Abstract:
  This monograph, along with a self-consistent presentation of the theory of
q-W-algebras including the construction of algebraic group analogues of Slodowy
slices, contains a description of q-W-algebras in terms of Zhelobenko type
operators introduced in the book. This description is applied to prove the De
Concini-Kac-Procesi conjecture on the dimensions of irreducible modules over
quantum groups at roots of unity.


Abstract:
  Facial expressions vary from the visible to the subtle. In recent years, the
analysis of micro-expressions $-$ a natural occurrence resulting from the
suppression of one's true emotions, has drawn the attention of researchers with
a broad range of potential applications. However, spotting microexpressions in
long videos becomes increasingly challenging when intertwined with normal or
macro-expressions. In this paper, we propose a shallow optical flow
three-stream CNN (SOFTNet) model to predict a score that captures the
likelihood of a frame being in an expression interval. By fashioning the
spotting task as a regression problem, we introduce pseudo-labeling to
facilitate the learning process. We demonstrate the efficacy and efficiency of
the proposed approach on the recent MEGC 2020 benchmark, where state-of-the-art
performance is achieved on CAS(ME)$^{2}$ with equally promising results on SAMM
Long Videos.


Abstract:
  Face recognition is a popular and well-studied area with wide applications in
our society. However, racial bias had been proven to be inherent in most State
Of The Art (SOTA) face recognition systems. Many investigative studies on face
recognition algorithms have reported higher false positive rates of African
subjects cohorts than the other cohorts. Lack of large-scale African face image
databases in public domain is one of the main restrictions in studying the
racial bias problem of face recognition. To this end, we collect a face image
database namely CASIA-Face-Africa which contains 38,546 images of 1,183 African
subjects. Multi-spectral cameras are utilized to capture the face images under
various illumination settings. Demographic attributes and facial expressions of
the subjects are also carefully recorded. For landmark detection, each face
image in the database is manually labeled with 68 facial keypoints. A group of
evaluation protocols are constructed according to different applications,
tasks, partitions and scenarios. The performances of SOTA face recognition
algorithms without re-training are reported as baselines. The proposed database
along with its face landmark annotations, evaluation protocols and preliminary
results form a good benchmark to study the essential aspects of face biometrics
for African subjects, especially face image preprocessing, face feature
analysis and matching, facial expression recognition, sex/age estimation,
ethnic classification, face image generation, etc. The database can be
downloaded from our http://www.cripacsir.cn/dataset/


Abstract:
  We study supervised learning algorithms in which a quantum device is used to
perform a computational subroutine - either for prediction via probability
estimation, or to compute a kernel via estimation of quantum states overlap. We
design implementations of these quantum subroutines using Boson Sampling
architectures in linear optics, supplemented by adaptive measurements. We then
challenge these quantum algorithms by deriving classical simulation algorithms
for the tasks of output probability estimation and overlap estimation. We
obtain different classical simulability regimes for these two computational
tasks in terms of the number of adaptive measurements and input photons. In
both cases, our results set explicit limits to the range of parameters for
which a quantum advantage can be envisaged with adaptive linear optics compared
to classical machine learning algorithms: we show that the number of input
photons and the number of adaptive measurements cannot be simultaneously small
compared to the number of modes. Interestingly, our analysis leaves open the
possibility of a near-term quantum advantage with a single adaptive
measurement.


Abstract:
  We present calculations of the photoionization (PI) cross sections of
rubidium and cesium Rydberg atoms for light with wavelengths ranging from the
infrared to the ultraviolet, using model potentials from [M. Marinescu, H. R.
Sadeghpour, and A. Dalgarno, Phys. Rev. A 49, 982 (1994)]. The origins of
pronounced PI minima are identified by investigating the free-electron
wavefunctions. These include broad PI minima in the $nS$ to $\epsilon P$ PI
channels of both Rb and Cs, with free-electron energy $\epsilon$, which are
identified as Cooper minima. Much narrower PI minima in the $nD$ to $\epsilon
F$ channels are due to shape resonances of the free-electron states. We
describe possible experimental procedures for measuring the PI minima, and we
discuss their implications in fundamental atomic physics as well as in
practical applications.


Abstract:
  We study the problem of how to construct a set of policies that can be
composed together to solve a collection of reinforcement learning tasks. Each
task is a different reward function defined as a linear combination of known
features. We consider a specific class of policy compositions which we call set
improving policies (SIPs): given a set of policies and a set of tasks, a SIP is
any composition of the former whose performance is at least as good as that of
its constituents across all the tasks. We focus on the most conservative
instantiation of SIPs, set-max policies (SMPs), so our analysis extends to any
SIP. This includes known policy-composition operators like generalized policy
improvement. Our main contribution is a policy iteration algorithm that builds
a set of policies in order to maximize the worst-case performance of the
resulting SMP on the set of tasks. The algorithm works by successively adding
new policies to the set. We show that the worst-case performance of the
resulting SMP strictly improves at each iteration, and the algorithm only stops
when there does not exist a policy that leads to improved performance. We
empirically evaluate our algorithm on a grid world and also on a set of domains
from the DeepMind control suite. We confirm our theoretical results regarding
the monotonically improving performance of our algorithm. Interestingly, we
also show empirically that the sets of policies computed by the algorithm are
diverse, leading to different trajectories in the grid world and very distinct
locomotion skills in the control suite.


Abstract:
  In this paper, we present an update to the NISQA speech quality prediction
model that is focused on distortions that occur in communication networks. In
contrast to the previous version, the model is trained end-to-end and the
time-dependency modelling and time-pooling is achieved through a Self-Attention
mechanism. Besides overall speech quality, the model also predicts the four
speech quality dimensions Noisiness, Coloration, Discontinuity, and Loudness,
and in this way gives more insight into the cause of a quality degradation.
Furthermore, new datasets with over 13,000 speech files were created for
training and validation of the model. The model was finally tested on a new,
live-talking test dataset that contains recordings of real telephone calls.
Overall, NISQA was trained and evaluated on 81 datasets from different sources
and showed to provide reliable predictions also for unknown speech samples. The
code, model weights, and datasets are open-sourced.


Abstract:
  We extend the GENEVA Monte Carlo framework using the transverse momentum of a
colour-singlet system as the resolution variable. This allows us to use
next-to-next-to-next-to leading logarithm (N$^3$LL) resummation via the RadISH
formalism to obtain precise predictions for any colour-singlet production
process at the fully exclusive level. Thanks to the implementation of two
different resolution variables within the GENEVA framework, we are able to
assess the impact of such a choice on differential observables for the first
time. As a first application we present predictions for Drell-Yan lepton pair
production at next-to-next-to-leading order (NNLO) in QCD interfaced to a
parton shower simulation that includes additional all-order radiative
corrections. We provide fully showered and hadronised events using PYTHIA 8,
while retaining the NNLO QCD accuracy for observables which are inclusive over
the additional radiation. We compare our final predictions to LHC data at 13
TeV, finding good agreement across several distributions.


Abstract:
  We show that ultradilute quantum liquids can be formed with ultracold bosonic
dipolar atoms in a bilayer geometry. Contrary to previous realizations of
ultradilute liquids, there is no need of stabilizing the system with an
additional repulsive short-range potential. The advantage of the proposed
system is that dipolar interactions on their own are sufficient for creation of
a self-bound state and no additional short-range potential is needed for the
stabilization. We perform quantum Monte Carlo simulations and find a rich
ground state phase diagram that contains quantum phase transitions between
liquid, solid, atomic gas, and molecular gas phases. The stabilization
mechanism of the liquid phase is consistent with the microscopic scenario in
which the effective dimer-dimer attraction is balanced by an effective
three-dimer repulsion. The equilibrium density of the liquid, which is
extremely small, can be controlled by the interlayer distance. From the
equation of state, we extract the spinodal density, below which the homogeneous
system breaks into droplets. Our results offer a new example of a
two-dimensional interacting dipolar liquid in a clean and highly controllable
setup.


Abstract:
  We study the jumps in the archimedean height of the Ceresa cycle, as
introduced by R. Hain in his work on normal functions on moduli spaces of
curves, and as further analyzed by P. Brosnan and G. Pearlstein in terms of
asymptotic Hodge theory. Our work is based on a study of the asymptotic
behavior of the Hain-Reed beta-invariant in degenerating families of curves. We
show that the height jump of the Ceresa cycle at a given stable curve is equal
to the so-called "slope" of the dual graph of the curve, and we characterize
those stable curves for which the height jump vanishes. We also obtain an
analytic formula for the height of the Ceresa cycle for a curve over a function
field over the complex numbers, and characterize in analytic terms when the
height of the Ceresa cycle vanishes.


Abstract:
  The N-body gauge allows the introduction of relativistic effects in Newtonian
cosmological simulations. Here we extend this framework to general Horndeski
gravity theories, and investigate the relativistic effects that the scalar
field introduces in the matter power spectrum at intermediate and large scales.
In particular, we show that the kineticity function at these scales enhances
the amplitude of the signal of contributions coming from the extra degree of
freedom. Using the Quasi-Static Approximation (QSA), we separate modified
gravity effects into two parts: one that only affects small-scale physics, and
one that is due to relativistic effects. This allows our formalism to be
readily implemented in modified gravity N-body codes in a straightforward
manner, e.g., relativistic effects can be included as an additional linear
density field in simulations. We identify the emergence of gravity acoustic
oscillations (GAOs) in the matter power spectrum at large scales, $k \sim
10^{-3}-10^{-2}$ Mpc$^{-1}$. GAO features have a purely relativistic origin,
coming from the dynamical nature of the scalar field. GAOs may be enhanced to
detectable levels by the rapid evolution of the dark energy sound horizon in
certain modified gravity models and can be seen as a new test of gravity at
scales probed by future galaxy and intensity-mapping surveys.


Abstract:
  Simultaneous Localization And Mapping (SLAM) is a task to estimate the robot
location and to reconstruct the environment based on observation from sensors
such as LIght Detection And Ranging (LiDAR) and camera. It is widely used in
robotic applications such as autonomous driving and drone delivery. Traditional
LiDAR-based SLAM algorithms mainly leverage the geometric features from the
scene context, while the intensity information from LiDAR is ignored. Some
recent deep-learning-based SLAM algorithms consider intensity features and
train the pose estimation network in an end-to-end manner. However, they
require significant data collection effort and their generalizability to
environments other than the trained one remains unclear. In this paper we
introduce intensity features to a SLAM system. And we propose a novel full SLAM
framework that leverages both geometry and intensity features. The proposed
SLAM involves both intensity-based front-end odometry estimation and
intensity-based back-end optimization. Thorough experiments are performed
including both outdoor autonomous driving and indoor warehouse robot
manipulation. The results show that the proposed method outperforms existing
geometric-only LiDAR SLAM methods.


Abstract:
  Locomotion over soft terrain remains a challenging problem for legged robots.
Most of the work done on state estimation for legged robots is designed for
rigid contacts, and does not take into account the physical parameters of the
terrain. That said, this letter answers the following questions: how and why
does soft terrain affect state estimation for legged robots? To do so, we
utilized a state estimator that fuses IMU measurements with leg odometry that
is designed with rigid contact assumptions. We experimentally validated the
state estimator with the HyQ robot trotting over both soft and rigid terrain.
We demonstrate that soft terrain negatively affects state estimation for legged
robots, and that the state estimates have a noticeable drift over soft terrain
compared to rigid terrain.


Abstract:
  When a single-layer graphene sheet is contacted with metallic electrodes,
tunnel barriers are formed as a result of the doping of graphene by the metal
in the contact region. If the Fermi energy level is modulated by a gate
voltage, the phenomenon of Klein tunneling results in specific features in the
conductance and noise. Here we obtain analytically exact solutions for the
transmission and reflection probability amplitudes using a trapezoidal
potential barrier, allowing us to calculate the differential conductance and
the Fano factor for a graphene sheet in the ballistic regime. We put in
evidence an unexpected global symmetry - the transmission probability is the
same for energies symmetric with respect to half of the barrier height. We
outline a proposal for the experimental verification of these ideas using
realistic sample parameters.


Abstract:
  A maximum sequence $S$ of vertices in a graph $G$, so that every vertex in
$S$ has a neighbor which is independent, or is itself independent, from all
previous vertices in $S$, is called a Grundy dominating sequence. The Grundy
domination number, $\gamma_{gr}(G)$, is the length of $S$. We show that for any
forest $F$, $\gamma_{gr}(F)=|V(T)|-|\mathcal{P}|$ where $\mathcal{P}$ is a
minimum partition of the non-isolate vertices of $F$ into caterpillars in which
if two caterpillars of $\mathcal{P}$ have an edge between them in $F$, then
such an edge must be incident to a non-leaf vertex in at least one of the
caterpillars. We use this result to show the strong product conjecture of B.
Bre\v{s}ar, Cs. Bujt\'{a}s, T. Gologranc, S. Klav\v{z}ar, G. Ko\v{s}mrlj, B.
Patk\'{o}s, Zs. Tuza, and M. Vizer, Dominating sequences in grid-like and
toroidal graphs, Electron. J. Combin. 23(4): P4.34 (2016), for all forests.
Namely, we show that for any forest $G$ and graph $H$, $\gamma_{gr}(G \boxtimes
H) = \gamma_{gr}(G) \gamma_{gr}(H)$. We also show that every connected graph
$G$ has a spanning tree $T$ so that $\gamma_{gr}(G)\le \gamma_{gr}(T)$ and that
every non-complete connected graph contains a Grundy dominating set $S$ so that
the induced subgraph of $S$ contains no isolated vertices.


Abstract:
  A positive integer is called an $E_j$-number if it is the product of $j$
distinct primes. We prove that there are infinitely many triples of
$E_2$-numbers within a gap size of $32$ and infinitely many triples of
$E_3$-numbers within a gap size of $15$. Assuming the Elliot-Halberstam
conjecture for primes and $E_2$-numbers, we can improve these gaps to $12$ and
$5$, respectively. We can obtain even smaller gaps for almost primes, almost
prime powers, or integers having the same exponent pattern in the their prime
factorizations. In particular, if $d(x)$ denotes the number of divisors of $x$,
we prove that there are integers $a,b$ with $1\leq a < b \leq 9$ such that
$d(x)=d(x+a)=d(x+b) = 192$ for infinitely many $x$. Assuming Elliot-Halberstam,
we prove that there are integers $a,b$ with $1\leq a < b \leq 4$ such that
$d(x)=d(x+a)=d(x+b) = 24$ for infinitely many $x$.


Abstract:
  We study the Thermodynamic Bethe Ansatz (TBA) equations for pure $T\bar{T}$
perturbations of some simple integrable quantum field theories with a single
bosonic or fermionic particle, in particular the massive sinh-Gordon model and
its ultraviolet (UV) limit which is a deformation of the conformally invariant
free massless boson. Whereas the TBA equations for $T\bar{T}$ deformations of
massive theories are in principle known, the TBA equations we propose for the
deformations of conformal field theories (CFT's) are relatively new and require
a special factorization in rapidity variables of the CDD factor for the
scattering of the massless particles. The latter TBA equations can be solved
exactly and reproduce the known results for the ground state energy on a
cylinder of circumference $R$ which were previously obtained using different
methods based for instance on the Burgers differential equation. Special
attention is paid to the c-theorem in this context which is discussed in some
detail. For positive infra-red (IR) central charge $c_{IR}$, for flows
consistent with the c-theorem the ground state energy develops a (previously
known) square-root singularity towards the UV, which strongly suggests the
theories are UV incomplete in these physically important cases. We suggest that
the singularity indicates a tachyonic vacuum instability. Other cases with
$c_{IR} < 0$ do not have this singularity and are interpreted as being UV
complete with $c_{UV} = 0$. We extend our results to a continuously variable
$c_{IR}$ by introducing a chemical potential and suggest this as a possible toy
model for the $T\bar{T}$ perturbed Liouville theory.


Abstract:
  This paper focuses on the numerical simulation of geothermal systems in
complex geological settings. The physical model is based on two-phase Darcy
flows coupling the mass conservation of the water component with the energy
conservation and the liquid vapor thermodynamical equilibrium. The
discretization exploits the flexibility of unstructured meshes to model complex
geology including conductive faults as well as complex wells. The polytopal and
essentially nodal Vertex Approximate Gradient scheme is used for the
approximation of the Darcy and Fourier fluxes combined with a Control Volume
approach for the transport of mass and energy. Particular attention is paid to
the faults which are modelled as two-dimensional interfaces defined as
collection of faces of the mesh and to the flow inside deviated or multi-branch
wells defined as collection of edges of the mesh with rooted tree data
structure. By using an explicit pressure drop calculation, the well model
reduces to a single equation based on complementarity constraints with only one
well implicit unknown. The coupled systems are solved fully implicitely at each
time step using efficient nonlinear and linear solvers on parallel distributed
architectures. The convergence of the discrete model is investigated
numerically on a simple test case with a Cartesian geometry and a single
vertical producer well. Then, the ability of our approach to deal efficiently
with realistic test cases is assessed on a high energy faulted geothermal
reservoir operated using a doublet of two deviated wells.


Abstract:
  Cosmological domain walls can be formed as a result of symmetry breaking at
any epoch during the evolution of our universe. We study their interaction with
a classical macroscopic object, like Earth or a satellite in Earth's orbit. We
set up an action that includes the interaction term between the massive
classical object and the scalar field that the domain wall is made of. We use
numerical calculations to solve the coupled equations of motion which describe
the crossing between the domain wall and the classical object. Depending on the
strength of the interaction, relative velocity and size, the object can be
either stopped by the wall, or it can pass through it inducing deformations in
the wall that cost energy. At the same time, the coupling to the scalar filed
might change the object's mass during the crossover. The fact that satellites
in Earth's orbit (or planets in Sun's orbit) can change their mass and/or lose
energy interacting with walls can be used as a new domain wall detection probe.
For example, a typical velocity precision of a satellite is about $0.5$ mm/s,
which directly puts an upper limit on its mass change to $\Delta M/M
\lessapprox 5\times 10^{-17} $. Alternatively, a known satellite flyby anomaly
can easily be explained as an interaction with a closed domain wall. We also
show that the presence of matter modifies the scalar filed potential and can
locally create a bubble of the true vacuum, and thus trigger the decay of the
false vacuum. For a critical bubble which is able to expand, such an
interaction with the domain wall must be strong enough.


Abstract:
  In active galactic nuclei (AGN), fluorescent Fe K$\alpha$ (iron) line
emission is generally interpreted as originating from obscuring material around
a supermassive black hole (SMBH) on the scale of a few parsecs (pc). However,
recent Chandra studies indicate the existence of iron line emission extending
to kpc scales in the host galaxy. The connection between iron line emission and
large-scale material can be spatially resolved directly only in nearby
galaxies, but could be inferred in more distant AGNs by a connection between
line emission and star-forming gas and dust that is more extended than the
pc-scale torus. Here we present the results from a stacking analysis and X-ray
spectral fitting performed on sources in the Chandra Deep Field South (CDFS) 7
Ms observations. From the deep stacked spectra, we select sources with stellar
mass $\log(M_*/M_\odot)>10$ at $0.5<z<2$, obtaining 25 sources with high
infrared luminosity ($ {\rm SFR}_{\rm FIR} \geq 17\;M_{\odot}\;{\rm yr}^{-1}$)
and 32 sources below this threshold. We find that the equivalent width of the
iron line EW(Fe) is a factor of three higher with 3$\sigma$ significance for
high infrared luminosity measured from Herschel observations, indicating a
connection between iron line emission and star-forming material on galaxy
scales. We show that there is no significant dependence in EW(Fe) on $M_*$ or
X-ray luminosity, suggesting the reflection of AGN X-ray emission over large
scales in their host galaxies may be widespread.


Abstract:
  For fixed functions $G,H:[0,\infty)\to[0,\infty)$, consider the rotationally
invariant probability density on $\mathbb{R}^n$ of the form \[ \mu^n(ds) =
\frac{1}{Z_n} G(\|s\|_2)\, e^{ - n H( \|s\|_2)} ds. \] We show that when $n$ is
large, the Euclidean norm $\|Y^n\|_2$ of a random vector $Y^n$ distributed
according to $\mu^n$ satisfies a Gaussian thin-shell property: the distribution
of $\|Y^n\|_2$ concentrates around a certain value $s_0$, and the fluctuations
of $\|Y^n\|_2$ are approximately Gaussian with the order $1/\sqrt{n}$. We apply
this thin shell property to the study of rotationally invariant random
simplices, simplices whose vertices consist of the origin as well as
independent random vectors $Y_1^n,\ldots,Y_p^n$ distributed according to
$\mu^n$. We show that the logarithmic volume of the resulting simplex exhibits
highly Gaussian behavior, providing a generalizing and unifying setting for the
objects considered in Grote-Kabluchko-Th\"ale [Limit theorems for random
simplices in high dimensions, ALEA, Lat. Am. J. Probab. Math. Stat. 16,
141--177 (2019)]. Finally, by relating the volumes of random simplices to
random determinants, we show that if $A^n$ is an $n \times n$ random matrix
whose entries are independent standard Gaussian random variables, then there
are explicit constants $c_0,c_1\in(0,\infty)$ and an absolute constant
$C\in(0,\infty)$ such that \[\sup_{ s \in \mathbb{R}} \left| \mathbb{P} \left[
\frac{ \log \mathrm{det}(A^n) - \log(n-1)! - c_0 }{ \sqrt{ \frac{1}{2} \log n +
c_1 }} < s \right] - \int_{-\infty}^s \frac{e^{ - u^2/2} du}{ \sqrt{ 2 \pi }}
\right| < \frac{C}{\log^{3/2}n}, \] sharpening the $1/\log^{1/3 + o(1)}n$ bound
in Nguyen and Vu [Random matrices: Law of the determinant, Ann. Probab. 42 (1)
(2014), 146--167].


Abstract:
  The compression quality losses of depth sequences determine quality of view
synthesis in free-viewpoint video. The depth map intra prediction in 3D
extensions of the HEVC applies intra modes with auxiliary depth modeling modes
(DMMs) to better preserve depth edges and handle motion discontinuities.
Although such modes enable high efficiency compression, but at the cost of very
high encoding complexity. Skipping conventional intra coding modes and DMMs in
depth coding limits practical applicability of the HEVC for 3D display
applications. In this paper, we introduce a novel low-complexity scheme for
depth video compression based on low-rank tensor decomposition and HEVC intra
coding. The proposed scheme leverages spatial and temporal redundancy by
compactly representing the depth sequence as a high-order tensor. Tensor
factorization into a set of factor matrices following CANDECOMP PARAFAC (CP)
decomposition via alternating least squares give a low-rank approximation of
the scene geometry. Further, compression of factor matrices with HEVC intra
prediction support arbitrary target accuracy by flexible adjustment of bitrate,
varying tensor decomposition ranks and quantization parameters. The results
demonstrate proposed approach achieves significant rate gains by efficiently
compressing depth planes in low-rank approximated representation. The proposed
algorithm is applied to encode depth maps of benchmark Ballet and Breakdancing
sequences. The decoded depth sequences are used for view synthesis in a
multi-view video system, maintaining appropriate rendering quality.


Abstract:
  Adversarial regularization has been shown to improve the generalization
performance of deep learning models in various natural language processing
tasks. Existing works usually formulate the method as a zero-sum game, which is
solved by alternating gradient descent/ascent algorithms. Such a formulation
treats the adversarial and the defending players equally, which is undesirable
because only the defending player contributes to the generalization
performance. To address this issue, we propose Stackelberg Adversarial
Regularization (SALT), which formulates adversarial regularization as a
Stackelberg game. This formulation induces a competition between a leader and a
follower, where the follower generates perturbations, and the leader trains the
model subject to the perturbations. Different from conventional approaches, in
SALT, the leader is in an advantageous position. When the leader moves, it
recognizes the strategy of the follower and takes the anticipated follower's
outcomes into consideration. Such a leader's advantage enables us to improve
the model fitting to the unperturbed data. The leader's strategic information
is captured by the Stackelberg gradient, which is obtained using an unrolling
algorithm. Our experimental results on a set of machine translation and natural
language understanding tasks show that SALT outperforms existing adversarial
regularization baselines across all tasks. Our code is publicly available.


Abstract:
  Substructured domain decomposition (DD) methods have been extensively
studied, and they are usually associated with nonoverlapping decompositions. We
introduce here a substructured version of Restricted Additive Schwarz (RAS)
which we call SRAS, and we discuss its advantages compared to the standard
volume formulation of the Schwarz method when they are used both as iterative
solvers and preconditioners for a Krylov method. To extend SRAS to nonlinear
problems, we introduce SRASPEN (Substructured Restricted Additive Schwarz
Preconditioned Exact Newton), where SRAS is used as a preconditioner for
Newton's method. We study carefully the impact of substructuring on the
convergence and performance of these methods as well as their implementations.
We finally introduce two-level versions of nonlinear SRAS and SRASPEN.
Numerical experiments confirm the advantages of formulating a Schwarz method at
the substructured level.


Abstract:
  More than 10% of the barred galaxies with a direct measurement of the bar
pattern speed host an ultrafast bar. These bars extend beyond the corotation
radius and challenge our understanding of the orbital structure of barred
galaxies. Most of them are found in spiral galaxies, rather than in lenticular
ones. We analysed the properties of the ultrafast bars detected in the CALIFA
Survey to investigate whether they are an artefact resulting from an
overestimation of the bar radius and/or an underestimation of the corotation
radius or a new class of bars, whose orbital structure has not yet been
understood. We revised the available measurements of the bar radius based on
ellipse fitting and Fourier analysis and of the bar pattern speed from the
Tremaine-Weinberg method. In addition, we measured the bar radius from the
analysis of the maps tracing the transverse-to-radial force ratio, which we
obtained from the deprojected i-band images of the galaxies retrieved from the
SDSS Survey. We found that nearly all the sample galaxies are spirals with an
inner ring or pseudo-ring circling the bar and/or strong spiral arms, which
hamper the measurement of the bar radius from the ellipse fitting and Fourier
analysis. According to these methods, the bar ends overlap the ring or the
spiral arms making the adopted bar radius unreliable. On the contrary, the bar
radius from the ratio maps are shorter than the corotation radius. This is in
agreement with the theoretical predictions and findings of numerical
simulations about the extension and stability of the stellar orbits supporting
the bars. We conclude that ultrafast bars are no longer observed when the
correct measurement of the bar radius is adopted. Deriving the bar radius in
galaxies with rings and strong spiral arms is not straightforward and a solid
measurement method based on both photometric and kinematic data is still
missing.


Abstract:
  We present an elegant derivation of the Doppler factor in the
Lienard-Wiechert potentials of an extended particle, based on a theorem
authored by J. L. Synge and dealing with cross-sections through worldtubes.


Abstract:
  We introduce a notion of emergence for coarse-grained macroscopic variables
associated with highly-multivariate microscopic dynamical processes, in the
context of a coupled dynamical environment. Dynamical independence instantiates
the intuition of an emergent macroscopic process as one possessing the
characteristics of a dynamical system "in its own right", with its own
dynamical laws distinct from those of the underlying microscopic dynamics. We
quantify (departure from) dynamical independence by a transformation-invariant
Shannon information-based measure of dynamical dependence. We emphasise the
data-driven discovery of dynamically-independent macroscopic variables, and
introduce the idea of a multiscale "emergence portrait" for complex systems. We
show how dynamical dependence may be computed explicitly for linear systems via
state-space modelling, in both time and frequency domains, facilitating
discovery of emergent phenomena at all spatiotemporal scales. We discuss
application of the state-space operationalisation to inference of the emergence
portrait for neural systems from neurophysiological time-series data. We also
examine dynamical independence for discrete- and continuous-time deterministic
dynamics, with potential application to Hamiltonian mechanics and classical
complex systems such as flocking and cellular automata.


Abstract:
  Crossbar resistive memory with the 1 Selector 1 Resistor (1S1R) structure is
attractive for nonvolatile, high-density, and low-latency storage-class memory
applications. As technology scales down to the single-nm regime, the increasing
resistivity of wordline/bitline becomes a limiting factor to device
reliability. This paper presents write/read communication channels while
considering the line resistance and device variabilities by statistically
relating the degraded write/read margins and the channel parameters. Binary
asymmetric channel (BAC) models are proposed for the write/read operations.
Simulations based on these models suggest that the bit-error rate of devices
are highly non-uniform across the memory array. These models provide
quantitative tools for evaluating the trade-offs between memory reliability and
design parameters, such as array size, technology nodes, and aspect ratio, and
also for designing coding-theoretic solutions that would be most effective for
crossbar memory. Method for optimizing the read threshold is proposed to reduce
the raw bit-error rate (RBER). We propose two schemes for efficient channel
coding based on Bose-Chaudhuri-Hocquenghem (BCH) codes. An interleaved coding
scheme is proposed to mitigate the non-uniformity of reliability and a location
dependent coding framework is proposed to leverage this non-uniformity. Both of
our proposed coding schemes effectively reduce the undetected bit-error rate
(UBER).


Abstract:
  In this paper we investigate the amount of spatial context required for
channel attention. To this end we study the popular squeeze-and-excite (SE)
block which is a simple and lightweight channel attention mechanism. SE blocks
and its numerous variants commonly use global average pooling (GAP) to create a
single descriptor for each channel. Here, we empirically analyze the amount of
spatial context needed for effective channel attention and find that limited
localcontext on the order of seven rows or columns of the original image is
sufficient to match the performance of global context. We propose tiled
squeeze-and-excite (TSE), which is a framework for building SE-like blocks that
employ several descriptors per channel, with each descriptor based on local
context only. We further show that TSE is a drop-in replacement for the SE
block and can be used in existing SE networks without re-training. This implies
that local context descriptors are similar both to each other and to the global
context descriptor. Finally, we show that TSE has important practical
implications for deployment of SE-networks to dataflow AI accelerators due to
their reduced pipeline buffering requirements. For example, using TSE reduces
the amount of activation pipeline buffering in EfficientDetD2 by 90% compared
to SE (from 50M to 4.77M) without loss of accuracy. Our code and pre-trained
models will be publicly available.


Abstract:
  When data is of an extraordinarily large size or physically stored in
different locations, the distributed nearest neighbor (NN) classifier is an
attractive tool for classification. We propose a novel distributed adaptive NN
classifier for which the number of nearest neighbors is a tuning parameter
stochastically chosen by a data-driven criterion. An early stopping rule is
proposed when searching for the optimal tuning parameter, which not only speeds
up the computation but also improves the finite sample performance of the
proposed Algorithm. Convergence rate of excess risk of the distributed adaptive
NN classifier is investigated under various sub-sample size compositions. In
particular, we show that when the sub-sample sizes are sufficiently large, the
proposed classifier achieves the nearly optimal convergence rate. Effectiveness
of the proposed approach is demonstrated through simulation studies as well as
an empirical application to a real-world dataset.


Abstract:
  We investigate the exact overlaps between eigenstates of integrable spin
chains and a special class of states called "integrable initial/final states".
These states satisfy a special integrability constraint, and they are closely
related to integrable boundary conditions. We derive new algebraic relations
for the integrable states, which lead to a set of recursion relations for the
exact overlaps. We solve these recursion relations and thus we derive new
overlap formulas, valid in the XXX Heisenberg chain and its integrable higher
spin generalizations. Afterwards we generalize the integrability condition to
twisted boundary conditions, and derive the corresponding exact overlaps.
Finally, we embed the integrable states into the "Separation of Variables"
framework, and derive an alternative representation for the exact overlaps of
the XXX chain. Our derivations and proofs are rigorous, and they can form the
basis of future investigations involving more complicated models such as nested
or long-range deformed systems.


Abstract:
  An important class of cyber-physical systems relies on multiple agents that
jointly perform a task by coordinating their actions over a wireless network.
Examples include self-driving cars in intelligent transportation and production
robots in smart manufacturing. However, the scalability of existing
control-over-wireless solutions is limited as they cannot resolve overload
situations in which the communication demand exceeds the available bandwidth.
This paper presents a novel co-design of distributed control and wireless
communication that overcomes this limitation by dynamically allocating the
available bandwidth to agents with the greatest need to communicate.
Experiments on a real cyber-physical testbed with 20 agents, each consisting of
a wireless node and a cart-pole system, demonstrate that our solution achieves
significantly better control performance under overload than the state of the
art. We further prove that our co-design guarantees closed-loop stability for
physical systems with stochastic linear time-invariant dynamics.


Abstract:
  We analyze behavior of weak solutions to compressible fluid flows in a
bounded domain in $\mathbb{R}^3$, randomly perforated by tiny balls with random
size. Assuming the radii of the balls scale like $\varepsilon^\alpha$, $\alpha
> 3$, with $\varepsilon$ denoting the average distance between the balls, the
problem homogenize with the same limiting equation. Our main contribution is a
construction of the Bogovski\u{\i} operator, uniformly in $\varepsilon$,
without any assumptions on the minimal distance between the balls.


Abstract:
  Cuspy shadow was first reported for hairy rotating black holes, whose metrics
deviate significantly from the Kerr one. The non-smooth edge of the shadow is
attributed to a transition between different branches of unstable but bounded
orbits, known as the fundamental photon orbits, which end up at the light
rings. In searching for a minimal theoretical setup to reproduce such a salient
feature, in this work, we devise a toy model with axisymmetry, a slowly
rotating Kerr black hole enveloped by a thin slowly rotating dark matter shell.
Despite its simplicity, we show rich structures regarding fundamental photon
orbits explicitly in such a system. We observe two disconnected branches of
unstable spherical photon orbits, and the jump between them gives rise to a
pair of cusps in the resultant black hole shadow. Besides the cuspy shadow, we
explore other intriguing phenomena when the Maxwell construction cannot be
established. We find that it is possible to have an incomplete arc of Einstein
rings and a "fractured" shadow. The potential astrophysical significance of the
corresponding findings is addressed.


Abstract:
  The ever-continuing explosive growth of on-demand content requests has
imposed great pressure on mobile/wireless network infrastructures. To ease
congestion in the network and increase perceived user experience, caching
popular content closer to the end-users can play a significant role and as such
this issue received significant attention over the last few years.
Additionally, energy efficiency is treated as a fundamental requirement in the
design of next-generation mobile networks. However, there has been little
attention to the overlapping area between energy efficiency and network caching
especially when considering multipath routing. To this end, this paper proposes
an energy-efficient caching with multipath routing support. The proposed scheme
provides a joint anchoring of popular content into a set of potential caching
nodes with optimized multi-path support whilst ensuring a balance between
transmission and caching energy cost. The proposed model also considers
different content delivery modes, such as multicast and unicast. Two separated
Integer-Linear Programming (ILP) models are formulated for each delivery mode.
To tackle the curse of dimensionality we then provide a greedy simulated
annealing algorithm, which not only reduces the time complexity but also
provides a competitive performance as ILP models. A wide set of numerical
investigations has shown that the proposed scheme is more energy-efficient
compared with other widely used approaches in caching under the premise of
network resource limitation.


Abstract:
  Consider an elliptic self-adjoint pseudodifferential operator $A$ acting on
$m$-columns of half-densities on a closed manifold $M$, whose principal symbol
is assumed to have simple eigenvalues. Relying on a basis of pseudodifferential
projections commuting with $A$, we construct an almost-unitary
pseudodifferential operator that diagonalizes $A$ modulo an infinitely
smoothing operator. We provide an invariant algorithm for the computation of
its full symbol, as well as an explicit closed formula for its subprincipal
symbol. Finally, we give a quantitative description of the relation between the
spectrum of $A$ and the spectrum of its approximate diagonalization, and
discuss the implications at the level of spectral asymptotics.


Abstract:
  The Ces\`aro limit - the asymptotic average of a sequence of real numbers -
is an operator of fundamental importance in probability, statistics and
mathematical analysis. To better understand sequences with Ces\`aro limits,
this paper considers the space $\mathcal{F}$ comprised of all binary sequences
with a Ces\`aro limit, and the associated functional $\nu: \mathcal{F}
\rightarrow [0,1]$ mapping each such sequence to its Ces\`aro limit. The basic
properties of $\mathcal{F}$ and $\nu$ are enumerated, and chains (totally
ordered sets) in $\mathcal{F}$ on which $\nu$ is countably additive are studied
in detail. The main result of the paper concerns a structural property of the
pair $(\mathcal{F},\nu)$, specifically that $\mathcal{F}$ can be factored (in a
certain sense) to produce a monotone class on which $\nu$ is countably
additive. In the process, a slight generalisation and clarification of the
monotone class theorem for Boolean algebras is proved.


Abstract:
  We establish the local existence and uniqueness of solutions to the
free-boundary ideal compressible magnetohydrodynamic equations with surface
tension in three spatial dimensions by a suitable modification of the
Nash--Moser iteration scheme. The main ingredients in proving the convergence
of the scheme are the tame estimates and unique solvability of the linearized
problem in the anisotropic Sobolev spaces $H_*^m$ for $m$ large enough. In
order to derive the tame estimates, we make full use of the boundary regularity
enhanced from the surface tension. The unique solution of the linearized
problem is constructed by designing some suitable $\varepsilon$--regularization
and passing to the limit $\varepsilon\to 0$.


Abstract:
  In this work, we argue that the observed differences in the value of the
vector coupling constant extracted from the decays $\rho\to \pi\pi$ ($g_{\rho}
= 6.0$), $\rho\to l^+l^-$ ($g_{\rho} = 5.0 $) and $\omega\to l^+ l^-$
($g_{\rho} = 5.7$), where $l=e,\mu $, are an indication of the important role
played by the $1/N_c$ corrections in the description of these processes. We
show that an emission of a photon by charged meson loops in the $\rho^0,
\omega,\phi\to\gamma$ transitions is a key process that allows to describe
above vector meson decays into two leptons with a single value $g_\rho = 6.0$.
Our result supports the idea of universality of neutral vector mesons and
clarifies the role of accounting of $1/N_c$ corrections to its fulfilment.


Abstract:
  Starting from the pseudo-differential decomposition
$\mathbf{D}=(-\Delta)^{\frac{1}{2}}\mathcal{H}$ of the Dirac operator
$\displaystyle \mathbf{D}=\sum_{j=1}^n\mathbf{e}_j\partial_{x_j}$ in terms of
the fractional operator $(-\Delta)^{\frac{1}{2}}$ of order $1$ and of the
Riesz-Hilbert type operator $\mathcal{H}$ we will investigate the fundamental
solutions of the space-fractional Dirac equation of L\'evy-Feller type
$$\partial_t\Phi_\alpha(\mathbf{x},t;\theta)=-(-\Delta)^{\frac{\alpha}{2}}\exp\left(
\frac{i\pi\theta}{2} \mathcal{H}\right)\Phi_\alpha(\mathbb{x},t;\theta) $$
involving the fractional Laplacian $-(-\Delta)^{\frac{\alpha}{2}}$ of order
$\alpha$, with $2m\leq \alpha <2m+2$ ($m\in \mathbb{N}$), and the
exponentiation operator $\exp\left( \frac{i\pi\theta}{2} \mathcal{H}\right)$ as
the hypercomplex counterpart of the fractional Riesz-Hilbert transform carrying
the \textit{skewness parameter} $\theta$, with values in the range
$|\theta|\leq \min\{\alpha-2m,2m+2-\alpha\}$. Such model problem permits us to
obtain hypercomplex counterparts for the fundamental solutions of higher-order
heat-type equations $\partial_t F_M(x,t)=\kappa_M(\partial_x)^M F_M(x,t)$
$(M=2,3,\ldots)$ in case where the even powers resp. odd powers
$\mathbf{D}^{2m}=(-\Delta)^{m}$ ($M=\alpha=2m$) resp.
$\mathbf{D}^{2m+1}=(-\Delta)^{m+\frac{1}{2}}\mathcal{H}$ ($M=\alpha=2m+1$) of
$\mathbf{D}$ are being considered.


Abstract:
  We compute the second (and the first) cohomology groups of $^*$-algebras
associated to the universal quantum unitary groups of not neccesarily Kac type,
extending our earlier results for the free unitary group $U_d^+$. The extended
setup forces us to use infinite-dimensional representations to construct the
cocycles.


Abstract:
  In the recent COVID-19 pandemic, a wide range of epidemiological modelling
approaches have been used to predict the effective reproduction number, R(t),
and other COVID-19 related measures such as the daily rate of exponential
growth, r(t). These candidate models use different modelling approaches or
differing assumptions about spatial or age mixing, and some capture genuine
uncertainty in scientific understanding of disease dynamics. Combining
estimates using appropriate statistical methodology from multiple candidate
models is important to better understand the variation of these outcome
measures to help inform decision making. In this paper, we combine these
estimates for specific UK nations and regions using random effects meta
analyses techniques, utilising the restricted maximum likelihood (REML) method
to estimate the heterogeneity variance parameter, and two approaches to
calculate the confidence interval for the combined estimate: the standard
Wald-type intervals; and the Knapp and Hartung (KNHA) method. As estimates in
this setting are derived using model predictions, each with varying degrees of
uncertainty, equal weighting is favoured over the more standard
inverse-variance weighting in order avoid potential up-weighting of models
providing estimates with lower levels of uncertainty that are not fully
accounting for inherent uncertainties. Utilising these meta-analysis techniques
has allowed for statistically robust combined estimates to be calculated for
key COVID-19 outcome measures. This in turn allows timely and informed decision
making based on all of the available information.


Abstract:
  A cosmological zoom-in simulation which develops into a Milky Way-like halo
is started at redshift 7. The initial dark matter distribution is seeded with
dense star clusters, median mass $5\times 10^5 M_\sun$, placed in the largest
sub-halos present, which have a median peak circular velocity of 25 \kms. Three
simulations are initialized using the same dark matter distribution, with the
star clusters started on approximately circular orbits having initial median
radii 6.8 kpc, 0.14 kpc, and, at the exact center of the sub-halos. The
simulations are evolved to the current epoch at which time the median galactic
orbital radii of the three sets of clusters are 30, 5 and 16 kpc, with the
clusters losing about 2, 50 and 15\% of their mass, respectively. Clusters
started at small orbital radii have so much tidal forcing that they are often
not in equilibrium. Clusters started at larger sub-halo radii have a velocity
dispersion that declines smoothly to $\simeq$20\% of the central value at
$\simeq$20 half mass radii. The clusters started at the sub-halo centers can
show a rise in velocity dispersion beyond 3-5 half mass radii. That is, the
clusters formed without local dark matter always have stellar mass dominated
kinematics at all radii, whereas about 25\% of the clusters started at sub-halo
centers have remnant local dark matter.


Abstract:
  Off-policy evaluation (OPE) holds the promise of being able to leverage
large, offline datasets for both evaluating and selecting complex policies for
decision making. The ability to learn offline is particularly important in many
real-world domains, such as in healthcare, recommender systems, or robotics,
where online data collection is an expensive and potentially dangerous process.
Being able to accurately evaluate and select high-performing policies without
requiring online interaction could yield significant benefits in safety, time,
and cost for these applications. While many OPE methods have been proposed in
recent years, comparing results between papers is difficult because currently
there is a lack of a comprehensive and unified benchmark, and measuring
algorithmic progress has been challenging due to the lack of difficult
evaluation tasks. In order to address this gap, we present a collection of
policies that in conjunction with existing offline datasets can be used for
benchmarking off-policy evaluation. Our tasks include a range of challenging
high-dimensional continuous control problems, with wide selections of datasets
and policies for performing policy selection. The goal of our benchmark is to
provide a standardized measure of progress that is motivated from a set of
principles designed to challenge and test the limits of existing OPE methods.
We perform an evaluation of state-of-the-art algorithms and provide open-source
access to our data and code to foster future research in this area.


Abstract:
  Recent research has recognized interpretability and robustness as essential
properties of trustworthy classification. Curiously, a connection between
robustness and interpretability was empirically observed, but the theoretical
reasoning behind it remained elusive. In this paper, we rigorously investigate
this connection. Specifically, we focus on interpretation using decision trees
and robustness to $l_{\infty}$-perturbation. Previous works defined the notion
of $r$-separation as a sufficient condition for robustness. We prove upper and
lower bounds on the tree size in case the data is $r$-separated. We then show
that a tighter bound on the size is possible when the data is linearly
separated. We provide the first algorithm with provable guarantees both on
robustness, interpretability, and accuracy in the context of decision trees.
Experiments confirm that our algorithm yields classifiers that are both
interpretable and robust and have high accuracy. The code for the experiments
is available at https://github.com/yangarbiter/interpretable-robust-trees .


Abstract:
  In this paper, we propose a novel edge and corner detection algorithm for an
unorganized point cloud. Our edge detection method classifies a query point as
an edge point by evaluating the distribution of local neighboring points around
the query point. The proposed technique has been tested on generic items such
as dragons, bunnies, and coffee cups from the Stanford 3D scanning repository.
The proposed technique can be directly applied to real and unprocessed point
cloud data of random clutter of objects. To demonstrate the proposed
technique's efficacy, we compare it to the other solutions for 3D edge
extractions in an unorganized point cloud data. We observed that the proposed
method could handle the raw and noisy data with little variations in parameters
compared to other methods. We also extend the algorithm to estimate the 6D pose
of known objects in the presence of dense clutter while handling multiple
instances of the object. The overall approach is tested for a warehouse
application, where an actual UR5 robot manipulator is used for robotic pick and
place operations in an autonomous mode.


Abstract:
  Robot arm placements are oftentimes a limitation in surgical preoperative
procedures, relying on trained staff to evaluate and decide on the optimal
positions for the arms. Given new and different patient anatomies, it can be
challenging to make an informed choice, leading to more frequently colliding
arms or limited manipulator workspaces. In this paper, we develop a method to
generate the optimal manipulator base positions for the multi-port da Vinci
surgical system that minimizes self-collision and environment-collision, and
maximizes the surgeon's reachability inside the patient. Scoring functions are
defined for each criterion so that they may be optimized over. Since for
multi-manipulator setups, a large number of free parameters are available to
adjust the base positioning of each arm, a challenge becomes how one can
expediently assess possible setups. We thus also propose methods that perform
fast queries of each measure with the use of a proxy collision-checker. We then
develop an optimization method to determine the optimal position using the
scoring functions. We evaluate the optimality of the base positions for the
robot arms on canonical trajectories, and show that the solution yielded by the
optimization program can satisfy each criterion. The metrics and optimization
strategy are generalizable to other surgical robotic platforms so that
patient-side manipulator positioning may be optimized and solved.


Abstract:
  Quantification represents the problem of predicting class distributions in a
given target set. It also represents a growing research field in supervised
machine learning, for which a large variety of different algorithms has been
proposed in recent years. However, a comprehensive empirical comparison of
quantification methods that supports algorithm selection is not available yet.
In this work, we close this research gap by conducting a thorough empirical
performance comparison of 24 different quantification methods. To consider a
broad range of different scenarios for binary as well as multiclass
quantification settings, we carried out almost 3 million experimental runs on
40 data sets. We observe that no single algorithm generally outperforms all
competitors, but identify a group of methods including the Median Sweep and the
DyS framework that perform significantly better in binary settings. For the
multiclass setting, we observe that a different, broad group of algorithms
yields good performance, including the Generalized Probabilistic Adjusted
Count, the readme method, the energy distance minimization method, the EM
algorithm for quantification, and Friedman's method. More generally, we find
that the performance on multiclass quantification is inferior to the results
obtained in the binary setting. Our results can guide practitioners who intend
to apply quantification algorithms and help researchers to identify
opportunities for future research.


Abstract:
  Industrial processes rely on sensory data for decision-making processes, risk
assessment, and performance evaluation. Extracting actionable insights from the
collected data calls for an infrastructure that can ensure the dissemination of
trustworthy data. For the physical data to be trustworthy, it needs to be
cross-validated through multiple sensor sources with overlapping fields of
view. Cross-validated data can then be stored on the blockchain, to maintain
its integrity and trustworthiness. Once trustworthy data is recorded on the
blockchain, product lifecycle events can be fed into data-driven systems for
process monitoring, diagnostics, and optimized control. In this regard, Digital
Twins (DTs) can be leveraged to draw intelligent conclusions from data by
identifying the faults and recommending precautionary measures ahead of
critical events. Empowering DTs with blockchain in industrial use-cases targets
key challenges of disparate data repositories, untrustworthy data
dissemination, and the need for predictive maintenance. In this survey, while
highlighting the key benefits of using blockchain-based DTs, we present a
comprehensive review of the state-of-the-art research results for
blockchain-based DTs. Based on the current research trends, we discuss a
trustworthy blockchain-based DTs framework. We highlight the role of Artificial
Intelligence (AI) in blockchain-based DTs. Furthermore, we discuss current and
future research and deployment challenges of blockchain-supported DTs that
require further investigation.


Abstract:
  We present an overview of the EmotionGIF2020 Challenge, held at the 8th
International Workshop on Natural Language Processing for Social Media
(SocialNLP), in conjunction with ACL 2020. The challenge required predicting
affective reactions to online texts, and included the EmotionGIF dataset, with
tweets labeled for the reaction categories. The novel dataset included 40K
tweets with their reaction GIFs. Due to the special circumstances of year 2020,
two rounds of the competition were conducted. A total of 84 teams registered
for the task. Of these, 25 teams success-fully submitted entries to the
evaluation phase in the first round, while 13 teams participated successfully
in the second round. Of the top participants, five teams presented a technical
report and shared their code. The top score of the winning team using the
Recall@K metric was 62.47%.


Abstract:
  In this work we provide a detailed specification of how we extended and
implemented Classic Paxos (CP) to execute Read-Modify-Writes. In addition, we
also specify how we implemented All-aboard Paxos over CP and how we use
carstamps, to also add ABD reads and writes, to accelerate the common case,
where RMWs are not needed. Our specification targets a Key-Value-Store that is
deployed within the datacenter, is replicated across 3 to 7 machines and
supports reads, writes and RMWs.


Abstract:
  We report the first detection of a hydroxyl radical (OH) emission signature
in the planetary atmosphere outside the solar system, in this case, in the
day-side of WASP-33b. We analyze high-resolution near-infrared emission spectra
of WASP-33b taken using the InfraRed Doppler spectrograph on the 8.2-m Subaru
telescope. The telluric and stellar lines are removed using a de-trending
algorithm, SysRem. The residuals are then cross-correlated with OH and H$_{2}$O
planetary spectrum templates produced using several different line-lists. We
check and confirm the accuracy of OH line-lists by cross-correlating with the
spectrum of GJ 436. As a result, we detect the emission signature of OH at
$K_\mathrm{p}$ of 230.9$^{+6.9}_{-7.4}$ km s$^{-1}$ and $v_{\mathrm{sys}}$ of
$-$0.3$^{+5.3}_{-5.6}$ km s$^{-1}$ with S/N of 5.4 and significance of
5.5$\sigma$. Additionally, we marginally detect H$_{2}$O emission in the H-band
with S/N of 4.0 and significance of 5.2$\sigma$ using the POKAZATEL line-list.
However, no significant signal is detected using the HITEMP 2010, which might
be due to differences in line positions and strengths, as well as the
incompleteness of the line-lists. Nonetheless, this marginal detection is
consistent with the prediction that H$_{2}$O is mostly thermally dissociated in
the upper atmosphere of the ultra-hot Jupiters. Therefore, along with CO, OH is
expected to be one of the most abundant O-bearing molecules in the day-side
atmosphere of ultra-hot Jupiters and should be considered when studying their
atmosphere.


Abstract:
  Mathematical models describing the behavior of viscoelastic materials are
often based on evolution equations that measure the change in stress depending
on its material parameters such as stiffness, viscosity or relaxation time. In
this article, we introduce a Maxwell-based rheological model, define the
associated forward operator and the inverse problem in order to determine the
number of Maxwell elements and the material parameters of the underlying
viscoelastic material. We perform a relaxation experiment by applying a strain
to the material and measure the generated stress. Since the measured data
varies with the number of Maxwell elements, the forward operator of the
underlying inverse problem depends on parts of the solution. By introducing
assumptions on the relaxation times, we propose a clustering algorithm to
resolve this problem. We provide the calculations that are necessary for the
minimization process and conclude with numerical results by investigating
unperturbed as well as noisy data. We present different reconstruction
approaches based on minimizing a least squares functional. Furthermore, we look
at individual stress components to analyze different displacement rates.
Finally, we study reconstructions with shortened data sets to obtain assertions
on how long experiments have to be performed to identify conclusive material
parameters.


Abstract:
  Algebraic structures involving both multiplications and comultiplications
(such as, e.g., bialgebras or Hopf algebras) can be encoded using PROPs
(categories with PROducts and Permutations) of Adams and MacLane. To encode
such structures on objects of a braided monoidal category, we need PROBs
(braided analogs of PROPs). Colored PROBs correspond to multi-sorted
structures.
  In particular, we have a colored PROB B governing non-negatively graded
bialgebras in braided categories. As a category, B splits into blocks B_n
according to the grading. We relate B_n with the category P_n of perverse
sheaves on the n-th symmetric product of the complex line, smooth with respect
to the natural stratification by multiplicities. More precisely, we show that
P_n is equivalent to the category of functors from B_n to vector spaces. This
gives a natural quiver description of P_n.


Abstract:
  The flow-based generative model is a deep learning generative model, which
obtains the ability to generate data by explicitly learning the data
distribution. Theoretically its ability to restore data is stronger than other
generative models. However, its implementation has many limitations, including
limited model design, too many model parameters and tedious calculation. In
this paper, a bi-parallel linear flow model for facial emotion generation from
emotion set images is constructed, and a series of improvements have been made
in terms of the expression ability of the model and the convergence speed in
training. The model is mainly composed of several coupling layers superimposed
to form a multi-scale structure, in which each coupling layer contains 1*1
reversible convolution and linear operation modules. Furthermore, this paper
sorted out the current public data set of facial emotion images, made a new
emotion data, and verified the model through this data set. The experimental
results show that, under the traditional convolutional neural network, the
3-layer 3*3 convolution kernel is more conducive to extracte the features of
the face images. The introduction of principal component decomposition can
improve the convergence speed of the model.


Abstract:
  We present the first observational constraint on the Br-gamma recombination
line emission associated with the supermassive black hole at the center of our
Galaxy, known as Sgr A*. By combining 13 years of data with the Adaptive Optics
fed integral field spectrograph OSIRIS at the W. M. Keck Observatory obtained
as part of the Galactic Center Orbits Initiative, we extract the near-infrared
spectrum within ~0.2'' of the black hole and we derive an upper limit on the
Br-gamma flux. The aperture was set to match the size of the disk-like
structure that was recently reported based on millimeter-wave ALMA observations
of the hydrogen recombination line, H30-alpha. Our stringent upper limit is at
least a factor of 80 (and up to a factor of 245) below what would be expected
from the ALMA measurements and strongly constrains possible interpretation of
emission from this highly under-luminous supermassive black hole.


Abstract:
  I searched for the ground state 6.8 and 9.2 GHz hyperfine transitions of
rubidium and cesium toward M- and L-dwarfs that show Rb and Cs optical
resonance lines. The optical lines can pump the hyperfine transitions,
potentially forming masers. These spin-flip transitions of Rb and Cs are the
principal transitions used in atomic clocks (the $^{133}$Cs hyperfine
transition defines the second). If they are detected in stellar atmospheres,
these transitions would provide exceptionally precise clocks that can be used
as accelerometers, as exoplanet detectors, as probes of the predictions of
general relativity, as probes of light propagation effects, and as a means to
do fundamental physics with telescopes. Observations of 21 M- and L-dwarfs,
however, show no evidence for Rb or Cs maser action, and a previous survey of
giant stars made no Rb maser detections.


Abstract:
  A central question in a large class of strongly correlated electron systems,
including heavy fermion compounds and iron pnictides, is the identification of
different phases and their origins. It has been shown that the
antiferromagnetic (AFM) phase in some heavy fermion compounds is induced by
Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction between localized moments, and
that the competition between this interaction and Kondo effect is responsible
for quantum criticality. However, conclusive experimental evidence of the RKKY
interaction in pnictides is lacking. Here, using high resolution $^{23}$Na NMR
measurements on lightly Cu-doped metallic single crystals of
NaFe$_{1-x}$Cu$_{x}$As ~($x \approx 0.01$) and numerical simulation, we show
direct evidence of the RKKY interaction in this pnictide system. Aided by
computer simulation, we identify the $^{23}$Na NMR satellite resonances with
the RKKY oscillations of spin polarization at Fe sites. Our $^{23}$Na
spin-lattice and spin-spin relaxation data exhibits signature of an itinerant
and inhomogeneous AFM phase in this system, accompanied by a simulation of
Cu-induced perturbation to the ordered moments on the Fe sites. Our NMR results
indicate coexistence of local and itinerant magnetism in lightly Cu-doped
NaFe$_{1-x}$Cu$_{x}$As.


Abstract:
  Neural-network quantum states (NQS) have been shown to be a suitable
variational ansatz to simulate out-of-equilibrium dynamics in two-dimensional
systems using time-dependent variational Monte Carlo (t-VMC). In particular,
stable and accurate time propagation over long time scales has been observed in
the square-lattice Heisenberg model using the Restricted Boltzmann machine
architecture. However, achieving similar performance in other systems has
proven to be more challenging. In this article, we focus on the two-leg
Heisenberg ladder driven out of equilibrium by a pulsed excitation as a
benchmark system. We demonstrate that unmitigated noise is strongly amplified
by the nonlinear equations of motion for the network parameters, which causes
numerical instabilities in the time evolution. As a consequence, the achievable
accuracy of the simulated dynamics is a result of the interplay between network
expressiveness and measures required to remedy these instabilities. We show
that stability can be greatly improved by appropriate choice of regularization.
This is particularly useful as tuning of the regularization typically imposes
no additional computational cost. Inspired by machine learning practice, we
propose a validation-set based diagnostic tool to help determining optimal
regularization hyperparameters for t-VMC based propagation schemes. For our
benchmark, we show that stable and accurate time propagation can be achieved in
regimes of sufficiently regularized variational dynamics.


Abstract:
  This paper addresses the problem of sheet-image-based on-line audio-to-score
alignment also known as score following. Drawing inspiration from object
detection, a conditional neural network architecture is proposed that directly
predicts x,y coordinates of the matching positions in a complete score sheet
image at each point in time for a given musical performance. Experiments are
conducted on a synthetic polyphonic piano benchmark dataset and the new method
is compared to several existing approaches from the literature for
sheet-image-based score following as well as an Optical Music Recognition
baseline. The proposed approach achieves new state-of-the-art results and
furthermore significantly improves the alignment performance on a set of
real-world piano recordings by applying Impulse Responses as a data
augmentation technique.


Abstract:
  Point-of-Interest recommendation is an increasing research and developing
area within the widely adopted technologies known as Recommender Systems. Among
them, those that exploit information coming from Location-Based Social Networks
(LBSNs) are very popular nowadays and could work with different information
sources, which pose several challenges and research questions to the community
as a whole. We present a systematic review focused on the research done in the
last 10 years about this topic. We discuss and categorize the algorithms and
evaluation methodologies used in these works and point out the opportunities
and challenges that remain open in the field. More specifically, we report the
leading recommendation techniques and information sources that have been
exploited more often (such as the geographical signal and deep learning
approaches) while we also alert about the lack of reproducibility in the field
that may hinder real performance improvements.


Abstract:
  It is shown that a singular equivalence induced by tensoring with a suitable
complex of bimodules defines a singular equivalence of Morita type with level,
in the sense of Wang. This result is applied to homological ideals and
idempotents to produce new reduction techniques for testing the properties of
syzygy-finite and injectives generation of finite dimensional algebras over a
field.


Abstract:
  In the past decades, massive efforts involving companies, non-profit
organizations, governments, and others have been put into supporting the
concept of data democratization, promoting initiatives to educate people to
confront information with data. Although this represents one of the most
critical advances in our free world, access to data without concrete facts to
check or the lack of an expert to help on understanding the existing patterns
hampers its intrinsic value and lessens its democratization. So the benefits of
giving full access to data will only be impactful if we go a step further and
support the Data Analytics Democratization, assisting users in transforming
findings into insights without the need of domain experts to promote
unconstrained access to data interpretation and verification. In this paper, we
present Explainable Patterns (ExPatt), a new framework to support lay users in
exploring and creating data storytellings, automatically generating plausible
explanations for observed or selected findings using an external (textual)
source of information, avoiding or reducing the need for domain experts. ExPatt
applicability is confirmed via different use-cases involving world demographics
indicators and Wikipedia as an external source of explanations, showing how it
can be used in practice towards the data analytics democratization.


Abstract:
  This article presents a novel computational model to study the selective
filtering of biological hydrogels due to the surface charge and size of
diffusing particles. It is the first model that includes the random 3D fiber
orientation and connectivity of the biopolymer network and that accounts for
elastic deformations of the fibers by means of beam theory. As a key component
of the model, novel formulations are proposed both for the electrostatic and
repulsive steric interactions between a spherical particle and a beam. In
addition to providing a thorough validation of the model, the presented
computational studies yield new insights into the underlying mechanisms of
hindered particle mobility, especially regarding the influence of the
aforementioned aspects that are unique to this model. It is found that the
precise distribution of fiber and thus charge agglomerations in the network
have a crucial influence on the mobility of oppositely charged particles and
gives rise to distinct motion patterns. Considering the high practical
significance for instance with respect to targeted drug release or infection
defense, the provided proof of concept motivates further advances of the model
toward a truly predictive computational tool that allows a case- and
patient-specific assessment for real (biological) systems.


Abstract:
  To perform manipulation tasks in the real world, robots need to operate on
objects with various shapes, sizes and without access to geometric models. It
is often unfeasible to train monolithic neural network policies across such
large variance in object properties. Towards this generalization challenge, we
propose to learn modular task policies which compose object-centric task-axes
controllers. These task-axes controllers are parameterized by properties
associated with underlying objects in the scene. We infer these controller
parameters directly from visual input using multi-view dense correspondence
learning. Our overall approach provides a simple, modular and yet powerful
framework for learning manipulation tasks. We empirically evaluate our approach
on multiple different manipulation tasks and show its ability to generalize to
large variance in object size, shape and geometry.


Abstract:
  We present new results of the backscattering dominated prompt emission model
in which the photons generated through pair annihilation at the centre of a
gamma ray burst (GRB) are backscattered through Compton scattering by an
outflowing stellar cork. %We show that the produced spectral features are
capable of producing spectral features that are widely observed in GRBs. Using
Comptonized pair annihilation spectrum accompanied by bremsstrahlung radiation
for seed photons, we show that the obtained spectra produce low energy photon
index in the range $\alpha \sim -1.95$ to $-1.1$, steeper high energy slopes
$\beta \sim -3.5$ to $-2.4$ and spectral peak energies $\sim$ few KeV to few
$10\times$ MeV. These findings are consistent with the values covered in GRB
prompt phase observations.


Abstract:
  We study the confinement/deconfinement phase transition of the radion field
in a warped model with a polynomial bulk potential. The backreaction of the
radion on the metric is taken into account by using the superpotential
formalism, while the radion effective potential is obtained from a novel
formulation which can incorporate the backreaction. The phase transition leads
to a stochastic gravitational wave background that depends on the energy scale
of the first Kaluza-Klein resonance, $m_{\textrm{KK}}$. This work completes
previous studies in the following aspects: i) we detail the evaluation of the
radion spectrum; ii) we report on the mismatches between the thick wall
approximation and the numerical bounce solution; iii) we include a suppression
factor in the spectrum of sound waves accounting for their finite lifetime;
and, iv) we update the bound on $m_{\textrm{KK}}$ in view of the O3 LIGO and
Virgo data. We find that the forthcoming gravitational wave interferometers can
probe scenarios where $m_{\textrm{KK}} \lesssim 10^9$ TeV, while the O3-run
bounds rule out warped models with $10^4 \textrm{TeV} \lesssim m_{\textrm{KK}}
\lesssim 10^7$ TeV exhibiting an extremely strong confinement/deconfinement
phase transition.


Abstract:
  When obtaining interior 3D voxel data from triangular meshes, most existing
methods fail to handle low quality meshes which happens to take up a big
portion on the internet. In this work we present a robust voxelization method
that is based on tetrahedral mesh generation within a user defined error bound.
Comparing to other tetrahedral mesh generation methods, our method produces
much higher quality tetrahedral meshes as the intermediate outcome, which
allows us to utilize a faster voxelization algorithm that is based on a
stronger assumption. We show the results comparing to various methods including
the state-of-the-art. Our contribution includes a framework which takes
triangular mesh as an input and produces voxelized data, a proof to an unproved
algorithm that performs better than the state-of-the-art, and various
experiments including parallelization built on the GPU and CPU. We further
tested our method on various dataset including Princeton ModelNet and Thingi10k
to show the robustness of the framework, where near 100% availability is
achieved, while others can only achieve around 50%.


Abstract:
  The recently proposed RCube network is a cube-based server-centric data
center network (DCN), including two types of heterogeneous servers, called core
and edge servers. Remarkably, it takes the latter as backup servers to deal
with server failures and thus achieve high availability. This paper first
points out that RCube is suitable as a candidate topology of DCNs for edge
computing. Three transmission types are among core and edge servers based on
the demand for applications' computation and instant response. We then employ
protection routing to analyze the transmission failure of RCube DCNs. Unlike
traditional protection routing, which only tolerates a single link or node
failure, we use the multi-protection routing scheme to improve fault-tolerance
capability. To configure a protection routing in a network, according to
Tapolcai's suggestion, we need to construct two completely independent spanning
trees (CISTs). A logic graph of RCube, denoted by $L$-$RCube(n,m,k)$, is a
network with a recursive structure. Each basic building element consists of $n$
core servers and $m$ edge servers, where the order $k$ is the number of
recursions applied in the structure. In this paper, we provide algorithms to
construct $\min\{n,\lfloor(n+m)/2\rfloor\}$ CISTs in $L$-$RCube(n,m,k)$ for
$n+m\geqslant 4$ and $n>1$. From a combination of the multiple CISTs, we can
configure the desired multi-protection routing. In our simulation, we configure
up to 10 protection routings for RCube DCNs. As far as we know, in past
research, there were at most three protection routings developed in other
network structures. Finally, we summarize some crucial analysis viewpoints
about the transmission efficiency of DCNs with heterogeneous edge-core servers
from the simulation results.


Abstract:
  We provide a microscopic-level derivation of earlier results showing that, in
the critical vicinity of the superconductor-to-insulator transition (SIT),
disorder and localization become negligible and the structure of the emergent
phases is determined by topological effects arising from the competition
between two quantum orders, superconductivity and superinsulation. We find
that, around the critical point, the ground state is a composite incompressible
quantum fluid of Cooper pairs and vortices coexisting with an intertwined
Wigner crystal for the excess (with respect to integer filling) excitations of
the two types.


Abstract:
  We study the zero-field optical spin noise spectra (OSN) for a thermal state
hot 87Rb vapor with strong spin exchange coupling. Our main findings are: (1)
The OSN spectrum consists of two components representing a positive and a
negative hyperfine spin correlation (HSC), the relative power of which varies
dramatically with the detuning frequency of the probe. (2) There exist two
polar frequencies at which the OSN spectrum is completely polarized with one
type of HSC. (3) At the limit of far detuning, the power ratio of the positive
and negative HSC component of the OSN is 4:5. (4) The total power of the OSN is
independent of the strength of SE coupling. (5) We give a simple way of
deriving the OSN using the eigensolution of the density matrix equation.


Abstract:
  Planning for legged-wheeled machines is typically done using trajectory
optimization because of many degrees of freedom, thus rendering legged-wheeled
planners prone to falling prey to bad local minima. We present a combined
sampling and optimization-based planning approach that can cope with
challenging terrain. The sampling-based stage computes whole-body
configurations and contact schedule, which speeds up the optimization
convergence. The optimization-based stage ensures that all the system
constraints, such as non-holonomic rolling constraints, are satisfied. The
evaluations show the importance of good initial guesses for optimization.
Furthermore, they suggest that terrain/collision (avoidance) constraints are
more challenging than the robot model's constraints. Lastly, we extend the
optimization to handle general terrain representations in the form of elevation
maps.


Abstract:
  Nonlinear interactions are recognized as potential resources for quantum
metrology, facilitating parameter estimation precisions that scale as the
exponential Heisenberg limit of $2^{-N}$. We explore such nonlinearity and
propose an associated quantum measurement scenario based on the nonlinear
interaction of $N$-probe entanglement generating form. This scenario provides
an enhanced precision scaling of $D^{-N}/(N-1)!$ with $D > 2$ a tunable
parameter. In addition, it can be readily implemented in a variety of
experimental platforms and applied to measurements of a wide range of
quantities, including local gravitational acceleration $g$, magnetic field, and
its higher-order gradients.


Abstract:
  Ionic gating is a very popular tool to investigate and control the electric
charge transport and electronic ground state in a wide variety of different
materials. This is due to its capability to induce large modulations of the
surface charge density by means of the electric-double-layer field-effect
transistor (EDL-FET) architecture, and has been proven to be capable of tuning
even the properties of metallic systems. In this short review, I summarize the
main results which have been achieved so far in controlling the superconducting
(SC) properties of thin films of conventional metallic superconductors by means
of the ionic gating technique. I discuss how the gate-induced charge doping,
despite being confined to a thin surface layer by electrostatic screening,
results in a long-range "bulk" modulation of the SC properties by the coherent
nature of the SC condensate, as evidenced by the observation of suppressions in
the critical temperature of films much thicker than the electrostatic screening
length, and by the pronounced thickness-dependence of their magnitude. I review
how this behavior can be modelled in terms of proximity effect between the
charge-doped surface layer and the unperturbed bulk with different degrees of
approximation, and how first-principles calculations have been employed to
determine the origin of an anomalous increase in the electrostatic screening
length at ultrahigh electric fields, thus fully confirming the validity of the
proximity effect model. Finally, I discuss a general framework - based on the
combination of ab-initio Density Functional Theory and the Migdal-Eliashberg
theory of superconductivity - by which the properties of any gated thin film of
a conventional metallic superconductor can be determined purely from first
principles.


Abstract:
  Heterogeneity in medical data, e.g., from data collected at different sites
and with different protocols in a clinical study, is a fundamental hurdle for
accurate prediction using machine learning models, as such models often fail to
generalize well. This paper leverages a recently proposed
normalizing-flow-based method to perform counterfactual inference upon a
structural causal model (SCM), in order to achieve harmonization of such data.
A causal model is used to model observed effects (brain magnetic resonance
imaging data) that result from known confounders (site, gender and age) and
exogenous noise variables. Our formulation exploits the bijection induced by
flow for the purpose of harmonization. We infer the posterior of exogenous
variables, intervene on observations, and draw samples from the resultant SCM
to obtain counterfactuals. This approach is evaluated extensively on multiple,
large, real-world medical datasets and displayed better cross-domain
generalization compared to state-of-the-art algorithms. Further experiments
that evaluate the quality of confounder-independent data generated by our model
using regression and classification tasks are provided.


Abstract:
  The low-energy excitations of graphene are relativistic massless Dirac
fermions with opposite chiralities at valleys K and K'. Breaking the chiral
symmetry could lead to gap opening in analogy to dynamical mass generation in
particle physics. Here we report direct experimental evidences of chiral
symmetry breaking (CSB) from both microscopic and spectroscopic measurements in
a Li-intercalated graphene. The CSB is evidenced by gap opening at the Dirac
point, Kekul\'e-O type modulation, and chirality mixing near the gap edge. Our
work opens up opportunities for investigating CSB related physics in a
Kekul\'e-ordered graphene.


Abstract:
  Artificial Neural networks are mathematical models at their core. This
truismpresents some fundamental difficulty when networks are tasked with
Natural Language Processing. A key problem lies in measuring the similarity or
distance among vectors in NLP embedding space, since the mathematical concept
of distance does not always agree with the linguistic concept. We suggest that
the best way to measure linguistic distance among vectors is by employing the
Language Model (LM) that created them. We introduce Language Model Distance
(LMD) for measuring accuracy of vector transformations based on the
Distributional Hypothesis ( LMD Accuracy ). We show the efficacy of this metric
by applying it to a simple neural network learning the Procrustes algorithm for
bilingual word mapping.


Abstract:
  We present a straightforward and efficient way to control unstable robotic
systems using an estimated dynamics model. Specifically, we show how to exploit
the differentiability of Gaussian Processes to create a state-dependent
linearized approximation of the true continuous dynamics that can be integrated
with model predictive control. Our approach is compatible with most Gaussian
process approaches for system identification, and can learn an accurate model
using modest amounts of training data. We validate our approach by learning the
dynamics of an unstable system such as a segway with a 7-D state space and 2-D
input space (using only one minute of data), and we show that the resulting
controller is robust to unmodelled dynamics and disturbances, while
state-of-the-art control methods based on nominal models can fail under small
perturbations. Code is open sourced at
https://github.com/learning-and-control/core .


Abstract:
  We consider weighted tree automata over strong bimonoids (for short: wta). A
wta $\mathcal{A}$ has the finite-image property if its recognized weighted tree
language $[\![\mathcal{A}]\!]$ has finite image; moreover, $\mathcal{A}$ has
the preimage property if the preimage under $[\![\mathcal{A}]\!]$ of each
element of the underlying strong bimonoid is a recognizable tree language. For
each wta $\mathcal{A}$ over a past-finite monotonic strong bimonoid we prove
the following results. In terms of $\mathcal{A}$'s structural properties, we
characterize whether it has the finite-image property. We characterize those
past-finite monotonic strong bimonoids such that for each wta $\mathcal{A}$ it
is decidable whether $\mathcal{A}$ has the finite-image property. In
particular, the finite-image property is decidable for wta over past-finite
monotonic semirings. Moreover, we prove that $\mathcal{A}$ has the preimage
property. All our results also hold for weighted string automata.


Abstract:
  We give a proof the monodromy conjecture relating the poles of motivic zeta
functions with roots of b-functions for isolated quasihomogeneous
hypersurfaces, and more generally for semi-quasihomogeneous hypersurfaces. We
also give a strange generalization allowing a twist by certain differential
forms.


Abstract:
  Recently, generalizations of the classical Three Gap Theorem to higher
dimensions attracted a lot of attention. In particular, upper bounds for the
number of nearest neighbor distances have been established for the Euclidean
and the maximum metric. It was proved that a generic multi-dimensional
Kronecker attains the maximal possible number of different gap lengths for
every sub-exponential subsequence. We mirror this result in dimension $d \in
\left\{ 2, 3 \right\}$ by constructing Kronecker sequences which have a
surprisingly low number of different nearest neighbor distances for infinitely
$N \in \mathbb{N}$. Our proof relies on simple arguments from the theory of
continued fractions.


Abstract:
  We study radiation pressure due to Lyman alpha line photons, obtaining and
exploring analytical expressions for the force-multiplier, $M_F(N_H, Z) =
F_\alpha/(L_\alpha/c)$, as a function of gas column density, $N_H$, and
metallicity, $Z$, for both dust-free and dusty media, employing a WKB approach
for the latter case. Solutions for frequency offset emission to emulate
non-static media moving with a bulk velocity $v$, have also been obtained. We
find that, in static media, Ly$\alpha$ pressure dominates over both
photoionization and dust-mediated UV radiation pressure in a very wide
parameter range ($16 < \log N_H < 23$; $-4 < \log[Z/Z_\odot] < 0$). For
example, it overwhelms the other two forces by 10 (300) times in standard
(low-$Z$) star-forming clouds. Thus, in agreement with previous studies, we
conclude that Ly$\alpha$ pressure plays a dominant role in the initial
acceleration of the gas around luminous sources, and must be implemented in
galaxy formation, evolution and outflow models and simulations.


Abstract:
  Super-Earths and sub-Neptunes are commonly thought to have accreted
hydrogen/helium envelopes, consisting of a few to ten percent of their total
mass, from the primordial gas disk. Subsequently, hydrodynamic escape driven by
core-powered mass-loss and/or photo-evaporation likely stripped much of these
primordial envelopes from the lower-mass and closer-in planets to form the
super-Earth population. In this work we show that after undergoing core-powered
mass-loss, some super-Earths can retain small residual H/He envelopes. This
retention is possible because, for significantly depleted atmospheres, the
density at the radiative-convective boundary drops sufficiently such that thhe
cooling time-scale becomes less than the mass-loss time-scale. The residual
envelope is therefore able to contract, terminating further mass loss. Using
analytic calculations and numerical simulations, we show that the mass of
primordial H/He envelope retained as a fraction of the planet's total mass,
$f_{ret}$, increases with increasing planet mass, $M_{c}$, and decreases with
increasing equilibrium temperature, $T_{eq}$, scaling as $f_{ret} \propto
M_{c}^{3/2} T_{eq}^{-1/2} \exp{[M_{c}^{3/4} T_{eq}^{-1}]}$. $f_{ret}$ varies
from $< 10^{-8}$ to about $10^{-3}$ for typical super-Earth parameters. To
first order, the exact amount of left-over H/He depends on the initial envelope
mass, the planet mass, its equilibrium temperature, and the envelope's opacity.
These residual hydrogen envelopes reduce the atmosphere's mean molecular weight
compared to a purely secondary atmosphere, a signature observable by current
and future facilities. These remnant atmospheres may, however, in many cases be
vulnerable to long-term erosion by photo-evaporation. Any residual hydrogen
envelope likely plays an important role in the long-term physical evolution of
super-Earths, including their geology and geochemistry.


Abstract:
  Reaction-diffusion equations are one of the most common mathematical models
in the natural sciences and are used to model systems that combine reactions
with diffusive motion. However, rather than normal diffusion, anomalous
subdiffusion is observed in many systems and is especially prevalent in cell
biology. What are the reaction-subdiffusion equations describing a system that
involves first-order reactions and subdiffusive motion? In this paper, we
answer this question. We derive fractional reaction-subdiffusion equations
describing an arbitrary number of molecular species which react at first-order
rates and move subdiffusively with general space-dependent diffusivities and
drifts. Importantly, different species may have different diffusivities and
drifts, which contrasts previous approaches to this question which assume that
each species has the same movement dynamics. We derive the equations by
combining results on time-dependent fractional Fokker-Planck equations with
methods of analyzing stochastically switching evolution equations. Furthermore,
we construct the stochastic description of individual molecules whose
deterministic concentrations follow these reaction-subdiffusion equations. This
stochastic description involves subordinating a diffusion process whose
dynamics are controlled by a subordinated Markov jump process. We illustrate
our results in several examples and show that solutions of the
reaction-subdiffusion equations agree with stochastic simulations of individual
molecules.


Abstract:
  Zariski decomposition is a fundamental tool for studying linear systems of
divisors on algebraic surfaces. Bauer, K\"uronya and Szemberg obtained a
decomposition of the big cone of a smooth projective surface into chambers,
called Zariski chambers, in each of which the support of the negative part of
the Zariski decomposition is constant. On the other hand, thanks to the work of
Boucksom, on a compact complex manifold we have the divisorial Zariski
decomposition which, on an irreducible holomorphic symplectic manifold (IHS),
is a higher dimensional Zariski decomposition with respect to the
Beauville-Bogomolov-Fujiki quadratic form. Similarly to the case of surfaces,
we provide for the big cone of a projective irreducible holomorphic symplectic
manifold a decomposition into chambers (which we describe in detail), in each
of which the support of the negative part of the divisorial Zariski
decomposition is constant. As for surfaces, the obtained decomposition of the
big cone allows to describe the volume function, which turns out to behave very
well. Moreover, always inspired by the surface case, we also see that the big
cone of a projective IHS manifold admits a decomposition into simple Weyl
chambers, which we compare to that induced by the divisorial Zariski
decomposition. To conclude, we investigate the boundary of the big cone and
determine the structure of the algebraic pseudo-effective cone.


Abstract:
  Quantum shell effects are known to affect the formation of fragments in
nuclear fission. Shell effects also affect quasi-fission reactions occurring in
heavy-ion collisions. Systematic time-dependent Hartree-Fock simulations of
50Ca+176Yb collisions show that the mass equilibration between the fragments in
quasi-fission is stopped when they reach similar properties to those in the
asymmetric fission mode of the 226Th compound nucleus. Similar shell effects
are then expected to determine the final repartition of nucleons between the
nascent fragments in both mechanisms. Future experimental studies that could
test these observations are discussed.


Abstract:
  Solar grade silicon (SoG-Si) is a key material for the development of
crystalline silicon photovoltaics (PV), which is expected to reach the
tera-watt level in the next years and around 50TW in 2050. Upgraded
metallurgical grade silicon (UMG-Si) has already demonstrated to be a viable
alternative to standard polysilicon in terms of cost and quality. This study
presents the life cycle assessment (LCA) of UMG obtained by the FerroSolar
process. Moreover, it shows the environmental impacts of PV modules and
electricity generation based on this material. For this, an exhaustive review
of the life cycle inventory (LCI) of PV value chain, from metallurgical grade
silicon (MG-Si) down to electricity generation, has been carried out updating
inputs for all processes. The Balance of System (BoS) has also been updated
with real state of the art data for a fixed open ground large PV site (100 MW).
Two different electricity mixes, with low and high carbon intensities, have
been considered. The results reveal that for PV electricity generation using
UMG instead of polysilicon leads to an overall reduction of Climate change (CC)
emissions of over 20%, along with an improvement of the Energy Payback Time
(EPBT) of 25%, achieving significantly low values, 12 gCO2eq / kWhe and 0.52
years, respectively. Moreover, it is shown that UMG silicon feedstock is not
the main contributor to the carbon and energy footprint of the produced
electricity, leaving the first place to PV module manufacturing.