title:
The ALMA REBELS Survey: Specific Star-Formation Rates in the
  Reionization Era

abstract:
  We present specific star-formation rates for 40 UV-bright galaxies at
$z\sim7-8$ observed as part of the Reionization Era Bright Emission Line Survey
(REBELS) ALMA large program. The sSFRs are derived using improved measures of
SFR and stellar masses, made possible by measurements of far-infrared (FIR)
continuum emission and [CII]-based spectroscopic redshifts. For each source in
the sample, we derive stellar masses from SED fitting and total SFRs from
calibrations of the UV and FIR emission. The median sSFR is $18_{-5}^{+7}$
Gyr$^{-1}$, significantly larger than literature measurements lacking
constraints in the FIR. The increase in sSFR reflects the larger obscured SFRs
we derive from the dust continuum relative to that implied by the UV+optical
SED. We suggest that such differences may reflect spatial variations in dust
across these luminous galaxies, with the component dominating the FIR distinct
from that dominating the UV. We demonstrate that the inferred stellar masses
(and hence sSFRs) are strongly-dependent on the assumed star formation history
(SFH) in reionization-era galaxies. When large sSFR galaxies are modeled with
non-parametric SFHs, the derived stellar masses can increase by an order of
magnitude relative to constant star formation models, owing to the presence of
a significant old stellar population that is outshined by the recent burst. The
[CII] line widths in the largest sSFR systems are often very broad, suggesting
dynamical masses that are easily able to accommodate the dominant old stellar
population suggested by non-parametric models. Regardless of these systematic
uncertainties in the derived parameters, we find that the sSFR increases
rapidly toward higher redshifts for massive galaxies ($9.6<\log(\rm
M_*/M_{\odot})<9.8$), with a power law that goes as $(1+z)^{1.7\pm0.3}$,
broadly consistent with expectations from the evolving baryon accretion rates.


title:
Probing the bulk plasmon continuum of layered materials through electron
  energy loss spectroscopy in a reflection geometry

abstract:
  A periodic arrangement of 2D conducting planes is known to host a (bulk)
plasmon dispersion that interpolates between the typical, gapped behavior of 3D
metals and a gapless, acoustic regime as a function of the out-of-plane
wavevector. The semi-infinite system -- the configuration relevant to Electron
Energy Loss Spectroscopy (EELS) in a reflection geometry, as in High Resolution
EELS (HREELS) -- is known to host a surface plasmon that ceases to propagate
below a cutoff wavevector. As the f-sum rule requires a finite response whether
or not there exist sharp excitations, we demonstrate that what remains in the
surface loss function -- the material response probed by HREELS -- is the
contribution from the (bulk) plasmon of the infinite system. In particular, we
provide a one-to-one mapping between the plasmon continuum and the spectral
weight in the surface loss function. In light of this result, we suggest that
HREELS be considered a long wavelength probe of the plasmon continuum in
layered materials.

categories: cond-mat.str-el cond-mat.mes-hall cond-mat.mtrl-sci physics.optics


title:
Tunneling Spectroscopy of Two-Dimensional Materials Based on Via
  Contacts

abstract:
  We introduce a novel planar tunneling architecture for van der Waals
heterostructures based on via contacts, namely metallic contacts embedded into
through-holes in hexagonal boron nitride (hBN). We use the via-based tunneling
method to study the single-particle density of states of two different
two-dimensional (2D) materials, superconducting NbSe$_2$ and monolayer graphene
in zero magnetic field as well as in the quantum Hall regime. In NbSe$_2$
devices, we characterize the barrier strength and interface disorder, within
the Blonder-Tinkham-Klapwijk model, for tunnel barrier thicknesses of 0, 1 and
2 layers of hBN and study the area dependence of the tunneling contacts down to
$30^2$ nm$^2$, demonstrating that the effect of interface disorder can be
suppressed in the small-area limit. Additionally, we demonstrate that for 0
layers of hBN, we cross over from diffusive to point contacts in the small-area
limit. In graphene, we show that reducing the tunneling area can suppress the
effects of phonon-assisted tunneling and defects in the hBN barriers. These
via-based tunneling devices overcome limitations of other planar tunneling
designs and can be used to produce high-quality, ultra-clean tunneling
structures from a variety of 2D materials.


title: Towards experimental classical verification of quantum computation

abstract:
  With today's quantum processors venturing into regimes beyond the
capabilities of classical devices [1-3], we face the challenge to verify that
these devices perform as intended, even when we cannot check their results on
classical computers [4,5]. In a recent breakthrough in computer science [6-8],
a protocol was developed that allows the verification of the output of a
computation performed by an untrusted quantum device based only on classical
resources. Here, we follow these ideas, and demonstrate in a first,
proof-of-principle experiment a verification protocol using only classical
means on a small trapped-ion quantum processor. We contrast this to
verification protocols, which require trust and detailed hardware knowledge, as
in gate-level benchmarking [9], or additional quantum resources in case we do
not have access to or trust in the device to be tested [5]. While our
experimental demonstration uses a simplified version [10] of Mahadev's protocol
[6] we demonstrate the necessary steps for verifying fully untrusted devices. A
scaled-up version of our protocol will allow for classical verification,
requiring no hardware access or detailed knowledge of the tested device. Its
security relies on post-quantum secure trapdoor functions within an interactive
proof [11]. The conceptually straightforward, but technologically challenging
scaled-up version of the interactive proofs, considered here, can be used for a
variety of additional tasks such as verifying quantum advantage [8], generating
[12] and certifying quantum randomness [7], or composable remote state
preparation [13].


title: Heavy Elements -- They came out of the blue

abstract:
  How are the heavy elements formed? This has been a key open question in
physics for decades. Recent direct detections of neutron star mergers and
observations of evolved stars show signatures of chemical elements in the blue
range of their spectra that bear witness of recent nuclear processes that led
to heavy element production. The formation of heavy elements typically takes
place through neutron-capture reactions creating radioactive isotopes, which
following beta-decay turn into the stable isotopes we today can measure
indirectly in the surfaces of cool, low-mass stars or meteoritic grains. The
conditions (such as the neutron density or entropy) of these n-capture
reactions remains to date poorly constrained, and only through a
multidisciplinary effort can we, by combining and comparing observations,
experiments, and theoretical predictions, improve on one of the top 10 most
important open physics questions posed at the turn of the century. This
emphasises the need for detailed observations of the near-UV to blue wavelength
region. The shortage of spectrographs and hence spectra covering this range
with high-resolution and high signal-to-noise has for decades played a limiting
factor in our understanding of how heavy elements form in the nuclear reactions
as well as how they behave in the stellar surfaces. With CUBES we can finally
improve the observations, by covering the crucial blue range in more remote
stars and also achieve a higher signal-to-noise ratio (SNR). This is much
needed to detect and accurately deblend the absorption lines and in turn derive
more accurate and precise abundances of the heavy elements.


title:
The Gauge Structure of Double Field Theory follows from Yang-Mills
  Theory

abstract:
  We show that to cubic order double field theory is encoded in Yang-Mills
theory. To this end we use algebraic structures from string field theory as
follows: The $L_{\infty}$-algebra of Yang-Mills theory is the tensor product
${\cal K}\otimes \mathfrak{g}$ of the Lie algebra $\mathfrak{g}$ of the gauge
group and a `kinematic algebra' ${\cal K}$ that is a $C_{\infty}$-algebra. This
structure induces a cubic truncation of an $L_{\infty}$-algebra on the subspace
of level-matched states of the tensor product ${\cal K}\otimes \bar{\cal K}$ of
two copies of the kinematic algebra. This $L_{\infty}$-algebra encodes double
field theory. More precisely, this construction relies on a particular form of
the Yang-Mills $L_{\infty}$-algebra following from string field theory or from
the quantization of a suitable worldline theory.


title:
AMICO galaxy clusters in KiDS-DR3: Constraints on cosmological
  parameters and on the normalisation of the mass-richness relation from
  clustering

abstract:
  We analysed the clustering of a photometric sample of galaxy clusters
selected from the Third Data Release of the Kilo-Degree Survey, focusing on the
redshift-space two-point correlation function (2PCF). We compared our
measurements to theoretical predictions of the standard $\Lambda$ cold dark
matter ($\Lambda$CDM) cosmological model. We measured the 2PCF of the sample in
the cluster centric radial range $r\in[5,80]$ $h^{-1}$Mpc, considering 4934
galaxy clusters with richness $\lambda^*\geq15$ in the redshift range
$z\in[0.1,0.6]$. A Markov chain Monte Carlo analysis has been performed to
constrain the cosmological parameters $\Omega_{\rm m}$, $\sigma_8$, and $S_8
\equiv \sigma_8(\Omega_{\rm m}/0.3)^{0.5}$, assuming Gaussian priors on the
mass-richness relation given by the posteriors obtained from a joint analysis
of cluster counts and weak lensing. In addition, we constrained the
normalisation of the mass-richness relation, $\alpha$, with fixed cosmological
parameters. We obtained $\Omega_{\rm m}=0.28^{+0.05}_{-0.04}$,
$\sigma_8=0.82^{+0.14}_{-0.12}$, and $S_8=0.80^{+0.08}_{-0.08}$. The constraint
on $S_8$ is consistent within 1$\sigma$ with the results from WMAP and Planck.
Furthermore, by fixing the cosmological parameters to those provided by Planck,
we obtained $\alpha=0.12^{+0.06}_{-0.06}$, which is fully consistent with the
result obtained from the joint analysis of cluster counts and weak lensing
performed for this sample.


title: Non-equilibrium pre-thermal states in a two-dimensional photon fluid

abstract:
  Thermalization is the dynamical process by which a many-body system evolves
toward a thermal equilibrium state that maximizes its entropy. In certain
cases, however, the establishment of thermal equilibrium is significantly
slowed down and a phenomenon of pre-thermalization can emerge. It describes the
initial relaxation toward a quasi-steady state after a perturbation. While
having similar properties to their thermal counterparts, pre-thermal states
exhibit a partial memory of initial conditions. Here, we observe the dynamical
formation of a pre-thermal state in a non-equilibrium, two-dimensional (2D)
fluid of light after an interaction quench. Direct measurements of the fluid's
first-order correlation function reveal the spontaneous emergence of long-range
algebraic correlations spreading within a light-cone, providing a clear
signature of a quasi steady-state strongly similar to a 2D thermal superfluid.
Detailed experimental characterization of the algebraic order is presented and
a partial memory of the initial conditions is demonstrated, in agreement with
recent theoretical predictions. Furthermore, by a controlled increase of the
fluid fluctuations, we unveil a cross-over from algebraic to short-range
(exponential) correlations, analogous to the celebrated Kosterlitz-Thouless
transition observed at thermal equilibrium. These results suggest the existence
of non-equilibrium precursors for thermodynamic phase transitions.


title: Direction reversal of non-Hermitian skin effect via coherent coupling

abstract:
  Absolute negative mobility (ANM) in nonequilibrium systems depicts the
possibility of particles propagating toward the opposite direction of an
external force. We uncover in this work a phenomenon analogous to ANM regarding
eigenstate localization and particle transport in non-Hermitian systems under
the influence of the non-Hermitian skin effect (NHSE). A coherent coupling
between two non-Hermitian chains individually possessing the same preferred
direction of NHSE is shown to cause a direction reversal of NHSE for all
eigenmodes. This concept is further investigated in terms of time evolution
dynamics using a non-Hermitian quantum walk platform within reach of current
experiments. Our findings are explained both qualitatively and quantitatively.
The possible direction reversal of NHSE can potentially lead to interesting
applications.


title:
Multi-Parameter Analysis of Finding Minors and Subgraphs in Edge
  Periodic Temporal Graphs

abstract:
  We study the computational complexity of determining structural properties of
edge periodic temporal graphs (EPGs). EPGs are time-varying graphs that
compactly represent periodic behavior of components of a dynamic network, for
example, train schedules on a rail network. In EPGs, for each edge $e$ of the
graph, a binary string $s_e$ determines in which time steps the edge is
present, namely $e$ is present in time step $t$ if and only if $s_e$ contains a
$1$ at position $t \mod |s_e|$. Due to this periodicity, EPGs serve as very
compact representations of complex periodic systems and can even be
exponentially smaller than classic temporal graphs representing one period of
the same system, as the latter contain the whole sequence of graphs explicitly.
In this paper, we study the computational complexity of fundamental questions
of the new concept of EPGs such as what is the shortest traversal time between
two vertices; is there a time step in which the graph (1) is minor-free; (2)
contains a minor; (3) is subgraph-free; (4) contains a subgraph; with respect
to a given minor or subgraph. We give a detailed parameterized analysis for
multiple combinations of parameters for the problems stated above including
several parameterized algorithms.


title:
Revisiting the Compositional Generalization Abilities of Neural Sequence
  Models

abstract:
  Compositional generalization is a fundamental trait in humans, allowing us to
effortlessly combine known phrases to form novel sentences. Recent works have
claimed that standard seq-to-seq models severely lack the ability to
compositionally generalize. In this paper, we focus on one-shot primitive
generalization as introduced by the popular SCAN benchmark. We demonstrate that
modifying the training distribution in simple and intuitive ways enables
standard seq-to-seq models to achieve near-perfect generalization performance,
thereby showing that their compositional generalization abilities were
previously underestimated. We perform detailed empirical analysis of this
phenomenon. Our results indicate that the generalization performance of models
is highly sensitive to the characteristics of the training data which should be
carefully considered while designing such benchmarks in future.


title:
Analysing international events through the lens of statistical physics:
  the case of Ukraine

abstract:
  During the last years, statistical physics has received an increasing
attention as a framework for the analysis of real complex systems; yet, this is
less clear in the case of international political events, partly due to the
complexity in securing relevant quantitative data on them. Here we analyse a
detailed data set of violent events that took place in Ukraine since January
2021, and analyse their temporal and spatial correlations through entropy and
complexity metrics, and functional networks. Results depict a complex scenario,
with events appearing in a non-random fashion, but with eastern-most regions
functionally disconnected from the remainder of the country -- something
opposing the widespread "two Ukraines" view. We further draw some lessons and
venues for future analyses.


title:
Respecting causality is all you need for training physics-informed
  neural networks

abstract:
  While the popularity of physics-informed neural networks (PINNs) is steadily
rising, to this date PINNs have not been successful in simulating dynamical
systems whose solution exhibits multi-scale, chaotic or turbulent behavior. In
this work we attribute this shortcoming to the inability of existing PINNs
formulations to respect the spatio-temporal causal structure that is inherent
to the evolution of physical systems. We argue that this is a fundamental
limitation and a key source of error that can ultimately steer PINN models to
converge towards erroneous solutions. We address this pathology by proposing a
simple re-formulation of PINNs loss functions that can explicitly account for
physical causality during model training. We demonstrate that this simple
modification alone is enough to introduce significant accuracy improvements, as
well as a practical quantitative mechanism for assessing the convergence of a
PINNs model. We provide state-of-the-art numerical results across a series of
benchmarks for which existing PINNs formulations fail, including the chaotic
Lorenz system, the Kuramoto-Sivashinsky equation in the chaotic regime, and the
Navier-Stokes equations in the turbulent regime. To the best of our knowledge,
this is the first time that PINNs have been successful in simulating such
systems, introducing new opportunities for their applicability to problems of
industrial complexity.

categories: cs.LG cs.NA math.NA nlin.CD physics.flu-dyn stat.ML


title: Symplectic Actions and Central Extensions

abstract:
  We give a proof of the fact that a simply-connected symplectic homogeneous
space $(M,\omega)$ of a connected Lie group $G$ is the universal cover of a
coadjoint orbit of a one-dimensional central extension of $G$. We emphasise the
r\^ole of symplectic group cocycles and the relationship between such cocycles,
left-invariant presymplectic structures on $G$ and central extensions of $G$.


title: SMEFT at the LHC and Beyond: A Snowmass White Paper

abstract:
  I detail an optimistic future vision for the use of SMEFT at high-energy
colliders, and describe the use of the studies which could result for
interpretation of future models. I also explore some of the potential pitfalls
which could significantly degrade the utility of those results and discuss
approaches to avoid them and ensure that results are actually accurate when
applied to new models.


title:
Computational Complexity of Multi-Player Evolutionarily Stable
  Strategies

abstract:
  In this paper we study the computational complexity of computing an
evolutionary stable strategy (ESS) in multi-player symmetric games. For
two-player games, deciding existence of an ESS is complete for {\Sigma} 2 , the
second level of the polynomial time hierarchy. We show that deciding existence
of an ESS of a multi-player game is closely connected to the second level of
the real polynomial time hierarchy. Namely, we show that the problem is hard
for a complexity class we denote as \exists D . \forall R and is a member of
\exists\forall R, where the former class restrict the latter by having the
existentially quantified variables be Boolean rather then real-valued. As a
special case of our results it follows that deciding whether a given strategy
is an ESS is complete for \forall R. A concept strongly related to ESS is that
of a locally superior strategy (LSS). We extend our results about ESS and show
that deciding existence of an LSS of a multiplayer game is likewise hard for
\exists D \forall R and a member of \exists\forall R, and as a special case
that deciding whether a given strategy is an LSS is complete for \forall R.


title:
Dexterous holographic trapping of dark-seeking particles with Zernike
  holograms

abstract:
  The intensity distribution of a holographically-projected optical trap can be
tailored to the physical properties of the particles it is intended to trap.
Dynamic optimization is especially desirable for manipulating dark-seeking
particles that are repelled by conventional optical tweezers, and even more so
when dark-seeking particles coexist in the same system as light-seeking
particles. We address the need for dexterous manipulation of dark-seeking
particles by introducing a class of "dark" traps created from the superposition
of two out-of-phase Gaussian modes with different waist diameters. Interference
in the difference-of-Gaussians (DoG) trap creates a dark central core that is
completely surrounded by light that can trap dark-seeking particles rigidly in
three dimensions. DoG traps can be combined with conventional optical tweezers
and other types of traps for use in heterogeneous samples. The ideal hologram
for a DoG trap being purely real-valued, we introduce a method based on the
Zernike phase-contrast principle to project real-valued holograms with the
phase-only diffractive optical elements used in standard holographic optical
trapping systems. Zernike phase holography should be useful for improving the
diffraction efficiency of holograms projected with phase-only diffractive
optical elements.


title:
Equivariant cohomology, deformations and central extension of Hom Lie
  triple systems

abstract:
  In this paper, we study equivariant cohomolgy theory of Hom Lie Triple
Systems. Using this cohomology, we study 1-parameter formal deformation and
central extensions of Hom Lie Triple Systems in the equivariant context.


title: The Scale of New Physics from the Higgs Couplings to gg

abstract:
  Measuring the Higgs couplings accurately at colliders is one of the best
routes for finding physics Beyond the Standard Model (BSM). If the measured
couplings deviate from the SM predictions, then this would give rise to
energy-growing processes that violate tree-level unitarity at some energy
scale, indicating new physics. In this paper, we extend previous work on
unitarity bounds from the Higgs potential and the Higgs couplings to vector
bosons, the top quark, $\gamma\gamma$ and $\gamma Z$; to the Higgs coupling to
gg. We show that the scale of new physics could be as low as $\sim 10$ TeV,
which although beyond the reach of the LHC, is well within the reach of the
future 100-TeV collider, and can also be probed indirectly in the High
Luminosity LHC (HL-LHC).


title:
On Connecting Deep Trigonometric Networks with Deep Gaussian Processes:
  Covariance, Expressivity, and Neural Tangent Kernel

abstract:
  Deep Gaussian Process as a Bayesian learning model is promising because it is
expressive and capable of uncertainty estimation. With Bochner's theorem, we
can view the deep Gaussian process with squared exponential kernels as a deep
trigonometric network consisting of the random feature layers, sine and cosine
activation units, and random weight layers. Focusing on this particular class
of models allows us to obtain analytical results. We shall show that the weight
space view yields the same effective covariance functions which were obtained
previously in function space. The heavy statistical tails can be studied with
multivariate characteristic function. In addition, the trig networks are
flexible and expressive as one can freely adopt different prior distributions
over the parameters in weight and feature layers. Lastly, the deep
trigonometric network representation of deep Gaussian process allows the
derivation of its neural tangent kernel, which can reveal the mean of
predictive distribution from the intractable inference.


title: ShapeNet: Shape Constraint for Galaxy Image Deconvolution

abstract:
  Deep Learning (DL) has shown remarkable results in solving inverse problems
in various domains. In particular, the Tikhonet approach is very powerful to
deconvolve optical astronomical images (Sureau et al. 2020). Yet, this approach
only uses the $\ell_2$ loss, which does not guarantee the preservation of
physical information (e.g. flux and shape) of the object reconstructed in the
image. In Nammour et al. (2021), a new loss function was proposed in the
framework of sparse deconvolution, which better preserves the shape of galaxies
and reduces the pixel error. In this paper, we extend Tikhonet to take into
account this shape constraint, and apply our new DL method, called ShapeNet, to
optical and radio-interferometry simulated data set. The originality of the
paper relies on i) the shape constraint we use in the neural network framework,
ii) the application of deep learning to radio-interferometry image
deconvolution for the first time, and iii) the generation of a simulated radio
data set that we make available for the community. A range of examples
illustrates the results.


title:
Switch Trajectory Transformer with Distributional Value Approximation
  for Multi-Task Reinforcement Learning

abstract:
  We propose SwitchTT, a multi-task extension to Trajectory Transformer but
enhanced with two striking features: (i) exploiting a sparsely activated model
to reduce computation cost in multi-task offline model learning and (ii)
adopting a distributional trajectory value estimator that improves policy
performance, especially in sparse reward settings. These two enhancements make
SwitchTT suitable for solving multi-task offline reinforcement learning
problems, where model capacity is critical for absorbing the vast quantities of
knowledge available in the multi-task dataset. More specifically, SwitchTT
exploits switch transformer model architecture for multi-task policy learning,
allowing us to improve model capacity without proportional computation cost.
Also, SwitchTT approximates the distribution rather than the expectation of
trajectory value, mitigating the effects of the Monte-Carlo Value estimator
suffering from poor sample complexity, especially in the sparse-reward setting.
We evaluate our method using the suite of ten sparse-reward tasks from the
gym-mini-grid environment.We show an improvement of 10% over Trajectory
Transformer across 10-task learning and obtain up to 90% increase in offline
model training speed. Our results also demonstrate the advantage of the switch
transformer model for absorbing expert knowledge and the importance of value
distribution in evaluating the trajectory.


title: Redshift weighted galaxy number counts

abstract:
  In this paper we introduce the `redshift fluctuation' as a gauge-invariant
cosmological observable and give its fully relativistic expression at first
order in cosmological perturbation theory. In a detailed analysis of its
angular power spectrum, we study the relevance of different relativistic
contributions, and how it differs from the conventional observable galaxy
number count fluctuations. In order to investigate its utility for future
spectroscopic surveys, we perform Fisher forecasts for a Euclid-like and an
SKAII-like configuration, as examples. Particular focus is placed on the
dependence of the results on the size of the redshift bins and on the cutoff in
$\ell$ adopted in the analysis.


title:
Scaling of Fock-space propagator and multifractality across the
  many-body localization transition

abstract:
  We implement a recursive Green function method to extract the Fock space (FS)
propagator and associated self-energy across the many-body localization (MBL)
transition, for one-dimensional interacting fermions in a random onsite
potential. We show that the typical value of the imaginary part of the local FS
self-energy, \Delta_t, related to the decay rate of an initially localized
state, acts as a probabilistic order parameter for the thermal to MBL phase
transition; and can be used to characterize critical properties of the
transition as well as the multifractal nature of MBL states as a function of
disorder strength W. In particular, we show that a fractal dimension D_s
extracted from \Delta_t jumps discontinuously across the transition, from D_s<1
in the MBL phase to D_s= 1 in the thermal phase. Moreover, \Delta_t follows an
asymmetrical finite-size scaling form across the thermal-MBL transition, where
a non-ergodic volume in the thermal phase diverges with a Kosterlitz-Thouless
like essential singularity at the critical point W_c, and controls the
continuous vanishing of \Delta_t as W_c is approached. In contrast, a
correlation length ({\xi}) extracted from \Delta_t exhibits a power-law
divergence on approaching W_c from the MBL phase.


title: Refined Hardness of Distance-Optimal Multi-Agent Path Finding

abstract:
  We study the computational complexity of multi-agent path finding (MAPF).
Given a graph $G$ and a set of agents, each having a start and target vertex,
the goal is to find collision-free paths minimizing the total distance
traveled. To better understand the source of difficulty of the problem, we aim
to study the simplest and least constrained graph class for which it remains
hard. To this end, we restrict $G$ to be a 2D grid, which is a ubiquitous
abstraction, as it conveniently allows for modeling well-structured
environments (e.g., warehouses). Previous hardness results considered highly
constrained 2D grids having only one vertex unoccupied by an agent, while the
most restricted hardness result that allowed multiple empty vertices was for
(non-grid) planar graphs. We therefore refine previous results by
simultaneously considering both 2D grids and multiple empty vertices. We show
that even in this case distance-optimal MAPF remains NP-hard, which settles an
open problem posed by Banfi et al. (2017). We present a reduction directly from
3-SAT using simple gadgets, making our proof arguably more informative than
previous work in terms of potential progress towards positive results.
Furthermore, our reduction is the first linear one for the case where $G$ is
planar, appearing nearly four decades after the first related result. This
allows us to go a step further and exploit the Exponential Time Hypothesis
(ETH) to obtain an exponential lower bound for the running time of the problem.
Finally, as a stepping stone towards our main results, we prove the NP-hardness
of the monotone case, in which agents move one by one with no intermediate
stops.


title:
Buoyancy driven bubbly flows: role of meso-scale structures on the
  relative motion between phases, and scaling of velocities in bubble columns
  operated up to the heterogeneous regime

abstract:
  The hydrodynamics of bubble columns is revisited, with an emphasis on the
origin of the increase in the relative velocity observed in the heterogeneous
regime. We focus on air-water systems, with no or limited coalescence and a
quasi-fully developed region where the flow self-organizes when it reaches the
heterogeneous regime.
  We show from dimensional analysis that, in the heterogeneous regime, buoyancy
equilibrates inertia, and that velocities scale as $(gD\varepsilon)^{1/2}$,
where $D$ is the bubble column diameter, $\varepsilon$ the void fraction and
$g$ the gravitational acceleration. This scaling holds for both liquid and gas
mean velocities and for their standard deviations, and it is valid over a large
range of column diameter and of gas superficial velocities, corresponding to
Froude numbers $Fr = V_{sg}/(gD)^{1/2}$ from 0.02 to 0.5.
  To investigate further the analogy with turbulent convection, experiments are
performed in a $0.4$m diameter column with $O(10^3)$ particle Reynolds numbers
bubbles. The self-organization prevailing in the heterogeneous regime is
confirmed, with a recirculating liquid flow rate that only depends on the
column diameter $D$. The void fraction at small scales is analysed using
Vorono\"i tessellations, that allow to identify meso-scale structures
(clusters), void and intermediate regions. A series of arguments are presented
that demonstrate that the large relative velocity observed in the heterogeneous
regime originates from the presence of meso-scale structures. Concerning
velocity fluctuations, a flow picture is presented that seems consistent with a
fast track mechanism which, for moderate Rouse numbers, leads to liquid
velocity fluctuations proportional to the relative velocity.


title: Nonlocal operators related to nonsymmetric forms I: H\"older estimates

abstract:
  The aim of this article is to develop the regularity theory for parabolic
equations driven by nonlocal operators associated with nonsymmetric forms.
H\"older regularity and weak Harnack inequalities are proved using extensions
of recently established nonlocal energy methods. We are able to connect the
theory of nonsymmetric nonlocal operators with the important results of
Aronson-Serrin in the local linear case. This connection is exemplified by
nonlocal-to-local convergence results identifying the limiting class of
operators as second order differential operators with drift terms.


title:
Torsion subgroups, solvability and the Engel condition in associative
  rings

abstract:
  The connections between the properties of associative rings that are
Lie-solvable (Engel, n-Engel, locally finite, respectively) and the properties
of their adjoin subgroups are investigated.


title: Optical Wavelength Meter with Machine Learning Enhanced Precision

abstract:
  Diverse applications in photonics and microwave engineering require a means
of measurement of the instantaneous frequency of a signal. A photonic
implementation typically applies an interferometer equipped with three or more
output ports to measure the frequency dependent phase shift provided by an
optical delay line. The components constituting the interferometer are prone to
impairments which results in erroneous measurements. It is shown that the
information to be retrieved is encoded by a three-component vector that lies on
a circular cone within a three-dimensional Cartesian object space. The measured
data belongs to the image of the object space under a linear map that describes
the action of the interferometer. Assisted by a learning algorithm, an inverse
map from the image space into the object space is constructed. The inverse map
compensates for a variety of impairments while being robust to noise.
Simulation results demonstrate that, to the extent the interferometer model
captures all significant impairments, a precision limited only by the level of
random noise is attainable. A wavelength meter architecture is fabricated on
Si3N4 photonic integration platform to prove the method experimentally. Applied
to the measured data, greater than an order of magnitude improvement in
precision is achieved by the proposed method compared to the conventional
method.


title:
Gravitational thermodynamics without the conformal factor problem:
  Partition functions and Euclidean saddles from Lorentzian Path Integrals

abstract:
  Thermal partition functions for gravitational systems have traditionally been
studied using Euclidean path integrals. But in Euclidean signature the
gravitational action suffers from the conformal factor problem, which renders
the action unbounded below. This makes it difficult to take the Euclidean
formulation as fundamental. However, despite their familiar association with
periodic imaginary time, thermal gravitational partition functions can also be
described by real-time path integrals over contours defined by real Lorentzian
metrics. The one caveat is that we should allow certain codimension-2
singularities analogous to the familiar Euclidean conical singularities. With
this understanding, we show that the usual Euclidean-signature black holes (or
their complex rotating analogues) define saddle points for the real-time path
integrals that compute our partition functions. Furthermore, when the black
holes have positive specific heat, we provide evidence that a codimension-2
subcontour of our real Lorentz-signature contour of integration can be deformed
so as to show that these black holes saddles contribute with non-zero weight to
the semiclassical limit, and that the same is then true of the remaining two
integrals.


title:
Bayesian-EUCLID: discovering hyperelastic material laws with
  uncertainties

abstract:
  Within the scope of our recent approach for Efficient Unsupervised
Constitutive Law Identification and Discovery (EUCLID), we propose an
unsupervised Bayesian learning framework for discovery of parsimonious and
interpretable constitutive laws with quantifiable uncertainties. As in
deterministic EUCLID, we do not resort to stress data, but only to
realistically measurable full-field displacement and global reaction force
data; as opposed to calibration of an a priori assumed model, we start with a
constitutive model ansatz based on a large catalog of candidate functional
features; we leverage domain knowledge by including features based on existing,
both physics-based and phenomenological, constitutive models. In the new
Bayesian-EUCLID approach, we use a hierarchical Bayesian model with
sparsity-promoting priors and Monte Carlo sampling to efficiently solve the
parsimonious model selection task and discover physically consistent
constitutive equations in the form of multivariate multi-modal probabilistic
distributions. We demonstrate the ability to accurately and efficiently recover
isotropic and anisotropic hyperelastic models like the Neo-Hookean, Isihara,
Gent-Thomas, Arruda-Boyce, Ogden, and Holzapfel models in both elastostatics
and elastodynamics. The discovered constitutive models are reliable under both
epistemic uncertainties - i.e. uncertainties on the true features of the
constitutive catalog - and aleatoric uncertainties - which arise from the noise
in the displacement field data, and are automatically estimated by the
hierarchical Bayesian model.


title: Sustainability Considerations for Accelerator and Collider Facilities

abstract:
  As the next generation of large accelerator-based facilities are being
considered at the Snowmass 2021 study high priority has to be given to
environmental sustainability including energy consumption, natural resource use
and the environmental impact of effluents. Typically, increased performance -
higher beam energies and intensities - of proposed new facilities have come
with increased electric power consumption. In the following we discuss the most
important areas of development for the sustainability of accelerator-based
research infrastructures in three categories - technologies, concepts and
general aspects. To achieve the goal of increased performance with reduced
energy consumption a focused R&D effort is required with the same or even
higher priority as the traditional performance-related R&D. Such a
recommendation was included in the recent European Strategy for Particle
Physics Accelerator R&D Roadmap.


title:
Hercules: Heterogeneity-Aware Inference Serving for At-Scale
  Personalized Recommendation

abstract:
  Personalized recommendation is an important class of deep-learning
applications that powers a large collection of internet services and consumes a
considerable amount of datacenter resources. As the scale of production-grade
recommendation systems continues to grow, optimizing their serving performance
and efficiency in a heterogeneous datacenter is important and can translate
into infrastructure capacity saving. In this paper, we propose Hercules, an
optimized framework for personalized recommendation inference serving that
targets diverse industry-representative models and cloud-scale heterogeneous
systems. Hercules performs a two-stage optimization procedure - offline
profiling and online serving. The first stage searches the large under-explored
task scheduling space with a gradient-based search algorithm achieving up to
9.0x latency-bounded throughput improvement on individual servers; it also
identifies the optimal heterogeneous server architecture for each
recommendation workload. The second stage performs heterogeneity-aware cluster
provisioning to optimize resource mapping and allocation in response to
fluctuating diurnal loads. The proposed cluster scheduler in Hercules achieves
47.7% cluster capacity saving and reduces the provisioned power by 23.7% over a
state-of-the-art greedy scheduler.


title: Computational Perspective of the Fog Node

abstract:
  Fog computing is a recent computational paradigm that was proposed to solve
some weaknesses in cloud-based systems. For this reason, this technology has
been extensively studied by several technology areas. It is still in a maturing
stage, so there is no consensus in academia about its concepts and definitions,
and each area adopts the ones that are convenient for each use case. This
article proposes a definition and a classification relying on a computational
perspective for the fog node, which is a fundamental element in a fog computing
environment. In addition, the main challenges related to the fog node are also
presented.


title:
Sememe Prediction for BabelNet Synsets using Multilingual and Multimodal
  Information

abstract:
  In linguistics, a sememe is defined as the minimum semantic unit of
languages. Sememe knowledge bases (KBs), which are built by manually annotating
words with sememes, have been successfully applied to various NLP tasks.
However, existing sememe KBs only cover a few languages, which hinders the wide
utilization of sememes. To address this issue, the task of sememe prediction
for BabelNet synsets (SPBS) is presented, aiming to build a multilingual sememe
KB based on BabelNet, a multilingual encyclopedia dictionary. By automatically
predicting sememes for a BabelNet synset, the words in many languages in the
synset would obtain sememe annotations simultaneously. However, previous SPBS
methods have not taken full advantage of the abundant information in BabelNet.
In this paper, we utilize the multilingual synonyms, multilingual glosses and
images in BabelNet for SPBS. We design a multimodal information fusion model to
encode and combine this information for sememe prediction. Experimental results
show the substantial outperformance of our model over previous methods (about
10 MAP and F1 scores). All the code and data of this paper can be obtained at
https://github.com/thunlp/MSGI.


title: What to learn from few visible transitions' statistics?

abstract:
  Interpreting partial information collected from systems subject to noise is a
key problem across scientific disciplines. Theoretical frameworks often focus
on the dynamics of variables that result from coarse-graining the internal
states of a physical system. However, most experimental apparatuses are rather
sensitive to specific transitions, while internal states of the physical system
are blurred or inaccessible. Here we consider an observer who records a time
series of occurrences of one or several transitions performed by a system,
under the single assumption that its underlying dynamics is Markovian. We pose
the question of how one can use the transitions' information to make inferences
of dynamical, thermodynamical, and biochemical properties. First, putting
forward first-passage time techniques, we derive analytical expressions for the
probabilities of consecutive transitions and for the time elapsed between them,
which we call inter-transition times. Second, we develop an estimate lower
bound to the entropy production rate which can be split into two non-negative
contributions, one due to the statistics of transitions and a second due to the
statistics of inter-transition times. We also show that when only one current
is measured, our estimate still detects irreversibility even in the absence of
net currents in the transition time series. Third, we verify our results with
numerical simulations of biophysical models of Kinesin, of Dynein, and of
template-directed polymerization by molecular motors. Our numerical results
reveal that while entropy production is entailed in the statistics of two
successive transitions of the same type (i.e. repeated transitions), the
statistics of two different successive transitions (i.e. alternated
transitions) can probe the existence of an underlying disorder in the motion of
a molecular motor.


title:
Space-like strong unique continuation for some fractional parabolic
  equations

abstract:
  In this paper we establish the \emph{space-like} strong unique continuation
for nonlocal equations of the type $(\partial_t - \Delta)^s u= Vu$, for $0<s
<1$. The proof of our main result, Theorem 1.1, is achieved via a conditional
elliptic type doubling property for solutions to the appropriate extension
problem, followed by a blowup analysis.


title:
Bipedal Locomotion with Nonlinear Model Predictive Control: Online Gait
  Generation using Whole-Body Dynamics

abstract:
  The ability to generate dynamic walking in real-time for bipedal robots with
compliance and underactuation has the potential to enable locomotion in complex
and unstructured environments. Yet, the high-dimensional nature of bipedal
robots has limited the use of full-order rigid body dynamics to gaits which are
synthesized offline and then tracked online, e.g., via whole-body controllers.
In this work we develop an online nonlinear model predictive control approach
that leverages the full-order dynamics to realize diverse walking behaviors.
Additionally, this approach can be coupled with gaits synthesized offline via a
terminal cost that enables a shorter prediction horizon; this makes rapid
online re-planning feasible and bridges the gap between online reactive control
and offline gait planning. We demonstrate the proposed method on the planar
robot AMBER-3M, both in simulation and on hardware.


title:
$\mathcal{H}_{\infty}$-optimal Interval Observer Synthesis for Uncertain
  Nonlinear Dynamical Systems via Mixed-Monotone Decompositions

abstract:
  This paper introduces a novel $\mathcal{H}_{\infty}$-optimal interval
observer synthesis for bounded-error/uncertain locally Lipschitz nonlinear
continuous-time (CT) and discrete-time (DT) systems with noisy nonlinear
observations. Specifically, using mixed-monotone decompositions, the proposed
observer is correct by construction, i.e., the interval estimates readily frame
the true states without additional constraints or procedures. In addition, we
provide sufficient conditions for input-to-state (ISS) stability of the
proposed observer and for minimizing the $\mathcal{H}_{\infty}$ gain of the
framer error system in the form of semi-definite programs (SDPs) with Linear
Matrix Inequalities (LMIs) constraints. Finally, we compare the performance of
the proposed $\mathcal{H}_{\infty}$-optimal interval observers with some
benchmark CT and DT interval observers.


title: Conditional Contextual Refinement (CCR)

abstract:
  Contextual refinement (CR) is one of the standard notions of specifying open
programs. CR has two main advantages: (i) (horizontal and vertical)
compositionality that allows us to decompose a large contextual refinement into
many smaller ones enabling modular and incremental verification, and (ii) no
restriction on programming features thereby allowing, e.g., mutually recursive,
pointer-value passing, and higher-order functions. However, CR has a downside
that it cannot impose conditions on the context since it quantifies over all
contexts, which indeed plays a key role in support of full compositionality and
programming features.
  In this paper, we address the problem of finding a notion of refinement that
satisfies all three requirements: support of full compositionality, full
(sequential) programming features, and rich conditions on the context. As a
solution, we propose a new theory of refinement, called CCR (Conditional
Contextual Refinement), and develop a verification framework based on it, which
allows us to modularly and incrementally verify a concrete module against an
abstract module under separation-logic-style pre and post conditions about
external modules. It is fully formalized in Coq and provides a proof mode that
combines (i) simulation reasoning about preservation of sideffects such as IO
events and termination and (ii) propositional reasoning about pre and post
conditions. Also, the verification results are combined with CompCert, so that
we formally establish behavioral refinement from top-level abstract programs,
all the way down to their assembly code.


title:
Nontrivial doping evolution of electronic properties in
  Ising-superconducting alloys

abstract:
  Transition metal dichalcogenides offer unprecedented versatility to engineer
2D materials with tailored properties to explore novel structural and
electronic phase transitions. In this work, we present the atomic-scale
evolution of the electronic ground state of a monolayer of
Nb$_{1-\delta}$Mo$_{\delta}$Se$_2$ across the entire alloy composition range (0
< ${\delta}$ < 1) using low-temperature (300 mK) scanning tunneling microscopy
and spectroscopy (STM/STS). In particular, we investigate the atomic and
electronic structure of this 2D alloy throughout the metal to semiconductor
transition (monolayer NbSe$_2$ to MoSe$_2$). Our measurements let us extract
the effective doping of Mo atoms, the bandgap evolution and the band shifts,
which are monotonic with ${\delta}$. Furthermore, we demonstrate that
collective electronic phases (charge density wave and superconductivity) are
remarkably robust against disorder. We further show that the superconducting TC
changes non-monotonically with doping. This contrasting behavior in the normal
and superconducting state is explained using first-principles calculations. We
show that Mo doping decreases the density of states at the Fermi level and the
magnitude of pair-breaking spin fluctuations as a function of Mo content. Our
results paint a detailed picture of the electronic structure evolution in 2D
TMD alloys, which is of utmost relevance for future 2D materials design.

categories: cond-mat.mtrl-sci cond-mat.mes-hall cond-mat.str-el cond-mat.supr-con


title:
Characterizing Reddit Participation of Users Who Engage in the QAnon
  Conspiracy Theories

abstract:
  Widespread conspiracy theories may significantly impact our society. This
paper focuses on the QAnon conspiracy theory, a consequential conspiracy theory
that started on and disseminated successfully through social media. Our work
characterizes how Reddit users who have participated in QAnon-focused
subreddits engage in activities on the platform, especially outside their own
communities. Using a large-scale Reddit moderation action against QAnon-related
activities in 2018 as the source, we identified 13,000 users active in the
early QAnon communities. We collected the 2.1 million submissions and 10.8
million comments posted by these users across all of Reddit from October 2016
to January 2021. The majority of these users were only active after the
emergence of the QAnon Conspiracy theory and decreased in activity after
Reddit's 2018 QAnon ban. A qualitative analysis of a sample of 915 subreddits
where the "QAnon-enthusiastic" users were especially active shows that they
participated in a diverse range of subreddits, often of unrelated topics to
QAnon. However, most of the users' submissions were concentrated in subreddits
that have sympathetic attitudes towards the conspiracy theory, characterized by
discussions that were pro-Trump, or emphasized unconstricted behavior (often
anti-establishment and anti-interventionist). Further study of a sample of
1,571 of these submissions indicates that most consist of links from
low-quality sources, bringing potential harm to the broader Reddit community.
These results point to the likelihood that the activities of early QAnon users
on Reddit were dedicated and committed to the conspiracy, providing
implications on both platform moderation design and future research.


title: Floquet Engineering of Non-Equilibrium Superradiance

abstract:
  We demonstrate the emergence of a non-equilibrium superradiant phase in the
dissipative Rabi-Dicke model. This phase is characterized by a photonic steady
state that oscillates with a frequency close to the cavity frequency, in
contrast to the constant photonic steady state of the equilibrium superradiant
phase in the Dicke model. We relate this superradiant phase to the population
inversion of Floquet states by introducing a Schwinger representation of the
driven two-level systems in the cavity. This inversion is depleted near Floquet
energies that are resonant with the cavity frequency to sustain a coherent
light-field. In particular, our model applies to solids within a two-band
approximation, in which the electrons act as Schwinger fermions. We propose to
use this Floquet-assisted superradiant phase to obtain controllable optical
gain for a laser-like operation.


title:
New LZ and PW(Z) relations of RR Lyrae stars calibrated with Gaia EDR3
  parallaxes

abstract:
  We present new luminosity-metallicity (LZ; MV-[Fe/H] and MG-[Fe/H]) relations
and, for the first time, empirical, Gaia three-band (G,GBP,GRP)
period-Wesenheit-metallicity (PWZ) relations of RR Lyrae stars (RRLs) derived
using a hierarchical Bayesian approach and new accurate parallaxes published
for these variables in the Gaia Early Data Release 3 (EDR3). In a previous
study we obtained Bayesian hierarchically-derived LZ relations from a sample of
about four hundred Milky Way field RRLs with G-band light curves and
trigonometric parallaxes published in the Gaia Data Release 2 (DR2), using V
mean magnitudes, metallicities, absorptions and pulsation periods available in
the literature. We now extend that study in two directions. Firstly, we update
our previous results using trigonometric parallaxes from Gaia EDR3 and
incorporate the Bayesian analysis of a first empirical PWZ relation derived
using those field RRLs with G, GBP and GRP time-series photometry available in
Gaia DR2. Secondly, we use Bayesian inference to derive LZ relations and
empirical PW Gaia three-band relations from 385 RRLs belonging to 15 Milky Way
globular clusters (GC) with literature-compiled spectroscopic metallicities
ranging from -0.36 to -2.39 dex and prior distances extending from 2.2 to 41.2
kpc. From the samples of RRLs analysed in this paper we infer a mean Gaia EDR3
zero-point offset of -0.028 mas with median values ranging from -0.033 (LZ and
PWZ models for field stars) to -0.024 mas (LZ model in the V-band for GC RRLs).


title: Panoptic animal pose estimators are zero-shot performers

abstract:
  Animal pose estimation is critical in applications ranging from life science
research, agriculture, to veterinary medicine. Compared to human pose
estimation, the performance of animal pose estimation is limited by the size of
available datasets and the generalization of a model across datasets. Typically
different keypoints are labeled regardless of whether the species are the same
or not, leaving animal pose datasets to have disjoint or partially overlapping
keypoints. As a consequence, a model cannot be used as a plug-and-play solution
across datasets. This reality motivates us to develop panoptic animal pose
estimation models that are able to predict keypoints defined in all datasets.
In this work we propose a simple yet effective way to merge differentially
labeled datasets to obtain the largest quadruped and lab mouse pose dataset.
Using a gradient masking technique, so called SuperAnimal-models are able to
predict keypoints that are distributed across datasets and exhibit strong
zero-shot performance. The models can be further improved by (pseudo) labeled
fine-tuning. These models outperform ImageNet-initialized models.


title: Unsupervised Clustering of Roman Potsherds via Variational Autoencoders

abstract:
  In this paper we propose an artificial intelligence imaging solution to
support archaeologists in the classification task of Roman commonware
potsherds. Usually, each potsherd is represented by its sectional profile as a
two dimensional black-white image and printed in archaeological books related
to specific archaeological excavations. The partiality and handcrafted variance
of the fragments make their matching a challenging problem: we propose to pair
similar profiles via the unsupervised hierarchical clustering of non-linear
features learned in the latent space of a deep convolutional Variational
Autoencoder (VAE) network. Our contribution also include the creation of a
ROman COmmonware POTtery (ROCOPOT) database, with more than 4000 potsherds
profiles extracted from 25 Roman pottery corpora, and a MATLAB GUI software for
the easy inspection of shape similarities. Results are commented both from a
mathematical and archaeological perspective so as to unlock new research
directions in both communities.


title:
Bayesian inference in single-line spectroscopic binaries with a visual
  orbit

abstract:
  We present a Bayesian inference methodology for the estimation of orbital
parameters on single-line spectroscopic binaries with astrometric data, based
on the No-U-Turn sampler Markov chain Monte Carlo algorithm. Our approach is
designed to provide a precise and efficient estimation of the joint posterior
distribution of the orbital parameters in the presence of partial and
heterogeneous observations. This scheme allows us to directly incorporate prior
information about the system - in the form of a trigonometric parallax, and an
estimation of the mass of the primary component from its spectral type - to
constrain the range of solutions, and to estimate orbital parameters that
cannot be usually determined (e.g. the individual component masses), due to the
lack of observations or imprecise measurements. Our methodology is tested by
analyzing the posterior distributions of well-studied double-line spectroscopic
binaries treated as single-line binaries by omitting the radial velocity data
of the secondary object. Our results show that the system's mass ratio can be
estimated with an uncertainty smaller than 10% using our approach. As a proof
of concept, the proposed methodology is applied to twelve single-line
spectroscopic binaries with astrometric data that lacked a joint
astrometric-spectroscopic solution, for which we provide full orbital elements.
Our sample-based methodology allows us also to study the impact of different
posterior distributions in the corresponding observations space. This novel
analysis provides a better understanding of the effect of the different sources
of information on the shape and uncertainty of the orbit and radial velocity
curve.

categories: astro-ph.SR astro-ph.IM physics.comp-ph physics.data-an


title:
DarkSUSY 6.3: Freeze-in, out-of-equilibrium freeze-out, cosmic-ray
  upscattering and further new features

abstract:
  DarkSUSY is a versatile tool for precision calculations of a large variety of
dark matter-related signals, ranging from predictions for the dark matter relic
density to dark matter self-interactions and rates relevant for direct and
indirect detection experiments. In all of these areas significant new code
additions have been made in recent years, since the release of DarkSUSY 6 in
2018, which we summarize in this overview. In particular, DarkSUSY now allows
users to compute the relic density for feebly interacting massive particles via
the freeze-in mechanism, but also offers new routines for freeze-out
calculations in the presence of secluded dark sectors as well as for models
where kinetic equilibrium is not fully established during the freeze-out
process. On the direct detection side, the effect of cosmic-ray upscattering of
dark matter has been fully implemented, leading to a subdominant relativistic
component in the expected dark matter flux at Earth. Finally, updated yields
relevant for indirect searches with gamma rays, neutrinos or charged cosmic
rays have been added; the new default spectra are based on a large number of
Pythia 8 runs, but users can also easily switch between various alternative
spectra. Further code details, including a manual and various concrete example
applications, are provided at www.darksusy.org.


title:
Constraining decaying dark matter with BOSS data and the effective field
  theory of large-scale structures

abstract:
  We update cosmological constraints on two decaying dark matter models in
light of BOSS-DR12 data analyzed under the Effective Field Theory of
Large-Scale Structures (EFTofLSS) formalism, together with Planck, Pantheon and
other BOSS measurements of the baryonic acoustic oscillation (BAO). In the
first model, a fraction $f_{\rm dcdm}$ of cold dark matter (CDM) decays into
dark radiation (DR) with a lifetime $\tau$. In the second model (recently
suggested as a potential resolution to the $S_8$ tension), all the CDM decays
with a lifetime $\tau$ into DR and a massive warm dark matter (WDM) particle,
with a fraction $\varepsilon$ of the CDM rest mass energy transferred to the
DR. Using numerical codes from the recent literature, we perform the first
calculation of the mildly non-linear (matter and galaxy) power spectra with the
EFTofLSS for these two models. In the case of DR products, we obtain the
constraints $f_{\rm dcdm}\lesssim0.022$ (95\% C.L.) for lifetimes shorter than
the age of the universe, and $\tau/f_{\rm dcdm} \gtrsim 250$ Gyr in the
long-lived regime assuming $f_{\rm dcdm}\to1$. We show that Planck data
contributes the most to these constraints, with EFTofBOSS providing a marginal
improvement over conventional BAO and redshift space distortions ($f\sigma_8$)
data. In the case of DR and WDM decay products, we find that EFTofBOSS data
significantly improves the constraints at 68\% C.L. on the CDM lifetime with a
$S_8$ prior from KiDS-1000. We show that, in order to fit EFTofBOSS data while
lowering $S_8$ to match KiDS-1000, the best-fit model has a longer lifetime
$\tau = 120$ Gyr, with a larger kick velocity $v_{\rm kick}/c \simeq
\varepsilon \simeq 1.2\%$, than that without EFTofBOSS ($\tau = 43$ Gyr,
$\varepsilon =0.6\%$). We anticipate that future surveys will provide exquisite
constraints on such models.


title:
Radiopurity of a kg-scale PbWO$_4$ cryogenic detector produced from
  archaeological Pb for the RES-NOVA experiment

abstract:
  RES-NOVA is a newly proposed experiment for the detection of neutrinos from
astrophysical sources, mainly Supernovae, using an array of cryogenic detectors
made of PbWO$_4$ crystals produced from archaeological Pb. This unconventional
material, characterized by intrinsic high radiopurity, enables to achieve
low-background levels in the region of interest for the neutrino detection via
Coherent Elastic neutrino-Nucleus Scattering (CE$\nu$NS). This signal lies at
the detector energy threshold, O(1 keV), and it is expected to be hidden by
naturally occurring radioactive contaminants of the crystal absorber. Here, we
present the results of a radiopurity assay on a 0.84 kg PbWO$_4$ crystal
produced from archaeological Pb operated as a cryogenic detector. The crystal
internal radioactive contaminations are: $^{232}$Th $<$40 $\mu$Bq/kg, $^{238}$U
$<$30 $\mu$Bq/kg, $^{226}$Ra 1.3 mBq/kg and $^{210}$Pb 22.5 mBq/kg. We present
also a background projection for the final experiment and possible mitigation
strategies for further background suppression. The achieved results demonstrate
the feasibility of realizing this new class of detectors.


title: The multipolar structure of rotating boson stars

abstract:
  The relativistic multipole moments provide a key ingredient to characterize
the gravitational field around compact astrophysical objects. They play a
crucial role in the description of the orbital evolution of coalescing binary
systems and encode valuable information on the nature of the binary's
components, which leaves a measurable imprint in their gravitational-wave
emission. We present a new study on the multipolar structure of a class of
arbitrarily spinning boson stars with quartic self-interactions in the large
coupling limit, where these solutions are expected to be stable. Our results
strengthen and extend previous numerical analyses, showing that even for the
most compact configurations the multipolar structure deviates significantly
from that of a Kerr black hole. We provide accurate data for the multipole
moments as functions of the object's mass and spin, which can be directly used
to construct inspiral waveform approximants and to perform parameter
estimations and searches for boson star binaries.


title: Locality optimization for parent Hamiltonians of Tensor Networks

abstract:
  Tensor Network states form a powerful framework for both the analytical and
numerical study of strongly correlated phases. Vital to their analytical
utility is that they appear as the exact ground states of associated parent
Hamiltonians, where canonical proof techniques guarantee a controlled ground
space structure. Yet, while those Hamiltonians are local by construction, the
known techniques often yield complex Hamiltonians which act on a rather large
number of spins. In this paper, we present an algorithm to systematically
simplify parent Hamiltonians, breaking them down into any given basis of
elementary interaction terms. The underlying optimization problem is a
semidefinite program, and thus the optimal solution can be found efficiently.
Our method exploits a degree of freedom in the construction of parent
Hamiltonians -- the excitation spectrum of the local terms -- over which it
optimizes such as to obtain the best possible approximation. We benchmark our
method on the AKLT model and the Toric Code model, where we show that the
canonical parent Hamiltonians (acting on 3 or 4 and 12 sites, respectively) can
be broken down to the known optimal 2-body and 4-body terms. We then apply our
method to the paradigmatic Resonating Valence Bond (RVB) model on the kagome
lattice. Here, the simplest previously known parent Hamiltonian acts on all the
12 spins on one kagome star. With our optimization algorithm, we obtain a
vastly simpler Hamiltonian: We find that the RVB model is the exact ground
state of a parent Hamiltonian whose terms are all products of at most four
Heisenberg interactions, and whose range can be further constrained, providing
a major improvement over the previously known 12-body Hamiltonian.


title: Minimum Partition into Plane Subgraphs: The CG:SHOP Challenge 2022

abstract:
  We give an overview of the 2022 Computational Geometry Challenge targeting
the problem Minimum Partition into Plane Subsets, which consists of
partitioning a given set of line segments into a minimum number of non-crossing
subsets.


title:
Fluctuation Spectroscopy of Two-Level Systems in Superconducting
  Resonators

abstract:
  Superconducting quantum computing is experiencing a tremendous growth.
Although major milestones have already been achieved, useful quantum-computing
applications are hindered by a variety of decoherence phenomena. Decoherence
due to two-level systems (TLSs) hosted by amorphous dielectric materials is
ubiquitous in planar superconducting devices. We use high-quality quasilumped
element resonators as quantum sensors to investigate TLS-induced loss and
noise. We perform two-tone experiments with a probe and pump electric field;
the pump is applied at different power levels and detunings. We measure and
analyze time series of the quality factor and resonance frequency for very long
time periods, up to 1000 h. We additionally carry out simulations based on the
TLS interacting model in presence of a pump field. We find that loss and noise
are reduced at medium and high power, matching the simulations, but not at low
power.


title:
A radio, optical, UV and X-ray view of the enigmatic changing look
  Active Galactic Nucleus 1ES~1927+654 from its pre- to post-flare states

abstract:
  The nearby type-II AGN 1ES1927+654 went through a violent changing-look (CL)
event beginning December 2017 during which the optical and UV fluxes increased
by four magnitudes over a few months, and broad emission lines newly appeared
in the optical/UV. By July 2018 the X-ray coronal emission had completely
vanished, only to reappear a few months later. In this work we report the
evolution of the radio, optical, UV and X-rays from the pre-flare state through
mid-2021 with new and archival data from the Very Long Baseline Array (VLBA),
the European VLBI Network, the Very Large Array (VLA), the Telescopio Nazionale
Galileo (TNG), Gran Telescopio Canarias (GTC), The Neil Gehrels Swift
observatory and XMM-Newton. The main results from our work are: (1) The source
has returned to its pre-CL state in optical, UV, and X-ray; the disk-corona
relation has been re-established as has been in the pre-CL state, with an
$\alpha_{\rm OX}\sim 1.02$. The optical spectra are dominated by narrow
emission lines. (2) The UV light curve follows a shallower slope of $\propto
t^{-0.91\pm 0.04}$ compared to that predicted by a tidal disruption event. We
conjecture that a magnetic flux inversion event is the possible cause for this
enigmatic event. (3) The compact radio emission which we tracked in the pre-CL
(2014), during CL (2018) and post-CL(2021) at spatial scales $<1$ pc was at its
lowest level during the changing look event in 2018, nearly contemporaneous
with a low $2-10$ keV emission. The radio to X-ray ratio of the compact source
$L_{\rm Radio}/L_{\rm X-ray}\sim 10^{-5.5}$, follows the Gudel-Benz relation,
typically found in coronally active stars, and several AGN. (4) We do not
detect any presence of nascent jets at the spatial scales of $\sim 5-10$ pc.


title:
Observability analysis and observer design of networks of R\"ossler
  systems

abstract:
  We address the problem of retrieving the full state of a network of R\"ossler
systems from the knowledge of the actual state of a limited set of nodes. The
selection of the nodes where sensors are placed is carried out in a
hierarchical way through a procedure based on graphical and symbolic
observability approaches. By using a map directly obtained from the governing
equations, we design a nonlinear network observer which is able to unfold the
state of the non measured nodes with minimal error. For sparse networks, the
number of sensors scales with half the network size and node reconstruction
errors are lower in networks with heterogeneous degree distributions. The
method performs well even in the presence of parameter mismatch and
non-coherent dynamics and, therefore, we expect it to be useful for designing
robust network control laws.


title: Observation of dynamical topology in 1D

abstract:
  Nontrivial topology in lattices is characterized by invariants--such as the
Zak phase for one dimensional (1D) lattices--derived from wave functions
covering the Brillouin zone. We realized the 1D bipartite Rice-Mele (RM)
lattice using ultracold $^{87}$Rb and focus on lattice configurations
possessing various combinations of chiral, time-reversal and particle-hole
symmetries. We quenched between configurations and used a form of quantum state
tomography, enabled by diabatically tuning lattice parameters, to directly
follow the time evolution of the Zak phase as well as a chiral winding number.
The Zak phase evolves continuously; however, when chiral symmetry transiently
appears in the out-of-equilibrium system, the chiral winding number is well
defined and can take on different integer values. When quenching between two
configurations obeying all three symmetries the Zak phase is time independent;
we confirm the contrasting prediction of [M. McGinley and N. R.Cooper, PRL 121
090401 (2018)] that chiral symmetry is periodically restored, at which times
the winding number changes by $\pm 2$, yielding values that are not present in
the native RM Hamiltonian.


title:
Recovering information in an asymptotically flat spacetime in quantum
  gravity

abstract:
  As an extension of arXiv:\{2002.02448, 2008.10740\} we present a physical
protocol that a set of observers can use to detect a pure state in the bulk
when they are spread across a small cut near $\mathcal{I}^+_-$ in flat
spacetime. The protocol involves the modification of a bulk state using simple
unitary operators and measurements of the energy of the state. The states that
we study are constructed by acting with low energy operators on a vacuum state
such that a perturbative analysis is valid. We restrict ourselves to $3+1$
dimensional spacetimes and only consider massless excitations. From this
analysis, the principle of holography of information becomes manifest in the
case of asymptotically flat spacetime.


title: A Neural Pairwise Ranking Model for Readability Assessment

abstract:
  Automatic Readability Assessment (ARA), the task of assigning a reading level
to a text, is traditionally treated as a classification problem in NLP
research. In this paper, we propose the first neural, pairwise ranking approach
to ARA and compare it with existing classification, regression, and
(non-neural) ranking methods. We establish the performance of our model by
conducting experiments with three English, one French and one Spanish datasets.
We demonstrate that our approach performs well in monolingual single/cross
corpus testing scenarios and achieves a zero-shot cross-lingual ranking
accuracy of over 80% for both French and Spanish when trained on English data.
Additionally, we also release a new parallel bilingual readability dataset in
English and French. To our knowledge, this paper proposes the first neural
pairwise ranking model for ARA, and shows the first results of cross-lingual,
zero-shot evaluation of ARA with neural models.


title: On the Inhomogeneous Phase of the Chiral Gross-Neveu Model

abstract:
  There is substantial evidence that the ground state of the 2d chiral
Gross-Neveu model, in the presence of a $U(1)$ fermion number chemical
potential $\mu$ and in the large $N$ limit, is given by a chiral spiral phase,
namely an inhomogeneous phase with a chiral condensate having a spatially
periodic phase. We show that the chiral spiral configuration persists at finite
$N$ for any $\mu>0$. Our analysis is based on non-abelian bosonization, that
relates the model to a $U(N)_1$ WZW model deformed by current-current
interactions. In this description the appearance of the inhomogeneous phase is
surprisingly simple. We also rederive the phase diagram of the large $N$ chiral
Gross-Neveu model via a direct diagrammatic computation, finding agreement with
previous results in the literature.


title:
A deep learning pipeline for breast cancer ki-67 proliferation index
  scoring

abstract:
  The Ki-67 proliferation index is an essential biomarker that helps
pathologists to diagnose and select appropriate treatments. However, automatic
evaluation of Ki-67 is difficult due to nuclei overlapping and complex
variations in their properties. This paper proposes an integrated pipeline for
accurate automatic counting of Ki-67, where the impact of nuclei separation
techniques is highlighted. First, semantic segmentation is performed by
combining the Squeez and Excitation Resnet and Unet algorithms to extract
nuclei from the background. The extracted nuclei are then divided into
overlapped and non-overlapped regions based on eight geometric and statistical
features. A marker-based Watershed algorithm is subsequently proposed and
applied only to the overlapped regions to separate nuclei. Finally, deep
features are extracted from each nucleus patch using Resnet18 and classified
into positive or negative by a random forest classifier. The proposed
pipeline's performance is validated on a dataset from the Department of
Pathology at H\^opital Nord Franche-Comt\'e hospital.


title: Dissipation Induced Flat Bands

abstract:
  Flat bands are an ideal environment to realize unconventional electronic
phases. Here, we show that fermionic systems with dissipation governed by a
Bloch Lindbladian can realize dispersionless bands for sufficiently strong
coupling to an appropriately engineered bath. These flat bands emerge in a
"dark space" of the system-environment coupling and are long-lived by virtue of
symmetry protection from dissipation. We exhibit the robustness of this
mechanism for general one and two band models with and without spin, and
discuss conditions for their experimental realization such as in a 2D material
on a superconducting substrate.


title: L2Explorer: A Lifelong Reinforcement Learning Assessment Environment

abstract:
  Despite groundbreaking progress in reinforcement learning for robotics,
gameplay, and other complex domains, major challenges remain in applying
reinforcement learning to the evolving, open-world problems often found in
critical application spaces. Reinforcement learning solutions tend to
generalize poorly when exposed to new tasks outside of the data distribution
they are trained on, prompting an interest in continual learning algorithms. In
tandem with research on continual learning algorithms, there is a need for
challenge environments, carefully designed experiments, and metrics to assess
research progress. We address the latter need by introducing a framework for
continual reinforcement-learning development and assessment using Lifelong
Learning Explorer (L2Explorer), a new, Unity-based, first-person 3D exploration
environment that can be continuously reconfigured to generate a range of tasks
and task variants structured into complex and evolving evaluation curricula. In
contrast to procedurally generated worlds with randomized components, we have
developed a systematic approach to defining curricula in response to controlled
changes with accompanying metrics to assess transfer, performance recovery, and
data efficiency. Taken together, the L2Explorer environment and evaluation
approach provides a framework for developing future evaluation methodologies in
open-world settings and rigorously evaluating approaches to lifelong learning.


title: Complex Scalar Singlet Model Benchmarks for Snowmass

abstract:
  In this contribution to Snowmass 2021, we present benchmark parameters for
the general complex scalar singlet model. The complex scalar singlet extension
has three massive scalar states with interesting decay chains which will depend
on the exact mass hierarchy of the system. We find maximum branching ratios for
resonant double Standard Model-like Higgs production, resonant production of a
Standard Model-like Higgs and a new scalar, and double resonant new scalar
production. These branching ratios are between 0.7 and 1. This is particularly
interesting because instead of direct production, the main production of a new
scalar resonance may be from the $s$-channel production and decay of another
scalar resonance. That is, it is still possible for discovery of new scalar
resonances to be from the cascade of one resonance to another. We choose our
benchmark points to have to have a large range of signatures: multi-$b$
production, multi-$W$ and $Z$ production, and multi-125 GeV SM-like Higgs
production. These benchmark points can provide various spectacular signatures
that are consistent with current experimental and theoretical bounds. This is a
summary of results in Ref. [1].


title:
Designing Underactuated Graspers with Dynamically Variable Geometry
  Using Potential Energy Map Based Analysis

abstract:
  In this paper we present a potential energy map based approach that provides
a framework for the design and control of a robotic grasper. Unlike other
potential energy map approaches, our framework is able to consider friction for
a more realistic perspective on grasper performance. Our analysis establishes
the importance of including variable geometry in a grasper design, namely with
regards to palm width, link lengths, and transmission ratio. We demonstrate the
use of this method specifically for a two-phalanx tendon-pulley underactuated
grasper, and show how various design parameters - palm width, link lengths, and
transmission ratios - impact the grasping and manipulation performance of a
specific design across a range of object sizes and friction coefficients.
Optimal grasping designs have palms that scale with object size, and
transmission ratios that scale with the coefficient of friction. Using a custom
manipulation metric we compared a grasper that only dynamically varied its
geometry to a grasper with a variable palm and distinct actuation commands. The
analysis revealed the advantage of the compliant reconfiguration ability
intrinsic to underactuated mechanisms; by varying only the geometry of the
grasper, manipulation of a wide range of objects could be performed.


title: Efficiency Studies of Fast Neutron Tracking using MCNP

abstract:
  Fast neutron identification and spectroscopy is of great interest to nuclear
physics experiments. Using the neutron elastic scattering, the fast neutron
momentum can be measured. (Wang and Morris, 2013) introduced the theoretical
concept that the initial fast neutron momentum can be derived from up to three
consecutive elastic collisions between the neutron and the target, including
the information of two consecutive recoil ion tracks and the vertex position of
the third collision or two consecutive elastic collisions with the timing
information. Here we also include the additional possibility of measuring the
deposited energies from the recoil ions. In this paper, we simulate the neutron
elastic scattering using the Monte Carlo N-Particle Transport Code (MCNP) and
study the corresponding neutron detection and tracking efficiency. The
corresponding efficiency and the scattering distances are simulated with
different target materials, especially natural silicon (92.23$\%$ $^{28}$Si,
4.67$\%$ $^{29}$Si, and 3.1$\%$ $^{30}$Si) and helium-4 ($^4$He). The timing of
collision and the recoil ion energy are also investigated, which are important
characters for the detector design. We also calculate the ion travelling range
for different energies using the software, "The Stopping and Range of Ions in
Matter (SRIM)", showing that the ion track can be most conveniently observed in
$^4$He unless sub-micron spatial resolution can be obtained in silicon.


title:
On the deterministic-shift extended CIR model in a negative interest
  rate framework

abstract:
  In this paper, we propose a new exogenous model to address the problem of
negative interest rates that preserves the analytical tractability of the
original Cox-Ingersoll-Ross (CIR) model with a perfect fit to the observed
term-structure. We use the difference of two independent CIR processes and
apply the deterministic-shift extension technique. To allow for a fast
calibration to the market swaption surface, we apply the Gram-Charlier
expansion to calculate the swaption prices in our model. We run several
numerical tests to demonstrate the strengths of this model by using Monte-Carlo
techniques. In particular, the model produces close Bermudan swaption prices
compared to Bloomberg's Hull-White one-factor model. Moreover, it finds
constant maturity swap (CMS) rates very close to Bloomberg's CMS rates.


title:
Channeling Acceleration in Crystals and Nanostructures and Studies of
  Solid Plasmas: New Opportunities

abstract:
  Plasma wakefield acceleration (PWA) has shown illustrious progress over the
past two decades of active research and resulted in an impressive demonstration
of O(10 GeV) particle acceleration in O(1 m) long single structures. While
already potentially sufficient for some applications, like, e.g., FELs, the
traditional laser- and beam-driven acceleration in gaseous plasma faces
enormous challenges when it comes to the design of the PWA-based O(1-10 TeV)
high energy $e^+e^-$ colliders due to the complexity of energy staging, low
average geometric gradients, and unprecedented transverse and longitudinal
stability requirements. Channeling acceleration in solid-state plasma of
crystals or nanostructures, e.g., carbon nanotubes (CNTs) or alumna honeycomb
holes, has the promise of ultra-high accelerating gradients O(1-10 TeV/m),
continuous focusing of channeling particles without need of staging, and
ultimately small equilibrium beam emittances naturally obtained while
accelerating.


title: Machine Learning and LHC Event Generation

abstract:
  First-principle simulations are at the heart of the high-energy physics
research program. They link the vast data output of multi-purpose detectors
with fundamental theory predictions and interpretation. This review illustrates
a wide range of applications of modern machine learning to event generation and
simulation-based inference, including conceptional developments driven by the
specific requirements of particle physics. New ideas and tools developed at the
interface of particle physics and machine learning will improve the speed and
precision of forward simulations, handle the complexity of collision data, and
enhance inference as an inverse simulation problem.


title:
Mineral Snowflakes on Exoplanets and Brown Dwarfs: Coagulation and
  Fragmentation of Cloud Particles with {\sc HyLandS}

abstract:
  Brown dwarfs and exoplanets provide unique atmospheric regimes that hold
information about their formation routes and evolutionary states. Modelling
mineral cloud particle formation is key to prepare for missions and instruments
like CRIRES+, JWST and ARIEL as well as possible polarimetry missions like {\sc
PolStar}. The aim is to support more detailed observations that demand greater
understanding of microphysical cloud processes. We extend our kinetic cloud
formation model that treats nucleation, condensation, evaporation and settling
of mixed material cloud particles to consistently model cloud particle-particle
collisions. The new hybrid code, {\sc HyLandS}, is applied to a grid of {\sc
Drift-Phoenix} (T, p)-profiles. Effective medium theory and Mie theory are used
to investigate the optical properties. Turbulence is the main driving process
of collisions, with collisions becoming the dominant process at the cloud base
($p>10^{-4}\,{\rm bar}$). Collisions produce one of three outcomes: fragmenting
atmospheres ($\log_{10}(g)=3$), coagulating atmospheres ($\log_{10}(g)=5$,
$T_{\rm eff} \leq 1800\, {\rm K}$) and condensational growth dominated
atmospheres ($\log_{10}(g\,)=5$, $T_{\rm eff} > 1800\, {\rm K}$). Cloud
particle opacity slope at optical wavelengths (HST) is increased with
fragmentation, as are the silicate features at mid-infrared wavelengths. The
hybrid moment-bin method {\sc HyLandS} demonstrates the feasibility of
combining a moment and a bin method whilst assuring element conservation. It
provides a powerful and fast tool for capturing general trends of particle
collisions, consistently with other microphysical processes. Collisions are
important in exoplanet and brown dwarf atmospheres but cannot be assumed to be
hit-and-stick only. The spectral effects of collisions complicates inferences
of cloud particle size and material composition from observational data.


title: Jets and Jet Substructure at Future Colliders

abstract:
  Even though jet substructure was not an original design consideration for the
Large Hadron Collider (LHC) experiments, it has emerged as an essential tool
for the current physics program. We examine the role of jet substructure on the
motivation for and design of future energy frontier colliders. In particular,
we discuss the need for a vibrant theory and experimental research and
development program to extend jet substructure physics into the new regimes
probed by future colliders. Jet substructure has organically evolved with a
close connection between theorists and experimentalists and has catalyzed
exciting innovations in both communities. We expect such developments will play
an important role in the future energy frontier physics program.


title:
Simultaneous Learning of the Inputs and Parameters in Neural
  Collaborative Filtering

abstract:
  Neural network-based collaborative filtering systems focus on designing
network architectures to learn better representations while fixing the input to
the user/item interaction vectors and/or ID. In this paper, we first show that
the non-zero elements of the inputs are learnable parameters that determine the
weights in combining the user/item embeddings, and fixing them limits the power
of the models in learning the representations. Then, we propose to learn the
value of the non-zero elements of the inputs jointly with the neural network
parameters. We analyze the model complexity and the empirical risk of our
approach and prove that learning the input leads to a better generalization
bound. Our experiments on several real-world datasets show that our method
outperforms the state-of-the-art methods, even using shallow network structures
with a smaller number of layers and parameters.


title: A singularly perturbed fractional Kirchhoff problem

abstract:
  In this paper, we first establish the uniqueness and non-degeneracy of
positive solutions to the fractional Kirchhoff problem \begin{equation*}
\Big(a+b{\int_{\mathbb{R}^{N}}}|(-\Delta)^{\frac{s}{2}}u|^2dx\Big)(-\Delta)^su+mu=|u|^{p-2}u,\quad
\text{in}\ \mathbb{R}^{N}, \end{equation*} where $a,b,m>0$,
$0<\frac{N}{4}<s<1$, $2<p<2^*_s=\frac{2N}{N-2s}$ and $(-\Delta )^s$ is the
fractional Laplacian. Then, combining this non-degeneracy result and
Lyapunov-Schmidt reduction method, we derive the existence of semiclassical
solutions to the singularly perturbation problem \begin{equation*}
\Big(\varepsilon^{2s}a+\varepsilon^{4s-N}
b{\int_{\mathbb{R}^{N}}}|(-\Delta)^{\frac{s}{2}}u|^2dx\Big)(-\Delta)^su+V(x)u=|u|^{p-2}u,\quad
\text{in}\ \mathbb{R}^{N}, \end{equation*} for $\varepsilon> 0$ sufficiently
small and a potential function $V$.


title:
Simultaneous view of the FRB~180301 with FAST and NICER during a
  bursting phase

abstract:
  FRB180301 is one of the most actively repeating fast radio bursts (FRBs)
which has shown polarization angle changes in its radio burst emission, an
indication for their likely origin in the magnetosphere of a highly-magnetized
neutron star. We carried out a multi-wavelength campaign with the FAST radio
telescope and NICER X-ray observatory to investigate any possible X-ray
emission temporally coincident with the bright radio bursts. The observations
took place on 2021 March 4, 9 and 19. We detected five bright radio bursts with
FAST, four of which were strictly simultaneous with the NICER observations. The
peak flux-density of the radio bursts ranged between $28-105$ mJy, the burst
fluence between $27-170$ mJy-ms, and the burst durations between $1.7-12.3$ ms.
The radio bursts from FRB~180301 exhibited complex time domain structure, and
sub-pulses were detected in individual bursts, with no significant circular
polarisation. The linear degree of polarisation in L-band reduced significantly
compared to the 2019 observations. We do not detect any X-ray emission in
excess of the background during the 5ms, 10ms, 100ms, 1sec and 100sec time
intervals at/around the radio-burst barycenter-corrected arrival times, at a
$>5\sigma$ confidence level. The $5\sigma$ upper limits on the X-ray a)
persistent flux is $<7.64\times 10^{-12}\, \rm erg\, cm^{-2}\, s^{-1}$ ,
equivalent to $L_{\rm X}<2.50 \times 10^{45} \rm erg\, s^{-1}$ and b) 5 ms
fluence is $<2\times 10^{-11} \rm erg\, cm^{-2}$, at the radio burst regions.
Using the $5$ ms X-ray fluence upper limit, we can estimate the radio
efficiency $\eta_{R/X} \equiv L_{\rm Radio}/L_{\rm X-ray} \gtrsim 10^{-8}$. The
derived upper limit on $\eta_{R/X}$ is consistent with both magnetospheric
models and synchrotron maser models involving relativistic shocks.


title:
Local uniqueness of semiclassical bounded states for a singularly
  perturbed fractional Kirchhoff problem

abstract:
  In this paper, we consider the following singularly perturbed fractional
Kirchhoff problem \begin{equation*} \Big(\varepsilon^{2s}a+\varepsilon^{4s-N}
b{\int_{\mathbb{R}^{N}}}|(-\Delta)^{\frac{s}{2}}u|^2dx\Big)(-\Delta)^su+V(x)u=|u|^{p-2}u,\quad
\text{in}\ \mathbb{R}^{N}, \end{equation*} where $a,b>0$, $2s<N<4s$ with
$s\in(0,1)$, $2<p<2^*_s=\frac{2N}{N-2s}$ and $(-\Delta )^s$ is the fractional
Laplacian. For $\varepsilon> 0$ sufficiently small and a bounded continuous
function $V$, we establish a type of local Pohoz\v{a}ev identity by extension
technique and then we can obtain the local uniqueness of semiclassical bounded
solutions based on our recent results on the uniqueness and non-degeneracy of
positive solutions to the limit problem.


title: Current-driven Langmuir Oscillations and Streaming Instabilities

abstract:
  The Buneman and ion acoustic instabilities are usually associated with
different electron and ion drift velocities, in such a way that there is a
large current through the plasma. However, due to the recently discovered
current-driven Langmuir oscillations (1-3), the relative drift velocity in
these configurations will oscillate at the plasma frequency, and with an
amplitude of at least the initial drift velocity. In contrast, the textbooks
assume a constant drift velocity. Since the growth rates arrived at under that
assumption are far less than the plasma frequency, several oscillation periods
will take place during the linear growth phase, and this will dampen the
instabilities. We provide general theoretical derivations of these
oscillations, and show simulation results of the altered behavior of the
instabilities. Towards the end, we hypothesize that drift-averaging might be a
viable method of calculating the modified growth rates.


title:
Local uniqueness of multi-peak positive solutions to a class of
  fractional Kirchhoff equations

abstract:
  This paper is twofold. In the first part, combining the nondegeneracy result
and Lyapunov-Schmidt reduction method, we derive the existence of multi-peak
positive solutions to the singularly perturbation problem \begin{equation*}
\Big(\varepsilon^{2s}a+\varepsilon^{4s-N}
b{\int_{\mathbb{R}^{N}}}|(-\Delta)^{\frac{s}{2}}u|^2dx\Big)(-\Delta)^su+V(x)u=u^p,\quad
\text{in}\ \mathbb{R}^{N}, \end{equation*} for $\varepsilon> 0$ sufficiently
small, $2s<N<4s$, $1<p<2^*_s-1$ and some mild assumptions on the function $V$.
The main difficulties are from the nonlocal operator mixed the nonlocal term,
which cause the corresponding unperturbed problem turns out to be a system of
partial differential equations, but not a single fractional Kirchhoff equation.
In the second part, under some assumptions on $V$, we show the local uniqueness
of positive multi-peak solutions by using the local Pohoz\v{a}ev identity.


title: Quantum Information Elicitation

abstract:
  In the classic scoring rule setting, a principal incentivizes an agent to
truthfully report their probabilistic belief about some future outcome. This
paper addresses the situation when this private belief, rather than a classical
probability distribution, is instead a quantum mixed state. In the resulting
quantum scoring rule setting, the principal chooses both a scoring function and
a measurement function, and the agent responds with their reported density
matrix. Several characterizations of quantum scoring rules are presented, which
reveal a familiar structure based on convex analysis. Spectral scores, where
the measurement function is given by the spectral decomposition of the reported
density matrix, have particularly elegant structure and connect to quantum
information theory. Turning to property elicitation, eigenvectors of the belief
are elicitable, whereas eigenvalues and entropy have maximal elicitation
complexity. The paper concludes with a discussion of other quantum information
elicitation settings and connections to the literature.


title:
Steric hindrance in the on-surface synthesis of diethynyl-linked
  anthracene polymers

abstract:
  Hybrid sp-sp2 structures can be efficiently obtained on metal substrates via
on-surface synthesis. The choice of both the precursor and of the substrate
impacts on the effectiveness of the process and the stability of the formed
structures. Here we demonstrate that using anthracene-based molecules as
precursor, the formation on Au(111) of polymers hosting sp carbon chains is
affected by the steric hindrance between aromatic groups. In particular, by
scanning tunneling microscopy and density functional theory calculations we
show that the de-metalation of organometallic structures induces a lateral
separation of adjacent polymers preventing the formation of ordered domains.


title:
Robust Dynamic Walking for a 3D Dual-SLIP Model under One-Step
  Unilateral Stiffness Perturbations: Towards Bipedal Locomotion over Compliant
  Terrain

abstract:
  Bipedal walking is one of the most important hallmarks of human that robots
have been trying to mimic for many decades. Although previous control
methodologies have achieved robot walking on some terrains, there is a need for
a framework allowing stable and robust locomotion over a wide range of
compliant surfaces. This work proposes a novel biomechanics-inspired controller
that adjusts the stiffness of the legs in support for robust and dynamic
bipedal locomotion over compliant terrains. First, the 3D Dual-SLIP model is
extended to support for the first time locomotion over compliant surfaces with
variable stiffness and damping parameters. Then, the proposed controller is
compared to a Linear-Quadratic Regulator (LQR) controller, in terms of
robustness on stepping on soft terrain. The LQR controller is shown to be
robust only up to a moderate ground stiffness level of 174 kN/m, while it fails
in lower stiffness levels. On the contrary, the proposed controller can produce
stable gait in stiffness levels as low as 30 kN/m, which results in a vertical
ground penetration of the leg that is deeper than 10% of its rest length. The
proposed framework could advance the field of bipedal walking, by generating
stable walking trajectories for a wide range of compliant terrains useful for
the control of bipeds and humanoids, as well as by improving controllers for
prosthetic devices with tunable stiffness.


title: Uncertainty Estimation for Language Reward Models

abstract:
  Language models can learn a range of capabilities from unsupervised training
on text corpora. However, to solve a particular problem (such as text
summarization) it is typically necessary to fine-tune them on a task-specific
dataset. It is often easier for humans to choose between options than to
provide labeled data, and prior work has achieved state-of-the-art performance
by training a reward model from such preference comparisons. However,
collecting a large preference comparison dataset is still expensive -- and the
learned reward models are unreliable out-of-distribution. We seek to address
these problems via uncertainty estimation, which can improve sample efficiency
and robustness using active learning and risk-averse reinforcement learning
(RL). Specifically, we use bootstrap aggregating (bagging) to train an ensemble
of reward models differing in the initialization of their final layer.
Ensembles have proved successful in prior applications of active learning, but
we find that in our setting ensemble active learning does not outperform random
sampling. Further experiments show that while the aggregate predictions are
well-calibrated, the ensemble's estimated epistemic uncertainty is only weakly
correlated with model error. We suspect this is because the ensemble members
are fine-tuned from a single model and so are similar to one another. This
suggests current pre-training methods will need to be modified to support
uncertainty estimation, e.g. by training multiple language models.