Abstract:
  This paper considers the equilibrium positions of $n$ particles in one
dimension. Two forces act on the particles; a nonlocal repulsive
particle-interaction force and an external force which pushes them to an
impenetrable barrier. While the continuum limit as $n \to \infty$ is known for
a certain class of potentials, numerical simulations show that a discrete
boundary layer appears at the impenetrable barrier, i.e. the positions of
$o(n)$ particles do not fit to the particle density predicted by the continuum
limit. In this paper we establish a first-order $\Gamma$-convergence result
which guarantees that these $o(n)$ particles converge to a specific continuum
boundary-layer profile.


Abstract:
  Priors allow us to robustify inference and to incorporate expert knowledge in
Bayesian hierarchical models. This is particularly important when there are
random effects that are hard to identify based on observed data. The challenge
lies in understanding and controlling the joint influence of the priors for the
variance parameters, and makemyprior is an R package that guides the
formulation of joint prior distributions for variance parameters. A joint prior
distribution is constructed based on a hierarchical decomposition of the total
variance in the model along a tree, and takes the entire model structure into
account. Users input their prior beliefs or express ignorance at each level of
the tree. Prior beliefs can be general ideas about reasonable ranges of
variance values and need not be detailed expert knowledge. The constructed
priors lead to robust inference and guarantee proper posteriors. A graphical
user interface facilitates construction and assessment of different choices of
priors through visualization of the tree and joint prior. The package aims to
expand the toolbox of applied researchers and make priors an active component
in their Bayesian workflow.


Abstract:
  Consider a nonlinear wave equation for a massless scalar field with
self-interaction in the spatially flat Friedmann-Lema\^{i}tre-Robertson-Walker
spacetimes. For the case of accelerated expansion, we show that blow-up in a
finite time occurs for the equation with arbitrary power nonlinearity as well
as upper bounds of the lifespan of blow-up solutions. Comparing to the case of
the Minkowski spacetime, we discuss how the scale factor affects the lifespan
of blow-up solutions of the equation.


Abstract:
  In a geographically distributed population, assortative clustering plays an
important role in evolution by modifying local environments. To examine its
effects in a linear habitat, we consider a one-dimensional grid of cells, where
each cell is either empty or occupied by an organism whose replication strategy
is genetically inherited to offspring. The strategy determines whether to have
offspring in surrounding cells, as a function of the neighborhood
configuration. If more than one offspring compete for a cell, then they can be
all exterminated due to the cost of conflict depending on environmental
conditions. We find that the system is more densely populated in an unfavorable
environment than in a favorable one because only the latter has to pay the cost
of conflict. This observation agrees reasonably well with a mean-field analysis
which takes assortative clustering of strategies into consideration. Our
finding suggests a possibility of intrinsic nonlinearity between environmental
conditions and population density when an evolutionary process is involved.


Abstract:
  Ultrahigh repetition rate lasers will become vital light sources for many
future technologies; however, their realization is challenging because the
cavity size must be minimized. Whispering-gallery-mode (WGM) microresonators
are attractive for this purpose since they allow the strong light-matter
interaction usually needed to enable mode-locking. However, the optimum
parameter ranges are entirely unknown since no experiments have yet been
conducted. Here, we numerically investigate pulsed operation in a toroidal WGM
microresonator with gain and saturable absorption (SA) to study the
experimental feasibility. We show that dispersion is the key parameter for
achieving passive mode-locking in this system. Moreover, the design guideline
provided in this work can apply to any small resonators with gain and SA and is
not limited to a specific cavity system.


Abstract:
  Density functional theory (DFT) based modeling of electronic excited states
is of importance for investigation of the photophysical/photochemical
properties and spectroscopic characterization of large systems. The widely used
linear response time-dependent DFT (TDDFT) approach is however not effective at
modeling many types of excited states, including (but not limited to)
charge-transfer states, doubly excited states and core-level excitations. In
this perspective, we discuss state-specific orbital optimized (OO) DFT
approaches as an alterative to TDDFT for electronic excited states. We motivate
the use of OO-DFT methods and discuss reasons behind their relatively
restricted historical usage (vs TDDFT). We subsequently highlight modern
developments that address these factors and allow efficient and reliable OO-DFT
computations. Several successful applications of OO-DFT for challenging
electronic excitations are also presented, indicating their practical efficacy.
OO-DFT approaches are thus increasingly becoming a useful route for computing
excited states of large chemical systems. We conclude by discussing the
limitations and challenges still facing OO-DFT methods, as well as some
potential avenues for addressing them.


Abstract:
  Executing scientific workflows with heterogeneous tasks on HPC platforms
poses several challenges which will be further exacerbated by the upcoming
exascale platforms. At that scale, bespoke solutions will not enable effective
and efficient workflow executions. In preparation, we need to look at ways to
manage engineering effort and capability duplication across software systems by
integrating independently developed, production-grade software solutions. In
this paper, we integrate RADICAL-Pilot (RP) and Parsl and develop an MPI
executor to enable the execution of workflows with heterogeneous (non)MPI
Python functions at scale. We characterize the strong and weak scaling of the
integrated RP-Parsl system when executing two use cases from polar science, and
of the function executor on both SDSC Comet and TACC Frontera. We gain
engineering insight about how to analyze and integrate workflow and runtime
systems, minimizing changes in their code bases and overall development effort.
Our experiments show that the overheads of the integrated system are invariant
of resource and workflow scale, and measure the impact of diverse MPI
overheads. Together, those results define a blueprint towards an ecosystem
populated by specialized, efficient, effective and independently-maintained
software systems to face the upcoming scaling challenges.


Abstract:
  This paper proposes novel end-to-end framework for detecting suspicious
pulmonary nodules in chest CT scans. The method core idea is a new nodule
segmentation architecture with a model-based feature projection block on
three-dimensional convolutions. This block acts as a preliminary feature
extractor for a two-dimensional U-Net-like convolutional network. Using the
proposed approach along with an axial, coronal, and sagittal projection
analysis makes it possible to abandon the widely used false positives reduction
step. The proposed method achieves SOTA on LUNA2016 with 0.959 average
sensitivity, and 0.936 sensitivity if the false-positive level per scan is
0.25. The paper describes the proposed approach and represents the experimental
results on LUNA2016 as well as ablation studies.


Abstract:
  Pruning the weights of randomly initialized neural networks plays an
important role in the context of lottery ticket hypothesis. Ramanujan et al.
(2020) empirically showed that only pruning the weights can achieve remarkable
performance instead of optimizing the weight values. However, to achieve the
same level of performance as the weight optimization, the pruning approach
requires more parameters in the networks before pruning and thus more memory
space. To overcome this parameter inefficiency, we introduce a novel framework
to prune randomly initialized neural networks with iteratively randomizing
weight values (IteRand). Theoretically, we prove an approximation theorem in
our framework, which indicates that the randomizing operations are provably
effective to reduce the required number of the parameters. We also empirically
demonstrate the parameter efficiency in multiple experiments on CIFAR-10 and
ImageNet.


Abstract:
  In this paper, we use the Carnot-Caratheodory distance from sub-Riemanian
geometry to prove entropy decay estimates for all finite dimensional symmetric
quantum Markov semigroups. This estimate is independent of the environment size
and hence stable under tensorization. Our approach relies on the transference
principle, the existence of $t$-designs, and the sub-Riemannian diameter of
compact Lie groups and implies estimates for the spectral gap.


Abstract:
  We study online Multi-Agent Path Finding (MAPF), where new agents are
constantly revealed over time and all agents must find collision-free paths to
their given goal locations. We generalize existing complexity results of
(offline) MAPF to online MAPF. We classify online MAPF algorithms into
different categories based on (1) controllability (the set of agents that they
can plan paths for at each time) and (2) rationality (the quality of paths they
plan) and study the relationships between them. We perform a competitive
analysis for each category of online MAPF algorithms with respect to
commonly-used objective functions. We show that a naive algorithm that routes
newly-revealed agents one at a time in sequence achieves a competitive ratio
that is asymptotically bounded from both below and above by the number of
agents with respect to flowtime and makespan. We then show a counter-intuitive
result that, if rerouting of previously-revealed agents is not allowed, any
rational online MAPF algorithms, including ones that plan optimal paths for all
newly-revealed agents, have the same asymptotic competitive ratio as the naive
algorithm, even on 2D 4-neighbor grids. We also derive constant lower bounds on
the competitive ratio of any rational online MAPF algorithms that allow
rerouting. The results thus provide theoretical insights into the effectiveness
of using MAPF algorithms in an online setting for the first time.


Abstract:
  In this paper, we use the first-principles calculations based on the density
functional theory to investigate structural, electronic and magnetic properties
of Fe$_{2}$YSn with (Y = Mn, Ti and V). The generalized gradient approximation
(GGA) method is used for calculations. The Cu$_{2}$MnAl type structure is
energetically more stable than the Hg$_{2}$CuTi type structure. The negative
formation energy is shown as the evidence of thermodynamic stability of the
alloy. The calculated total spin moment is found as 3$\mu_\text{B}$ and
0$\mu_\text{B}$ at the equilibrium lattice constant for Fe$_{2}$MnSn and
Fe$_{2}$TiSn respectively, which agrees with the Slater-Pauling rule of $M_t=
Z_t-24$. The study of electronic and magnetic properties proves that
Fe$_{2}$MnSn and Fe$_{2}$TiSn full-Heusler alloys are complete half-metallic
ferromagnetic materials.


Abstract:
  The different thermo-elastic properties of glass fibers and polymer matrices
can generate residual thermal stresses in injection-molded fiber-reinforced
plastic (FRP) objects. During cooling from mold to room temperature, these
stresses can be relaxed by large deformations resulting from an instability of
the unwarped configuration (i.e., buckling). This article investigates the
thermal buckling of thin FRP disks via an analytical formulation based on the
Foppl-von Karman theory. Expanding on our previous work, cylindrical orthotropy
with material parameters varying over the disk thickness is assumed in order to
account for thickness dependency of the glass fiber orientation distribution. A
disk parameter generalizing the thermal anisotropy ratio for homogeneous
orthotropic disks is introduced and its relation with the occurrence and
periodicity of buckling is discussed. This is done for a skin-coreskin model,
for which the core-to-total thickness ratio is defined. For fiber orientation
distributions typical of injection-molded disks, it is found that there exists
a value of the thickness ratio for which no buckling occurs. It is also
demonstrated that the periodicity of the first buckling mode is described by
the generalized thermal anisotropy ratio, thus extending the results obtained
for a homogeneous fiber orientation distribution. Improvements in the accuracy
of the predictions for experimental data available in the literature when using
the skin-core-skin model are shown. Finally, we study the relation between
buckling temperature and disk thickness and propose an expression for the
dependence of the normalized buckling temperature on the thermal anisotropy
ratio. Results of FEM simulations are used to validate the proposed expression,
proving its applicability and accuracy.


Abstract:
  Neural networks serve as effective controllers in a variety of complex
settings due to their ability to represent expressive policies. The complex
nature of neural networks, however, makes their output difficult to verify and
predict, which limits their use in safety-critical applications. While
simulations provide insight into the performance of neural network controllers,
they are not enough to guarantee that the controller will perform safely in all
scenarios. To address this problem, recent work has focused on formal methods
to verify properties of neural network outputs. For neural network controllers,
we can use a dynamics model to determine the output properties that must hold
for the controller to operate safely. In this work, we develop a method to use
the results from neural network verification tools to provide probabilistic
safety guarantees on a neural network controller. We develop an adaptive
verification approach to efficiently generate an overapproximation of the
neural network policy. Next, we modify the traditional formulation of Markov
decision process (MDP) model checking to provide guarantees on the
overapproximated policy given a stochastic dynamics model. Finally, we
incorporate techniques in state abstraction to reduce overapproximation error
during the model checking process. We show that our method is able to generate
meaningful probabilistic safety guarantees for aircraft collision avoidance
neural networks that are loosely inspired by Airborne Collision Avoidance
System X (ACAS X), a family of collision avoidance systems that formulates the
problem as a partially observable Markov decision process (POMDP).


Abstract:
  We study four-point functions of scalars, conserved currents, and stress
tensors in a conformal field theory, generated by a local contact term in the
bulk dual description, in two different causal configurations. The first of
these is the standard Regge configuration in which the chaos bound applies. The
second is the `causally scattering configuration' in which the correlator
develops a bulk point singularity. We find an expression for the coefficient of
the bulk point singularity in terms of the bulk S matrix of the bulk dual
metric, gauge fields and scalars, and use it to determine the Regge scaling of
the correlator on the causally scattering sheet in terms of the Regge growth of
this S matrix. We then demonstrate that the Regge scaling on this sheet is
governed by the same power as in the standard Regge configuration, and so is
constrained by the chaos bound, which turns out to be violated unless the bulk
flat space S matrix grows no faster than $s^2$ in the Regge limit. It follows
that in the context of the AdS/CFT correspondence, the chaos bound applied to
the boundary field theory implies that the S matrices of the dual bulk scalars,
gauge fields, and gravitons obey the Classical Regge Growth (CRG) conjecture.


Abstract:
  This paper deals with data-driven output synchronization for heterogeneous
leader-follower linear multi-agent systems. Given a multi-agent system that
consists of one autonomous leader and a number of heterogeneous followers with
external disturbances, we provide necessary and sufficient data-based
conditions for output synchronization. We also provide a design method for
obtaining such output synchronizing protocols directly from data. The results
are then extended to the special case that the followers are disturbance-free.
Finally, a simulation example is provided to illustrate our results.


Abstract:
  In machine translation field, in both academia and industry, there is a
growing interest in increasingly powerful systems, using corpora of several
hundred million to several billion examples. These systems represent the
state-of-the-art. Here we defend the idea of developing in parallel <<frugal>>
bilingual translation systems, trained with relatively small corpora. Based on
the observation of a standard human professional translator, we estimate that
the corpora should be composed at maximum of a monolingual sub-corpus of 75
million examples for the source language, a second monolingual sub-corpus of 6
million examples for the target language, and an aligned bilingual sub-corpus
of 6 million bi-examples. A less desirable alternative would be an aligned
bilingual corpus of 47.5 million bi-examples.


Abstract:
  We survey the area of strongly regular graphs satisfying the 4-vertex
condition and find several new families. We describe a switching operation on
collinearity graphs of polar spaces that produces cospectral graphs. The
obtained graphs satisfy the 4-vertex condition if the original graph belongs to
a symplectic polar space.


Abstract:
  It is well known that two-sided markets are unfair in a number of ways. For
instance, female workers at Uber earn less than their male colleagues per mile
driven. Similar observations have been made for other minority subgroups in
other two-sided markets. Here, we suggest a novel market-clearing mechanism for
two-sided markets, which promotes equalisation of the pay per hour worked
across multiple subgroups, as well as within each subgroup. In the process, we
introduce a novel notion of subgroup fairness (which we call Inter-fairness),
which can be combined with other notions of fairness within each subgroup
(called Intra-fairness), and the utility for the customers (Customer-Care) in
the objective of the market-clearing problem. While the novel non-linear terms
in the objective complicate market clearing by making the problem non-convex,
we show that a certain non-convex augmented Lagrangian relaxation can be
approximated to any precision in time polynomial in the number of market
participants using semi-definite programming. This makes it possible to
implement the market-clearing mechanism efficiently. On the example of
driver-ride assignment in an Uber-like system, we demonstrate the efficacy and
scalability of the approach, and trade-offs between Inter- and Intra-fairness.


Abstract:
  We report the discovery of a bright, compact ultraviolet source at a
projected separation of 1.1~kpc from the known active galactic nucleus (AGN) in
Mrk~766 based on Astrosat/UVIT observations. We perform radial profile analysis
and derive the UV flux almost free from the nearby contaminating sources. The
new source is about 2.5 and 5.6 times fainter than the AGN in the far and near
UV bands. The two sources appear as a pair of nuclei in Mrk~766. We investigate
the nature of the new source based on the UV flux ratio, X-ray and optical
emission. The new source is highly unlikely to be another accreting
supermassive black hole in Mrk~766 as it lacks X-ray emission. We find that the
UV/Optical flux of the new source measured at four different bands closely
follow the shape of the template spectrum of starburst galaxies. This strongly
suggests that the new source is a compact star-forming region.


Abstract:
  Tate-Hochschild cohomology of an algebra is a generalization of ordinary
Hochschild cohomology, which is defined on positive and negative degrees and
has a ring structure. Our purpose of this paper is to study the eventual
periodicity of an algebra by using the Tate-Hochschild cohomology ring. First,
we deal with eventually periodic algebras and show that they are not
necessarily Gorenstein algebras. Secondly, we characterize the eventual
periodicity of a Gorenstein algebra as the existence of an invertible
homogeneous element of the Tate-Hochschild cohomology ring of the algebra,
which is our main result. Finally, we use tensor algebras to establish a way of
constructing eventually periodic Gorenstein algebras.


Abstract:
  The chiral anomaly is a fundamental quantum mechanical phenomenon which is of
great importance to both particle physics and condensed matter physics alike.
In the context of QED it manifests as the breaking of chiral symmetry in the
presence of electromagnetic fields. It is also known that anomalous chiral
symmetry breaking can occur through interactions alone, as is the case for
interacting one dimensional systems. In this paper we investigate the interplay
between these two modes of anomalous chiral symmetry breaking in the context of
interacting Weyl semimetals. Using Fujikawa's path integral method we show that
the chiral charge continuity equation is modified by the presence of
interactions which can be viewed as including the effect of the electric and
magnetic fields generated by the interacting quantum matter. This can be
understood further using dimensional reduction and a Luttinger liquid
description of the lowest Landau level. These effects manifest themselves in
the non-linear response of the system. In particular we find an interaction
dependent density response due to a change in the magnetic field as well as a
contribution to the non-equilibrium and inhomogeneous anomalous Hall response
while preserving its equilibrium value.


Abstract:
  We study a class of perturbative scalar quantum field theories where dynamics
is characterized by Lorentz-invariant or Lorentz-breaking non-local operators
of fractional order and the underlying spacetime has a varying spectral
dimension. These theories are either ghost free or power-counting
renormalizable but they cannot be both at the same time. However, some of them
are one-loop unitary and finite, and possibly unitary and finite at all orders.


Abstract:
  In this note we prove a sharp lower bound on the necessary number of nestings
of nested absolute-value functions of generalized hinging hyperplanes (GHH) to
represent arbitrary CPWL functions. Previous upper bound states that $n+1$
nestings is sufficient for GHH to achieve universal representation power, but
the corresponding lower bound was unknown. We prove that $n$ nestings is
necessary for universal representation power, which provides an almost tight
lower bound. We also show that one-hidden-layer neural networks don't have
universal approximation power over the whole domain. The analysis is based on a
key lemma showing that any finite sum of periodic functions is either
non-integrable or the zero function, which might be of independent interest.


Abstract:
  Purpose: To develop a knowledge-based voxel-wise dose prediction system using
a convolution neural network for high-dose-rate brachytherapy cervical cancer
treatments with a tandem-and-ovoid (T&O) applicator. Methods: A 3D U-NET was
utilized to output dose predictions using organ-at-risk (OAR), high-risk
clinical target volume (HRCTV), and possible source locations. A sample of
previous T&O treatments comprising 397 cases (273 training:62 validation:62
test), HRCTV and OARs (bladder/rectum/sigmoid) was used. Structures and dose
were interpolated to 1x1x2.5mm3 dose planes with two input channels (source
positions, voxel identification) and one output channel for dose. We evaluated
dose difference (\Delta D)(xyz)=D_(actual)(x,y,z)-D_(predicted)(x,y,z) and dice
similarity coefficients in all cohorts across the clinically-relevant dose
range (20-130% of prescription), mean and standard deviation. We also examined
discrete DVH metrics used for T&O plan quality assessment: HRCTV D_90%(dose to
hottest 90% volume) and OAR D_2cc, with \Delta
D_x=D_(x,actual)-D_(x,predicted). Pearson correlation coefficient, standard
deviation, and mean quantified model performance on the clinical metrics.
Results: Voxel-wise dose difference accuracy for 20-130% dose range for
training(test) ranges for mean (\Delta D) and standard deviation for all voxels
was [-0.3%+/-2.0% to +1.0%+/-12.0%] ([-0.1%+/-4% to +4.0%+/-26%]). Voxel-wise
dice similarity coefficients for 20-130% dose ranged from [0.96, 0.91]([0.94,
0.87]). DVH metric prediction in the training (test) set were HRCTV(\Delta
D_90)=-0.19+/-0.55 Gy (-0.09+/-0.67 Gy), bladder(\Delta D_2cc)=-0.06+/-0.54 Gy
(-0.17+/-0.67 Gy), rectum(\Delta D)_2cc=-0.03+/-0.36 Gy (-0.04+/-0.46 Gy), and
sigmoid(\Delta D_2cc)=-0.01+/-0.34 Gy (0.00+/-0.44 Gy). Conclusion: 3D
knowledge-based dose predictions for T&O brachytherapy provide accurate
voxel-level and DVH metric estimates.


Abstract:
  Using data samples collected with the BESIII detector operating at the BEPCII
storage ring at center-of-mass energies from 4.178 to 4.600 GeV, we study the
process $e^+e^-\rightarrow\pi^{0}X(3872)\gamma$ and search for
$Z_c(4020)^{0}\rightarrow X(3872)\gamma$. We find no significant signal and set
upper limits on
$\sigma(e^+e^-\rightarrow\pi^{0}X(3872)\gamma)\cdot\mathcal{B}(X(3872)\rightarrow\pi^{+}\pi^{-}J/\psi)$
and
$\sigma(e^+e^-\rightarrow\pi^{0}Z_c(4020)^{0})\cdot\mathcal{B}(Z_c(4020)^{0}\rightarrow
X(3872)\gamma)\cdot\mathcal{B}(X(3872)\rightarrow\pi^{+}\pi^{-}J/\psi)$ for
each energy point at $90\%$ confidence level, which is of the order of several
tenths pb.


Abstract:
  We study some sum-product problems over matrix rings. Firstly, for $A, B,
C\subseteq M_n(\mathbb{F}_q)$, we have $$ |A+BC|\gtrsim q^{n^2}, $$ whenever
$|A||B||C|\gtrsim q^{3n^2-\frac{n+1}{2}}$. Secondly, if a set $A$ in
$M_n(\mathbb{F}_q)$ satisfies $|A|\geq C(n)q^{n^2-1}$ for some sufficiently
large $C(n)$, then we have $$ \max\{|A+A|, |AA|\}\gtrsim
\min\left\{\frac{|A|^2}{q^{n^2-\frac{n+1}{4}}}, q^{n^2/3}|A|^{2/3}\right\}. $$
These improve the results due to The and Vinh (2020), and generalize the
results due to Mohammadi, Pham, and Wang (2021). We also give a new proof for a
recent result due to The and Vinh (2020). Our method is based on spectral graph
theory and linear algebra.


Abstract:
  Supernova remnants (SNRs) are observable for about 6-15x10^4 years before
they fade into the Galactic interstellar medium. With a Galactic supernova rate
of approximately two per century, we can expect to have of the order of 1200
SNRs in our Galaxy. However, only about 300 of them are known to date, with the
majority having been discovered in Galactic plane radio surveys. Given that
these SNRs represent the brightest tail of the distribution and are mostly
located close to the plane, they are not representative of the complete sample.
Here we report findings from the search for new SNRs in the eROSITA all-sky
survey data which led to the detection of one of the largest SNRs discovered at
wavelengths other than the radio: G249.5+24.5. This source is located at a
relatively high Galactic latitude, where SNRs are not usually expected to be
found. The remnant, 'Hoinga', has a diameter of about 4.4 degrees and shows a
circular shaped morphology with diffuse X-ray emission filling almost the
entire remnant. Spectral analysis of the remnant emission reveals that an APEC
spectrum from collisionally ionised diffuse gas and a plane-parallel shock
plasma model with non-equilibrium ionisation are both able to provide an
adequate description of the data, suggesting a gas temperature of the order of
kT = 0.1 keV and an absorbing column density of N_H=3.6 x 10^20 cm^-2.
Subsequent searches for a radio counterpart of the Hoinga remnant identified
its radio emission in archival data from the Continuum HI Parkes All-Sky Survey
(CHIPASS) and the 408-MHz `Haslam' all-sky survey. The radio spectral index
alpha=-0.69 +- 0.08 obtained from these data definitely confirms the SNR nature
of Hoinga. From its size and X-ray and radio spectral properties we conclude
that Hoinga is a middle-aged Vela-like SNR located at a distance of about twice
that of the Vela SNR, i.e. at ~500 pc.


Abstract:
  Cross-resolution image alignment is a key problem in multiscale gigapixel
photography, which requires to estimate homography matrix using images with
large resolution gap. Existing deep homography methods concatenate the input
images or features, neglecting the explicit formulation of correspondences
between them, which leads to degraded accuracy in cross-resolution challenges.
In this paper, we consider the cross-resolution homography estimation as a
multimodal problem, and propose a local transformer network embedded within a
multiscale structure to explicitly learn correspondences between the multimodal
inputs, namely, input images with different resolutions. The proposed local
transformer adopts a local attention map specifically for each position in the
feature. By combining the local transformer with the multiscale structure, the
network is able to capture long-short range correspondences efficiently and
accurately. Experiments on both the MS-COCO dataset and the real-captured
cross-resolution dataset show that the proposed network outperforms existing
state-of-the-art feature-based and deep-learning-based homography estimation
methods, and is able to accurately align images under $10\times$ resolution
gap.


Abstract:
  In this note, we are concerned with dark modes in a class of non-Markovian
open quantum systems. Based on a microscopic model, a time-convoluted linear
quantum stochastic differential equation and an output equation are derived to
describe the system dynamics. The definition of dark modes is given building on
the input-output structure of the system. Then, we present a necessary and
sufficient condition for the existence of dark modes. Also, the problem of dark
mode synthesis via Hamiltonian engineering is constructively solved and an
example is presented to illustrate our results.


Abstract:
  The data available in Electronic Health Records (EHRs) provides the
opportunity to transform care, and the best way to provide better care for one
patient is through learning from the data available on all other patients.
Temporal modelling of a patient's medical history, which takes into account the
sequence of past events, can be used to predict future events such as a
diagnosis of a new disorder or complication of a previous or existing disorder.
While most prediction approaches use mostly the structured data in EHRs or a
subset of single-domain predictions and outcomes, we present MedGPT a novel
transformer-based pipeline that uses Named Entity Recognition and Linking tools
(i.e. MedCAT) to structure and organize the free text portion of EHRs and
anticipate a range of future medical events (initially disorders). Since a
large portion of EHR data is in text form, such an approach benefits from a
granular and detailed view of a patient while introducing modest additional
noise. MedGPT effectively deals with the noise and the added granularity, and
achieves a precision of 0.344, 0.552 and 0.640 (vs LSTM 0.329, 0.538 and 0.633)
when predicting the top 1, 3 and 5 candidate future disorders on real world
hospital data from King's College Hospital, London, UK (\textasciitilde600k
patients). We also show that our model captures medical knowledge by testing it
on an experimental medical multiple choice question answering task, and by
examining the attentional focus of the model using gradient-based saliency
methods.


Abstract:
  We present the clustering analysis of photometric luminous red galaxies
(LRGs) at a redshift range of $0.1\leq z \leq 1.05$ using $615,317$ photometric
LRGs selected from the Hyper Suprime-Cam Subaru Strategic Program covering
$\sim124$ deg$^{2}$. Our sample covers a broad range of stellar masses and
photometric redshifts and enables a halo occupation distribution analysis to
study the redshift and stellar-mass dependence of dark halo properties of LRGs.
We find a tight correlation between the characteristic dark halo mass to host
central LRGs, $M_{\min}$, and the number density of LRGs independently of
redshifts, indicating that the formation of LRGs is associated with the global
environment. The $M_{\min}$ of LRGs depends only weakly on the stellar mass
$M_{\star}$ at $M_{\star} \lesssim 10^{10.75}h^{-2} M_{\odot}$ at $0.3<z<1.05$,
in contrast to the case for all photometrically selected galaxies for which
$M_{\min}$ shows significant dependence on $M_{\star}$ even at low $M_{\star}$.
The weak stellar mass dependence is indicative of the dark halo mass being the
key parameter for the formation of LRGs rather than the stellar mass. Our
result suggests that the halo mass of $\sim 10^{12.5 \pm 0.2}h^{-1} M_{\odot}$
is the critical mass for an efficient halo quenching due to the halo
environment. We compare our result with the result of the hydrodynamical
simulation to find that low-mass LRGs at $z \sim 1$ will increase their stellar
masses by an order magnitude from $z=1$ to $0$ through mergers and satellite
accretions, and a large fraction of massive LRGs at $z<0.9$ consist of LRGs
that are recently migrated from massive green valley galaxies or those evolved
from less massive LRGs through mergers and satellite accretions.


Abstract:
  In this work, we consider the nonlocal obstacle problem with a given obstacle
$\psi$ in a bounded Lipschitz domain $\Omega$ in $\mathbb{R}^{d}$, such that
$\mathbb{K}_\psi^s=\{v\in H^s_0(\Omega):v\geq\psi \text{ a.e. in
}\Omega\}\neq\emptyset$, given by
\[u\in\mathbb{K}_\psi^s:\langle\mathcal{L}_au,v-u\rangle\geq\langle
F,v-u\rangle\quad\forall v\in\mathbb{K}^s_\psi,\] for $F\in H^{-s}(\Omega)$,
the dual space of $H^s_0(\Omega)$, $0<s<1$. The nonlocal operator
$\mathcal{L}_a:H^s_0(\Omega)\to H^{-s}(\Omega)$ is defined with a measurable,
bounded, strictly positive singular kernel $a(x,y)$, possibly not symmetric, by
\[\langle\mathcal{L}_au,v\rangle=P.V.\int_{\mathbb{R}^d}\int_{\mathbb{R}^d}v(x)(u(x)-u(y))a(x,y)dydx=\mathcal{E}_a(u,v),\]
with $\mathcal{E}_a$ being a Dirichlet form. Also, the fractional operator
$\tilde{\mathcal{L}}_A=-D^s\cdot AD^s$ defined with the distributional Riesz
$s$-fractional derivative and a bounded matrix $A(x)$ gives a well defined
integral singular kernel. The corresponding $s$-fractional obstacle problem
converges as $s\nearrow1$ to the obstacle problem in $H^1_0(\Omega)$ with the
operator $-D\cdot AD$ given with the gradient $D$.
  We mainly consider problems involving the bilinear form $\mathcal{E}_a$ with
one or two obstacles, and the N-membranes problem, deriving a weak maximum
principle, comparison properties, approximation by bounded penalization, and
the Lewy-Stampacchia inequalities. This provides regularity of the solutions,
including a global estimate in $L^\infty(\Omega)$, local H\"older regularity
when $a$ is symmetric, and local regularity in $W^{2s,p}_{loc}(\Omega)$ and
$C^1(\Omega)$ for fractional $s$-Laplacian obstacle-type problems. These novel
results are complemented with the extension of the Lewy-Stampacchia
inequalities to the order dual of $H^s_0(\Omega)$ and some remarks on the
associated $s$-capacity for general $\mathcal{L}_a$.


Abstract:
  The application of reliable structural health monitoring (SHM) technologies
to operational wind turbine blades is a challenging task, due to the uncertain
nature of the environments they operate in. In this paper, a novel SHM
methodology, which uses Gaussian Processes (GPs) is proposed. The methodology
takes advantage of the fact that the blades on a turbine are nominally
identical in structural properties and encounter the same environmental and
operational variables (EOVs). The properties of interest are the first edgewise
frequencies of the blades. The GPs are used to predict the edge frequencies of
one blade given that of another, after these relationships between the pairs of
blades have been learned when the blades are in a healthy state. In using this
approach, the proposed SHM methodology is able to identify when the blades
start behaving differently from one another over time. To validate the concept,
the proposed SHM system is applied to real onshore wind turbine blade data,
where some form of damage was known to have taken place. X-bar control chart
analysis of the residual errors between the GP predictions and actual
frequencies show that the system successfully identified early onset of damage
as early as six months before it was identified and remedied.


Abstract:
  Multi-robot systems are an efficient method to explore and map an unknown
environment. The simulataneous localization and mapping (SLAM) algorithm is
common for single robot systems, however multiple robots can share respective
map data in order to merge a larger global map. This thesis contributes to the
multi-robot mapping problem by considering cases in which robots have
communication range limitations. The architecture coordinates a team of robots
and the central server to explore an unknown environment by exploiting a
hierarchical choice structure. The coordination algorithms ensure that the
hierarchy of robots choose frontier points that provide maximum information
gain, while maintaining viable communication amongst themselves and the central
computer through an ad-hoc relay network. In addition, the robots employ a
backup choice algorithm in cases when no valid frontier points remain by
arranging the communication relay network as a fireline back to the source.
  This work contributes a scalable, efficient, and robust architecture towards
hybrid multi-robot mapping systems that take into account communication range
limitations. The architecture is tested in a simulation environment using
various maps.


Abstract:
  We study pluripotential complex Monge-Amp\`ere flows in big cohomology
classes on compact K{\"a}hler manifolds. We use the Perron method, considering
pluripotential subsolutions to the Cauchy problem. We prove that, under natural
assumptions on the data, the upper envelope of all subsolutions is continuous
in space and semi-concave in time, and provides a unique pluripotential
solution with such regularity. We apply this theory to study pluripotential
K{\"a}hler-Ricci flows on compact K{\"a}hler manifolds of general type as well
as on stable varieties.


Abstract:
  We present the abundance analyses of 7 Carbon enhanced metal-poor (CEMP)
stars to understand the origin of carbon in them. We used high-resolution
optical spectra to derive abundances of various elements. We also used
low-resolution Near-Infrared (NIR) spectra to derive the abundance of O and
12C/13C from the CO molecular band and compared their values with those derived
from high-resolution optical spectra. We identified a good agreement between
the values. Thus, in cool CEMP stars, the NIR observations complement the
high-resolution optical observations to derive the oxygen abundance and the
12C/13C ratio. This enables us to probe fainter cool CEMP stars using NIR
spectroscopy. C, N, O abundances of all the program stars in this study show
abundances that are consistent with binary mass transfer from a low-mass
low-metallicity Asymptotic Giant Branch (AGB) companion which is further
supported by the presence of enhancement in neutron-capture elements and
detection of radial velocity variation. One of the stars show abundance
patterns similar to a CEMP-s star whereas the abundance pattern of the rest of
the stars satisfy the criteria required to classify them as CEMP-r/s stars. The
sub-classification of some of the stars studied here is revisited. The
abundance of neutron capture elements in these CEMP-r/s stars resembles to that
of i-process models where proton ingestion episodes in the companion low-mass
low-metallicity AGB stars produce the necessary neutron density required for
the onset of i-process.


Abstract:
  We study the feasibility of reaching the ultrastrong (USC) and deep-strong
coupling (DSC) regimes of light-matter interaction, in particular at resonance
condition, with a superconducting charge qubit, also known as Cooper-Pair box
(CPB). We show that by shunting the charge qubit with a high-impedance
LC-circuit, one can maximally reach both USC and DSC regimes exceeding the
classical upper bound $|g|\leq \sqrt{\omega_q\omega_r}/2$ between two harmonic
systems with frequencies $\omega_q$ and $\omega_r$. In our case, the
fundamental model corresponds to an enhanced quantum Rabi model, which contains
a displacement field operator that breaks its internal parity symmetry.
Furthermore, we consider a multipartite device consisting of two CPBs
ultrastrongly coupled to an oscillator as a mediator and study a quantum state
transfer protocol between a pair of transmon qubits, all of them subjected to
local incoherent noise channels with realistic parameters. This work opens the
door for studying light-matter interactions beyond the quantum Rabi model at
extreme coupling strengths, providing a new building block for applications
within quantum computation and quantum information processing.


Abstract:
  The observation of beam spin asymmetries in two-pion production in
semi-inclusive deep inelastic scattering off an unpolarized proton target is
reported. The data presented here were taken in the fall of 2018 with the
CLAS12 spectrometer using a 10.6 GeV longitudinally spin-polarized electron
beam delivered by CEBAF at JLab. The measured asymmetries provide the first
opportunity to extract the parton distribution function $e(x)$, which provides
information about the interaction between gluons and quarks, in a collinear
framework that offers cleaner access than previous measurements. The
asymmetries also constitute the first ever signal sensitive to the
helicity-dependent two-pion fragmentation function $G_1^\perp$. A clear sign
change is observed around the $\rho$ mass that appears in model calculations
and is indicative of the dependence of the produced pions on the helicity of
the fragmenting quark.


Abstract:
  There is a growing interest in the community in making an embodied AI agent
perform a complicated task while interacting with an environment following
natural language directives. Recent studies have tackled the problem using
ALFRED, a well-designed dataset for the task, but achieved only very low
accuracy. This paper proposes a new method, which outperforms the previous
methods by a large margin. It is based on a combination of several new ideas.
One is a two-stage interpretation of the provided instructions. The method
first selects and interprets an instruction without using visual information,
yielding a tentative action sequence prediction. It then integrates the
prediction with the visual information etc., yielding the final prediction of
an action and an object. As the object's class to interact is identified in the
first stage, it can accurately select the correct object from the input image.
Moreover, our method considers multiple egocentric views of the environment and
extracts essential information by applying hierarchical attention conditioned
on the current instruction. This contributes to the accurate prediction of
actions for navigation. A preliminary version of the method won the ALFRED
Challenge 2020. The current version achieves the unseen environment's success
rate of 4.45% with a single view, which is further improved to 8.37% with
multiple views.


Abstract:
  The behaviour of the generalised Riesz function defined by
\[S_{m,p}(x)=\sum_{k=0}^\infty \frac{(-)^{k-1}x^k}{k! \zeta(mk+p)}\qquad (m\geq
1,\ p\geq 1)\] is considered for large positive values of $x$. A numerical
scheme is given to compute this function which enables the visualisation of its
asymptotic form. The two cases $m=2$, $p=1$ and $m=p=2$ (introduced
respectively by Hardy and Littlewood in 1918 and Riesz in 1915) are examined in
detail. It is found on numerical evidence that these functions appear to
exhibit the $x^{-1/4}$ and $x^{-3/4}$ decay, superimposed on an oscillatory
structure, required for the truth of the Riemann hypothesis.


Abstract:
  We find stationary thin-brane geometries that are dual to
far-from-equilibrium steady states of two-dimensional holographic interfaces.
The flow of heat at the boundary agrees with the result of CFT and the known
energy-transport coefficients of the thin-brane model. We argue that by
entangling outgoing excitations the interface produces coarse-grained entropy
at a maximal rate, and point out similarities and differences with double-sided
black funnels. The non-compact, non-Killing and far from-equilibrium event
horizon of our solutions coincides with the local (apparent) horizon on the
colder side, but lies behind it on the hotter side of the interface. We also
show that the thermal conductivity of a pair of interfaces jumps at the
Hawking-Page phase transition from a regime described by classical scatterers
to a quantum regime in which heat flows unobstructed.


Abstract:
  Existing work on automated hate speech classification assumes that the
dataset is fixed and the classes are pre-defined. However, the amount of data
in social media increases every day, and the hot topics changes rapidly,
requiring the classifiers to be able to continuously adapt to new data without
forgetting the previously learned knowledge. This ability, referred to as
lifelong learning, is crucial for the real-word application of hate speech
classifiers in social media. In this work, we propose lifelong learning of hate
speech classification on social media. To alleviate catastrophic forgetting, we
propose to use Variational Representation Learning (VRL) along with a memory
module based on LB-SOINN (Load-Balancing Self-Organizing Incremental Neural
Network). Experimentally, we show that combining variational representation
learning and the LB-SOINN memory module achieves better performance than the
commonly-used lifelong learning techniques.


Abstract:
  Q-learning, which seeks to learn the optimal Q-function of a Markov decision
process (MDP) in a model-free fashion, lies at the heart of reinforcement
learning. When it comes to the synchronous setting (such that independent
samples for all state-action pairs are drawn from a generative model in each
iteration), substantial progress has been made towards understanding the sample
efficiency of Q-learning. Consider a $\gamma$-discounted infinite-horizon MDP
with state space $\mathcal{S}$ and action space $\mathcal{A}$: to yield an
entrywise $\varepsilon$-approximation of the optimal Q-function,
state-of-the-art theory for Q-learning requires a sample size exceeding the
order of $\frac{|\mathcal{S}||\mathcal{A}|}{(1-\gamma)^5\varepsilon^{2}}$,
which fails to match existing minimax lower bounds. This gives rise to natural
questions: what is the sharp sample complexity of Q-learning? Is Q-learning
provably sub-optimal? This paper addresses these questions for the synchronous
setting: (1) when $|\mathcal{A}|=1$ (so that Q-learning reduces to TD
learning), we prove that the sample complexity of TD learning is minimax
optimal and scales as $\frac{|\mathcal{S}|}{(1-\gamma)^3\varepsilon^2}$ (up to
log factor); (2) when $|\mathcal{A}|\geq 2$, we settle the sample complexity of
Q-learning to be on the order of
$\frac{|\mathcal{S}||\mathcal{A}|}{(1-\gamma)^4\varepsilon^2}$ (up to log
factor). Our theory unveils the strict sub-optimality of Q-learning when
$|\mathcal{A}|\geq 2$, and rigorizes the negative impact of over-estimation in
Q-learning. Finally, we extend our analysis to accommodate asynchronous
Q-learning (i.e., the case with Markovian samples), sharpening the horizon
dependency of its sample complexity to be $\frac{1}{(1-\gamma)^4}$.


Abstract:
  We examine the bound state solutions of the Dirac equation under the spin and
pseudospin symmetries for a new suggested combined potential, Hulten plus a
class of Yukawa potential including a Coulomb-like tensor interaction. An
improved scheme is employed to deal with the centrifugal (pseudo-centrifugal)
term. Using the Nikiforov-Uvarov and SUSYQM methods, we analytically develop
the relativistic energy eigenvalues and associated Dirac spinor components of
wave functions. We find that both methods give entirely the same results.
Modifiable of our results into some particular potential cases, useful for
other physical systems, are also discussed. We obtain complete agreement with
the findings of previous works. The spin and pseudospin bound state energy
spectra for various levels are presented in the absence as well as the presence
of tensor coupling. Both energy spectrums are sensitive with regards to the
quantum numbers $\kappa$ and $n$, as well as the parameter $\delta$. We also
notice that the degeneracies between Dirac spin and pseudospin doublet
eigenstate partners are completely removed by the tensor interaction. Finally,
we present the parameter space of allowable bound state regions of potential
strength $V_0$ with constants for both considered symmetry limits $C_S$ and
$C_{PS}$.


Abstract:
  Heterogeneous graph is a kind of data structure widely existing in real life.
Nowadays, the research of graph neural network on heterogeneous graph has
become more and more popular. The existing heterogeneous graph neural network
algorithms mainly have two ideas, one is based on meta-path and the other is
not. The idea based on meta-path often requires a lot of manual preprocessing,
at the same time it is difficult to extend to large scale graphs. In this
paper, we proposed the general heterogeneous message passing paradigm and
designed R-GSN that does not need meta-path, which is much improved compared to
the baseline R-GCN. Experiments have shown that our R-GSN algorithm achieves
the state-of-the-art performance on the ogbn-mag large scale heterogeneous
graph dataset.


Abstract:
  Since model quantization helps to reduce the model size and computation
latency, it has been successfully applied in many applications of mobile
phones, embedded devices and smart chips. The mixed-precision quantization
model can match different quantization bit-precisions according to the
sensitivity of different layers to achieve great performance. However, it is a
difficult problem to quickly determine the quantization bit-precision of each
layer in deep neural networks according to some constraints (e.g., hardware
resources, energy consumption, model size and computation latency). To address
this issue, we propose a novel sequential single path search (SSPS) method for
mixed-precision quantization,in which the given constraints are introduced into
its loss function to guide searching process. A single path search cell is used
to combine a fully differentiable supernet, which can be optimized by
gradient-based algorithms. Moreover, we sequentially determine the candidate
precisions according to the selection certainties to exponentially reduce the
search space and speed up the convergence of searching process. Experiments
show that our method can efficiently search the mixed-precision models for
different architectures (e.g., ResNet-20, 18, 34, 50 and MobileNet-V2) and
datasets (e.g., CIFAR-10, ImageNet and COCO) under given constraints, and our
experimental results verify that SSPS significantly outperforms their uniform
counterparts.


Abstract:
  This paper studies distributed resource allocation problem in multi-agent
systems, where all the agents cooperatively minimize the sum of their cost
functions with global resource constraints over stochastic communication
networks. This problem arises from many practical domains such as economic
dispatch in smart grid, task assignment, and power allocation in robotic
control. Most of existing works cannot converge to the optimal solution if
states deviate from feasible region due to disturbance caused by environmental
noise, misoperation, malicious attack, etc. To solve this problem, we propose a
distributed deviation-tracking resource allocation algorithm and prove that it
linearly converges to the optimal solution with constant stepsizes. We further
explore its resilience properties of the proposed algorithm. Most importantly,
the algorithm still converges to the optimal solution under the disturbance
injection and random communication failure. In order to improve the convergence
rate, the optimal stepsizes for the fastest convergence rate are established.
We also prove the algorithm converges linearly to the optimal solution in mean
square even with uncoordinated stepsizes, i.e., agents are allowed to employ
different stepsizes. Simulations are provided to verify the theoretical
results.


Abstract:
  Methods based on convolutional neural networks have improved the performance
of biomedical image segmentation. However, most of these methods cannot
efficiently segment objects of variable sizes and train on small and biased
datasets, which are common in biomedical use cases. While methods exist that
incorporate multi-scale fusion approaches to address the challenges arising
with variable sizes, they usually use complex models that are more suitable for
general semantic segmentation computer vision problems. In this paper, we
propose a novel architecture called MSRF-Net, which is specially designed for
medical image segmentation tasks. The proposed MSRF-Net is able to exchange
multi-scale features of varying receptive fields using a dual-scale dense
fusion block (DSDF). Our DSDF block can exchange information rigorously across
two different resolution scales, and our MSRF sub-network uses multiple DSDF
blocks in sequence to perform multi-scale fusion. This allows the preservation
of resolution, improved information flow, and propagation of both high- and
low-level features to obtain accurate segmentation maps. The proposed MSRF-Net
allows to capture object variabilities and provides improved results on
different biomedical datasets. Extensive experiments on MSRF-Net demonstrate
that the proposed method outperforms most of the cutting-edge medical image
segmentation state-of-the-art methods. MSRF-Net advances the performance on
four publicly available datasets, and also, MSRF-Net is more generalizable as
compared to state-of-the-art methods.


Abstract:
  Course estimation is a key component for the development of autonomous
navigation systems for robots. While state-of-the-art methods widely use
visual-based algorithms, it is worth noting that they all fail to deal with the
complexity of the real world by being computationally greedy and sometimes too
slow. They often require obstacles to be highly textured to improve the overall
performance, particularly when the obstacle is located within the focus of
expansion (FOE) where the optic flow (OF) is almost null. This study proposes
the FAst ITerative Half-plane (FAITH) method to determine the course of a micro
air vehicle (MAV). This is achieved by means of an event-based camera, along
with a fast RANSAC-based algorithm that uses event-based OF to determine the
FOE. The performance is validated by means of a benchmark on a simulated
environment and then tested on a dataset collected for indoor obstacle
avoidance. Our results show that the computational efficiency of our solution
outperforms state-of-the-art methods while keeping a high level of accuracy.
This has been further demonstrated onboard an MAV equipped with an event-based
camera, showing that our event-based FOE estimation can be achieved online
onboard tiny drones, thus opening the path towards fully neuromorphic solutions
for autonomous obstacle avoidance and navigation onboard MAVs.


Abstract:
  We investigate the asymptotic fluctuation of three interacting particle
systems: the geometric q-TASEP, the geometric q-PushTASEP and the q-PushASEP.
We prove that the rescaled particle position converges to the GUE Tracy-Widom
distribution in the homogeneous case. If the jump rates of the first finitely
many particles are perturbed in the first two models, we obtain Baik-Ben
Arous-Peche and Gaussian limiting fluctuations.


Abstract:
  The names of variables and functions serve as implicit documentation and are
instrumental for program comprehension. But choosing good meaningful names is
hard. We perform a sequence of experiments in which a total of 334 subjects are
required to choose names in given programming scenarios. The first experiment
shows that the probability that two developers would select the same name is
low: in the 47 instances in our experiments the median probability was only
6.9%. At the same time, given that a specific name is chosen, it is usually
understood by the majority of developers. Analysis of the names given in the
experiment suggests a model where naming is a (not necessarily cognizant or
serial) three-step process: (1) selecting the concepts to include in the name,
(2) choosing the words to represent each concept, and (3) constructing a name
using these words. A followup experiment, using the same experimental setup,
then checked whether using this model explicitly can improve the quality of
names. The results were that names selected by subjects using the model were
judged by two independent judges to be superior to names chosen in the original
experiment by a ratio of two-to-one. Using the model appears to encourage the
use of more concepts and longer names.


Abstract:
  We consider the Navier-Stokes system in three dimensions perturbed by a
transport noise which is sufficiently smooth in space and rough in time. The
existence of a weak solution was proved recently, however, as in the
deterministic setting the question of uniqueness remains a major open problem.
An important feature of systems with uniqueness is the semigroup property
satisfied by their solutions. Without uniqueness, this property cannot hold
generally. We select a system of solutions satisfying the semigroup property
with appropriately shifted rough path. In addition, the selected solutions
respect the well accepted admissibility criterium for physical solutions,
namely, maximization of the energy dissipation. Finally, under suitable
assumptions on the driving rough path, we show that the Navier-Stokes system
generates a measurable random dynamical system. To the best of our knowledge,
this is the first construction of a measurable single-valued random dynamical
system in the state space for an SPDE without uniqueness.


Abstract:
  This paper empirically examines how the opening of K-12 schools and colleges
is associated with the spread of COVID-19 using county-level panel data in the
United States. Using data on foot traffic and K-12 school opening plans, we
analyze how an increase in visits to schools and opening schools with different
teaching methods (in-person, hybrid, and remote) is related to the 2-weeks
forward growth rate of confirmed COVID-19 cases. Our debiased panel data
regression analysis with a set of county dummies, interactions of state and
week dummies, and other controls shows that an increase in visits to both K-12
schools and colleges is associated with a subsequent increase in case growth
rates. The estimates indicate that fully opening K-12 schools with in-person
learning is associated with a 5 (SE = 2) percentage points increase in the
growth rate of cases. We also find that the positive association of K-12 school
visits or in-person school openings with case growth is stronger for counties
that do not require staff to wear masks at schools. These results have a causal
interpretation in a structural model with unobserved county and time
confounders. Sensitivity analysis shows that the baseline results are robust to
timing assumptions and alternative specifications.


Abstract:
  In cell-free massive multiple-input multiple-output (MIMO) the fluctuations
of the channel gain from the access points to a user are large due to the
distributed topology of the system. Because of these fluctuations, data
decoding schemes that treat the channel as deterministic perform inefficiently.
A way to reduce the channel fluctuations is to design a precoding scheme that
equalizes the effective channel gain seen by the users. Conjugate beamforming
(CB) poorly contributes to harden the effective channel at the users. In this
work, we propose a variant of CB dubbed enhanced normalized CB (ECB), in that
the precoding vector consists of the conjugate of the channel estimate
normalized by its squared norm. For this scheme, we derive an exact closed-form
expression for an achievable downlink spectral efficiency (SE), accounting for
channel estimation errors, pilot reuse and user's lack of channel state
information (CSI), assuming independent Rayleigh fading channels. We also
devise an optimal max-min fairness power allocation based only on large-scale
fading quantities. ECB greatly boosts the channel hardening enabling the users
to reliably decode data relying only on statistical CSI. As the provided
effective channel is nearly deterministic, acquiring CSI at the users does not
yield a significant gain.


Abstract:
  The healthcare industry has witnessed significant transformations in e-health
services where Electronic Health Records (EHRs) are transferred to mobile edge
clouds to facilitate healthcare. Many edge cloud-based system designs have been
proposed, but some technical challenges still remain, such as low quality of
services (QoS), data privacy and system security due to centralized healthcare
architectures. In this paper, we propose a novel hybrid approach of data
offloading and data sharing for healthcare using edge cloud and blockchain.
First, an efficient data offloading scheme is proposed where IoT health data
can be offloaded to nearby edge servers for data processing with privacy
awareness. Then, a data sharing scheme is integrated to enable data exchange
among healthcare users via blockchain. Particularly, a trustworthy access
control mechanism is developed using smart contracts for access authentication
to achieve secure EHRs sharing. Implementation results from extensive
real-world experiments show the superior advantages of the proposal over the
existing schemes in terms of improved QoS, enhanced data privacy and security,
and low smart contract costs.


Abstract:
  Skew completable unimodular rows of odd length are completable over
polynomial extension of a local ring if dimension of local ring and length of
unimodular rows are same.


Abstract:
  In this paper, we design receivers for filter bank multicarrier-based
(FBMC-based) massive MIMO considering practical aspects such as channel
estimation and equalization. In particular, we propose a spectrally efficient
pilot structure and a channel estimation technique in the uplink to jointly
estimate all the users' channel impulse responses. We mathematically analyze
our proposed channel estimator and find the statistics of the channel
estimation errors. These statistics are incorporated into our proposed
equalizers to deal with the imperfect channel state information (CSI) effect.
We revisit the channel equalization problem for FBMC-based massive MIMO,
address the shortcomings of the existing equalizers in the literature, and make
them more applicable to practical scenarios. The proposed receiver in this
paper consists of two stages. In the first stage, a linear combining of the
received signals at the base station (BS) antennas provides a coarse channel
equalization and removes any multiuser interference. In the second stage, a per
subcarrier fractionally spaced equalizer (FSE) takes care of any residual
distortion of the channel for the user of interest. We propose an FSE design
based on the equivalent channel at the linear combiner output. This enables the
applicability of our proposed technique to small and/or distributed antenna
setups such as cell-free massive MIMO. Finally, the efficacy of the proposed
techniques is corroborated through numerical analysis.


Abstract:
  We study the band structure of self-adjoint elliptic operators $\mathbb{A}_g=
-\nabla \cdot \sigma_{g} \nabla$, where $\sigma_g$ has the symmetries of a
honeycomb tiling of $\mathbb{R}^2$. We focus on the case where $\sigma_{g}$ is
a real-valued scalar: $\sigma_{g}=1$ within identical, disjoint "inclusions",
centered at vertices of a honeycomb lattice, and $\sigma_{g}=g \gg1 $ (high
contrast) in the complement of the inclusion set (bulk). Such operators govern,
e.g. transverse electric (TE) modes in photonic crystal media consisting of
high dielectric constant inclusions (semi-conductor pillars) within a
homogeneous lower contrast bulk (air), a configuration used in many physical
studies. Our approach, which is based on monotonicity properties of the
associated energy form, extends to a class of high contrast elliptic operators
that model heterogeneous and anisotropic honeycomb media.
  Our results concern the global behavior of dispersion surfaces, and the
existence of conical crossings (Dirac points) occurring in the lowest two
energy bands as well as in bands arbitrarily high in the spectrum. Dirac points
are the source of important phenomena in fundamental and applied physics, e.g.
graphene and its artificial analogues, and topological insulators. The key
hypotheses are the non-vanishing of the Dirac (Fermi) velocity $v_D(g)$,
verified numerically, and a spectral isolation condition, verified analytically
in many configurations. Asymptotic expansions, to any order in $g^{-1}$, of
Dirac point eigenpairs and $v_D(g)$ are derived with error bounds.
  Our study illuminates differences between the high contrast behavior of
$\mathbb{A}_g$ and the corresponding strong binding regime for Schroedinger
operators.


Abstract:
  Effective theories describing black hole exteriors contain many open-system
features due to the large number of gapless degrees of freedom that lie beyond
reach across the horizon. A simple solvable Caldeira-Leggett type model of a
quantum field interacting within a small area with many unmeasured thermal
degrees of freedom was recently proposed in arXiv:2106.09854 to provide a toy
model of this kind of dynamics against which more complete black hole
calculations might be compared. We here compute the response of a simple
Unruh-DeWitt detector (or qubit) interacting with a massless quantum field
$\phi$ coupled to such a hotspot. Our treatment differs from traditional
treatments of Unruh-DeWitt detectors by using Open-EFT tools to reliably
calculate the qubit's late-time behaviour. We use these tools to determine the
efficiency with which the qubit thermalizes as a function of its proximity to
the hotspot. We identify a Markovian regime in which thermalization does occur,
though only for qubits closer to the hotspot than a characteristic distance
scale set by the $\phi$-hotspot coupling. We compute the thermalization time,
and find that it varies inversely with the $\phi$-qubit coupling strength in
the standard way.


Abstract:
  The realization of an efficient quantum optical interface for multi-qubit
systems is an outstanding challenge in science and engineering. Using two atoms
in individually-controlled optical tweezers coupled to a nanofabricated
photonic crystal cavity, we demonstrate entanglement generation, fast
non-destructive readout, and full quantum control of atomic qubits. The
entangled state is verified in free space after being transported away from the
cavity by encoding the qubits into long-lived states and using dynamical
decoupling. Our approach bridges quantum operations at an optical link and in
free space with a coherent one-way transport, potentially enabling an
integrated optical interface for atomic quantum processors.


Abstract:
  This paper provides a general overview of different perspectives and studies
on trust, offers a definition of trust, and provides factors that play a
substantial role in developing social trust, and shows from which perspectives
it can be fostered. The results showed that trust is playing an important role
in success for organizations involved in cross-national strategic partnerships.
Trust can reduce transaction costs, promotes inter-organizational
relationships, and improve subordinate relationships between managers.


Abstract:
  Artificial Intelligence (AI) technologies have long been positioned as a tool
to provide crucial data-driven decision support to people. In this survey
paper, we look at how AI in general, and collaboration assistants (CAs or
chatbots for short) in particular, have been used during a true global exigency
- the COVID-19 pandemic. The key observation is that chatbots missed their
"Apollo moment" when they could have really provided contextual, personalized,
reliable decision support at scale that the state-of-the-art makes possible. We
review the existing capabilities that are feasible and methods, identify the
potential that chatbots could have met, the use-cases they were deployed on,
the challenges they faced and gaps that persisted, and draw lessons that, if
implemented, would make them more relevant in future health emergencies.


Abstract:
  Knowledge distillation~(KD) is an effective learning paradigm for improving
the performance of lightweight student networks by utilizing additional
supervision knowledge distilled from teacher networks. Most pioneering studies
either learn from only a single teacher in their distillation learning methods,
neglecting the potential that a student can learn from multiple teachers
simultaneously, or simply treat each teacher to be equally important, unable to
reveal the different importance of teachers for specific examples. To bridge
this gap, we propose a novel adaptive multi-teacher multi-level knowledge
distillation learning framework~(AMTML-KD), which consists two novel insights:
(i) associating each teacher with a latent representation to adaptively learn
instance-level teacher importance weights which are leveraged for acquiring
integrated soft-targets~(high-level knowledge) and (ii) enabling the
intermediate-level hints~(intermediate-level knowledge) to be gathered from
multiple teachers by the proposed multi-group hint strategy. As such, a student
model can learn multi-level knowledge from multiple teachers through AMTML-KD.
Extensive results on publicly available datasets demonstrate the proposed
learning framework ensures student to achieve improved performance than strong
competitors.


Abstract:
  The vibrational quenching cross sections and corresponding low-temperature
rate constants for the v = 1 and v = 2 states of CN- colliding with He and Ar
atoms have been computed ab initio using new three dimensional potential energy
surfaces. Little work has so far been carried out on low-energy vibrationally
inelastic collisions for anions with neutral atoms. The cross sections and
rates calculated at energies and temperatures relevant for both ion traps and
astrochemical modelling, are found by the present calculations to be even
smaller than those of the similar C2- /He and C2-/Ar systems which are in turn
of the order of those existing for the collisions involving neutral diatom-atom
systems. The implications of our finding in the present case rather small
computed rate constants are discussed for their possible role in the dynamics
of molecular cooling and in the evolution of astrochemical modelling networks.


Abstract:
  Non-convex optimization is ubiquitous in modern machine learning. Researchers
devise non-convex objective functions and optimize them using off-the-shelf
optimizers such as stochastic gradient descent and its variants, which leverage
the local geometry and update iteratively. Even though solving non-convex
functions is NP-hard in the worst case, the optimization quality in practice is
often not an issue -- optimizers are largely believed to find approximate
global minima. Researchers hypothesize a unified explanation for this
intriguing phenomenon: most of the local minima of the practically-used
objectives are approximately global minima. We rigorously formalize it for
concrete instances of machine learning problems.


Abstract:
  Nowadays, analysis of Transparent Environmental Microorganism Images (T-EM
images) in the field of computer vision has gradually become a new and
interesting spot. This paper compares different deep learning classification
performance for the problem that T-EM images are challenging to analyze. We
crop the T-EM images into 8 * 8 and 224 * 224 pixel patches in the same
proportion and then divide the two different pixel patches into foreground and
background according to ground truth. We also use four convolutional neural
networks and a novel ViT network model to compare the foreground and background
classification experiments. We conclude that ViT performs the worst in
classifying 8 * 8 pixel patches, but it outperforms most convolutional neural
networks in classifying 224 * 224 pixel patches.


Abstract:
  Given finite configurations $P_1, \dots, P_n \subset \mathbb{R}^d$, let us
denote by $\mathbf{m}_{\mathbb{R}^d}(P_1, \dots, P_n)$ the maximum density a
set $A \subseteq \mathbb{R}^d$ can have without containing congruent copies of
any $P_i$. We will initiate the study of this geometrical parameter, called the
independence density of the considered configurations, and give several results
we believe are interesting. For instance we show that, under suitable size and
non-degeneracy conditions, $\mathbf{m}_{\mathbb{R}^d}(t_1 P_1, t_2 P_2, \dots,
t_n P_n)$ progressively `untangles' and tends to $\prod_{i=1}^n
\mathbf{m}_{\mathbb{R}^d}(P_i)$ as the ratios $t_{i+1}/t_i$ between consecutive
dilation parameters grow large; this shows an exponential decay on the density
when forbidding multiple dilates of a given configuration, and gives a common
generalization of theorems by Bourgain and by Bukh in geometric Ramsey theory.
We also consider the analogous parameter $\mathbf{m}_{S^d}(P_1, \dots, P_n)$ on
the more complicated framework of sets on the unit sphere $S^d$, obtaining the
corresponding results in this setting.


Abstract:
  In this paper, we present a distributed resource allocation mechanism in
cognitive radio networks, based on a new coopeti-tion methodology, which
combines advantages of nodes competition and cooperation. We postulate that
this new method allows for fully distributed resource management between
cognitive radio devices. The presented framework is generic, however, we
consider it for the application in OFDMA networks. Coopetition takes the best
from cooperative and competitive problem formulation and provides the
opportunity to control the balance between fairness and spectral efficiency
(SE) of resource allocation. Simulation results confirm that coopetition allows
for efficient resource utilization, and may be used practically in wireless
cognitive networks.


Abstract:
  We advocate profunctors between posets compared to order preserving maps. We
introduce the graph and ascent of such profunctors. We apply this in
commutative algebra where these give classes of Alexander dual square-free
monomial ideals giving the full and natural generalized setting of isotonian
ideals and letterplace ideals for posets. We study the poset of profunctors
from ${\mathbb N}$ to ${\mathbb N}$. Such profunctors identify as order
presering maps $f : {\mathbb N} \rightarrow {\mathbb N} \cup \{\infty \}$. For
our applications to infinite posets we also introduce a topology on the set of
profunctors between two posets and study its properties.


Abstract:
  The quasi-one-dimensional organic conductors (TMTTF)$_2X$ with
non-centrosymmetric anions commonly undergo charge- and anion-order transitions
upon cooling. While for compounds with tetrahedral anions ($X$ = BF$_4^-$,
ReO$_4^-$, and ClO$_4^-$) the charge-ordered phase is rather well understood,
the situation is less clear in the case of planar triangular anions, such as
(TMTTF)$_2$NO$_3$. Here we explore the electronic and structural transitions by
transport experiments, optical and magnetic spectroscopy. This way we analyze
the temperature dependence of the charge imbalance 2$\delta$ and an activated
behavior of $\rho(T)$ with $\Delta_{\rm CO}\approx 530$~K below $T_{\rm CO} =
250$~K. Since (TMTTF)$_2$NO$_3$ follows the universal relation between charge
imbalance 2$\delta$ and size of the gap $\Delta_{\rm CO}$, our findings suggest
that charge order is determined by TMTTF stacks with little influence of the
anions. Clear signatures of anion ordering are detected at $T_{\rm AO}=50$~K.
The tetramerization affects the dc transport, the vibrational features of
donors and acceptors, and leads to formation of spin singlets.


Abstract:
  Improving our knowledge of global Milky Way (MW) properties is critical for
connecting the detailed measurements only possible from within our Galaxy to
our understanding of the broader galaxy population. We train Gaussian Process
Regression (GPR) models on SDSS galaxies to map from galaxy properties (stellar
mass, apparent axis ratio, star formation rate, bulge-to-total ratio, disk
scale length, and bar vote fraction) to UV (GALEX $FUV/NUV$), optical (SDSS
$ugriz$) and IR (2MASS $JHKs$ and WISE $W1/W2/W3/W4$) fluxes and uncertainties.
With these models we estimate the photometric properties of the MW, resulting
in a full UV-to-IR spectral energy distribution (SED) as it would be measured
externally, viewed face-on. We confirm that the Milky Way lies in the green
valley in optical diagnostic diagrams, but show for the first time that the MW
is in the star-forming region in standard UV and IR diagnostics --
characteristic of the population of red spiral galaxies. Although our GPR
method predicts one band at a time, the resulting MW UV--IR SED is consistent
with SEDs of local spirals with characteristics broadly similar to the MW,
suggesting that these independent predictions can be combined reliably. Our
UV--IR SED will be invaluable for reconstructing the MW's star formation
history using the same tools employed for external galaxies, allowing
comparisons of results from \textit{in situ} measurements to those from the
methods used for extra-galactic objects.


Abstract:
  Preserving privacy is a growing concern in our society where sensors and
cameras are ubiquitous. In this work, for the first time, we propose a
trainable image acquisition method that removes the sensitive identity
revealing information in the optical domain before it reaches the image sensor.
The method benefits from a trainable optical convolution kernel which transmits
the desired information while filters out the sensitive content. As the
sensitive content is suppressed before it reaches the image sensor, it does not
enter the digital domain therefore is unretrievable by any sort of privacy
attack. This is in contrast with the current digital privacy-preserving methods
that are all vulnerable to direct access attack. Also, in contrast with the
previous optical privacy-preserving methods that cannot be trained, our method
is data-driven and optimized for the specific application at hand. Moreover,
there is no additional computation, memory, or power burden on the acquisition
system since this processing happens passively in the optical domain and can
even be used together and on top of the fully digital privacy-preserving
systems. The proposed approach is adaptable to different digital neural
networks and content. We demonstrate it for several scenarios such as smile
detection as the desired attribute while the gender is filtered out as the
sensitive content. We trained the optical kernel in conjunction with two
adversarial neural networks where the analysis network tries to detect the
desired attribute and the adversarial network tries to detect the sensitive
content. We show that this method can reduce 65.1% of sensitive content when it
is selected to be the gender and it only loses 7.3% of the desired content.
Moreover, we reconstruct the original faces using the deep reconstruction
method that confirms the ineffectiveness of reconstruction attacks to obtain
the sensitive content.


Abstract:
  Sixth-generation wireless communication (6G) will be an integrated
architecture of "space, air, ground and sea". One of the most difficult part of
this architecture is the underwater information acquisition which need to
transmitt information cross the interface between water and air.In this
senario, ocean of things (OoT) will play an important role, because it can
serve as a hub connecting Internet of things (IoT) and Internet of underwater
things (IoUT). OoT device not only can collect data through underwater methods,
but also can utilize radio frequence over the air. For underwater
communications, underwater acoustic communications (UWA COMMs) is the most
effective way for OoT devices to exchange information, but it is always
tormented by doppler shift and synchronization errors. In this paper, in order
to overcome UWA tough conditions, a deep neural networks based receiver for
underwater acoustic chirp communication, called C-DNN, is proposed. Moreover,
to improve the performance of DL-model and solve the problem of model
generalization, we also proposed a novel federated meta learning (FML) enhanced
acoustic radio cooperative (ARC) framework, dubbed ARC/FML, to do transfer.
Particularly, tractable expressions are derived for the convergence rate of FML
in a wireless setting, accounting for effects from both scheduling ratio, local
epoch and the data amount on a single node.From our analysis and simulation
results, it is shown that, the proposed C-DNN can provide a better BER
performance and lower complexity than classical matched filter (MF) in
underwater acoustic communications scenario. The ARC/FML framework has good
convergence under a variety of channels than federated learning (FL). In
summary, the proposed ARC/FML for OoT is a promising scheme for information
exchange across water and air.


Abstract:
  The U.S. Food & Drug Administration (FDA) requires that e-cigarette
advertisements include a prominent warning label that reminds consumers that
nicotine is addictive. However, the high volume of vaping-related posts on
social media makes compliance auditing expensive and time-consuming, suggesting
that an automated, scalable method is needed. We sought to develop and evaluate
a deep learning system designed to automatically determine if an Instagram post
promotes vaping, and if so, if an FDA-compliant warning label was included or
if a non-compliant warning label was visible in the image. We compiled and
labeled a dataset of 4,363 Instagram images, of which 44% were vaping-related,
3% contained FDA-compliant warning labels, and 4% contained non-compliant
labels. Using a 20% test set for evaluation, we tested multiple neural network
variations: image processing backbone model (Inceptionv3, ResNet50,
EfficientNet), data augmentation, progressive layer unfreezing, output bias
initialization designed for class imbalance, and multitask learning. Our final
model achieved an area under the curve (AUC) and [accuracy] of 0.97 [92%] on
vaping classification, 0.99 [99%] on FDA-compliant warning labels, and 0.94
[97%] on non-compliant warning labels. We conclude that deep learning models
can effectively identify vaping posts on Instagram and track compliance with
FDA warning label requirements.


Abstract:
  We describe the deployment and first tests on Sky of CONCERTO, a large
field-of-view (18.6arc-min) spectral-imaging instrument. The instrument
operates in the range 130-310GHz from the APEX 12-meters telescope located at
5100m a.s.l. on the Chajnantor plateau. Spectra with R=1-300 are obtained using
a fast (2.5Hz mechanical frequency) Fourier Transform Spectrometer (FTS),
coupled to a continuous dilution cryostat with a base temperature of 60mK. Two
2152-pixels arrays of Lumped Element Kinetic Inductance Detectors (LEKID) are
installed in the cryostat that also contains the cold optics and the front-end
electronics. CONCERTO, installed in April 2021, generates more than 20k spectra
per second during observations. We describe the final development phases, the
installation and the first results obtained on Sky.


Abstract:
  Discovery of quantized electric conductance by the group of van Wees in 1988
was a major breakthrough in physics. Later, the group of Schwab has proven the
existence of quantized thermal conductance. Advancing one step further, we
present that quantized entropy current can be interpreted and it ease the
description of a transferred quantized energy package. This might yield a
universal transport behavior of the microscopic world. During the transfer of a
single energy quantum, $h \nu$ between two neighboring domains the minimum
entropy increment is calculated. Furthermore, the possible existence of the
minimum entropy production can be formulated.


Abstract:
  For the one-dimensional mass-critical/supercritical pseudo-relativistic
nonlinear Schrodinger equation, a stationary solution can be constructed as an
energy minimizer under an additional kinetic energy constraint and the set of
energy minimizers is orbitally stable in \cite{BGV}. In this study, we proved
the local uniqueness and established the orbital stability of the solitary wave
by improving that of the energy minimizer set. A key aspect thereof is the
reformulation of the variational problem in the non-relativistic regime, which
we consider to be more natural because the proof extensively relies on the
subcritical nature of the limiting model. Thus, the role of the additional
constraint is clarified, a more suitable Gagliardo-Nirenberg inequality is
introduced, and the non-relativistic limit is proved. Subsequently, this limit
is employed to derive the local uniqueness and orbital stability.


Abstract:
  This paper proposes an ecological adaptive cruise control (EACC) concept with
the primary goal to minimize the fuel consumption in a city bus with an
internal combustion engine (ICE). A hybrid model predictive control (HMPC) is
implemented in this work to control both continuous and discrete-time
variables. Moreover, a multi-objective optimization problem for EACC is
formulated in time-domain as a mixed-integer quadratically constrained
quadratic programming (MIQCQP) problem. The proposed HMPC-EACC performs robust
vehicle-following while tracking a leading vehicle and plans fuel-efficient
acceleration and deceleration maneuvers for the host vehicle. Additionally, it
uses the signal phase and timing (SPaT) information to compute a green wave
reference speed for the host vehicle to cross the signalized intersections at a
green phase. Moreover, the proposed controller performs pulse and glide (PnG)
to optimally control the engine ON and OFF states and save additional fuel.
Furthermore, the performance of the proposed strategy is evaluated on a
real-world driving profile and compared against a baseline controller from the
literature. Finally, the influence of different prediction horizons on the fuel
savings and computation times are studied. The results reveal significant
reduction in fuel consumption with HMPC-EACC and demonstrate that the proposed
controller is real-time capable.


Abstract:
  Learning to model how the world changes as time elapses has proven a
challenging problem for the computer vision community. We propose a
self-supervised solution to this problem using temporal cycle consistency
jointly in vision and language, training on narrated video. Our model learns
modality-agnostic functions to predict forward and backward in time, which must
undo each other when composed. This constraint leads to the discovery of
high-level transitions between moments in time, since such transitions are
easily inverted and shared across modalities. We justify the design of our
model with an ablation study on different configurations of the cycle
consistency problem. We then show qualitatively and quantitatively that our
approach yields a meaningful, high-level model of the future and past. We apply
the learned dynamics model without further training to various tasks, such as
predicting future action and temporally ordering sets of images. Project page:
https://dave.ml/mmcc


Abstract:
  We present two Dialectica-like constructions for models of intensional
Martin-L\"of type theory based on G\"odel's original Dialectica interpretation
and the Diller-Nahm variant, bringing dependent types to categorical proof
theory. We set both constructions within a logical predicates style theory for
display map categories where we show that 'quasifibred' versions of dependent
products and universes suffice to construct their standard counterparts. To
support the logic required for dependent products in the first construction, we
propose a new semantic notion of finite sum for dependent types, generalizing
finitely-complete extensive categories. The second avoids extensivity
assumptions using biproducts in a Kleisli category for a fibred additive monad.


Abstract:
  In this paper, we propose a novel multi-color balance adjustment for color
constancy. The proposed method, called "n-color balancing," allows us not only
to perfectly correct n target colors on the basis of corresponding ground truth
colors but also to correct colors other than the n colors. In contrast,
although white-balancing can perfectly adjust white, colors other than white
are not considered in the framework of white-balancing in general. In an
experiment, the proposed multi-color balancing is demonstrated to outperform
both conventional white and multi-color balance adjustments including
Bradford's model.


Abstract:
  The Peer-to-Peer systems (P2P) were led these last years as the major
technology of access upon various resources on Internet. These systems build a
cluster witch contains a very large number of peers. As the result the
selection of peers who can answer for a given query is a very difficult
problem. The efficiency of the selection algorithms can be improved by
introducing of semantics into the process of queries routing. We present in
this paper a novel improved version of our semantic routing algorithm
LearningPeerSelection (LPS) presented in CORIA 2009, an incremental strategy of
updating knowledge bases and an advanced experimental study. To test the
proposed algorithm, we defined a layer of routing on the PeerSim simulator.


Abstract:
  Transit surveys have revealed a significant population of compact
multi-planet systems, containing several sub-Neptune-mass planets on close-in,
tightly-packed orbits. These systems are thought to have formed through a final
phase of giant impacts, which would tend to leave systems close to the edge of
stability. Here, we assess this hypothesis, comparing observed eccentricities
in systems exhibiting transit-timing variations (TTVs), with the maximum
eccentricities compatible with long-term stability. We use the machine-learning
classifier SPOCK (Tamayo et al. 2020) to rapidly classify the stability of
numerous initial configurations and hence determine these stability limits.
While previous studies have argued that multi-planet systems are often
maximally packed, in the sense that they could not host any additional planets,
we find that the existing planets in these systems have measured eccentricities
below the limits allowed by stability by a factor of 2--10. We compare these
results against predictions from the giant impact theory of planet formation,
derived from both $N$-body integrations and theoretical expectations that in
the absence of dissipation, the orbits of such planets should be distributed
uniformly throughout the phase space volume allowed by stability. We find that
the observed systems have systematically lower eccentricities than this
scenario predicts, with a median eccentricity about 4 times lower than
predicted. These findings suggest that if such systems formed through giant
impacts, then some dissipation must occur to damp their eccentricities. This
may take place during formation, perhaps through interactions with the natal
gas disk or a leftover population of planetesimals, or over longer timescales
through the coupling of tidal and secular processes.


Abstract:
  Let $\mathcal{O}$ be a discrete valuation ring with unique maximal ideal
$\mathfrak{p}$ and with finite residue field $\mathbb{F}_{q}$, the field with
$q$ elements where $q$ is a power of a prime $p$. For $r \ge 1$, we write
$\mathcal{O}_r$ for the reduction of $\mathcal{O}$ modulo the ideal
$\mathfrak{p}^r$. An irreducible representation of the finite group
$G_r=\mathrm{GL}_{N}(\mathcal{O}_{r})$ is called stable if its restriction to
the principal congruence kernel
$K^l=1+\mathfrak{p}^{l}\mathrm{M}_{N}(\mathcal{O}_r)$, where
$l=[\frac{r+1}{2}]$, consists of representations whose stabilisers modulo
$K^{l'}$ are centralisers of Jordan canonical matrix in
$\mathfrak{g}_{l'}=\mathrm{M}_{N}(\mathcal{{O}}_{l'})$, where $l'=r-l$.
  Their study is motivated by constructions of strongly semisimple
representations, introduced by the work of Hill, which is a special case of
stable representations. In this paper, we explore the construction of stable
(ordinary) irreducible representations of the finite group
$G_r=\mathrm{GL}_{N}(\mathcal{O}_{r})$ for $N \ge 2$.


Abstract:
  Current voice conversion (VC) methods can successfully convert timbre of the
audio. As modeling source audio's prosody effectively is a challenging task,
there are still limitations of transferring source style to the converted
speech. This study proposes a source style transfer method based on
recognition-synthesis framework. Previously in speech generation task, prosody
can be modeled explicitly with prosodic features or implicitly with a latent
prosody extractor. In this paper, taking advantages of both, we model the
prosody in a hybrid manner, which effectively combines explicit and implicit
methods in a proposed prosody module. Specifically, prosodic features are used
to explicit model prosody, while VAE and reference encoder are used to
implicitly model prosody, which take Mel spectrum and bottleneck feature as
input respectively. Furthermore, adversarial training is introduced to remove
speaker-related information from the VAE outputs, avoiding leaking source
speaker information while transferring style. Finally, we use a modified
self-attention based encoder to extract sentential context from bottleneck
features, which also implicitly aggregates the prosodic aspects of source
speech from the layered representations. Experiments show that our approach is
superior to the baseline and a competitive system in terms of style transfer;
meanwhile, the speech quality and speaker similarity are well maintained.


Abstract:
  Safety-critical software systems are in many cases designed and implemented
as families of products, usually referred to as Software Product Lines (SPLs).
Products within an SPL vary from each other in terms of which features they
include. Applying existing analysis techniques to SPLs and their safety cases
is usually challenging because of the potentially exponential number of
products with respect to the number of supported features. In this paper, we
present a methodology and infrastructure for certified \emph{lifting} of
existing single-product safety analyses to product lines. To ensure certified
safety of our infrastructure, we implement it in an interactive theorem prover,
including formal definitions, lemmas, correctness criteria theorems, and
proofs. We apply this infrastructure to formalize and lift a Change Impact
Assessment (CIA) algorithm. We present a formal definition of the lifted
algorithm, outline its correctness proof (with the full machine-checked proof
available online), and discuss its implementation within a model management
framework.


Abstract:
  The functional form of Coulomb interactions in the transition metal
dichalcogenides and other van der Waals solids is critical to many of their
unique properties, e.g. strongly-correlated electron states, superconductivity
and emergent ferromagnetism. This paper presents measurements of key excitonic
energy levels in MoSe2/WSe2 heterostructures. These measurements are obtained
from resonance Raman experiments on specific Raman peaks only observed at
excited states of the excitons. This data is used to validate a model of the
Coulomb potential in these structures which predicts the exciton energies to
within ~5 meV / 2.5%. This model is used to determine the effect of
heterostructure formation on the single-particle band gaps of the layers and
will have a wide applicability in designing the next generation of more complex
transition metal dichalcogenide structures.


Abstract:
  This paper deals with the fully parabolic chemotaxis system of local sensing
in higher dimensions. Despite the striking similarity between this system and
the Keller--Segel system, we prove the absence of finite-time blow-up
phenomenon in this system even in the supercritical case. It means that for any
regular initial data, independently of the magnitude of mass, the classical
solution exists globally in time in the higher dimensional setting. Moreover,
for the exponential decaying motility case, it is established that solutions
may blow up at infinite time for any magnitude of mass. In order to prove our
theorem, we deal with some auxiliary identity as an evolution equation with a
time dependent operator. In view of this new perspective, the direct
consequence of the abstract theory is rich enough to establish global existence
of the system.


Abstract:
  The Internet of Medical Things (IoMT) paradigm is becoming mainstream in
multiple clinical trials and healthcare procedures. Cardiovascular diseases
monitoring, usually involving electrocardiogram (ECG) traces analysis, is one
of the most promising and high-impact applications. Nevertheless, to fully
exploit the potential of IoMT in this domain, some steps forward are needed.
First, the edge-computing paradigm must be added to the picture. A certain
level of near-sensor processing has to be enabled, to improve the scalability,
portability, reliability, responsiveness of the IoMT nodes. Second, novel,
increasingly accurate, data analysis algorithms, such as those based on
artificial intelligence and Deep Learning, must be exploited. To reach these
objectives, designers and programmers of IoMT nodes, have to face challenging
optimization tasks, in order to execute fairly complex computing tasks on
low-power wearable and portable processing systems, with tight power and
battery lifetime budgets. In this work, we explore the implementation of a
cognitive data analysis algorithm, based on a convolutional neural network
trained to classify ECG waveforms, on a resource-constrained
microcontroller-based computing platform. To minimize power consumption, we add
an adaptivity layer that dynamically manages the hardware and software
configuration of the device to adapt it at runtime to the required operating
mode. Our experimental results show that adapting the node setup to the
workload at runtime can save up to 50% power consumption. Our optimized and
quantized neural network reaches an accuracy value higher than 97% for
arrhythmia disorders detection on MIT-BIH Arrhythmia dataset.