title:
Generalizable Person Re-Identification via Self-Supervised Batch Norm
  Test-Time Adaption

abstract:
  In this paper, we investigate the generalization problem of person
re-identification (re-id), whose major challenge is the distribution shift on
an unseen domain. As an important tool of regularizing the distribution, batch
normalization (BN) has been widely used in existing methods. However, they
neglect that BN is severely biased to the training domain and inevitably
suffers the performance drop if directly generalized without being updated. To
tackle this issue, we propose Batch Norm Test-time Adaption (BNTA), a novel
re-id framework that applies the self-supervised strategy to update BN
parameters adaptively. Specifically, BNTA quickly explores the domain-aware
information within unlabeled target data before inference, and accordingly
modulates the feature distribution normalized by BN to adapt to the target
domain. This is accomplished by two designed self-supervised auxiliary tasks,
namely part positioning and part nearest neighbor matching, which help the
model mine the domain-aware information with respect to the structure and
identity of body parts, respectively. To demonstrate the effectiveness of our
method, we conduct extensive experiments on three re-id datasets and confirm
the superior performance to the state-of-the-art methods.


title: Essay on modality across different logics

abstract:
  In this paper, we deal with the problem of putting together modal worlds that
operate in different logic systems. When evaluating a modal sentence $\Box
\varphi$, we argue that it is not sufficient to inspect the truth of $\varphi$
in accessed worlds (possibly in different logics). Instead, ways of
transferring more subtle semantic information between logical systems must be
established. Thus, we will introduce modal structures that accommodate
communication between logic systems by fixing a common lattice $L$ where
different logics build their semantics. The semantics of each logic being
considered in the modal structure is a sublattice of $L$. In this system,
necessity and possibility of a statement should not solely rely on the
satisfaction relation in each world and the accessibility relation. The value
of a formula $\Box \varphi$ will be defined in terms of a comparison between
the values of $\varphi$ in accessible worlds and the common lattice $L$. We
will investigate natural instances where formulas $\varphi$ can be said to be
necessary$/$possible even though all accessible world falsify $\varphi$.
Finally, we will discuss frames that characterize dynamic relations between
logic systems: classically increasing, classically decreasing and dialectic
frames.


title:
Advancing an Interdisciplinary Science of Conversation: Insights from a
  Large Multimodal Corpus of Human Speech

abstract:
  People spend a substantial portion of their lives engaged in conversation,
and yet our scientific understanding of conversation is still in its infancy.
In this report we advance an interdisciplinary science of conversation, with
findings from a large, novel, multimodal corpus of 1,656 recorded conversations
in spoken English. This 7+ million word, 850 hour corpus totals over 1TB of
audio, video, and transcripts, with moment-to-moment measures of vocal, facial,
and semantic expression, along with an extensive survey of speaker post
conversation reflections. We leverage the considerable scope of the corpus to
(1) extend key findings from the literature, such as the cooperativeness of
human turn-taking; (2) define novel algorithmic procedures for the segmentation
of speech into conversational turns; (3) apply machine learning insights across
various textual, auditory, and visual features to analyze what makes
conversations succeed or fail; and (4) explore how conversations are related to
well-being across the lifespan. We also report (5) a comprehensive mixed-method
report, based on quantitative analysis and qualitative review of each
recording, that showcases how individuals from diverse backgrounds alter their
communication patterns and find ways to connect. We conclude with a discussion
of how this large-scale public dataset may offer new directions for future
research, especially across disciplinary boundaries, as scholars from a variety
of fields appear increasingly interested in the study of conversation.


title:
Visualizing the out-of-plane electronic dispersions in an intercalated
  transition metal dichalcogenide

abstract:
  Layered transition metal dichalcogenides have rich phase diagram and they
feature two dimensionality on numerous physical properties. Co1/3NbS2 is one of
the newest members of this family where Co atoms are intercalated into the Van
der Waals gaps between NbS2 layers. We study the three-dimensional electronic
band structure of Co1/3NbS2 using both surface and bulk sensitive
angle-resolved photoemission spectroscopy. We show that the electronic bands do
not fit into the rigid-band-shift picture after the Co intercalation. Instead,
Co1/3NbS2 displays a different orbital character near the Fermi level compared
to the pristine NbS2 compound and has a clear band dispersion in kz direction
despite its layered structure. Our photoemission study demonstrates the
out-of-plane electronic correlations introduced by the Co intercalation, thus
offering a new perspective on this compound. Finally, we propose how Fermi
level tuning could lead to exotic phases such as spin density wave instability.


title:
The Schwinger function, confinement and positivity violation in pure
  gauge QED

abstract:
  The lattice regularized pure gauge compact U(1) theory is an ideal laboratory
to explore how confinement is realized as its phase diagram has a confined and
a deconfined phase that depends on the value of the coupling constant. Herein,
the connection between confinement and positivity violation through the
Schwinger function associated with the Landau gauge photon propagator is
investigated. The simulations reported show a very clear link between the
realization of confinement and positivity violation of the photon Schwinger
function and, therefore, of the photon K\"all\'en-Lehmann spectral density.
Furthermore, a mass scale that characterizes the decay of the Schwinger
function for small time separations is computed and used to distinguish the two
phases of the theory.


title:
Radiative Plateau Inflation with Conformal Invariance: Dynamical
  Generation of Electroweak and Seesaw Scales

abstract:
  We investigate in scale-invariant $B-L$ scenario where the Standard Model
(SM) is supplemented with a dark scalar $\phi$ associated with gauge \& Yukawa
interactions, described by the couplings $g_{BL}$ and $y$ respectively, leading
to radiative plateau inflation at scale $\phi=M$ in the ultraviolet (UV), while
dynamically generating the Electroweak and Seesaw scales via Coleman-Weinberg
in the infrared (IR). This is particularly achieved implementing threshold
correction at an energy scale $\mu_T$ arising due to the presence of
vector-like fermions. We show that implementing the inflationary observables
makes the couplings solely dependent on the plateau scale or inflection-point
scale $M$, leaving us with only two independent parameters $M$ and $\mu_T$.
Within the theoretically consistent parameter space regions defined by
$m_{Z_{BL}} > 850~\rm GeV$, from the assumption of independent evolution of the
dark sector couplings from the SM couplings and $M < 5.67$ times the Planck
scale ($M_P$) required for the realisation of inflationary plateau-like
behaviour of the potential around $\phi=M$, we identify the parameter space
that is excluded by current LHC results involving searches for the heavy
$Z_{BL}$ boson. For typical benchmark points in the viable parameter regions,
we estimate the reheating temperature to be $\sim\mathcal{O}(TeV)$ thus
consistent with the standard Big Bang Nucleosynthesis (BBN) constraints. For
typical benchmark points ($M=5.67,~1,~0.1~M_P$) we predict the scales of
inflation to be $\mathcal{H}_{inf}=2.79\times10^{12}$ GeV, $1.53\times10^{10}$
GeV and $1.53\times10^7$ GeV respectively.


title: Metastable Melting of Superconductivity in an Iron-Based Superconductor

abstract:
  The recently observed photoinduced metastable state in the iron-chalcogenide
superconductors has emerged as a novel platform to study the interactions that
lead to superconductivity. Here we directly observe the photoinduced changes to
the electronic band structure with high energy resolution to determine the
relationship between the metastability and superconductivity. Our results show
the simultaneous destruction of the superconducting gap and superconducting
peak that are consistent with the melting of superconductivity on ultrafast
timescales. Furthermore, these changes persist for > 100 ps, indicating their
metastability. We propose that the metastable state involves double stripe
antiferromagnetic correlations that are known to compete with
superconductivity, and that this new magnetic configuration is a byproduct of
the orbital redistribution resulting from photoexcitation.


title:
Details of Second-Order Partial Derivatives of Rigid-Body Inverse
  Dynamics

abstract:
  This document provides full details of second-order partial derivatives of
rigid-body inverse dynamics. Several properties and identities using an
extension of Spatial Vector Algebra for tensorial use are listed, along with
their detailed derivations. Using those, the expressions for second-order
derivatives are derived step-by-step in detail. The expressions build upon
previous work by the authors on first-order partial derivatives of inverse
dynamics.


title:
CrossPoint: Self-Supervised Cross-Modal Contrastive Learning for 3D
  Point Cloud Understanding

abstract:
  Manual annotation of large-scale point cloud dataset for varying tasks such
as 3D object classification, segmentation and detection is often laborious
owing to the irregular structure of point clouds. Self-supervised learning,
which operates without any human labeling, is a promising approach to address
this issue. We observe in the real world that humans are capable of mapping the
visual concepts learnt from 2D images to understand the 3D world. Encouraged by
this insight, we propose CrossPoint, a simple cross-modal contrastive learning
approach to learn transferable 3D point cloud representations. It enables a
3D-2D correspondence of objects by maximizing agreement between point clouds
and the corresponding rendered 2D image in the invariant space, while
encouraging invariance to transformations in the point cloud modality. Our
joint training objective combines the feature correspondences within and across
modalities, thus ensembles a rich learning signal from both 3D point cloud and
2D image modalities in a self-supervised fashion. Experimental results show
that our approach outperforms the previous unsupervised learning methods on a
diverse range of downstream tasks including 3D object classification and
segmentation. Further, the ablation studies validate the potency of our
approach for a better point cloud understanding. Code and pretrained models are
available at http://github.com/MohamedAfham/CrossPoint.


title: A General Framework for Distributed Partitioned Optimization

abstract:
  Decentralized optimization is widely used in large scale and privacy
preserving machine learning and various distributed control and sensing
systems. It is assumed that every agent in the network possesses a local
objective function, and the nodes interact via a communication network. In the
standard scenario, which is mostly studied in the literature, the local
functions are dependent on a common set of variables, and, therefore, have to
send the whole variable set at each communication round. In this work, we study
a different problem statement, where each of the local functions held by the
nodes depends only on some subset of the variables.
  Given a network, we build a general algorithm-independent framework for
decentralized partitioned optimization that allows to construct algorithms with
reduced communication load using a generalization of Laplacian matrix.
Moreover, our framework allows to obtain algorithms with non-asymptotic
convergence rates with explicit dependence on the parameters of the network,
including accelerated and optimal first-order methods. We illustrate the
efficacy of our approach on a synthetic example.


title:
ONIX: an X-ray deep-learning tool for 3D reconstructions from sparse
  views

abstract:
  Three-dimensional (3D) X-ray imaging techniques like tomography and confocal
microscopy are crucial for academic and industrial applications. These
approaches access 3D information by scanning the sample with respect to the
X-ray source. However, the scanning process limits the temporal resolution when
studying dynamics and is not feasible for some applications, such as surgical
guidance in medical applications. Alternatives to obtaining 3D information when
scanning is not possible are X-ray stereoscopy and multi-projection imaging.
However, these approaches suffer from limited volumetric information as they
only acquire a small number of views or projections compared to traditional 3D
scanning techniques. Here, we present ONIX (Optimized Neural Implicit X-ray
imaging), a deep-learning algorithm capable of retrieving 3D objects with
arbitrary large resolution from only a set of sparse projections. ONIX,
although it does not have access to any volumetric information, outperforms
current 3D reconstruction approaches because it includes the physics of image
formation with X-rays, and it generalizes across different experiments over
similar samples to overcome the limited volumetric information provided by
sparse views. We demonstrate the capabilities of ONIX compared to
state-of-the-art tomographic reconstruction algorithms by applying it to
simulated and experimental datasets, where a maximum of eight projections are
acquired. We anticipate that ONIX will become a crucial tool for the X-ray
community by i) enabling the study of fast dynamics not possible today when
implemented together with X-ray multi-projection imaging, and ii) enhancing the
volumetric information and capabilities of X-ray stereoscopic imaging in
medical applications.

categories: eess.IV physics.data-an physics.med-ph physics.optics


title: Conformal submersions whose total manifolds admit a Ricci soliton

abstract:
  In this paper, we study conformal submersions from Ricci solitons to
Riemannian manifolds. First, we study some properties of O' Neill tensor $A$,
which are changed in the case of conformal submersion. We also find a necessary
and sufficient condition for conformal submersion to be totally geodesic and
calculate Ricci tensors for such map with different conditions. Further, we
consider conformal submersion $F:M \to N$ from a Ricci soliton to a Riemannian
manifold and obtain necessary conditions for fibers of $F$ and base manifold
$N$ to be Ricci soliton, almost Ricci soliton and Einstein. Moreover, we find
necessary conditions for a vector field and it's horizontal lift to be
conformal on $N$ and $(KerF_\ast)^\bot,$ respectively. Also, we calculate the
scalar curvature of Ricci soliton $M$. Finally, we obtain a necessary and
sufficient condition for $F$ to be harmonic.


title:
Awareness and use of quantitative decision-making methods in
  pharmaceutical development

abstract:
  The pharmaceutical industry has experienced increasing costs and sustained
high attrition rates in drug development over the last years. One proposal that
addresses this challenge from a statistical perspective is the use of
quantitative decision-making (QDM) methods to support a data-driven, objective
appraisal of the evidence that forms the basis of decisions at different
development levels. Growing awareness among statistical leaders in the industry
has led to the creation of the European EFSPI/PSI special interest group (ESIG)
on quantitative decision making to share experiences, collect best practices,
and promote the use of QDM. In this paper, we introduce key components of QDM
and present examples of QDM methods on trial, program, and portfolio level. The
ESIG created a questionnaire to learn how and to what extent QDM methods are
currently used in the different development phases. We present the main
questionnaire findings, and we show where QDM is already used today but also
where areas for future improvement can be identified. In particular,
statisticians should increase their visibility, involvement, and leadership in
cross-functional decision-making.


title:
Investigation of Mixed Irradiation Effects in p-MCz Thin Silicon
  Microstrip Detector for the HL-LHC Experiments

abstract:
  A lot of R & D work is carried out in the CERN RD50 Collaboration to find out
the best material for the Si detectors that can be used in the harsh radiation
environment of HL-LHC, n and p-MCz Si was identified as one of the prime
candidates as a material for strip detector that can be chosen the phase 2
upgrade plan of the new Compact Muon Solenoid tracker detector in 2026. For the
very first time, in this work, an advanced four level deep-trap mixed
irradiation model for p-MCz Si is proposed by the comparison of experimental
data on the full depletion voltage and leakage current to the Shockley Read
Hall recombination statistics results on the mixed irradiated p-MCz Si PAD
detector. In this work, we have determined the effective introduction rate
{\eta}eff of shallower donor deep trap E30 K using SRH theory calculations for
exp. Neff and that can shown the behavior of space charges and electric field
distribution in the p-MCz Si strip detector and compared its value with the
Neff of shallower donor deep trap E30 K in the n -MCz Si microstrip detector.
Prediction uncertainty in the p-MCz Si radiation damage mixed irradiation model
considered in the full depletion voltage and leakage current. A very good
agreement is observed in the experimental and SRH results. This radiation
damage models also used to extrapolate the value of the full depletion voltage
at different mixed (proton + neutron) higher irradiation fluences for the thin
p-MCz Si microstrip detector.


title:
TCAD Simulation and Design Optimization of Radiation Hard n-MCz and n-Fz
  Si Microstrip Detector for the HL-LHC

abstract:
  A radiation hard Si detector is used in the new CMS tracker detector at
HL-LHC. It has been observed that n-MCz and n-Fz Si as a material can be used
for the Si micro strip detector. The detector design for this material should
be simulated and optimized to get high CCE. In order to understand the charge
collection behavior of the n-MCz/n-FzSi detector, it is required to simulate
and compare the radiation damage effects in the mixed irradiated n-MCz Si and
neutron irradiated n-FzSi micro strip detector equipped with metal overhang and
multiple guard rings. In this paper, we have done analysis and optimization of
the radiation hard n-MCz Si/n-Fz Si strip detector design for the HL-LHC
experiment in order to get high CCE.


title: Catalog of Properties of the First Isodynamic Point of a Triangle

abstract:
  The first isodynamic point of a triangle is one of many notable points
associated with a triangle. It is named X(15) in the Encyclopedia of Triangle
Centers. This paper surveys known results about this point and gives additional
properties that were discovered by computer.


title:
High-resolution absorption measurements with free-electron lasers using
  ghost spectroscopy

abstract:
  We demonstrate a simple and robust high-resolution ghost spectroscopy
approach for x-ray and extreme ultraviolet absorption spectroscopy at
free-electron laser sources. Our approach requires an on-line spectrometer
before the sample and a downstream bucket detector. We use this method to
measure the absorption spectrum of silicon, silicon carbide and silicon nitride
membranes in the vicinity of the silicon L2,3-edge. We show that ghost
spectroscopy allows the high-resolution reconstruction of the sample spectral
response using a coarse energy scan with self-amplified spontaneous emission
radiation. For the conditions of our experiment the energy resolution of the
ghost-spectroscopy reconstruction is higher than the energy resolution reached
by scanning the energy range by narrow spectral bandwidth radiation produced by
the seeded free-electron laser. When we set the photon energy resolution of the
ghost spectroscopy to be equal to the resolution of the measurement with the
seeded radiation, the measurement time with the ghost spectroscopy method is
shorter than scanning the photon energy with seeded radiation. The exact
conditions for which ghost spectroscopy can provide higher resolution at
shorter times than measurement with narrow band scans depend on the details of
the measurements and on the properties of the samples and should be addressed
in future studies.


title:
Kiloparsec view of a typical star-forming galaxy when the Universe was
  $\sim$1 Gyr old II. Regular rotating disk and evidence for baryon dominance
  on galactic scales

abstract:
  We present a kinematic analysis of the main-sequence galaxy HZ4 at $z=5.5$.
Our study is based on deep, spatially resolved observations of the [CII] 158
$\mu$m transition obtained with the Atacama Large Millimeter/Submillimeter
Array (ALMA). From the combined analysis of the disk morphology, the
two-dimensional velocity structure, and forward-modeling of the one-dimensional
velocity and velocity dispersion profiles, we conclude that HZ4 has a regular
rotating disk in place. The intrinsic velocity dispersion in HZ4 is high
($\sigma_{0}=65.8^{+2.9}_{-3.3}$ km s$^{-1}$), and the ratio between the
rotational velocity and the intrinsic velocity dispersion is $V_{\rm
rot}/\sigma_{0}=2.2$. These values are consistent with the expectations from
the trends of increasing $\sigma_{0}$ and decreasing $V_{\rm rot}/\sigma_{0}$
as a function of redshift observed in main-sequence galaxies up to $z\approx4$.
Galaxy evolution models suggest that the high level of turbulence observed in
HZ4 can only be achieved if, in addition to stellar feedback, there is radial
transport of gas within the disk. Finally, we find that HZ4 is baryon dominated
on galactic scales ($\lesssim2\times R_{\rm e}$), with a dark matter fraction
at one effective radius of $f_{\rm DM}(R_{\rm e})=0.41^{+0.25}_{-0.22}$. This
value is comparable to the dark matter fractions found in lower redshift
galaxies that could be the descendants of HZ4: massive
($M_{\star}\approx10^{11}~M_{\odot}$), star-forming galaxies at $z\sim2$, and
passive, early type galaxies at $z\approx0$.


title:
From Images to Dark Matter: End-To-End Inference of Substructure From
  Hundreds of Strong Gravitational Lenses

abstract:
  Constraining the distribution of small-scale structure in our universe allows
us to probe alternatives to the cold dark matter paradigm. Strong gravitational
lensing offers a unique window into small dark matter halos ($<10^{10}
M_\odot$) because these halos impart a gravitational lensing signal even if
they do not host luminous galaxies. We create large datasets of strong lensing
images with realistic low-mass halos, Hubble Space Telescope (HST)
observational effects, and galaxy light from HST's COSMOS field. Using a
simulation-based inference pipeline, we train a neural posterior estimator of
the subhalo mass function (SHMF) and place constraints on populations of lenses
generated using a separate set of galaxy sources. We find that by combining our
network with a hierarchical inference framework, we can both reliably infer the
SHMF across a variety of configurations and scale efficiently to populations
with hundreds of lenses. By conducting precise inference on large and complex
simulated datasets, our method lays a foundation for extracting dark matter
constraints from the next generation of wide-field optical imaging surveys.


title:
Tunable Nonlinearity and Efficient Harmonic Generation from a Strongly
  Coupled Light-Matter System

abstract:
  Strong light-matter coupling within electromagnetic environments provides a
promising path to modify and control chemical and physical processes. The
origin of the enhancement of nonlinear optical processes such as
second-harmonic and third-harmonic generation (SHG and THG) due to strong
light-matter coupling is attributed to distinct physical effects which
questions the relevance of strong coupling in these processes. In this work, we
leverage a first-principles approach to investigate the origins of the
experimentally observed enhancement of resonant SHG and THG under strong
light-matter coupling. We find that the enhancement of the nonlinear conversion
efficiency has its origins in a modification of the associated nonlinear
optical susceptibilities as polaritonic resonances emerge in the nonlinear
spectrum. Further, we find that the nonlinear conversion efficiency can be
tuned by increasing the light-matter coupling strength. Finally, we provide a
general framework to compute the harmonic generation spectra from the
displacement field as opposed to the standard approach which computes the
harmonic spectrum from the matter-only induced polarization. Our results
address a key debate in the field, and pave the way for predicting and
understanding quantum nonlinear optical phenomena in strongly coupled
light-matter systems.


title: Observing planet-driven dust spirals with ALMA

abstract:
  ALMA continuum observations of thermal emission from the dust component of
protoplanetary disks have revealed an abundance of substructures that may be
interpreted as evidence for embedded planets, but planet-driven spiral arms --
perhaps one of the most compelling lines of evidence -- have proven
comparatively elusive. In this work, we test the capabilities of ALMA to detect
the planet-driven spiral signal in continuum emission. Carrying out
hydrodynamic simulations and radiative transfer calculations, we present
synthetic Band 7 continuum images for a wide range of disk and observing
conditions. We show that thermal mass planets at tens of au typically drive
spirals detectable within a few hours of integration time, and the detectable
planet mass may be as low as $\sim$Neptune mass ($0.3 \, M_{\rm th}$). The
grains probed by ALMA form spirals morphologically identical to the underlying
gas spiral. The temperature of the dust spiral is crucial in determining its
contrast, and spirals are easier to detect in disks with an adiabatic equation
of state and longer cooling times. Resolving the spiral is not necessary for
its detection; with the help of residual maps, the optimal beam size is a few
times the spiral width at a constant noise level. Finally, we show how the
presence of gaps and rings can impair our ability to recognize co-located
spirals. Our work demonstrates the planet-finding potential of the current
design specification of ALMA, and suggests that observing capability is not the
bottleneck in searching for spirals induced by thermal mass planets.


title: Gravitational Waves from Incomplete Inflationary Phase Transitions

abstract:
  We study the observable implications of an incomplete first order phase
transition during inflation. In such a phase transition, the nucleated bubbles
do not percolate and instead are continuously produced until the onset of
reheating. The process creates an inhomogeneity with a distinct power spectrum
that depends on both the physics of the phase transition and the inflationary
dynamics. Upon horizon re-entry, this spectrum generates gravitational waves
through non-linear effects. This stochastic gravitational wave background is
predicted to have unique signatures that may be detectable by future
experiments spanning a wide frequency range. The discovery of such a
gravitational wave signal would shed a light on the detailed dynamics of
inflation.


title: Cuspy dark matter density profiles in massive dwarf galaxies

abstract:
  Rotation curves of galaxies probe their total mass distributions, including
dark matter. Dwarf galaxies are excellent systems to investigate the dark
matter density distribution, as they tend to have larger fractions of dark
matter compared to higher mass systems. The core-cusp problem describes the
discrepancy found in the slope of the dark matter density profile in the
centres of galaxies ($\beta^*$) between observations of dwarf galaxies
(shallower cores) and dark matter-only simulations (steeper cusps). We
investigate $\beta^*$ in six nearby spiral dwarf galaxies for which
high-resolution CO $J=1-0$ data were obtained with ALMA. We derive rotation
curves and decompose the mass profile of the dark matter using our CO rotation
curves as a tracer of the total potential and 4.5$\mu$m photometry to define
the stellar mass distribution. We find $\langle\beta^*\rangle = 0.6$ with a
standard deviation of $\pm0.1$ among the galaxies in this sample, in agreement
with previous measurements in this mass range. The galaxies studied are on the
high stellar mass end of dwarf galaxies and have cuspier profiles than lower
mass dwarfs, in agreement with other observations. When the same definition of
the slope is used, we observe steeper slopes than predicted by the FIRE and
NIHAO simulations. This may signal that these relatively massive dwarfs
underwent stronger gas inflows toward their centres than predicted by these
simulations, that these simulations over-predict the frequency of accretion or
feedback events, or that a combination of these or other effects are at work.


title: Calibrations of the Compton Spectrometer and Imager

abstract:
  The Compton Spectrometer and Imager (COSI) is a balloon-borne soft
$\gamma$-ray telescope (0.2-5 MeV) designed to study astrophysical sources.
COSI employs a compact Compton telescope design and is comprised of twelve
high-purity germanium semiconductor detectors. Tracking the locations and
energies of $\gamma$-ray scatters within the detectors permits high-resolution
spectroscopy, direct imaging over a wide field-of-view, polarization studies,
and effective suppression of background events. Critical to the precise
determination of each interaction's energy, position, and the subsequent event
reconstruction are several calibrations conducted in the field before launch.
Additionally, benchmarking the instrument's higher-level performance through
studies of its angular resolution, effective area, and polarization sensitivity
quantifies COSI's scientific capabilities. In May 2016, COSI became the first
science payload to be launched on NASA's superpressure balloon and was slated
for launch again in April 2020. Though the 2020 launch was canceled due to the
COVID-19 pandemic, the COSI team took calibration measurements prior to
cancellation. In this paper we provide a detailed overview of COSI
instrumentation, describe the calibration methods, and compare the calibration
and benchmarking results of the 2016 and 2020 balloon campaigns. These
procedures will be integral to the calibration and benchmarking of the NASA
Small Explorer satellite version of COSI scheduled to launch in 2025.


title: UV-Complete Gravity Amplitudes and the Triple Product

abstract:
  We construct an infinite class of new ultraviolet-complete four-graviton
scattering amplitudes that reduce to Einstein gravity at low energies, vanish
at high energies, are meromorphic, and exhibit a triple-product structure
${\cal A}(s){\cal A}(t){\cal A}(u)$. The spectrum invariantly exhibits
accumulation points in the form of infinite towers of states on each mass pole,
whose residue can be positively expanded on tree-level exchanges of irreducible
representations of the Lorentz group.


title:
Automated reconstruction of bound states in bilayer graphene quantum
  dots

abstract:
  Bilayer graphene is a nanomaterial that allows for well-defined, separated
quantum states to be defined by electrostatic gating and, therefore, provides
an attractive platform to construct tunable quantum dots. When a magnetic field
perpendicular to the graphene layers is applied, the graphene valley degeneracy
is lifted, and splitting of the energy levels of the dot is observed. Given the
experimental ability to engineer this energy valley splitting, bilayer graphene
quantum dots have a great potential for hosting robust qubits. Although bilayer
graphene quantum dots have been recently realized in experiments, it is
critically important to devise robust methods that can identify the observed
quantum states from accessible measurement data. Here, we develop an efficient
algorithm for extracting the model parameters needed to characterize the states
of a bilayer graphene quantum dot completely. We introduce a Hamiltonian-guided
random search method and demonstrate robust identification of quantum states on
both simulated and experimental data.


title:
Towards a SAT Encoding for Quantum Circuits: A Journey From Classical
  Circuits to Clifford Circuits and Beyond

abstract:
  Satisfiability Testing (SAT) techniques are well-established in classical
computing where they are used to solve a broad variety of problems, e.g., in
the design of classical circuits and systems. Analogous to the classical realm,
quantum algorithms are usually modelled as circuits and similar design tasks
need to be tackled. Thus, it is natural to pose the question whether these
design tasks in the quantum realm can also be approached using SAT techniques.
To the best of our knowledge, no SAT formulation for arbitrary quantum circuits
exists and it is unknown whether such an approach is feasible at all. In this
work, we define a propositional SAT encoding that, in principle, can be applied
to arbitrary quantum circuits. However, we show that due to the inherent
complexity of representing quantum states, constructing such an encoding is not
feasible in general. Therefore, we establish general criteria for determining
the feasibility of the proposed encoding and identify classes of quantum
circuits fulfilling these criteria. We explicitly demonstrate how the proposed
encoding can be applied to the class of Clifford circuits as a representative.
Finally, we empirically demonstrate the applicability and efficiency of the
proposed encoding for Clifford circuits. With these results, we lay the
foundation for continuing the ongoing success of SAT in classical circuit and
systems design for quantum circuits.


title:
Nature of supersonic turbulence and density distribution function in the
  multiphase interstellar medium

abstract:
  Supersonic flows in the interstellar medium (ISM) are believed to be a key
driver of the molecular cloud formation and evolution. Among molecular clouds'
properties, the ratio between the solenoidal and compressive modes of
turbulence plays important roles in determining the star formation efficiency.
We use numerical simulations of supersonic converging flows of the warm neutral
medium (WNM) resolving the thermal instability to calculate the early phase of
molecular cloud formation, and investigate the turbulence structure and the
density probability distribution function (density PDF) of the multiphase ISM.
We find that both the solenoidal and compressive modes have their power
spectrum similar to the Kolmogorov spectrum. The solenoidal (compressive) modes
account for >~80% (<~20%) of the total turbulence power. When we consider both
the cold neutral medium (CNM) and the thermally unstable neutral medium (UNM)
up to T <~ 400 K, the density PDF follows the log-normal distribution whose
width sigma_s is well explained by the known relation from the isothermal
turbulence as sigma_s = ln(1 + b^2 * M^2) (where b is the parameter
representing the turbulence mode ratio and M is the turbulent Mach number). The
density PDF of the CNM component alone (T <= 50 K), however, exhibits a
narrower sigma_s by a factor of ~ 2. These results suggest that observational
estimations of b based on the CNM density PDF requires the internal turbulence
within each CNM clump but not the inter-clump relative velocity, the latter of
which is instead powered by the WNM/UNM turbulence.


title:
Quasi-local energy and microcanonical entropy in two-dimensional nearly
  de Sitter gravity

abstract:
  We study the semi-classical thermodynamics of two-dimensional de Sitter space
($\text{dS}_{2}$) in Jackiw-Teitelboim (JT) gravity coupled to conformal
matter. We extend the quasi-local formalism of Brown and York to
$\text{dS}_{2}$, where a timelike boundary is introduced in the static patch to
uniquely define conserved charges, including quasi-local energy. The boundary
divides the static patch into two systems, a cosmological system and a black
hole system, the former being unstable under thermal fluctuations while the
latter is stable. A semi-classical quasi-local first law is derived, where the
Gibbons--Hawking entropy is replaced by the generalized entropy. In the
microcanonical ensemble the generalized entropy is stationary. Further, we show
the on-shell Euclidean microcanonical action of a causal diamond in
semi-classical JT gravity equals minus the generalized entropy of the diamond,
hence extremization of the entropy follows from minimizing the action. Thus, we
provide a first principles derivation of the island rule for $U(1)$ symmetric
$\text{dS}_{2}$ backgrounds, without invoking the replica trick. We discuss the
implications of our findings for static patch de Sitter holography.


title: Simulating continuous symmetry models with discrete ones

abstract:
  It is known that frustrated systems display unusual properties. Even the
seemingly weak contribution of topological frustration can bring an anisotropic
Heisenberg chain, which is defined by discrete local symmetries, to develop
characteristics that are usually associated to continuous symmetries and a
gapless spectrum, such as a finite Fermi momentum. We show that, in a region of
this model's phase diagram, an adiabatic change in the system's parameters
causes a singular orthogonality catastrophe so that neighboring ground states
are exactly orthogonal for any system size, due to a flow in the states'
quantum numbers. The chain also acquires unusual features such as ground states
with non-zero chirality and a breaking of invariance by translation. However,
the amplitude of these attributes decay to zero in the thermodynamic limit.
This region of parameter space (chiral region) is separated from the rest by a
transition line where also any non-analyticity vanishes in the thermodynamic
limit, even though the ground state degeneracy changes crossing this line. We
pose that this line can constitute a boundary-BKT-like transition.


title: Isotope dependence of muon-to-electron conversion

abstract:
  The lepton-flavor-violating conversion of a muon into an electron in the
field of a nucleus is one of the most sensitive probes of physics beyond the
Standard Model and the experiments Mu2e, COMET, and DeeMe will explore
uncharted terrain in the near future. An observation of this $\mu^-\to e^-$
conversion process opens up the possibility to distinguish the underlying
operator or new-physics model by exploiting the target-nucleus dependence of
the conversion rate. To facilitate the choice of optimal targets we provide
$\mu^-\to e^-$ conversion rates for all stable isotopes and estimate
nuclear-structure uncertainties. Our results enable studies of mixed or
enriched target materials that are particularly promising for distinguishing
scenarios in which the muon converts either on protons or neutrons.


title: Simulation Paths for Quantum Circuit Simulation with Decision Diagrams

abstract:
  Simulating quantum circuits on classical computers is a notoriously hard, yet
increasingly important task for the development and testing of quantum
algorithms. In order to alleviate this inherent complexity, efficient data
structures and methods such as tensor networks and decision diagrams have been
proposed. However, their efficiency heavily depends on the order in which the
individual computations are performed. For tensor networks the order is defined
by so-called contraction plans and a plethora of methods has been developed to
determine suitable plans. On the other hand, simulation based on decision
diagrams is mostly conducted in a straight-forward, i.e., sequential, fashion
thus far. In this work, we study the importance of the path that is chosen when
simulating quantum circuits using decision diagrams. We propose an open-source
framework that not only allows to investigate dedicated simulation paths, but
also to re-use existing findings, e.g., obtained from determining contraction
plans for tensor networks. Experimental evaluations show that translating
strategies from the domain of tensor networks can already yield speedups of up
to several orders of magnitude compared to the state of the art. Furthermore,
we show, both conceptually and experimentally, that choosing the right
simulation path can result in runtimes practically independent of the number of
qubits -- as opposed to exponential runtimes using the state-of-the-art
approach. The proposed framework is publicly available at
github.com/cda-tum/ddsim.


title: The Kohnen-Zagier formula for Maass forms for $\Gamma_0(4)$

abstract:
  We extend a formula of Duke, Imam\=oglu, and T\'oth (which itself is a
generalization of the Katok-Sarnak formula) to prove the Kohnen-Zagier formula
for Maass forms for $\Gamma_0(4)$.


title: The Complete Picard Vessiot Closure

abstract:
  Let F be a differential field with field of constants C. We assume C to be
algebraically closed and of characteristic 0. The complete Picard--Vessiot
closure of F is a differential field extension of F with the same constants C
as F, which has no Picard--Vessiot extensions, and is minimal over F with these
properties. There is a correspondence between subfields of the complete
Picard--Vessiot closure and subgroups of its differential automorphism group,
which arises because the complete Picard--Vessiot closure comes from F via
repeated Picard--Vessiot extensions. This correspondence also obtains for
certain normal subfields of the complete Picard--Vessiot closure, fields which
can be characterized independently of their embedding in the complete
Picard--Vessiot closure.


title:
Composable security for continuous variable quantum key distribution:
  Trust levels and practical key rates in wired and wireless networks

abstract:
  Continuous variable (CV) quantum key distribution (QKD) provides a powerful
setting for secure quantum communications, thanks to the use of
room-temperature off-the-shelf optical devices and the potential to reach much
higher rates than the standard discrete-variable counterpart. In this work, we
provide a general framework for studying the composable finite-size security of
CV-QKD with Gaussian-modulated coherent-state protocols under various levels of
trust for the loss and noise experienced by the parties. Our study considers
both wired (i.e., fiber-based) and wireless (i.e., free-space) quantum
communications. In the latter case, we show that high key rates are achievable
for short-range optical wireless (LiFi) in secure quantum networks with both
fixed and mobile devices. Finally, we extend our investigation to microwave
wireless (WiFi) discussing security and feasibility of CV-QKD for very
short-range applications.


title:
Advanced Methods for Connectome-Based Predictive Modeling of Human
  Intelligence: A Novel Approach Based on Individual Differences in Cortical
  Topography

abstract:
  Individual differences in human intelligence can be modeled and predicted
from in vivo neurobiological connectivity. Many established modeling frameworks
for predicting intelligence, however, discard higher-order information about
individual differences in brain network topology, and show only moderate
performance when generalized to make predictions in out-of-sample subjects. In
this paper, we propose that connectome-based predictive modeling, a common
predictive modeling framework for neuroscience data, can be productively
modified to incorporate information about brain network topology and individual
differences via the incorporation of bagged decision trees and the network
based statistic. These modifications produce a novel predictive modeling
framework that leverages individual differences in cortical tractography to
generate accurate regression predictions of intelligence scores. Network
topology-based feature selection provides for natively interpretable networks
as input features, increasing the model's explainability. Investigating the
proposed modeling framework's efficacy, we find that advanced connectome-based
predictive modeling generates neuroscience predictions that account for a
significantly greater proportion of variance in general intelligence scores
than previously established methods, advancing our scientific understanding of
the network architecture that underlies human intelligence.


title: Ion Acceleration at Magnetotail Plasma Jets

abstract:
  We investigate a series of Earthward bursty bulk flows (BBFs) observed by the
Magnetospheric Multiscale (MMS) spacecraft in Earth's magnetotail at (-24, 7,
4) RE in Geocentric Solar Magnetospheric (GSM) coordinates. At the leading
edges of the BBFs, we observe complex magnetic field structures. In particular,
we focus on one which presents a chain of small scale (~0.5 RE)
dipolarizations, and another with a large scale (~3.5 RE) dipolarization.
Although the two structures have different scales, both of these structures are
associated with flux increases of supra-thermal ions with energies > 100 keV.
We investigate the ion acceleration mechanism and its dependence on the mass
and charge state. We show that the ions with gyroradii smaller than the scale
of the structure are accelerated by the ion bulk flow. We show that whereas in
the small scale structure, ions with gyroradii comparable with the scale of the
structure undergo resonance acceleration, and the acceleration in the larger
scale structure is more likely due to a spatially limited electric field.


title:
Radial perturbations of scalar-Gauss-Bonnet black holes beyond
  spontaneous scalarization

abstract:
  Spontaneous scalarization of black holes in scalar-Gauss-Bonnet (sGB) gravity
is a very interesting phenomenon allowing black holes to circumvent the no-hair
theorem and acquire scalar hair while leaving the weak field regime of the
theory practically unaltered. It was recently shown that if we allow for a
different form of the coupling between the scalar field and the Gauss-Bonnet
invariant, a new type of scalarization is possible beyond the standard one that
can be excited only nonlinearly. In this case the spectrum of black hole
solutions can be more complicated, naturally opening the question about their
stability. The goal of the present paper is to study this question including
the possible loss of hyperbolicity of the radial perturbation equation. We show
that one of the nonlinearly scalarized phases of the Schwarzschild black hole
can be stable and hyperbolic for large enough black hole masses. These results
establish the studied hairy black holes as viable astrophysical candidates.


title:
Understanding Effects of Algorithmic vs. Community Label on Perceived
  Accuracy of Hyper-partisan Misinformation

abstract:
  Hyper-partisan misinformation has become a major public concern. In order to
examine what type of misinformation label can mitigate hyper-partisan
misinformation sharing on social media, we conducted a 4 (label type:
algorithm, community, third-party fact-checker, and no label) X 2 (post
ideology: liberal vs. conservative) between-subjects online experiment (N =
1,677) in the context of COVID-19 health information. The results suggest that
for liberal users, all labels reduced the perceived accuracy and believability
of fake posts regardless of the posts' ideology. In contrast, for conservative
users, the efficacy of the labels depended on whether the posts were
ideologically consistent: algorithmic labels were more effective in reducing
the perceived accuracy and believability of fake conservative posts compared to
community labels, whereas all labels were effective in reducing their belief in
liberal posts. Our results shed light on the differing effects of various
misinformation labels dependent on people's political ideology.


title:
A fast continuous time approach with time scaling for nonsmooth convex
  optimization

abstract:
  In a Hilbert setting we study the convergence properties of a second order in
time dynamical system combining viscous and Hessian-driven damping with time
scaling in relation with the minimization of a nonsmooth and convex function.
The system is formulated in terms of the gradient of the Moreau envelope of the
objective function with time-dependent parameter. We show fast convergence
rates for the Moreau envelope and its gradient along the trajectory, and also
for the velocity of the system. From here we derive fast convergence rates for
the objective function along a path which is the image of the trajectory of the
system through the proximal operator of the first. Moreover, we prove the weak
convergence of the trajectory of the system to a global minimizer of the
objective function. Finally, we provide multiple numerical examples which
illustrate the theoretical results.


title:
SUPER Scheme in Action: Experimental Demonstration of Red-detuned
  Excitation of a Quantum Dot

abstract:
  The quest for the perfect single-photon source includes finding the optimal
protocol for exciting the quantum emitter. Based on a recently proposed,
so-called SUPER (swing-up of quantum emitter population) scheme, we demonstrate
experimentally that two red-detuned laser pulses, neither of which could yield
a significant upper-level population individually, lead to the coherent
excitation of a semiconductor quantum dot. We characterize the emitted single
photons and show that they have properties comparable to those achieved under
resonant excitation schemes.


title: Determining Research Priorities for Astronomy Using Machine Learning

abstract:
  We summarize the first exploratory investigation into whether Machine
Learning techniques can augment science strategic planning. We find that an
approach based on Latent Dirichlet Allocation using abstracts drawn from high
impact astronomy journals may provide a leading indicator of future interest in
a research topic. We show two topic metrics that correlate well with the
high-priority research areas identified by the 2010 National Academies'
Astronomy and Astrophysics Decadal Survey science frontier panels. One metric
is based on a sum of the fractional contribution to each topic by all
scientific papers ("counts") while the other is the Compound Annual Growth Rate
of these counts. These same metrics also show the same degree of correlation
with the whitepapers submitted to the same Decadal Survey.
  Our results suggest that the Decadal Survey may under-emphasize fast growing
research. A preliminary version of our work was presented by Thronson et al.
2021.


title:
Subspace-Based Pilot Decontamination in User-Centric Scalable Cell-Free
  Wireless Networks

abstract:
  We consider a cell-free wireless system operated in Time Division Duplex
(TDD) mode with localized user-centric clusters of remote radio units (RUs).
Since the uplink pilot dimensions per channel coherence slot is limited,
co-pilot users might incur mutual pilot contamination. In the current
literature, it is assumed that the long-term statistical knowledge of all user
channels is available. This enables MMSE channel estimation or simplified
dominant subspace projection, which achieves significant pilot decontamination
under certain assumptions on the channel covariance matrices. However,
estimating the channel covariance matrix or even just its dominant subspace at
all RUs forming a user cluster is not an easy task. In fact, if not properly
designed, a piloting scheme for such long-term statistics estimation will also
be subject to the contamination problem. In this paper, we propose a new
channel subspace estimation scheme explicitly designed for cell-free wireless
networks. Our scheme is based on 1) a sounding reference signal (SRS) using
latin squares wideband frequency hopping, and 2) a subspace estimation method
based on robust Principal Component Analysis (R-PCA). The SRS hopping scheme
ensures that for any user and any RU participating in its cluster, only a few
pilot measurements will contain strong co-pilot interference. These few heavily
contaminated measurements are (implicitly) eliminated by R-PCA, which is
designed to regularize the estimation and discount the "outlier" measurements.
Our simulation results show that the proposed scheme achieves almost perfect
subspace knowledge, which in turns yields system performance very close to that
with ideal channel state information, thus essentially solving the problem of
pilot contamination in cell-free user-centric TDD wireless networks.


title: Learning Robust Real-Time Cultural Transmission without Human Data

abstract:
  Cultural transmission is the domain-general social skill that allows agents
to acquire and use information from each other in real-time with high fidelity
and recall. In humans, it is the inheritance process that powers cumulative
cultural evolution, expanding our skills, tools and knowledge across
generations. We provide a method for generating zero-shot, high recall cultural
transmission in artificially intelligent agents. Our agents succeed at
real-time cultural transmission from humans in novel contexts without using any
pre-collected human data. We identify a surprisingly simple set of ingredients
sufficient for generating cultural transmission and develop an evaluation
methodology for rigorously assessing it. This paves the way for cultural
evolution as an algorithm for developing artificial general intelligence.


title:
Computing Bounds on $L_{\infty}$-induced Norm for Linear Time-Invariant
  Systems Using Homogeneous Lyapunov Functions

abstract:
  Quadratic Lyapunov function has been widely used in the analysis of linear
time invariant (LTI) systems ever since it has shown that the existence of such
quadratic Lyapunov function certifies the stability of the LTI system. In this
work, the problem of finding upper and lower bounds for the
$L_{\infty}$-induced norm of the LTI system is considered. Quadratic Lyapunov
functions are used to find the star norm, the best upper on the
$L_{\infty}$-induced norm, by bounding the unit peak input reachable sets by
inescapable ellipsoids. Instead, a more general class of homogeneous Lyapunov
functions is used to get less conservative upper bounds on the
$L_{\infty}$-induced norm and better conservative approximations for the
reachable sets than those obtained using standard quadratic Lyapunov functions.
The homogeneous Lyapunov function for the LTI system is considered to be a
quadratic Lyapunov function for a higher-order system obtained by Lifting the
LTI system via Kronecker product. Different examples are provided to show the
significant improvements on the bounds obtained by using Homogeneous Lyapunov
functions.


title: Twisted hybrid algorithms for combinatorial optimization

abstract:
  Proposed hybrid algorithms encode a combinatorial cost function into a
problem Hamiltonian and optimize its energy by varying over a set of states
with low circuit complexity. Classical processing is typically only used for
the choice of variational parameters following gradient descent. As a
consequence, these approaches are limited by the descriptive power of the
associated states.
  We argue that for certain combinatorial optimization problems, such
algorithms can be hybridized further, thus harnessing the power of efficient
non-local classical processing. Specifically, we consider combining a quantum
variational ansatz with a greedy classical post-processing procedure for the
MaxCut-problem on $3$-regular graphs. We show that the average cut-size
produced by this method can be quantified in terms of the energy of a modified
problem Hamiltonian. This motivates the consideration of an improved algorithm
which variationally optimizes the energy of the modified Hamiltonian. We call
this a twisted hybrid algorithm since the additional classical processing step
is combined with a different choice of variational parameters. We exemplify the
viability of this method using the quantum approximate optimization algorithm
(QAOA), giving analytic lower bounds on the expected approximation ratios
achieved by twisted QAOA. These show that the necessary non-locality of the
quantum ansatz can be reduced compared to the original QAOA: We find that for
levels $p=2,\ldots, 6$, the level $p$ can be reduced by one while roughly
maintaining the expected approximation ratio. This reduces the circuit depth by
$4$ and the number of variational parameters by $2$.


title: Isoperimetric problem for the first curl eigenvalue

abstract:
  We consider an isoperimetric problem involving the smallest positive and
largest negative curl eigenvalues on abstract ambient manifolds, with a focus
on the standard model spaces. We in particular show that the corresponding
eigenvalues on optimal domains, assuming optimal domains exist, must be simple
in the Euclidean and hyperbolic setting. This generalises a recent result by
Enciso and Peralta-Salas who showed the simplicity for axisymmetric optimal
domains with connected boundary in Euclidean space. We then generalise another
recent result by Enciso and Peralta-Salas, namely that the points of any
rotationally symmetric optimal domain with connected boundary in Euclidean
space which are closest to the symmetry axis must disconnect the boundary, to
the hyperbolic setting, as well as strengthen it in the Euclidean case by
getting rid of the connected boundary assumption. Lastly, we show how a second
variation inequality related to the isoperimetric problem may be used in order
to relate the existence of Killing-Beltrami fields to the geometry of the
ambient space.


title:
CHEMOUT: CHEMical complexity in star-forming regions of the OUTer
  Galaxy. I. Organic molecules and tracers of star-formation activity

abstract:
  The outer Galaxy is an environment with metallicity lower than the Solar one.
Because of this, the formation and survival of molecules in star-forming
regions located in the inner and outer Galaxy is expected to be different. To
gain understanding on how chemistry changes throughout the Milky Way, it is
crucial to observe outer Galaxy star-forming regions to constrain models
adapted for lower metallicity environments. In this paper we present a new
observational project: chemical complexity in star-forming regions of the outer
Galaxy (CHEMOUT). The goal is to unveil the chemical composition in 35 dense
molecular clouds associated with star-forming regions of the outer Galaxy
through observations obtained with the IRAM 30m telescope. In this first paper,
we present the sample, and report the detection at 3~mm of simple organic
species HCO+, H13CO+, HCN, c-C3H2, HCO, C4H, and HCS+, of the complex
hydrocarbon CH3CCH, and of SiO, CCS and SO. From c-C3H2, we estimate new
kinematic heliocentric and Galactocentric distances based on an updated
rotation curve of the Galaxy. The detection of the molecular tracers does not
seem to have a clear dependence on the Galactocentric distance. We also analyse
the HCO+ line profiles. We find high velocity wings in ~71% of the targets, and
their occurrence does not depend on the Galactocentric distance. Our results,
confirmed by a statistical analysis, show that the presence of organic
molecules and tracers of protostellar activity is ubiquitous in the
low-metallicity environment of the outer Galaxy. Based on this, and on the
additional evidence that small, terrestrial planets are omnipresent in the
Galaxy, we support previous claims that the definition of Galactic Habitable
Zone should be rediscussed in view of the ubiquitous capacity of the
interstellar medium to form organic molecules.


title: The Long Tails of the Pegasus-Pisces Arch Intermediate Velocity Cloud

abstract:
  We present hydrodynamic simulations of the Pegasus-Pisces (PP Arch), an
intermediate velocity cloud in our Galaxy. The PP Arch, also known as IVC
86-36, is unique among intermediate and high velocity clouds, because its twin
tails are unusually long and narrow. Its -50 km/s line-of-sight velocity
qualifies it as an intermediate velocity cloud, but the tails' orientations
indicate that the cloud's total three-dimensional speed is at least ~100 km/s.
This speed is supersonic in the Reynold's Layer and thick disk. We simulated
the cloud as it travels supersonically through the Galactic thick and thin
disks at an oblique angle relative to the midplane. Our simulated clouds grow
long double tails and reasonably reproduce the H I 21~cm intensity and velocity
of the head of the PP Arch. A bow shock protects each simulated cloud from
excessive shear and lowers its Reynolds number. These factors may similarly
protect the PP Arch and enable the survival of its unusually long tails. The
simulations predict the future hydrodynamic behavior of the cloud when it
collides with denser gas nearer to the Galactic midplane. It appears that the
PP Arch's fate is to deform, dissipate, and merge with the Galactic disk.


title:
Validation of the Reduced Unified Continuum Formulation Against In Vitro
  4D-Flow MRI

abstract:
  In our recent work, we introduced the reduced unified continuum formulation
for vascular fluid-structure interaction (FSI) and demonstrated enhanced solver
accuracy, scalability, and performance compared to conventional approaches. We
further verified the formulation against Womersley's deformable wall theory. In
this study, we assessed its performance in a compliant patient-specific aortic
model by leveraging 3D printing, 2D magnetic resonance imaging (MRI), and
4D-flow MRI to extract high-resolution anatomical and hemodynamic information
from an in vitro flow circuit. To accurately reflect experimental conditions,
we additionally enabled in-plane vascular motion at each inlet and outlet, and
implemented viscoelastic external tissue support and vascular tissue
prestressing. Validation of our formulation is achieved through close
quantitative agreement in pressures, lumen area changes, pulse wave velocity,
and early systolic velocities, as well as qualitative agreement in late
systolic flow structures. Our validated suite of FSI techniques can be used to
investigate vascular disease initiation, progression, and treatment at a
computational cost on the same order as that of rigid-walled simulations. This
study is the first to validate a cardiovascular FSI formulation against an in
vitro flow circuit involving a compliant vascular phantom of complex
patient-specific anatomy.


title: Media Modulation based Molecular Communication

abstract:
  In conventional molecular communication (MC) systems, the signaling molecules
used for information transmission are stored, released, and then replenished by
a transmitter (TX). However, the replenishment of signaling molecules at the TX
is challenging in practice. Furthermore, in most envisioned MC applications,
e.g., in the medical field, it is not desirable to insert the TX into the MC
system, as this might impair natural biological processes. In this paper, we
propose the concept of media modulation based MC where the TX is placed outside
the channel and utilizes signaling molecules already present inside the system.
The signaling molecules can assume different states which can be switched by
external stimuli. Hence, in media modulation based MC, the TX modulates
information into the state of the signaling molecules. In particular, we
exploit the group of photochromic molecules, which undergo light-induced
reversible state transitions, for media modulation. We study the usage of these
molecules for information transmission in a three-dimensional duct system,
which contains an eraser, a TX, and a receiver for erasing, writing, and
reading of information via external light, respectively. We develop a
statistical model for the received signal. We adopt a maximum likelihood
detector and show that it can be reduced to a threshold based detector.
Furthermore, we derive analytical expressions for the optimal threshold value
and the resulting bit error rate (BER), respectively. Finally, we investigate
the impact of various system parameters on the BER of media modulation based
MC. Both the statistical model and BER results are verified by computer
simulations. Our results reveal that media modulation enables reliable
information transmission, validating it as a promising alternative to MC based
on molecule emitting TXs.


title: The Energetics of the Central Engine in the Powerful Quasar, 3C298

abstract:
  The compact steep spectrum radio source, 3C 298, (redshift of 1.44) has the
largest 178 MHz luminosity in the 3CR (revised Third Cambridge Catalogue)
catalog; its radio lobes are among the most luminous in the Universe. The
plasma state of the radio lobes is modeled with the aid of interferometric
radio observations (in particular, the new Low Frequency Array observation and
archival MERLIN data) and archival single-station data. It is estimated that
the long-term time-averaged jet power required to fill these lobes with
leptonic plasma is $\overline{Q} \approx 1.28 \pm 0.51 \times 10^{47}
\rm{erg}\,\rm{s}^{-1}$, rivaling the largest time averaged jet powers from any
quasar. Supporting this notion of extraordinary jet power is a 0.5 keV -10 keV
luminosity of $\approx 5.2 \times 10^{46} \rm{erg}\, \rm{s}^{-1}$, comparable
to luminous blazars, yet there is no other indication of strong relativistic
beaming. We combine two new high signal to noise optical spectroscopic
observations from the Hobby-Eberly Telescope with archival Hubble Space
Telescope, Two Micron Survey and Galaxy Evolutionary Explorer data to compute a
bolometric luminosity from the accretion flow of $L_{\rm{bol}} \approx 1.55 \pm
0.15 \times 10^{47} \rm{erg} \,\rm{s}^{-1}$. The ratio,
$\overline{Q}/L_{\rm{bol}}\approx 1$, is the approximate upper limit for
quasars. Characteristic of a large $\overline{Q}/L_{\rm{bol}}$, we find an
extreme ultraviolet (EUV) spectrum that is very steep (the "EUV deficit" of
powerful radio quasars relative to radio quiet quasars) and this weak ionizing
continuum is likely a contributing factor to the relatively small equivalent
widths of the broad emission lines in this quasar.


title:
On The large Time Asymptotics of Schr\"odinger type equations with
  General Data

abstract:
  For the Schr\"odinger equation with general interaction term, which may be
linear or nonlinear, time dependent and including charge transfer potentials,
we prove the global solutions are asymptotically given by a free wave and a
weakly localized part. The proof is based on constructing in a new way the Free
Channel wave operator, and further tools from the recent works
\cite{Liu-Sof1,Liu-Sof2,SW2020}. This work generalizes the results of the first
part of \cite{Liu-Sof1,Liu-Sof2} to arbitrary dimension, and non-radial data.


title: A Conformer Based Acoustic Model for Robust Automatic Speech Recognition

abstract:
  This study addresses robust automatic speech recognition (ASR) by introducing
a Conformer-based acoustic model. The proposed model builds on a
state-of-the-art recognition system using a bi-directional long short-term
memory (BLSTM) model with utterance-wise dropout and iterative speaker
adaptation, but employs a Conformer encoder instead of the BLSTM network. The
Conformer encoder uses a convolution-augmented attention mechanism for acoustic
modeling. The proposed system is evaluated on the monaural ASR task of the
CHiME-4 corpus. Coupled with utterance-wise normalization and speaker
adaptation, our model achieves $6.25\%$ word error rate, which outperforms the
previous best system by $8.4\%$ relatively. In addition, the proposed
Conformer-based model is $18.3\%$ smaller in model size and reduces total
training time by $79.6\%$.


title: Minimal invariable generating sets

abstract:
  A subset $S$ of a group $G$ invariably generates $G$ if, when each element of
$S$ is replaced by an arbitrary conjugate, the resulting set generates $G.$ An
invariable generating set $X$ of $G$ is called minimal if no proper subset of
$X$ invariably generates $G.$ We will address several questions related to the
behaviour of minimal invariable generating sets of a finite group.


title:
Repeated Robot-Assisted Unilateral Stiffness Perturbations Result in
  Significant Aftereffects Relevant to Post-Stroke Gait Rehabilitation

abstract:
  Due to hemiparesis, stroke survivors frequently develop a dysfunctional gait
that is often characterized by an overall decrease in walking speed and a
unilateral decrease in step length. With millions currently affected by this
dysfunctional gait, robust and effective rehabilitation protocols are needed.
Although robotic devices have been used in numerous rehabilitation protocols
for gait, the lack of significant aftereffects that translate to effective
therapy makes their application still questionable. This paper proposes a novel
type of robot-assisted intervention that results in significant aftereffects
that last much longer than any other previous study. With the utilization of a
novel robotic device, the Variable Stiffness Treadmill (VST), the stiffness of
the walking surface underneath one leg is decreased for a number of steps. This
unilateral stiffness perturbation results in a significant aftereffect that is
both useful for stroke rehabilitation and often lasts for over 200 gait cycles
after the intervention has concluded. More specifically, the aftereffect
created is an increase in both left and right step lengths, with the
unperturbed step length increasing significantly more than the perturbed. These
effects may be helpful in correcting two of the most common issues in
post-stroke gait: overall decrease in walking speed and a unilateral shortened
step length. The results of this work show that a robot-assisted therapy
protocol involving repeated unilateral stiffness perturbations can lead to a
more permanent and effective solution to post-stroke gait.


title: FHEBench: Benchmarking Fully Homomorphic Encryption Schemes

abstract:
  Fully Homomorphic Encryption (FHE) emerges one of the most promising
solutions to privacy-preserving computing in an untrusted cloud. FHE can be
implemented by various schemes, each of which has distinctive advantages, i.e.,
some are good at arithmetic operations, while others are efficient when
implementing Boolean logic operations. Therefore, it is difficult for even
cryptography experts let alone average users to choose the "right" FHE scheme
to efficiently implement a specific application. Prior work only qualitatively
compares few FHE schemes. In this paper, we present an empirical study,
FHEBench, to quantitatively compare major FHE schemes


title: The Economics and Econometrics of Gene-Environment Interplay

abstract:
  Economists and social scientists have debated the relative importance of
nature (one's genes) and nurture (one's environment) for decades, if not
centuries. This debate can now be informed by the ready availability of genetic
data in a growing number of social science datasets. This paper explores the
potential uses of genetic data in economics, with a focus on estimating the
interplay between nature (genes) and nurture (environment). We discuss how
economists can benefit from incorporating genetic data into their analyses even
when they do not have a direct interest in estimating genetic effects. We argue
that gene--environment (GxE) studies can be instrumental for (i) testing
economic theory, (ii) uncovering economic or behavioral mechanisms, and (iii)
analyzing treatment effect heterogeneity, thereby improving the understanding
of how (policy) interventions affect population subgroups. We introduce the
reader to essential genetic terminology, develop a conceptual economic model to
interpret gene-environment interplay, and provide practical guidance to
empirical researchers.


title: Low-energy spectrum and dynamics of the weakly interacting Bose gas

abstract:
  We consider a gas of N bosons with interactions in the mean-field scaling
regime. We review the proof of an asymptotic expansion of its low-energy
spectrum, eigenstates and dynamics, which provides corrections to Bogoliubov
theory to all orders in 1/N. This is based on joint works with S. Petrat, P.
Pickl, R. Seiringer and A. Soffer. In addition, we derive a full asymptotic
expansion of the ground state one-body reduced density matrix.


title:
A lower bound on the proportion of modular elliptic curves over Galois
  CM fields

abstract:
  We calculate an explicit lower bound on the proportion of elliptic curves
that are modular over any Galois CM field not containing $\zeta_5$. Applied to
imaginary quadratic fields, this proportion is at least $2/5$. Applied to
cyclotomic fields $\mathbb{Q}(\zeta_n)$ with $5\nmid n$, this proportion is at
least $1-\varepsilon$ with only finitely many exceptions of $n$, for any choice
of $\varepsilon > 0$.


title:
Attend, Memorize and Generate: Towards Faithful Table-to-Text Generation
  in Few Shots

abstract:
  Few-shot table-to-text generation is a task of composing fluent and faithful
sentences to convey table content using limited data. Despite many efforts
having been made towards generating impressive fluent sentences by fine-tuning
powerful pre-trained language models, the faithfulness of generated content
still needs to be improved. To this end, this paper proposes a novel approach
Attend, Memorize and Generate (called AMG), inspired by the text generation
process of humans. In particular, AMG (1) attends over the multi-granularity of
context using a novel strategy based on table slot level and traditional
token-by-token level attention to exploit both the table structure and natural
linguistic information; (2) dynamically memorizes the table slot allocation
states; and (3) generates faithful sentences according to both the context and
memory allocation states. Comprehensive experiments with human evaluation on
three domains (i.e., humans, songs, and books) of the Wiki dataset show that
our model can generate higher qualified texts when compared with several
state-of-the-art baselines, in both fluency and faithfulness.


title: Task-grasping from human demonstration

abstract:
  A challenge in robot grasping is to achieve task-grasping which is to select
a grasp that is advantageous to the success of tasks before and after grasps.
One of the frameworks to address this difficulty is Learning-from-Observation
(LfO), which obtains various hints from human demonstrations. This paper solves
three issues in the grasping skills in the LfO framework: 1) how to
functionally mimic human-demonstrated grasps to robots with limited grasp
capability, 2) how to coordinate grasp skills with reaching body mimicking, 3)
how to robustly perform grasps under object pose and shape uncertainty. A deep
reinforcement learning using contact-web based rewards and domain randomization
of approach directions is proposed to achieve such robust mimicked grasping
skills. Experiment results show that the trained grasping skills can be applied
in an LfO system and executed on a real robot. In addition, it is shown that
the trained skill is robust to errors in the object pose and to the uncertainty
of the object shape and can be combined with various reach-coordination.


title:
Knock, knock. Who's there? -- Identifying football player jersey numbers
  with synthetic data

abstract:
  Automatic player identification is an essential and complex task in sports
video analysis. Different strategies have been devised over the years, but
identification based on jersey numbers is one of the most common approaches
given its versatility and relative simplicity. However, automatic detection of
jersey numbers is still challenging due to changing camera angles, low video
resolution, small object size in wide-range shots and transient changes in the
player's posture and movement. In this paper we present a novel approach for
jersey number identification in a small, highly imbalanced dataset from the
Seattle Seahawks practice videos. Our results indicate that simple models can
achieve an acceptable performance on the jersey number detection task and that
synthetic data can improve the performance dramatically (accuracy increase of
~9% overall, ~18% on low frequency numbers) making our approach achieve state
of the art results.


title: Permutatorial Optimization via the Permutahedron

abstract:
  A water company decides to expand its network with a set of water lines, but
it cannot build them all at once. However, it starts reaping benefits from a
partial network expansion. In what order should the company build the water
lines? We formalize a class of permutatorial problems with
combinatorial/continuous subproblems capturing this and other applications of
incremental deployment. We show that, for additive/linear objective functions,
efficient polyhedral methods for the combinatorial/continuous subproblem extend
to the permutatorial problem. Our main technical ingredient is the
permutahedron.


title: 3D Skeleton-based Human Motion Prediction with Manifold-Aware GAN

abstract:
  In this work we propose a novel solution for 3D skeleton-based human motion
prediction. The objective of this task consists in forecasting future human
poses based on a prior skeleton pose sequence. This involves solving two main
challenges still present in recent literature; (1) discontinuity of the
predicted motion which results in unrealistic motions and (2) performance
deterioration in long-term horizons resulting from error accumulation across
time. We tackle these issues by using a compact manifold-valued representation
of 3D human skeleton motion. Specifically, we model the temporal evolution of
the 3D poses as trajectory, what allows us to map human motions to single
points on a sphere manifold. Using such a compact representation avoids error
accumulation and provides robust representation for long-term prediction while
ensuring the smoothness and the coherence of the whole motion. To learn these
non-Euclidean representations, we build a manifold-aware Wasserstein generative
adversarial model that captures the temporal and spatial dependencies of human
motion through different losses. Experiments have been conducted on CMU MoCap
and Human 3.6M datasets and demonstrate the superiority of our approach over
the state-of-the-art both in short and long term horizons. The smoothness of
the generated motion is highlighted in the qualitative results.


title: Runtime Detection of Executional Errors in Robot-Assisted Surgery

abstract:
  Despite significant developments in the design of surgical robots and
automated techniques for objective evaluation of surgical skills, there are
still challenges in ensuring safety in robot-assisted minimally-invasive
surgery (RMIS). This paper presents a runtime monitoring system for the
detection of executional errors during surgical tasks through the analysis of
kinematic data. The proposed system incorporates dual Siamese neural networks
and knowledge of surgical context, including surgical tasks and gestures, their
distributional similarities, and common error modes, to learn the differences
between normal and erroneous surgical trajectories from small training
datasets. We evaluate the performance of the error detection using Siamese
networks compared to single CNN and LSTM networks trained with different levels
of contextual knowledge and training data, using the dry-lab demonstrations of
the Suturing and Needle Passing tasks from the JIGSAWS dataset. Our results
show that gesture specific task nonspecific Siamese networks obtain micro F1
scores of 0.94 (Siamese-CNN) and 0.95 (Siamese-LSTM), and perform better than
single CNN (0.86) and LSTM (0.87) networks. These Siamese networks also
outperform gesture nonspecific task specific Siamese-CNN and Siamese-LSTM
models for Suturing and Needle Passing.


title:
The role of morphology on the emergence of topologically trivial surface
  states and selection rules in topological-insulator nano-particles

abstract:
  Confined electronic states and optical transitions in 3D topological
insulator nanoparticles have been studied in the literature, assuming idealized
geometries such as spheres or infinitely long cylinders, that allow to obtain
analytical solutions to the corresponding eigenvalue equation within such
geometries. In contrast, in this article we consider triangular-shaped
nanoplates as a more realistic approximation to the experimentally observed
morphologies of topological insulator nanoparticles. In this particular
geometry, we obtain analytical expressions for the confined eigenstates and the
corresponding energy spectrum. Moreover, by a spatial representation of the
probability density distribution of these states, we further identify the
conditions leading to the emergence of topologically trivial surface states as
a result of geometric confinement. Finally, we also study the optical
transitions and the corresponding selection rules imposed by the nanoparticle
size and morphology.


title:
Rapidity evolution of the entanglement entropy in quarkonium: parton and
  string duality

abstract:
  We investigate the quantum entanglement in rapidity space of the soft gluon
wave function of a quarkonium, in theories with non-trivial rapidity
evolutions. We found that the rapidity evolution drastically changes the
behavior of the entanglement entropy, at any given order in perturbation
theory. At large $N_c$, the reduced density matrices that "resum" the leading
rapidity-logs can be explicitly constructed, and shown to satisfy
Balitsky-Kovchegov (BK)-like evolution equations. We study their entanglement
entropy in a conformal but simplified $1+1$ toy model, and in non-conformal 3D
QCD. The entanglement entropy in these cases, after re-summation, is shown to
saturate the Kolmogorov-Sinai bound of 1. Remarkably, in non-conformal 3D QCD
the essential growth rate of the entanglement entropy is found to vanish at
large rapidities, a result of kinematical "quenching" in transverse space. The
one-body reduction of the entangled density matrix obeys a BFKL evolution
equation, which can be recast as an evolution in an emergent AdS space, at
large impact-parameter and large rapidity. This observation allows the
extension of the perturbative wee parton evolution at low-$x$, to a dual
non-perturbative evolution of string bits in curved AdS$_5$ space, with
manifest entanglement entropy in the confining regime.


title: Low-Energy Physics in Neutrino LArTPCs

abstract:
  In this white paper, we outline some of the scientific opportunities and
challenges related to detection and reconstruction of low-energy (less than 100
MeV) signatures in liquid argon time-projection chamber (LArTPC) detectors. Key
takeaways are summarized as follows. 1) LArTPCs have unique sensitivity to a
range of physics and astrophysics signatures via detection of event features at
and below the few tens of MeV range. 2) Low-energy signatures are an integral
part of GeV-scale accelerator neutrino interaction final states, and their
reconstruction can enhance the oscillation physics sensitivities of LArTPC
experiments. 3) BSM signals from accelerator and natural sources also generate
diverse signatures in the low-energy range, and reconstruction of these
signatures can increase the breadth of BSM scenarios accessible in LArTPC-based
searches. 4) Neutrino interaction cross sections and other nuclear physics
processes in argon relevant to sub-hundred-MeV LArTPC signatures are poorly
understood. Improved theory and experimental measurements are needed. Pion
decay-at-rest sources and charged particle and neutron test beams are ideal
facilities for experimentally improving this understanding. 5) There are
specific calibration needs in the low-energy range, as well as specific needs
for control and understanding of radiological and cosmogenic backgrounds. 6)
Novel ideas for future LArTPC technology that enhance low-energy capabilities
should be explored. These include novel charge enhancement and readout systems,
enhanced photon detection, low radioactivity argon, and xenon doping. 7)
Low-energy signatures, whether steady-state or part of a supernova burst or
larger GeV-scale event topology, have specific triggering, DAQ and
reconstruction requirements that must be addressed outside the scope of
conventional GeV-scale data collection and analysis pathways.


title:
Study of scalar and tensor power spectra in the generalized Starobinsky
  inflationary model using semiclassical methods

abstract:
  In this work we solved the equation of scalar and tensor perturbations for
the generalized Starobinsky inflationary model using the improved uniform
approximation method and the phase-integral method up to third-order in
deviation. We compare our results with the numerical integration. We have
obtained that both semiclassical methods reproduce the scalar power spectra
$P_{S,T}$, the scalar spectral index $n_S$, and the tensor-to-scalar ratio $r$.
Also we present our results in the $(n_S,r)$ plane.


title:
Measuring Changes in Regional Network Traffic Due to COVID-19
  Stay-at-Home Measures

abstract:
  During the 2020 pandemic caused by the COVID-19 virus, many countries
implemented stay-at-home measures, which led to many businesses and schools
moving from in-person to online mode of operation. We analyze sampled Netflow
records at a medium-sized US Regional Optical Network to quantify the changes
in the network traffic due to stay-at-home measures in that region. We find
that human-driven traffic in the network decreases to around 70%, and mostly
shifts to local ISPs, while VPN and online meeting traffic increases up to 5
times. We also find that networks adopt a variety of online meeting solutions
and favor one but continue using a few others. We find that educational and
government institutions experience large traffic changes, but aim to keep their
productivity via increased online meetings. Some scientific traffic also
reduces possibly leading to loss of research productivity. Businesses mostly
lose their traffic and few show VPN or online meeting activity. Most network
prefixes experience large loss of live addresses but a handful increase their
liveness. We also find increased incidence of network attacks. Our findings can
help plan network provisioning and management to prepare for future possible
infection outbreaks and natural disasters.


title:
Uncovering the dynamic effects of DEX treatment on lung cancer by
  integrating bioinformatic inference and multiscale modeling of scRNA-seq and
  proteomics data

abstract:
  Motivation: Lung cancer is one of the leading causes for cancer-related
death, with a five-year survival rate of 18%. It is a priority for us to
understand the underlying mechanisms that affect the implementation and
effectiveness of lung cancer therapeutics. In this study, we combine the power
of Bioinformatics and Systems Biology to comprehensively uncover functional and
signaling pathways of drug treatment using bioinformatics inference and
multiscale modeling of both scRNA-seq data and proteomics data. The innovative
and cross-disciplinary approach can be further applied to other computational
studies in tumorigenesis and oncotherapy. Results: A time series of lung
adenocarcinoma-derived A549 cells after DEX treatment were analysed. (1) We
first discovered the differentially expressed genes in those lung cancer cells.
Then through the interrogation of their regulatory network, we identified key
hub genes including TGF-\b{eta}, MYC, and SMAD3 varied underlie DEX treatment.
Further enrichment analysis revealed the TGF-\b{eta} signaling pathway as the
top enriched term. Those genes involved in the TGF-\b{eta} pathway and their
crosstalk with the ERBB pathway presented a strong survival prognosis in
clinical lung cancer samples. (2) Based on biological validation and further
curation, a multiscale model of tumor regulation centered on both
TGF-\b{eta}-induced and ERBB-amplified signaling pathways was developed to
characterize the dynamics effects of DEX therapy on lung cancer cells. Our
simulation results were well matched to available data of SMAD2, FOXO3,
TGF\b{eta}1, and TGF\b{eta}R1 over the time course. Moreover, we provided
predictions of different doses to illustrate the trend and therapeutic
potential of DEX treatment.


title: Effective forces between active polymers

abstract:
  The characterization of the interactions between two fully flexible
self-avoiding polymers is one of the classic and most important problems in
polymer physics. In this paper we measure these interactions in the presence of
active fluctuations. We introduce activity into the problem using two of the
most popular models in this field. One where activity is effectively embedded
into the monomers' dynamics, and the other where passive polymers fluctuate in
an explicit bath of active particles. We establish the conditions under which
the interaction between active polymers can be mapped into the classical
passive problem. We observe that the active bath can drive the development of
strong attractive interactions between the polymers and that, upon enforcing a
significant degree of overlap, they come together to form a single
double-stranded unit. A phase diagram tracing this change in conformational
behavior is also reported.


title:
Hierarchical team structure and multidimensional localization (or
  siloing) on networks

abstract:
  Knowledge silos emerge when structural properties of organizational
interaction networks limit the diffusion of information. These structural
barriers are known to take many forms at different scales - hubs in otherwise
sparse organisations, large dense teams, or global core-periphery structure -
but we lack an understanding of how these different structures interact. Here
we bridge the gap between the mathematical literature on localization of
spreading dynamics and the more applied literature on knowledge silos in
organizational interaction networks. To do so, we introduce a new model that
considers a layered structure of teams to unveil a new form of hierarchical
localization (i.e., the localization of information at the top or center of an
organization) and study its interplay with known phenomena of mesoscopic
localization (i.e., the localization of information in large groups), $k$-core
localization (i.e., around denser $k$-cores) and hub localization (i.e., around
high degree stars). We also include a complex contagion mechanism by
considering a general infection kernel which can depend on hierarchical level
(influence), degree (popularity), infectious neighbors (social reinforcement)
or team size (importance). This general model allows us to study the
multifaceted phenomenon of information siloing in complex organizational
interaction networks and opens the door to new optimization problems to promote
or hinder the emergence of different localization regimes.


title:
Multi-level Coordinated Energy Management for Energy Hub in Hybrid
  Markets with Distributionally Robust Scheduling

abstract:
  Maintaining energy balance and economical operation is significant for
multi-energy systems such as the energy hub. However, it is usually challenged
by the frequently changing and unpredictable uncertainties at different
timescales. Under this scope, this paper investigates the coordinated energy
management problem for day-ahead and intra-day conditions considering
uncertainties of source-load and market prices concurrently. Note that the
precise knowledge of distributions about uncertainties may be unaccessible
before the decision-making in day-ahead phase. A two-stage chance-constrained
model based on distributionally robust approach with ambiguous moment
information is proposed to immunize scheduling strategies against the
worst-case probability distributions. The first stage is dedicated to obtaining
more energy arbitrage and operation flexibility by optimizing bidding
strategies in day-ahead power, natural gas and carbon markets. The second stage
focuses on the optimization of worst-case expected operation cost. It provides
a robust energy equipment and load scheduling strategy for the reference of
subsequent intra-day arrangements. With respect to different variations of
electrical and thermal components, an intra-day two-timescale coordination is
implemented step by step. The energy scheduling is re-dispatched circularly to
minimize the operation and penalty costs. The possible energy imbalance is also
compensated by this way. As the energy management program is nonlinear,
chance-constrained and multi-stage, linearization and dual transformation
techniques are designed to enhance tractability of programs. Experimental
results show that the developed multi-level framework results in a carbon
emission decrease of 37%, and reduces energy cost averagely 3% compared with
corresponding contrasting cases. The obtained strategy validates a good
tradeoff between robustness and optimality.


title:
Asymptotics, Tur\'an inequalities, and the distribution of the BG-rank
  and 2-quotient rank of partitions

abstract:
  Let $\overline{p}_j(n)$ denote the number of partitions with BG-rank $j$, and
$\overline{p}_j(a,b;n)$ to be the number of partitions with BG-rank $j$ and
$2$-quotient rank congruent to $a \pmod{b}$. We give asymptotics for both
statistics, and show that $\overline{p}_j(a,b;n)$ is asymptotically
equidistributed over the congruence classes modulo $b$. We also show that each
of $\overline{p}_j(n)$ and $\overline{p}_j(a,b;n)$ asymptotically satisfy all
higher-order Tur\'{a}n inequalities.


title:
E-LANG: Energy-Based Joint Inferencing of Super and Swift Language
  Models

abstract:
  Building huge and highly capable language models has been a trend in the past
years. Despite their great performance, they incur high computational cost. A
common solution is to apply model compression or choose light-weight
architectures, which often need a separate fixed-size model for each desirable
computational budget, and may lose performance in case of heavy compression.
This paper proposes an effective dynamic inference approach, called E-LANG,
which distributes the inference between large accurate Super-models and
light-weight Swift models. To this end, a decision making module routes the
inputs to Super or Swift models based on the energy characteristics of the
representations in the latent space. This method is easily adoptable and
architecture agnostic. As such, it can be applied to black-box pre-trained
models without a need for architectural manipulations, reassembling of modules,
or re-training. Unlike existing methods that are only applicable to
encoder-only backbones and classification tasks, our method also works for
encoder-decoder structures and sequence-to-sequence tasks such as translation.
The E-LANG performance is verified through a set of experiments with T5 and
BERT backbones on GLUE, SuperGLUE, and WMT. In particular, we outperform T5-11B
with an average computations speed-up of 3.3$\times$ on GLUE and 2.9$\times$ on
SuperGLUE. We also achieve BERT-based SOTA on GLUE with 3.2$\times$ less
computations. Code and demo are available in the supplementary materials.


title: Stimulated Raman lineshapes in the large light-matter interaction limit

abstract:
  Stimulated Raman scattering (SRS) represents a powerful tool for accessing
the vibrational properties of molecular compounds or solid state systems. From
a spectroscopic perspective, SRS is able to capture Raman spectra free from
incoherent background processes and typically ensures a signal enhancement of
several orders of magnitude with respect to its spontaneous counterpart. Since
its discovery in 1962, SRS has been applied to develop technological
applications, such as Raman-based lasers, frequency shifters for pulsed sources
and Raman amplifiers. For the full exploitation of their potential, however, it
is crucial to have an accurate description of the SRS processes under the large
gain regime. Here, by taking as an example the stimulated Raman spectrum of a
model solvent, namely liquid cyclohexane, we discuss how the spectral profiles
and the lineshapes of Raman excitations critically depend on the pump
excitation regime. In particular, we show that in the large light-matter
interaction limit the Raman gain undergoes an exponential increase (decrease)
in the red (blue) side of the spectrum, with the Raman linewidths that appear
sharpened (broadened).


title:
Comments on "Direct measurement of the ionization quenching factor of
  nuclear recoils in germanium in the keV energy range"

abstract:
  We examine a recent measurement of the quenching factor (QF) in germanium at
80 K, noticing a number of inconsistencies capable of affecting a claimed
agreement with Lindhard's ion-stopping formalism in the sub-keV nuclear recoil
energy regime. Namely, an underestimated uncertainty in the energy scale and a
missing correction for a large instrumental non-linearity in this scale able to
severely distort the QF behavior at low energy, favoring reduced values. The
discussion is expanded to inspect the impact of QF model selection on a study
of neutrino electromagnetic properties using CE$\nu$NS data that supports a
non-zero electric charge at the 3.5 $\sigma$ level.


title:
Fair Cuts, Approximate Isolating Cuts, and Approximate Gomory-Hu Trees
  in Near-Linear Time

abstract:
  In this paper, we introduce a robust notion of $(1+\epsilon)$-approximate
$(s, t)$-mincuts in undirected graphs where every cut edge can be
simultaneously saturated (in the same direction) to a $\frac{1}{1+\epsilon}$
fraction by an $(s, t)$-flow. We call these $(1+\epsilon)$-fair cuts. Unlike
arbitrary approximate $(s, t)$-mincuts, fair cuts can be uncrossed, which is a
key property of $(s, t)$-mincuts used in many algorithms. We also give a
near-linear $\tilde O(m/\epsilon^3)$-time algorithm for computing an $(s,
t)$-fair cut. This offers a general tool for trading off a
$(1+\epsilon)$-approximation for near-linear running time in mincut based
algorithms.
  As an application of this new concept, we obtain a near-linear time algorithm
for constructing a $(1+\epsilon)$-approximate Gomory-Hu tree, thereby giving a
nearly optimal algorithm for the $(1+\epsilon)$-approximate all-pairs max-flows
(APMF) problem in undirected graphs. Our result is obtained via another
intermediate tool of independent interest. We obtain a near-linear time
algorithm for finding $(1+\epsilon)$-approximate isolating cuts in undirected
graphs, a concept that has gained wide traction over the past year.