title: Asymptotics of the hypergraph bipartite Tur\'an problem

abstract:
  For positive integers $s,t,r$, let $K_{s,t}^{(r)}$ denote the $r$-uniform
hypergraph whose vertex set is the union of pairwise disjoint sets
$X,Y_1,\dots,Y_t$, where $|X| = s$ and $|Y_1| = \dots = |Y_t| = r-1$, and whose
edge set is $\{\{x\} \cup Y_i: x \in X, 1\leq i\leq t\}$. The study of the
Tur\'an function of $K_{s,t}^{(r)}$ received considerable interest in recent
years. Our main results are as follows. First, we show that
  \begin{equation}
  \mathrm{ex}(n,K_{s,t}^{(r)}) = O_{s,r}(t^{\frac{1}{s-1}}n^{r -
\frac{1}{s-1}})
  \end{equation} for all $s,t\geq 2$ and $r\geq 3$, improving the power of $n$
in the previously best bound and resolving a question of Mubayi and
Verstra\"ete about the dependence of $\mathrm{ex}(n,K_{2,t}^{(3)})$ on $t$.
Second, we show that this upper bound is tight when $r$ is even and $t \gg s$.
This disproves a conjecture of Xu, Zhang and Ge. Third, we show that the above
upper bound is not tight for $r = 3$, namely that $\mathrm{ex}(n,K_{s,t}^{(3)})
= O_{s,t}(n^{3 - \frac{1}{s-1} - \varepsilon_s})$ (for all $s\geq 3$). This
indicates that the behaviour of $\mathrm{ex}(n,K_{s,t}^{(r)})$ might depend on
the parity of $r$. Lastly, we prove a conjecture of Ergemlidze, Jiang and
Methuku on the hypergraph analogue of the bipartite Tur\'an problem for graphs
with bounded degrees on one side. Our tools include a novel twist on the
dependent random choice method as well as a variant of the celebrated norm
graphs constructed by Koll\'ar, R\'onyai and Szab\'o.


title:
Dielectric and thermodynamic study of Carbon dioxide (CO$_2$) - methanol
  (CH$_3$OH) mixture

abstract:
  In this paper is calculated and analyzed the dielectric behavior of carbon
dioxide - methanol mixture and evaluate how the correct reproduction of the
electrical properties of this molecules influences in the mixture of these two
substances. A comparison of two carbon dioxide models and three methanol models
is made, where the CO2/$\epsilon$ model reproduces and improves the dielectric
constant and for the methanol the TraPPE 2015 and MeOH-4P models improve the
dielectric constant $\epsilon$, the surface tension {\gamma} , and the liquid
density \r{ho} of the pure liquid at T=298.15 K and p=1 bar.


title: Optimizing Ghost Imaging via Analysis and Design of Speckle Patterns

abstract:
  We study the influence rules of the speckle size of light source on ghost
imaging, and propose a new type of speckle patterns to improve the quality of
ghost imaging. The results show that the image quality will first increase and
then decrease with the increase of the speckle size, and there is an optimal
speckle size for a specific object. Moreover, by using the random distribution
of speckle positions, a new type of displacement speckle patterns is designed,
and the imaging quality is better than that of the random speckle patterns.
These results are of great significances for finding the best speckle patterns
suitable for detecting targets, which further promotes the practical
applications of ghost imaging.


title: Modular quasi-Hopf algebras and groups with one involution

abstract:
  In a previous paper the authors constructed a class of quasi-Hopf algebras
$D^{\omega}(G, A)$ associated to a finite group $G$, generalizing the twisted
quantum double construction. We gave necessary and sufficient conditions,
cohomological in nature, that the corresponding module category
$Rep(D^{\omega}(G, A))$ is a modular tensor category.\ In the present paper we
verify the cohomological conditions for the class of groups $G$ which
\emph{contain a unique involution}, and in this way we obtain an explicit
construction of a new class of modular quasi-Hopf algebras.\ We develop the
basic theory for general finite groups $G$, and also a parallel theory
concerned with the question of when $Rep(D^{\omega}(G, A))$ is super-modular
rather than modular. We give some explicit examples involving binary polyhedral
groups and some sporadic simple groups.


title:
Modeling of porous lithium metal electrodes: turning the Li-dendrite
  problem around

abstract:
  The properties of rechargeable lithium-ion batteries are determined by the
electrochemical and kinetic properties of their constituent materials as well
as by their underlying microstructure. Microstructural design can be leveraged
to achieve a leap in performance and durability. Here we investigate a porous
electrode structure, as a strategy to increase the surface area, and provide
structural stability for Li-metal anodes. The porous architecture consists of a
mixed electron/ion conductor that function as a scaffold for lithium metal
deposition. A new finite element model was developed to simulate the large
topological changes associated with Li plating/stripping. This model is used to
predict the current density distribution as a function of material and
structural properties. A dimensionless quantity that combines Li-ion
conductivity, surface impedance and average pore size is shown to be a good
indicator to predict the peak current density. Preventing current localization
at the separator reduces the risk of cell shorting. The analyses show that the
peak current scales as $(hG)^{1/2}$, where $h$ is the ratio between surface and
bulk conductivity and $G$ is the average pore size. Stability analyses suggest
that the growth is morphologically stable, and that confining Li-plating into
pores can enable high-energy density solid-state batteries. In addition to
optimizing porous electrodes design, this finite element method can be extended
to studying other Li-battery structures.


title: Searches for Long-Lived Particles at the Future FCC-ee

abstract:
  The electron-positron stage of the Future Circular Collider, FCC-ee, is a
frontier factory for Higgs, top, electroweak, and flavour physics. It is
designed to operate in a 100 km circular tunnel built at CERN, and will serve
as the first step towards $\geq$ 100 TeV proton-proton collisions. In addition
to an essential and unique Higgs program, it offers powerful opportunities to
discover direct or indirect evidence of physics beyond the Standard Model.
  Direct searches for long-lived particles at FCC-ee could be particularly
fertile in the high-luminosity $Z$ run, where $5\times 10^{12}$ $Z$ bosons are
anticipated to be produced for the configuration with two interaction points.
The high statistics of Higgs bosons, $W$ bosons and top quarks in very clean
experimental conditions could offer additional opportunities at other collision
energies. Three physics cases producing long-lived signatures at FCC-ee are
highlighted and studied in this paper: heavy neutral leptons (HNLs), axion-like
particles (ALPs), and exotic decays of the Higgs boson. These searches motivate
out-of-the-box optimization of experimental conditions and analysis techniques,
that could lead to improvements in other physics searches.


title:
Real- and Fourier-space observation of the anomalous $\pi$-mode in
  Floquet engineered plasmonic waveguide arrays

abstract:
  We present a joint experimental and theoretical study of the driven
Su-Schrieffer-Heeger model implemented by arrays of evanescently coupled
plasmonic waveguides. Floquet theory predicts that this system hosts for
suitable driving frequencies a topologically protected edge state that has no
counterpart in static systems, the so-called anomalous Floquet topological
$\pi$-mode. By using real- and Fourier-space leakage radiation microscopy in
combination with edge- and bulk excitation, we unequivocally identify the
anomalous Floquet topological $\pi$-mode and study its frequency dependence.


title: On the monoid of partial isometries of a finite star graph

abstract:
  In this paper we consider the monoid $DPS_n$ of all partial isometries of a
star graph $S_n$ with $n$ vertices. Our main objectives are to determine the
rank and to exhibit a presentation of $DPS_n$. We also describe Green's
relations of $DPS_n$ and calculate its cardinal.


title:
Testing Lepton Flavor Universality and CKM Unitarity with Rare Pion
  Decays in the PIONEER experiment

abstract:
  The physics motivation and the conceptual design of the PIONEER experiment, a
next-generation rare pion decay experiment testing lepton flavor universality
and CKM unitarity, are described. Phase I of the PIONEER experiment, which was
proposed and approved at Paul Scherrer Institut, aims at measuring the
charged-pion branching ratio to electrons vs.\ muons, $R_{e/\mu}$, 15 times
more precisely than the current experimental result, reaching the precision of
the Standard Model (SM) prediction at 1 part in $10^4$. Considering several
inconsistencies between the SM predictions and data pointing towards the
potential violation of lepton flavor universality, the PIONEER experiment will
probe non-SM explanations of these anomalies through sensitivity to quantum
effects of new particles up to the PeV mass scale. The later phases of the
PIONEER experiment aim at improving the experimental precision of the branching
ratio of pion beta decay (BRPB), $\pi^+\to \pi^0 e^+ \nu (\gamma)$, currently
at $1.036(6)\times10^{-8}$, by a factor of three (Phase II) and an order of
magnitude (Phase III). Such precise measurements of BRPB will allow for tests
of CKM unitarity in light of the Cabibbo Angle Anomaly and the theoretically
cleanest extraction of $|V_{ud}|$ at the 0.02\% level, comparable to the
deduction from superallowed beta decays.


title: The PDF4LHC21 combination of global PDF fits for the LHC Run III

abstract:
  A precise knowledge of the quark and gluon structure of the proton, encoded
by the parton distribution functions (PDFs), is of paramount importance for the
interpretation of high-energy processes at present and future lepton-hadron and
hadron-hadron colliders. Motivated by recent progress in the PDF determinations
carried out by the CT, MSHT, and NNPDF groups, we present an updated
combination of global PDF fits: PDF4LHC21. It is based on the Monte Carlo
combination of the CT18, MSHT20, and NNPDF3.1 sets followed by either its
Hessian reduction or its replica compression. Extensive benchmark studies are
carried out in order to disentangle the origin of the differences between the
three global PDF sets. In particular, dedicated fits based on almost identical
theory settings and input datasets are performed by the three groups,
highlighting the role played by the respective fitting methodologies. We
compare the new PDF4LHC21 combination with its predecessor, PDF4LHC15,
demonstrating their good overall consistency and a modest reduction of PDF
uncertainties for key LHC processes such as electroweak gauge boson production
and Higgs boson production in gluon fusion. We study the phenomenological
implications of PDF4LHC21 for a representative selection of inclusive,
fiducial, and differential cross sections at the LHC. The PDF4LHC21 combination
is made available via the LHAPDF library and provides a robust, user-friendly,
and efficient method to estimate the PDF uncertainties associated to
theoretical calculations for the upcoming Run III of the LHC and beyond.


title:
A Look into the Problem of Preferential Sampling from the Lens of Survey
  Statistics

abstract:
  An evolving problem in the field of spatial and ecological statistics is that
of preferential sampling, where biases may be present due to a relationship
between sample data locations and a response of interest. This field of
research bears a striking resemblance to the longstanding problem of
informative sampling within survey methodology, although with some important
distinctions. With the goal of promoting collaborative effort within and
between these two problem domains, we make comparisons and contrasts between
the two problem statements. Specifically, we review many of the solutions
available to address each of these problems, noting the important differences
in modeling techniques. Additionally, we construct a series of simulation
studies to examine some of the methods available for preferential sampling, as
well as a comparison analyzing heavy metal biomonitoring data.


title: Towards Less Constrained Macro-Neural Architecture Search

abstract:
  Networks found with Neural Architecture Search (NAS) achieve state-of-the-art
performance in a variety of tasks, out-performing human-designed networks.
However, most NAS methods heavily rely on human-defined assumptions that
constrain the search: architecture's outer-skeletons, number of layers,
parameter heuristics and search spaces. Additionally, common search spaces
consist of repeatable modules (cells) instead of fully exploring the
architecture's search space by designing entire architectures (macro-search).
Imposing such constraints requires deep human expertise and restricts the
search to pre-defined settings. In this paper, we propose LCMNAS, a method that
pushes NAS to less constrained search spaces by performing macro-search without
relying on pre-defined heuristics or bounded search spaces. LCMNAS introduces
three components for the NAS pipeline: i) a method that leverages information
about well-known architectures to autonomously generate complex search spaces
based on Weighted Directed Graphs with hidden properties, ii) a evolutionary
search strategy that generates complete architectures from scratch, and iii) a
mixed-performance estimation approach that combines information about
architectures at initialization stage and lower fidelity estimates to infer
their trainability and capacity to model complex functions. We present
experiments showing that LCMNAS generates state-of-the-art architectures from
scratch with minimal GPU computation. We study the importance of different NAS
components on a macro-search setting. Code for reproducibility is public at
\url{https://github.com/VascoLopes/LCMNAS}.


title:
Phenomenology of the Dark Matter sector in the Two Higgs Doublet Model
  with Complex Scalar Singlet extension

abstract:
  Extensions of the Two Higgs Doublet model with a complex scalar singlet
(2HDMS) can accommodate all current experimental constraints and are highly
motivated candidates for Beyond Standard Model Physics. It can successfully
provide a dark matter candidate as well as explain baryogenesis and provides
gravitational wave signals. In this work, we focus on the dark matter
phenomenology of the 2HDMS with the complex scalar singlet as the dark matter
candidate. We study variations of dark matter observables with respect to the
model parameters and present representative benchmark points in the light and
heavy dark matter mass regions allowed by existing experimental constraints
from dark matter, flavour physics and collider searches. We also compare real
and complex scalar dark matter in the context of 2HDMS. Further, we discuss the
discovery potential of such scenarios at the HL-LHC and at future $e^+e^-$
colliders.


title:
Controlling the flexibility of non-Gaussian processes through shrinkage
  priors

abstract:
  The normal inverse Gaussian (NIG) and generalized asymmetric Laplace (GAL)
distributions can be seen as skewed and heavy-tailed extensions of the Gaussian
distribution. Models driven by these more flexible noise distributions are then
regarded as generalizations of simpler Gaussian models. Inferential procedures
tend to overestimate the degree of non-Gaussianity in the data and in this
paper we propose controlling the flexibility of these non-Gaussian models by
adding sensible priors in the inferential framework that contract the model
towards Gaussianity. The methods are derived for a generic class of
non-Gaussian models that include non-Gaussian spatial Mat\'ern fields,
autoregressive models for time series, and simultaneous autoregressive models
for aerial data. The results are illustrated with a simulation study, where
priors that penalize model complexity were shown to lead to more robust
estimation and give preference to the Gaussian model, while at the same time
allowing for non-Gaussianity if there is sufficient evidence of asymmetry and
leptokurtosis in the data. We also propose a new method for assessing if there
is enough support in the data for choosing the non-Gaussian model over the
simpler Gaussian model and show how to determine at which time points or
regions in space the Gaussian model lacks flexibility.


title:
Multi-Party Quantum Purity Distillation with Bounded Classical
  Communication

abstract:
  We consider the task of distilling local purity from a noisy quantum state
$\rho^{ABC}$, wherein we provide a protocol for three parties, Alice, Bob and
Charlie, to distill local purity (at a rate $P$) from many independent copies
of a given quantum state $\rho^{ABC}$. The three parties have access to their
respective subsystems of $\rho^{ABC}$, and are provided with pure ancilla
catalytically, i.e., with the promise of returning them unaltered after the end
of the protocol. In addition, Alice and Bob can communicate with Charlie using
a one-way multiple-access dephasing channel of link rates $R_1$ and $R_2$,
respectively. The objective of the protocol is to minimize the usage of the
dephasing channel (in terms of rates $R_1$ and $R_2$) while maximizing the
asymptotic purity that can be jointly distilled from $\rho^{ABC}$. To achieve
this, we employ ideas from distributed measurement compression protocols, and
in turn, characterize a set of sufficient conditions on $(P,R_1,R_2)$ in terms
of quantum information theoretic quantities such that $P$ amount of purity can
be distilled using rates $R_1$ and $R_2$. Finally, we also incorporate the
technique of asymptotic algebraic structured coding, and provide a unified
approach of characterizing the performance limits.


title: Thermodynamic uncertainty relation for Langevin dynamics by scaling time

abstract:
  The thermodynamic uncertainty relation (TUR) quantifies a relationship
between current fluctuations and dissipation in out-of-equilibrium overdamped
Langevin dynamics, making it a natural counterpart of the
fluctuation-dissipation theorem in equilibrium statistical mechanics. For
underdamped Langevin dynamics, the situation is known to be more complicated,
with dynamical activity also playing a role in limiting the magnitude of
current fluctuations. Progress on those underdamped TUR-like bounds have thus
far come from applications of the information-theoretic Cram\'er-Rao
inequality. Here, we present an alternative perspective by employing the
contraction principle of large deviation theory. The approach offers a general,
unified treatment of TUR-like bounds for both overdamped and underdamped
Langevin dynamics built upon current fluctuations achieved by scaling time. The
resulting bounds are similar to known results but with differences we discuss
and rationalize.


title:
OneWeb Satellite Brightness -- Characterized From 80,000 Visible Light
  Magnitudes

abstract:
  The mean apparent magnitude and the mean of magnitudes adjusted to a standard
distance are reported. The illumination phase function for OneWeb satellites is
determined and it differs strongly from that of VisorSat spacecraft. Brightness
flares are characterized and the mean rate of magnitude variation during a pass
is determined. Tools for planning observations that minimize interference from
bright satellites are illustrated and discussed.


title: Geodesics on adjoint orbits of $SL(n, \mathbb{R})$

abstract:
  In this paper we study geodesics on adjoint orbits of $SL(n,\mathbb{R})$
equipped with $SO(n)$-invariant metrics (maximal compact subgroup). Our main
technique is translate this problem into a geometric problem in the tangent
bundle of certain $SO(n)$-flag manifolds and describe the geodesics equations
with respect to the Sasaki metric on tangent bundle. We also use tools of Lie
Theory in order to obtain some explicit description of families of geodesics.
We deal with the case of $SL(2,\mathbb{R})$ in full details.


title:
A weight-formula for all highest weight modules, and a higher order
  parabolic category $\mathcal{O}$

abstract:
  Let $\mathfrak{g}$ be a complex Kac-Moody algebra, with Cartan subalgebra
$\mathfrak{h}$. Also fix a weight $\lambda\in\mathfrak{h}^*$. For
$M(\lambda)\twoheadrightarrow V$ an arbitrary highest weight
$\mathfrak{g}$-module, we provide a cancellation-free, non-recursive formula
for the weights of $V$. This is novel even in finite type, and is obtained from
$\lambda$ and a collection $\mathcal{H}=\mathcal{H}_V$ of independent sets in
the Dynkin diagram of $\mathfrak{g}$ that are associated to $V$.
  Our proofs use and reveal a finite family (for each $\lambda$) of "higher
order Verma modules" $\mathbb{M}(\lambda,\mathcal{H})$ - these are all of the
universal modules for weight-considerations. They (i) generalize and subsume
parabolic Verma modules $M(\lambda,J)$, and (ii) have pairwise distinct
weight-sets, which exhaust the weight-sets of all modules
$M(\lambda)\twoheadrightarrow V$. As an application, we explain the sense in
which the modules $M(\lambda)$ of Verma and $M(\lambda, J_V)$ of Lepowsky are
respectively the zeroth and first order upper-approximations of every $V$, and
continue to higher order upper-approximations $\mathbb{M}_k(\lambda,
\mathcal{H}_V)$ (and to lower-approximations). We also determine the $k$th
order integrability of $V$, for all $k\geq 0$.
  We then introduce the category $\mathcal{O}^\mathcal{H}\subset\mathcal{O}$,
which is a higher order parabolic analogue that contains the higher order Verma
modules $\mathbb{M}(\lambda,\mathcal{H})$. We show that
$\mathcal{O}^{\mathcal{H}}$ has enough projectives, and also initiate the study
of BGG reciprocity, by proving it for all $\mathcal{O}^\mathcal{H}$ over
$\mathfrak{g}=\mathfrak{sl}_2^{\oplus n}$. Finally, we provide a BGG resolution
for the universal modules $\mathbb{M}(\lambda,\mathcal{H})$ in certain cases;
this yields a Weyl-type character formula for them, and involves the action of
a parabolic Weyl semigroup.


title: VirtualSync+: Timing Optimization with Virtual Synchronization

abstract:
  In digital circuit designs, sequential components such as flip-flops are used
to synchronize signal propagations. Logic computations are aligned at and thus
isolated by flip-flop stages. Although this fully synchronous style can reduce
design efforts significantly, it may affect circuit performance negatively,
because sequential components can only introduce delays into signal
propagations but never accelerate them. In this paper, we propose a new timing
model, VirtualSync+, in which signals, specially those along critical paths,
are allowed to propagate through several sequential stages without flip-flops.
Timing constraints are still satisfied at the boundary of the optimized circuit
to maintain a consistent interface with existing designs. By removing
clock-to-q delays and setup time requirements of flip-flops on critical paths,
the performance of a circuit can be pushed even beyond the limit of traditional
sequential designs. In addition, we further enhance the optimization with
VirtualSync+ by fine-tuning with commercial design tools, e.g., Design Compiler
from Synopsys, to achieve more accurate result. Experimental results
demonstrate that circuit performance can be improved by up to 4% (average 1.5%)
compared with that after extreme retiming and sizing, while the increase of
area is still negligible. This timing performance is enhanced beyond the limit
of traditional sequential designs. It also demonstrates that compared with
those after retiming and sizing, the circuits with VirtualSync+ can achieve
better timing performance under the same area cost or smaller area cost under
the same clock period, respectively.


title:
Analysis of Multipartite Entanglement Distribution using a Central
  Quantum-Network Node

abstract:
  We study the performance (rate and fidelity) of distributing multipartite
entangled states in a quantum network through the use of a central node.
Specifically, we consider the scenario where the multipartite entangled state
is first prepared locally at a central node, and then transmitted to the end
nodes of the network through quantum teleportation. As our first result, we
present leading-order analytical expressions and lower bounds for both the rate
and fidelity at which a specific class of multipartite entangled states, namely
Greenberger-Horne-Zeilinger (GHZ) states, are distributed. Our analytical
expressions for the fidelity accurately account for time-dependent depolarizing
noise encountered by individual quantum bits while stored in quantum memory, as
verified using Monte Carlo simulations. As our second result, we compare the
performance to the case where the central node is an entanglement switch and
the GHZ state is created by the end nodes in a distributed fashion. Apart from
these two results, we outline how the teleportation-based scheme could be
physically implemented using trapped ions or nitrogen-vacancy centers in
diamond.


title: Long-range current-induced spin accumulation in chiral crystals

abstract:
  The chirality-induced spin selectivity (CISS) refers to a conversion between
charge and spin currents occurring in chiral molecules and solids, which
reveals a great potential for applications in various areas of technology.
Nevertheless, after two decades of studies, the understanding of its
microscopic origin remains elusive. Here, the CISS effect in materials with
strong spin-orbit coupling is explained in terms of the unconventional
current-induced spin accumulation, an analog of the Rashba-Edelstein effect
that may manifest in systems with a radial spin texture reversing the sign for
opposite enantiomers. The calculations of spin accumulation based on the
density functional theory, performed for the first time for Te and TaSi$_2$,
provide a rationale for the experimental studies reporting CISS in these
materials. The analysis of a general Weyl-type spin-orbit coupling locally
describing the spin texture at specific momentum vectors, reveals that a
quasi-persistent spin helix emerges in the real space as a consequence of the
crystal symmetries, and prevents the spins from decoherence, which
qualitatively supports the observation of the long-range spin transport in CISS
experiments. These results open the perspectives for DFT calculations of CISS
for any chiral crystals and represent a step forward to its understanding in
general systems.


title: Singular multiple integrals and nonlinear potentials

abstract:
  We prove sharp partial regularity criteria of nonlinear potential theoretic
nature for the Lebesgue-Serrin-Marcellini extension of nonhomogeneous singular
multiple integrals featuring $(p,q)$-growth conditions.


title: Opportunities and Limitations in Broadband Sensing

abstract:
  We consider estimating the magnitude of a monochromatic AC signal that
couples to a two-level sensor. For any detection protocol, the precision
achieved depends on the signal's frequency and can be quantified by the quantum
Fisher information. To study limitations in broadband sensing, we introduce the
integrated quantum Fisher information and derive inequality bounds that embody
fundamental tradeoffs in any sensing protocol. These inequalities show that
sensitivity in one frequency range must come at a cost of reduced sensitivity
elsewhere. For many protocols, including those with small phase accumulation
and those consisting of $\pi$-pulses, we find the integrated Fisher information
scales linearly with $T$. We also find protocols with substantial phase
accumulation can have integrated QFI that grows quadratically with $T$, which
is optimal. These protocols may allow the very rapid detection of a signal with
unknown frequency over a very wide bandwidth.


title: Fluctuating Quantum Kinetic Theory

abstract:
  We consider a quantum Langevin kinetic equation for a system of fermions. We
first derive the Langevin force noise correlation functions in Landau's
Fermi-liquid kinetic theory from general considerations. We then use the
resulting equation to calculate the equilibrium dynamic structure factor in the
collisionless regime at low temperatures. The result is in agreement with the
conventional many-body result. We then use the theory to derive both the
fluctuating Navier-Stokes equations for a quantum fluid and the fluctuating
hydrodynamic equations for fermions in the presence of quenched disorder. We
also discuss the modifications needed for the fluctuating hydrodynamic
equations to describe an electron fluid with long-ranged interactions, and we
prove an H-theorem for the nonlinear Landau kinetic equation.


title:
Data-driven Abstractions with Probabilistic Guarantees for Linear PETC
  Systems

abstract:
  We employ the scenario approach to compute probably approximately correct
(PAC) bounds on the average inter-sample time (AIST) generated by an unknown
PETC system, based on a finite number of samples. We extend the scenario
approach to multiclass SVM algorithms in order to construct a PAC map between
the concrete, unknown state-space and the inter-sample times. We then build a
traffic model applying an $\ell$-complete relation and find, in the underlying
graph, the cycles of minimum and maximum average weight: these provide lower
and upper bounds on the AIST. Numerical benchmarks show the practical
applicability of our method, which is compared against model-based
state-of-the-art tools.


title:
SoftSNN: Low-Cost Fault Tolerance for Spiking Neural Network
  Accelerators under Soft Errors

abstract:
  Specialized hardware accelerators have been designed and employed to maximize
the performance efficiency of Spiking Neural Networks (SNNs). However, such
accelerators are vulnerable to transient faults (i.e., soft errors), which
occur due to high-energy particle strikes, and manifest as bit flips at the
hardware layer. These errors can change the weight values and neuron operations
in the compute engine of SNN accelerators, thereby leading to incorrect outputs
and accuracy degradation. However, the impact of soft errors in the compute
engine and the respective mitigation techniques have not been thoroughly
studied yet for SNNs. A potential solution is employing redundant executions
(re-execution) for ensuring correct outputs, but it leads to huge latency and
energy overheads. Toward this, we propose SoftSNN, a novel methodology to
mitigate soft errors in the weight registers (synapses) and neurons of SNN
accelerators without re-execution, thereby maintaining the accuracy with low
latency and energy overheads. Our SoftSNN methodology employs the following key
steps: (1) analyzing the SNN characteristics under soft errors to identify
faulty weights and neuron operations, which are required for recognizing faulty
SNN behavior; (2) a Bound-and-Protect technique that leverages this analysis to
improve the SNN fault tolerance by bounding the weight values and protecting
the neurons from faulty operations; and (3) devising lightweight hardware
enhancements for the neural hardware accelerator to efficiently support the
proposed technique. The experimental results show that, for a 900-neuron
network with even a high fault rate, our SoftSNN maintains the accuracy
degradation below 3%, while reducing latency and energy by up to 3x and 2.3x
respectively, as compared to the re-execution technique.


title:
Chiral phonons in binary compounds $A$Bi ($A$ = K, Rb, Cs) with P2$_1$/c
  structure

abstract:
  Binary compounds $A$Bi ($A$ = K, Rb, Cs) crystallize in P2$_1$/c structure
containing both clockwise and anticlockwise chiral chains of Bi atoms.
Electronic band structure exhibits the insulating nature of these systems, with
the band gap about 0.25 eV. The presented study of dynamical properties confirm
a stability of the system with P2$_1$/c symmetry. Independently of the absence
of the three-fold (or higher) rotational symmetry axes, the chiral modes
propagate along the Bi atom chains in these systems. We discuss basic
properties of these modes in monoatomic chiral chains.


title:
Passive low energy nuclear recoil detection with color centers --
  PALEOCCENE

abstract:
  The PALEOCCENE concept offers the potential for room-temperature, passive and
robust detectors in the gram to kilogram range for the detection of low-energy
nuclear recoil events. Nuclear recoil events can be caused by neutron
scattering, coherent elastic neutrino nucleus scattering (CEvNS) or dark matter
scattering and therefore, PALEOCCENE could find applications in all three
areas. In this white paper we present current and planned R&D efforts to study
the feasibility of this technique.


title: Efficient classical simulation of open bosonic quantum systems

abstract:
  We propose a computationally efficient method to solve the dynamics of
operators of bosonic quantum systems coupled to their environments. The method
maps the operator under interest to a set of complex-valued functions, and its
adjoint master equation to a set of partial differential equations for these
functions, which are subsequently solved numerically. In the limit of weak
coupling to the environment, the mapping of the operator enables storing the
operator efficiently during the simulation, leading to approximately quadratic
improvement in the memory consumption compared with the direct approach of
solving the adjoint master equation in the number basis, while retaining the
computation time comparable. Moreover, the method enables efficient
parallelization which allows to optimize for the actual computational time to
reach an approximately quadratic speed up, while retaining the memory
consumption comparable to the direct approach. We foresee the method to prove
useful, e.g., for the verification of the operation of superconducting quantum
processors.


title:
A robust tip-less positioning device for near-field investigations:
  Press and Roll Scan (PROscan)

abstract:
  Scanning probe microscopes scan and manipulate a sharp tip in the immediate
vicinity of a sample surface. The limited bandwidth of the feedback mechanism
used for stabilizing the separation between the tip and the sample makes the
fragile nanoscopic tip very susceptible to mechanical instabilities. We
propose, demonstrate and characterize a new alternative device based on bulging
a thin substrate against a second substrate and rolling them with respect each
other. We showcase the power of this method by placing gold nanoparticles and
semiconductor quantum dots on the two opposite substrates and positioning them
with nanometer precision to enhance the fluorescence intensity and emission
rate. We exhibit the passive mechanical stability of the system over more than
one hour. The design concept presented in this work holds promise in a variety
of other contexts, where nanoscopic features have to be positioned and kept
near contact with each other.


title: Model reduction methods for nuclear emulators

abstract:
  The field of model order reduction (MOR) is growing in importance due to its
ability to extract the key insights from complex simulations while discarding
computationally burdensome and superfluous information. We provide an overview
of MOR methods for the creation of fast & accurate emulators of memory- and
compute-intensive nuclear systems. As an example, we describe how "eigenvector
continuation" is a special case of a much more general and well-studied MOR
formalism for parameterized systems. We continue with an introduction to the
Ritz and Galerkin projection methods that underpin many such emulators, while
pointing to the relevant MOR theory and its successful applications along the
way. We believe that this will open the door to broader applications in nuclear
physics and facilitate communication with practitioners in other fields.


title: Commentary on Sp(6) hypergeometric groups

abstract:
  We study the examples mentioned in [2,Tables A & C] and establish the
arithmeticity of four examples of symplectic hypergeometric groups of degree
six. Following [2] we know that there are 458 inequivalent symplectic
hypergeometric groups of degree six, and combining the results of this article
with the work of [1,2,11], we now know that at least 384 are arithmetic and at
least 63 are thin whereas the arithmeticity and thinness of remaining 11
examples are still unknown.


title: Perspectives of microwave quantum key distribution in open-air

abstract:
  One of the cornerstones of quantum communication is the unconditionally
secure distribution of classical keys between remote parties. This key feature
of quantum technology is based on the quantum properties of propagating
electromagnetic waves, such as entanglement, or the no-cloning theorem.
However, these quantum resources are known to be susceptible to noise and
losses, which are omnipresent in open-air communication scenarios. In this
work, we theoretically investigate the perspectives of continuous-variable
open-air quantum key distribution at microwave frequencies. In particular, we
present a model describing the coupling of propagating microwaves with a noisy
environment. Using a protocol based on displaced squeezed states, we
demonstrate that continuous-variable quantum key distribution with propagating
microwaves can be unconditionally secure at room temperature up to distances of
around 200 meters. Moreover, we show that microwaves can potentially outperform
conventional quantum key distribution at telecom wavelength at imperfect
weather conditions.


title: Review of Neutral Naturalness

abstract:
  The hierarchy between the mass of the Higgs boson and larger mass scales
becomes ever more puzzling as experiments push to higher energies. Neutral
naturalness is the umbrella term for symmetry-based explanations for these
hierarchies whose quark symmetry partners are not charged under the $SU(3)_c$
color gauge group of the Standard Model. Though the first manifestations of
this idea predate the physics runs of the Large Hadron Collider, since the
Higgs discovery this paradigm has grown and developed to include a wide variety
of models, connections to intriguing collider signals, various dark matter
candidates, intersections with astrophysics and cosmology, motivations for
lattice studies, and ties to neutrinos and flavor. In this review we orient the
reader within this growing literature and reveal interesting directions for
further study.


title: Robust Radical Sylvester-Gallai Theorem for Quadratics

abstract:
  We prove a robust generalization of a Sylvester-Gallai type theorem for
quadratic polynomials, generalizing the result in [S'20]. More precisely, given
a parameter $0 < \delta \leq 1$ and a finite collection $\mathcal{F}$ of
irreducible and pairwise independent polynomials of degree at most 2, we say
that $\mathcal{F}$ is a $(\delta, 2)$-radical Sylvester-Gallai configuration if
for any polynomial $F_i \in \mathcal{F}$, there exist $\delta(|\mathcal{F}|
-1)$ polynomials $F_j$ such that $|\mathrm{rad}(F_i, F_j) \cap \mathcal{F}|
\geq 3$, that is, the radical of $F_i, F_j$ contains a third polynomial in the
set.
  In this work, we prove that any $(\delta, 2)$-radical Sylvester-Gallai
configuration $\mathcal{F}$ must be of low dimension: that is $$\dim
\mathrm{span}(\mathcal{F}) = \mathrm{poly}(1/\delta).$$


title:
Lee-Yang zeroes of the Curie-Weiss ferromagnet, unitary Hermite
  polynomials, and the backward heat flow

abstract:
  The backward heat flow on the real line started from the initial condition
$z^n$ results in the classical $n$-th Hermite polynomial whose zeroes are
distributed according to the Wigner semicircle law in the large $n$ limit.
Similarly, the backward heat flow with the periodic initial condition $(\sin
\frac \theta 2)^n$ leads to trigonometric or unitary analogues of the Hermite
polynomials. These polynomials are closely related to the partition function of
the Curie-Weiss model and appeared in the work of Mirabelli on finite free
probability. We relate the $n$-th unitary Hermite polynomial to the expected
characteristic polynomial of a unitary random matrix obtained by running a
Brownian motion on the unitary group $U(n)$. We identify the global
distribution of zeroes of the unitary Hermite polynomials as the free unitary
normal distribution. We also compute the asymptotics of these polynomials or,
equivalently, the free energy of the Curie-Weiss model in a complex external
field. We identify the global distribution of the Lee-Yang zeroes of this
model. Finally, we show that the backward heat flow applied to a high-degree
real-rooted polynomial (respectively, trigonometric polynomial) induces, on the
level of the asymptotic distribution of its roots, a free Brownian motion
(respectively, free unitary Brownian motion).


title:
AGCN: Augmented Graph Convolutional Network for Lifelong Multi-label
  Image Recognition

abstract:
  The Lifelong Multi-Label (LML) image recognition builds an online
class-incremental classifier in a sequential multi-label image recognition data
stream. The key challenges of LML image recognition are the construction of
label relationships on Partial Labels of training data and the Catastrophic
Forgetting on old classes, resulting in poor generalization. To solve the
problems, the study proposes an Augmented Graph Convolutional Network (AGCN)
model that can construct the label relationships across the sequential
recognition tasks and sustain the catastrophic forgetting. First, we build an
Augmented Correlation Matrix (ACM) across all seen classes, where the
intra-task relationships derive from the hard label statistics while the
inter-task relationships leverage both hard and soft labels from data and a
constructed expert network. Then, based on the ACM, the proposed AGCN captures
label dependencies with dynamic augmented structure and yields effective class
representations. Last, to suppress the forgetting of label dependencies across
old tasks, we propose a relationship-preserving loss as a constraint to the
construction of label relationships. The proposed method is evaluated using two
multi-label image benchmarks and the experimental results show that the
proposed method is effective for LML image recognition and can build convincing
correlation across tasks even if the labels of previous tasks are missing. Our
code is available at https://github.com/Kaile-Du/AGCN.


title: Small fractional parts of binary forms

abstract:
  We obtain bounds on fractional parts of binary forms of the shape
$$\Psi(x,y)=\alpha_k x^k+\alpha_l
x^ly^{k-l}+\alpha_{l-1}x^{l-1}y^{k-l+1}+\cdots+\alpha_0 y^k$$ with
$\alpha_k,\alpha_l,\ldots,\alpha_0\in\mathbb{R}$ and $l\leq k-2.$ By exploiting
recent progress on Vinogradov's mean value theorem and earlier work on
exponential sums over smooth numbers, we derive estimates superior to those
obtained hitherto for the best exponent $\sigma$, depending on $k$ and $l,$
such that \begin{equation*} \min_{\substack{0\leq x,y\leq X\\(x,y)\neq
(0,0)}}\|\Psi(x,y)\|\leq X^{-\sigma+\epsilon}.\end{equation*}


title:
Non-zero $\theta_{13}$, CP-violation and Neutrinoless Double Beta Decay
  for Neutrino Mixing in the $A_4\times Z_2\times Z_2^\prime$ Flavor Symmetry
  Model

abstract:
  We study the modification of the Altarelli-Feruglio $A_4$ flavor symmetry
model by adding three singlet flavons $\xi'$, $\xi''$ and $\rho$ and the model
is augmented with extra $Z_2\times Z_2^ \prime$ symmetry to prevent the
unwanted terms in our study. The addition of these three flavons lead to two
higher order corrections in the form of two perturbation parameters $\epsilon$
and $\epsilon^\prime$. These corrections yield the deviation from exact
tri-bimaximal (TBM) neutrino mixing pattern by producing a non-zero
$\theta_{13}$ and other neutrino oscillation parameters which are consistent
with the latest experimental data. In both the corrections, the neutrino masses
are generated via Weinberg operator. The analysis of the perturbation
parameters $\epsilon$ and $\epsilon^\prime$, shows that normal hierarchy (NH)
and inverted hierarchy (IH) for $\epsilon$ does not change much. However, as
the values of $\epsilon^\prime$ increases, $\theta_{23}$ occupies the lower
octant for NH case. We further investigate the neutrinoless double beta decay
parmeter $m_{\beta\beta}$ using the parameter space of the model for both
normal and inverted hierarchies of neutrino masses.


title:
Supernova remnant G46.8-0.3, a new case of interaction with molecular
  material

abstract:
  We present the first detailed study on the Galactic supernova remnant (SNR)
G46.8-0.3 and its environment since its discovery more than 50 years ago. With
new flux measurements from radio surveys plus a compilation from the literature
we created an improved integrated radio spectrum of G46.8-0.3, which is well
fitted by a simple power-law with an index alpha = -0.535 +/- 0.012, then
excluding the presence of absorption by abundant ionised gas in the line of
sight, or in the SNR's proximity. The analysis of local changes in the radio
spectral index across G46.8-0.3 suggests a slight steepening at about 1 GHz,
which does not impact on the integrated spectrum of the source. From atomic
hydrogen (HI) spectra we placed the remnant at 8.7 +/- 1.0 kpc, and revisited
the distance to the nearby HII region G046.495-00.241 to 7.3 +/- 1.2 kpc. From
evolutionary models and our distance estimate, we conclude G46.8-0.3 is a
middle-aged ($\sim$1 x 10^4 yr) SNR. We recognised several 12CO and 13CO
molecular structures in the proximity of the remnant and used combined CO-HI
profiles to derive the kinematic distances to these features and characterise
their physical properties. We also provided compelling evidences for
environmental molecular clouds physically linked to G46.8-0.3 at its centre, on
its eastern edge, and towards the northern and southwestern rims on the far
side of the SNR shell. Our study does not confirm that the remnant is embedded
in a molecular cavity as previously suggested. G46.8-0.3 shows a line-of-sight
coincidence with the gamma-ray source 4FGL J1918.1+1215c, detected at GeV
energies by the space telescope Fermi. A rough analysis based on the properties
of the interstellar matter close G46.8-0.3 indicates that the GeV gamma-rays
photons detected in direction to the SNR can be plausibly attributed to
hadronic collisions and/or bremsstrahlung radiation.


title: Multicopy metrology with many-particle quantum states

abstract:
  We consider quantum metrology with several copies of bipartite and
multipartite quantum states. We characterize the metrological usefulness by
determining how much the state outperforms separable states. We identify a
large class of entangled states that become maximally useful for metrology in
the limit of infinite number of copies. The maximally achievable metrological
usefulness is attained exponentially fast in the number of copies. We show
that, on the other hand, pure entangled states with even a small amount of
white noise do not become maximally useful even in the limit of infinite number
of copies. We also make general statements about the usefulness of a single
copy of pure entangled states. We show that the multiqubit states presented in
Hyllus et al. [Phys. Rev. A 82, 012337 (2010)], which are not useful, become
useful if we embed the qubits locally in qutrits. We discuss the relation of
our scheme to error correction, and possible use for quantum information
processing in a noisy environment.


title:
Spin Evolution of Stellar-mass Black Holes Embedded in AGN disks:
  Orbital Eccentricity Produces Retrograde Circumstellar Flows

abstract:
  Spin evolution of stellar-mass Black Holes (sBHs) embedded in AGN accretion
disks is an important process relevant to production of gravitaional waves from
binary Black Hole (BBH) merger events through the AGN channel. Since embedded
sBHs are surrounded by circum-stellar disks (CSDs), the rotation of CSD gas
flows determine the direction of the angular momentum it accretes. In this
Letter, we use global 2D hydrodynamic simulations to show that while a
disk-embedded sBH on a circular orbit transforms the initial retrograde
Keplerian shear of the background accretion disk into a prograde CSD flow, as
in the classical picture of companion-disk interaction theory, moderate orbital
eccentricity could disrupt the steady-state tidal perturbation and preserve a
retrograde CSD flow around the sBH. This switch of CSD orientation occurs at a
transition eccentricity that scales nearly proportional with local sound speed.
This bifurcation in the CSD flow and thereafter spin-up direction of SBHs leads
to formation of a population of nearly anti-aligned sBHs and should be
incorporated in future population models of sBH and BBH evolutions.

categories: astro-ph.HE astro-ph.EP astro-ph.GA astro-ph.SR


title: Plank theorems and their applications: a survey

abstract:
  Plank problems concern the covering of convex bodies by planks in Euclidean
space and are related to famous open problems in convex geometry. In this
survey, we introduce plank problems and present surprising applications of
plank theorems in various areas of mathematics.


title:
Rational points on hyperelliptic Atkin-Lehner quotients of modular
  curves and their coverings

abstract:
  We complete the computation of all $\mathbb{Q}$-rational points on all the
$64$ maximal Atkin-Lehner quotients $X_0(N)^*$ such that the quotient is
hyperelliptic. To achieve this, we use a combination of various methods, namely
the classical Chabauty--Coleman, elliptic curve Chabauty, quadratic Chabauty,
and the bielliptic quadratic Chabauty method combined with the Mordell-Weil
sieve. Additionally, for square-free levels $N$, we classify all
$\mathbb{Q}$-rational points as cusps, CM points (including their CM field and
$j$-invariants) and exceptional ones. We further indicate how to use this to
compute the $\mathbb{Q}$-rational points on all of their modular coverings.


title:
The Observed Age Gradient in the Milky Way -- as a Test for theories of
  spiral arm structure

abstract:
  Some important predictions from 4 main models of spiral arm formation are
tested here, using observational data acquired for the Milky Way galaxy. Many
spiral arm models (density wave, tidal wave, nuclear Lyapunov tube, or dynamic
transient wave) have some consistencies with some of the observations, and some
inconsistencies. Our 4 tests consist of the relative locations and relative
speeds of different arm tracers away from the dust lane, and the global arm
pitch angle as obtained over two Galactic quadrants and several Galactic radii,
as well as the arm's continuity of shape from Galactic quadrant IV to Galactic
quadrant I. In the Milky Way, an age gradient is observed from different arm
tracers, amounting to 12.9 +/-1.1 Myrs/kpc, or a relative speed away from the
dust lane of 76 +/-10 km/s. The presence of an age gradient is predicted by the
density waves, but is not consistent with the predictions of the tidal waves,
of the nuclear Lyapunov tubes, nor of the dynamic transient recurrent waves.


title: Experimental realization of a fermionic spin-momentum lattice

abstract:
  We experimentally realize a spin-momentum lattice with a homogeneously
trapped Fermi gas. The lattice is created via cyclically-rotated atom-laser
couplings between three bare atomic spin states, and are such that they form a
triangular lattice in a synthetic spin-momentum space. We demonstrate the
lattice and explore its dynamics with spin- and momentum-resolved absorption
imaging. This platform will provide new opportunities for synthetic spin
systems and the engineering of topological bands. In particular, the use of
three spin states in two spatial dimensions would allow the simulation of
synthetic magnetic fields of high spatial uniformity, which would lead to
ultra-narrow Chern bands that support robust fractional quantum Hall states.


title: B-physics anomalies: UV models

abstract:
  B-anomalies hint towards New Physics violating Lepton Flavor Universality at
the TeV scale. The way they manifest points out to very particular structures
for this New Physics. In this talk I review some UV-complete models to explain
the B-anomalies, and possible directions these models suggest for Physics
beyond the TeV scale.


title: Optimal times to buy and sell a home

abstract:
  We consider a financial market in which the risk-free rate of interest is
modeled as a Markov diffusion. We suppose that home prices are set by a
representative home-buyer, who can afford to pay only a fixed cash-flow per
unit time for housing. The cash-flow is a fraction of the representative
home-buyer's salary, which grows at a rate that is proportional to the
risk-free rate of interest. As a result, in the long-run, higher interest rates
lead to faster growth of home prices. The representative home-buyer finances
the purchase of a home by taking out a mortgage. The mortgage rate paid by the
home-buyer is fixed at the time of purchase and equal to the risk-free rate of
interest plus a positive constant. As the home-buyer can only afford to pay a
fixed cash-flow per unit time, a higher mortgage rate limits the size of the
loan the home-buyer can take out. As a result, the short-term effect of higher
interest rates is to lower the value of homes. In this setting, we consider an
investor who wishes to buy and then sell a home in order to maximize his
discounted expected profit. This leads to a nested optimal stopping problem. We
use a nonnegative concave majorant approach to derive the investor's optimal
buying and selling strategies. Additionally, we provide a detailed analytic and
numerical study of the case in which the risk-free rate of interest is modeled
by a Cox-Ingersoll-Ross (CIR) process. We also examine, in the case of CIR
interest rates, the expected time that the investor waits before buying and
then selling a home when following the optimal strategies.


title:
Quantum yield of an emitter proximate to nanostructures: a quantitative
  understanding

abstract:
  Exciton-surface plasmon coupling is at the heart of the most elementary
light-matter interactions and is a result of not only an intrinsic property of
the emitter but that of emitter-environment interaction. Thus, change of
electromagnetic environment, as in case of metallic nanoplasmonic structures
and an emitter, significantly modifies the near field light-matter interaction,
which leads to energy transfer in the form of exciton between metallic
nanostructure and the emitter. However, this mechanism remains largely
unexplored. Here, we developed and applied semi-classical electrodynamics
theory and modeling techniques to analyze the energy transfer mechanism in
exciton-surface plasmon coupling. The quantum efficiency of an emitter was
investigated as a function of the location of the emitter with respect to
nanoparticles and their assembles whose local plasmonic field modified by
forming complex coupling modes as well as the local dielectric environment. The
research provided a theoretical insight into fundamental science of
nanophotonics and shed light on unprecedented applications in wide range fields
such as ultra-low power lasers, quantum information processing, photovoltaics,
photocatalysis, and chemical sensing.


title:
Probing the Dark Solar System: Detecting Binary Asteroids with a
  Space-Based Interferometric Asteroid Explorer

abstract:
  With the inception of gravitational wave astronomy, astrophysical studies
using interferometric techniques have begun to probe previously unknown parts
of the universe. In this work, we investigate the potential of a new
interferometric experiment to study a unique group of gravitationally
interacting sources within our solar system: binary asteroids. We present the
first study into binary asteroid detection via gravitational signals. We
identify the interferometer sensitivity necessary for detecting a population of
binary asteroids in the asteroid belt. We find that the space-based
gravitational wave detector LISA will have negligible ability to detect these
sources as these signals will be well below the LISA noise curve. Consequently,
we propose a 4.6 AU and a 1 AU arm-length interferometers specialized for
binary asteroid detection, targeting frequencies between $10^{-6}$ and
$10^{-4}$ Hz. Our results demonstrate that the detection of binary asteroids
with space-based gravitational wave interferometers is possible though very
difficult, requiring substantially improved interferometric technology over
what is presently proposed for space-based missions. If that threshold can be
met, an interferometer may be used to map the asteroid belt, allowing for new
studies into the evolution of our solar system.


title:
LiDAR Aided Future Beam Prediction in Real-World Millimeter Wave V2I
  Communications

abstract:
  This paper presents the first large-scale real-world evaluation for using
LiDAR data to guide the mmWave beam prediction task. A machine learning (ML)
model that leverages the LiDAR sensory data to predict the current and future
beams was developed. Based on the large-scale real-world dataset, DeepSense 6G,
this model was evaluated in a vehicle-to-infrastructure communication scenario
with highly-mobile vehicles. The experimental results show that the developed
LiDAR-aided beam prediction and tracking model can predict the optimal beam in
$95\%$ of the cases and with more than $90\%$ reduction in the beam training
overhead. The LiDAR-aided beam tracking achieves comparable accuracy
performance to a baseline solution that has perfect knowledge of the previous
optimal beams, without requiring any knowledge about the previous optimal beam
information and without any need for beam calibration. This highlights a
promising solution for the critical beam alignment challenges in mmWave and
terahertz communication systems.


title: Context is Everything: Implicit Identification for Dynamics Adaptation

abstract:
  Understanding environment dynamics is necessary for robots to act safely and
optimally in the world. In realistic scenarios, dynamics are non-stationary and
the causal variables such as environment parameters cannot necessarily be
precisely measured or inferred, even during training. We propose Implicit
Identification for Dynamics Adaptation (IIDA), a simple method to allow
predictive models to adapt to changing environment dynamics. IIDA assumes no
access to the true variations in the world and instead implicitly infers
properties of the environment from a small amount of contextual data. We
demonstrate IIDA's ability to perform well in unseen environments through a
suite of simulated experiments on MuJoCo environments and a real robot dynamic
sliding task. In general, IIDA significantly reduces model error and results in
higher task performance over commonly used methods. Our code and robot videos
are at https://bennevans.github.io/iida/


title: An Empirical Investigation of 3D Anomaly Detection and Segmentation

abstract:
  Anomaly detection and segmentation in images has made tremendous progress in
recent years while 3D information has often been ignored. The objective of this
paper is to further understand the benefit and role of 3D as opposed to color
in image anomaly detection. Our study begins by presenting a surprising
finding: standard color-only anomaly segmentation methods, when applied to 3D
datasets, significantly outperform all current methods. On the other hand, we
observe that color-only methods are insufficient for images containing
geometric anomalies where shape cannot be unambiguously inferred from 2D. This
suggests that better 3D methods are needed. We investigate different
representations for 3D anomaly detection and discover that handcrafted
orientation-invariant representations are unreasonably effective on this task.
We uncover a simple 3D-only method that outperforms all recent approaches while
not using deep learning, external pretraining datasets, or color information.
As the 3D-only method cannot detect color and texture anomalies, we combine it
with 2D color features, granting us the best current results by a large margin
(Pixel-wise ROCAUC: 99.2%, PRO: 95.9% on MVTec 3D-AD). We conclude by
discussing future challenges for 3D anomaly detection and segmentation.


title:
Cellular automata can classify data by inducing trajectory phase
  coexistence

abstract:
  We show that cellular automata can classify data by inducing a form of
dynamical phase coexistence. We use Monte Carlo methods to search for general
two-dimensional deterministic automata that classify images on the basis of
activity, the number of state changes that occur in a trajectory initiated from
the image. When the depth of the automaton is a trainable parameter, the search
scheme identifies automata that generate a population of dynamical trajectories
displaying high or low activity, depending on initial conditions. Automata of
this nature behave as nonlinear activation functions with an output that is
effectively binary, resembling an emergent version of a spiking neuron. Our
work connects machine learning and reservoir computing to phenomena
conceptually similar to those seen in physical systems such as magnets and
glasses.


title:
Set-theoretical solutions to the Zamolodchikov tetrahedron equation on
  associative rings and Liouville integrability

abstract:
  This paper is devoted to tetrahedron maps, which are set-theoretical
solutions to the Zamolodchikov tetrahedron equation. We construct a family of
tetrahedron maps on associative rings.
  We show that matrix tetrahedron maps presented in [arXiv:2110.05998] are a
particular case of our construction. This provides an algebraic explanation of
the fact that the matrix maps from [arXiv:2110.05998] satisfy the tetrahedron
equation.
  Also, Liouville integrability is established for some of the constructed
maps.


title:
Transfer of Representations to Video Label Propagation: Implementation
  Factors Matter

abstract:
  This work studies feature representations for dense label propagation in
video, with a focus on recently proposed methods that learn video
correspondence using self-supervised signals such as colorization or temporal
cycle consistency. In the literature, these methods have been evaluated with an
array of inconsistent settings, making it difficult to discern trends or
compare performance fairly. Starting with a unified formulation of the label
propagation algorithm that encompasses most existing variations, we
systematically study the impact of important implementation factors in feature
extraction and label propagation. Along the way, we report the accuracies of
properly tuned supervised and unsupervised still image baselines, which are
higher than those found in previous works. We also demonstrate that augmenting
video-based correspondence cues with still-image-based ones can further improve
performance. We then attempt a fair comparison of recent video-based methods on
the DAVIS benchmark, showing convergence of best methods to performance levels
near our strong ImageNet baseline, despite the usage of a variety of
specialized video-based losses and training particulars. Additional comparisons
on JHMDB and VIP datasets confirm the similar performance of current methods.
We hope that this study will help to improve evaluation practices and better
inform future research directions in temporal correspondence.


title:
Floquet engineering of optical nonlinearities: a quantum many-body
  approach

abstract:
  Subjecting a physical system to a time-periodic drive can substantially
modify its properties and applications. This Floquet-engineering approach has
been extensively applied to a wide range of classical and quantum settings in
view of designing synthetic systems with exotic properties. Considering a
general class of two-mode nonlinear optical devices, we show that effective
optical nonlinearities can be created by subjecting the light field to a
repeated pulse sequence, which couples the two modes in a fast and
time-periodic manner. The strength of these drive-induced optical
nonlinearities, which include an emerging four-wave mixing, can be varied by
simply adjusting the pulse sequence. This leads to topological changes in the
system's phase space, which can be detected through light intensity and phase
measurements. Our proposal builds on an effective-Hamiltonian approach, which
derives from a parent quantum many-body Hamiltonian describing driven
interacting bosons. As a corollary, our results equally apply to Bose-Einstein
condensates in driven double-well potentials, where pair tunneling effectively
arises from the periodic pulse sequence. Our scheme offers a practical route to
engineer and finely tune exotic nonlinearities and interactions in photonics
and ultracold quantum gases.


title: Constraining the Nuclear Equation of State from Rotating Neutron Stars

abstract:
  We demonstrate how observables of slowly rotating neutron stars can be used
to constrain the nuclear equation of state. By building a Bayesian framework we
demonstrate how combining different types of neutron star measurements,
motivated by the upcoming multi-messenger era, provide informative posterior
distributions of commonly used equation of state parametrizations. Exemplarily,
we chose the Read et al. four parameter model that is widely used to represent
realistic equations of states in neutron star physics. Since future
observational campaigns will not only provide mass, radius and rotation rate
measurements, but also neutron star oscillation modes, a unified framework to
analyze the joint knowledge becomes necessary. While several attempts exist in
the literature, the here presented framework explicitly takes into account
rotational effects by using state-of-the-art universal relations for their
oscillation spectra. To demonstrate its performance we apply it to observables
that take into account rotational effects to best possible precision and use
our independent model for the reconstruction. Only a few high precision neutron
star measurements are required to put tight constraints on the underlying
equation of state.


title: On Embeddings for Numerical Features in Tabular Deep Learning

abstract:
  Recently, Transformer-like deep architectures have shown strong performance
on tabular data problems. Unlike traditional models, e.g., MLP, these
architectures map scalar values of numerical features to high-dimensional
embeddings before mixing them in the main backbone. In this work, we argue that
embeddings for numerical features are an underexplored degree of freedom in
tabular DL, which allows constructing more powerful DL models and competing
with GBDT on some traditionally GBDT-friendly benchmarks. We start by
describing two conceptually different approaches to building embedding modules:
the first one is based on a piecewise linear encoding of scalar values, and the
second one utilizes periodic activations. Then, we empirically demonstrate that
these two approaches can lead to significant performance boosts compared to the
embeddings based on conventional blocks such as linear layers and ReLU
activations. Importantly, we also show that embedding numerical features is
beneficial for many backbones, not only for Transformers. Specifically, after
proper embeddings, simple MLP-like models can perform on par with the
attention-based architectures. Overall, we highlight embeddings for numerical
features as an important design aspect with good potential for further
improvements in tabular DL.


title: Conditional Prompt Learning for Vision-Language Models

abstract:
  With the rise of powerful pre-trained vision-language models like CLIP, it
becomes essential to investigate ways to adapt these models to downstream
datasets. A recently proposed method named Context Optimization (CoOp)
introduces the concept of prompt learning -- a recent trend in NLP -- to the
vision domain for adapting pre-trained vision-language models. Specifically,
CoOp turns context words in a prompt into a set of learnable vectors and, with
only a few labeled images for learning, can achieve huge improvements over
intensively-tuned manual prompts. In our study we identify a critical problem
of CoOp: the learned context is not generalizable to wider unseen classes
within the same dataset, suggesting that CoOp overfits base classes observed
during training. To address the problem, we propose Conditional Context
Optimization (CoCoOp), which extends CoOp by further learning a lightweight
neural network to generate for each image an input-conditional token (vector).
Compared to CoOp's static prompts, our dynamic prompts adapt to each instance
and are thus less sensitive to class shift. Extensive experiments show that
CoCoOp generalizes much better than CoOp to unseen classes, even showing
promising transferability beyond a single dataset; and yields stronger domain
generalization performance as well. Code is available at
https://github.com/KaiyangZhou/CoOp.


title:
Understanding the Nature of an Unusual Post-Starburst Quasar with
  Exceptionally Strong Ne V Emission

abstract:
  We present a $z = 0.94$ quasar, SDSS J004846.45-004611.9, discovered in the
SDSS-III BOSS survey. A visual analysis of this spectrum reveals highly
broadened and blueshifted narrow emission lines, in particular
[Ne~V]$\lambda3426$ and [O~III]$\lambda5007$, with outflow velocities of 4000
km s$^{-1}$, along with unusually large [Ne V]$\lambda3426$/[Ne
III]$\lambda3869$ ratios. The gas shows higher ionization at higher outflow
velocities, indicating a connection between the powerful outflow and the
unusual strength of the high ionization lines. The SED and the $i - \text{W3}$
color of the source reveal that it is likely a "core" Extremely Red Quasar
(core ERQ); a candidate population of young AGN that are violently "blowing
out" gas and dust from their centers. The dominance of host galaxy light in its
spectrum and its fortuitous position in the SDSS S82 region allows us to
measure its star formation history and investigate for variability for the
first time in an ERQ. Our analysis indicates that SDSS J004846.45-004611.9
underwent a short-lived starburst phase 400 Myr ago and was subsequently
quenched, possibly indicating a time-lag between star formation quenching and
the onset of AGN activity. We also find that the strong extinction can be
uniquely attributed to the AGN and does not persist in the host galaxy,
contradicting a scenario where the source has recently transitioned from being
a dusty sub-mm galaxy. In our relatively shallow photometric data, the source
does not appear to be variable at $0.24-2.4~\mu$m in the restframe, most likely
due to the dominant contribution of host galaxy starlight at these wavelengths.


title: Weak gravity versus scale separation

abstract:
  We give evidence that fully supersymmetric Anti-de Sitter vacua of extended
supergravity with a residual gauge group containing an abelian factor cannot be
scale separated as a consequence of the weak gravity conjecture. We prove this
for N=2 and N=8 supergravity and we explain how our argument applies also to
vacua with partially broken, but extended residual supersymmetry. We finally
discuss possible loopholes and especially how certain N=1 models can evade our
reasoning. Our results suggest that N=0,1 supersymmetry at the lagrangian level
might be the most promising chances to obtain a truly four-dimensional
effective description of quantum gravity.


title: On the radio spectra of Galactic millisecond pulsars

abstract:
  With recent advances in the sensitivity of radio surveys of the Galactic
disk, the number of millisecond pulsars (MSPs) has increased substantially in
recent years such that it is now possible to study their demographic properties
in more detail than in the past. We investigate what can be learned about the
radio spectra of the MSP population. Using a sample of 179 MSPs detected in
eleven surveys carried out at radio frequencies in the range 0.135-6.6 GHz, we
carry out detailed modeling of MSP radio spectral behaviour in this range.
Employing Markov Chain Monte Carlo simulations to explore a multi-dimensional
parameter space, and accurately accounting for observational selection effects,
we find strong evidence in favour of the MSP population having a two-component
power-law spectral model scaling with frequency, $\nu$. Specifically, we find
that MSP flux density spectra are approximately independent of frequency below
320 MHz, and proportional to $\nu^{-1.5}$ at higher frequencies. This
parameterization performs significantly better than single power-law models
which over predict the number of MSPs seen in low-frequency (100-200 MHz)
surveys. We compared our results with earlier work, and current understanding
of the normal pulsar population, and use our model to make predictions for MSP
yields in upcoming surveys. We demonstrate that the observed sample of MSPs
could triple in the coming decade.


title: Deep Learning for the Benes Filter

abstract:
  The Benes filter is a well-known continuous-time stochastic filtering model
in one dimension that has the advantage of being explicitly solvable. From an
evolution equation point of view, the Benes filter is also the solution of the
filtering equations given a particular set of coefficient functions. In
general, the filtering stochastic partial differential equations (SPDE) arise
as the evolution equations for the conditional distribution of an underlying
signal given partial, and possibly noisy, observations. Their numerical
approximation presents a central issue for theoreticians and practitioners
alike, who are actively seeking accurate and fast methods, especially for such
high-dimensional settings as numerical weather prediction, for example. In this
paper we present a brief study of a new numerical method based on the mesh-free
neural network representation of the density of the solution of the Benes model
achieved by deep learning. Based on the classical SPDE splitting method, our
algorithm includes a recursive normalisation procedure to recover the
normalised conditional distribution of the signal process. Within the
analytically tractable setting of the Benes filter, we discuss the role of
nonlinearity in the filtering model equations for the choice of the domain of
the neural network. Further we present the first study of the neural network
method with an adaptive domain for the Benes model.


title:
Updated Lagrangian unsaturated periporomechanics for extreme large
  deformation in unsaturated porous media

abstract:
  Unsaturated periporomechanics is a strong nonlocal poromechanics based on
peridynamic state and effective force concept. In the previous periporomechnics
the total Lagrangian formulation is adopted for the solid skeleton of porous
media. In this article as a new contribution we formulate and implement an
updated Lagrangian unsaturated periporomechanics framework for modeling extreme
large deformation in unsaturated soils under drained condition, e.g., soil
column collapse. In this new framework the so-called bond-associated
sub-horizon concept is utilized to enhance the stability and accuracy at
extreme large deformation of solid skeleton. The stabilized nonlocal velocity
gradient in the deformed configuration is used to update the effective force
state from a critical state based visco-plastic model for unsaturated soils.
The updated Lagrangian periporomechanics is implemented numerically through an
explicit Newmark scheme for high-performance computing. Numerical examples are
presented to demonstrate the efficacy of the updated Lagrangian
periporomechanics and its robustness in modeling unsaturated soil column
collapse under drained condition.


title:
Artificial Intelligence Solution for Effective Treatment Planning for
  Glioblastoma Patients

abstract:
  Glioblastomas are the most common malignant brain tumors in adults.
Approximately 200000 people die each year from Glioblastoma in the world.
Glioblastoma patients have a median survival of 12 months with optimal therapy
and about 4 months without treatment. Glioblastomas appear as heterogeneous
necrotic masses with irregular peripheral enhancement, surrounded by vasogenic
edema. The current standard of care includes surgical resection, radiotherapy
and chemotherapy, which require accurate segmentation of brain tumor
subregions. For effective treatment planning, it is vital to identify the
methylation status of the promoter of Methylguanine Methyltransferase (MGMT), a
positive prognostic factor for chemotherapy. However, current methods for brain
tumor segmentation are tedious, subjective and not scalable, and current
techniques to determine the methylation status of MGMT promoter involve
surgically invasive procedures, which are expensive and time consuming. Hence
there is a pressing need to develop automated tools to segment brain tumors and
non-invasive methods to predict methylation status of MGMT promoter, to
facilitate better treatment planning and improve survival rate. I created an
integrated diagnostics solution powered by Artificial Intelligence to
automatically segment brain tumor subregions and predict MGMT promoter
methylation status, using brain MRI scans. My AI solution is proven on large
datasets with performance exceeding current standards and field tested with
data from teaching files of local neuroradiologists. With my solution,
physicians can submit brain MRI images, and get segmentation and methylation
predictions in minutes, and guide brain tumor patients with effective treatment
planning and ultimately improve survival time.


title:
HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps
  in Digital Twins for Cardiac Analysis

abstract:
  Synthetic digital twins based on medical data accelerate the acquisition,
labelling and decision making procedure in digital healthcare. A core part of
digital healthcare twins is model-based data synthesis, which permits the
generation of realistic medical signals without requiring to cope with the
modelling complexity of anatomical and biochemical phenomena producing them in
reality. Unfortunately, algorithms for cardiac data synthesis have been so far
scarcely studied in the literature. An important imaging modality in the
cardiac examination is three-directional CINE multi-slice myocardial velocity
mapping (3Dir MVM), which provides a quantitative assessment of cardiac motion
in three orthogonal directions of the left ventricle. The long acquisition time
and complex acquisition produce make it more urgent to produce synthetic
digital twins of this imaging modality. In this study, we propose a hybrid deep
learning (HDL) network, especially for synthetic 3Dir MVM data. Our algorithm
is featured by a hybrid UNet and a Generative Adversarial Network with a
foreground-background generation scheme. The experimental results show that
from temporally down-sampled magnitude CINE images (six times), our proposed
algorithm can still successfully synthesise high temporal resolution 3Dir MVM
CMR data (PSNR=42.32) with precise left ventricle segmentation (DICE=0.92).
These performance scores indicate that our proposed HDL algorithm can be
implemented in real-world digital twins for myocardial velocity mapping data
simulation. To the best of our knowledge, this work is the first one in the
literature investigating digital twins of the 3Dir MVM CMR, which has shown
great potential for improving the efficiency of clinical studies via
synthesised cardiac data.


title: LiftReg: Limited Angle 2D/3D Deformable Registration

abstract:
  We propose LiftReg, a 2D/3D deformable registration approach. LiftReg is a
deep registration framework which is trained using sets of digitally
reconstructed radiographs (DRR) and computed tomography (CT) image pairs. By
using simulated training data, LiftReg can use a high-quality CT-CT image
similarity measure, which helps the network to learn a high-quality deformation
space. To further improve registration quality and to address the inherent
depth ambiguities of very limited angle acquisitions, we propose to use
features extracted from the backprojected 2D images and a statistical
deformation model. We test our approach on the DirLab lung registration dataset
and show that it outperforms an existing learning-based pairwise registration
approach.


title:
SUPERNOVA: Automating Test Selection and Defect Prevention in AAA Video
  Games Using Risk Based Testing and Machine Learning

abstract:
  Testing video games is an increasingly difficult task as traditional methods
fail to scale with growing software systems. Manual testing is a very
labor-intensive process, and therefore quickly becomes cost prohibitive. Using
scripts for automated testing is affordable, however scripts are ineffective in
non-deterministic environments, and knowing when to run each test is another
problem altogether. The modern game's complexity, scope, and player
expectations are rapidly increasing where quality control is a big portion of
the production cost and delivery risk. Reducing this risk and making production
happen is a big challenge for the industry currently. To keep production costs
realistic up-to and after release, we are focusing on preventive quality
assurance tactics alongside testing and data analysis automation. We present
SUPERNOVA (Selection of tests and Universal defect Prevention in External
Repositories for Novel Objective Verification of software Anomalies), a system
responsible for test selection and defect prevention while also functioning as
an automation hub. By integrating data analysis functionality with machine and
deep learning capability, SUPERNOVA assists quality assurance testers in
finding bugs and developers in reducing defects, which improves stability
during the production cycle and keeps testing costs under control. The direct
impact of this has been observed to be a reduction in 55% or more testing hours
for an undisclosed sports game title that has shipped, which was using these
test selection optimizations. Furthermore, using risk scores generated by a
semi-supervised machine learning model, we are able to detect with 71%
precision and 77% recall the probability of a change-list being bug inducing,
and provide a detailed breakdown of this inference to developers. These efforts
improve workflow and reduce testing hours required on game titles in
development.


title: Recovering medical images from CT film photos

abstract:
  While medical images such as computed tomography (CT) are stored in DICOM
format in hospital PACS, it is still quite routine in many countries to print a
film as a transferable medium for the purposes of self-storage and secondary
consultation. Also, with the ubiquitousness of mobile phone cameras, it is
quite common to take pictures of CT films, which unfortunately suffer from
geometric deformation and illumination variation. In this work, we study the
problem of recovering a CT film, which marks \textbf{the first attempt} in the
literature, to the best of our knowledge. We start with building a large-scale
head CT film database CTFilm20K, consisting of approximately 20,000 pictures,
using the widely used computer graphics software Blender. We also record all
accompanying information related to the geometric deformation (such as 3D
coordinate, depth, normal, and UV maps) and illumination variation (such as
albedo map). Then we propose a deep framework called \textbf{F}ilm
\textbf{I}mage \textbf{Re}covery \textbf{Net}work (\textbf{FIReNet}) to tackle
geometric deformation and illumination variation using the multiple maps
extracted from the CT films to collaboratively guide the recovery process.
Finally, we convert the dewarped images to DICOM files with our cascade model
for further analysis such as radiomics feature extraction. Extensive
experiments demonstrate the superiority of our approach over the previous
approaches. We plan to open source the simulated images and deep models for
promoting the research on CT film image analysis.


title: Unfolded Deep Kernel Estimation for Blind Image Super-resolution

abstract:
  Blind image super-resolution (BISR) aims to reconstruct a high-resolution
image from its low-resolution counterpart degraded by unknown blur kernel and
noise. Many deep neural network based methods have been proposed to tackle this
challenging problem without considering the image degradation model. However,
they largely rely on the training sets and often fail to handle images with
unseen blur kernels during inference. Deep unfolding methods have also been
proposed to perform BISR by utilizing the degradation model. Nonetheless, the
existing deep unfolding methods cannot explicitly solve the data term of the
unfolding objective function, limiting their capability in blur kernel
estimation. In this work, we propose a novel unfolded deep kernel estimation
(UDKE) method, which, for the first time to our best knowledge, explicitly
solves the data term with high efficiency. The UDKE based BISR method can
jointly learn image and kernel priors in an end-to-end manner, and it can
effectively exploit the information in both training data and image degradation
model. Experiments on benchmark datasets and real-world data demonstrate that
the proposed UDKE method could well predict complex unseen non-Gaussian blur
kernels in inference, achieving significantly better BISR performance than
state-of-the-art. The source code of UDKE is available at:
https://github.com/natezhenghy/UDKE.


title:
Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance
  Imaging

abstract:
  Image corruption by motion artifacts is an ingrained problem in Magnetic
Resonance Imaging (MRI). In this work, we propose a neural network-based
regularization term to enhance Autofocusing, a classic optimization-based
method to remove motion artifacts. The method takes the best of both worlds:
the optimization-based routine iteratively executes the blind demotion and deep
learning-based prior penalizes for unrealistic restorations and speeds up the
convergence. We validate the method on three models of motion trajectories,
using synthetic and real noisy data. The method proves resilient to noise and
anatomic structure variation, outperforming the state-of-the-art demotion
methods.


title: neos: End-to-End-Optimised Summary Statistics for High Energy Physics

abstract:
  The advent of deep learning has yielded powerful tools to automatically
compute gradients of computations. This is because training a neural network
equates to iteratively updating its parameters using gradient descent to find
the minimum of a loss function. Deep learning is then a subset of a broader
paradigm; a workflow with free parameters that is end-to-end optimisable,
provided one can keep track of the gradients all the way through. This work
introduces neos: an example implementation following this paradigm of a fully
differentiable high-energy physics workflow, capable of optimising a learnable
summary statistic with respect to the expected sensitivity of an analysis.
Doing this results in an optimisation process that is aware of the modelling
and treatment of systematic uncertainties.


title:
Deep Convolutional Neural Networks for Molecular Subtyping of Gliomas
  Using Magnetic Resonance Imaging

abstract:
  Knowledge of molecular subtypes of gliomas can provide valuable information
for tailored therapies. This study aimed to investigate the use of deep
convolutional neural networks (DCNNs) for noninvasive glioma subtyping with
radiological imaging data according to the new taxonomy announced by the World
Health Organization in 2016. Methods: A DCNN model was developed for the
prediction of the five glioma subtypes based on a hierarchical classification
paradigm. This model used three parallel, weight-sharing, deep residual
learning networks to process 2.5-dimensional input of trimodal MRI data,
including T1-weighted, T1-weighted with contrast enhancement, and T2-weighted
images. A data set comprising 1,016 real patients was collected for evaluation
of the developed DCNN model. The predictive performance was evaluated via the
area under the curve (AUC) from the receiver operating characteristic analysis.
For comparison, the performance of a radiomics-based approach was also
evaluated. Results: The AUCs of the DCNN model for the four classification
tasks in the hierarchical classification paradigm were 0.89, 0.89, 0.85, and
0.66, respectively, as compared to 0.85, 0.75, 0.67, and 0.59 of the radiomics
approach. Conclusion: The results showed that the developed DCNN model can
predict glioma subtypes with promising performance, given sufficient,
non-ill-balanced training data.


title: A new model for nulling and moding in radio pulsars

abstract:
  Single pulse behaviour of radio pulsars is usually interpreted in terms of
the ExB drift of a radio beam. It is shown that antisymmetric arrangement of
the radio-bright zones can produce several types of observed single pulse
phenomena: the half-cycle jump in subpulse modulation phase, left-right-middle
subpulse sequence and switching between the core-dominated and cone-dominated
pulsation modes. The geometry can also produce nulling, both sporadic and
intermodal. The model implies that the radio-quiet intervals that separate the
main pulse and interpulse in PSR B0826-34 correspond to azimuthal breaks in the
radio beam, instead of breaks in colatitude.


title: Self Pre-training with Masked Autoencoders for Medical Image Analysis

abstract:
  Masked Autoencoder (MAE) has recently been shown to be effective in
pre-training Vision Transformers (ViT) for natural image analysis. By
performing the pretext task of reconstructing the original image from only
partial observations, the encoder, which is a ViT, is encouraged to aggregate
contextual information to infer content in masked image regions. We believe
that this context aggregation ability is also essential to the medical image
domain where each anatomical structure is functionally and mechanically
connected to other structures and regions. However, there is no ImageNet-scale
medical image dataset for pre-training. Thus, in this paper, we investigate a
self pre-training paradigm with MAE for medical images, i.e., models are
pre-trained on the same target dataset. To validate the MAE self pre-training,
we consider three diverse medical image tasks including chest X-ray disease
classification, CT abdomen multi-organ segmentation and MRI brain tumor
segmentation. It turns out MAE self pre-training benefits all the tasks
markedly. Specifically, the mAUC on lung disease classification is increased by
9.4%. The average DSC on brain tumor segmentation is improved from 77.4% to
78.9%. Most interestingly, on the small-scale multi-organ segmentation dataset
(N=30), the average DSC improves from 78.8% to 83.5% and the HD95 is reduced by
60%, indicating its effectiveness in limited data scenarios. The segmentation
and classification results reveal the promising potential of MAE self
pre-training for medical image analysis.


title: On-the-Fly Test-time Adaptation for Medical Image Segmentation

abstract:
  One major problem in deep learning-based solutions for medical imaging is the
drop in performance when a model is tested on a data distribution different
from the one that it is trained on. Adapting the source model to target data
distribution at test-time is an efficient solution for the data-shift problem.
Previous methods solve this by adapting the model to target distribution by
using techniques like entropy minimization or regularization. In these methods,
the models are still updated by back-propagation using an unsupervised loss on
complete test data distribution. In real-world clinical settings, it makes more
sense to adapt a model to a new test image on-the-fly and avoid model update
during inference due to privacy concerns and lack of computing resource at
deployment. To this end, we propose a new setting - On-the-Fly Adaptation which
is zero-shot and episodic (i.e., the model is adapted to a single image at a
time and also does not perform any back-propagation during test-time). To
achieve this, we propose a new framework called Adaptive UNet where each
convolutional block is equipped with an adaptive batch normalization layer to
adapt the features with respect to a domain code. The domain code is generated
using a pre-trained encoder trained on a large corpus of medical images. During
test-time, the model takes in just the new test image and generates a domain
code to adapt the features of source model according to the test data. We
validate the performance on both 2D and 3D data distribution shifts where we
get a better performance compared to previous test-time adaptation methods.
Code is available at https://github.com/jeya-maria-jose/On-The-Fly-Adaptation


title: Constraints on Early Dark Energy from the Axion Weak Gravity Conjecture

abstract:
  A popular proposal for resolving the Hubble tension involves an early phase
of dark energy, driven by an axion field with a periodic potential. In this
paper, we argue that these models are tightly constrained by the axion weak
gravity conjecture: for typical parameter values, the axion decay constant must
satisfy $f < 0.008 M_{\textrm{Pl}}$, which is smaller than the axion decay
constants appearing in the vast majority of early dark energy models to date.
We discuss possible ways to evade or loosen this constraint, arguing that its
loopholes are small and difficult to thread. This suggests that it may prove
challenging to realize early dark energy models in a UV complete theory of
quantum gravity.


title: Soft limits of the wavefunction in exceptional scalar theories

abstract:
  We study the structure of the flat space wavefunctional in scalar field
theories with nonlinearly realized symmetries. These symmetries imply soft
theorems that are satisfied by wavefunction coefficients in the limit where one
of the external momenta is scaled to zero. After elucidating the structure of
these soft theorems in the nonlinear sigma model, Dirac-Born-Infeld, and
galileon scalar theories, we combine them with information about the
singularity structure of the wavefunction to bootstrap the wavefunction
coefficients of these theories. We further systematize this construction
through two types of recursion relations: one that utilizes the flat space
scattering amplitude plus minimal information about soft limits, and an
alternative that does not require amplitude input, but does require subleading
soft information.


title: The role of fluctuations in quantum and classical time crystals

abstract:
  Discrete time crystals (DTCs) are a many-body state of matter whose dynamics
are slower than the forces acting on it. The same is true for classical systems
with period-doubling bifurcations. Hence, the question naturally arises what
differentiates classical from quantum DTCs. Here, we analyze a variant of the
Bose-Hubbard model, which describes a plethora of physical phenomena and has
both a classical and a quantum time-crystalline limit. We study the role of
fluctuations on the stability of the system and find no distinction between
quantum and classical DTCs. This allows us to probe the fluctuations in an
experiment using two strongly coupled parametric resonators subject to
classical noise.


title: Photometric Signature of Ultra-Harmonic Resonances in Barred Galaxies

abstract:
  Bars may induce morphological features, such as rings, through their
resonances. Previous studies suggested that the presence of 'dark-gaps', or
regions of a galaxy where the difference between the surface brightness along
the bar major axis and along the bar minor axis are maximal, can be attributed
to the location of bar corotation. Here, using GALAKOS, a high-resolution
N-body simulation of a barred galaxy, we test this photometric method's ability
to identify the bar corotation resonance. Contrary to previous work, our
results indicate that 'dark-gaps' are a clear sign of the location of the 4:1
ultra-harmonic resonance instead of bar corotation. Measurements of the bar
corotation can indirectly be inferred using kinematic information, e.g., by
measuring the shape of the rotation curve. We demonstrate our concept on a
sample of 578 face-on barred galaxies with both imaging and integral field
observations and find the sample likely consists primarily of fast bars.


title: Minimal realization of light thermal Dark Matter

abstract:
  We propose a minimal UV-complete model for kinematically forbidden Dark
Matter (DM) leading to a sub-GeV thermal relic. Our crucial realization is that
the two-Higgs-doublet model can provide a light mediator through which the DM
can annihilate into SM leptons, avoiding indirect detection constraints. The DM
mass is predicted to be very close to the mass of the leptons, which can
potentially be identified from DM annihilation into gamma-rays. Due to sizable
couplings to muons in reproducing the DM relic abundance, this framework
naturally favors a resolution to the $(g-2)_\mu$ anomaly. Furthermore, by
embedding this setup to the Zee model, we show that the phenomenon of neutrino
oscillations is inherently connected to the observed relic abundance of DM. All
new physics involved in our framework lies at or below the electroweak scale,
making it testable at upcoming colliders, beam-dump experiments, and future
sub-GeV gamma-ray telescopes.


title: A hidden population of massive black holes in simulated dwarf galaxies

abstract:
  We explore the characteristics of actively accreting MBHs within dwarf
galaxies in the \textsc{Romulus25} cosmological hydrodynamic simulation. We
examine the MBH occupation fraction, x-ray active fractions, and AGN scaling
relations within dwarf galaxies of stellar mass $10^{8} < M_{\rm star} <
10^{10} M_\odot$ out to redshift $z=2$. In the local universe, the MBH
occupation fraction is consistent with observed constraints, dropping below
unity at $M_{\rm star} < 3\times10^{10} M_{\odot}$, $M_{\rm 200} <
3\times10^{11} M_\odot$. Local dwarf AGN in \textsc{Romulus25} follow observed
scaling relations between AGN x-ray luminosity, stellar mass, and star
formation rate, though they exhibit slightly higher active fractions and number
densities than comparable x-ray observations. Since $z=2$, the MBH occupation
fraction has decreased, the population of dwarf AGN has become overall less
luminous, and as a result, the overall number density of dwarf AGN has
diminished. We predict the existence of a large population of MBHs in the local
universe with low x-ray luminosities and high contamination from x-ray binaries
and the hot interstellar medium that are undetectable by current x-ray surveys.
These hidden MBHs make up $76\%$ of all MBHs in local dwarf galaxies, and
include many MBHs that are undermassive relative to their host galaxy's stellar
mass. Their detection relies not only on greater instrument sensitivity but on
better modeling of x-ray contaminants or multi-wavelength surveys. Our results
indicate dwarf AGN were substantially more active in the past despite being
low-luminosity today, and indicate future deep x-ray surveys may uncover many
hidden MBHs in dwarf galaxies out to at least $z=2$.