title:
Asynchronous Distributed-Memory Triangle Counting and LCC with RMA
  Caching

abstract:
  Triangle count and local clustering coefficient are two core metrics for
graph analysis. They find broad application in analyses such as community
detection and link recommendation. Current state-of-the-art solutions suffer
from synchronization overheads or expensive pre-computations needed to
distribute the graph, achieving limited scaling capabilities. We propose a
fully asynchronous implementation for triangle counting and local clustering
coefficient based on 1D partitioning, using remote memory accesses for
transferring data and avoid synchronization. Additionally, we show how these
algorithms present data reuse on remote memory accesses and how the overall
communication time can be improved by caching these accesses. Finally, we
extend CLaMPI, a software-layer caching system for MPI RMA, to include
application-specific scores for cached entries and influence the eviction
procedure to improve caching efficiency. Our results show improvements on
shared memory, and we achieve 14x speedup from 4 to 64 nodes for the
LiveJournal1 graph on distributed memory. Moreover, we demonstrate how caching
remote accesses reduces total running time by up to 73% with respect to a
non-cached version. Finally, we compare our implementation to TriC, the 2020
graph champion paper, and achieve up to 100x faster results for scale-free
graphs.


title: Pure pairs. X. Tournaments and the strong Erdos-Hajnal property

abstract:
  A pure pair in a tournament $G$ is an ordered pair $(A,B)$ of disjoint
subsets of $V(G)$ such that every vertex in $B$ is adjacent from every vertex
in $A$. Which tournaments $H$ have the property that if $G$ is a tournament not
containing $H$ as a subtournament, and $|G|>1$, there is a pure pair $(A,B)$ in
$G$ with $|A|,|B|\ge c|G|$, where $c>0$ is a constant independent of $G$? Let
us say that such a tournament $H$ has the strong EH-property.
  As far as we know, it might be that a tournament $H$ has this property if and
only if its vertex set has a linear ordering in which its backedges form a
forest. Certainly this condition is necessary, but we are far from proving
sufficiency. We make a small step in this direction, showing that if a
tournament can be ordered with at most three backedges then it has the strong
EH-property (except for one case, that we could not decide). In particular,
every tournament with at most six vertices has the property, except for three
that we could not decide. We also give a seven-vertex tournament that does not
have the strong EH-property.
  This is related to the Erdos-Hajnal conjecture, which in one form says that
for every tournament $H$ there exists $\tau>0$ such that every tournament $G$
not containing $H$ as a subtournament has a transitive subtournament of
cardinality at least $|G|^\tau$. Let us say that a tournament $H$ satisfying
this has the EH-property. It is known that every tournament with the strong
EH-property also has the EH-property; so our result extends work by Berger,
Choromanski and Chudnovsky, who proved that every tournament with at most six
vertices has the EH-property, except for one that they did not decide.


title: Alternating runs of permutations and the central factorial numbers

abstract:
  Let R(n,k) be the number of permutations of $\{1,2,\ldots,n\}$ with k
alternating runs. In this paper, we establish the relationships between R(n,k)
and the central factorial numbers of even indices as well as the number of
signed permutations with a given number of alternating runs and the central
factorial numbers of odd indices. The explicit formulas of the peak and left
peak polynomials for permutations and the derivative polynomials of the tangent
and secant functions are also established.


title:
Plasmonic grating for circularly-polarized out-coupling of
  waveguide-enhanced spontaneous emission

abstract:
  Plasmonic metasurfaces form a convenient platform for light manipulation at
the nanoscale due to their specific localized surface plasmons. Nevertheless,
despite the high degree of light localization in metals, their intrinsic Joule
losses are often considered prevention from applications in high-quality
dielectric structures. Here, we experimentally demonstrate that in some cases,
the capabilities of plasmonic particles for light manipulation prevail over the
negative impact of absorption. We show the lattice of plasmonic nanoparticles
onto a dielectric waveguide that efficiently couples the light of both circular
polarizations to guided modes propagating in opposite directions. We
demonstrate 80% degree of circular polarization for the out-coupled emission of
GaAs-waveguide-embedded quantum dots. The results allow us to consider the
lattice as a circular-polarization-controlled grating coupler operating at
normal incidence and make this structure prospective for further implementation
as an efficient coupling interface for various integrated devices.


title:
Evaluation of non-pharmaceutical interventions and optimal strategies
  for containing the COVID-19 pandemic

abstract:
  Given multiple new COVID-19 variants are continuously emerging,
non-pharmaceutical interventions are still primary control strategies to curb
the further spread of coronavirus. However, implementing strict interventions
over extended periods of time is inevitably hurting the economy. With an aim to
solve this multi-objective decision-making problem, we investigate the
underlying associations between policies, mobility patterns, and virus
transmission. We further evaluate the relative performance of existing COVID-19
control measures and explore potential optimal strategies that can strike the
right balance between public health and socio-economic recovery for individual
states in the US. The results highlight the power of state of emergency
declaration and wearing face masks and emphasize the necessity of pursuing
tailor-made strategies for different states and phases of epidemiological
transmission. Our framework enables policymakers to create more refined designs
of COVID-19 strategies and can be extended to inform policy makers of any
country about best practices in pandemic response.


title: "If you could see me through my eyes": Predicting Pedestrian Perception

abstract:
  Pedestrians are particularly vulnerable road users in urban traffic. With the
arrival of autonomous driving, novel technologies can be developed specifically
to protect pedestrians. We propose a machine learning toolchain to train
artificial neural networks as models of pedestrian behavior. In a preliminary
study, we use synthetic data from simulations of a specific pedestrian crossing
scenario to train a variational autoencoder and a long short-term memory
network to predict a pedestrian's future visual perception. We can accurately
predict a pedestrian's future perceptions within relevant time horizons. By
iteratively feeding these predicted frames into these networks, they can be
used as simulations of pedestrians as indicated by our results. Such trained
networks can later be used to predict pedestrian behaviors even from the
perspective of the autonomous car. Another future extension will be to re-train
these networks with real-world video data.


title: Combinatorial logic devices based on a multi-path active ring circuit

abstract:
  In this work, we describe a logic device in which an act of computation is
associated with finding a path connecting input and output ports. The device is
based on an active ring circuit comprising electric and magnetic parts. The
electric part includes an amplifier, a phase shifter, and an attenuator. The
magnetic part is a multi-port magnetic matrix comprising delay lines and
frequency filters. Signals propagating on different paths may accumulate
different phase shifts. Auto-oscillations occur in the circuit when the
magnetic and electric parts match each other to meet the resonance amplitude
and phase conditions. The system naturally searches for a resonance path that
depends on the position of the electric phase shifter and amplification level.
The path is detected by the set of power sensors. The proposed logic device can
be used for solving a variety of computational problems. We present the results
of numerical modeling illustrating prime factorization and finding the shortest
path connected selected points on the mesh.


title: Radio Number for the Cartesian Product of Two Trees

abstract:
  Let $G$ be a simple connected graph. For any two vertices $u$ and $v$, let
$d(u,v)$ denote the distance between $u$ and $v$ in $G$, and let $diam(G)$
denote the diameter of $G$. A radio-labeling of $G$ is a function $f$ which
assigns to each vertex a non-negative integer (label) such that for every
distinct vertices $u$ and $v$ in $G$, it holds that $|f(u)-f(v)| \geq diam(G) -
d(u,v) +1$. The span of $f$ is the difference between the largest and smallest
labels of $f(V)$. The radio number of $G$, denoted by $rn(G)$, is the smallest
span of a radio labeling admitted by $G$. In this paper, we give a lower bound
for the radio number of the Cartesian product of two trees. Moreover, we
present three necessary and sufficient conditions, and three sufficient
conditions for the product of two trees to achieve this bound. Applying these
results, we determine the radio number of the Cartesian product of two stars as
well as a path and a star.


title: A growth estimate for the monodromy matrix of a canonical system

abstract:
  We investigate the spectrum of 2-dimensional canonical systems in the limit
circle case. It is discrete and, by the Krein-de Branges formula, cannot be
more dense than the integers. But in many cases it will be more sparse. The
spectrum of a particular selfadjoint realisation coincides with the zeroes of
one entry of the monodromy matrix of the system. Classical function theory thus
establishes an immediate connection between the growth of the monodromy matrix
and the distribution of the spectrum. We prove a generic and flexibel upper
estimate for the monodromy matrix, use it to prove a bound for the case of a
continuous Hamiltonian, and construct examples which show that this bound is
sharp. The first two results run along the lines of earlier work of R.Romanov,
but significantly improve upon these results. This is seen even on the rough
scale of exponential order.


title: The dangers in algorithms learning humans' values and irrationalities

abstract:
  For an artificial intelligence (AI) to be aligned with human values (or human
preferences), it must first learn those values. AI systems that are trained on
human behavior, risk miscategorising human irrationalities as human values --
and then optimising for these irrationalities. Simply learning human values
still carries risks: AI learning them will inevitably also gain information on
human irrationalities and human behaviour/policy. Both of these can be
dangerous: knowing human policy allows an AI to become generically more
powerful (whether it is partially aligned or not aligned at all), while
learning human irrationalities allows it to exploit humans without needing to
provide value in return. This paper analyses the danger in developing
artificial intelligence that learns about human irrationalities and human
policy, and constructs a model recommendation system with various levels of
information about human biases, human policy, and human values. It concludes
that, whatever the power and knowledge of the AI, it is more dangerous for it
to know human irrationalities than human values. Thus it is better for the AI
to learn human values directly, rather than learning human biases and then
deducing values from behaviour.


title:
Contact-Implicit Trajectory Optimization with Hydroelastic Contact and
  iLQR

abstract:
  Contact-implicit trajectory optimization offers an appealing method of
automatically generating complex and contact-rich behaviors for robot
manipulation and locomotion. The scalability of such techniques has been
limited, however, by the challenge of ensuring both numerical reliability and
physical realism. In this paper, we present preliminary results suggesting that
the Iterative Linear Quadratic Regulator (iLQR) algorithm together with the
recently proposed pressure-field-based hydroelastic contact model enables
reliable and physically realistic trajectory optimization through contact. We
use this approach synthesize contact-rich behaviors like quadruped locomotion
and whole-arm manipulation. Furthermore, open-loop playback on a Kinova Gen3
robot arm demonstrates the physical accuracy of the whole-arm manipulation
trajectories. Code is available at https://bit.ly/ilqr_hc and videos can be
found at https://youtu.be/IqxJKbM8_ms .


title: Virial Clumps in Central Molecular Zone Clouds

abstract:
  CMZoom survey observations with the Submillimeter Array are analyzed to
describe the virial equilibrium (VE) and star-forming potential of 755 clumps
in 22 clouds in the Central Molecular Zone (CMZ) of the Milky Way. In each
cloud, nearly all clumps follow the column-density-mass trend N~M^s, where s =
0.38 +- 0.03 is near the pressure-bounded limit s=1/3. This trend is expected
when gravitationally unbound clumps in VE have similar velocity dispersion and
external pressure. Nine of these clouds also harbor one or two distinctly more
massive clumps. These properties allow a VE model of bound and unbound clumps
in each cloud, where the most massive clump has the VE critical mass. These
models indicate that 213 clumps have velocity dispersion 1-2 km s^(-1), mean
external pressure 0.5-4 x 10^8 cm^(-3) K, bound clump fraction 0.06, and
typical virial parameter alpha=4-15. These mostly unbound clumps may be in VE
with their turbulent cloud pressure, possibly driven by inflow from the
galactic bar. In contrast, most Sgr B2 clumps are bound according to their
associated sources and N-M trends. When the CMZ clumps are combined into mass
distributions, their typical power-law slope is analyzed with a stopped
accretion model. It also indicates that most clumps are unbound and cannot grow
significantly, due to their similar time scales of accretion and dispersal,
~0.2 Myr. Thus, virial and dynamical analyses of the most extensive clump
census available indicate that star formation in the CMZ may be suppressed by a
significant deficit of gravitationally bound clumps.


title: On Global Asymptotic Stability for the diffusive Carr-Penrose Model

abstract:
  This paper is concerned with large time behavior of the solution to a
diffusive perturbation of the linear LSW model introduced by Carr and Penrose.
Like the LSW model, the Carr-Penrose model has a family of rapidly decreasing
self-similar solutions, depending on a parameter $\beta$ with $0<\beta\le 1$.
It is shown that if the initial data has compact support then the solution to
the diffusive model at large time approximates the $\beta=1$ self-similar
solution. This result supports the intuition that diffusion provides the
mechanism whereby the $\beta=1$ self-similar solution of the LSW model is the
only physically relevant one.


title: Measurement of high-pressure xenon gas absorption in acrylic

abstract:
  Acrylic is a popular structural material in experiments requiring low
background because of its radio-purity, machinability, and mechanical strength.
However, its porosity may cause significant gas absorption and influence the
detector stability in the long term. The interaction between acrylic and other
detector materials becomes one of the key concerns in the detector design. In
this paper, we carry out an experiment to measure quantitatively the absorption
process of high-pressure xenon gas into acrylic. A specific setup is designed
for the measurement, and systematic measurements are done to obtain a result of
the absorption amount: 0.98 g xenon into 332 g of acrylic.


title: On Codes and Learning With Errors over Function Fields

abstract:
  It is a long standing open problem to find search to decision reductions for
structured versions of the decoding problem of linear codes. Such results in
the lattice-based setting have been carried out using number fields:
Polynomial-LWE, Ring-LWE, Module-LWE and so on. We propose a function field
version of the LWE problem. This new framework leads to another point of view
on structured codes, e.g. quasi-cyclic codes, strengthening the connection
between lattice-based and code-based cryptography. In particular, we obtain the
first search to decision reduction for structured codes. Following the
historical constructions in lattice-based cryptography, we instantiate our
construction with function fields analogues of cyclotomic fields, namely
Carlitz extensions, leading to search to decision reductions on various
versions of Ring-LPN, which have applications to secure multi party computation
and to an authentication protocol.


title: Lagrangian Grassmannians, CKP hierarchy and hyperdeterminantal relations

abstract:
  This work concerns the relation between the geometry of Lagrangian
Grassmannians and the CKP integrable hierarchy. For a complex vector space $V$
of dimension $N$, and ${\mathcal H}_N:=V\oplus V^*$ the associated symplectic
space, with canonical symplectic structure $\omega_N$, the exterior space
$\Lambda({\mathcal H}_N)$ is decomposed into a direct sum of irreducible
$Sp({\mathcal H}_N, \omega_N)$ submodules, and a basis adapted to this
decomposition is constructed. The Lagrangian map ${\mathcal L}: Gr^{\mathcal
L}_V({\mathcal H}_N, \omega_N) \rightarrow P(\Lambda(V))$ is defined by
restricting the Pl\"ucker map to the Lagrangian Grassmannian $Gr^{\mathcal
L}_V({\mathcal H}_N,\omega_N)$ of maximal isotropic subspaces and composing it
with projection to the subspace of symmetric elements of $\Lambda^N({\mathcal
H}_N)$ under dualization. In terms of the affine coordinate matrix on the big
cell, this reduces to the principal minors map, whose image is cut out by the
$2 \times 2 \times 2$ quartic hyperdeterminantal relations. To apply this to
the CKP hierarchy, the Lagrangian Grassmannian framework is extended to
infinite dimensions, with ${\mathcal H}_N$ replaced by a polarized Hilbert
space $ {\mathcal H}$, with symplectic form $\omega$. The fermionic Fock space
${\mathcal F}= \Lambda^{\infty/2}{\mathcal H}$ is decomposed into a direct sum
of irreducible $Sp({\mathcal H},\omega)$ representations and the infinite
dimensional Lagrangian map is defined. The linear constraints defining
reduction to the CKP hierarchy are expressed as a fermionic null condition and
the infinite analogue of the hyperdeterminantal relations is deduced. A
multiparametric family of such relations is shown to be satisfied by the
evaluation of the $\tau$-function at translates of a point in the space of odd
flow variables along the cubic lattices generated by power sums in the
parameters.


title: Dynamic and thermodynamic bounds for collective motor-driven transport

abstract:
  Molecular motors work collectively to transport cargo within cells, with
anywhere from one to several hundred motors towing a single cargo. For a broad
class of collective-transport systems, we use tools from stochastic
thermodynamics to derive a new lower bound for the entropy production rate
which is tighter than the second law. This implies new bounds on the velocity,
efficiency, and precision of general transport systems and a set of analytic
Pareto frontiers for identical motors. In a specific model, we identify
conditions for saturation of these Pareto frontiers.


title: A tensor norm approach to quantum compatibility

abstract:
  Measurement incompatibility is one of the most striking examples of how
quantum physics is different from classical physics. Two measurements are
incompatible if they cannot arise via classical post-processing from a third
one. A natural way to quantify incompatibility is in terms of noise robustness.
In the present article, we review recent results on the maximal noise
robustness of incompatible measurements, which have been obtained by the
present authors using free spectrahedra, and rederive them using tensor norms.
In this way, we make them accessible to a broader audience from quantum
information theory and mathematical physics and contribute to the fruitful
interactions between Banach space theory and quantum information theory. We
also describe incompatibility witnesses using tensor norm and matrix convex set
duality, emphasizing the relation between the different notions of witnesses.


title:
Optical measurement of electron spins in quantum dots: Quantum Zeno
  effects

abstract:
  We describe the effects of the quantum back action under continuous optical
measurement of electron spins in quantum dots. We consider the system
excitation by elliptically polarized light close to the trion resonance, which
allows for the simultaneous spin orientation and measurement. We
microscopically demonstrate that the nuclei-induced spin relaxation can be both
suppressed and accelerated by the continuous spin measurement due to the
quantum Zeno and anti-Zeno effects, respectively. Our theoretical predictions
can be directly compared with the future experimental results and
straightforwardly generalized for the pump-probe experiments.


title:
Multi-FPGA Designs and Scaling of HPC Challenge Benchmarks via MPI and
  Circuit-Switched Inter-FPGA Networks

abstract:
  While FPGA accelerator boards and their respective high-level design tools
are maturing, there is still a lack of multi-FPGA applications, libraries, and
not least, benchmarks and reference implementations towards sustained HPC usage
of these devices. As in the early days of GPUs in HPC, for workloads that can
reasonably be decoupled into loosely coupled working sets, multi-accelerator
support can be achieved by using standard communication interfaces like MPI on
the host side. However, for performance and productivity, some applications can
profit from a tighter coupling of the accelerators. FPGAs offer unique
opportunities here when extending the dataflow characteristics to their
communication ininterfaces. In this work, we extend the HPCC FPGA benchmark
suite by multi-FPGA support and three missing benchmarks that particularly
characterize or stress inter-device communication: b_eff, PTRANS, and LINPACK.
With all benchmarks implemented for current boards with Intel and Xilinx FPGAs,
we established a baseline for multi-FPGA performance. Additionally, for the
communication-centric benchmarks, we explored the potential of direct
FPGA-to-FPGA communication with a circuit-switched inter-FPGA network that is
currently only available for one of the boards. The evaluation with parallel
execution on up to 26 FPGA boards makes use of one of the largest academic FPGA
installations.


title: Risk-Neutral Market Simulation

abstract:
  We develop a risk-neutral spot and equity option market simulator for a
single underlying, under which the joint market process is a martingale. We
leverage an efficient low-dimensional representation of the market which
preserves no static arbitrage, and employ neural spline flows to simulate
samples which are free from conditional drifts and are highly realistic in the
sense that among all possible risk-neutral simulators, the obtained
risk-neutral simulator is the closest to the historical data with respect to
the Kullback-Leibler divergence. Numerical experiments demonstrate the
effectiveness and highlight both drift removal and fidelity of the calibrated
simulator.

categories: q-fin.CP cs.LG q-fin.GN q-fin.RM stat.ML


title: Classical Verification of Quantum Computations in Linear Time

abstract:
  In the quantum computation verification problem, a quantum server wants to
convince a client that the output of evaluating a quantum circuit $C$ is some
result that it claims. This problem is considered very important both
theoretically and practically in quantum computation [arXiv:1709.06984],
[arXiv:1704.04487], [arXiv:1209.0449]. The client is considered to be limited
in computational power, and one desirable property is that the client can be
completely classical, which leads to the classical verification of quantum
computation (CVQC) problem. In terms of total time complexity of both the
client and the server, the fastest single-server CVQC protocol so far has
complexity $O(poly(\kappa)|C|^3)$ where $|C|$ is the size of the circuit to be
verified, given by Mahadev [arXiv:1804.01082].
  In this work, by developing new techniques, we give a new CVQC protocol with
complexity $O(poly(\kappa)|C|)$, which is significantly faster than existing
protocols. Our protocol is secure in the quantum random oracle model
[arXiv:1008.0931] assuming the existence of noisy trapdoor claw-free functions
[arXiv:1804.00640], which are both extensively used assumptions in quantum
cryptography. Along the way, we also give a new classical channel remote state
preparation protocol for states in
$\{|+_\theta\rangle=\frac{1}{\sqrt{2}}(|0\rangle+e^{i\theta\pi/4}|1\rangle):\theta\in
\{0,1\cdots 7\}\}$, another basic primitive in quantum cryptography. Our
protocol allows for parallel verifiable preparation of $L$ independently random
states in this form (up to a constant overall error and a possibly unbounded
server-side isometry), and runs in only $O(poly(\kappa)L)$ time and constant
rounds; for comparison, existing works (even for possibly simpler state
families) all require very large or unestimated time and round complexities
[arXiv:1904.06320][arXiv:1904.06303][arXiv:2201.13445][arXiv:2201.13430].


title: Global-in-time semiclassical regularity for the Hartree-Fock equation

abstract:
  For arbitrarily large times $T>0$, we prove the uniform in $\hbar$
propagation of semiclassical regularity for the solutions to the Hartree-Fock
equation with singular interactions of the form $V(x)=\pm\,|x|^{-a}$ with
$a\in(0,\frac12)$. As a byproduct of this result, we extend to arbitrarily long
times the derivation of the Hartree-Fock and the Vlasov equations from the
many-body dynamics provided in [J. Chong, L. Lafleche, C. Saffirio:
arXiv:2103.10946 (2021)].


title: Using the Dafermos Entropy Rate Criterion in Numerical Schemes

abstract:
  The following work concerns the construction of an entropy dissipative finite
volume solver based on the convex combination of an entropy conservative and an
entropy dissipative flux. We aim to construct a semidiscrete scheme that is
entropy stable in the sense of the entropy criterion of Dafermos as well as in
the classical sense entropy dissipative. The proposed semidiscrete scheme shows
nice properties like $2p$ order accuracy in smooth regions as well as a
non-oscillatory behavior around shocks.


title: Resolving label uncertainty with implicit posterior models

abstract:
  We propose a method for jointly inferring labels across a collection of data
samples, where each sample consists of an observation and a prior belief about
the label. By implicitly assuming the existence of a generative model for which
a differentiable predictor is the posterior, we derive a training objective
that allows learning under weak beliefs. This formulation unifies various
machine learning settings; the weak beliefs can come in the form of noisy or
incomplete labels, likelihoods given by a different prediction mechanism on
auxiliary input, or common-sense priors reflecting knowledge about the
structure of the problem at hand. We demonstrate the proposed algorithms on
diverse problems: classification with negative training examples, learning from
rankings, weakly and self-supervised aerial imagery segmentation,
co-segmentation of video frames, and coarsely supervised text classification.


title:
Measurement and analysis of lobar lung deformation after a change of
  patient position during video-assisted thoracoscopic surgery

abstract:
  Video-assisted thoracoscopic surgery (VATS) is a minimally invasive surgical
technique for the diagnosis and treatment of early-stage lung cancer. During
VATS, large lung deformation occurs as a result of a change of patient position
and a pneumothorax (lung deflation), which hinders the intraoperative
localization of pulmonary nodules. Modeling lung deformation during VATS for
surgical navigation is desirable, but the mechanisms causing such deformation
are yet not well-understood. In that regard, in this first study, we estimate,
quantify and analyze the lung deformation occurring after a change of patient
position during VATS. We used deformable image registration to estimate the
lung deformation between a preoperative CT (in supine position) and an
intraoperative CBCT (in lateral decubitus position) of six VATS clinical cases.
We accounted for sliding motion between lobes and against the thoracic wall by
individually registering each structure, and obtained consistently low average
target registration errors ($\le$ 1.0 mm). We observed large lung displacement
(up to 40 mm); considerable sliding motion between lobes and against the
thoracic wall (up to 30 mm); and localized volume changes indicating
deformation. These findings demonstrate the complexity of the change of patient
position phenomenon, which should necessarily be taken into account to model
lung deformation for intraoperative guidance during VATS.


title: Systematic Stabilization of Constrained Piecewise Affine Systems

abstract:
  This paper presents an efficient, offline method to simultaneously synthesize
controllers and seek closed-loop Lyapunov functions for constrained piecewise
affine systems on triangulated subsets of the admissible states. Triangulation
refinements explore a rich class of controllers and Lyapunov functions. Since
an explicit Lipschitz Lyapunov function is found, an invariant subset of the
closed-loop region of attraction is obtained. Moreover, it is a control
Lyapunov function, so minimum-norm controllers can be realized through online
quadratic programming. It is formulated as a sequence of semi-definite
programs. The method avoids computationally burdensome non-convex optimizations
and a-priori design choices that are typical of similar existing methods.


title: Subconvexity in inhomogeneous Vinogradov systems

abstract:
  When $k$ and $s$ are natural numbers and $\mathbf h\in \mathbb Z^k$, denote
by $J_{s,k}(X;\mathbf h)$ the number of integral solutions of the system \[
\sum_{i=1}^s(x_i^j-y_i^j)=h_j\quad (1\le j\le k), \] with $1\le x_i,y_i\le X$.
When $s<k(k+1)/2$ and $(h_1,\ldots ,h_{k-1})\ne {\mathbf 0}$, Brandes and
Hughes have shown that $J_{s,k}(X;\mathbf h)=o(X^s)$. In this paper we improve
on quantitative aspects of this result, and, subject to an extension of the
main conjecture in Vinogradov's mean value theorem, we obtain an asymptotic
formula for $J_{s,k}(X;\mathbf h)$ in the critical case $s=k(k+1)/2$. The
latter requires minor arc estimates going beyond square-root cancellation.


title: Adams-type maps are not stable under composition

abstract:
  We give a simple counterexample to the plausible conjecture that Adams-type
maps of ring spectra are stable under composition. We then show that over a
field, this failure is quite extreme, as any map of
$\mathbb{E}_{\infty}$-algebras is a transfinite composition of Adams-type maps.


title: Deep, Deep Learning with BART

abstract:
  Purpose: To develop a deep-learning-based image reconstruction framework for
reproducible research in MRI.
  Methods: The BART toolbox offers a rich set of implementations of calibration
and reconstruction algorithms for parallel imaging and compressed sensing. In
this work, BART was extended by a non-linear operator framework that provides
automatic differentiation to allow computation of gradients. Existing
MRI-specific operators of BART, such as the non-uniform fast Fourier transform,
are directly integrated into this framework and are complemented by common
building blocks used in neural networks. To evaluate the use of the framework
for advanced deep-learning-based reconstruction, two state-of-the-art unrolled
reconstruction networks, namely the Variational Network [1] and MoDL [2], were
implemented.
  Results: State-of-the-art deep image-reconstruction networks can be
constructed and trained using BART's gradient based optimization algorithms.
The BART implementation achieves a similar performance in terms of training
time and reconstruction quality compared to the original implementations based
on TensorFlow.
  Conclusion: By integrating non-linear operators and neural networks into
BART, we provide a general framework for deep-learning-based reconstruction in
MRI.


title: The scatter in the galaxy-halo connection: a machine learning analysis

abstract:
  We apply machine learning, a powerful method for uncovering complex
correlations in high-dimensional data, to the galaxy-halo connection of
cosmological hydrodynamical simulations. The mapping between galaxy and halo
variables is stochastic in the absence of perfect information, but conventional
machine learning models are deterministic and hence cannot capture its
intrinsic scatter. To overcome this limitation, we design an ensemble of neural
networks with a Gaussian loss function that predict probability distributions,
allowing us to model statistical uncertainties in the galaxy-halo connection as
well as its best-fit trends. We extract a number of galaxy and halo variables
from the Horizon-AGN and IllustrisTNG100-1 simulations and quantify the extent
to which knowledge of some subset of one enables prediction of the other. This
allows us to identify the key features of the galaxy-halo connection and
investigate the origin of its scatter in various projections. We find that
while halo properties beyond mass account for up to 50 per cent of the scatter
in the halo-to-stellar mass relation, the prediction of stellar half-mass
radius or total gas mass is not substantially improved by adding further halo
properties. We also use these results to investigate semi-analytic models for
galaxy size in the two simulations, finding that assumptions relating galaxy
size to halo size or spin are not successful.


title:
Thermodynamic phase transition of Euler-Heisenberg-AdS black hole on
  free energy landscape

abstract:
  We study the first order phase transition of Euler-Heisenberg-AdS black hole
based on free energy landscape. By imposing the reflecting boundary conditions
and the initial conditions, we discuss the probabilistic evolution for the
black hole states using the Fokker-Planck equation. With the Euler-Heisenberg
parameter (quantum electrodynamics parameter) $a<0$, we observe that the large
(small) Euler-Heisenberg-AdS black hole can have the probability to switch to
the small (large) black hole. The coexistent small and large black hole states
can be acquired for a certain condition. With $0\leq a\leq \frac{32}{7} Q^2 $,
the system may be in no black hole state for some conditions. For the case of
the initial wave packet is located at the small (large) black hole state, we
observe that it can have the chance to transition to large (small) black hole
state. Furthermore, we can obtain the thermodynamic stable state from the final
stationary distribution which is determined by the Euler-Heisenberg parameter
and the temperature.


title:
The Aronsson Equation for Absolute Minimizers of Supremal Functionals in
  Carnot-Carath\'eodory Spaces

abstract:
  Given a $C^2$ family of vector fields $X_1,...,X_m$ which induces a
continuous Carnot-Carath\'eodory distance, we show that any absolute minimizer
of a supremal functional defined by a $C^2$ quasiconvex Hamiltonian $f(x, z,
p)$, allowing $z$-variable dependence, is a viscosity solution to the Aronsson
equation $-\langle X(f(x, u(x), Xu(x))), D_p f(x, u(x), Xu(x))\rangle = 0.$


title: SUNet: Swin Transformer UNet for Image Denoising

abstract:
  Image restoration is a challenging ill-posed problem which also has been a
long-standing issue. In the past few years, the convolution neural networks
(CNNs) almost dominated the computer vision and had achieved considerable
success in different levels of vision tasks including image restoration.
However, recently the Swin Transformer-based model also shows impressive
performance, even surpasses the CNN-based methods to become the
state-of-the-art on high-level vision tasks. In this paper, we proposed a
restoration model called SUNet which uses the Swin Transformer layer as our
basic block and then is applied to UNet architecture for image denoising. The
source code and pre-trained models are available at
https://github.com/FanChiMao/SUNet.


title:
Proceedings of the Artificial Intelligence for Cyber Security (AICS)
  Workshop at AAAI 2022

abstract:
  The workshop will focus on the application of AI to problems in cyber
security. Cyber systems generate large volumes of data, utilizing this
effectively is beyond human capabilities. Additionally, adversaries continue to
develop new attacks. Hence, AI methods are required to understand and protect
the cyber domain. These challenges are widely studied in enterprise networks,
but there are many gaps in research and practice as well as novel problems in
other domains.
  In general, AI techniques are still not widely adopted in the real world.
Reasons include: (1) a lack of certification of AI for security, (2) a lack of
formal study of the implications of practical constraints (e.g., power, memory,
storage) for AI systems in the cyber domain, (3) known vulnerabilities such as
evasion, poisoning attacks, (4) lack of meaningful explanations for security
analysts, and (5) lack of analyst trust in AI solutions. There is a need for
the research community to develop novel solutions for these practical issues.


title: Classification Under Partial Reject Options

abstract:
  We study set-valued classification for a Bayesian model where data originates
from one of a finite number $N$ of possible hypotheses. Thus we consider the
scenario where the size of the classified set of categories ranges from 0 to
$N$. Empty sets corresponds to an outlier, size 1 represents a firm decision
that singles out one hypotheses, size $N$ corresponds to a rejection to
classify, whereas sizes $2\ldots,N-1$ represent a partial rejection, where some
hypotheses are excluded from further analysis. We introduce a general framework
of reward functions with a set-valued argument and derive the corresponding
optimal Bayes classifiers, for a homogeneous block of hypotheses and for when
hypotheses are partitioned into blocks, where ambiguity within and between
blocks are of different severity. We illustrate classification using an
ornithological dataset, with taxa partitioned into blocks and parameters
estimated using MCMC. The associated reward function's tuning parameters are
chosen through cross-validation.


title: Markov properties for Gaussian fields associated with Dirichlet forms

abstract:
  We prove the equivalence of the local property for an irreducible regular
Dirichlet form and the Markov property for the Gaussian field associated with
the Dirichlet form. Moreover we introduce a strong Markov property for Gaussian
fields and present some sufficient conditions for this to hold.


title:
Szeg\H{o} kernel asymptotics and concentration of Husimi Distributions
  of eigenfunctions

abstract:
  We work on the boundary $\partial M_\tau$ of a Grauert tube of a closed, real
analytic Riemannian manifold $M$. The Toeplitz operator $\Pi_\tau
D_{\sqrt{\rho}} \Pi_\tau$ associated to the Reeb vector field is a positive,
self-adjoint, elliptic operator on $H^2(\partial M_\tau)$. We compute $\lambda
\to \infty$ asymptotics under parabolic rescaling in a neighborhood of the
geodesic (Reeb) flow $G^{t}_{\tau} = \exp t\Xi_{\sqrt{\rho}}$ for the spectral
projection kernel $\Pi_{\chi, \lambda}$ associated to $\Pi_\tau D_{\sqrt{\rho}}
\Pi_\tau$. We also compute scaling asymptotics for tempered sums of Husimi
distributions (analytic continuations) on $\partial M_\tau$ of Laplace
eigenfunctions on $M$. Both asymptotic formulae can be expressed in terms of
the metaplectic representation of the linearization of the geodesic flow
$G^{t}_\tau$ on Bargmann--Fock space. As a corollary, we obtain sharp $L^p \to
L^{q}$ norm estimates for $\Pi_{\chi, \lambda}$ and sharp $L^p$ estimates for
Husimi distributions.


title:
Distributed-MPC with Data-Driven Estimation of Bus Admittance Matrix in
  Voltage Stabilization

abstract:
  This paper presents a distributed model-predictive control (MPC) design for
real-time voltage stabilization in power systems, allowing the bus admittance
matrix $\mathbf{Y} = \mathbf{G} + j\mathbf{B}$ to be not known a priori (it may
be time-varying), and so is estimated online. The prevalent control designs are
either centralized and optimal but lack scalability and are subject to network
attacks, or decentralized that are scalable and less vulnerable to attacks but
are suboptimal. The proposed distributed solution offers the attractive
features of both types of schemes, namely, optimality, scalability, as well as
enhanced security to network attacks. In addition, since acquiring the exact
knowledge of the line conductance and susceptance, which are required to form
$\mathbf{Y}$, is in general challenging, the presented framework integrates
data-driven estimation of $\mathbf{Y}$ to circumvent this challenge. We first
introduce the centralized version of the formulation, and next transfer it to a
distributed version for efficiency, scalability, and attack-resilience,
leveraging the graph structure of the power system. Only local computation and
communication are used for (i) computing local control via distributed
optimization (which is solved by the alternating direction method of
multipliers ADMM), as well as (ii) data-driven estimation of $\mathbf{Y}$. The
performance of the proposed methodology is validated using numerical examples
of the IEEE-30 Bus and IEEE-57 Bus systems.


title: Indirect searches for New Physics via flavour observables

abstract:
  Precision measures of electroweak and flavour observables, at both low and
high energies, are highly complementary to direct searches for New Physics at
high-energy colliders. Despite the discovery of the Higgs boson at the Large
Hadron Collider, and of the overwhelming successes of the Standard Model,
several observational and theoretical problems remain to be addressed. In
addition to neutrino oscillation phenomena, the Standard Model fails to explain
the baryon asymmetry of the Universe, and does not offer a viable dark matter
candidate. In recent years, numerous deviations between the Standard Model
prediction and experimental measurements have been identified; interestingly,
most are closely connected to lepton flavours.
  In this thesis we have explored several aspects of flavour physics, focusing
on the phenomenological implications of models of massive neutrinos, and of
several Standard Model extensions capable of accommodating current tensions on
anomalous magnetic moments of charged leptons and several $B$-meson decay
observables.


title:
The generalized quasiharmonic approximation via space group irreducible
  derivatives

abstract:
  The quasiharmonic approximation (QHA) is the simplest nontrivial
approximation for interacting phonons under constant pressure, bringing the
effects of anharmonicity into temperature dependent observables. Nonetheless,
the QHA is often implemented with additional approximations due to the
complexity of computing phonons under arbitrary strains, and the generalized
QHA, which employs constant stress boundary conditions, has not been completely
developed. Here we formulate the generalized QHA, providing a practical
algorithm for computing the strain state and other observables as a function of
temperature and true stress. We circumvent the complexity of computing phonons
under arbitrary strains by employing irreducible second order displacement
derivatives of the Born-Oppenheimer potential and their strain dependence,
which are efficiently and precisely computed using the lone irreducible
derivative approach. We formulate two complementary strain parametrizations: a
discretized strain grid interpolation and a Taylor series expansion in
symmetrized strain. We illustrate our approach by evaluating the temperature
and pressure dependence of the elastic constant tensor and the thermal
expansion in thoria (ThO$_2$) using density functional theory with three
exchange-correlation functionals. The QHA results are compared to our
measurements of the elastic constant tensor using time domain Brillouin
scattering and inelastic neutron scattering. Our irreducible derivative
approach simplifies the implementation of the generalized QHA, which will
facilitate reproducible, data driven applications.


title: Reduced Order Model Closures: A Brief Tutorial

abstract:
  In this paper, we present a brief tutorial on reduced order model (ROM)
closures. First, we carefully motivate the need for ROM closure modeling in
under-resolved simulations. Then, we construct step by step the ROM closure
model by extending the classical Galerkin framework to the spaces of resolved
and unresolved scales. Finally, we develop the data-driven variational
multiscale ROM closure and then we test it in fluid flow simulations.
  Our tutorial on ROM closures is structured as a sequence of questions and
answers, and is aimed at first year graduate students and advanced
undergraduate students. Our goal is not to explain the "how," but the "why."
That is, we carefully explain the principles used to develop ROM closures,
without focusing on particular approaches. Furthermore, we try to keep the
technical details to a minimum and describe the general ideas in broad terms
while citing appropriate references for details.


title: Description Logic EL++ Embeddings with Intersectional Closure

abstract:
  Many ontologies, in particular in the biomedical domain, are based on the
Description Logic EL++. Several efforts have been made to interpret and exploit
EL++ ontologies by distributed representation learning. Specifically, concepts
within EL++ theories have been represented as n-balls within an n-dimensional
embedding space. However, the intersectional closure is not satisfied when
using n-balls to represent concepts because the intersection of two n-balls is
not an n-ball. This leads to challenges when measuring the distance between
concepts and inferring equivalence between concepts. To this end, we developed
EL Box Embedding (ELBE) to learn Description Logic EL++ embeddings using
axis-parallel boxes. We generate specially designed box-based geometric
constraints from EL++ axioms for model training. Since the intersection of
boxes remains as a box, the intersectional closure is satisfied. We report
extensive experimental results on three datasets and present a case study to
demonstrate the effectiveness of the proposed method.


title:
Domain Knowledge-Informed Self-Supervised Representations for Workout
  Form Assessment

abstract:
  Maintaining proper form while exercising is important for preventing injuries
and maximizing muscle mass gains. While fitness apps are becoming popular, they
lack the functionality to detect errors in workout form. Detecting such errors
naturally requires estimating users' body pose. However, off-the-shelf pose
estimators struggle to perform well on the videos recorded in gym scenarios due
to factors such as camera angles, occlusion from gym equipment, illumination,
and clothing. To aggravate the problem, the errors to be detected in the
workouts are very subtle. To that end, we propose to learn exercise-specific
representations from unlabeled samples such that a small dataset annotated by
experts suffices for supervised error detection. In particular, our domain
knowledge-informed self-supervised approaches exploit the harmonic motion of
the exercise actions, and capitalize on the large variances in camera angles,
clothes, and illumination to learn powerful representations. To facilitate our
self-supervised pretraining, and supervised finetuning, we curated a new
exercise dataset, Fitness-AQA, comprising of three exercises: BackSquat,
BarbellRow, and OverheadPress. It has been annotated by expert trainers for
multiple crucial and typically occurring exercise errors. Experimental results
show that our self-supervised representations outperform off-the-shelf 2D- and
3D-pose estimators and several other baselines.


title:
State-of-the-Art in the Architecture, Methods and Applications of
  StyleGAN

abstract:
  Generative Adversarial Networks (GANs) have established themselves as a
prevalent approach to image synthesis. Of these, StyleGAN offers a fascinating
case study, owing to its remarkable visual quality and an ability to support a
large array of downstream tasks. This state-of-the-art report covers the
StyleGAN architecture, and the ways it has been employed since its conception,
while also analyzing its severe limitations. It aims to be of use for both
newcomers, who wish to get a grasp of the field, and for more experienced
readers that might benefit from seeing current research trends and existing
tools laid out. Among StyleGAN's most interesting aspects is its learned latent
space. Despite being learned with no supervision, it is surprisingly
well-behaved and remarkably disentangled. Combined with StyleGAN's visual
quality, these properties gave rise to unparalleled editing capabilities.
However, the control offered by StyleGAN is inherently limited to the
generator's learned distribution, and can only be applied to images generated
by StyleGAN itself. Seeking to bring StyleGAN's latent control to real-world
scenarios, the study of GAN inversion and latent space embedding has quickly
gained in popularity. Meanwhile, this same study has helped shed light on the
inner workings and limitations of StyleGAN. We map out StyleGAN's impressive
story through these investigations, and discuss the details that have made
StyleGAN the go-to generator. We further elaborate on the visual priors
StyleGAN constructs, and discuss their use in downstream discriminative tasks.
Looking forward, we point out StyleGAN's limitations and speculate on current
trends and promising directions for future research, such as task and target
specific fine-tuning.


title:
A probabilistic result on impulsive noise reduction in Topological Data
  Analysis through Group Equivariant Non-Expansive Operators

abstract:
  In recent years, group equivariant non-expansive operators (GENEOs) have
attracted attention in the fields of Topological Data Analysis and Machine
Learning. In this paper we show how these operators can be of use also for the
removal of impulsive noise and to increase the stability of TDA in the presence
of noisy data. In particular, we prove that GENEOs can control the expected
value of the perturbation of persistence diagrams caused by uniformly
distributed impulsive noise, when data are represented by $L$-Lipschitz
functions from $\mathbb{R}$ to $\mathbb{R}$.


title: Rigidity of Automorphic Galois Representations over CM Fields

abstract:
  We show the vanishing of adjoint Bloch-Kato Selmer groups of automorphic
Galois representations over CM fields. This proves their rigidity in the sense
that they have no deformations which are de Rham. In order for this to make
sense we also prove that automorphic Galois representations over CM fields are
de Rham themselves. Our methods draw heavily from the 10 author paper, where
these Galois representations were studied extensively. Another crucial piece of
inspiration comes from the work of P. Allen who used the smoothness of certain
local deformation rings in characteristic $0$ to obtain rigidity in the
polarized case.


title:
Confinement in QCD and generic Yang-Mills theories with matter
  representations

abstract:
  We derive the low-energy limit of quantum chromodynamics (QCD) and show that
in the 't~Hooft limit, i.e.\ for a very large number of colors and increasing
't Hooft coupling, quark confinement is recovered. The low energy limit of the
theory turns out to be a non-local Nambu--Jona-Lasinio (NJL) model. The effect
of non-locality, arising from a gluon propagator that fits quite well to the
profile of an instanton liquid, is to produce a phase transition from a chiral
condensate to an instanton liquid, as the coupling increases with lower
momentum. This phase transition suffices to move the poles of the quark
propagator to the complex field. As a consequence, free quarks are no longer
physical states in the spectrum of the theory.


title:
Generalized Oppenheimer-Snyder Gravitational Collapse into Regular Black
  holes

abstract:
  We shall study the formation of a particular class of regular black holes
from the gravitational collapse of a massive star. The inside geometry is
described by spatially flat Friedmann-Robertson-Walker metric and the stellar
matter is distributed uniformly without any pre-assumption about its equation
of state. Our model is a generalization of Oppenheimer-Snyder collapse for
regular black holes. We have obtained the density and pressure of star by
applying the condition of smooth joining of metrics at the freely falling
surface of star. Specifying the regular black holes to Hayward and Bardeen
cases, we see that the stellar matter is described by a polytropic equation of
state and moreover, for the radius smaller than a certain value, the strong
energy condition becomes invalid. Then for both black holes, the interior
apparent and event horizons and also the stellar surface are obtained as
functions of the proper time of star. At the end, we have constructed a new two
parametric family of regular black holes jointed smoothly to the flat
Friedmann-Robertson-Walker interior metric of a polytropic star with an
arbitrary index.


title: Arline Benchmarks: Automated Benchmarking Platform for Quantum Compilers

abstract:
  Efficient compilation of quantum algorithms is vital in the era of Noisy
Intermediate-Scale Quantum (NISQ) devices. While multiple open-source quantum
compilation and circuit optimization frameworks are available, e.g. IBM Qiskit,
CQC Tket, Google Cirq, Rigetti Quilc, PyZX, their relative performance is not
always clear to a quantum programmer. The growth of complexity and diversity of
quantum circuit compilation algorithms creates a demand for a dedicated tool
for cross-benchmarking and profiling of inner workflow of the quantum
compilation stack. We present an open-source software package, Arline
Benchmarks, that is designed to perform automated benchmarking of quantum
compilers with the focus on NISQ applications. The name "Arline" was given in
honour of Arline Greenbaum Feynman, the first wife of Richard Feynman, the
pioneer of quantum computing. We compared several quantum compilation
frameworks based on a set of important metrics such as post-optimization gate
counts, circuit depth, hardware-dependent circuit cost function, compiler run
time etc. with a detailed analysis of metrics for each compilation stage. We
performed a variety of compiler tests for random circuits and structured
quantum algorithms (VQE, Trotter decomposition, Grover search, Option Pricing
via Amplitude Estimation) for several popular quantum hardware architectures.
Leveraging cross-platform functionality of Arline, we propose a concept of
composite compilation pipeline that combines compiler-specific circuit
optimization subroutines in a single compilation stack and finds an optimized
sequence of compilation passes. By providing detailed insights into the
compilation flow of quantum compilers, Arline Benchmarks offers a valuable
toolkit for quantum computing researchers and software developers to gain
additional insights into compilers' characteristics.


title: Robust Training under Label Noise by Over-parameterization

abstract:
  Recently, over-parameterized deep networks, with increasingly more network
parameters than training samples, have dominated the performances of modern
machine learning. However, when the training data is corrupted, it has been
well-known that over-parameterized networks tend to overfit and do not
generalize. In this work, we propose a principled approach for robust training
of over-parameterized deep networks in classification tasks where a proportion
of training labels are corrupted. The main idea is yet very simple: label noise
is sparse and incoherent with the network learned from clean data, so we model
the noise and learn to separate it from the data. Specifically, we model the
label noise via another sparse over-parameterization term, and exploit implicit
algorithmic regularizations to recover and separate the underlying corruptions.
Remarkably, when trained using such a simple method in practice, we demonstrate
state-of-the-art test accuracy against label noise on a variety of real
datasets. Furthermore, our experimental results are corroborated by theory on
simplified linear models, showing that exact separation between sparse noise
and low-rank data can be achieved under incoherent conditions. The work opens
many interesting directions for improving over-parameterized models by using
sparse over-parameterization and implicit regularization.


title: Fingering Instability of Active Nematic Droplets

abstract:
  From the mitotic spindle up to tissues and biofilms, many biological systems
behave as active droplets, which often break symmetry and change shape
spontaneously. Here, I show that active nematic droplets can experience a
fingering instability. I consider an active fluid that acquires nematic order
through anchoring at the droplet interface, and I predict its morphological
stability in terms of three dimensionless parameters: the anchoring angle, the
penetration length of nematic order compared to droplet size, and an active
capillary number. Droplets with extensile (contractile) stresses and planar
(homeotropic) anchoring are unstable above a critical activity or droplet size.
This instability is interfacial in nature: It arises through the coupling of
active flows with interface motion, even when the bulk instability of active
nematics cannot take place. In contrast to the dynamic states characteristic of
active matter, the instability could produce static fingering patterns. The
number of fingers increases with activity but varies non-monotonically with the
nematic penetration length. Overall, these results can help to understand the
self-organized shapes of biological systems, and to design patterns in active
materials.


title:
Discordances in cosmology and the violation of slow-roll inflationary
  dynamics

abstract:
  We identify examples of single field inflationary trajectories beyond the
slow-roll regime which improve the fit to Planck 2018 data compared to baseline
$\Lambda$CDM model with power law form of primordial spectrum and at the same
time alleviate existing tensions between different data sets in the estimate of
cosmological parameters such as $H_0$ and $S_8$. A damped oscillation in the
first Hubble flow function - or equivalently a feature in the potential - and
the corresponding localized oscillations in the primordial power spectrum
partially mimic the improvement in the fit of Planck data due to $A_L$ or
$\Omega_K$. Compared to the baseline model, this model can lead simultaneously
to larger value of $H_0$ and a smaller value of $S_8$, a trend which can be
enhanced when the most recent SH0ES measurement for $H_0$ is combined with
Planck and BK18 data. Large scale structure data and more precise CMB
polarization measurements will further provide critical tests of this
intermediate fast roll phase.


title: On the transmission of turbulent structures across the Earth's Bow Shock

abstract:
  Collisionless shocks and plasma turbulence are crucial ingredients for a
broad range of astrophysical systems. The shock-turbulence interaction, and in
particular the transmission of fully developed turbulence across the
quasi-perpendicular Earth's bow shock, is here addressed using a combination of
spacecraft observations and local numerical simulations. An alignment between
the Wind (upstream) and MMS (downstream) spacecraft is used to study the
transmission of turbulent structures across the shock, revealing an increase of
their magnetic helicity content in its downstream. Local kinetic simulations,
in which the dynamics of turbulent structures is followed through their
transmission across a perpendicular shock, confirm this scenario, revealing
that the observed magnetic helicity increase is associated with the compression
of turbulent structures at the shock front.


title: Learning Semantic Segmentation from Multiple Datasets with Label Shifts

abstract:
  With increasing applications of semantic segmentation, numerous datasets have
been proposed in the past few years. Yet labeling remains expensive, thus, it
is desirable to jointly train models across aggregations of datasets to enhance
data volume and diversity. However, label spaces differ across datasets and may
even be in conflict with one another. This paper proposes UniSeg, an effective
approach to automatically train models across multiple datasets with differing
label spaces, without any manual relabeling efforts. Specifically, we propose
two losses that account for conflicting and co-occurring labels to achieve
better generalization performance in unseen domains. First, a gradient conflict
in training due to mismatched label spaces is identified and a
class-independent binary cross-entropy loss is proposed to alleviate such label
conflicts. Second, a loss function that considers class-relationships across
datasets is proposed for a better multi-dataset training scheme. Extensive
quantitative and qualitative analyses on road-scene datasets show that UniSeg
improves over multi-dataset baselines, especially on unseen datasets, e.g.,
achieving more than 8% gain in IoU on KITTI averaged over all the settings.


title: Succinct Amyloid and Non-Amyloid Patterns in Hexapeptides

abstract:
  Hexapeptides are widely applied as a model system for studying
amyloid-forming properties of polypeptides, including proteins. Recently, large
experimental databases have become publicly available with amyloidogenic
labels. Using these datasets for training and testing purposes, one may build
artificial intelligence (AI)-based classifiers for predicting the amyloid state
of peptides. In our previous work (Biomolecules, 11(4) 500, (2021)) we
described the Support Vector Machine (SVM)-based Budapest Amyloid Predictor
(\url{https://pitgroup.org/bap}). Here we apply the Budapest Amyloid Predictor
for discovering numerous amyloidogenic and non-amyloidogenic hexapeptide
patterns with accuracy between 80\% and 84\%, as surprising and succinct novel
rules for further understanding the amyloid state of peptides. For example, we
have shown that for any independently mutated residue (position marked by
``x''), the patterns CxFLWx, FxFLFx, or xxIVIV are predicted to be
amyloidogenic, while those of PxDxxx, xxKxEx, and xxPQxx non-amyloidogenic at
all. We note that each amyloidogenic pattern with two x's (e.g.,CxFLWx)
describes succinctly $20^2=400$ hexapeptides, while the non-amyloidogenic
patterns comprising four point mutations (e.g.,PxDxxx) gives $20^4=160,000$
hexapeptides in total. To our knowledge, no similar applications of artificial
intelligence tools or succinct amyloid patterns were described before the
present work.


title: New Stepping-Up Constructions for Multicoloured Hypergraphs

abstract:
  Generalizing the classical Ramsey numbers, $r_k(t; q, p)$ is the smallest
integer $n$ such that every $q$-colouring of the $k$-sets on $n$ vertices
contains a set of $t$ vertices spanning fewer than $p$ colours. We prove the
first tower-type lower bounds on these numbers via two new stepping-up
constructions, both variants of the original stepping-up lemma due to Erd\H{o}s
and Hajnal. We use these to resolve a problem of Conlon, Fox, and R\"{o}dl.
More precisely, we construct a family of hypergraphs with arbitrarily large
tower height separation between their $2$-colour and $q$-colour Ramsey numbers.


title:
Quantum transport in quasi-periodic lattice systems in presence of
  B\"uttiker probes

abstract:
  Quasi-periodic lattice systems offer diverse transport properties. In this
work, we investigate the environment induced effects on transport properties
for quasi-periodic systems, namely the one-dimensional Aubry-Andr\'e-Harper
(AAH) lattice chain and its generalized version (GAAH) by considering the
B\"uttiker probe approach. We first consider voltage probe situation and study
the electrical conductance properties in the linear response regime. At zero
temperature, we observe enhancement in conductance at all the no-transport
regimes, located both inside and outside of the band of the original system,
for small probe coupling strength $\gamma$ with a power-law scaling $\gamma^4$.
Whereas, for large probe coupling strengths, the conductance at all Fermi
energies is the same and decays as a power-law with scaling $1/\gamma^4$. This
particular scaling survives even in the finite-temperature limit.
Interestingly, this scaling result is different from the one recently predicted
following the local Lindblad master equation approach. The transport eventually
becomes diffusive for all energy ranges and in all regimes of the original
model for a sufficiently strong coupling with the probes. We further extend our
study and consider voltage-temperature probes to analyze the thermoelectric
performance of the chain in terms of the figure of merit. We also demonstrate
the validity of two recently obtained bounds on thermoelectric efficiency that
are tighter than the seminal Carnot bound and express the same in terms of the
Onsager's transport coefficients.

categories: cond-mat.mes-hall cond-mat.dis-nn cond-mat.stat-mech quant-ph


title:
Attribute Descent: Simulating Object-Centric Datasets on the Content
  Level and Beyond

abstract:
  This article aims to use graphic engines to simulate a large number of
training data that have free annotations and possibly strongly resemble to
real-world data. Between synthetic and real, a two-level domain gap exists,
involving content level and appearance level. While the latter is concerned
with appearance style, the former problem arises from a different mechanism,
i.e., content mismatch in attributes such as camera viewpoint, object placement
and lighting conditions. In contrast to the widely-studied appearance-level
gap, the content-level discrepancy has not been broadly studied. To address the
content-level misalignment, we propose an attribute descent approach that
automatically optimizes engine attributes to enable synthetic data to
approximate real-world data. We verify our method on object-centric tasks,
wherein an object takes up a major portion of an image. In these tasks, the
search space is relatively small, and the optimization of each attribute yields
sufficiently obvious supervision signals. We collect a new synthetic asset
VehicleX, and reformat and reuse existing the synthetic assets ObjectX and
PersonX. Extensive experiments on image classification and object
re-identification confirm that adapted synthetic data can be effectively used
in three scenarios: training with synthetic data only, training data
augmentation and numerically understanding dataset content.


title: ParaNames: A Massively Multilingual Entity Name Corpus

abstract:
  This preprint describes work in progress on ParaNames, a multilingual
parallel name resource consisting of names for approximately 14 million
entities. The included names span over 400 languages, and almost all entities
are mapped to standardized entity types (PER/LOC/ORG). Using Wikidata as a
source, we create the largest resource of this type to-date. We describe our
approach to filtering and standardizing the data to provide the best quality
possible. ParaNames is useful for multilingual language processing, both in
defining tasks for name translation/transliteration and as supplementary data
for tasks such as named entity recognition and linking. We demonstrate an
application of ParaNames by training a multilingual model for canonical name
translation to and from English. Our resource is released at
\url{https://github.com/bltlab/paranames} under a Creative Commons license (CC
BY 4.0).


title:
How Well Do My Results Generalize Now? The External Validity of Online
  Privacy and Security Surveys

abstract:
  Security and privacy researchers often rely on data collected through online
crowdsourcing platforms such as Amazon Mechanical Turk (MTurk) and Prolific.
Prior work -- which used data collected in the United States between 2013 and
2017 -- found that MTurk responses regarding security and privacy were
generally representative for people under 50 or with some college education.
However, the landscape of online crowdsourcing has changed significantly over
the last five years, with the rise of Prolific as a major platform and the
increasing presence of bots. This work attempts to replicate the prior results
about the external validity of online privacy and security surveys. We conduct
an online survey on MTurk (n=800), a gender-balanced survey on Prolific
(n=800), and a representative survey on Prolific (n=800) and compare the
responses to a probabilistic survey conducted by the Pew Research Center
(n=4272). We find that MTurk responses are no longer representative of the U.S.
population, even when responses that fail attention check questions or CAPTCHAs
are excluded. Data collected through Prolific is generally representative for
questions about user perceptions and experience, but not for questions about
security and privacy knowledge. We also evaluate the impact of Prolific
settings (e.g., gender-balanced sample vs. representative sample), various
attention check questions, and statistical methods on the external validity of
surveys conducted through Prolific, and we develop recommendations about best
practices for conducting online privacy and security surveys.


title:
Understanding Contrastive Learning Requires Incorporating Inductive
  Biases

abstract:
  Contrastive learning is a popular form of self-supervised learning that
encourages augmentations (views) of the same input to have more similar
representations compared to augmentations of different inputs. Recent attempts
to theoretically explain the success of contrastive learning on downstream
classification tasks prove guarantees depending on properties of {\em
augmentations} and the value of {\em contrastive loss} of representations. We
demonstrate that such analyses, that ignore {\em inductive biases} of the
function class and training algorithm, cannot adequately explain the success of
contrastive learning, even {\em provably} leading to vacuous guarantees in some
settings. Extensive experiments on image and text domains highlight the
ubiquity of this problem -- different function classes and algorithms behave
very differently on downstream tasks, despite having the same augmentations and
contrastive losses. Theoretical analysis is presented for the class of linear
representations, where incorporating inductive biases of the function class
allows contrastive learning to work with less stringent conditions compared to
prior analyses.


title: The Three Hundred project: The Gizmo-Simba runs

abstract:
  We introduce \textsc{Gizmo-Simba}, a new suite of galaxy cluster simulations
within \textsc{The Three Hundred} project. \textsc{The Three Hundred} consists
of zoom re-simulations of 324 clusters with $M_{200}\gtrsim 10^{14.8}M_\odot$
drawn from the MultiDark-Planck $N$-body simulation, run using several
hydrodynamic and semi-analytic codes. The \textsc{Gizmo-Simba} suite adds a
state-of-the-art galaxy formation model based on the highly successful {\sc
Simba} simulation, mildly re-calibrated to match $z=0$ cluster stellar
properties. Comparing to \textsc{The Three Hundred} zooms run with
\textsc{Gadget-X}, we find intrinsic differences in the evolution of the
stellar and gas mass fractions, BCG ages, and galaxy colour-magnitude diagrams,
with \textsc{Gizmo-Simba} generally providing a good match to available data at
$z \approx 0$. \textsc{Gizmo-Simba}'s unique black hole growth and feedback
model yields agreement with the observed BH scaling relations at the
intermediate-mass range and predicts a slightly different slope at high masses
where few observations currently lie. \textsc{Gizmo-Simba} provides a new and
novel platform to elucidate the co-evolution of galaxies, gas, and black holes
within the densest cosmic environments.


title: AI-based approach for improving the detection of blood doping in sports

abstract:
  Sports officials around the world are facing incredible challenges due to the
unfair means of practices performed by the athletes to improve their
performance in the game. It includes the intake of hormonal based drugs or
transfusion of blood to increase their strength and the result of their
training. However, the current direct test of detection of these cases includes
the laboratory-based method, which is limited because of the cost factors,
availability of medical experts, etc. This leads us to seek for indirect tests.
With the growing interest of Artificial Intelligence in healthcare, it is
important to propose an algorithm based on blood parameters to improve decision
making. In this paper, we proposed a statistical and machine learning-based
approach to identify the presence of doping substance rhEPO in blood samples.


title:
SUTD-PRCM Dataset and Neural Architecture Search Approach for Complex
  Metasurface Design

abstract:
  Metasurfaces have received a lot of attentions recently due to their
versatile capability in manipulating electromagnetic wave. Advanced designs to
satisfy multiple objectives with non-linear constraints have motivated
researchers in using machine learning (ML) techniques like deep learning (DL)
for accelerated design of metasurfaces. For metasurfaces, it is difficult to
make quantitative comparisons between different ML models without having a
common and yet complex dataset used in many disciplines like image
classification. Many studies were directed to a relatively constrained datasets
that are limited to specified patterns or shapes in metasurfaces. In this
paper, we present our SUTD polarized reflection of complex metasurfaces
(SUTD-PRCM) dataset, which contains approximately 260,000 samples of complex
metasurfaces created from electromagnetic simulation, and it has been used to
benchmark our DL models. The metasurface patterns are divided into different
classes to facilitate different degree of complexity, which involves
identifying and exploiting the relationship between the patterns and the
electromagnetic responses that can be compared in using different DL models.
With the release of this SUTD-PRCM dataset, we hope that it will be useful for
benchmarking existing or future DL models developed in the ML community. We
also propose a classification problem that is less encountered and apply neural
architecture search to have a preliminary understanding of potential
modification to the neural architecture that will improve the prediction by DL
models. Our finding shows that convolution stacking is not the dominant element
of the neural architecture anymore, which implies that low-level features are
preferred over the traditional deep hierarchical high-level features thus
explains why deep convolutional neural network based models are not performing
well in our dataset.

categories: cs.LG physics.app-ph physics.comp-ph physics.data-an


title:
Interfacing Finite Elements with Deep Neural Operators for Fast
  Multiscale Modeling of Mechanics Problems

abstract:
  Multiscale modeling is an effective approach for investigating multiphysics
systems with largely disparate size features, where models with different
resolutions or heterogeneous descriptions are coupled together for predicting
the system's response. The solver with lower fidelity (coarse) is responsible
for simulating domains with homogeneous features, whereas the expensive
high-fidelity (fine) model describes microscopic features with refined
discretization, often making the overall cost prohibitively high, especially
for time-dependent problems. In this work, we explore the idea of multiscale
modeling with machine learning and employ DeepONet, a neural operator, as an
efficient surrogate of the expensive solver. DeepONet is trained offline using
data acquired from the fine solver for learning the underlying and possibly
unknown fine-scale dynamics. It is then coupled with standard PDE solvers for
predicting the multiscale systems with new boundary/initial conditions in the
coupling stage. The proposed framework significantly reduces the computational
cost of multiscale simulations since the DeepONet inference cost is negligible,
facilitating readily the incorporation of a plurality of interface conditions
and coupling schemes. We present various benchmarks to assess accuracy and
speedup, and in particular we develop a coupling algorithm for a time-dependent
problem, and we also demonstrate coupling of a continuum model (finite element
methods, FEM) with a neural operator representation of a particle system
(Smoothed Particle Hydrodynamics, SPH) for a uniaxial tension problem with
hyperelastic material. What makes this approach unique is that a well-trained
over-parametrized DeepONet can generalize well and make predictions at a
negligible cost.


title:
A Dynamic Mode Decomposition Approach for Decentralized Spectral
  Clustering of Graphs

abstract:
  We propose a novel robust decentralized graph clustering algorithm that is
provably equivalent to the popular spectral clustering approach. Our proposed
method uses the existing wave equation clustering algorithm that is based on
propagating waves through the graph. However, instead of using a fast Fourier
transform (FFT) computation at every node, our proposed approach exploits the
Koopman operator framework. Specifically, we show that propagating waves in the
graph followed by a local dynamic mode decomposition (DMD) computation at every
node is capable of retrieving the eigenvalues and the local eigenvector
components of the graph Laplacian, thereby providing local cluster assignments
for all nodes. We demonstrate that the DMD computation is more robust than the
existing FFT based approach and requires 20 times fewer steps of the wave
equation to accurately recover the clustering information and reduces the
relative error by orders of magnitude. We demonstrate the decentralized
approach on a range of graph clustering problems.


title: Uniform convergence of translation operators

abstract:
  It is well known that there are entire functions whose orbit approximates any
other entire function under the action of a sequence of translation operators .
This result also holds for an uncountable family of sequences of translation
operators when the convergence is pointwise .We prove that this result does not
hold uniformly .


title:
Towards Reducing the Need for Speech Training Data To Build Spoken
  Language Understanding Systems

abstract:
  The lack of speech data annotated with labels required for spoken language
understanding (SLU) is often a major hurdle in building end-to-end (E2E)
systems that can directly process speech inputs. In contrast, large amounts of
text data with suitable labels are usually available. In this paper, we propose
a novel text representation and training methodology that allows E2E SLU
systems to be effectively constructed using these text resources. With very
limited amounts of additional speech, we show that these models can be further
improved to perform at levels close to similar systems built on the full speech
datasets. The efficacy of our proposed approach is demonstrated on both intent
and entity tasks using three different SLU datasets. With text-only training,
the proposed system achieves up to 90% of the performance possible with full
speech training. With just an additional 10% of speech data, these models
significantly improve further to 97% of full performance.


title:
GCN-Transformer for short-term passenger flow prediction on holidays in
  urban rail transit systems

abstract:
  The short-term passenger flow prediction of the urban rail transit system is
of great significance for traffic operation and management. The emerging deep
learning-based models provide effective methods to improve prediction accuracy.
However, most of the existing models mainly predict the passenger flow on
general weekdays, while few studies focus on predicting the holiday passenger
flow, which can provide more significant information for operators because
congestions or accidents generally occur on holidays. To this end, we propose a
deep learning-based model named GCN-Transformer comprising graph conventional
neural network (GCN) and Transformer for short-term passenger flow prediction
on holidays. The GCN is applied to extract the spatial features of passenger
flows and the Transformer is applied to extract the temporal features of
passenger flows. Moreover, in addition to the historical passenger flow data,
social media data are also incorporated into the prediction model, which has
been proven to have a potential correlation with the fluctuation of passenger
flow. The GCN-Transformer is tested on two large-scale real-world datasets from
Nanning, China during the New Year holiday and is compared with several
conventional prediction models. Results demonstrate its better robustness and
advantages among baseline methods, which provides overwhelming support for
practical applications of short-term passenger flow prediction on holidays


title:
Learned end-to-end high-resolution lensless fiber imaging toward
  intraoperative real-time cancer diagnosis

abstract:
  Endomicroscopy is indispensable for minimally invasive diagnostics in
clinical practice. For optical keyhole monitoring of surgical interventions,
high-resolution fiber endoscopic imaging is considered to be very promising,
especially in combination with label-free imaging techniques to realize in vivo
diagnosis. However, the inherent honeycomb-artifacts of coherent fiber bundles
(CFB) reduce the resolution and limit the clinical applications. We propose an
end-to-end lensless fiber imaging scheme toward intraoperative real-time cancer
diagnosis. The framework includes resolution enhancement and classification
networks that use single-shot fiber bundle images to provide both
high-resolution images and tumor diagnosis result. The well-trained resolution
enhancement network not only recovers high-resolution features beyond the
physical limitations of CFB, but also helps improving tumor recognition rate.
Especially for glioblastoma, the resolution enhancement network helps
increasing the classification accuracy from 90.8% to 95.6%. The novel technique
can enable histological real-time imaging through lensless fiber endoscopy and
is promising for rapid and minimal-invasive intraoperative diagnosis in
clinics.


title:
A geometrical point of view for branching problems for holomorphic
  discrete series of conformal Lie groups

abstract:
  This article is devoted to branching problems for holomorphic discrete series
representations of a conformal group $G$ of a tube domain $T_Omega$ over a
symmetric cone $\Omega$. More precisely, we analyse restrictions of such
representations to the conformal group $G'$ of a tube domain $T_{\Omega'}$
holomorphically embedded in $T_\Omega$. The goal of this work is the explicit
construction of the symmetry breaking and holographic operators in this
geometrical setting. To do so, a stratification space for a symmetric cone is
introduced. This structure put the light on a new functional model, called the
\emph{stratified model}, for such infinite dimensional representations. The
main idea of this work is to give a geometrical interpretation for the
branching laws of infinite dimensional representations. The stratified model
answers this program by relating branching laws of holomorphic discrete series
representations to the theory of orthogonal polynomials on the stratification
space. This program is developed in three cases. First, we consider the
$n$-fold tensor product of holomorphic discrete series of the universal
covering of $SL_2(\mathbb{R})$. Then, it is tested on the restrictions of a
member of the scalar-valued holomorphic discrete series of the conformal group
$SO(2,n)$ to the subgroup $SO(2,n-p)$, and finally to the subgroup
$SO(2,n-p)\times SO(p)$.


title:
Path-integral Monte Carlo worm algorithm for Bose systems with periodic
  boundary conditions

abstract:
  We provide a detailed description of the path-integral Monte Carlo worm
algorithm used to exactly calculate the thermodynamics of Bose systems in the
canonical ensemble. The algorithm is fully consistent with periodic boundary
conditions, that are applied to simulate homogeneous phases of bulk systems,
and it does not require any limitation in the length of the Monte Carlo moves
realizing the sampling of the probability distribution function in the space of
path configurations. The result is achieved adopting a representation of the
path coordinates where only the initial point of each path is inside the
simulation box, the remaining ones being free to span the entire space.
Detailed balance can thereby be ensured for any update of the path
configurations without the ambiguity of the selection of the periodic image of
the particles involved. We benchmark the algorithm using the non-interacting
Bose gas model for which exact results for the partition function at finite
number of particles can be derived. Convergence issues and the approach to the
thermodynamic limit are also addressed for interacting systems of hard spheres
in the regime of high density.


title: Junction conditions and sharp gradients in generalized coupling theories

abstract:
  In this article, we develop the formalism for singular hypersurfaces and
junction conditions in generalized coupling theories using a variational
approach. We then employ this formalism to examine the behavior of sharp matter
density gradients in generalized coupling theories. We find that such gradients
do not necessarily lead to the pathologies present in other theories of gravity
with auxiliary fields. A detailed example, based on a simple instance of a
generalized coupling theory called the MEMe model, is also provided. In the
static case, we show that sharp boundaries do not generate singularities in the
dynamical frame despite the presence of an auxiliary field. Instead, in the
case of a collapsing spherical density distribution with a general profile an
additional force compresses over-densities and expands under-densities. These
results can also be used to deduce additional constraints on the parameter of
this model.


title: Quantum Capacities of Transducers

abstract:
  High-performance quantum transducers, which can faithfully convert quantum
information between disparate physical carriers, are essential elements in
quantum science and technology. To assess their ability to coherently transfer
quantum information, quantum transducers are typically characterized by
different figures of merit including conversion efficiency, bandwidth, and
added noise. Here we utilize the concept of quantum capacity, the highest
achievable qubit communication rate through a channel, to quantify the
performance of a transducer. By evaluating the continuous-time quantum capacity
across the conversion band, quantum capacity can serve as a single metric that
unifies various desirable criteria of a transducer -- high efficiency, large
bandwidth, and low noise. Moreover, using the quantum capacities of bosonic
pure-loss channels as benchmarks, we investigate the optimal designs of generic
quantum transduction schemes implemented by transmitting external signals
through a coupled bosonic chain. Under the physical constraint of a bounded
maximal coupling rate $g_{\rm max}$, the highest continuous-time quantum
capacity $Q^{\rm max} \approx 31.4 g_{\rm max}$ is achieved by transducers with
a maximally flat conversion frequency response, analogous to Butterworth
electric filters. We further extend our method to include thermal noise by
considering upper and lower bounds on the quantum capacities of transducers and
characterize the performance of maximally flat transducers under the effect of
thermal loss.


title:
Foraging with MUSHROOMS: A Mixed-Integer Linear Programming Scheduler
  for Multimessenger Target of Opportunity Searches with the Zwicky Transient
  Facility

abstract:
  Electromagnetic follow-up of gravitational wave detections is very resource
intensive, taking up hours of limited observation time on dozens of telescopes.
Creating more efficient schedules for follow-up will lead to a commensurate
increase in counterpart location efficiency without using more telescope time.
Widely used in operations research and telescope scheduling, mixed integer
linear programming (MILP) is a strong candidate to produce these
higher-efficiency schedules, as it can make use of powerful commercial solvers
that quickly and efficiently find globally optimal solutions to provided
problems. We detail a new target of opportunity scheduling algorithm designed
with Zwicky Transient Facility in mind that uses mixed integer linear
programming. We compare its performance to \texttt{gwemopt}, the tuned
heuristic scheduler used by the Zwicky Transient Facility and other facilities
during the third LIGO-Virgo gravitational wave observing run. This new
algorithm uses variable-length observing blocks to enforce cadence requirements
and ensure field observability, along with having a secondary optimization step
to minimize slew time. Our algorithm shows improvement over \texttt{gwemopt} in
successfully scheduling observations for a simulated binary neutron star merger
data set consistent with LIGO-Virgo's third observing run. We demonstrate the
ability to achieve better efficiencies with this straightforward algorithm,
highlighting the potential of mixed integer target of opportunity schedulers
for future multimessenger follow-up surveys.


title:
The Exploration of Local VolumE Satellites (ELVES) Survey: A Nearly
  Volume-Limited Sample of Nearby Dwarf Satellite Systems

abstract:
  We present the final results of the Exploration of Local VolumE Satellites
(ELVES) Survey, a survey of the dwarf satellites of a nearly volume-limited
sample of Milky Way (MW)-like hosts in the Local Volume. Hosts are selected
simply via a cut in luminosity ($M_{K_s}<-22.1$ mag) and distance ($D<12$ Mpc).
We have cataloged the satellites of 25 of the 31 such hosts, with another five
taken from the literature. All hosts are surveyed out to at least 150 projected
kpc ($\sim R_\mathrm{vir}/2$) with the majority surveyed to 300 kpc ($\sim
R_\mathrm{vir}$). Satellites are detected using a consistent semi-automated
algorithm specialized for low surface brightness dwarfs. As shown through
extensive tests with injected galaxies, the catalogs are complete to
$M_V\sim-9$ mag and $\mu_{0,V}\sim26.5$ mag arcsec$^{-2}$. Candidates are
confirmed to be real satellites through distance measurements including
redshift, tip of the red giant branch, and surface brightness fluctuations.
Across all 30 surveyed hosts, there are 338 confirmed satellites with a further
105 candidates awaiting distance measurement. For the vast majority of these,
we provide consistent multi-band S\'{e}rsic photometry. We show that satellite
abundance correlates with host mass, with the MW being quite typical amongst
comparable systems, and that satellite quenched fraction rises steeply with
decreasing satellite mass, mirroring the quenched fraction for the MW and M31.
The ELVES survey represents a massive increase in the statistics of surveyed
systems with known completeness, and the provided catalogs are a unique dataset
to explore various aspects of small-scale structure and dwarf galaxy evolution.


title:
Topological fracton quantum phase transitions by tuning exact tensor
  network states

abstract:
  Gapped fracton phases of matter generalize the concept of topological order
and broaden our fundamental understanding of entanglement in quantum many-body
systems. However, their analytical or numerical description beyond exactly
solvable models remains a formidable challenge. Here we employ an exact 3D
quantum tensor-network approach that allows us to study a $\mathbb{Z}_N$
generalization of the prototypical X cube fracton model and its quantum phase
transitions between distinct topological states via fully tractable
wavefunction deformations. We map the (deformed) quantum states exactly to a
combination of a classical lattice gauge theory and a plaquette clock model,
and employ numerical techniques to calculate various entanglement order
parameters. For the $\mathbb{Z}_N$ model we find a family of (weakly)
first-order fracton confinement transitions that in the limit of $N\to\infty$
converge to a continuous phase transition beyond the Landau-Ginzburg-Wilson
paradigm. We also discover a line of 3D conformal quantum critical points (with
critical magnetic flux loop fluctuations) which, in the $N\to\infty$ limit,
appears to coexist with a gapless deconfined fracton state.


title:
Massive central galaxies of galaxy groups in the Romulus simulations: an
  overview of galaxy properties at z=0

abstract:
  Contrary to many stereotypes about massive galaxies, observed brightest group
galaxies (BGGs) are diverse in their star formation rates, kinematic
properties, and morphologies. Studying how they evolve into and express such
diverse characteristics is an important piece of the galaxy formation puzzle.
We use a high-resolution cosmological suite of simulations Romulus and compare
simulated central galaxies in group-scale halos at $z=0$ to observed BGGs. The
comparison encompasses the stellar mass-halo mass relation, various kinematic
properties and scaling relations, morphologies, and the star formation rates.
Generally, we find that Romulus reproduces the full spectrum of diversity in
the properties of the BGGs very well, albeit with a tendency toward lower than
the observed fraction of quenched BGGs. We find both early-type S0 and
elliptical galaxies as well as late-type disk galaxies; we find Romulus
galaxies that are fast-rotators as well as slow-rotators; and we observe
galaxies transforming from late-type to early-type following strong dynamical
interactions with satellites. We also carry out case studies of selected
Romulus galaxies to explore the link between their properties, and the recent
evolution of the stellar system as well as the surrounding
intragroup/circumgalactic medium. In general, mergers/strong interactions
quench star-forming activity and disrupt the stellar disk structure. Sometimes,
however, such interactions can also trigger star-formation and galaxy
rejuvenation. Black hole feedback can also lead to a decline of the star
formation rate but by itself, it does not typically lead to complete quenching
of the star formation activity in the BGGs.


title:
BASS XXIV: The BASS DR2 Spectroscopic Line Measurements and AGN
  Demographics

abstract:
  We present the second catalog and data release of optical spectral line
measurements and AGN demographics of the BAT AGN Spectroscopic Survey, which
focuses on the of Swift-BAT hard X-ray detected AGNs. We use spectra from
dedicated campaigns and publicly available archives to investigate spectral
properties of most of the AGNs listed in the 70-month Swift-BAT all-sky
catalog; specifically, 743 of the 746 unbeamed and unlensed AGNs (99.6%). We
find a good correspondence between the optical emission line widths and the
hydrogen column density distributions using the X-ray spectra, with a clear
dichotomy of AGN types for NH = 10^22 cm-2. Based on optical emission-line
diagnostics, we show that 48%-75% of BAT AGNs are classified as Seyfert,
depending on the choice of emission lines used in the diagnostics. The fraction
of objects with upper limits on line emission varies from 6% to 20%. Roughly 4%
of the BAT AGNs have lines too weak to be placed on the most commonly used
diagnostic diagram, [O III]{\lambda}5007/H\b{eta} versus [N
II]{\lambda}6584/H{\alpha}, despite the high signal-to-noise ratio (S/N) of
their spectra. This value increases to 35% in the [O III]{\lambda}5007/[O
II]{\lambda}3727 diagram, owing to difficulties in line detection. Compared to
optically-selected narrow-line AGNs in the Sloan Digital Sky Survey, the BAT
narrow-line AGNs have a higher rate of reddening/extinction, with
H{\alpha}/H\b{eta} > 5 (~ 36%), indicating that hard X-ray selection more
effectively detects obscured AGNs from the underlying AGN population. Finally,
we present a subpopulation of AGNs that feature complex broad-lines (34%,
250/743) or double-peaked narrow emission lines (2%, 17/743).


title:
Spectroscopic confirmation of a gravitationally lensed Lyman break
  galaxy at z$_{[CII]}$ = 6.827 using NOEMA

abstract:
  We present the spectroscopic confirmation of the brightest known
gravitationally lensed Lyman break galaxy in the Epoch of Reionisation,
A1703-zD1, through the detection of [C II] at a redshift of z = 6.8269 +/-
0.0004. This source was selected behind the strong lensing cluster Abell 1703,
with an intrinsic L$_{UV}$ ~ L$^*$$_{z=7}$ luminosity and a very blue
Spitzer/IRAC [3.6]-[4.5] colour, implying high equivalent width line emission
of [O III]+H$\beta$. [C II] is reliably detected at 6.1$\sigma$ co-spatial with
the rest-frame UV counterpart, showing similar spatial extent. Correcting for
the lensing magnification, the [C II] luminosity in A1703-zD1 is broadly
consistent with the local L$_{[CII]}$ - SFR relation. We find a clear velocity
gradient of 103 +/- 22 km/s across the source which possibly indicates rotation
or an ongoing merger. We furthermore present spectral scans with no detected [C
II] above 4.6$\sigma$ in two unlensed Lyman break galaxies in the EGS-CANDELS
field at z ~ 6.6 - 6.9. This is the first time that NOEMA has been successfully
used to observe [C II] in a 'normal' star-forming galaxy at z > 6, and our
results demonstrate its capability to complement ALMA in confirming galaxies in
the Epoch of Reionisation.


title: Advances in QED with intense background fields

abstract:
  Upcoming and planned experiments combining increasingly intense lasers and
energetic particle beams will access new regimes of nonlinear, relativistic,
quantum effects. This improved experimental capability has driven substantial
progress in QED in intense background fields. We review here the advances made
during the last decade, with a focus on theory and phenomenology. As ever
higher intensities are reached, it becomes necessary to consider processes at
higher orders in both the number of scattered particles and the number of
loops, and to account for non-perturbative physics (e.g. the Schwinger effect),
with extreme intensities requiring resummation of the loop expansion. In
addition to increased intensity, experiments will reach higher accuracy, and
these improvements are being matched by developments in theory such as in
approximation frameworks, the description of finite-size effects, and the range
of physical phenomena analysed. Topics on which there has been substantial
progress include: radiation reaction, spin and polarisation, nonlinear quantum
vacuum effects and connections to other fields including physics beyond the
Standard Model.