title:
The Streaming Instability Cannot Form Planetesimals from mm-size Grains
  in Pressure Bumps

abstract:
  We present evidence that it is unlikely that the streaming instability (SI)
can form planetesimals from mm grains inside axisymmetric pressure bumps. We
conducted the largest simulation of the SI so far (7 million CPU hours),
consisting of a large slice of the disk with mm grains, a solar-like
dust-to-gas ratio ($Z = 0.01$), and the largest pressure bump that does not
cause gravitational instability (GI) in the particle layer. We used a high
resolution of $1000/H$ to resolve as many unstable modes of the SI as possible.
The simulation produced a long-lived particle over-density far exceeding the SI
criteria (i.e., a critical solid abundance to headwind parameter ratio $Z/\Pi$)
where past studies predict strong clumping; yet we observed none. The likely
reason is that the time it takes particles to cross the high-$Z/\Pi$ region
($t_{\rm cross}$) is shorter than the growth timescale of the SI ($t_{\rm
grow}$). We propose an added criterion for planetesimal formation by the SI --
that $t_{\rm cross} > t_{\rm grow}$. We show that any bump larger than the one
in this run would form planetesimals by the GI instead of the SI. Our results
significantly restrict the pathways to planet formation: Either protoplanetary
disks regularly form grains larger than 1~mm, or planetesimals do not form by
the SI in axisymmetric pressure bumps. Since bumps large enough to induce the
GI are likely Rossby-wave unstable, we propose that mm grains may only form
planetesimals in vortices.


title: Symmetric Mass Generation

abstract:
  The most well-known mechanism for fermions to acquire a mass is the
Nambu-Goldstone-Anderson-Higgs mechanism, i.e. after a spontaneous symmetry
breaking, a bosonic field that couples to the fermion mass term condenses,
which grants a mass gap for the fermionic excitation. In the last few years, it
was gradually understood that there is a new mechanism of mass generation for
fermions without involving any symmetry breaking within an anomaly-free
symmetry group. This new mechanism is generally referred to as the "Symmetric
Mass Generation (SMG)." It is realized that the SMG has deep connections with
interacting topological insulator/superconductors, symmetry-protected
topological states, perturbative local and non-perturbative global anomaly
cancellations, and deconfined quantum criticality. It has strong implications
for the lattice regularization of chiral gauge theories. This article defines
the SMG, summarizes current numerical results, introduces a novel unifying
theoretical framework (including the parton-Higgs and the s-confinement
mechanisms, as well as the symmetry-extension construction), and overviews
various features and applications of SMG.


title:
End-to-end Spoken Conversational Question Answering: Task, Dataset and
  Model

abstract:
  In spoken question answering, the systems are designed to answer questions
from contiguous text spans within the related speech transcripts. However, the
most natural way that human seek or test their knowledge is via human
conversations. Therefore, we propose a new Spoken Conversational Question
Answering task (SCQA), aiming at enabling the systems to model complex dialogue
flows given the speech documents. In this task, our main objective is to build
the system to deal with conversational questions based on the audio recordings,
and to explore the plausibility of providing more cues from different
modalities with systems in information gathering. To this end, instead of
directly adopting automatically generated speech transcripts with highly noisy
data, we propose a novel unified data distillation approach, DDNet, which
effectively ingests cross-modal information to achieve fine-grained
representations of the speech and language modalities. Moreover, we propose a
simple and novel mechanism, termed Dual Attention, by encouraging better
alignments between audio and text to ease the process of knowledge transfer. To
evaluate the capacity of SCQA systems in a dialogue-style interaction, we
assemble a Spoken Conversational Question Answering (Spoken-CoQA) dataset with
more than 40k question-answer pairs from 4k conversations. The performance of
the existing state-of-the-art methods significantly degrade on our dataset,
hence demonstrating the necessity of cross-modal information integration. Our
experimental results demonstrate that our proposed method achieves superior
performance in spoken conversational question answering tasks.


title: Coherently coupled quantum oscillators for quantum reservoir computing

abstract:
  We analyze the properties of a quantum system composed of two coherently
coupled quantum oscillators and show through simulations that it fulfills the
two properties required for reservoir computing: non-linearity and fading
memory. We first show that the basis states of this system apply a set of
nonlinear transformations on the input signals and thus can implement neurons.
We then show that the system exhibits a fading memory that can be controlled by
its dissipation rates. Finally we show that a strong coupling between the
oscillators is important in order to ensure complex dynamics and to populate a
number of basis state neurons that is exponential in the number of physical
devices.


title: Dark Photon Dark Matter without Stueckelberg Mass

abstract:
  We study the scenario of dark photon Dark Matter where the mass is generated
through the Higgs mechanism rather than the constant Stueckelberg mass. In this
construction the dark sector contains necessarily extra degrees of freedom and
interactions that lead to non-trivial dynamics including thermalization, phase
transitions, cosmic string production. As a consequence the predictions of
Stueckelberg theories are vastly modified, strongly depending on the couplings
to curvature and on the scale of inflation $H_I$ compared to the scale $f$ of
spontaneous symmetry breaking. We find in particular that only in extreme
regions of parameter space the phenomenology of Stueckelberg dark photon is
reproduced. These scenarios are strongly constrained by isocurvature
perturbations unless the dark sector is approximately Weyl invariant.


title:
Brainish: Formalizing A Multimodal Language for Intelligence and
  Consciousness

abstract:
  Having a rich multimodal inner language is an important component of human
intelligence that enables several necessary core cognitive functions such as
multimodal prediction, translation, and generation. Building upon the Conscious
Turing Machine (CTM), a machine model for consciousness proposed by Blum and
Blum (2021), we describe the desiderata of a multimodal language called
Brainish, comprising words, images, audio, and sensations combined in
representations that the CTM's processors use to communicate with each other.
We define the syntax and semantics of Brainish before operationalizing this
language through the lens of multimodal artificial intelligence, a vibrant
research area studying the computational tools necessary for processing and
relating information from heterogeneous signals. Our general framework for
learning Brainish involves designing (1) unimodal encoders to segment and
represent unimodal data, (2) a coordinated representation space that relates
and composes unimodal features to derive holistic meaning across multimodal
inputs, and (3) decoders to map multimodal representations into predictions
(for fusion) or raw data (for translation or generation). Through discussing
how Brainish is crucial for communication and coordination in order to achieve
consciousness in the CTM, and by implementing a simple version of Brainish and
evaluating its capability of demonstrating intelligence on multimodal
prediction and retrieval tasks on several real-world image, text, and audio
datasets, we argue that such an inner language will be important for advances
in machine models of intelligence and consciousness.


title: A Theory of Natural Intelligence

abstract:
  Introduction: In contrast to current AI technology, natural intelligence --
the kind of autonomous intelligence that is realized in the brains of animals
and humans to attain in their natural environment goals defined by a repertoire
of innate behavioral schemata -- is far superior in terms of learning speed,
generalization capabilities, autonomy and creativity. How are these strengths,
by what means are ideas and imagination produced in natural neural networks?
  Methods: Reviewing the literature, we put forward the argument that both our
natural environment and the brain are of low complexity, that is, require for
their generation very little information and are consequently both highly
structured. We further argue that the structures of brain and natural
environment are closely related.
  Results: We propose that the structural regularity of the brain takes the
form of net fragments (self-organized network patterns) and that these serve as
the powerful inductive bias that enables the brain to learn quickly, generalize
from few examples and bridge the gap between abstractly defined general goals
and concrete situations.
  Conclusions: Our results have important bearings on open problems in
artificial neural network research.


title: Development of a fast simulator for GEM-based neutron detectors

abstract:
  Gas Electron Multiplier (GEM)-based detectors using a layer of 10B as a
neutron converter is becoming popular for thermal neutron detection. A common
strategy to simulate this kind of detector is based on two frameworks: Geant4
and Garfield++. The first one provides the simulation of the nuclear
interaction between neutrons and the 10B layer, while the second allows the
simulation of the interaction of the reaction products with the detector gas
leading to the ionization and excitation of the gas molecules. Given the high
ionizing power of these nuclear reaction products, a full simulation is very
time consuming and must be optimized to become viable. In this work, we present
a strategy to develop a fast simulator based on these two frameworks that will
allow us to generate enough data for a proper evaluation of the expected
performance and optimization of this kind of detector. We will show the first
results obtained with this tool concentrating on its validation and
performance.


title:
Magnetic characterization of oblique angle deposited Co/CoO on gold
  nanoparticles

abstract:
  The influence of a patterned substrate on obliquely deposited, exchange
biased Co/CoO films was studied. It was found that substrates decorated with
nanoparticle patterns provide the option to manipulate the orientation of the
magnetic easy axis in obliquely deposited thin films. The complementary methods
of SQUID magnetometry and polarized neutron reflectometry were used to
disentangle the different contributions to the magnetic hysteresis of such
complex magnetic systems.


title: Optically detected magnetic resonance with an open source platform

abstract:
  Localized electronic spins in solid-state environments form versatile and
robust platforms for quantum sensing, metrology and quantum information
processing. With optically detected magnetic resonance (ODMR), it is possible
to prepare and readout highly coherent spin systems, up to room temperature,
with orders of magnitude enhanced sensitivities and spatial resolutions
compared to induction-based techniques, allowing single spin manipulations.
While ODMR was first observed in organic molecules, many other systems are
nowadays intensively being searched for, discovered and studied. Among them is
the nitrogen-vacany (NV) center in diamond. Beyond ODMR it is notably already
widely and successfully used both both as a high-resolution high-sensitivity
quantum sensors for external fields and as a qubit. Others are rare earth ions
used as quantum memories and many other color centers trapped in bulk or
2-dimensional materials. In order to allow the broadest possible community of
researchers and engineers to investigate and develop novel ODMR-based materials
and applications, we overview here the setting up of ODMR experiments using
commercially available hardware. We also present in detail a dedicated
collaborative open-source interface named Qudi and describe the original
features we have added to speed-up data acquisition, relax instrumental
requirements and widen its applicability to individual and ensemble ODMR
systems. Associating hardware and software discussions, this article aims to
steepen the learning curve of newcomers in ODMR from a variety of scientific
backgrounds, to optimize the experimental development time, preempt the common
measurement pitfalls, and to provide an efficient, portable and collaborative
interface to explore innovative experiments.

categories: quant-ph physics.app-ph physics.ins-det physics.optics


title: Evaluating the Evidence for Water World Populations using Mixture Models

abstract:
  Water worlds have been hypothesized as an alternative to photo-evaporation in
order to explain the gap in the radius distribution of Kepler exoplanets. We
explore water worlds within the framework of a joint mass-radius-period
distribution of planets fit to a sample of transiting Kepler exoplanets, a
subset of which have radial velocity mass measurements. We employ hierarchical
Bayesian modeling to create a range of ten mixture models that include multiple
compositional subpopulations of exoplanets. We model these subpopulations -
including planets with gaseous envelopes, evaporated rocky cores, evaporated
icy cores, intrinsically rocky planets, and intrinsically icy planets - in
different combinations in order to assess which combinations are most favored
by the data. Using cross-validation, we evaluate the support for models that
include planets with icy compositions compared to the support for models that
do not, finding broad support for both. We find significant population-level
degeneracies between subpopulations of water worlds and planets with primordial
envelopes. Among models that include one or more icy-core subpopulations, we
find a wide range for the fraction of planets with icy compositions, with a
rough upper limit of 50%. Improved datasets or alternative modeling approaches
may better be able to distinguish between these subpopulations of planets.


title:
Graph Learning from Multivariate Dependent Time Series via a
  Multi-Attribute Formulation

abstract:
  We consider the problem of inferring the conditional independence graph (CIG)
of a high-dimensional stationary multivariate Gaussian time series. In a time
series graph, each component of the vector series is represented by distinct
node, and associations between components are represented by edges between the
corresponding nodes. We formulate the problem as one of multi-attribute graph
estimation for random vectors where a vector is associated with each node of
the graph. At each node, the associated random vector consists of a time series
component and its delayed copies. We present an alternating direction method of
multipliers (ADMM) solution to minimize a sparse-group lasso penalized negative
pseudo log-likelihood objective function to estimate the precision matrix of
the random vector associated with the entire multi-attribute graph. The time
series CIG is then inferred from the estimated precision matrix. A theoretical
analysis is provided. Numerical results illustrate the proposed approach which
outperforms existing frequency-domain approaches in correctly detecting the
graph edges.


title:
Brill-Noether-general Limit Root Bundles: Absence of vector-like Exotics
  in F-theory Standard Models

abstract:
  Root bundles appear prominently in studies of vector-like spectra of 4d
F-theory compactifications. Of particular importance to phenomenology are the
Quadrillion F-theory Standard Models (F-theory QSMs). In this work, we identify
$\mathcal{O}(10^{11})$ F-theory QSM geometries without vector-like exotics in
the representations $(\mathbf{3}, \mathbf{2})_{1/6}$, $(\mathbf{\bar{3}},
\mathbf{1})_{-2/3}$ and $(\mathbf{1}, \mathbf{1})_{1}$.
  The QSM geometries come in families of toric 3-folds $B_3( \Delta^\circ )$
obtained from triangulations of certain 3-dimensional polytopes $\Delta^\circ$.
The matter curves in $X_\Sigma \in B_3( \Delta^\circ )$ can be deformed to
nodal curves which are the same for all spaces in $B_3( \Delta^\circ )$.
Therefore, one can probe the vector-like spectra on the entire family $B_3(
\Delta^\circ )$ from studies of five nodal curves. For the family $B_3(
\Delta_4^\circ )$, consisting of $\mathcal{O}(10^{11})$ 3-folds, we argue that
all limit root bundles on the nodal curves $C_{(\mathbf{3},
\mathbf{2})_{1/6}}^\bullet$, $C_{(\mathbf{\bar{3}},
\mathbf{1})_{-2/3}}^\bullet$ and $C_{(\mathbf{1}, \mathbf{1})_{1}}^\bullet$
have exactly three sections. This rules out vector-like exotics.
  We compute the cohomologies of all limit roots on selected nodal curves. In
our applications, for the majority of limit roots this requires line bundle
cohomology on rational tree-like curves. For this, we present a computer
algorithm. The remaining limit roots, corresponding to circuit-like graphs, are
handled by hand. The cohomologies are independent of the relative position of
the nodes, except for a few circuits. On these \emph{jumping circuits}, jumps
happen if the nodes are specially aligned. This mirrors classical Brill-Noether
jumps. $B_3(\Delta_4^\circ)$ admits one jumping circuit. We derive explicit
conditions which guarantee the absence of vector-like exotics.


title: An intuition for physicists: information gain from experiments

abstract:
  How much one has learned from an experiment is quantifiable by the
information gain, also known as the Kullback-Leibler divergence. The narrowing
of the posterior parameter distribution $P(\theta|D)$ compared with the prior
parameter distribution $\pi(\theta)$, is quantified in units of bits, as: $
D_{\mathrm{KL}}(P|\pi)=\int\log_{2}\left(\frac{P(\theta|D)}{\pi(\theta)}\right)\,P(\theta|D)\,d\theta
$. This research note gives an intuition what one bit of information gain
means. It corresponds to a Gaussian shrinking its standard deviation by a
factor of three.


title: NGC 5236's stars as tracers of arms and arm widths in spiral galaxies

abstract:
  Generally, identifying the spiral arms of a spiral galaxy is not a hard task.
However, defining the main characteristics, width and length of those structure
is not a common task. Previous studies have used different tracers: Star
clusters, Massers, H$\alpha$. It was until recently that individual stars were
used as tracers of spiral structures. The basic method of measuring the width
of spiral arms assumes a Gaussian distribution around the mean concentration,
either of gas or other tracer. In this work we use NGC 5236's stars as tracers.
We estimated the surface stellar density of arms and inter-arm regions to
measure the width of the arms. As a test case, this works focused on NGC 5236
(M83). We find that field stellar populations can trace the (two) main spiral
arms of NGC 5236. We find a correlation between the arm width and the
galactocentric radii, found using other tracers. The slope of the growth of the
width of the arm correlates with the morphological types of spiral galaxies. A
second finding of our study suggest the possible correlation between the width
of the arms and the corrotation radius, result that will be presented in a
follow up paper.


title:
A new look at local ultraluminous infrared galaxies: the atlas and
  radiative transfer models of their complex physics

abstract:
  We present the ultraviolet to submillimetre spectral energy distributions
(SEDs) of the HERschel Ultra Luminous Infrared Galaxy Survey (HERUS) sample of
42 local ultraluminous infrared galaxies (ULIRGs) and fit them with a Markov
chain Monte Carlo (MCMC) code using the CYprus models for Galaxies and their
NUclear Spectra (CYGNUS) radiative transfer models for starbursts, active
galactic nucleus (AGN) tori and host galaxy. The Spitzer IRS spectroscopy data
are included in the fitting. Our bayesian SED fitting method takes comparable
time to popular energy balance methods but it is more physically motivated and
versatile. All HERUS galaxies harbor high rates of star formation but we also
find bolometrically significant AGN in all of the galaxies of the sample. We
estimate the correction of the luminosities of the AGN in the ULIRGs due to the
anisotropic emission of the torus and find that it could be up to a factor of
$\sim10$ for nearly edge-on tori. We present a comparison of our results with
the smooth torus model of Fritz et al. and the two-phase models of Siebenmorgen
et al. and SKIRTOR. We find that the CYGNUS AGN torus models fit significantly
better the SEDs of our sample compared to all other models. We find no evidence
that strong AGN appear either at the beginning or end of a starburst episode or
that starbursts and AGN affect each other. IRAS 01003-2238 and Mrk 1014 show
evidence for dual AGN in their SED fits suggesting a minimum dual AGN fraction
in the sample of 5%.


title:
Turbulence in Milky Way Star-Forming Regions Traced by Young Stars and
  Gas

abstract:
  The interstellar medium (ISM) is turbulent on all scales and in all phases.
In this paper, we study turbulence with different tracers in four nearby
star-forming regions: Orion, Ophiuchus, Perseus, and Taurus. We combine the
APOGEE-2 and Gaia surveys to obtain the full 6-dimensional measurements of
positions and velocities of young stars in these regions. The velocity
structure functions (VSFs) of the stars show a universal scaling of turbulence.
We also obtain H{\alpha} gas kinematics in these four regions from the
Wisconsin H-Alpha Mapper. The VSFs of the H{\alpha} are more diverse compared
to the stars. In regions with recent supernova activities, they show
characteristics of local energy injections and higher amplitudes compared to
the VSFs of stars and of CO from the literature. Such difference in amplitude
of the VSFs can be explained by the different energy and momentum transport
from supernovae into different phases of the ISM, thus resulting in higher
levels of turbulence in the warm ionized phase traced by H{\alpha}. In regions
without recent supernova activities, the VSFs of young stars, H{\alpha}, and CO
are generally consistent, indicating well-coupled turbulence between different
phases. Within individual regions, the brighter parts of the H{\alpha} gas tend
to have a higher level of turbulence than the low-emission parts. Our findings
support a complex picture of the Milky Way ISM, where turbulence can be driven
at different scales and inject energy unevenly into different phases.


title: Boostless Cosmological Collider Bootstrap

abstract:
  Cosmological correlation functions contain valuable information about the
primordial Universe, with possible signatures of new massive particles at very
high energies. Recent developments, including the cosmological bootstrap, bring
new perspectives and powerful tools to study these observables. In this paper,
we systematically classify inflationary three-point correlators of scalar
perturbations using the bootstrap method. For the first time, we derive a
complete set of single-exchange cosmological collider bispectra with new shapes
and potentially detectable signals. Specifically, we focus on the primordial
scalar bispectra generated from the exchange of massive particles with all
possible boost-breaking interactions during inflation. We introduce three-point
"seed" functions, from which we bootstrap the inflationary bispectra of scalar
and spinning exchanges using weight-shifting and spin-raising operators. The
computation of the seed function requires solving an ordinary differential
equation in comoving momenta, a boundary version of the equation of motion
satisfied by a propagator that linearly mixes a massive particle with the
external light scalars. The resulting correlators are presented in analytic
form, for any kinematics. These shapes are of interest for near-future
cosmological surveys, as the primordial non-Gaussianity in boost-breaking
theories can be large. We also identify new features in these shapes, which are
phenomenologically distinct from the de Sitter invariant cases. For example,
the oscillatory shapes around the squeezed limit have different phases.
Furthermore, when the massive particle has much lower speed of sound than the
inflaton, oscillatory features appear around the equilateral configuration.


title: A Triple Star Origin For T Pyx and Other Short-Period Recurrent Novae

abstract:
  Recurrent novae are star systems in which a massive white dwarf accretes
material at such a high rate that it undergoes thermonuclear runaways every 1 -
100 years. They are the only class of novae in which the white dwarf can grow
in mass, making some of these systems strong Type Ia supernova progenitor
candidates. Almost all known recurrent novae are long-period (P_orb > 12 hrs)
binary systems in which the requisite mass supply rate can be provided by an
evolved (sub-)giant donor star. However, at least two recurrent novae are
short-period (P_orb < 3 hrs) binaries in which mass transfer would normally be
driven by gravitational radiation at rates 3-4 orders of magnitude smaller than
required. Here, we show that the prototype of this class -- T Pyxidis -- has a
distant proper motion companion and therefore likely evolved from a
hierarchical triple star system. Triple evolution can naturally produce exotic
compact binaries as a result of three-body dynamics, either by Kozai-Lidov
eccentricity cycles in dynamically stable systems or via mass-loss-induced
dynamical instabilities. By numerically evolving triple progenitors with
physically reasonable parameters forward in time, we show explicitly that the
inner binary can become so eccentric that mass transfer is triggered at
periastron, driving the secondary out of thermal equilibrium. We suggest that
short-period recurrent novae likely evolved via this extreme state, explaining
their departure from standard binary evolution tracks.


title: Critical topological Anderson insulator

abstract:
  Two-dimensional topological insulators (TI) feature robust metallic edges
with quantized conductance protected by an insulating bulk gap. Strong
disorder, however, eventually drives the system to the trivial Anderson
insulator (AI), suppressing edge conductance. We study the nature of this
transition and establish it as happening through a distinct phase called the
critical topological insulator (CTI). Scaling behavior with increasing system
size can distinguish these three phases with the longitudinal conductance of
the TI/CTI phase converging exponentially/polynomially to the quantized value,
while the conductance manifest in the exponential AI phase showing no size
dependence. We claim analytically this observation using the self-consistent
Born approximation. In particular, we associate the boundary between these
phases with the imaginary part of the effective self-energy, introducing an
analytical tool to predict various aspects of the disorder-topology interplay.
We use our analytical theory, supported by direct numerical simulations, to
study the effect of disorder range on the topological Anderson insulator.
Interestingly, while this phase is quite generic for uncorrelated or
short-range disorder, it is strongly suppressed by long-range disorder, perhaps
explaining why it has never been seen in solid state systems.


title: Circuit knitting with classical communication

abstract:
  The scarcity of qubits is a major obstacle to the practical usage of quantum
computers in the near future. To circumvent this problem, various circuit
knitting techniques have been developed to partition large quantum circuits
into subcircuits that fit on smaller devices, at the cost of a simulation
overhead. In this work, we study a particular method of circuit knitting based
on quasiprobability simulation of nonlocal gates with operations that act
locally on the subcircuits. We investigate whether classical communication
between these local quantum computers can help. We provide a positive answer by
showing that for circuits containing $n$ nonlocal CNOT gates connecting two
circuit parts, the simulation overhead can be reduced from $O(9^n)$ to $O(4^n)$
if one allows for classical information exchange. Similar improvements can be
obtained for general Clifford gates and, at least in a restricted form, for
other gates such as controlled rotation gates.


title: What is the temperature of a quantum state?

abstract:
  Temperature is usually defined for physical systems at thermal equilibrium.
Nevertheless one may wonder if it would be possible to attribute a meaningful
notion of temperature to an arbitrary quantum state, beyond simply the thermal
(Gibbs) state. In this work, we propose such a notion of temperature
considering an operational task, inspired by the Zeroth Law of thermodynamics.
Specifically, we define two effective temperatures for quantifying the ability
of a quantum system to cool down or heat up a thermal environment. In this way
we can associate an operationally meaningful notion of temperature to any
quantum density matrix. We provide general expressions for these effective
temperatures, for both single- and many-copy systems, establishing connections
to concepts previously discussed in the literature. Finally, we consider a more
sophisticated scenario where the heat exchange between the system and the
thermal environment is assisted by a quantum reference frame. This leads to an
effect of "coherent quantum catalysis", where the use of a coherent catalyst
allows for exploiting quantum energetic coherences in the system, now leading
to much colder or hotter effective temperatures.


title:
Subaru HSC weak lensing of SDSS redMaPPer cluster satellite galaxies:
  Empirical upper limit on orphan fractions

abstract:
  Weak gravitational lensing directly probes the matter distribution
surrounding satellite galaxies in galaxy clusters. We measure the weak lensing
signal induced on the shapes of background galaxies around SDSS redMaPPer
cluster satellite galaxies, which have their central galaxies assigned with a
probability $P_{\rm cen}>0.95$ in the redshift range, $0.1\leq z\leq 0.33$. We
use the galaxy shapes from the Subaru Hyper Suprime-Cam (HSC) survey for this
purpose. We bin satellite galaxies by their distance from the cluster centre
and compare it to the signal around a control sample of galaxies which do not
reside in clusters but have similar colours and magnitudes. We explore the
effect of environmental processes on the dark matter mass around satellites. We
see hints of a difference in the mass of the subhalo of the satellite compared
to the halo masses of galaxies in our control sample, especially in the
innermost cluster-centric radial bin ($0.1<r<0.3$ [$h^{-1}\rm Mpc$]). For the
first time, we put an upper limit on the prevalence of orphan galaxies which
have entirely lost their dark matter halos with cluster-centric distances with
the help of our measurements. However, these upper limits could be relaxed if
there is substantial contamination in the satellite galaxy sample.


title:
Coideals as remainders of groups distinguishing between combinatorial
  covering properties

abstract:
  In this paper we construct consistent examples of subgroups of $2^\omega$
with Menger remainders which fail to have other stronger combinatorial covering
properties. This answers several open questions asked by Bella, Tokgoz and
Zdomskyy (Arch. Math. Logic 55 (2016), 767--784).


title:
Constraints on cosmic rays in the Milky Way circumgalactic medium from
  OVIII observations

abstract:
  We constrain the cosmic ray (CR) population in the circumgalactic medium
(CGM) of Milky Way by comparing the observations of absorption lines of OVIII
ion with predictions from analytical models of CGM : precipitation (PP) and
isothermal (IT) model. For a CGM in hydrostatic equilibrium, the introduction
of CR suppresses thermal pressure, and affects the OVIII ion abundance. We
explore the allowances given to the ratio of CR pressure to thermal pressure
($\rm{P}_{\rm{CR}}/\rm{P}_{\rm{th}}=\eta$), with varying boundary conditions,
CGM mass content, photoionization by extragalactic ultraviolet background and
temperature fluctuations. We find that the allowed maximum values of $\eta$ are
: $\eta\lesssim10$ in the PP model and $\eta\lesssim6$ in the IT model. We also
explore the spatial variation of $\eta$ : rising ($\eta=Ax$) or declining
($\eta=A/x$) with radius, where A is the normalization of the profiles. In
particular, the models with declining ratio of CR to thermal pressure fare
better than those with rising ratio with suitable temperature fluctuation
(larger $\sigma_{\rm ln T}$ for PP and lower for IT). The declining profiles
allow $A\lesssim8$ and $A\lesssim10$ in the case of IT and PP models,
respectively, thereby accommodating a large value of $\eta \,(\simeq 200)$ in
the central region, but not in the outer regions. These limits, combined with
the limits derived from $\gamma$-ray and radio background, can be useful for
building models of Milky Way CGM including CR population. However, the larger
amount of CR can be packed in cold phase which may be one way to circumvent
these constraints.


title: Widespread, strong outflows in XQR-30 quasars at the Reionisation epoch

abstract:
  Luminous quasars powered by accretion onto billion solar mass black holes
already exist at the epoch of Reionisation, when the Universe was 0.5-1 Gyr
old. These objects likely reside in over-dense regions of the Universe, and
will grow to form today's giant galaxies. How their huge black holes formed in
such short times is debated, particularly as they lie above the local black
hole mass-galaxy dynamical mass correlation, thus following the black
hole-dominance growth path. It is unknown what slowed down the black hole
growth, leading towards the symbiotic growth observed in the local Universe,
and when this process started, although black hole feedback is a likely driver.
This deadlock is due to the lack of large, homogeneous samples of high-redshift
quasars with high-quality, broad-band spectroscopic information. Here we report
results from a VLT/X-shooter survey of 30 quasars at redshift 5.8$\le$z$\le$6.6
(XQR-30). About 50% of their spectra reveal broad blue-shifted absorption line
(BAL) throughs, tracing powerful ionised winds. The BAL fraction in z$\gtrsim$6
quasars is 2-3 times higher than in quasars at z~2-4.5. XQR-30 BAL quasars
exhibit extreme outflow velocities, up to 17% of the light speed, rarely
observed at lower redshift. These outflows inject large amounts of energy into
the galaxy interstellar medium, which can contrast nuclear gas accretion,
slowing down the black-hole growth. The star-formation rate in high-z quasar
hosts is generally $>$100 M$_\odot$/yr, so these galaxies are growing at a fast
rate. The BAL phase may then mark the beginning of significant feedback, acting
first on black hole growth and possibly later on galaxy growth. The red optical
colors of BAL quasars at z$\gtrsim$6 indeed suggest that these systems are
dusty and may be caught during an initial quenching phase of obscured
accretion.


title:
Prospects for the observation of continuous gravitational waves from
  spinning neutron stars lensed by the galactic supermassive black hole

abstract:
  We study the prospects of detecting continuous gravitational waves (CGWs)
from spinning neutron stars, gravitationally lensed by the galactic
supermassive black hole. Assuming various astrophysically motivated spatial
distributions of galactic neutron stars, we find that CGW signals from a few
($\sim 0-6$) neutron stars should be strongly lensed. Lensing will produce two
copies of the signal (with time delays of seconds to minutes) that will
interfere with each other. The relative motion of the neutron star with respect
to the lensing optical axis will change the interference pattern, which will
help us to identify a lensed signal. Accounting for the magnifications and time
delays of the lensed signals, we investigate their detectability by
ground-based detectors. Assuming an ellipticity of $\epsilon = 10^{-7}$ and the
spin distribution of known pulsars, lensed CGWs are unlikely to be detectable
by LIGO and Virgo in realistic searches involving $\mathcal{O}(10^{12})$
templates. However, third-generation detectors are likely to observe some of
them. For the spatial and spin distributions of NSs that we consider, the
probability of detecting at least one lensed NS is $\sim 1\%-44\%$. Such an
observation will enable interesting probes of the supermassive black hole and
its environment.


title: Cofinal types below $\aleph_\omega$

abstract:
  It is proved that for every positive integer $n$, the number of
non-Tukey-equivalent directed sets of cardinality $\leq \aleph_n$ is at least
$c_{n+2}$, the $(n+2)$-Catalan number. Moreover, the Tukey class $\mathcal
D_{\aleph_n} $ of directed sets of cardinality $\leq \aleph_n$ contains an
isomorphic copy of the poset of Dyck $(n+2)$-paths. Furthermore, we give a
complete description whether two successive elements in the copy contain
another directed set in between or not.


title:
Local density of states and particle entanglement in topological quantum
  fluids

abstract:
  The understanding of particle entanglement is an important goal in the
studies of correlated quantum matter. The widely-used method of scanning
tunneling spectroscopy -- which measures the local density of states (LDOS) of
a many-body system by injecting or removing an electron from it -- is expected
to be sensitive to particle entanglement. In this paper, we systematically
investigate the relation between the particle entanglement spectrum (PES) and
the LDOS of fractional quantum Hall (FQH) states, the paradigmatic
strongly-correlated phases of electrons with topological order. Using exact
diagonalization, we show that the counting of levels in both the LDOS and PES
in the Jain sequence of FQH states can be predicted from the composite fermion
theory. We point out the differences between LDOS and PES characterization of
the bulk quasihole excitations, and we discuss the conditions under which the
LDOS counting can be mapped to that of PES. Our results affirm that tunneling
spectroscopy is a sensitive tool for identifying the nature of FQH states.


title: Narain CFTs and Quantum Codes at Higher Genus

abstract:
  Code CFTs are 2d conformal field theories defined by error-correcting codes.
Recently, Dymarsky and Shapere generalized the construction of code CFTs to
include quantum error-correcting codes. In this paper, we explore this
connection at higher genus. We prove that the higher-genus partition functions
take the form of polynomials of higher-weight theta functions, and that the
higher-genus modular group acts as simple linear transformations on these
polynomials. We explain how to solve the modular constraints explicitly, which
we do for genus 2. The result is that modular invariance at genus 1 and genus 2
is much more constraining than genus 1 alone. This allows us to drastically
reduce the space of possible code CFTs. We also consider a number of examples
of "isospectral theories" -- CFTs with the same genus 1 partition function --
and we find that they have different genus 2 partition functions. Finally, we
make connection to some 2d CFTs known from the modular bootstrap. The $n = 4$
theory conjectured to have the largest possible gap, the $SO(8)$ WZW model, is
a code CFT, allowing us to give an expression for its genus 2 partition
function. We also find some other known CFTs which are not code theories but
whose partition functions satisfy the same simple polynomial ansatz as the code
theories. This leads us to speculate about the usefulness of the code
polynomial form beyond the study of code CFTs.


title:
Quantum advantage in charging cavity and spin batteries by repeated
  interactions

abstract:
  Recently, an unconditional advantage has been demonstrated for the process of
charging of a quantum battery in a collisional model. Motivated by the question
of whether such an advantage could be observed experimentally, we consider a
model where the battery is modeled by a quantum harmonic oscillator or a large
spin, charged via repeated interactions with a stream of non-equilibrium qubit
units. For both setups, we show that a quantum protocol can significantly
outperform the most general adaptive classical schemes, leading to 90\% and
38\% higher charging power for the cavity and large spin batteries
respectively. Towards an experimental realization, we also characterise the
robustness of this quantum advantage to imperfections (noise and decoherence)
considering implementations with state-of-the-art micromasers and hybrid
superconducting devices.


title:
Mind the gap: how multiracial individuals get left behind when we talk
  about race, ethnicity, and ancestry in genomic research

abstract:
  It is widely acknowledged that there is a diversity problem in genomics
stemming from the vast underrepresentation of non-European genetic ancestry
populations. While many challenges exist to address this gap, a major
complicating factor is the misalignment between (1) how society defines and
labels individuals; (2) how populations are defined for research; and (3) how
research findings are translated to benefit human health. Recent conversations
to address the lack of clarity in terminology in genomics have largely focused
on ontologies that acknowledge the difference between genetic ancestry and
race. Yet, these ontological frameworks for ancestry often follow the
subjective discretization of people, normalized by historical racial
categories; this perpetuates exclusion at the expense of inclusion. In order to
make the benefits of genomics research accessible to all, standards around
race, ethnicity, and genetic ancestry must deliberately and explicitly address
multiracial, genetically admixed individuals who make salient the limitations
of discrete categorization in genomics and society. Starting with the need to
clarify terminology, we outline current practices in genomic research and
translation that fail those who are 'binned' for failing to fit into a specific
bin. We conclude by offering concrete solutions for future research in order to
share the benefits of genomics research with the full human population.


title:
Group action Markov chain Monte Carlo for accelerated sampling of energy
  landscapes with discrete symmetries and energy barriers

abstract:
  Monte Carlo sampling of the canonical distribution presents a formidable
challenge when the potential energy landscape is characterized by a large
number of local minima separated by high barriers. The principal observation of
this work is that the multiple local minima and energy barriers in a landscape
can often occur as a result of discrete symmetries in the potential energy
function. A new Monte Carlo method is proposed, group action Markov chain Monte
Carlo (GA-MCMC), which augments more conventional trial moves (e.g. random
jumps, hybrid Monte Carlo, etc.) with the application of a group action from a
well-chosen generating set of the discrete symmetry group; the result is a
framework for symmetry-adapted MCMC. It is shown that conventional trial moves
are generally optimal for "local mixing" rates, i.e. sampling a single energy
well; whereas the group action portion of the GA-MCMC trial move allows the
Markov chain to propagate between energy wells and can vastly improve the rate
of "global mixing". The proposed method is compared with standard jumps and
umbrella sampling (a popular alternative for energy landscapes with barriers)
for potential energies with translational, reflection, and rotational
symmetries. GA-MCMC is shown to consistently outperform the considered
alternatives, even when the symmetry of the potential energy function is
broken. The work culminates by extending GA-MCMC to a clustering-type algorithm
for interacting dielectric polymer chains. Not only does GA-MCMC again
outperform the considered alternatives, but it is the only method which
consistently converges for all of the cases considered. Some new and
interesting phenomena regarding the electro-elasticity of dielectric polymer
chains, unveiled via GA-MCMC, is briefly discussed.


title: Self-Aware Feedback-Based Self-Learning in Large-Scale Conversational AI

abstract:
  Self-learning paradigms in large-scale conversational AI agents tend to
leverage user feedback in bridging between what they say and what they mean.
However, such learning, particularly in Markov-based query rewriting systems
have far from addressed the impact of these models on future training where
successive feedback is inevitably contingent on the rewrite itself, especially
in a continually updating environment. In this paper, we explore the
consequences of this inherent lack of self-awareness towards impairing the
model performance, ultimately resulting in both Type I and II errors over time.
To that end, we propose augmenting the Markov Graph construction with a
superposition-based adjacency matrix. Here, our method leverages an induced
stochasticity to reactively learn a locally-adaptive decision boundary based on
the performance of the individual rewrites in a bi-variate beta setting. We
also surface a data augmentation strategy that leverages template-based
generation in abridging complex conversation hierarchies of dialogs so as to
simplify the learning process. All in all, we demonstrate that our self-aware
model improves the overall PR-AUC by 27.45%, achieves a relative defect
reduction of up to 31.22%, and is able to adapt quicker to changes in global
preferences across a large number of customers.


title: GRAND for Rayleigh Fading Channels

abstract:
  Guessing Random Additive Noise Decoding (GRAND) is a code-agnostic decoding
technique for short-length and high-rate channel codes. GRAND tries to guess
the channel noise by generating test error patterns (TEPs), and the sequence of
the TEPs is the main difference between different GRAND variants. In this work,
we extend the application of GRAND to multipath frequency non-selective
Rayleigh fading communication channels, and we refer to this GRAND variant as
Fading-GRAND. The proposed Fading-GRAND adapts its TEP generation to the fading
conditions of the underlying communication channel, outperforming traditional
channel code decoders in scenarios with $L$ spatial diversity branches as well
as scenarios with no diversity. Numerical simulation results show that the
Fading-GRAND outperforms the traditional Berlekamp-Massey (B-M) decoder for
decoding BCH code $(127,106)$ and BCH code $(127,113)$ by $\mathbf{0.5\sim6.5}$
dB at a target FER of $10^{-7}$. Similarly, Fading-GRAND outperforms GRANDAB,
the hard-input variation of GRAND, by $0.2\sim8$ dB at a target FER of
$10^{-7}$ with CRC $(128,104)$ code and RLC $(128,104)$. Furthermore the
average complexity of Fading-GRAND, at $\frac{E_b}{N_0}$ corresponding to
target FER of $10^{-7}$, is $\frac{1}{2}\times\sim \frac{1}{46}\times$ the
complexity of GRANDAB.


title: Physics at Future Colliders: the Interplay Between Energy and Luminosity

abstract:
  In this note, as an input to the Snowmass studies, we provide a broad-brush
picture of the physics output of future colliders as a function of their center
of mass energies and luminosities. Instead of relying on precise projections of
physics reaches, which are lacking in many cases, we mainly focused on simple
benchmarks of physics yields, such as the number of Higgs boson produced. More
detailed considerations for lepton colliders are given since there have been
various recent proposals. A brief summary for hadron colliders based on a
simple scaling estimate of the physics reaches is also included.


title: Improving the Deferred Acceptance with Minimal Compromise

abstract:
  In school choice problems, the motivation for students' welfare (efficiency)
is restrained by concerns to respect schools' priorities (fairness). Even the
best matching in terms of welfare among all fair matchings (SOSM) is in general
inefficient. Moreover, any mechanism that improves welfare over the SOSM is
manipulable by the students. First, we characterize the "least manipulable"
mechanisms in this class: upper-manipulation-proofness ensures that no student
is better off through strategic manipulation over the objects that are better
than their assigned school. Second, we use the notion that a matching is less
unfair if it yields a smaller set of students whose priorities are violated,
and define minimal unfairness accordingly. We then show that the Efficiency
Adjusted Deferred Acceptance (EADA) mechanism is minimally unfair in the class
of efficient and upper-manipulation-proof mechanisms. When the objective is to
improve students' welfare over the SOSM, this characterization implies an
important insight on the frontier of the main axioms in school choice.


title:
A Human-Centric Perspective on Fairness and Transparency in Algorithmic
  Decision-Making

abstract:
  Automated decision systems (ADS) are increasingly used for consequential
decision-making. These systems often rely on sophisticated yet opaque machine
learning models, which do not allow for understanding how a given decision was
arrived at. This is not only problematic from a legal perspective, but
non-transparent systems are also prone to yield unfair outcomes because their
sanity is challenging to assess and calibrate in the first place -- which is
particularly worrisome for human decision-subjects. Based on this observation
and building upon existing work, I aim to make the following three main
contributions through my doctoral thesis: (a) understand how (potential)
decision-subjects perceive algorithmic decisions (with varying degrees of
transparency of the underlying ADS), as compared to similar decisions made by
humans; (b) evaluate different tools for transparent decision-making with
respect to their effectiveness in enabling people to appropriately assess the
quality and fairness of ADS; and (c) develop human-understandable technical
artifacts for fair automated decision-making. Over the course of the first half
of my PhD program, I have already addressed substantial pieces of (a) and (c),
whereas (b) will be the major focus of the second half.


title: What do we Really Know about State of the Art NER?

abstract:
  Named Entity Recognition (NER) is a well researched NLP task and is widely
used in real world NLP scenarios. NER research typically focuses on the
creation of new ways of training NER, with relatively less emphasis on
resources and evaluation. Further, state of the art (SOTA) NER models, trained
on standard datasets, typically report only a single performance measure
(F-score) and we don't really know how well they do for different entity types
and genres of text, or how robust are they to new, unseen entities. In this
paper, we perform a broad evaluation of NER using a popular dataset, that takes
into consideration various text genres and sources constituting the dataset at
hand. Additionally, we generate six new adversarial test sets through small
perturbations in the original test set, replacing select entities while
retaining the context. We also train and test our models on randomly generated
train/dev/test splits followed by an experiment where the models are trained on
a select set of genres but tested genres not seen in training. These
comprehensive evaluation strategies were performed using three SOTA NER models.
Based on our results, we recommend some useful reporting practices for NER
researchers, that could help in providing a better understanding of a SOTA
model's performance in future.


title: A fast point charge interacting with the screened Vlasov-Poisson system

abstract:
  We consider the long-time behavior of a fast, charged particle interacting
with an initially spatially homogeneous background plasma. The background is
modeled by the screened Vlasov-Poisson equations, whereas the interaction
potential of the point charge is assumed to be smooth. We rigorously prove the
validity of the stopping power theory in physics, which predicts a decrease of
the velocity $V(t)$ of the point charge given by $\dot{V} \sim -|V|^{-3} V$, a
formula that goes back to Bohr (1915). Our result holds for all initial
velocities larger than a threshold value that is larger than the velocity of
all background particles and remains valid until (i) the particle slows down to
the threshold velocity, or (ii) the time is exponentially long compared to the
velocity of the point charge. The long-time behavior of this coupled system is
related to the question of Landau damping which has remained open in this
setting so far. Contrary to other results in nonlinear Landau damping, the
long-time behavior of the system is driven by the non-trivial electric field of
the plasma, and the damping only occurs in regions that the point charge has
already passed.


title: Tropical medians by transportation

abstract:
  Fermat-Weber points with respect to an asymmetric tropical distance function
are studied. It turns out that they correspond to the optimal solutions of a
transportation problem. The results are applied to obtain a new method for
computing consensus trees in phylogenetics. This method has several desirable
properties; e.g., it is Pareto and co-Pareto on rooted triplets.


title:
Stellar and black hole assembly in z<0.3 infrared-luminous mergers:
  intermittent starbursts vs. super-Eddington accretion

abstract:
  We study stellar and black hole mass assembly in a sample of 42
infrared-luminous galaxy mergers at z<0.3 by combining results from radiative
transfer modelling with archival measures of molecular gas and black hole mass.
The ratios of stellar mass, molecular gas mass, and black hole mass to each
other are consistent with those of massive gas-rich galaxies at z<0.3. The
advanced mergers may show increased black hole mass to stellar mass ratios,
consistent with the transition from AGN to ellipticals and implying substantial
black hole mass growth over the course of the merger. Star formation rates are
enhanced relative to the local main sequence, by factors of ~100 in the
starburst and ~1.8 in the host. The starburst star formation rates appear
distinct to star formation in the main sequence at all redshifts up to at least
z~5. Starbursts may prefer late-stage mergers, but are observed at any merger
stage. We do not find evidence that the starbursts in these low-redshift
systems substantially increase the total stellar mass, with a soft upper limit
on the stellar mass increase from starburst activity of about a factor of two.
In contrast, 12 objects show evidence for super-Eddington accretion, associated
with late-stage mergers, suggesting that many AGN in infrared-luminous mergers
go through a super-Eddington phase. The super-Eddington phase may increase
black hole mass by up to an order of magnitude at an accretion efficiency of
42+/-33% over a period of 44+/-22Myr. Our results imply that super-Eddington
accretion is an important black hole growth channel in infrared-luminous
galaxies at all redshifts.


title:
Heteroclinic-structure transition of the pure quartic modulation
  instability

abstract:
  We show that, in the pure-quartic systems, modulation instability (MI)
undergoes heteroclinic-structure transitions (HSTs) at two critical frequencies
of {\omega} c1 and {\omega} c2 ( {\omega} c2 > {\omega} c1 ), which indicates
that there are significantchanges of the spatiotemporal behavior in the system.
The complicated heteroclinic structure of instability obtained by the mode
truncation method reveals all possible dynamic trajectories of nonlinear waves,
which allows us to discover the various types of Fermi-Pasta-Ulam (FPU)
recurrences and Akhmediev breathers (ABs). When the modulational frequency
satisfies {\omega} < {\omega} c2 , the heteroclinic structure encompasses two
separatrixes corresponding to the ABs and the nonlinear wave with a modulated
final state, which individually separate FPU recurrences into three different
regions. Remarkably, crossing critical frequency {\omega} c1 , both the
staggered FPU recurrences and ABs essentially switch their patterns. These HST
behaviors will give vitality to the study of MI.


title:
Correlations between flow and transverse momentum in Xe+Xe and Pb+Pb
  collisions at the LHC with the ATLAS detector: a probe of the heavy-ion
  initial state and nuclear deformation

abstract:
  The correlations between flow harmonics $v_n$ for $n=2$, 3 and 4 and mean
transverse momentum $[p_\mathrm{T}]$ in $^{129}$Xe+$^{129}$Xe and
$^{208}$Pb+$^{208}$Pb collisions at $\sqrt{s_{\mathrm{NN}}}=5.44$ TeV and 5.02
TeV, respectively, are measured using charged particles with the ATLAS
detector. The correlations are sensitive to the shape and size of the initial
geometry, nuclear deformation, and initial momentum anisotropy. The effects
from non-flow and centrality fluctuations are minimized, respectively, via a
subevent cumulant method and event activity selection based on particle
production in the very forward rapidity. The results show strong dependences on
centrality, harmonic number $n$, $p_{\mathrm{T}}$ and pseudorapidity range.
Current models describe qualitatively the overall centrality- and
system-dependent trends but fail to quantitatively reproduce all the data. In
the central collisions, where models generally show good agreement, the
$v_2$-$[p_\mathrm{T}]$ correlations are sensitive to the triaxiality of the
quadruple deformation. The comparison of model to the Pb+Pb and Xe+Xe data
suggests that the $^{129}$Xe nucleus is a highly deformed triaxial ellipsoid
that is neither a prolate nor an oblate shape. This provides strong evidence
for a triaxial deformation of $^{129}$Xe nucleus using high-energy heavy-ion
collision.


title:
Evidence for the Late Arrival of Hot Jupiters in Systems with High
  Host-star Obliquities

abstract:
  It has been shown that hot Jupiters systems with massive, hot stellar
primaries exhibit a wide range of stellar obliquities. On the other hand, hot
Jupiter systems with low-mass, cool primaries often have stellar obliquities
close to zero. Efficient tidal interactions between hot Jupiters and the
convective envelopes present in lower-mass main sequence stars have been a
popular explanation for these observations. If this explanation is accurate,
then aligned systems should be older than misaligned systems. Likewise, the
convective envelope mass of a hot Jupiter's host star should be an effective
predictor of its obliquity. We derive homogeneous stellar parameters --
including convective envelope masses -- for hot Jupiter host stars with
high-quality sky-projected obliquity inferences. Using a thin disk stellar
population's Galactic velocity dispersion as a relative age proxy, we find that
hot Jupiter host stars with larger-than-median obliquities are older than hot
Jupiter host stars with smaller-than-median obliquities. The relative age
difference between the two populations is larger for hot Jupiter host stars
with smaller-than-median fractional convective envelope masses and is
significant at the 3.6-$\sigma$ level. We identify stellar mass, not convective
envelope mass, as the best predictor of stellar obliquity in hot Jupiter
systems. The best explanation for these observations is that many hot Jupiters
in misaligned systems arrived in the close proximity of their host stars long
after their parent protoplanetary disks dissipated. The dependence of observed
age offset on convective envelope mass suggests that tidal realignment
contributes to the population of aligned hot Jupiters orbiting stars with
convective envelopes.


title: Hyperexponential solutions of elliptic difference equations

abstract:
  Consider an elliptic curve $\mathcal{C}$ with coefficients in $\mathbb{K}$
with $[\mathbb{K}:\mathbb{Q}]<\infty$ and $\delta \in \mathcal{C}(\mathbb{K})$
a non torsion point. We consider an elliptic difference equation $\sum_{i=0}^l
a_i(p) f(p\oplus i.\delta)=0$ with $\oplus$ the elliptic addition law and $a_i$
polynomials on $\mathcal{C}$. We present an algorithm to compute rational
solutions, then an intermediary class we call pseudo-rational solutions, and
finally hyperexponential solutions, which are functions $f$ such that
$f(p\oplus \delta)/f(p)$ is rational over $\mathcal{C}$.


title: Answer Consolidation: Formulation and Benchmarking

abstract:
  Current question answering (QA) systems primarily consider the single-answer
scenario, where each question is assumed to be paired with one correct answer.
However, in many real-world QA applications, multiple answer scenarios arise
where consolidating answers into a comprehensive and non-redundant set of
answers is a more efficient user interface. In this paper, we formulate the
problem of answer consolidation, where answers are partitioned into multiple
groups, each representing different aspects of the answer set. Then, given this
partitioning, a comprehensive and non-redundant set of answers can be
constructed by picking one answer from each group. To initiate research on
answer consolidation, we construct a dataset consisting of 4,699 questions and
24,006 sentences and evaluate multiple models. Despite a promising performance
achieved by the best-performing supervised models, we still believe this task
has room for further improvements.


title:
Tail Adversarial Stability for Regularly Varying Linear Processes and
  their Extensions

abstract:
  The recently introduced notion of tail adversarial stability has been proven
useful in studying tail dependent time series and obtaining their limit
theorems. Its implication and advantage over the classical strong mixing
framework has been examined for max-linear processes, but not yet studied for
additive linear processes that have also been commonly used in modeling
extremal clusters and tail dependence in time series. In this article, we fill
this gap by verifying the tail adversarial stability condition for regularly
varying additive linear processes. A comparison with the classical strong
mixing condition is also given in the context of tail autocorrelation
estimation. We in addition consider extensions of the result on an additive
linear process to its stochastic volatility generalization and to its
max-linear counterpart.


title: Local adaptation, phenotypic plasticity, and species coexistence

abstract:
  Understanding the mechanisms of species coexistence has always been a
fundamental topic in ecology. Classical theory predicts that interspecific
competition may select for traits that stabilize niche differences, although
recent work shows that this is not strictly necessary. Here we ask whether
adaptive phenotypic plasticity could allow species coexistence (i.e., some
stability at an equilibrium point) without ecological differentiation in
habitat use. We used individual-based stochastic simulations defining a
landscape composed of spatially uncorrelated or autocorrelated environmental
patches, where two species with the same competitive strategies, not able to
coexist without some form of phenotypic plasticity, expanded their ranges in
the absence of a competition-colonization trade-off (a well-studied mechanism
for species diversity). In such a scenario, only local adaptation and gene flow
may interact to promote genetic variation and coexistence in the
metapopulation. Results show that a competitively superior nonplastic species
cannot always displace from the metapopulation an inferior competitor that
displays some adaptive phenotypic plasticity. This plastic and inferior
competitor has also some chances of invading a metapopulation already occupied
by the ecologically superior native species.


title:
Azimuthal correlations within exclusive dijets with large momentum
  transfer in photon-lead collisions

abstract:
  The first measurement of the azimuthal angular correlations of exclusively
produced events with two jets in photon-lead interactions at large momentum
transfer is presented. Theoretical predictions relate such correlations to the
polarization of gluons within nuclei. This study uses a data sample of
lead-lead collisions at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV, corresponding to an
integrated luminosity of 0.38 nb$^{-1}$, collected with the CMS experiment. One
of the jets is required to have a transverse momentum above 30 GeV, while that
of the second is greater than 20 GeV. The measured second harmonic of the
correlation between the sum and difference of the two jet momenta is found to
be positive, and rising, as the dijet momentum increases. The model
representing the state-of-the-art calculations in electromagnetic interactions
with protons overestimates this angular correlation. The measurement is also
compared to a recent theoretical calculation that includes final state
interactions.


title:
Single Whispering-Gallery-Mode Resonator With Microfluidic Chip as a
  Basis for Multifrequency Microwave Permittivity Measurement of Liquids

abstract:
  The accurate measurement of complex liquid permittivity in a frequency range
provides important information on liquid properties in comparison to single
frequency permittivity investigations. The multifrequency microwave
characterization technique based on a single quartz whispering-gallery-mode
(WGM) resonator with a microfluidic chip are proposed, developed, and
demonstrated. This technique allows the complex permittivity of small volumes
of liquid to be measured at six resonant frequencies in the 30-40 GHz frequency
range. The calibration is performed by simulating the measurement cell and by
plotting the calibration nomogram charts for all six investigated frequencies.
The novel approach is applied in studies of the complex permittivity of the
L-lysine in water solutions. The results open up possibilities to investigate
the complex permittivity of biological liquids at several frequencies and to
further develop the microwave dielectrometry of small liquid volumes in a
certain frequency range using the quasi-optical nature of a single high-Q WGM
resonator.


title: Logically Consistent Adversarial Attacks for Soft Theorem Provers

abstract:
  Recent efforts within the AI community have yielded impressive results
towards "soft theorem proving" over natural language sentences using language
models. We propose a novel, generative adversarial framework for probing and
improving these models' reasoning capabilities. Adversarial attacks in this
domain suffer from the logical inconsistency problem, whereby perturbations to
the input may alter the label. Our Logically consistent AdVersarial Attacker,
LAVA, addresses this by combining a structured generative process with a
symbolic solver, guaranteeing logical consistency. Our framework successfully
generates adversarial attacks and identifies global weaknesses common across
multiple target models. Our analyses reveal naive heuristics and
vulnerabilities in these models' reasoning capabilities, exposing an incomplete
grasp of logical deduction under logic programs. Finally, in addition to
effective probing of these models, we show that training on the generated
samples improves the target model's performance.


title: Joint Multisided Exposure Fairness for Recommendation

abstract:
  Prior research on exposure fairness in the context of recommender systems has
focused mostly on disparities in the exposure of individual or groups of items
to individual users of the system. The problem of how individual or groups of
items may be systemically under or over exposed to groups of users, or even all
users, has received relatively less attention. However, such systemic
disparities in information exposure can result in observable social harms, such
as withholding economic opportunities from historically marginalized groups
(allocative harm) or amplifying gendered and racialized stereotypes
(representational harm). Previously, Diaz et al. developed the expected
exposure metric -- that incorporates existing user browsing models that have
previously been developed for information retrieval -- to study fairness of
content exposure to individual users. We extend their proposed framework to
formalize a family of exposure fairness metrics that model the problem jointly
from the perspective of both the consumers and producers. Specifically, we
consider group attributes for both types of stakeholders to identify and
mitigate fairness concerns that go beyond individual users and items towards
more systemic biases in recommendation. Furthermore, we study and discuss the
relationships between the different exposure fairness dimensions proposed in
this paper, as well as demonstrate how stochastic ranking policies can be
optimized towards said fairness goals.


title: Prompt Consistency for Zero-Shot Task Generalization

abstract:
  One of the most impressive results of recent NLP history is the ability of
pre-trained language models to solve new tasks in a zero-shot setting. To
achieve this, NLP tasks are framed as natural language prompts, generating a
response indicating the predicted output. Nonetheless, the performance in such
settings often lags far behind its supervised counterpart, suggesting a large
space for potential improvement. In this paper, we explore methods to utilize
unlabeled data to improve zero-shot performance. Specifically, we take
advantage of the fact that multiple prompts can be used to specify a single
task, and propose to regularize prompt consistency, encouraging consistent
predictions over this diverse set of prompts. Our method makes it possible to
fine-tune the model either with extra unlabeled training data, or directly on
test input at inference time in an unsupervised manner. In experiments, our
approach outperforms the state-of-the-art zero-shot learner, T0 (Sanh et al.,
2022), on 9 out of 11 datasets across 4 NLP tasks by up to 10.6 absolute points
in terms of accuracy. The gains are often attained with a small number of
unlabeled examples.


title:
Fractional powers of first order differential operators and new families
  of polynomials associated to inverse measures

abstract:
  First, we establish the theory of fractional powers of first order
differential operators with zero order terms, obtaining PDE properties and
analyzing the corresponding fractional Sobolev spaces. In particular, our study
shows that Lebesgue and Sobolev spaces with inverse measures (like the inverse
Gaussian measure) play a fundamental role in the theory of fractional powers of
the first order operators. Second, and motivated in part by such a theory, we
lay out the foundations for the development of the harmonic analysis for
\emph{inverse} measures. We discover new families of polynomials related to the
inverse Gaussian, Laguerre, and Jacobi measures, and characterize them using
generating and Rodrigues formulas, and three-term recurrence relations.
Moreover, we prove boundedness of several fundamental singular integral
operators in these inverse measure settings.


title: Useful formulas for non-magnetized electron cooling

abstract:
  Recent success of Low Energy RHIC Electron Cooler (LEReC) opened a road for
development of high energy electron coolers based on non-magnetized electron
bunches accelerated by RF cavities. Electrons in such coolers can have velocity
distribution with various unequal horizontal, vertical and longitudinal
velocity spreads. In this paper we revisit a formula of friction force in
non-magnetized cooling and derive a number of useful expressions for different
limiting cases.


title: Novel magnetic ordering in LiYbO2 probed by muon spin relaxation

abstract:
  The stretched diamond lattice material LiYbO2 has recently been reported to
exhibit two magnetic transitions ($T_{N1} = 1.1 K$, $T_{N2} = 0.45 K$) via
specific heat, magnetization, and neutron scattering measurements [Bordelon et
al., Phys. Rev. B 103, 014420 (2021)]. Here we report complementary magnetic
measurements down to T = 0.28 K via the local probe technique of muon spin
relaxation. While we observe a rapid increase in the zero-field muon
depolarization rate at $T_{N1}$, we do not observe any spontaneous muon
precession for $T < T_{N1}$, which is typically associated with long-range
magnetic ordering. The depolarization rate in the ordered state shows a
surprising sensitivity to magnetic fields applied along the initial spin
polarization direction. Using a simple one-dimensional model, we show that
these results are consistent with the unusual random-phase bipartite
incommensurate magnetic structure proposed by Bordelon et al. for the
intermediate temperature range $T_{N2} < T < T_{N1}$. We also find evidence for
temperature-independent magnetic fluctuations persisting to our lowest
temperatures, but no obvious signature of the transition or spontaneous muon
precession at and below TN2, respectively. This result is suggestive of quantum
dynamics within a highly degenerate ground state.


title:
Plasmon damping rates in Coulomb-coupled two-dimensional layers in a
  heterostructure

abstract:
  The Coulomb excitations of charge density oscillation are calculated for a
double-layer heterostructure. Specifically, we consider two-dimensional (2D)
layers of silicene and graphene on a substrate. From the obtained surface
response function, we calculated the plasmon dispersion relations which
demonstrate the way in which the Coulomb coupling renormalizes the plasmon
frequencies. Additionally, we present a novel result for the damping rates of
the plasmons in this Coulomb coupled heterostructure and compare these results
as the separation between layers is varied.


title:
Heterogeneity of Interaction Strengths and Its Consequences on
  Ecological Systems

abstract:
  Ecosystems are formed by networks of species and their interactions.
Traditional models of such interactions assume a constant interaction strength
between a given pair of species. However, there is often significant trait
variation among individual organisms even within the same species, causing
heterogeneity in their interaction strengths with other species. The
consequences of such heterogeneous interactions for the ecosystem have not been
studied systematically. As a theoretical exploration, we analyze a simple
ecosystem with trophic interactions between two predators and a shared prey,
which would exhibit competitive exclusion in models with homogeneous
interactions. We consider several scenarios where individuals of the prey
species differentiate into subpopulations with different interaction strengths.
We show that in all these cases, whether the heterogeneity is inherent,
reversible, or adaptive, the ecosystem can stabilize at a new equilibrium where
all three species coexist. Moreover, the prey population that has heterogeneous
interactions with its predators reaches a higher density than it would without
heterogeneity, and can even reach a higher density in the presence of two
predators than with just one. Our results suggest that heterogeneity may be a
naturally selected feature of ecological interactions that have important
consequences for the stability and diversity of ecosystems.


title:
From Service Quality to E-Service Quality: Measurement, Dimensions and
  Model

abstract:
  With the global increase in online services, there is a paradigm shift from
service quality to e-service quality. In order to sustain this strategic
change, there is need to measure and evaluate the quality of e-services.
Consequently, the paper seeks to determine the relevant e-service quality
dimensions for e-channels. The aim is to generate a concise set of dimensions
that managers can use to measure e-service quality. The paper proposed an
e-service quality model comprising seven e-service quality dimensions (website
appearance, ease of use, reliability, security, personalisation, fulfilment and
responsiveness) and overall e-service quality. The study employed a
cross-sectional research design and quantitative research approach. The data
were collected via a questionnaire from 400 e-channel users in Lagos State,
Nigeria. However, 318 copies of the questionnaire were found useful. The data
were analysed using mean, frequency, percentages, correlation and multiple
regression analysis. The results revealed that the relevant e-service quality
dimensions influencing overall e-service quality are reliability, security,
fulfilment, ease of use and responsiveness. These e-service quality dimensions
are expected to provide information for managers to evaluate and improve their
e-channel service delivery.


title: Mitigating Low-volume DoS Attacks with Data-driven Resource Accounting

abstract:
  Low-volume Denial-of-Service ({\mu}DoS) attacks have been demonstrated to
fundamentally bypass traditional DoS mitigation schemes based on the flow and
volume of network packets. In this paper, we propose a data-driven approach,
called ROKI, that accurately tracks internal resource utilization and
allocation associated with each packet (or session), making it possible to tame
resource exhaustion caused by {\mu}DoS attacks. Since ROKI focuses on capturing
the symptom of DoS, it can effectively mitigate previously unknown {\mu}DoS
attacks. To enable a finer-grain resource tracking, ROKI provided in concept
the accounting capabilities to each packet itself, so we called data-driven: it
monitors resource utilization at the link, network, transport layers in the
kernel, as well as application layers, and attributes back to the associated
packet. Given the resource usages of each packet, ROKI can reclaim (or prevent)
the system resources from malicious packets (or attackers) whenever it
encounters system-wide resource exhaustion. To provide lightweight resource
tracking, ROKI carefully multiplexes hardware performance counters whenever
necessary. Our evaluation shows that ROKI's approach is indeed effective in
mitigating real-world {\mu}DoS attacks with negligible performance overheads -
incurring 3%-4% throughput and latency overheads on average when the system is
throttled.


title:
Joint Track Machine Learning: An autonomous method for measuring 6DOF
  TKA kinematics from single-plane x-ray images

abstract:
  Dynamic radiographic measurement of 3D TKA kinematics has provided important
information for implant design and surgical technique for over 30 years.
However, current methods of measuring TKA kinematics are too cumbersome or
time-consuming for practical clinical application. Even state-of-the-art
techniques require human-supervised initialization or human supervision
throughout the entire optimization process. Elimination of human supervision
could potentially bring this technology into clinical practicality. Therefore,
we propose a fully autonomous pipeline for quantifying TKA kinematics from
single-plane imaging. First, a convolutional neural network segments the
femoral and tibial implants from the image. Second, segmented images are
compared to Normalized Fourier Descriptor shape libraries for initial pose
estimates. Lastly, a Lipschitzian optimization routine minimizes the difference
between the segmented image and the projected implant. This technique reliably
reproduces human-supervised kinematics measurements from internal datasets and
external validation studies, with RMS differences of less than 0.7mm and
4{\deg} for internal studies and 0.8mm and 1.7{\deg} for external validation
studies. This performance indicates that it will soon be practical to perform
these measurements in a clinical setting.


title:
Implicit Regularization Properties of Variance Reduced Stochastic Mirror
  Descent

abstract:
  In machine learning and statistical data analysis, we often run into
objective function that is a summation: the number of terms in the summation
possibly is equal to the sample size, which can be enormous. In such a setting,
the stochastic mirror descent (SMD) algorithm is a numerically efficient method
-- each iteration involving a very small subset of the data. The variance
reduction version of SMD (VRSMD) can further improve SMD by inducing faster
convergence. On the other hand, algorithms such as gradient descent and
stochastic gradient descent have the implicit regularization property that
leads to better performance in terms of the generalization errors. Little is
known on whether such a property holds for VRSMD. We prove here that the
discrete VRSMD estimator sequence converges to the minimum mirror interpolant
in the linear regression. This establishes the implicit regularization property
for VRSMD. As an application of the above result, we derive a model estimation
accuracy result in the setting when the true model is sparse. We use numerical
examples to illustrate the empirical power of VRSMD.


title: Fractional Poisson Analysis in Dimension one

abstract:
  In this paper, we use a biorthogonal approach (Appell system) to construct
and characterize the spaces of test and generalized functions associated to the
fractional Poisson measure $\pi_{\lambda,\beta}$, that is, a probability
measure in the set of natural (or real) numbers. The Hilbert space
$L^{2}(\pi_{\lambda,\beta})$ of complex-valued functions plays a central role
in the construction, namely, the test function spaces
$(N)_{\pi_{\lambda,\beta}}^{\kappa}$, $\kappa\in[0,1]$ is densely embedded in
$L^{2}(\pi_{\lambda,\beta})$. Moreover, $L^{2}(\pi_{\lambda,\beta})$ is also
dense in the dual
$((N)_{\pi_{\lambda,\beta}}^{\kappa})'=(N)_{\pi_{\lambda,\beta}}^{-\kappa}$.
Hence, we obtain a chain of densely embeddings
$(N)_{\pi_{\lambda,\beta}}^{\kappa}\subset
L^{2}(\pi_{\lambda,\beta})\subset(N)_{\pi_{\lambda,\beta}}^{-\kappa}$. The
characterization of these spaces is realized via integral transforms and chain
of spaces of entire functions of different types and order of growth. Wick
calculus extends in a straightforward manner from Gaussian analysis to the
present non-Gaussian framework. Finally, in Appendix B we give an explicit
relation between (generalized) Appell polynomials and Bell polynomials.


title:
Designing High-Tc Superconductors with BCS-inspired Screening, Density
  Functional Theory and Deep-learning

abstract:
  Recent advances in first principles calculations and machine learning
techniques allow for a systematic search for phonon-mediated superconductors.
We develop a multi-step workflow for the discovery of conventional
superconductors, starting with a Bardeen-Cooper-Schrieffer inspired
pre-screening of 1736 materials with high Debye temperature and electronic
density of states at the Fermi-level. Next, we perform electron-phonon coupling
calculations for 988 of them to establish a large and systematic database of
BCS superconducting properties. Using the McMillan-Allen-Dynes formula, we
identify 112 dynamically stable materials with transition temperatures, Tc > 5
K. In addition, we analyze trends in our dataset and individual materials
including MoN, VC, VTe, KB6, Ru3NbC, V3Pt, ScN, LaN2, RuO2, and TaC. Finally,
we demonstrate that deep-learning models can predict superconductor properties,
including the Eliashberg function, thousands of times faster than direct first
principles computations. We apply the trained model on the crystallographic
open database and pre-screen 8293 candidates for further DFT calculations.


title:
The Directional Bias Helps Stochastic Gradient Descent to Generalize in
  Kernel Regression Models

abstract:
  We study the Stochastic Gradient Descent (SGD) algorithm in nonparametric
statistics: kernel regression in particular. The directional bias property of
SGD, which is known in the linear regression setting, is generalized to the
kernel regression. More specifically, we prove that SGD with moderate and
annealing step-size converges along the direction of the eigenvector that
corresponds to the largest eigenvalue of the Gram matrix. In addition, the
Gradient Descent (GD) with a moderate or small step-size converges along the
direction that corresponds to the smallest eigenvalue. These facts are referred
to as the directional bias properties; they may interpret how an SGD-computed
estimator has a potentially smaller generalization error than a GD-computed
estimator. The application of our theory is demonstrated by simulation studies
and a case study that is based on the FashionMNIST dataset.


title: On the Inf-Sup Stabillity of Crouzeix-Raviart Stokes Elements in 3D

abstract:
  We consider non-conforming discretizations of the stationary Stokes equation
in three spatial dimensions by Crouzeix-Raviart type elements. The original
definition in the seminal paper by M. Crouzeix and P.-A. Raviart in 1973 is
implicit and also contains substantial freedom for a concrete choice. In this
paper, we introduce canonical Crouzeix-Raviart basis functions in 3D in analogy
to the 2D case in a fully explicit way. We prove that this canonical
CrouzeixRaviart element for the Stokes equation is inf-sup stable for
polynomial degree $k =2$ (quadratic velocity approximation). We identify
spurious pressure modes for the conforming $(k; k - 1)$ 3D Stokes element and
show that these are eliminated by using the canonical Crouzeix-Raviart space.


title:
Comparing four hard-sphere approximations for the low-temperature WCA
  melting line

abstract:
  By combining interface-pinning simulations with numerical integration of the
Clausius-Clapeyron equation we determine accurately the melting-line
coexistence pressure and fluid/crystal densities of the Weeks-Chandler-Andersen
(WCA) system covering four decades of temperature. The data are used for
comparing the melting-line predictions of the Boltzmann,
Andersen-Weeks-Chandler, Barker-Henderson, and Stillinger hard-sphere
approximations. The Andersen-Weeks-Chandler and the Barker-Henderson theories
give the most accurate predictions, and they both work excellently in the
zero-temperature limit for which analytical expressions are derived here.


title:
A nearly linearly convergent first-order method for nonsmooth functions
  with quadratic growth

abstract:
  Classical results show that gradient descent converges linearly to minimizers
of smooth strongly convex functions. A natural question is whether there exists
a locally nearly linearly convergent method for nonsmooth functions with
quadratic growth. This work designs such a method for a wide class of nonsmooth
and nonconvex locally Lipschitz functions, including max-of-smooth, Shapiro's
decomposable class, and generic semialgebraic functions. The algorithm is
parameter-free and derives from Goldstein's conceptual subgradient method.


title: Imaging of the Quiet Sun in the Frequency Range of 20-80 MHz

abstract:
  Radio emission of the quiet Sun is considered to be due to thermal
bremsstrahlung emission of the hot solar atmosphere. The properties of the
quiet Sun in the microwave band have been well studied, and they can be well
described by the spectrum of bremsstrahlung emission. In the meter-wave and
decameter-wave bands, properties of the quiet Sun have rarely been studied due
to the instrumental limitations. In this work, we use the LOw Frequency ARray
(LOFAR) telescope to perform high quality interferometric imaging spectroscopy
observations of quiet Sun coronal emission at frequencies below 90~MHz. We
present the brightness temperature spectrum, and size of the Sun in the
frequency range of 20-80~MHz. We report on dark coronal regions with low
brightness temperature that persist with frequency. The brightness temperature
spectrum of the quiet Sun is discussed and compared with the bremsstrahlung
emission of a coronal model and previous quiet Sun observations.


title: Disorder effects on hot spots in electron-doped cuprates

abstract:
  Antiferromagnetic fluctuations in two dimensions cause a decrease in spectral
weight at so-called hot spots associated with the pseudogap in electron-doped
cuprates. In the 2D Hubbard model, these hot spots occur when the Vilk
criterion is satisfied, namely when the spin correlation length exceeds the
thermal de Broglie wavelength. Using the two-particle self-consistent approach,
here we show that this criterion may be violated near the antiferromagnetic
quantum critical point when disorder is added to the model. Static disorder
decreases inelastic scattering, in contradiction with Mathiessen's rule,
leading to a shift in the position of the quantum critical point and a
modification of the conditions for the appearance of hot spots. This opens the
road to a study of the interplay between disorder, antiferromagnetic
fluctuations and superconductivity in electron-doped cuprates.


title:
A novel framework for semi-Bayesian radial velocities through template
  matching

abstract:
  The detection and characterization of an increasing variety of exoplanets has
been in part possible thanks to the continuous development of high-resolution,
stable spectrographs, and using the Doppler radial-velocity (RV) method. The
Cross Correlation Function (CCF) method is one of the traditional approaches
for RV extraction. More recently, template matching was introduced as an
advantageous alternative for M-dwarf stars. In this paper, we describe a new
implementation of template matching within a semi-Bayesian framework, providing
a more statistically principled characterization of the RV measurements. In
this context, a common RV shift is used to describe the difference between each
spectral order of a given stellar spectrum and a template built from the
available observations. Posterior probability distributions are obtained for
the relative RV associated with each spectrum, after marginalizing with respect
to the continuum. This methodology was named S-BART: Semi-Bayesian Approach for
RVs with Template-matching, and it can be applied to HARPS and ESPRESSO. The
application of our method to HARPS archival observations of Barnard's star
allowed us to validate our implementation against HARPS-TERRA and SERVAL. Then,
we applied it to 33 ESPRESSO targets, evaluating its performance and comparing
it with the CCF method. We found a decrease in the median RV scatter of \sim
10\% and \sim 4\% for M- and K-type stars, respectively. S-BART yields more
precise RV estimates than the CCF method, particularly in the case of M-type
stars where a median uncertainty of \sim 15 cm/s is achieved over 309
observations. Further, we estimated the nightly zero point (NZP) of ESPRESSO,
finding a weighted NZP scatter below \sim 0.7 m/s. As this includes stellar
variability, photon noise, and potential planetary signals, it should be taken
as an upper limit of the RV precision attainable with ESPRESSO data.


title:
Shortcuts to adiabatic population inversion via time-rescaling:
  stability and thermodynamic cost

abstract:
  A shortcut to adiabaticity (STA) is concerned with the fast and robust
manipulation of the dynamics of a quantum system that reproduces the effect of
an adiabatic process. A recently proposed method enables the generation of
shortcuts from a prescribed slow dynamics by simply rescaling the time variable
of the quantum evolution operator [B. L. Bernardo, Phys. Rev. Research 2,
013133 (2020)]. This time-rescaling method does not demand knowledge about the
eigenvalues and eigenstates of the Hamiltonian, and in many cases no additional
coupling fields. Here, we use this approach to study the problem of speeding up
the population inversion of a two-level quantum system. The fidelity of the
dynamics versus systematic errors in the control parameters are shown to be
comparable with other STA schemes. From a quantum thermodynamic viewpoint, we
also demonstrate that the main properties of the distribution of work required
to drive the system along the shortcuts are unchanged with respect to the
reference (slow) protocol.


title:
Birds' Eye View: Measuring Behavior and Posture of Chickens as a Metric
  for Their Well-Being

abstract:
  Chicken well-being is important for ensuring food security and better
nutrition for a growing global human population. In this research, we represent
behavior and posture as a metric to measure chicken well-being. With the
objective of detecting chicken posture and behavior in a pen, we employ two
algorithms: Mask R-CNN for instance segmentation and YOLOv4 in combination with
ResNet50 for classification. Our results indicate a weighted F1 score of 88.46%
for posture and behavior detection using Mask R-CNN and an average of 91%
accuracy in behavior detection and 86.5% average accuracy in posture detection
using YOLOv4. These experiments are conducted under uncontrolled scenarios for
both posture and behavior measurements. These metrics establish a strong
foundation to obtain a decent indication of individual and group behaviors and
postures. Such outcomes would help improve the overall well-being of the
chickens. The dataset used in this research is collected in-house and will be
made public after the publication as it would serve as a very useful resource
for future research. To the best of our knowledge no other research work has
been conducted in this specific setup used for this work involving multiple
behaviors and postures simultaneously.


title:
Queues with Delayed Information: Analyzing the Impact of the Choice
  Model Function

abstract:
  In this paper, we study queueing systems with delayed information that use a
generalization of the multinomial logit choice model as its arrival process.
Previous literature assumes that the functional form of the multinomial logit
model is exponential. However, in this work we generalize this to different
functional forms. In particular, we compute the critical delay and analyze how
it depends on the choice of the functional form. We highlight how the
functional form of the model can be interpreted as an exponential model where
the exponential rate parameter is uncertain. Furthermore, the rate parameter
distribution is given by the inverse Laplace-Stieltjes transform of the
functional form when it exists. We perform numerous numerical experiments to
confirm our theoretical insights.


title: Preferential attachment hypergraph with vertex deactivation

abstract:
  In the area of complex networks so far hypergraph models have received
significantly less attention than the graphs. However, many real-life networks
feature multiary relations (co-authorship, protein reactions) thus may be
modeled way better by hypergraphs. Also, recent study by Broido and Clauset
suggests that a power-law degree distribution is not as ubiquitous in the
natural systems as it was thought so far. They experimentally confirm that
majority of networks (56% of around 1000 social, biological, technological,
transportation, and information networks that undergone the test) favor a
power-law with an exponential cutoff over other distributions. We address two
above observations by introducing a preferential attachment hypergraph model
which allows for a vertex deactivation. The phenomenon of a vertex deactivation
is rare in existing theoretical models and omnipresent in real-life scenarios
(think of social network accounts which are not maintained forever,
collaboration networks in which people eventually retire or technological
networks in which devices may break down). We prove that the degree
distribution of a proposed model follows a power-law with an exponential
cutoff. We also check experimentally that a Scopus collaboration network has
the same characteristic. We believe that our model will predict well the
behavior of the systems from variety of domains.


title: Doubting AI Predictions: Influence-Driven Second Opinion Recommendation

abstract:
  Effective human-AI collaboration requires a system design that provides
humans with meaningful ways to make sense of and critically evaluate
algorithmic recommendations. In this paper, we propose a way to augment
human-AI collaboration by building on a common organizational practice:
identifying experts who are likely to provide complementary opinions. When
machine learning algorithms are trained to predict human-generated assessments,
experts' rich multitude of perspectives is frequently lost in monolithic
algorithmic recommendations. The proposed approach aims to leverage productive
disagreement by (1) identifying whether some experts are likely to disagree
with an algorithmic assessment and, if so, (2) recommend an expert to request a
second opinion from.


title: On Negative Sampling for Audio-Visual Contrastive Learning from Movies

abstract:
  The abundance and ease of utilizing sound, along with the fact that auditory
clues reveal a plethora of information about what happens in a scene, make the
audio-visual space an intuitive choice for representation learning. In this
paper, we explore the efficacy of audio-visual self-supervised learning from
uncurated long-form content i.e movies. Studying its differences with
conventional short-form content, we identify a non-i.i.d distribution of data,
driven by the nature of movies. Specifically, we find long-form content to
naturally contain a diverse set of semantic concepts (semantic diversity),
where a large portion of them, such as main characters and environments often
reappear frequently throughout the movie (reoccurring semantic concepts). In
addition, movies often contain content-exclusive artistic artifacts, such as
color palettes or thematic music, which are strong signals for uniquely
distinguishing a movie (non-semantic consistency). Capitalizing on these
observations, we comprehensively study the effect of emphasizing within-movie
negative sampling in a contrastive learning setup. Our view is different from
those of prior works who consider within-video positive sampling, inspired by
the notion of semantic persistency over time, and operate in a short-video
regime. Our empirical findings suggest that, with certain modifications,
training on uncurated long-form videos yields representations which transfer
competitively with the state-of-the-art to a variety of action recognition and
audio classification tasks.