposing and
exploiting the property of Discriminative Semantic Transitive Consistency --
ensuring that the data points are correctly classified even after being
transferred to the other modality. Along with semantic transitive consistency,
we also enforce the traditional distance minimizing constraint which makes the
projections of the corresponding data points from both the modalities to come
closer in the representation space. We analyze and compare the contribution of
both the loss terms and their interaction, for the task. In addition, we
incorporate semantic cycle-consistency for each of the modality. We empirically
demonstrate better performance owing to the different components with clear
ablation studies. We also provide qualitative results to support the proposals.


Abstract:
  We report a single-shot-based projective readout of a semiconductor hybrid
qubit formed by three electrons in a GaAs double quantum dot.
Voltage-controlled adiabatic transitions between the qubit operations and
readout conditions allow high-fidelity mapping of quantum states. We show that
a large ratio both in relaxation time vs. tunneling time (~ 50) and
singlet-triplet splitting vs. thermal energy (~ 20) allow energy-selective
tunneling-based spin-to-charge conversion with readout visibility ~ 92.6%.
Combined with ac driving, we demonstrate high visibility coherent Rabi and
Ramsey oscillations of a hybrid qubit in GaAs. Further, we discuss the
generality of the method for use in other materials, including silicon.


Abstract:
  Let $G$ be an acylindrically hyperbolic group and $E$ an exponential equation
over $G$. We show that if $E$ is solvable in $G$, then there exists a solution
whose components, corresponding to loxodromic elements, can be linearly
estimated in terms of lengths of the coefficients of $E$. We give a more
precise answer in the case where $G$ is a relatively hyperbolic group. Under
some assumption of general character, the solvability and the search problems
for exponential equations over $G$ can be reduced to the peripheral subgroups
of $G$.


Abstract:
  For the Hodge--Laplace equation in finite element exterior calculus, we
introduce several families of discontinuous Galerkin methods in the extended
Galerkin framework. For contractible domains, this framework utilizes seven
fields and provides a unifying inf-sup analysis with respect to all
discretization and penalty parameters. It is shown that the proposed methods
can be hybridized as a reduced two-field formulation.


Abstract:
  Based on decision trees, it is efficient to handle tabular data. Conventional
decision tree growth methods often result in suboptimal trees because of their
greedy nature. Their inherent structure limits the options of hardware to
implement decision trees in parallel. Here is a compact representation of
binary decision trees to overcome these deficiencies. We explicitly formulate
the dependence of prediction on binary tests for binary decision trees and
construct a function to guide the input sample from the root to the appropriate
leaf node. And based on this formulation we introduce a new interpretation of
binary decision trees. Then we approximate this formulation via continuous
functions. Finally, we interpret the decision tree as a model combination
method. And we propose the selection-prediction scheme to unify a few learning
methods.


Abstract:
  We prove bounds on the saturation degrees of homogeneous ideals (and their
powers) defining smooth complex projective varieties. For example, we show that
a classical statement due to Macualay for zero-dimensional complete
intersection ideals holds for any smooth variety. For curves, we bound the
saturation degree of powers in terms of the regularity.


Abstract:
  Vision transformers (ViTs) have been successfully applied in image
classification tasks recently. In this paper, we show that, unlike convolution
neural networks (CNNs)that can be improved by stacking more convolutional
layers, the performance of ViTs saturate fast when scaled to be deeper. More
specifically, we empirically observe that such scaling difficulty is caused by
the attention collapse issue: as the transformer goes deeper, the attention
maps gradually become similar and even much the same after certain layers. In
other words, the feature maps tend to be identical in the top layers of deep
ViT models. This fact demonstrates that in deeper layers of ViTs, the
self-attention mechanism fails to learn effective concepts for representation
learning and hinders the model from getting expected performance gain. Based on
above observation, we propose a simple yet effective method, named
Re-attention, to re-generate the attention maps to increase their diversity at
different layers with negligible computation and memory cost. The pro-posed
method makes it feasible to train deeper ViT models with consistent performance
improvements via minor modification to existing ViT models. Notably, when
training a deep ViT model with 32 transformer blocks, the Top-1 classification
accuracy can be improved by 1.6% on ImageNet. Code is publicly available at
https://github.com/zhoudaquan/dvit_repo.


Abstract:
  Despite their remarkable performance on a wide range of visual tasks, machine
learning technologies often succumb to data distribution shifts. Consequently,
a range of recent work explores techniques for detecting these shifts.
Unfortunately, current techniques offer no explanations about what triggers the
detection of shifts, thus limiting their utility to provide actionable
insights. In this work, we present Concept Bottleneck Shift Detection (CBSD): a
novel explainable shift detection method. CBSD provides explanations by
identifying and ranking the degree to which high-level human-understandable
concepts are affected by shifts. Using two case studies (dSprites and
3dshapes), we demonstrate how CBSD can accurately detect underlying concepts
that are affected by shifts and achieve higher detection accuracy compared to
state-of-the-art shift detection methods.


Abstract:
  A wall-resolved large eddy simulation is performed to study secondary tones
generated by a NACA0012 airfoil at $\alpha = 3^{\circ}$ with freestream Mach
number $M_{\infty} = 0.3$ and Reynolds number $Re = 5 \times 10^4$. Laminar
separation bubbles are observed over the suction side and near the trailing
edge on the pressure side. Flow visualization and spectral analysis are
employed to investigate vortex shedding aft of the suction side separation
bubble. Vortex interaction results in merging or bursting such that coherent
structures or turbulent packets are advected towards the trailing edge leading
to different levels of noise emission. Despite the intermittent occurrence of
laminar-turbulent transition, the noise spectrum depicts a main tone with
multiple equidistant secondary tones. To understand the role of flow
instabilities on the tones, the linearized Navier-Stokes equations are examined
in its operator form through bi-global stability and resolvent analyses, and by
time evolution of disturbances using a matrix-free method. These linear global
analyses reveal amplification of disturbances over the suction side separation
bubble. Non-normality of the linear operator leads to further transient
amplification due to modal interaction among eigenvectors. Two-point
correlations of pressure along the spanwise direction elucidate aspects of the
acoustic feedback loop mechanism in both the linear and non-linear solutions.
This feedback process is self-sustained by acoustic waves radiated from the
trailing edge, which reach the most sensitive flow location between 10 and 18\%
of the airfoil chord as identified by the resolvent analysis.


Abstract:
  Earth's modern climate is characterized by wet, rainy deep tropics, however
paleoclimate and planetary science have revealed a wide range of hydrological
cycle regimes connected to different external parameters. Here we investigate
how surface wetness affects the tropical hydrological cycle. When surface
wetness is decreased in an Earth-like general circulation model, the tropics
remain wet but transition from a rainy to rain-free regime. The rain-free
regime occurs when surface precipitation is suppressed as negative evaporation
(surface condensation) balances moisture flux convergence. The regime
transition is dominated by near-surface relative humidity changes in contrast
to the hypothesis that relative humidity changes are small. We show
near-surface relative humidity changes responsible for the regime transition
are controlled by re-evaporation of stratiform precipitation near the lifting
condensation level. Re-evaporation impacts the near-surface through vertical
mixing. Our results reveal a new rain-free tropical hydrological cycle regime
that goes beyond the wet/dry paradigm.


Abstract:
  With the widespread success of deep learning in biomedical image
segmentation, domain shift becomes a critical and challenging problem, as the
gap between two domains can severely affect model performance when deployed to
unseen data with heterogeneous features. To alleviate this problem, we present
a novel unsupervised domain adaptation network, for generalizing models learned
from the labeled source domain to the unlabeled target domain for
cross-modality biomedical image segmentation. Specifically, our approach
consists of two key modules, a conditional domain discriminator~(CDD) and a
category-centric prototype aligner~(CCPA). The CDD, extended from conditional
domain adversarial networks in classifier tasks, is effective and robust in
handling complex cross-modality biomedical images. The CCPA, improved from the
graph-induced prototype alignment mechanism in cross-domain object detection,
can exploit precise instance-level features through an elaborate prototype
representation. In addition, it can address the negative effect of class
imbalance via entropy-based loss. Extensive experiments on a public benchmark
for the cardiac substructure segmentation task demonstrate that our method
significantly improves performance on the target domain.


Abstract:
  In quantum computation, the computation is achieved by linear operators in
Hilbert spaces. In this work, we explain an idea of a new computation scheme,
in which the linear operators are replaced by (higher) functors between two
(higher) categories. The fundamental problem in realizing this idea is the
physical realization of (higher) functors. We provide a theoretical idea of
realizing (higher) functors based on the physics of topological orders.


Abstract:
  In this paper, as the third part of the third step of our study on developing
data analysis procedures for using 3-dimensional information offered by
directional direct Dark Matter detection experiments in the future, we
introduce a 3-dimensional effective velocity distribution of halo Weakly
Interacting Massive Particles (WIMPs), which, instead of the theoretically
prediction of the entire Galactic Dark Matter particles, describes the actual
velocity distribution of WIMPs scattering off (specified) target nuclei in an
underground detector. Its target and WIMP-mass dependences as well as ("annual"
modulations of) its "anisotropy" in the Equatorial/laboratory and even the
Galactic coordinate systems will be demonstrated and discussed in detail. For
readers' reference, all simulation plots presented in this paper (and more) can
be found "in animation" on our online (interactive) demonstration webpage
(http://www.tir.tw/phys/hep/dm/amidas-2d/).


Abstract:
  Two-way online correlated selection (two-way OCS) is an online algorithm
that, at each timestep, takes a pair of elements from the ground set and
irrevocably chooses one of the two elements, while ensuring negative
correlation in the algorithm's choices. Whilst OCS was initially invented by
Fahrbach, Huang, Tao, and Zadimoghaddam to solve the edge-weighted online
bipartite matching problem, it is an interesting technique on its own due to
its capability of introducing a powerful algorithmic tool, namely negative
correlation, to online algorithms. As such, Fahrbach et al. posed two
tantalizing open questions in their paper, one of which was the following: Can
we obtain n-way OCS for n>2, in which the algorithm can be given n>2 elements
to choose from at each timestep?
  In this paper, we affirmatively answer this open question by presenting a
three-way OCS. Our algorithm uses two-way OCS as its building block and is
simple to describe; however, as it internally runs two instances of two-way
OCS, one of which is fed with the output of the other, the final output
probability distribution becomes highly elusive. We tackle this difficulty by
approximating the output distribution of OCS by a flat, less correlated
function and using it as a safe "surrogate" of the real distribution. Our
three-way OCS also yields a 0.5093-competitive algorithm for edge-weighted
online matching, demonstrating its usefulness.


Abstract:
  We determine the effect on the computational complexity of a conformal
anomaly using the Complexity=Action prescription of the gauge/gravity
correspondence. To allow the involvement of said anomaly, we extend previous
studies to include arbitrary values for the anisotropic parameter and the
magnetic field respectively on the Mateos-Trancanelli and the D'Hoker-Kraus
holographic models. Our main result is that the rate of change of the
computational complexity is independent of the conformal anomaly in both cases.
In addition, this allows us to also show that, if so desired, the saturation of
Lloyd's bound at infinite time can be used as a renormalization condition.


Abstract:
  We present the largest sample of brown dwarf (BD) protoplanetary disk
spectral energy distributions modeled to date. We compile 49 objects with ALMA
observations from four star-forming regions: $\rho$ Ophiuchus, Taurus, Lupus,
and Upper Scorpius. Studying multiple regions with various ages enables us to
probe disk evolution over time. Specifically, from our models we obtain values
for dust grain sizes, dust settling, and disk mass; we compare how each of
these parameters vary between the regions. We find that disk mass decreases
with age. We also find evidence of disk evolution (i.e., grain growth and
significant dust settling) in all four regions, indicating that planet
formation and disk evolution may begin to occur at earlier stages. We generally
find these disks contain too little mass to form planetary companions, though
we cannot rule out that planet formation may have already occurred. Finally, we
examine the disk mass -- host mass relationship and find that BD disks are
largely consistent with previously-determined relationships for disks around T
Tauri stars.


Abstract:
  For graph $G$, $F$ and integer $n$, the generalized Tu\'an number $ex(n,G,F)$
denotes the maximum number of copies of $G$ that an $F$-free $n$-vertex graph
can have. We study this parameter when both $G$ and $F$ are complete bipartite
graphs.


Abstract:
  Expanding on MacKay (1992), we argue that conventional model-based methods
for active learning - like BALD - have a fundamental shortfall: they fail to
directly account for the test-time distribution of the input variables. This
can lead to pathologies in the acquisition strategy, as what is maximally
informative for model parameters may not be maximally informative for
prediction: for example, when the data in the pool set is more dispersed than
that of the final prediction task, or when the distribution of pool and test
samples differs. To correct this, we revisit an acquisition strategy that is
based on maximizing the expected information gained about possible future
predictions, referring to this as the Expected Predictive Information Gain
(EPIG). As EPIG does not scale well for batch acquisition, we further examine
an alternative strategy, a hybrid between BALD and EPIG, which we call the
Joint Expected Predictive Information Gain (JEPIG). We consider using both for
active learning with Bayesian neural networks on a variety of datasets,
examining the behavior under distribution shift in the pool set.


Abstract:
  Prior works on formalizing explanations of a graph neural network (GNN) focus
on a single use case - to preserve the prediction results through identifying
important edges and nodes. In this paper, we develop a multi-purpose
interpretation framework by acquiring a mask that indicates topology
perturbations of the input graphs. We pack the framework into an interactive
visualization system (GNNViz) which can fulfill multiple purposes:
Preserve,Promote, or Attack GNN's predictions. We illustrate our approach's
novelty and effectiveness with three case studies: First, GNNViz can assist non
expert users to easily explore the relationship between graph topology and
GNN's decision (Preserve), or to manipulate the prediction (Promote or Attack)
for an image classification task on MS-COCO; Second, on the Pokec social
network dataset, our framework can uncover unfairness and demographic biases;
Lastly, it compares with state-of-the-art GNN explainer baseline on a synthetic
dataset.


Abstract:
  In the present investigation compact stellar models are dealt with in the
framework of the modified gravity theory, specifically of
$f(\mathbb{T},\mathcal{T})$ type. We have considered that the compact objects
are following a spherically symmetric static metric and obtained the Einstein
field equations in the spacetime of $f(\mathbb{T},\mathcal{T})$. To make the
Einstein equations solvable we employ the methodology of conformal Killing
vectors. Thereafter by using the MIT bag equation of state to the compact
stars, considering that the stars are formed by strange quark, we find the
solutions set. The solutions are examined via several physical tastings which
exhibit viability of the model.


Abstract:
  A recent study demonstrated that freedom of convection and strength of
magnetic field in the photospheric feet of active-region (AR) coronal loops,
together, can engender or quench heating in them. Other studies stress that
magnetic flux cancellation at the loop-feet potentially drives heating in
loops. We follow 24-hour movies of a bipolar AR, using EUV images from SDO/AIA
and line-of-sight (LOS) magnetograms from SDO/HMI, to examine magnetic
polarities at the feet of 23 of the brightest coronal loops. We derived FeXVIII
emission (hot-94) images (using the Warren et al. method) to select the
hottest/brightest loops, and confirm their footpoint locations via
non-force-free field extrapolations. From 6"$\times$6" boxes centered at each
loop foot in LOS magnetograms we find that $\sim$40\% of the loops have both
feet in unipolar flux, and $\sim$60\% of the loops have at least one foot in
mixed-polarity flux. The loops with both feet unipolar are $\sim$15\% shorter
lived on average than the loops having mixed-polarity foot-point flux, but
their peak-intensity averages are equal. The presence of mixed-polarity
magnetic flux in at least one foot of majority of the loops suggests that flux
cancellation at the footpoints may drive most of the heating. But, the absence
of mixed-polarity magnetic flux (to the detection limit of HMI) in $\sim$40\%
of the loops suggests that flux cancellation may not be necessary to drive
heating in coronal loops -- magnetoconvection and field strength at both loop
feet possibly drive much of the heating, even in the cases where a loop foot
presents mixed-polarity magnetic flux.


Abstract:
  What limits the value of the superconducting transition temperature ($T_c$)
is a question of great fundamental and practical importance. Various heuristic
upper bounds on $T_c$ have been proposed, expressed as fractions of the Fermi
temperature, $T_F$, the zero-temperature superfluid stiffness, $\rho_s(0)$, or
a characteristic Debye frequency, $\omega_0$. We show that while these bounds
are physically motivated and are certainly useful in many relevant situations,
none of them serve as a fundamental bound on $T_c$. To demonstrate this, we
provide explicit models where $T_c/T_F$ (with an appropriately defined $T_F$),
$T_c/\rho_s(0)$, and $T_c/\omega_0$ are unbounded.


Abstract:
  The specific heat capacity $c_v$ of glass formers undergoes a hysteresis when
subjected to a cooling-heating cycle, with a larger $c_v$ and a more pronounced
hysteresis for fragile glasses than for strong ones. Here, we show that these
experimental features, including the unusually large magnitude of $c_v$ of
fragile glasses, are well reproduced by kinetic Monte Carlo and equilibrium
study of a distinguishable particle lattice model (DPLM) incorporating a
two-state picture of particle interactions. The large $c_v$ in fragile glasses
is caused by a dramatic transfer of probabilistic weight from high-energy
particle interactions to low-energy ones as temperature decreases.


Abstract:
  In this article, we give an in-depth analysis of the problem of optimising
the total population size for a standard logistic-diffusive model. This
optimisation problem stems from the study of spatial ecology and amounts to the
following question: assuming a species evolves in a domain, what is the best
way to spread resources in order to ensure a maximal population size at
equilibrium? {In recent years, many authors contributed to this topic.} We
settle here the proof of two fundamental properties of optimisers: the
bang-bang one which had so far only been proved under several strong
assumptions, and the other one is the fragmentation of maximisers. Here, we
prove the bang-bang property in all generality using a new spectral method. The
technique introduced to demonstrate the bang-bang character of optimizers can
be adapted and generalized to many optimization problems with other classes of
bilinear optimal control problems where the state equation is semilinear and
elliptic. We comment on it in a conclusion section.Regarding the geometry of
maximisers, we exhibit a blow-up rate for the $BV$-norm of maximisers as the
diffusivity gets smaller: if $\O$ is an orthotope and if $m_\mu$ is an optimal
control, then $\Vert m_\mu\Vert_{BV}\gtrsim \sqrt{\mu}$. The proof of this
results relies on a very fine energy argument.


Abstract:
  The phenomenon of negative refraction generally requires negative refractive
indices or phase discontinuities, which can be realized using metamaterials or
metasurfaces. Recent theories have proposed a novel mechanism for negative
refraction based on synthetic gauge fields, which affect classical waves as if
they were charged particles in electromagnetic fields, but this has not
hitherto been demonstrated in experiment. Here, we report on the experimental
demonstration of gauge-field-induced negative refraction in a twisted bilayer
acoustic metamaterial. The bilayer twisting produces a synthetic gauge field
for sound waves propagating within a projected two-dimensional geometry, with
the magnitude of the gauge field parameterized by the choice of wavenumber
along the third dimension. Waveguiding with backward propagating modes is also
demonstrated in a trilayer configuration that implements strong gauge fields.
These results provide an alternative route to achieving negative refraction in
synthetic materials.


Abstract:
  Accelerated gradient-based methods are being extensively used for solving
non-convex machine learning problems, especially when the data points are
abundant or the available data is distributed across several agents. Two of the
prominent accelerated gradient algorithms are AdaGrad and Adam. AdaGrad is the
simplest accelerated gradient method, which is particularly effective for
sparse data. Adam has been shown to perform favorably in deep learning problems
compared to other methods. In this paper, we propose a new fast optimizer,
Generalized AdaGrad (G-AdaGrad), for accelerating the solution of potentially
non-convex machine learning problems. Specifically, we adopt a state-space
perspective for analyzing the convergence of gradient acceleration algorithms,
namely G-AdaGrad and Adam, in machine learning. Our proposed state-space models
are governed by ordinary differential equations. We present simple convergence
proofs of these two algorithms in the deterministic settings with minimal
assumptions. Our analysis also provides intuition behind improving upon
AdaGrad's convergence rate. We provide empirical results on MNIST dataset to
reinforce our claims on the convergence and performance of G-AdaGrad and Adam.


Abstract:
  In "The Organization of Behavior" (Hebb, 1949), Hebb suggested that the
propagation of activity between transiently grouped neurons plays an important
role in behavior. Since then, multiple studies have provided evidence
supporting Hebb's claim; however, most findings have been found locally in
confined brain regions during unimodal tasks. Here we report on brain-wide
behavioral-specific sequences in humans performing a multimodal task. To
investigate the structure of these sequences, we used MEG to record brain
activity in multiple brain regions simultaneously in participants performing a
sensory-motor synchronization task. We detected local transient events
corresponding to synchronously activating populations of pyramidal neurons and
searched for their global organization as spatio-temporal patterns of
activation sequences between distant neural populations. We focused our
analysis on two types of spatio-temporal patterns: the most frequently
repeating patterns and the most discriminative patterns, to concentrate on
patterns with high relevancy to behavior. The findings revealed that global
temporally precise sequences can be found and that these sequences have
partially stereotypical characteristics, both temporally and spatially, with
consistent properties across subjects. By implementing a simplistic
single-trial decoding approach, we found that brain-wide sequences have a
temporal precision of 17-31 milliseconds, which resembles the temporal
precision found locally in neural assemblies.


Abstract:
  Repeated Compton scattering of photons with thermal electrons is one of the
fundamental processes at work in many astrophysical plasma. Solving the exact
evolution equations is hard and one common simplification is based on
Fokker-Planck (FP) approximations of the Compton collision term. Here we carry
out a detailed numerical comparison of several FP approaches with the exact
scattering kernel solution for a range of test problems assuming isotropic
media and thermal electrons at various temperatures. The Kompaneets equation,
being one of the most widely used FP approximations, fails to account for
Klein-Nishina corrections and enhanced Doppler boosts and recoil at high
energies. These can be accounted for with an alternative FP approach based on
the exact first and second moments of the scattering kernel. As demonstrated
here, the latter approach works very well in dilute media, but inherently fails
to reproduce the correct equilibrium solution in the limit of many scattering.
Conditions for the applicability of the FP approximations are clarified,
overall showing that the Kompaneets equation provides the most robust
approximation to the full problem, even if inaccurate in many cases. We close
our numerical analysis by briefly illustrating the solutions for the spectral
distortions of the cosmic microwave background (CMB) after photon injection at
redshift $z\lesssim 10^5$, when double Compton and Bremsstrahlung emission can
be omitted. We demonstrate that the exact treatment using the scattering kernel
computed with {\tt CSpack} is often needed. This work should provide an
important step towards accurate computations of the CMB spectral distortions
from high-energy particle cascades.


Abstract:
  Robot-assisted surgery is an established clinical practice. The automatic
identification of surgical actions is needed for a range of applications,
including performance assessment of trainees and surgical process modeling for
autonomous execution and monitoring. However, supervised action identification
is not feasible, due to the burden of manually annotating recordings of
potentially complex and long surgical executions. Moreover, often few example
executions of a surgical procedure can be recorded. This paper proposes a novel
fast algorithm for unsupervised identification of surgical actions in a
standard surgical training task, the ring transfer, executed with da Vinci
Research Kit. Exploiting kinematic and semantic visual features automatically
extracted from a very limited dataset of executions, we are able to
significantly outperform state-of-the-art results on a dataset of non-expert
executions (58\% vs. 24\% F1-score), and improve performance in the presence of
noise, short actions and non-homogeneous workflows, i.e. non repetitive action
sequences.


Abstract:
  Camera-based Deep Learning algorithms are increasingly needed for perception
in Automated Driving systems. However, constraints from the automotive industry
challenge the deployment of CNNs by imposing embedded systems with limited
computational resources. In this paper, we propose an approach to embed a
multi-task CNN network under such conditions on a commercial prototype
platform, i.e. a low power System on Chip (SoC) processing four surround-view
fisheye cameras at 10 FPS.
  The first focus is on designing an efficient and compact multi-task network
architecture. Secondly, a pruning method is applied to compress the CNN,
helping to reduce the runtime and memory usage by a factor of 2 without
lowering the performances significantly. Finally, several embedded optimization
techniques such as mixed-quantization format usage and efficient data transfers
between different memory areas are proposed to ensure real-time execution and
avoid bandwidth bottlenecks. The approach is evaluated on the hardware
platform, considering embedded detection performances, runtime and memory
bandwidth. Unlike most works from the literature that focus on classification
task, we aim here to study the effect of pruning and quantization on a compact
multi-task network with object detection, semantic segmentation and soiling
detection tasks.


Abstract:
  A novel way to arrive at a single molecule junction in the partially wet
phase is described. The fabrication of this device makes use of a natural
drying process. Details are provided concerning the fabrication of the device
and the related material choices. In addition a process description is provided
with a step by step approach for creating the partially wet phase molecular MCB
junction.


Abstract:
  The promise of Deep Neural Network (DNN) powered Internet of Thing (IoT)
devices has motivated a tremendous demand for automated solutions to enable
fast development and deployment of efficient (1) DNNs equipped with
instantaneous accuracy-efficiency trade-off capability to accommodate the
time-varying resources at IoT devices and (2) dataflows to optimize DNNs'
execution efficiency on different devices. Therefore, we propose InstantNet to
automatically generate and deploy instantaneously switchable-precision networks
which operate at variable bit-widths. Extensive experiments show that the
proposed InstantNet consistently outperforms state-of-the-art designs.


Abstract:
  We prove that the Bowen-Franks group classifies the Leavitt path algebras of
purely infinite simple finite graphs over a regular supercoherent commutative
ring with involution where $2$ is invertible, equipped with their standard
involutions, up to matricial stabilization and involution preserving homotopy
equivalence. We also consider a twisting of the standard involution on Leavitt
path algebras and obtain partial results in the same direction for purely
infinite simple graphs. Our tools are $K$-theoretic, and we prove several
results about (Hermitian, bivariant) $K$-theory of Leavitt path algebras.


Abstract:
  Self-supervised pre-training using so-called "pretext" tasks has recently
shown impressive performance across a wide range of modalities. In this work,
we advance self-supervised learning from permutations, by pre-training a model
to reorder shuffled parts of the spectrogram of an audio signal, to improve
downstream classification performance. We make two main contributions. First,
we overcome the main challenges of integrating permutation inversions into an
end-to-end training scheme, using recent advances in differentiable ranking.
This was heretofore sidestepped by casting the reordering task as
classification, fundamentally reducing the space of permutations that can be
exploited. Our experiments validate that learning from all possible
permutations improves the quality of the pre-trained representations over using
a limited, fixed set. Second, we show that inverting permutations is a
meaningful pretext task for learning audio representations in an unsupervised
fashion. In particular, we improve instrument classification and pitch
estimation of musical notes by reordering spectrogram patches in the
time-frequency space.


Abstract:
  Collaborative multi-agent robotic systems where agents coordinate by
modifying a shared environment often result in undesired dynamical couplings
that complicate the analysis and experiments when solving a specific problem or
task. Simultaneously, biologically-inspired robotics rely on simplifying agents
and increasing their number to obtain more efficient solutions to such
problems, drawing similarities with natural processes. In this work we focus on
the problem of a biologically-inspired multi-agent system solving collaborative
foraging. We show how mean field techniques can be used to re-formulate such a
stochastic multi-agent problem into a deterministic autonomous system. This
de-couples agent dynamics, enabling the computation of limit behaviours and the
analysis of optimality guarantees. Furthermore, we analyse how having finite
number of agents affects the performance when compared to the mean field limit
and we discuss the implications of such limit approximations in this
multi-agent system, which have impact on more general collaborative stochastic
problems.


Abstract:
  We compare three first-principles methods of calculating the Curie
temperature in two-dimensional (2D) ferromagnetic materials (FM), modeled using
the Heisenberg model, and propose a simple formula for estimating the Curie
temperature with high accuracy that works for all common 2D lattice types.
First, we study the effect of exchange anisotropy on the Curie temperature
calculated using the Monte-Carlo (MC), the Green's function method, and the
renormalized spin-wave (RNSW). We find that the Green's function overestimates
the Curie temperature in high-anisotropy regimes compared to MC, whereas RNSW
underestimates the Curie temperature compared to the MC and the Green's
function. Next, we propose a closed-form formula for calculating the Curie
temperature of 2D FMs, which provides an estimate of the Curie temperature
greatly improving over the mean-field expression for magnetic material
screening. We apply the closed-form formula to predict the Curie temperature 2D
magnets screened from the C2DB database and discover several high Curie
temperature FMs with Fe2F2 and MoI2 emerging as the most promising 2D
ferromagnets. Finally, comparing to experimental results for CrI3, CrCl3, and
CrBr3, we conclude that for small effective anisotropies, the Green's
function-based equations are preferable, while, for larger anisotropies
MC-based results are more predictive.


Abstract:
  We investigate and quantify the impact of mixed (cold and warm) dark matter
models on large-scale structure observables. In this scenario, dark matter
comes in two phases, a cold one (CDM) and a warm one (WDM): the presence of the
latter causes a suppression in the matter power spectrum which is allowed by
current constraints and may be detected in present-day and upcoming surveys. We
run a large set of $N$-body simulations in order to build an efficient and
accurate emulator to predict the aforementioned suppression with percent
precision over a wide range of values for the WDM mass, $M_\mathrm{wdm}$, and
its fraction with respect to the totality of dark matter, $f_\mathrm{wdm}$. The
suppression in the matter power spectrum is found to be independent of changes
in the cosmological parameters at the 2% level for $k\lesssim 10 \ h/$Mpc and
$z\leq 3.5$. In the same ranges, by applying a baryonification procedure on
both $\Lambda$CDM and CWDM simulations to account for the effect of feedback,
we find a similar level of agreement between the two scenarios. We examine the
impact that such suppression has on weak lensing and angular galaxy clustering
power spectra. Finally, we discuss the impact of mixed dark matter on the shape
of the halo mass function and which analytical prescription yields the best
agreement with simulations. We provide the reader with an application to galaxy
cluster number counts.


Abstract:
  The novel technique introduced here aims to accomplish the first stage of
transferring low-level cognitive skills between two individuals (e.g. from
expert to learner) to ease the consecutive higher level declarative learning
process for the target "learner" individual in a game environment. Such
low-level cognitive skill is associated with the procedural knowledge and
established at low-level of mind which can be unveiled and transferred by only
a novel technique (rather than by a traditional educational environment ) like
a highly interactive computer game domain in which a user exposes his/her
unconscious mind behaviors via the game-hero non-deliberately during the game
sessions. The cognitive data exposed by the game-hero would be recorded, and
then be modelled by the artificial intelligence technique like Bayesian
networks for an early stage of cognitive skill transfer and the cognitive
stimuli are also generated to be used as game agents to train the learner.


Abstract:
  Despite the continued successes of computationally efficient deep neural
network architectures for video object detection, performance continually
arrives at the great trilemma of speed versus accuracy versus computational
resources (pick two). Current attempts to exploit temporal information in video
data to overcome this trilemma are bottlenecked by the state-of-the-art in
object detection models. We present, a technique which performs video object
detection through the use of off-the-shelf object detectors alongside existing
optical flow based motion estimation techniques in parallel. Through a set of
experiments on the benchmark MOT20 dataset, we demonstrate that our approach
significantly reduces the baseline latency of any given object detector without
sacrificing any accuracy. Further latency reduction, up to 25x lower than the
original latency, can be achieved with minimal accuracy loss. MOVEX enables low
latency video object detection on common CPU based systems, thus allowing for
high performance video object detection beyond the domain of GPU computing. The
code is available at https://github.com/juliantrue/movex.


Abstract:
  We establish a DMV-strong uniqueness result for the compressible
Navier-Stokes system with potential temperature transport. The concept of
generalized, the so-called dissipative measure-valued (DMV), solutions was
proposed in [7], where their global-in-time existence was proved. Here we show
that strong solutions are stable in the class of DMV solutions. More precisely,
a DMV solution coincides with a strong solution emanating from the same initial
data as long as the strong solution exists.


Abstract:
  We introduce "Simulations Beyond The Local Universe" (SIBELIUS) that connect
the Local Group to its cosmic environment. We show that introducing
hierarchical small-scale perturbations to a density field constrained on large
scales by observations provides an efficient way to explore the sample space of
Local Group analogues. From more than 60 000 simulations, we identify a
hierarchy of Local Group characteristics emanating from different scales: the
total mass, orientation, orbital energy and the angular momentum are largely
determined by modes above $\lambda$ = 1.6 comoving Mpc (cMpc) in the primordial
density field.
  Smaller scale variations are mostly manifest as perturbations to the MW-M31
orbit, and we find that the observables commonly used to describe the Local
Group -- the MW-M31 separation and radial velocity -- are transient and depend
on specifying scales down to 0.2 cMpc in the primordial density field. We
further find that the presence of M33/LMC analogues significantly affects the
MW-M31 orbit and its sensitivity to small-scale perturbations. We construct
initial conditions that lead to the formation of a Local Group whose primary
observables precisely match the current observations.


Abstract:
  Toxic Spans Detection(TSD) task is defined as highlighting spans that make a
text toxic. Many works have been done to classify a given comment or document
as toxic or non-toxic. However, none of those proposed models work at the token
level. In this paper, we propose a self-attention-based bidirectional gated
recurrent unit(BiGRU) with a multi-embedding representation of the tokens. Our
proposed model enriches the representation by a combination of GPT-2, GloVe,
and RoBERTa embeddings, which led to promising results. Experimental results
show that our proposed approach is very effective in detecting span tokens.


Abstract:
  In this work we present a spatial-temporal convolutional neural network for
predicting future COVID-19 related symptoms severity among a population, per
region, given its past reported symptoms. This can help approximate the number
of future Covid-19 patients in each region, thus enabling a faster response,
e.g., preparing the local hospital or declaring a local lockdown where
necessary. Our model is based on a national symptom survey distributed in
Israel and can predict symptoms severity for different regions daily. The model
includes two main parts - (1) learned region-based survey responders profiles
used for aggregating questionnaires data into features (2) Spatial-Temporal 3D
convolutional neural network which uses the above features to predict symptoms
progression.


Abstract:
  In Southern Italy, since 2013, there has been an ongoing Olive Quick Decline
Syndrome (OQDS) outbreak, due to the bacterium Xylella fastidiosa. In a couple
of previous papers, the authors have proposed a mathematical approach for
identifying possible control strategies for eliminating or at least reduce the
economic impact of such event. The main players involved in OQDS are
represented by the insect vector, Philaenus spumarius, its host plants (olive
trees and weeds) and the bacterium, X. fastidiosa. A basic mathematical model
has been expressed in terms of a system of ordinary differential equations; a
preliminary analysis already provided interesting results about possible
control strategies within an integrated pest management framework, not
requiring the removal of the productive resource represented by the olive
trees. The same conjectures have been later confirmed by analyzing the impact
of possible spatial heterogeneities on controlling a X. fastidiosa epidemic.
These encouraging facts have stimulated a more detailed and rigorous
mathematical analysis of the same system, as presented in this paper. A clear
picture of the possible steady states (equilibria) and their stability
properties has been outlined, within a variety of different parameter
scenarios, for the original spatially homogeneous ecosystem. The results
obtained here confirm, in a mathematically rigorous way, what had been
conjectured in the previous papers, i.e. that the removal of a suitable amount
of weed biomass (reservoir of the juvenile stages of the insect vector of X.
fastidiosa from olive orchards and surrounding areas is the most acceptable
strategy to control the spread of the OQDS. In addition, as expected, the
adoption of more resistant olive tree cultivars has been shown to be a good
strategy, though less cost-effective, in controlling the pathogen.