Abstract:
  Topic models are widely used analysis techniques for clustering documents and
surfacing thematic elements of text corpora. These models remain challenging to
optimize and often require a "human-in-the-loop" approach where domain experts
use their knowledge to steer and adjust. However, the fragility,
incompleteness, and opacity of these models means even minor changes could
induce large and potentially undesirable changes in resulting model. In this
paper we conduct a simulation-based analysis of human-centered interactions
with topic models, with the objective of measuring the sensitivity of topic
models to common classes of user actions. We find that user interactions have
impacts that differ in magnitude but often negatively affect the quality of the
resulting modelling in a way that can be difficult for the user to evaluate. We
suggest the incorporation of sensitivity and "multiverse" analyses to topic
model interfaces to surface and overcome these deficiencies.


Abstract:
  We present bright [CII] 158 $\mu$m line detections from a strongly magnified
and multiply-imaged ($\mu\sim20-160$) sub-$L^{*}$ ($M_{\rm UV}$ =
$-19.75^{+0.55}_{-0.44}$) Lyman-break galaxy (LBG) at $z=6.0719\pm0.0004$ from
the ALMA Lensing Cluster Survey (ALCS). Emission lines are identified at 268.7
GHz at $\geq$ 8$\sigma$ exactly at positions of two multiple images of the LBG
behind the massive galaxy cluster RXCJ0600$-$2007. Our lens models, updated
with the latest spectroscopy from VLT/MUSE, indicate that a sub region of the
LBG crosses the caustic and is lensed into a long ($\sim6''$) arc with a local
magnification of $\mu\sim 160$, for which the [CII] line is also significantly
detected. The source-plane reconstruction resolves the interstellar medium
(ISM) structure, showing that the [CII] line is co-spatial with the rest-frame
UV continuum at the scale of $\sim$300 pc. The [CII] line properties suggest
that the LBG is a rotation-dominated system whose velocity gradient explains a
slight difference of redshifts between the whole LBG and its sub region. The
star formation rate (SFR)-$L_{\rm [CII]}$ relations from the sub to the whole
regions of the LBG are consistent with those of local galaxies. We evaluate the
lower limit of the faint-end of the [CII] luminosity function at $z=6$, and
find that it is consistent with predictions from semi analytical models and
from the local SFR-$L_{\rm [CII]}$ relation with a SFR function at $z=6$. These
results imply that the local SFR-$L_{\rm [CII]}$ relation is universal for a
wide range of scales including the spatially resolved ISM, the whole region of
galaxy, and the cosmic scale, even in the epoch of reionization.


Abstract:
  Recently a continuous description of the particle swarm optimization (PSO)
based on a system of stochastic differential equations was proposed by Grassi
and Pareschi in arXiv:2012.05613 where the authors formally showed the link
between PSO and the consensus based optimization (CBO) through zero-inertia
limit. This paper is devoted to solving this theoretical open problem proposed
in arXiv:2012.05613 by providing a rigorous derivation of CBO from PSO through
the limit of zero inertia, and a quantified convergence rate is obtained as
well. The proofs are based on a probabilistic approach by investigating the
weak convergence of the corresponding stochastic differential equations (SDEs)
of Mckean type in the continuous path space and the results are illustrated
with some numerical examples.


Abstract:
  It is generally believed that wormholes are supported by exotic matter
violating Null Energy Condition (NEC). However, various studies of wormhole
geometries under Lovelock theories of gravity have reported existence of
wormhole supported by matter satisfying NEC. Being inspired by these results,
we explore the possibility of the existence of wormhole supported by normal
quark matter in third order Lovelock gravity theory. Well known MIT Bag Model
Equation of state is chosen for describing the quark matter. Taking physically
acceptable approximations, we solve the field equations for shape function
which satisfies flare out condition. The residual of the approximate solution
is studied for accuracy and found to be acceptable.


Abstract:
  Knowledge bases are collections of domain-specific and commonsense facts.
Recently, the sizes of KBs are rocketing due to automatic extraction for
knowledge and facts. For example, the number of facts in WikiData is up to 974
million! According to our observation, current KBs, especially domain KBs, show
strong relevance in relations according to some topics. These patterns can be
used to conclude and infer for part of facts in the KBs. Therefore, the
original KBs can be minimzed by extracting patterns and essential facts.


Abstract:
  In this work, we consider the problem of learning a feed-forward neural
network (NN) controller to safely steer an arbitrarily shaped planar robot in a
compact and obstacle-occluded workspace. Unlike existing methods that depend
strongly on the density of data points close to the boundary of the safe state
space to train NN controllers with closed-loop safety guarantees, we propose an
approach that lifts such assumptions on the data that are hard to satisfy in
practice and instead allows for graceful safety violations, i.e., of a bounded
magnitude that can be spatially controlled. To do so, we employ reachability
analysis methods to encapsulate safety constraints in the training process.
Specifically, to obtain a computationally efficient over-approximation of the
forward reachable set of the closed-loop system, we partition the robot's state
space into cells and adaptively subdivide the cells that contain states which
may escape the safe set under the trained control law. To do so, we first
design appropriate under- and over-approximations of the robot's footprint to
adaptively subdivide the configuration space into cells. Then, using the
overlap between each cell's forward reachable set and the set of infeasible
robot configurations as a measure for safety violations, we introduce penalty
terms into the loss function that penalize this overlap in the training
process. As a result, our method can learn a safe vector field for the
closed-loop system and, at the same time, provide numerical worst-case bounds
on safety violation over the whole configuration space, defined by the overlap
between the over-approximation of the forward reachable set of the closed-loop
system and the set of unsafe states. Moreover, it can control the tradeoff
between computational complexity and tightness of these bounds. Finally, we
provide a simulation study that verifies the efficacy of the proposed scheme.


Abstract:
  The problem of covert communication over Multiple-Input Multiple-Output
(MIMO) Additive White Gaussian Noise (AWGN) channels is investigated, in which
a transmitter attempts to reliably communicate with a legitimate receiver while
avoiding detection by a passive adversary. The covert capacity of the MIMO AWGN
is characterized under a variational distance covertness constraint when the
MIMO channel matrices are static and known. The characterization of the covert
capacity is also extended to a class of channels in which the legitimate
channel matrix is known but the adversary's channel matrix is only known up to
a rank and a spectral norm constraint.


Abstract:
  We explore emergent geometry of the spacetime at the microscopic scale by
adiabatic transport of a quasi-coherent state of a fermionic string, with
quantum spacetime described by the matrix theory (BFSS matrix model). We show
that the generator of the Berry phase is the shift vector of the spacetime
foliation by spacelike surfaces associated with the quasi-coherent state. The
operator-valued generator of the geometric phase of weak adiabatic transport is
the Lorentz connection of the emergent geometry which is not torsion free at
the microscopic scale. The effects of the torsion seem consistent with the
usual interpretation of the Berry curvature as a pseudo magnetic field.


Abstract:
  A toric code is an error-correcting code determined by a toric variety or its
associated integral convex polytope. We investigate $4$- and $5$-dimensional
toric $3$-fold codes, which are codes arising from polytopes in $\mathbf{R}^3$
with four and five lattice points, respectively. By computing the minimum
distances of each code, we fully classify the $4$-dimensional codes. We further
present progress toward the same goal for dimension $5$ codes. In particular,
we classify the $5$-dimensional toric $3$-fold codes arising from polytopes of
width 1.


Abstract:
  The aim of this paper is to derive macroscopic equations for processes on
large co-evolving networks, examples being opinion polarization with the
emergence of filter bubbles or other social processes such as norm development.
This leads to processes on graphs (or networks), where both the states of
particles in nodes as well as the weights between them are updated in time. In
our derivation we follow the basic paradigm of statistical mechanics: We start
from paradigmatic microscopic models and derive a Liouville-type equation in a
high-dimensional space including not only the node states in the network
(corresponding to positions in mechanics), but also the edge weights between
them. We then derive a natural (finite) marginal hierarchy and pass to an
infinite limit.
  We will discuss the closure problem for this hierarchy and see that a simple
mean-field solution can only arise if the weight distributions between nodes of
equal states are concentrated. In a more interesting general case we propose a
suitable closure at the level of a two-particle distribution (including the
weight between them) and discuss some properties of the arising kinetic
equations. Moreover, we highlight some structure-preserving properties of this
closure and discuss its analysis in a minimal model. We discuss the application
of our theory to some agent-based models in literature and discuss some open
mathematical issues.


Abstract:
  We propose a novel technique to analyse spacings of the observed CMB
temperature angular power spectrum (APS) motivated by the behaviour of level
spacings in the presence or absence of correlations between eigenvalues of a
random matrix. We use data from WMAP 9 year ILC and 2018 Planck maps
(Commander, NILC and SMICA) and estimate minimum, maximum, average spacings and
ratios of maximum to minimum spacings between consecutive multipoles of APS
measures ($C_{\ell}$ and $\frac{\ell(\ell+1)}{2\pi}C_\ell$). Anomalous
multipole ranges given by $\ell_{max}$ and $\ell_{min}$ exist for multipoles
$\in[2,31]$ of this work. Sans parity distinctions, average spacing of
$8\leq\ell \leq 31$ for $C_\ell$'s, and maximum and average spacings of
$\ell_{min}\in[8,12]$ of $\frac{\ell(\ell+1)}{2\pi}C_\ell$'s are anomalously
low for all maps, with most anomalously low maximum spacing at $99.93\%$ C.L.
for $\frac{\ell(\ell+1)}{2\pi}C_\ell\text{ (Planck NILC)}$. With parity
distinctions, even multipoles indicate anomalously low maximum and average
spacings relative to odd multipoles, most consistently for
$\ell_{max}\in[6,30]$ of $C_\ell$'s of all maps, the most outstanding being the
even multipole average spacing of $2\leq\ell\leq27$ for
$\frac{\ell(\ell+1)}{2\pi}C_\ell\text{ (WMAP)}$, at $99.60\%$ C.L. For spacing
ratios, multipole ranges are similar to those for anomalous spacings, and odd
multipole spacing ratios are mostly anomalous, with odd multipole spacing ratio
of $2\leq\ell\leq7$ being consistently low for all maps. Overall, we infer an
unusual deficit of correlations between APS measures and deviations from
isotropy due to consistently low, parity-biased anomalous spacings, which can
be argued to have arisen primordially. The physical origin of these findings
may be probed in future work.


Abstract:
  Ultracold polar molecules possess long-range, anisotropic, and tunable
dipolar interactions, providing the opportunities to probe quantum phenomena
inaccessible with existing cold gas platforms. However, experimental progress
has been hindered by the dominance of two-body loss over elastic interactions,
which prevents efficient evaporative cooling. Though recent work has
demonstrated controlled interactions by confining molecules to a
two-dimensional geometry, a general approach for tuning molecular interactions
in a three-dimensional (3D), stable system has been lacking. Here, we
demonstrate tunable elastic dipolar interactions in a bulk gas of ultracold
40K87Rb molecules in 3D, facilitated by an electric field-induced shielding
resonance which suppresses the reactive loss by a factor of thirty. This
improvement in the ratio of elastic to inelastic collisions enables direct
thermalization. The thermalization rate depends on the angle between the
collisional axis and the dipole orientation controlled by an external electric
field, a direct manifestation of the anisotropic dipolar interaction. We
achieve evaporative cooling mediated by the dipolar interactions in three
dimensions. This work demonstrates full control of a long-lived bulk quantum
gas system with tunable long-range interactions, paving the way for the study
of collective quantum many-body physics.


Abstract:
  This paper presents an investigation of local elastic properties of
polystyrene doped with a SiO2 nanoparticle. The local elasticity is analyzed
using the local density fluctuations at various length scales. A significant
enhancement of local elastic properties of polystyrene was observed around the
nanoparticle, and it was revealed that the additional stiffness has an
exponential behavior with a typical length scale R0 ~ 1.4 nm. At the same time,
it was shown that the density and other structural properties of polystyrene at
this distance from the nanoparticle surface are identical to those for the bulk
polystyrene. The enhancement of the local elastic properties was attributed to
the effect of non-affine deformations caused by the strong spatial fluctuations
of polystyrene stiffness at the length scale below 1 nm. The effect was
confirmed using the random matrix model with variable strength of disorder.


Abstract:
  The $\eta N$ interactions are investigated in the hot magnetized asymmetric
nuclear matter using chiral SU(3) model and chiral perturbation theory (ChPT).
In the chiral model, the in-medium properties of $\eta$-meson are calculated by
the medium modified scalar densities under the influence of an external
magnetic field. Further, in the combined approach of chiral model and ChPT,
off-shell contributions of $\eta N$ interactions are evaluated from the ChPT
effective $\eta N$ Lagrangian, and the in-medium effect of scalar densities are
incorporated from the chiral SU(3) model. We observe a significant effect of
magnetic field on the in-medium mass and optical potential of $\eta$ meson. We
observe a deeper mass-shift in the combined approach of ChPT and chiral model
compared to the effect of solo chiral SU(3) model. In both approaches, no
additional mass-shift is observed due to the uncharged nature of $\eta$ mesons
in the presence of magnetic field.


Abstract:
  Mina and Andersen, authors of the Perspectives in Science: COVID-19 Testing:
One Size Does Not Fit All have referred to results and adopted conclusions from
recently published governmental report Pavelka et al. "The effectiveness of
population wide, rapid antigen test based screening in reducing SARS-CoV-2
infection prevalence in Slovakia" without critical consideration, and rigorous
verification. We demonstrate that the authors refer to conclusions that are not
supported by experimental data. Further, there is a lack of objective,
independent information and studies regarding the widespread, public testing
program currently in force in the Slovak Republic. We offer an alternative
explanation of observed data as they have been provided by the Slovak Republic
government to fill this information gap. We also provide explanations and
conclusions that more accurately describe viral spread dynamics. Drawing from
available public data and our simple but rigorous analysis, we show that it is
not possible to make clear conclusions about any positive impact of the public
testing program in the Slovak Republic. In particular, it is not possible to
conclude that this testing program forces the curve down for the SARS-CoV-2
virus outbreak. We think that Pavelka et al. did not consider many fundamental
phenomena in their proposed computer simulations and data analysis - in
particular: the complexity of SARS-CoV-2 virus spread. In complex
spatio-temporal dynamical systems, small spatio-temporal fluctuations can
dramatically change the dynamics of virus spreading on large scales.


Abstract:
  In this paper we introduce the class of bilinear Hilbert--Carleson operators
$\{BC^a\}_{a>0}$ defined by $$ BC^{a}(f,g)(x):= \sup_{\lambda\in {\mathbb R}}
\Big|\int f(x-t)\, g(x+t)\, e^{i\lambda t^a} \, \frac{dt}{t} \Big| $$ and show
that in the non-resonant case $a\in (0,\infty)\setminus\{1,2\}$ the operator
$BC^a$ extends continuously from $L^p({\mathbb R})\times L^q({\mathbb R})$ into
$L^r({\mathbb R})$ whenever $\frac{1}{p}+\frac{1}{q}=\frac{1}{r}$ with
$1<p,\,q\leq\infty$ and $\frac{2}{3}<r<\infty$.
  A key novel feature of these operators is that -- in the non-resonant case --
$BC^{a}$ has a \emph{hybrid} nature enjoying both (1) ``zero curvature''
features inherited from the modulation invariance property of the classical
bilinear Hilbert transform (BHT), and (2) ``non-zero curvature'' features
arising from the Carleson-type operator with nonlinear phase $\lambda t^a$.


Abstract:
  A path-following collision-avoidance model predictive control (MPC) method is
proposed which approximates obstacle shapes as convex polygons.
Collision-avoidance is ensured by means of the signed distance function which
is calculated efficiently as part of the MPC problem by making use of a dual
formulation. The overall MPC problem can be solved by standard nonlinear
programming (NLP) solvers. The dual signed distance formulation yields, besides
the (dual) collision-avoidance constraints, norm, and consistency constraints.
A novel approach is presented that combines the arising norm equality with the
dual collision-avoidance inequality constraints to yield an alternative
formulation reduced in complexity. Moving obstacles are considered using
separate convex sets of linearly predicted obstacle positions in the dual
problem. The theoretical findings and simplifications are compared with the
often-used ellipsoidal obstacle formulation and are analyzed with regard to
efficiency by the example of a simulated path-following autonomous surface
vessel during a realistic maneuver and AIS obstacle data from the Kiel bay
area.


Abstract:
  In this study, recommendations of the AAPM TG- 43 (U1) report have been
followed to characterize the new 192Ir pulse dose rate source, provided by the
Applied Radiation Research School, Nuclear Science and Technology Research
Institute in Iran. Dose rate constant, radial dose function, geometry factors,
and anisotropy function were calculated according to the relevant American
Association of Physicists in Medicine AAPM and TG43 (U1) reports. In this
study, 192Ir source was characterized using Monte Carlo simulation in water
phantom, and in addition, experimental measurements were carried out using
thermoluminescent dosimeters (TLD-100) in plexiglass (PMMA) phantoms. The
dose-rate constant for the 192Ir PDR was found to be equal to 1.131 cGyh-1U-1
and 1.173 cGyh-1U-1 with TLD measurement and Monte Carlo simulation,
respectively. Also in this study, the geometry function, radial dose functions
g(r), and the anisotropy function have been calculated at distances from 0.1 to
16 cm. The dose-rate constant of these calculations has been compared with
measured values for an actual 192Ir seed. The results of dosimetry parameters,
presented in tabulated and graphical formats, exhibited good agreement to those
reported from other commercially available PDR 192Ir sources. The results
obtained in this study are in close agreement with the characteristics of the
commercially available 192Ir sources. The results obtained in this study can be
treated as an initial assessment of this source to be employed in the
conventional treatment planning systems subsequent to complementary
investigations.


Abstract:
  CzeV404 is an SU UMa-type dwarf nova in the period gap. Kara et al. (2021)
(arXiv:2107.02664) recently published photometric and spectroscopic
observations and obtained a mass ratio q=0.16, which is in severe disagreement
of q~0.32 estimated from superhump observations (Bakowska et al., 2014). I here
present what analysis was wrong or outdated in Bakowska et al. (2014) and
provide a new value of q=0.247(5), consistent with the known behavior of
superhumps and the evolution of cataclysmic variables. CzeV404 does not look
like an unusual dwarf nova as suggested by Kara et al. (2021) and I discuss
that the link between SW Sex and SU UMa systems suggested by Kara et al. (2021)
is not supported.


Abstract:
  By planning through a learned dynamics model, model-based reinforcement
learning (MBRL) offers the prospect of good performance with little environment
interaction. However, it is common in practice for the learned model to be
inaccurate, impairing planning and leading to poor performance. This paper aims
to improve planning with an importance sampling framework that accounts and
corrects for discrepancy between the true and learned dynamics. This framework
also motivates an alternative objective for fitting the dynamics model: to
minimize the variance of value estimation during planning. We derive and
implement this objective, which encourages better prediction on trajectories
with larger returns. We observe empirically that our approach improves the
performance of current MBRL algorithms on two stochastic control problems, and
provide a theoretical basis for our method.


Abstract:
  Research evaluations and comparison of the assessments of academic
institutions (scientific areas, departments, universities etc.) are among the
major issues in recent years in higher education systems. One method, followed
by some national evaluation agencies, is to assess the research quality by the
evaluation of a limited number of publications in a way that each publication
is rated among $n$ classes. This method produces, for each institution, a
distribution of the publications in the $n$ classes. In this paper we introduce
a natural geometric way to compare these assessments by introducing an ad hoc
distance from the performance of an institution to the best possible achievable
assessment. Moreover, to avoid the methodological error of comparing
non-homogeneous institutions, we introduce a {\em geometric score} based on
such a distance. The latter represents the probability that an ideal
institution, with the same configuration as the one under evaluation, performs
worst. We apply our method, based on the geometric score, to rank, in two
specific scientific areas, the Italian universities using the results of the
evaluation exercise VQR 2011-2014.


Abstract:
  A promising method for measuring the cosmological parameter combination
fsigma_8 is to compare observed peculiar velocities with peculiar velocities
predicted from a galaxy density field using perturbation theory. We use N-body
simulations and semi-analytic galaxy formation models to quantify the accuracy
and precision of this method. Specifically, we examine a number of technical
aspects, including the optimal smoothing length applied to the density field,
the use of dark matter halos or galaxies as tracers of the density field, the
effect of noise in the halo mass estimates or in the stellar-to-halo mass
relation, and the effect of finite survey volumes. We find that for a Gaussian
smoothing of 4 Mpc/h, the method has only small systematic biases at the level
of 5%. Cosmic variance affects current measurements at the 5% level due to the
volume of current redshift data sets.


Abstract:
  We measure the scalar induced gravitational waves from the cosmic microwave
background (CMB) observations and the gravitational wave observations. In the
$\Lambda$CDM+$r$ model, we constrain the cosmological parameters within the
evolution of the scalar induced gravitational waves by the additional scalar
power spectrum. The two special cases called narrow power spectrum and wide
power spectrum have influence on the cosmological parameters, especially the
combinations of Planck18+BAO+BK15+LISA. We also compare these numerical results
from four datasets within LIGO, LISA, IPTA and FAST projects, respectively. The
constraints from FAST have a significant impact on tensor-to-scalar ratio.


Abstract:
  Some interpersonal verbs can implicitly attribute causality to either their
subject or their object and are therefore said to carry an implicit causality
(IC) bias. Through this bias, causal links can be inferred from a narrative,
aiding language comprehension. We investigate whether pre-trained language
models (PLMs) encode IC bias and use it at inference time. We find that to be
the case, albeit to different degrees, for three distinct PLM architectures.
However, causes do not always need to be implicit -- when a cause is explicitly
stated in a subordinate clause, an incongruent IC bias associated with the verb
in the main clause leads to a delay in human processing. We hypothesize that
the temporary challenge humans face in integrating the two contradicting
signals, one from the lexical semantics of the verb, one from the
sentence-level semantics, would be reflected in higher error rates for models
on tasks dependent on causal links. The results of our study lend support to
this hypothesis, suggesting that PLMs tend to prioritize lexical patterns over
higher-order signals.


Abstract:
  This paper proposes a simultaneous control method for the angle and stiffness
of the joint in an antagonistic pneumatic artificial muscle (PAM) actuator
system using only pressure measurements, and clarifies the allowable references
for the PAM actuator system. To achieve a sensor-less control, the proposed
method estimates the joint angle and contraction forces using an unscented
Kalman filter that employs a detailed model of the actuator system. Unlike
previous control methods, the proposed method does not require any encoder and
force sensor to achieve angle and stiffness control of the PAM actuator system.
Experimental validations using three control scenarios confirm that the
proposed method can control the joint angle and stiffness simultaneously and
independently. Moreover, it is shown that a reference admissible set can be
used as an indicator to establish reference values by demonstrating that the
reference set covers the experimentally obtained trajectories of the angle and
stiffness.


Abstract:
  We propose to tackle data-to-text generation tasks by directly splicing
together retrieved segments of text from "neighbor" source-target pairs. Unlike
recent work that conditions on retrieved neighbors but generates text
token-by-token, left-to-right, we learn a policy that directly manipulates
segments of neighbor text, by inserting or replacing them in partially
constructed generations. Standard techniques for training such a policy require
an oracle derivation for each generation, and we prove that finding the
shortest such derivation can be reduced to parsing under a particular weighted
context-free grammar. We find that policies learned in this way perform on par
with strong baselines in terms of automatic and human evaluation, but allow for
more interpretable and controllable generation.


Abstract:
  We report the optical absorption characteristics and the photocurrent spectra
for large-area molecular junctions containing different molecular structures
and thicknesses. Through experimental assessment of the optical absorption of
molecules in solution and chemisorbed onto transparent, conductive carbon
films, the energy ranges and intensity of absorption for a variety of molecular
layers as a function of thickness were determined. Our findings show that
optical absorption is red-shifted for a chemisorbed molecular layer relative to
the molecule in solution, indicating electronic coupling between the molecule
and the surface. However, we also find that from 2-22 nm in thickness, the
absorption characteristics follow Beers law, with no further shifts in the
wavelength of maximum absorption or low-energy absorption onset. For energies
where no absorption occurs, an internal photoemission process takes place
resulting in photocurrent that can be sustained over a limited range. For
energies at which the molecular component absorbs light, photocurrent can
proceed over longer distances, and the trends in photocurrent yield versus
thickness shows interesting trends that may be related to the distance scale
over which charge carriers can communicate with the conductive contacts. This
method is particularly relevant for strongly coupled systems that rely on
molecular properties to mediate charge transport, since excitation takes place
directly within the molecular layer, and provides a direct probe of energetics
in systems that show bulk-limited transport.


Abstract:
  Emotion recognition in conversations is an important step in various virtual
chat bots which require opinion-based feedback, like in social media threads,
online support and many more applications. Current Emotion recognition in
conversations models face issues like (a) loss of contextual information in
between two dialogues of a conversation, (b) failure to give appropriate
importance to significant tokens in each utterance and (c) inability to pass on
the emotional information from previous utterances.The proposed model of
Advanced Contextual Feature Extraction (AdCOFE) addresses these issues by
performing unique feature extraction using knowledge graphs, sentiment lexicons
and phrases of natural language at all levels (word and position embedding) of
the utterances. Experiments on the Emotion recognition in conversations dataset
show that AdCOFE is beneficial in capturing emotions in conversations.


Abstract:
  CHARA/SPICA (Stellar Parameters and Images with a Cophased Array) is
currently being developed at Observatoire de la C\^ote d'Azur. It will be
installed at the visible focus of the CHARA Array by the end of 2021. It has
been designed to perform a large survey of fundamental stellar parameters with,
in the possible cases, a detailed imaging of the surface or environment of
stars. To reach the required precision and sensitivity, CHARA/SPICA combines a
low spectral resolution mode R = 140 in the visible and single-mode fibers fed
by the AO stages of CHARA. This setup generates additional needs before the
interferometric combination: the compensation of atmospheric refraction and
longitudinal dispersion, and the fringe stabilization. In this paper, we
present the main features of the 6-telescopes fibered visible beam combiner
(SPICA-VIS) together with the first laboratory and on-sky results of the fringe
tracker (SPICA-FT). We describe also the new fringe-tracker simulator developed
in parallel to SPICA-FT.


Abstract:
  Many computational problems are subject to a quantum speed-up: one might find
that a problem having an O(n^3)-time or O(n^2)-time classic algorithm can be
solved by a known O(n^1.5)-time or O(n)-time quantum algorithm. The question
naturally arises: how much quantum speed-up is possible?
  The area of fine-grained complexity allows us to prove optimal lower-bounds
on the complexity of various computational problems, based on the conjectured
hardness of certain natural, well-studied problems. This theory has recently
been extended to the quantum setting, in two independent papers by Buhrman,
Patro, and Speelman (arXiv:1911.05686), and by Aaronson, Chia, Lin, Wang, and
Zhang (arXiv:1911.01973).
  In this paper, we further extend the theory of fine-grained complexity to the
quantum setting. A fundamental conjecture in the classical setting states that
the 3SUM problem cannot be solved by (classical) algorithms in time O(n^{2-a}),
for any a>0. We formulate an analogous conjecture, the Quantum-3SUM-Conjecture,
which states that there exist no sublinear O(n^{1-b})-time quantum algorithms
for the 3SUM problem.
  Based on the Quantum-3SUM-Conjecture, we show new lower-bounds on the time
complexity of quantum algorithms for several computational problems. Most of
our lower-bounds are optimal, in that they match known upper-bounds, and hence
they imply tight limits on the quantum speedup that is possible for these
problems.


Abstract:
  In practice, the use of rounding is ubiquitous. Although researchers have
looked at the implications of rounding continuous random variables, rounding
may be applied to functions of discrete random variables as well. For example,
to infer on suicide difference between two time periods, authorities may
provide a rounded average of deaths for each period. Suicide rates tend to be
relatively low around the world and such rounding may seriously affect
inference on the change of suicide rate. In this paper, we study the scenario
when a rounded to nearest integer average is used to estimate a non-negative
discrete random variable. Specifically, our interest is in drawing inference on
a parameter from the pmf of Y, when we get U=n[Y/n]as a proxy for Y. The
probability generating function of U, E(U), and Var(U) capture the effect of
the coarsening of the support of Y. Also, moments and estimators of
distribution parameters are explored for some special cases. Under certain
conditions, there is little impact from rounding. However, we also find
scenarios where rounding can significantly affect statistical inference as
demonstrated in two applications. The simple methods we propose are able to
partially counter rounding error effects.


Abstract:
  We give an analytic treatment of the time resolution and efficiency of Single
Photon Avalanche Diodes (SPADs) and Silicon Photomultipliers (SiPMs). We
provide closed-form expressions for structures with uniform electric fields and
efficient numerical prescriptions for arbitrary electric field configurations.
We discuss the sensor performance for single photon detection and also for
charged particle detection.


Abstract:
  We consider the family of $r$-parallel sets in $\mathbb{R}^d$, that is sets
of the form $A_r=A+rB_2^n$, where $B_2^n$ is the unit Euclidean ball and $A$ is
an arbitrary Borel set. We show that the ratio between the upper surface area
measure of an $r$-parallel set and its volume is upper bounded by $d/r$.
Equality is achieved for $A$ being a single point. As a consequence of our main
result we show that the Gaussian upper surface area measure of an $r$-parallel
set is upper bounded by $18d \max(\sqrt{d},r^{-1})$. Moreover, we observe that
there exists a $1$-parallel set with Gaussian surface area measure at least
$0.28 \cdot d^{1/4}$.


Abstract:
  Attractor inflation is a particularly robust framework for developing
inflationary models that are insensitive to the details of the potential. Such
models are most often considered in the metric formulation of gravity. However,
non-minimal models may not necessarily maintain their attractor nature in the
Palatini formalism where the connection is independent of the metric. In this
work, we employ the $\beta$-function formalism to classify the strong coupling
limit of inflationary models in both the metric and the Palatini approaches.
Furthermore, we determine the range of values for the non-minimal coupling that
lead to theories being observationally indistinguishable in metric and Palatini
within current accuracy. Finally, we reconstruct the Jordan frame potential for
$\xi$-attractors by imposing an explicit form for the $\beta$-function,
demonstrating the effect that the choice of metric or Palatini has on the
inflationary observables of the theory.


Abstract:
  Pseudo-labeling is a key component in semi-supervised learning (SSL). It
relies on iteratively using the model to generate artificial labels for the
unlabeled data to train against. A common property among its various methods is
that they only rely on the model's prediction to make labeling decisions
without considering any prior knowledge about the visual similarity among the
classes. In this paper, we demonstrate that this degrades the quality of
pseudo-labeling as it poorly represents visually similar classes in the pool of
pseudo-labeled data. We propose SemCo, a method which leverages label semantics
and co-training to address this problem. We train two classifiers with two
different views of the class labels: one classifier uses the one-hot view of
the labels and disregards any potential similarity among the classes, while the
other uses a distributed view of the labels and groups potentially similar
classes together. We then co-train the two classifiers to learn based on their
disagreements. We show that our method achieves state-of-the-art performance
across various SSL tasks including 5.6% accuracy improvement on Mini-ImageNet
dataset with 1000 labeled examples. We also show that our method requires
smaller batch size and fewer training iterations to reach its best performance.
We make our code available at https://github.com/islam-nassar/semco.


Abstract:
  In many indigenous societies, people are categorised into several cultural
groups, or clans, within which they believe to share ancestors. Clan
attributions provide certain rules for marriage and descent. Such rules between
clans constitute kinship structures. Anthropologists have revealed several
kinship structures. Here, we propose an agent-based model of indigenous
societies to reveal the evolution of kinship structures. In the model, several
societies compete. Societies themselves comprise multiple families with
parameters for cultural traits and mate preferences. These values determine
with whom each family cooperates and competes and are transmitted to a new
generation with mutation. The growth rate of each family is determined by the
number of cooperators and competitors. Through this multi-level evolution,
family traits and preferences diverge to form clusters that can be regarded as
clans. Subsequently, kinship structures emerge, including dual organisation and
generalised or restricted exchange, as well as patrilineal, matrilineal, and
double descent systems. These structures emerge depending on the necessity of
cooperation and the strength of mating competition. Their dependence is also
estimated analytically. Finally, statistical analysis using the Standard
Cross-Cultural Sample, a global ethnographic database, empirically verified
theoretical results. Such collaboration between theoretical and empirical
approaches will unveil universal features in anthropology.


Abstract:
  The univariate generalized extreme value (GEV) distribution is the most
commonly used tool for analysing the properties of rare events. The ever
greater utilization of Bayesian methods for extreme value analysis warrants
detailed theoretical investigation, which has thus far been underdeveloped.
Even the most basic asymptotic results are difficult to obtain because the GEV
fails to satisfy standard regularity conditions. Here, we prove that the
posterior distribution of the GEV parameter vector, given an independent and
identically distributed sequence of observations, converges to a normal
distribution centred at the true parameter. The proof necessitates analysing
integrals of the GEV likelihood function over the entire parameter space, which
requires considerable care because the support of the GEV density depends on
the parameters in complicated ways.


Abstract:
  A family of subsets of $[n]$ is intersecting if every pair of its sets
intersects. Determining the structure of large intersecting families is a
central problem in extremal combinatorics. Frankl-Kupavskii and
Balogh-Das-Liu-Sharifzadeh-Tran independently showed that for $n\geq 2k +
c\sqrt{k\ln k}$, almost all $k$-uniform intersecting families are stars.
Improving their result, we show that the same conclusion holds for $n\geq 2k+
100\ln k$. Our proof uses, among others, Sapozhenko's graph container lemma and
the Das-Tran removal lemma.


Abstract:
  In this paper, we propose multi-input multi-output (MIMO) beamforming designs
towards joint radar sensing and multi-user communications. We employ the
Cram\'er-Rao bound (CRB) as a performance metric of target estimation, under
both point and extended target scenarios. We then propose minimizing the CRB of
radar sensing while guaranteeing a pre-defined level of
signal-to-interference-plus-noise ratio (SINR) for each communication user. For
the single-user scenario, we derive a closed form for the optimal solution for
both cases of point and extended targets. For the multi-user scenario, we show
that both problems can be relaxed into semidefinite programming by using the
semidefinite relaxation approach, and prove that the global optimum can always
be obtained. Finally, we demonstrate numerically that the globally optimal
solutions are reachable via the proposed methods, which provide significant
gains in target estimation performance over state-of-the-art benchmarks.


Abstract:
  We report on the transport study of a double quantum dot (DQD) device made
from a freestanding, single crystalline InSb nanosheet. The freestanding
nanosheet is grown by molecular beam epitaxy and the DQD is defined by top gate
technique. Through the transport measurements, we demonstrate how a single
quantum dot (QD) and a DQD can be defined in an InSb nanosheet by tuning
voltages applied to the top gates. We also measure the charge stability
diagrams of the DQD and show that the charge states and the inter-dot coupling
between the two individual QDs in the DQD can be efficiently regulated by the
top gates. Numerical simulations for the potential profile and charge density
distribution in the DQD have been performed and the results support the
experimental findings and provide a better understanding of fabrication and
transport characteristics of the DQD in the InSb nanosheet. The achieved DQD in
the two-dimensional InSb nanosheet possesses pronounced benefits in lateral
scaling and can thus serve as a new building block for developments of quantum
computation and quantum simulation technologies.


Abstract:
  It has previously been shown that 2D spectral mammography can be used to
discriminate between (likely benign) cystic and (potentially malignant) solid
lesions in order to reduce unnecessary recalls in mammography. One limitation
of the technique is, however, that the composition of overlapping tissue needs
to be interpolated from a region surrounding the lesion. The purpose of this
investigation was to demonstrate that lesion characterization can be done with
spectral tomosynthesis, and to investigate whether the 3D information available
in tomosynthesis can reduce the uncertainty from the interpolation of
surrounding tissue. A phantom experiment was designed to simulate a cyst and a
tumor, where the tumor was overlaid with a structure that made it mimic a cyst.
In 2D, the two targets appeared similar in composition, whereas spectral
tomosynthesis revealed the exact compositional difference. However, the loss of
discrimination signal due to spread from the plane of interest was of the same
strength as the reduction of anatomical noise. Results from a preliminary
investigation on clinical tomosynthesis images of solid lesions yielded results
that were consistent with the phantom experiments, but were still to some
extent inconclusive. We conclude that lesion characterization is feasible in
spectral tomosynthesis, but more data, as well as refinement of the calibration
and discrimination algorithms, are needed to draw final conclusions about the
benefit compared to 2D.


Abstract:
  Classification, a heavily-studied data-driven machine learning task, drives
an increasing number of prediction systems involving critical human decisions
such as loan approval and criminal risk assessment. However, classifiers often
demonstrate discriminatory behavior, especially when presented with biased
data. Consequently, fairness in classification has emerged as a high-priority
research area. Data management research is showing an increasing presence and
interest in topics related to data and algorithmic fairness, including the
topic of fair classification. The interdisciplinary efforts in fair
classification, with machine learning research having the largest presence,
have resulted in a large number of fairness notions and a wide range of
approaches that have not been systematically evaluated and compared. In this
paper, we contribute a broad analysis of 13 fair classification approaches and
additional variants, over their correctness, fairness, efficiency, scalability,
robustness to data errors, sensitivity to underlying ML model, data efficiency,
and stability using a variety of metrics and real-world datasets. Our analysis
highlights novel insights on the impact of different metrics and high-level
approach characteristics on different aspects of performance. We also discuss
general principles for choosing approaches suitable for different practical
settings, and identify areas where data-management-centric solutions are likely
to have the most impact.


Abstract:
  Cyber attacks constitute a significant threat to organizations with
implications ranging from economic, reputational, and legal consequences. As
cybercriminals' techniques get sophisticated, information security
professionals face a more significant challenge to protecting information
systems. In today's interconnected realm of computer systems, each attack
vector has a network dimension. The present study investigates network
intrusion attempts with anomaly-based machine learning models to provide better
protection than the conventional misuse-based models. Two models, namely an
ensemble learning model and a convolutional neural network model, were built
and implemented on a data set gathered from a real-life, institutional
production environment. To demonstrate the models' reliability and validity,
they were applied to the UNSW-NB15 benchmarking data set. The type of attack
was limited to probing attacks to keep the scope of the study manageable. The
findings revealed high accuracy rates, the CNN model being slightly more
accurate.


Abstract:
  Active learning is a decision-making process. In both abstract and physical
settings, active learning demands both analysis and action. This is a review of
active learning in robotics, focusing on methods amenable to the demands of
embodied learning systems. Robots must be able to learn efficiently and
flexibly through continuous online deployment. This poses a distinct set of
control-oriented challenges -- one must choose suitable measures as objectives,
synthesize real-time control, and produce analyses that guarantee performance
and safety with limited knowledge of the environment or robot itself. In this
work, we survey the fundamental components of robotic active learning systems.
We discuss classes of learning tasks that robots typically encounter, measures
with which they gauge the information content of observations, and algorithms
for generating action plans. Moreover, we provide a variety of examples -- from
environmental mapping to nonparametric shape estimation -- that highlight the
qualitative differences between learning tasks, information measures, and
control techniques. We conclude with a discussion of control-oriented open
challenges, including safety-constrained learning and distributed learning.


Abstract:
  In this paper we propose a novel observer to solve the problem of visual
simultaneous localization and mapping (SLAM), only using the information from a
single monocular camera and an inertial measurement unit (IMU). The system
state evolves on the manifold $SE(3)\times \mathbb{R}^{3n}$, on which we design
dynamic extensions carefully in order to generate an invariant foliation, such
that the problem is reformulated into online \emph{constant parameter}
identification. Then, following the recently introduced parameter
estimation-based observer (PEBO) and the dynamic regressor extension and mixing
(DREM) procedure, we provide a new simple solution. A notable merit is that the
proposed observer guarantees almost global asymptotic stability requiring
neither persistency of excitation nor uniform complete observability, which,
however, are widely adopted in most existing works with guaranteed stability.


Abstract:
  PAC-Bayesian bounds are known to be tight and informative when studying the
generalization ability of randomized classifiers. However, when applied to some
family of deterministic models such as neural networks, they require a loose
and costly derandomization step. As an alternative to this step, we introduce
new PAC-Bayesian generalization bounds that have the originality to provide
disintegrated bounds, i.e., they give guarantees over one single hypothesis
instead of the usual averaged analysis. Our bounds are easily optimizable and
can be used to design learning algorithms. We illustrate the interest of our
result on neural networks and show a significant practical improvement over the
state-of-the-art framework.


Abstract:
  The functionalization of single-walled carbon nanotubes (SWCNTs) with
luminescent sp$^{3}$ defects has greatly improved their performance in
applications such as quantum light sources and bioimaging. Here, we report the
covalent functionalization of purified semiconducting SWCNTs with stable
organic radicals (perchlorotriphenylmethyl, PTM) carrying a net spin. This
model system allows us to use the near-infrared photoluminescence arising from
the defect-localized exciton as a highly sensitive probe for the short-range
interaction between the PTM radical and the SWCNT. Our results point toward an
increased triplet exciton population due to radical-enhanced intersystem
crossing, which could provide access to the elusive triplet manifold in SWCNTs.
Furthermore, this simple synthetic route to spin-labeled defects could enable
magnetic resonance studies complementary to in vivo fluorescence imaging with
functionalized SWCNTs and facilitate the scalable fabrication of spintronic
devices with magnetically switchable charge transport.


Abstract:
  Spike-timing-dependent plasticity(STDP) is a biological process of synaptic
modification caused by the difference of firing order and timing between
neurons. One of the neurodynamical roles of STDP is to form a macroscopic
geometrical structure in the neuronal state space in response to a periodic
input. This work proposes a practical memory model based on STDP that can store
and retrieve high-dimensional associative data. The model combines STDP
dynamics with an encoding scheme for distributed representations and can handle
multiple composite data in a continuous manner. In the auto-associative memory
task where a group of images is continuously streamed to the model, the images
are successfully retrieved from an oscillating neural state whenever a proper
cue is given. In the second task that deals with semantic memories embedded
from sentences, the results show that words can recall multiple sentences
simultaneously or one exclusively, depending on their grammatical relations.


Abstract:
  As cameras are increasingly deployed in new application domains such as
autonomous driving, performing 3D object detection on monocular images becomes
an important task for visual scene understanding. Recent advances on monocular
3D object detection mainly rely on the ``pseudo-LiDAR'' generation, which
performs monocular depth estimation and lifts the 2D pixels to pseudo 3D
points. However, depth estimation from monocular images, due to its poor
accuracy, leads to inevitable position shift of pseudo-LiDAR points within the
object. Therefore, the predicted bounding boxes may suffer from inaccurate
location and deformed shape. In this paper, we present a novel neighbor-voting
method that incorporates neighbor predictions to ameliorate object detection
from severely deformed pseudo-LiDAR point clouds. Specifically, each feature
point around the object forms their own predictions, and then the ``consensus''
is achieved through voting. In this way, we can effectively combine the
neighbors' predictions with local prediction and achieve more accurate 3D
detection. To further enlarge the difference between the foreground region of
interest (ROI) pseudo-LiDAR points and the background points, we also encode
the ROI prediction scores of 2D foreground pixels into the corresponding
pseudo-LiDAR points. We conduct extensive experiments on the KITTI benchmark to
validate the merits of our proposed method. Our results on the bird's eye view
detection outperform the state-of-the-art performance by a large margin,
especially for the ``hard'' level detection.


Abstract:
  The singlet pseudoscalar sector of light mesons is investigated in the
soft-wall model, a bottom-up approach to the AdS/QCD correspondence. The
$\eta^\prime$ mass results from the mixing among the fields dual to the axial
current, pseudoscalar current and the $G\tilde G$ operator. The topological
susceptibility is computed for any quark mass, and the Witten-Veneziano
relation is obtained in the large $N_c$ limit.


Abstract:
  We give a proof of a recent combinatorial conjecture due to the first author,
which was discovered in the framework of commutative algebra. This result gives
rise to new companions to the famous Andrews-Gordon identities. Our tools
involve graded quotient rings, Durfee squares and rectangles for integer
partitions, and $q$-series identities.


Abstract:
  What is the difference between goal-directed and habitual behavior? We
propose a novel computational framework of decision making with Bayesian
inference, in which everything is integrated as an entire neural network model.
The model learns to predict environmental state transitions by self-exploration
and generating motor actions by sampling stochastic internal states ${z}$.
Habitual behavior, which is obtained from the prior distribution of ${z}$, is
acquired by reinforcement learning. Goal-directed behavior is determined from
the posterior distribution of ${z}$ by planning, using active inference which
optimizes the past, current and future ${z}$ by minimizing the variational free
energy for the desired future observation constrained by the observed sensory
sequence. We demonstrate the effectiveness of the proposed framework by
experiments in a sensorimotor navigation task with camera observations and
continuous motor actions.


Abstract:
  We explore a method of encoding secret messages using factoradic numbering of
permuted lists of text or numeric elements. Encoding and decoding methods are
provided, with code, and key aspects of the correctness of the methods are
formally proven. The method of encoding is simple and provides a working
example of using textual and numeric lists as a stenagographic channel. Given
the ubiquity of lists, such channels are already present but are often unused.


Abstract:
  In this paper, we consider a problem of Lang about finiteness of torsion
points on plane rational curves, and prove some results towards a matrix
analogue of this problem, including a full analogue for $2\times 2$ matrices
defined over $\mathbb{C}$.


Abstract:
  Purpose: Image classification may be the fundamental task in imaging
artificial intelligence. We have recently shown that reinforcement learning can
achieve high accuracy for lesion localization and segmentation even with
minuscule training sets. Here, we introduce reinforcement learning for image
classification. In particular, we apply the approach to normal vs.
tumor-containing 2D MRI brain images.
  Materials and Methods: We applied multi-step image classification to allow
for combined Deep Q learning and TD(0) Q learning. We trained on a set of 30
images (15 normal and 15 tumor-containing). We tested on a separate set of 30
images (15 normal and 15 tumor-containing). For comparison, we also trained and
tested a supervised deep-learning classification network on the same set of
training and testing images.
  Results: Whereas the supervised approach quickly overfit the training data
and as expected performed poorly on the testing set (57% accuracy, just over
random guessing), the reinforcement learning approach achieved an accuracy of
100%.
  Conclusion: We have shown a proof-of-principle application of reinforcement
learning to the classification of brain tumors. We achieved perfect testing set
accuracy with a training set of merely 30 images.


Abstract:
  We present the beta functions of gauge and Yukawa couplings in general
four-dimensional quantum field theory, at four and three loops, respectively.
The essence of our approach is fixing unknown coefficients in the most general
ansatz for beta-functions by direct calculation in several simplified models.
We apply our results to the Standard Model and its extension with an arbitrary
number of Higgs doublets and provide expressions for all four-loop gauge
couplings beta-functions with matrix Yukawa interactions.


Abstract:
  In light of a gap found by Krupi\'{n}ski, we give a new proof of
associativity for the Morley (or "nonforking") product of invariant measures in
NIP theories.


Abstract:
  The study of complex oxides and oxide heterostructures have dominated the
field of experimental and theoretical condensed matter research for the better
part of the last few decades. Powerful experimental techniques like molecular
beam epitaxy and pulsed laser deposition have made fabrication of oxide
heterostructures with atomically sharp interfaces possible, while more and more
sophisticated handling of exchange and correlations within first principles
methods including density functional theory (DFT) supplemented with Hubbard U
corrections and hybrid functionals, and beyond DFT techniques like dynamical
mean field theory (DMFT) have made understanding of such correlated oxides and
oxide interfaces easier. The emergence of the high mobility two dimensional
electron gas with fascinating properties like giant photoconductance, large
negative magnetoresistance, superconductivity, ferromagnetism, and the
mysterious coexistence of the latter two have indeed caught the attention of
condensed matter community at large. Similarly strain tuning of oxides have
generated considerable interest particularly after the recent discovery of
piezoelectric methods of strain generation. Theoretical understanding and
prediction of the possible exotic phases emerging in such complex oxides both
under strain and in heterostructures will eventually lead to better design of
device applications in this new emerging field of oxide electronics, along with
possible discovery of exotic physics in condensed matter systems which may be
of wider significance! In this review we briefly look at theoretical studies of
novel phenomena in oxides under strain and oxide heterostructures, and try to
understand the role of exchange and particularly correlation in giving rise to
such exotic electronic states.


Abstract:
  The manpower scheduling problem is a kind of critical combinational
optimization problem. Researching solutions to scheduling problems can improve
the efficiency of companies, hospitals, and other work units. This paper
proposes a new model combined with deep learning to solve the multi-shift
manpower scheduling problem based on the existing research. This model first
solves the objective function's optimized value according to the current
constraints to find the plan of employee arrangement initially. It will then
use the scheduling table generation algorithm to obtain the scheduling result
in a short time. Moreover, the most prominent feature we propose is that we
will use the neural network training method based on the time series to solve
long-term and long-period scheduling tasks and obtain manpower arrangement. The
selection criteria of the neural network and the training process are also
described in this paper. We demonstrate that our model can make a precise
forecast based on the improvement of neural networks. This paper also discusses
the challenges in the neural network training process and obtains enlightening
results after getting the arrangement plan. Our research shows that neural
networks and deep learning strategies have the potential to solve similar
problems effectively.


Abstract:
  In this paper we study the identifiability of specific forms (symmetric
tensors), with the target of extending recent methods for the case of $3$
variables to more general cases. In particular, we focus on forms of degree $4$
in $5$ variables. By means of tools coming from classical algebraic geometry,
such as Hilbert function, liaison procedure and Serre's construction, we give a
complete geometric description and criteria of identifiability for ranks $\geq
9$, filling the gap between rank $\leq 8$, covered by Kruskal's criterion, and
$15$, the rank of a general quartic in $5$ variables. For the case $r=12$, we
construct an effective algorithm that guarantees that a given decomposition is
unique.


Abstract:
  The jamming transition is accompanied by a rich phenomenology, such as
hysteresis or non-local effects, which is still not well understood. Here we
experimentally investigate a model frictionless granular layer flowing down an
inclined plane, as a way to disentangle generic collective effects from those
arising from frictional interactions. We find that thin frictionless granular
layers are devoid of hysteresis, yet the layer stability is increased as it
gets thinner. Rheological laws obtained for different layer thicknesses can be
collapsed into a unique master curve, supporting that non-local effects are the
consequence of the usual finite-size effects associated to the presence of a
critical point. This collapse indicates that the so-called isostatic length
$l^*$ governs the effect of boundaries on flow, and rules out other
propositions made in the past.


Abstract:
  In some short gamma-ray bursts, precursor flares occurring $\sim$ seconds
prior to the main episode have been observed. These flares may then be
associated with the last few cycles of the inspiral when the orbital frequency
is a few hundred Hz. During these final cycles, tidal forces can resonantly
excite quasi-normal modes in the inspiralling stars, leading to a rapid
increase in their amplitude. It has been shown that these modes can exert
sufficiently strong strains onto the neutron star crust to instigate yieldings.
Due to the typical frequencies of $g$-modes being $\sim 100\text{ Hz}$, their
resonances with the orbital frequency match the precursor timings and warrant
further investigation. Adopting realistic equations of state and solving the
general-relativistic pulsation equations, we study $g$-mode resonances in
coalescing quasi-circular binaries, where we consider various stellar rotation
rates, degrees of stratification, and magnetic field structures. We show that
for some combination of stellar parameters, the resonantly excited $g_1$- and
$g_2$-modes may lead to crustal failure and trigger precursor flares.


Abstract:
  Ellerman bombs are regions with enhanced Balmer line wing emission and mark
magnetic reconnection in the deep solar atmosphere in active regions and quiet
Sun. They are often found in regions where opposite magnetic polarities are in
close proximity. Recent high resolution observations suggest that Ellerman
bombs are more prevalent than thought before. We aim to determine the
occurrence of Ellerman bombs in the penumbra of sunspots. We analyze high
spatial resolution observations of sunspots in the Balmer H-alpha and H-beta
lines as well as auxiliary continuum channels obtained with the Swedish 1-m
Solar Telescope and apply the k-means clustering technique to systematically
detect and characterize Ellerman Bombs. Features with all the defining
characteristics of Ellerman bombs are found in large numbers over the entire
penumbra. The true prevalence of these events is only fully appreciated in the
H-beta line due to highest spatial resolution and lower chromospheric opacity.
We find that the penumbra hosts some of the highest Ellerman bomb densities,
only surpassed by the moat in the immediate surroundings of the sunspot. Some
penumbral Ellerman bombs show flame morphology and rapid dynamical evolution.
Many penumbral Ellerman bombs are fast moving with typical speed of 3.7 km/s
and sometimes more than 10 km/s. Many penumbral Ellerman bombs migrate from the
inner to the outer penumbra over hundreds of km and some continue moving beyond
the outer penumbral boundary into the moat. Many penumbral Ellerman bombs are
found in the vicinity of regions with opposite magnetic polarity. We conclude
that reconnection is a near continuous process in the low atmosphere of the
penumbra of sunspots as manifest in the form of penumbral Ellerman bombs. These
are so prevalent that they may be a major sink of sunspot magnetic energy.


Abstract:
  Spin-orbit coupling is an essential mechanism underlying quantum phenomena
such as the spin Hall effect and topological insulators. It has been widely
studied in well-isolated Hermitian systems, but much less is known about the
role dissipation plays in spin-orbit-coupled systems. Here, we implement
dissipative spin-orbit-coupled bands filled with ultracold fermions, and
observe parity-time symmetry breaking as a result of the competition between
the spin-orbit coupling and dissipation. Tunable dissipation, introduced by
state-selective atom loss, enables us to tune the energy gap and close it at
the critical dissipation value, the so-called exceptional point. In the
vicinity of the critical point, the state evolution exhibits a chiral response,
which enables us to tune the spin-orbit coupling and dissipation dynamically,
revealing topologically robust chiral spin transfer when the quantum state
encircles the exceptional point. This demonstrates that we can explore
non-Hermitian topological states with spin-orbit coupling.


Abstract:
  We generalize the shuffle theorem and its $(km,kn)$ version, as conjectured
by Haglund et al. and Bergeron et al., and proven by Carlsson and Mellit, and
Mellit, respectively. In our version the $(km,kn)$ Dyck paths on the
combinatorial side are replaced by lattice paths lying under a line segment
whose $x$ and $y$ intercepts need not be integers, and the algebraic side is
given either by a Schiffmann algebra operator formula or an equivalent explicit
raising operator formula.
  We derive our combinatorial identity as the polynomial truncation of an
identity of infinite series of $GL_{l}$ characters, expressed in terms of
infinite series versions of LLT polynomials. The series identity in question
follows from a Cauchy identity for non-symmetric Hall-Littlewood polynomials.


Abstract:
  We present a new algorithm for estimating the Point Spread Function (PSF) in
wide-field astronomical images with extreme source crowding. Robust and
accurate PSF estimation in crowded astronomical images dramatically improves
the fidelity of astrometric and photometric measurements extracted from
wide-field sky monitoring imagery. Our radically new approach utilizes
convolutional sparse representations to model the continuous functions involved
in the image formation. This approach avoids the need to detect and precisely
localize individual point sources that is shared by existing methods. In
experiments involving simulated astronomical imagery, it significantly
outperforms the recent alternative method with which it is compared.


Abstract:
  We report the result of a search for neutrinos in coincidence with solar
flares from the GOES flare database. The search was performed on a 10.8
kton-year exposure of KamLAND collected from 2002 to 2019. This large exposure
allows us to explore previously unconstrained parameter space for solar flare
neutrinos. We found no statistical excess of neutrinos and established 90%
confidence level upper limits of $8.4 \times 10^7$ cm$^{-2}$ ($3.0 \times
10^{9}$ cm$^{-2}$) on electron anti-neutrino (electron neutrino) fluence at 20
MeV normalized to the X12 flare, assuming that the neutrino fluence is
proportional to the X-ray intensity.


Abstract:
  In this short review we pass through the milestones in the studies of the
electronic topological transitions (ETT) and focus on some recent applications
of the ideas worked out in their classical theory. These are: two-dimensional
electron systems, de Haas-van Alphen effect, classification of ETT in
multidimensional systems, superconductivity in systems close to ETT,
thermoelectricity in heavy-fermion systems, where the cascades of topological
changes of Fermi surface (FS) are generated by magnetic field. The history of
studies of ETT is inextricably linked with Kharkov school of condensed matter
physics, with such names as I.M. Lifshitz, V.G.Bar'yakhtar and many other.
Among them is Moisey Isaakovich Kaganov, who contributed much in studies of the
role of geometry and topology of FS in physical properties of the metals. Two
of the authors (A.V. and Y.G.) had a honor and pleasure to work with "Musik",
as all friends called Kaganov; all of us have been learning the niceties of
science from his books. "The Fermi surface is the stage on which the drama of
the life of the electron is played out" wrote Kaganov and Lifshitz. We devote
this work to their memory.


Abstract:
  Here we provide the most comprehensive determinations of the rest-frame $UV$
LF available to date with HST at z~2, 3, 4, 5, 6, 7, 8, and 9. Essentially all
of the non-cluster extragalactic legacy fields are utilized, including the
Hubble Ultra Deep Field (HUDF), the Hubble Frontier Field parallel fields, and
all five CANDELS fields, for a total survey area of 1136 arcmin^2. Our
determinations include galaxies at z~2-3 leveraging the deep HDUV, UVUDF, and
ERS WFC3/UVIS observations available over a ~150 arcmin^2 area in the GOODS
North and GOODS South regions. All together, our collective samples include
>24,000 sources, >2.3x larger than previous selections with HST. 5766, 6332,
7240, 3449, 1066, 601, 246, and 33 sources are identified at z~2, 3, 4, 5, 6,
7, 8, and 9, respectively. Combining our results with an earlier z~10 LF
determination by Oesch+2018a, we quantify the evolution of the $UV$ LF. Our
results indicate that there is (1) a smooth flattening of the faint-end slope
alpha from alpha~-2.4 at z~10 to -1.5 at z~2, (2) minimal evolution in the
characteristic luminosity M* at z>~2.5, and (3) a monotonic increase in the
normalization log_10 phi* from z~10 to z~2, which can be well described by a
simple second-order polynomial, consistent with an "accelerated" evolution
scenario. We find that each of these trends (from z~10 to z~2.5 at least) can
be readily explained on the basis of the evolution of the halo mass function
and a simple constant star formation efficiency model.


Abstract:
  This paper is devoted to the analysis of Steklov eigenvalues and Steklov
eigenfunctions on a class of warped product Riemannian manifolds $(M,g)$ whose
boundary $\partial M$ consists in two distinct connected components $\Gamma_0$
and $\Gamma_1$. First, we show that the Steklov eigenvalues can be divided into
two families $(\lambda_m^\pm)_{m \geq 0}$ which satisfy accurate asymptotics as
$m \to \infty$. Second, we consider the associated Steklov eigenfunctions which
are the harmonic extensions of the boundary Dirichlet to Neumann
eigenfunctions. In the case of symmetric warped product, we prove that the
Steklov eigenfunctions are exponentially localized on the whole boundary
$\partial M$ as $m \to \infty$. Whenever we add an asymmetric perturbation to a
symmetric warped product, we observe a flea on the elephant effect. Roughly
speaking, we prove that "half" the Steklov eigenfunctions are exponentially
localized on one connected component of the boundary, say $\Gamma_0$, and the
other half on the other connected component $\Gamma_1$ as $m \to \infty$.


Abstract:
  Having numerous potential applications and great impact, end-to-end speech
translation (ST) has long been treated as an independent task, failing to fully
draw strength from the rapid advances of its sibling - text machine translation
(MT). With text and audio inputs represented differently, the modality gap has
rendered MT data and its end-to-end models incompatible with their ST
counterparts. In observation of this obstacle, we propose to bridge this
representation gap with Chimera. By projecting audio and text features to a
common semantic representation, Chimera unifies MT and ST tasks and boosts the
performance on ST benchmarks, MuST-C and Augmented Librispeech, to a new
state-of-the-art. Specifically, Chimera obtains 27.1 BLEU on MuST-C EN-DE,
improving the SOTA by a +1.9 BLEU margin. Further experimental analyses
demonstrate that the shared semantic space indeed conveys common knowledge
between these two tasks and thus paves a new way for augmenting training
resources across modalities. Code, data, and resources are available at
https://github.com/Glaciohound/Chimera-ST.


Abstract:
  The novel coronavirus disease (COVID-19) pneumonia has posed a great threat
to the world recent months by causing many deaths and enormous economic damage
worldwide. The first case of COVID-19 in Morocco was reported on 2 March 2020,
and the number of reported cases has increased day by day. In this work, we
extend the well-known SIR compartmental model to deterministic and stochastic
time-delayed models in order to predict the epidemiological trend of COVID-19
in Morocco and to assess the potential role of multiple preventive measures and
strategies imposed by Moroccan authorities. The main features of the work
include the well-posedness of the models and conditions under which the
COVID-19 may become extinct or persist in the population. Parameter values have
been estimated from real data and numerical simulations are presented for
forecasting the COVID-19 spreading as well as verification of theoretical
results.


Abstract:
  OCTO-TIGER is an astrophysics code to simulate the evolution of
self-gravitating and rotat-ing systems of arbitrary geometry based on the fast
multipole method, using adaptive mesh refinement. OCTO-TIGER is currently
optimised to simulate the merger of well-resolved stars that can be
approximated by barotropic structures, such as white dwarfs or main sequence
stars. The gravity solver conserves angular momentum to machine precision,
thanks to a correction algorithm. This code uses HPX parallelization, allowing
the overlap of work and communication and leading to excellent scaling
properties, allowing for the computation of large problems in reasonable
wall-clock times. In this paper, we investigate the code performance and
precision by running benchmarking tests. These include simple problems, such as
the Sod shock tube, as well as sophisticated, full, white-dwarf binary
simulations. Results are compared to analytic solutions, when known, and to
other grid based codes such as FLASH. We also compute the interaction between
two white dwarfs from the early mass transfer through to the merger and compare
with past simulations of similar systems. We measure OCTO-TIGERs scaling
properties up to a core count of 80,000, showing excellent performance for
large problems. Finally, we outline the current and planned areas of
development aimed at tackling a number of physical phenomena connected to
observations of transients.


Abstract:
  Debris disks are tenuous, dusty belts surrounding main sequence stars
generated by collisions between planetesimals. HD 206893 is one of only two
stars known to host a directly imaged brown dwarf orbiting interior to its
debris ring, in this case at a projected separation of 10.4 au. Here we resolve
structure in the debris disk around HD 206893 at an angular resolution of 0.6"
(24 au) and wavelength of 1.3 mm with the Atacama Large
Millimeter/submillimeter Array (ALMA). We observe a broad disk extending from a
radius of <51 au to 194^{+13}_{-2} au. We model the disk with a continuous,
gapped, and double power-law model of the surface density profile, and find
strong evidence for a local minimum in the surface density distribution near a
radius of 70 au, consistent with a gap in the disk with an inner radius of
63^{+8}_{-16} au and width 31^{+11}_{-7} au. Gapped structure has been observed
in four other debris disks -- essentially every other radially resolved debris
disk observed with sufficient angular resolution and sensitivity with ALMA --
and could be suggestive of the presence of an additional planetary-mass
companion.


Abstract:
  In conjunction with a previous southern-hemisphere work, we present the
largest radio survey of jets from massive protostars to date with
high-resolution, ($\sim 0.04^{\prime\prime}$) VLA observations towards two
subsamples of massive star-forming regions of different evolutionary statuses:
48 infrared-bright, massive YSOs and 8 IRDCs containing 16 luminous (${\rm
L_{bol}}>10^3\,{\rm L_\odot}$) cores. For $94\%$ of the MYSO sample we detect
thermal radio ($\alpha \geq -0.1$ whereby $S_\nu \propto \nu^\alpha$) sources
coincident with the protostar, of which $84\%$ (13 jets and 25 candidates) are
jet-like. Radio luminosity is found to scale with ${\rm L_{bol}}$ similarly to
the low-mass case supporting a common mechanism for jet production across all
masses. Associated radio lobes tracing shocks are seen towards $52\%$ of
jet-like objects and are preferentially detected towards jets of higher radio
and bolometric luminosities, resulting from our sensitivity limitations. We
find jet mass loss rate scales with bolometric luminosity as $\dot{m}_{\rm jet}
\propto {\rm L_{bol}}^{0.9\pm0.2}$, thereby discarding radiative, line-driving
mechanisms as the dominant jet-launching process. Calculated momenta show that
the majority of jets are mechanically capable of driving the massive, molecular
outflow phenomena since $p_{\rm jet}>p_{\rm outflow}$. Finally, from their
physical extent we show that the radio emission can not originate from small,
optically-thick \textsc{Hii} regions. Towards the IRDC cores, we observe
increasing incidence rates/radio fluxes with age using the proxy of increasing
luminosity-to-mass $\left( \frac{L}{M} \right)$ and decreasing infrared flux
ratios $\left(\frac{S_{70 \rm \mu m}}{S_{24 \rm \mu m}}\right)$. Cores with
$\frac{L}{M}<40\,{\rm L_{sol}}{\rm M_{sol}}^{-1}$ are not detected above
($5.8{\rm GHz}$) radio luminosities of $\sim1{\rm mJy\,kpc}^2$.


Abstract:
  We consider the problem of driving a finite-state classical system from some
initial distribution $p$ to some final distribution $p'$ with vanishing entropy
production (EP), under the constraint that the driving protocols can only use
some limited set of energy functions $\mathcal{E}$. Assuming no other
constraints on the driving protocol, we derive a simple condition that
guarantees that such a transformation can be carried out, which is stated in
terms of the smallest probabilities in $\{p,p'\}$ and a graph-theoretic
property defined in terms of $\mathcal{E}$. Our results imply that a
surprisingly small amount of control over the energy function is sufficient (in
particular, any transformation $p\to p'$ can be carried out as soon as one can
control some one-dimensional parameter of the energy function, e.g., the energy
of a single state). We also derive a lower bound on the EP under more general
constraints on the transition rates, which is formulated in terms of a convex
optimization problem.


Abstract:
  We benchmark the quantum processing units of the largest quantum annealers to
date, the 5000+ qubit quantum annealer Advantage and its 2000+ qubit
predecessor D-Wave 2000Q, using tail assignment and exact cover problems from
aircraft scheduling scenarios. The benchmark set contains small, intermediate,
and large problems with both sparsely connected and almost fully connected
instances. We find that Advantage outperforms D-Wave 2000Q for almost all
problems, with a notable increase in success rate and problem size. In
particular, Advantage is also able to solve the largest problems with 120
logical qubits that D-Wave 2000Q cannot solve anymore. Furthermore, problems
that can still be solved by D-Wave 2000Q are solved faster by Advantage. We
find that D-Wave 2000Q can only achieve better success rates for a few very
sparsely connected problems.


Abstract:
  This document describes the Generalized Moving Peaks Benchmark (GMPB) that
generates continuous dynamic optimization problem instances. The landscapes
generated by GMPB are constructed by assembling several components with a
variety of controllable characteristics ranging from unimodal to highly
multimodal, symmetric to highly asymmetric, smooth to highly irregular, and
various degrees of variable interaction and ill-conditioning. In this document,
we explain how these characteristics can be generated by different parameter
settings of GMPB. The MATLAB source code of GMPB is also explained.


Abstract:
  The Dicke superradiance from an optically thin nanocomposite slab represented
by metal nanoparticles dispersed in a dielectric matrix is predicted and its
theory is developed from first principles. It is shown that the superradiance
signal evolution is determined by the eigenvalues of the field susceptibility
tensor for the slab. The excitation of the system by a pumping pulse in
different polarizations as well as in the attenuated total reflection
configuration is considered. It is demonstrated that the relaxation rates are
enhanced when surface plasmon polaritons are excited at the interface between
the substrate and superstrate. These findings can pave a way to the extension
of the optical techniques based on the Dicke superradiance to the femtosecond
time domain.


Abstract:
  The classical nova YZ Reticuli was discovered in July 2020. Shortly after
this we commenced a sustained, highly time-sampled coverage of its subsequent
rapid evolution with time-resolved spectroscopy from the Global Jet Watch
observatories. Its H-alpha complex exhibited qualitatively different spectral
signatures in the following weeks and months. We find that these H-alpha
complexes are well described by the same five Gaussian emission components
throughout the six months following eruption. These five components appear to
constitute two pairs of lines, from jet outflows and an accretion disc,
together with an additional central component. The correlated, symmetric
patterns that these jet/accretion disc pairs exhibit suggest precession,
probably in response to the large perturbation caused by the nova eruption. The
jet and accretion disc signatures persist from the first ten days after
brightening -- evidence that the accretion disc survived the disruption. We
also compare another classical nova (V6568 Sgr) that erupted in July 2020 whose
H-alpha complex can be described analogously, but with faster line-of-sight jet
speeds exceeding 4000 km/s. We suggest that classical novae with higher mass
white dwarfs bridge the gap between recurrent novae and classical novae such as
YZ Reticuli.


Abstract:
  Training deep neural networks on large datasets containing high-dimensional
data requires a large amount of computation. A solution to this problem is
data-parallel distributed training, where a model is replicated into several
computational nodes that have access to different chunks of the data. This
approach, however, entails high communication rates and latency because of the
computed gradients that need to be shared among nodes at every iteration. The
problem becomes more pronounced in the case that there is wireless
communication between the nodes (i.e. due to the limited network bandwidth). To
address this problem, various compression methods have been proposed including
sparsification, quantization, and entropy encoding of the gradients. Existing
methods leverage the intra-node information redundancy, that is, they compress
gradients at each node independently. In contrast, we advocate that the
gradients across the nodes are correlated and propose methods to leverage this
inter-node redundancy to improve compression efficiency. Depending on the node
communication protocol (parameter server or ring-allreduce), we propose two
instances of the LGC approach that we coin Learned Gradient Compression (LGC).
Our methods exploit an autoencoder (i.e. trained during the first stages of the
distributed training) to capture the common information that exists in the
gradients of the distributed nodes. We have tested our LGC methods on the image
classification and semantic segmentation tasks using different convolutional
neural networks (ResNet50, ResNet101, PSPNet) and multiple datasets (ImageNet,
Cifar10, CamVid). The ResNet101 model trained for image classification on
Cifar10 achieved an accuracy of 93.57%, which is lower than the baseline
distributed training with uncompressed gradients only by 0.18%.


Abstract:
  By considering the tiling of an $N$-board (a linear array of $N$ square cells
of unit width) with new types of tile that we refer to as combs, we give a
combinatorial interpretation of the product of two consecutive generalized
Fibonacci numbers $s_n$ (where $s_{n}=\sum_{i=1}^q v_i s_{n-m_i}$, $s_0=1$,
$s_{n<0}=0$, where $v_i$ and $m_i$ are positive integers and $m_1<\cdots<m_q$)
each raised to an arbitrary non-negative integer power. A $(w,g;m)$-comb is a
tile composed of $m$ rectangular sub-tiles of dimensions $w\times1$ separated
by gaps of width $g$. The interpretation is used to give combinatorial proof of
new convolution-type identities relating $s_n^2$ for the cases $q=2$, $v_i=1$,
$m_1=M$, $m_2=m+1$ for $M=0,m$ to the permanent of a (0,1) Toeplitz matrix with
3 nonzero diagonals which are $-2$, $M-1$, and $m$ above the leading diagonal.
When $m=1$ these identities reduce to ones connecting the Padovan and
Narayana's cows numbers.


Abstract:
  In this work, we study two different approaches to defining the entropy of a
quantum channel. One of these is based on the von Neumann entropy of the
corresponding Choi-Jamio{\l}kowski state. The second one is based on the
relative entropy of the output of the extended channel relative to the output
of the extended completely depolarizing channel. This entropy then needs to be
optimized over all possible input states. Our results first show that the
former entropy provides an upper bound on the latter. Next, we show that for
unital qubit channels, this bound is saturated. Finally, we conjecture and
provide numerical intuitions that the bound can also be saturated for random
channels as their dimension tends to infinity.


Abstract:
  In 2021, Japan recorded more than three times as much snowfall as usual, so
road user maybe come across dangerous situation. The poor visibility caused by
snow triggers traffic accidents. For example, 2021 January 19, due to the dry
snow and the strong wind speed of 27 m / s, blizzards occurred and the outlook
has been ineffective. Because of the whiteout phenomenon, multiple accidents
with 17 casualties occurred, and 134 vehicles were stacked up for 10 hours over
1 km. At the night time zone, the temperature drops and the road surface tends
to freeze. CCTV images on the road surface have the advantage that we enable to
monitor the status of major points at the same time. Road managers are required
to make decisions on road closures and snow removal work owing to the road
surface conditions even at night. In parallel, they would provide road users to
alert for hazardous road surfaces. This paper propose a method to automate a
snow hazard indicator that the road surface region is generated from the night
snow image using the Conditional GAN, pix2pix. In addition, the road surface
and the snow covered ROI are predicted using the semantic segmentation
DeepLabv3+ with a backbone MobileNet, and the snow hazard indicator to
automatically compute how much the night road surface is covered with snow. We
demonstrate several results applied to the cold and snow region in the winter
of Japan January 19 to 21 2021, and mention the usefulness of high similarity
between snowy night-to-day fake output and real snowy day image for night snow
visibility.


Abstract:
  Star clusters form via clustering star formation inside molecular clouds. In
order to understand the dynamical evolution of star clusters in their early
phase, in which star clusters are still embedded in their surrounding gas, we
need an accurate integration of individual stellar orbits without gravitational
softening in the systems including both gas and stars, as well as modeling
individual stars with a realistic mass function. We develop a new tree-direct
hybrid smoothed particle hydrodynamics/N-body code, ASURA+BRIDGE, in which
stars are integrated using a direct N-body scheme or PeTar, a particle-particle
particle-tree scheme code, without gravitational softening. In ASURA+BRIDGE,
stars are assumed to have masses randomly drawn from a given initial mass
function. With this code, we perform star-cluster formation simulations
starting from molecular clouds without gravitational softening. We find that
artificial dense cores in star-cluster centers due to the softening disappear
when we do not use softening. We further demonstrate that star clusters are
built up via mergers of smaller clumps. Star clusters formed in our simulations
include some dynamically formed binaries with the minimum semi-major axes of a
few au, and the binary fraction is higher for more massive stars.


Abstract:
  Image harmonization has been significantly advanced with large-scale
harmonization dataset. However, the current way to build dataset is still
labor-intensive, which adversely affects the extendability of dataset. To
address this problem, we propose to construct a large-scale rendered
harmonization dataset RHHarmony with fewer human efforts to augment the
existing real-world dataset. To leverage both real-world images and rendered
images, we propose a cross-domain harmonization network CharmNet to bridge the
domain gap between two domains. Moreover, we also employ well-designed style
classifiers and losses to facilitate cross-domain knowledge transfer. Extensive
experiments demonstrate the potential of using rendered images for image
harmonization and the effectiveness of our proposed network. Our dataset and
code are available at
https://github.com/bcmi/Rendered_Image_Harmonization_Datasets.


Abstract:
  The human aorta is a high-risk area for vascular diseases, which are commonly
restored by thoracic endovascular aortic repair. In this paper, we report a
promising shear-activated targeted nanoparticle drug delivery strategy to
assist in the treatment of coarctation of the aorta and aortic aneurysm.
Idealized three-dimensional geometric models of coarctation of the aorta and
aortic aneurysm are designed, respectively. The unique hemodynamic environment
of the diseased aorta is used to improve nanoparticle drug delivery.
Micro-carriers with nanoparticle drugs would be targeting activated to release
nanoparticle drugs by local abnormal shear stress rate (SSR). Coarctation of
the aorta provides a high SSR hemodynamic environment, while the aortic
aneurysm is exposed to low SSR. We propose a method to calculate the SSR
thresholds for the diseased aorta. Results show that the upstream near-wall
area of the diseased location is an ideal injection location for the
micro-carriers, which could be activated by the abnormal SSR. Released
nanoparticle drugs would be successfully targeted delivered to the aortic
diseased wall. Besides, the high diffusivity of the micro-carriers and
nanoparticle drugs has a significant impact on the surface drug concentrations
of the diseased aortic walls, especially for aortic aneurysms. This study
preliminary demonstrates the feasibility of shear-activated targeted
nanoparticle drug delivery in the treatment of aortic diseases and provides a
theoretical basis for developing the drug delivery system and novel therapy.


Abstract:
  Aerodynamic forces render accurate high-speed trajectory tracking with
quadrotors extremely challenging. These complex aerodynamic effects become a
significant disturbance at high speeds, introducing large positional tracking
errors, and are extremely difficult to model. To fly at high speeds, feedback
control must be able to account for these aerodynamic effects in real-time.
This necessitates a modelling procedure that is both accurate and efficient to
evaluate. Therefore, we present an approach to model aerodynamic effects using
Gaussian Processes, which we incorporate into a Model Predictive Controller to
achieve efficient and precise real-time feedback control, leading to up to 70%
reduction in trajectory tracking error at high speeds. We verify our method by
extensive comparison to a state-of-the-art linear drag model in synthetic and
real-world experiments at speeds of up to 14m/s and accelerations beyond 4g.


Abstract:
  We address the question of whether a non-nomological (i.e., anomic)
interpretation of the wavefunction is compatible with the quantum formalism.
After clarifying the distinction between ontic, epistemic, nomic and anomic
models we focus our attention on two famous no-go theorems due to Pusey,
Barrett, and Rudolph (PBR) on the one side and Hardy on the other side which
forbid the existence of anomic-epistemic models. Moreover, we demonstrate that
the so called restricted ontic indifference introduced by Hardy induces new
constraints. We show that after modifications the Hardy theorem actually rules
out all anomic models of the wavefunction assuming only restricted ontic
indifference and preparation independence.


Abstract:
  The investigation of evaporating liquid drop deposited onto a flat surface is
of great importance for physical, engineering and medical applications. Novel
analytical expressions are proposed to calculate the evaporation rate of
sessile drop (mass loss per unit surface area per unit time) and total
evaporation rate (mass loss per unit time). To obtain these results, the H.M.
Macdonald's solution for a flat wedge was transformed by method of inversion in
a sphere originally developed by J.C. Maxwell in the Treatise on Electricity
and Magnetism with further derivation the solution for a lens based on
consideration given in bipolar coordinates by G.A. Grinberg. These solutions
are mathematically equivalent to expressions proposed earlier by Yuri O. Popov
[Phys. Rev. E 71, 036313 (2005)], but, in some cases, probably, the new
solutions can be more useful from a computational point of view.


Abstract:
  The ex-ante evaluation of policies using structural econometric models is
based on estimated parameters as a stand-in for the true parameters. This
practice ignores uncertainty in the counterfactual policy predictions of the
model. We develop a generic approach that deals with parametric uncertainty
using uncertainty sets and frames model-informed policy-making as a decision
problem under uncertainty. The seminal human capital investment model by Keane
and Wolpin (1997) provides a well-known, influential, and empirically-grounded
test case. We document considerable uncertainty in the models's policy
predictions and highlight the resulting policy recommendations obtained from
using different formal rules of decision-making under uncertainty.


Abstract:
  Consider two independent Poisson point processes of unit intensity in the
Euclidean space of dimension $d$ at least 3. We construct a perfect matching
between the two point sets that is a factor (i.e., an equivariant measurable
function of the point configurations), and with the property that the distance
between a configuration point and its pair has a tail distribution that decays
as fast as possible, namely, as $b\exp (-cr^d)$ with suitable constants
$b,c>0$. Our proof relies on two earlier results: an allocation rule of similar
tail for a Poisson point process, and a recent theorem that enables one to
obtain perfect matchings from fractional perfect matchings in our setup.


Abstract:
  We propose a parameter sharing method for Transformers (Vaswani et al.,
2017). The proposed approach relaxes a widely used technique, which shares
parameters for one layer with all layers such as Universal Transformers
(Dehghani et al., 2019), to increase the efficiency in the computational time.
We propose three strategies: Sequence, Cycle, and Cycle (rev) to assign
parameters to each layer. Experimental results show that the proposed
strategies are efficient in the parameter size and computational time.
Moreover, we indicate that the proposed strategies are also effective in the
configuration where we use many training data such as the recent WMT
competition.


Abstract:
  In inland fisheries, transporting fishery resource individuals from a habitat
to spatially apart habitat(s) has recently been considered for fisheries stock
management in the natural environment. However, its mathematical optimization,
especially finding when and how much of the population should be transported,
is still a fundamental unresolved issue. We propose a new impulse control
framework to tackle this issue based on a simple but new stochastic growth
model of individual fishes. The novel growth model governing individuals' body
weights uses a Wright-Fisher model as a latent driver to reproduce plausible
growth dynamics. The optimization problem is formulated as an impulse control
problem of a cost-benefit functional constrained by a degenerate parabolic
Fokker-Planck equation of the stochastic growth dynamics. Because the growth
dynamics have an observable variable and an unobservable variable (a variable
difficult or impossible to observe), we consider both full-information and
partial-information cases. The latter is more involved but more realistic
because of not explicitly using the unobservable variable in designing the
controls. In both cases, resolving an optimization problem reduces to solving
the associated Fokker-Planck and its adjoint equations, the latter being
non-trivial. We present a derivation procedure of the adjoint equation and its
internal boundary conditions in time to efficiently derive the optimal
transporting strategy. We finally provide a demonstrative computational example
of a transporting problem of Ayu sweetfish (Plecoglossus altivelis altivelis)
based on the latest real data set.