title:
How to optimally combine pre-reconstruction full shape and
  post-reconstruction BAO signals

abstract:
  We review the different approaches for combining the cosmological information
from the full shape of the pre-reconstructed power spectrum - usually referred
as redshift-space distortion (RSD) analysis - and from the baryon acoustic
oscillation (BAO) peak position in the post-reconstructed power spectrum with
the aim of finding the optimal procedure. We focus on combining the pre- and
post-reconstructed derived quantities at different compression levels: 1) the
two-point summary statistics, the power spectrum multipoles, $P^{(\ell)}(k)$;
2) the compressed BAO variables, $\alpha_{\parallel,\perp}$; and 3) an hybrid
approach between 1) and 2). We apply these methods to the publicly available
eBOSS Luminous Red Galaxy catalogues, for both data and synthetic EZ-mocks. We
find that the three approaches result in very consistent posteriors when the
appropriate covariance matrix estimator is used. On average, the combination at
$P^{(\ell)}(k)$ level retrieves $5-10\%$ tighter constraints than the other two
approaches, demonstrating that the standard approach of combining at the level
of the BAO variables is nearly optimal. We conclude that combining both BAO
post-reconstructed and full shape pre-reconstructed signals for the one single
data realization at the level of the summary statistics is faster, as it does
not require running the whole pipeline on the individual mocks, and brings a
moderate $10\%$ improvement, with respect to the other two studied methods.
Moreover, we check for potential systematics, such as, the way the matrix is
built and the effect of the finite number of mocks on the likelihood estimator
and find none of these have a significant impact in the final results.
Combining the pre- and post-reconstruction signals at the level of the summary
statistics is an attractive, faster and accurate method to be used in future
and on-going spectroscopic surveys.


title: Quantum information with top quarks in QCD production

abstract:
  We present here the general framework to study the quantum state of a
top-antitop ($t\bar{t}$) quark pair produced through quantum chromodynamics
(QCD) in a high-energy collider. We show that, in general, the total quantum
state that can be probed in a collider is given in terms of the production spin
density matrix, which gives rise to a mixed state. We compute the quantum state
of the $t\bar{t}$ pair produced from the most elementary QCD processes, finding
the presence of entanglement and CHSH violation in different regions of phase
space. We show that any realistic hadronic production of a $t\bar{t}$ pair is a
statistical mixture of these elementary processes. We focus on the
experimentally relevant cases of proton-proton and proton-antiproton
collisions, performed at the LHC and the Tevatron, analyzing the dependence of
the quantum state with the energy of the collisions. We provide experimentally
relevant observables for entanglement and CHSH violation signatures. At the
LHC, these signatures are given by the measurement of a single observable. In
the case of entanglement, this signature represents the violation of a
Cauchy-Schwarz inequality. We extend the quantum tomography protocol for the
$t\bar{t}$ pair proposed in the literature to more general quantum states,
finding that it is valid for any $t\bar{t}$ production mechanism. Finally, we
argue that the CHSH violation detected in a collider is only a weak form of
violation of Bell's theorem, necessarily containing a number of loopholes.


title: LINNA: Likelihood Inference Neural Network Accelerator

abstract:
  Bayesian posterior inference of modern multi-probe cosmological analyses
incurs massive computational costs. For instance, depending on the combinations
of probes, a single posterior inference for the Dark Energy Survey (DES) data
had a wall-clock time that ranged from 1 to 21 days using a state-of-the-art
computing cluster with 100 cores. These computational costs have severe
environmental impacts and the long wall-clock time slows scientific
productivity. To address these difficulties, we introduce LINNA: the Likelihood
Inference Neural Network Accelerator. Relative to the baseline DES analyses,
LINNA reduces the computational cost associated with posterior inference by a
factor of 8--50. If applied to the first-year cosmological analysis of Rubin
Observatory's Legacy Survey of Space and Time (LSST Y1), we conservatively
estimate that LINNA will save more than US $\$300,000$ on energy costs, while
simultaneously reducing $\rm{CO}_2$ emission by $2,400$ tons. To accomplish
these reductions, LINNA automatically builds training data sets, creates neural
network surrogate models, and produces a Markov chain that samples the
posterior. We explicitly verify that LINNA accurately reproduces the first-year
DES (DES Y1) cosmological constraints derived from a variety of different data
vectors with our default code settings, without needing to retune the algorithm
every time. Further, we find that LINNA is sufficient for enabling accurate and
efficient sampling for LSST Y10 multi-probe analyses. We make LINNA publicly
available at https://github.com/chto/linna, to enable others to perform fast
and accurate posterior inference in contemporary cosmological analyses.


title:
Long-term Evolution of Tightly-Packed Stellar Black Holes in AGN Disks:
  Formation of Merging Black-Hole Binaries via Close Encounters

abstract:
  We study the long-term evolution of two or more stellar black holes (BHs) on
initially separated but unstable circular orbits around a supermassive BH
(SMBH). Such a close-packed orbital configuration can naturally arise from BH
migrations in the AGN disk. Dynamical instability of the orbits leads to
recurring close encounters between two BHs, during which the BH separation
$r_{\rm p}$ becomes less than the Hill radius $R_{\rm H}$. In the rare very
close encounters (with $r_{\rm p}$ several orders of magnitude less than
$R_{\rm H}$), a tight merging BH binary can form with the help of gravitational
wave emission. We use $N$-body simulations to study the time evolution of close
encounters of various degrees of "closeness" and the property of the resulting
binary BH mergers. For a typical "SMBH + 2 BHs" system, the averaged cumulative
number of close encounters (with $r_{\rm p} \lesssim R_{\rm H}$) scales
approximately as $\propto t^{0.5}$. The minimum encounter separation $r_{\rm
p}$ follows a linear cumulative distribution $P(<r_{\rm p}) \propto r_{\rm p}$
for $r_{\rm p} \ll R_{\rm H}$. From these, we obtain a semi-analytical
expression for the averaged rate of binary captures that lead to BH mergers.
Our results suggest that close-packed BHs in AGN disks may take a long time
($\gtrsim 10^7$ orbits around the SMBH) to experience a sufficiently close
encounter and form a bound binary, although this time can be shorter if the
initial BH orbits are highly aligned. The BH binary mergers produced in this
scenario always have high eccentricities when entering the LIGO band, and have
a broad distribution of orbital inclinations relative to the original AGN disk.
We also explore the effects of the gas disk and find that simple gas drags on
the BHs do not necessarily lead to an enhanced BH binary capture rate.


title: End-to-End Learning to Grasp from Object Point Clouds

abstract:
  The ability to grasp objects is an essential skill that enables many robotic
manipulation tasks. Recent works have studied point cloud-based methods for
object grasping by starting from simulated datasets and have shown promising
performance in real-world scenarios. Nevertheless, many of them still strongly
rely on ad-hoc geometric heuristics to generate grasp candidates, which fail to
generalize to objects with significantly different shapes with respect to those
observed during training. Moreover, these methods are generally inefficient
with respect to the number of training samples and the time needed during
deployment. In this paper, we propose an end-to-end learning solution to
generate 6-DOF parallel-jaw grasps starting from the partial view of the
object. Our Learning to Grasp (L2G) method takes as input object point clouds
and is guided by a principled multi-task optimization objective that generates
a diverse set of grasps combining contact point sampling, grasp regression, and
grasp evaluation. With a thorough experimental analysis, we show the
effectiveness of the proposed method as well as its robustness and
generalization abilities.


title:
Control of nonlinear processes using versatile random photonic sources:
  Application to the energy deposition in a dielectric material

abstract:
  We report on the properties of a non-conventional stochastic photonic source.
We first describe the principle of nonlinear intensity filtering by using a
modified Mach-Zehnder interferometer. The latter alters the characteristics of
an input stochastic source based on Bose-Einstein emission. Computed output
intensity fluctuations are compared to an analytical model. Adjusting the
interferometer parameters, we show theoretically and numerically that the
statistical properties of light such as its probability density function can be
tailored. Depending on the parameters, the probability density may exhibit
large overshoots or smoother fluctuations. We further evaluate the impact of
these modified statistics on simple nonlinear processes. Compared to
Bose-Einstein emitters, the yield of nonlinear phenomena varies by several
orders of magnitude. We finally simulate the nonlinear interaction of such a
laser source with a dielectric material (fused silica) within the framework of
a more realistic model, including a statistical analysis. It confirms that the
deposited energy varies over many decades and can be largely enhanced due to
the properties of the presented laser source.


title: How to avoid the appearance of a classical world in gravity experiments

abstract:
  Quantum states of gravitational source masses can lead to experimental
outcomes that are inconsistent with the predictions of a purely classical field
theory of gravity. Environmental decoherence places strict boundary conditions
to the potential realization of such experiments: sufficiently mild not to act
as a fundamental show-stopper, yet sufficiently demanding to represent a
formidable challenge to the next generation of quantum experiment(er)s.


title: Convex geometries over induced paths with bounded length

abstract:
  Graph convexity spaces have been studied in many contexts. In particular,
some studies are devoted to determine if a graph equipped with a convexity
space is a {\em convex geometry}. It is well known that chordal and Ptolemaic
graphs can be characterized as convex geometries with respect to the geodesic
and monophonic convexities, respectively. Weak polarizable graphs, interval
graphs, and proper interval graphs can also be characterized in this way. In
this paper we introduce the notion of {\em $l^k$-convexity}, a natural
restriction of the monophonic convexity. Let $G$ be a graph and $k\geq 2$ an
integer. A subset $S\subseteq V(G)$ is \textit{$l^k$-convex} if and only if for
any pair of vertices $x,y$ of $S$, each induced path of length {\em at most}
$k$ connecting $x$ and $y$ is completely contained in the subgraph induced by
$S$. The {\em $l^k$-convexity} consists of all $l^k$-convex subsets of $G$. In
this work, we characterize {\em $l^k$-convex geometries} (graphs that are
convex geometries with respect to the $l^k$-convexity) for $k\in\{2,3\}$. We
show that a graph $G$ is an $l^2$-convex geometry if and only if $G$ is a
chordal $P_4$-free graph, and an $l^3$-convex geometry if and only if $G$ is a
chordal graph with diameter at most three such that its induced gems satisfy a
special "solving" property. As far as the authors know, the class of
$l^3$-convex geometries is the first example of a non-hereditary class of
convex geometries.


title: Generalized quasi-topological gravities: the whole shebang

abstract:
  Generalized quasi-topological gravities (GQTGs) are higher-curvature
extensions of Einstein gravity in $D$-dimensions. Their defining properties
include possessing second-order linearized equations of motion around maximally
symmetric backgrounds as well as non-hairy generalizations of Schwarzschild's
black hole characterized by a single function, $f(r)\equiv -
g_{tt}=g_{rr}^{-1}$, which satisfies a second-order differential equation. In
arXiv:1909.07983 GQTGs were shown to exist at all orders in curvature and for
general $D$. In this paper we prove that, in fact, $n-1$ inequivalent classes
of order-$n$ GQTGs exist for $D\geq 5$. Amongst these, we show that one -- and
only one -- type of densities is of the Quasi-topological kind, namely, such
that the equation for $f(r)$ is algebraic. Our arguments do not work for $D=4$,
in which case there seems to be a single unique GQT density at each order which
is not of the Quasi-topological kind. We compute the thermodynamic charges of
the most general $D$-dimensional order-$n$ GQTG, verify that they satisfy the
first law and provide evidence that they can be entirely written in terms of
the embedding function which determines the maximally symmetric vacua of the
theory.


title: An all-dielectric metasurface polarimeter

abstract:
  The polarization state of light is a key parameter in many imaging systems.
For example, it can image mechanical stress and other physical properties that
are not seen with conventional imaging, and can also play a central role in
quantum sensing. However, polarization is more difficult to image and
polarimetry typically involves several independent measurements with moving
parts in the measurement device. Metasurfaces with interleaved designs have
demonstrated sensitivity to either linear or circular/elliptical polarization
states. Here we present an all-dielectric meta-polarimeter for direct
measurement of any arbitrary polarization states from a single unit-cell
design. By engineering a completely asymmetric design, we obtained a
metasurface that can excite eigenmodes of the nanoresonators, thus displaying a
unique diffraction pattern for not only any linear polarization state but all
elliptical polarization states (and handedness) as well. The unique diffraction
patterns are quantified into Stokes parameters with a resolution of 5$^{\circ}$
and with a polarization state fidelity of up to $99\pm1$%. This holds promise
for applications in polarization imaging and quantum state tomography.


title: Tau Neutrinos in the Next Decade: from GeV to EeV

abstract:
  Tau neutrinos are the least studied particle in the Standard Model. This
whitepaper discusses the current and expected upcoming status of tau neutrino
physics with attention to the broad experimental and theoretical landscape
spanning long-baseline, beam-dump, collider, and astrophysical experiments.
This whitepaper was prepared as a part of the NuTau2021 Workshop.


title:
Galactic chemical evolution]{Galactic chemical evolution of the solar
  neighborhood, solar twins and exoplanet indicators

abstract:
  Galactic chemical evolution (GCE), solar analogues or twins, and
peculiarities of the solar composition with respect to the twins are
inextricably related. We examine GCE parameters from the literature and present
newly derived values using a quadratic fit that gives zero for a Solar age
(i.e., 4.6 Gyr). We show how the GCE parameters may be used not only to
"correct" abundances to the solar age, but to predict relative elemental
abundances as a function of age. We address the question of whether the solar
abundances are depleted in refractories and enhanced in volatiles and find that
the answer is sensitive to the selection of a representative standard. The best
quality data sets do not support the notion that the Sun is depleted in
refractories and enhanced in volatiles. A simple model allows us to estimate
the amount of refractory-rich material missing from the Sun or alternately
added to the average solar twin. The model gives between zero and 1.4 earth
masses.


title: Labor Demand on a Tight Leash

abstract:
  Although theory highlights search frictions in tight labor markets, standard
models of labor demand do not account for labor market tightness. Given the
universe of administrative employment data on Germany, we study the effect of
labor market tightness on firms' labor demand using novel Bartik instruments
that rely on predetermined firm shares and national shifts at the occupation
level. In line with theory, the IV results suggest that a 10 percent increase
in labor market tightness reduces firms' employment by 0.5 percent. When
accounting for search externalities, we find that the individual-firm wage
elasticity of labor demand reduces from -0.7 to -0.5 at the aggregate level.
For the 2015 minimum wage introduction, the elasticities imply only modest
disemployment effects mirroring empirical ex-post evaluations. Moreover, the
doubling of tightness between 2012 and 2019 led to a significant slowdown in
employment growth by 1.1 million jobs.


title: Split Electrons in Partition Density Functional Theory

abstract:
  Partition Density Functional Theory (P-DFT) is a density embedding method
that partitions a molecule into fragments by minimizing the sum of fragment
energies subject to a local density constraint and a global electron-number
constraint. To perform this minimization, we study a two-stage procedure in
which the sum of fragment energies is lowered when electrons flow from
fragments of lower electronegativity to fragments of higher electronegativity.
The global minimum is reached when all electronegativities are equal. The
non-integral fragment populations are dealt with in two different ways: (1) An
ensemble approach (ENS) that involves averaging over calculations with
different numbers of electrons (always integers); and (2) A simpler approach
that involves fractionally occupying orbitals (FOO). We compare and contrast
these two approaches and examine their performance in some of the simplest
systems where one can transparently apply both, including simple models of
heteronuclear diatomic molecules and actual diatomic molecules with 2 and 4
electrons. We find that, although both ENS and FOO methods lead to the same
total energy and density, the ENS fragment densities are less distorted than
those of FOO when compared to their isolated counterparts, and they tend to
retain integer numbers of electrons. We establish the conditions under which
the ENS populations can become fractional and observe that, even in those
cases, the total charge transferred is always lower in ENS than in FOO.
Similarly, the FOO fragment dipole moments provide an upper bound to the ENS
dipoles. We explain why, and discuss implications.


title: Social Networks and Spatial Mobility: Evidence from Facebook in India

abstract:
  This paper studies the role of social networks in spatial mobility across
India. Using aggregated and de-identified data from the world's largest online
social network, we (i) document new descriptive findings on the structure of
social networks and spatial mobility in India; (ii) quantify the effects of
social networks on annual migration choice; and (iii) embed these estimates in
a spatial equilibrium model to study the wage implications of increasing social
connectedness. Across millions of individuals, we find that multiple measures
of social capital are concentrated among the rich and educated and among
migrants. Across destinations, both mobility patterns and social networks are
concentrated toward richer areas. A model of migration suggests individuals are
indifferent between a 10% increase in destination wages and a 12-16% increase
in destination social networks. Accounting for networks reduces the
migration-distance relationship by 19%. In equilibrium, equalizing social
networks across locations improves average wages by 3% (24% for the bottom
wage-quartile), a larger impact than removing the marginal cost of distance. We
find evidence of an economic support mechanism, with destination economic
improvements reducing the migration-network elasticity. We also find suggestive
evidence for an emotional support mechanism from qualitative surveys among
Facebook users. Difference-in-difference estimates suggest college attendance
delivers a 20% increase in network size and diversity. Taken together, our data
suggest that - by reducing effective moving costs - increasing social
connectedness across space may have considerable economic gains.


title:
The Lick Observatory Supernova Search follow-up program: photometry data
  release of 70 stripped-envelope supernovae

abstract:
  We present BVRI and unfiltered Clear light curves of 70 stripped-envelope
supernovae (SESNe), observed between 2003 and 2020, from the Lick Observatory
Supernova Search (LOSS) follow-up program. Our SESN sample consists of 19
spectroscopically normal SNe~Ib, two peculiar SNe Ib, six SN Ibn, 14 normal SNe
Ic, one peculiar SN Ic, ten SNe Ic-BL, 15 SNe IIb, one ambiguous SN IIb/Ib/c,
and two superluminous SNe. Our follow-up photometry has (on a per-SN basis) a
mean coverage of 81 photometric points (median of 58 points) and a mean cadence
of 3.6d (median of 1.2d). From our full sample, a subset of 38 SNe have
pre-maximum coverage in at least one passband, allowing for the peak brightness
of each SN in this subset to be quantitatively determined. We describe our data
collection and processing techniques, with emphasis toward our automated
photometry pipeline, from which we derive publicly available data products to
enable and encourage further study by the community. Using these data products,
we derive host-galaxy extinction values through the empirical colour evolution
relationship and, for the first time, produce accurate rise-time measurements
for a large sample of SESNe in both optical and infrared passbands. By modeling
multiband light curves, we find that SNe Ic tend to have lower ejecta masses
and lower ejecta velocities than SNe~Ib and IIb, but higher $^{56}$Ni masses.


title: Bounds on the number of maximal subgroups of finite groups

abstract:
  In this paper we obtain significant bounds for the number of maximal
subgroups of a given index of a finite group. These results allow us to give
new bounds for the number of random generators needed to generate a finite
$d$-generated group with high probability.


title:
A new approach to calculating BERTScore for automatic assessment of
  translation quality

abstract:
  The study of the applicability of the BERTScore metric was conducted to
translation quality assessment at the sentence level for English -> Russian
direction. Experiments were performed with a pre-trained Multilingual BERT as
well as with a pair of Monolingual BERT models. To align monolingual
embeddings, an orthogonal transformation based on anchor tokens was used. It
was demonstrated that such transformation helps to prevent mismatching issue
and shown that this approach gives better results than using embeddings of the
Multilingual model. To improve the token matching process it is proposed to
combine all incomplete WorkPiece tokens into meaningful words and use simple
averaging of corresponding vectors and to calculate BERTScore based on anchor
tokens only. Such modifications allowed us to achieve a better correlation of
the model predictions with human judgments. In addition to evaluating machine
translation, several versions of human translation were evaluated as well, the
problems of this approach were listed.


title: Memory Compression with Quantum Random-Access Gates

abstract:
  In the classical RAM, we have the following useful property. If we have an
algorithm that uses $M$ memory cells throughout its execution, and in addition
is sparse, in the sense that, at any point in time, only $m$ out of $M$ cells
will be non-zero, then we may "compress" it into another algorithm which uses
only $m \log M$ memory and runs in almost the same time. We may do so by
simulating the memory using either a hash table, or a self-balancing tree.
  We show an analogous result for quantum algorithms equipped with quantum
random-access gates. If we have a quantum algorithm that runs in time $T$ and
uses $M$ qubits, such that the state of the memory, at any time step, is
supported on computational-basis vectors of Hamming weight at most $m$, then it
can be simulated by another algorithm which uses only $O(m \log M)$ memory, and
runs in time $\tilde O(T)$.
  We show how this theorem can be used, in a black-box way, to simplify the
presentation in several papers. Broadly speaking, when there exists a need for
a space-efficient history-independent quantum data-structure, it is often
possible to construct a space-inefficient, yet sparse, quantum data structure,
and then appeal to our main theorem. This results in simpler and shorter
arguments.


title: Discrete Mechanical Systems in a Dirac Setting: a Proposal

abstract:
  In these notes, we present an alternative version of discrete Dirac mechanics
using Dirac structures. We first establish a notion of 'continuous Dirac
system' and then propose a definition of discrete Dirac system, proving that it
is possible to recover discrete Lagrangian and Hamiltonian systems as
particular cases. We also note that this approach allows for kinematic as well
as variational constraints.


title:
Human Face Recognition from Part of a Facial Image based on Image
  Stitching

abstract:
  Most of the current techniques for face recognition require the presence of a
full face of the person to be recognized, and this situation is difficult to
achieve in practice, the required person may appear with a part of his face,
which requires prediction of the part that did not appear. Most of the current
forecasting processes are done by what is known as image interpolation, which
does not give reliable results, especially if the missing part is large. In
this work, we adopted the process of stitching the face by completing the
missing part with the flipping of the part shown in the picture, depending on
the fact that the human face is characterized by symmetry in most cases. To
create a complete model, two facial recognition methods were used to prove the
efficiency of the algorithm. The selected face recognition algorithms that are
applied here are Eigenfaces and geometrical methods. Image stitching is the
process during which distinctive photographic images are combined to make a
complete scene or a high-resolution image. Several images are integrated to
form a wide-angle panoramic image. The quality of the image stitching is
determined by calculating the similarity among the stitched image and original
images and by the presence of the seam lines through the stitched images. The
Eigenfaces approach utilizes PCA calculation to reduce the feature vector
dimensions. It provides an effective approach for discovering the
lower-dimensional space. In addition, to enable the proposed algorithm to
recognize the face, it also ensures a fast and effective way of classifying
faces. The phase of feature extraction is followed by the classifier phase.


title:
Gesture based Arabic Sign Language Recognition for Impaired People based
  on Convolution Neural Network

abstract:
  The Arabic Sign Language has endorsed outstanding research achievements for
identifying gestures and hand signs using the deep learning methodology. The
term "forms of communication" refers to the actions used by hearing-impaired
people to communicate. These actions are difficult for ordinary people to
comprehend. The recognition of Arabic Sign Language (ArSL) has become a
difficult study subject due to variations in Arabic Sign Language (ArSL) from
one territory to another and then within states. The Convolution Neural Network
has been encapsulated in the proposed system which is based on the machine
learning technique. For the recognition of the Arabic Sign Language, the
wearable sensor is utilized. This approach has been used a different system
that could suit all Arabic gestures. This could be used by the impaired people
of the local Arabic community. The research method has been used with
reasonable and moderate accuracy. A deep Convolutional network is initially
developed for feature extraction from the data gathered by the sensing devices.
These sensors can reliably recognize the Arabic sign language's 30 hand sign
letters. The hand movements in the dataset were captured using DG5-V hand
gloves with wearable sensors. For categorization purposes, the CNN technique is
used. The suggested system takes Arabic sign language hand gestures as input
and outputs vocalized speech as output. The results were recognized by 90% of
the people.


title: Some applications of TDA on financial markets

abstract:
  The Topological Data Analysis (TDA) has had many applications. However,
financial markets has been studied slightly through TDA. Here we present a
quick review of some recent applications of TDA on financial markets and
propose a new turbulence index based on persistent homology -- the fundamental
tool for TDA -- that seems to capture critical transitions on financial data,
based on our experiment with SP500 data before 2020 stock market crash in
February 20, 2020, due to the COVID-19 pandemic. We review applications in the
early detection of turbulence periods in financial markets and how TDA can help
to get new insights while investing and obtain superior risk-adjusted returns
compared with investing strategies using classical turbulence indices as VIX
and the Chow's index based on the \textit{Mahalanobis} distance. Furthermore,
we include an introduction to persistent homology so the reader could be able
to understand this paper without knowing TDA.


title: Deep Learning-Based Perceptual Stimulus Encoder for Bionic Vision

abstract:
  Retinal implants have the potential to treat incurable blindness, yet the
quality of the artificial vision they produce is still rudimentary. An
outstanding challenge is identifying electrode activation patterns that lead to
intelligible visual percepts (phosphenes). Here we propose a PSE based on CNN
that is trained in an end-to-end fashion to predict the electrode activation
patterns required to produce a desired visual percept. We demonstrate the
effectiveness of the encoder on MNIST using a psychophysically validated
phosphene model tailored to individual retinal implant users. The present work
constitutes an essential first step towards improving the quality of the
artificial vision provided by retinal implants.


title:
Optically coherent nitrogen-vacancy defect centers in diamond
  nanostructures

abstract:
  Optically active solid-state spin defects have the potential to become a
versatile resource for quantum information processing applications.
Nitrogen-vacancy defect centers (NV) in diamond act as quantum memories and can
be interfaced by coherent photons as demonstrated in entanglement protocols.
However, in particular in diamond nanostructures, the effect of spectral
diffusion leads to optical decoherence hindering entanglement generation. In
this work, we present strategies to significantly reduce the electric noise in
diamond nanostructures. We demonstrate single NVs in nanopillars exhibiting
lifetime-limited linewidth on the time scale of one second and long-term
spectral stability with inhomogeneous linewidth as low as 150 MHz over three
minutes. Excitation power and energy-dependent measurements in combination with
nanoscopic Monte Carlo simulations contribute to a better understanding of the
impact of bulk and surface defects on the NV's spectral properties. Finally, we
propose an entanglement protocol for nanostructure-coupled NVs providing
entanglement generation rates up to hundreds of kHz.


title:
The origin of MOND acceleration and deep-MOND behavior from mass and
  energy cascade in dark matter flow

abstract:
  The MOND paradigm is an empirical theory with modified gravity to reproduce
many astronomical observations without invoking the dark matter hypothesis.
Instead of falsifying the existence of dark matter, we propose that MOND is an
effective theory naturally emerging from the long-range and collisionless
nature of dark matter flow. It essentially describes the dynamics of baryonic
mass suspended in fluctuating dark matter fluid. We first review the unique
properties of self-gravitating collisionless dark matter flow (SG-CFD),
followed by their implications in the origin of MOND theory. To maximize system
entropy, the long-range interaction requires a broad size of halos to be
formed. These halos facilitate an inverse mass and energy cascade from small to
large mass scales that involves a constant rate of energy transfer
$\epsilon_u$=$-4.6\times10^{-7}m^2/s^3$. In addition to the velocity
fluctuation with a typical scale $u$, the long-range interaction leads to a
fluctuation in acceleration with a typical scale $a_0$ that matches the value
of critical MOND acceleration. The velocity and acceleration fluctuations in
dark matter flow satisfy the equality $\epsilon_u$=$-a_0u/(3\pi)^2$ such that
$a_0$ can be determined. A notable (unexplained) coincidence of cosmological
constant $\Lambda\propto (a_0/c)^2$ might point to a dark energy density
proportional to acceleration fluctuation, i.e. $\rho_{vac}\propto a_0^2/G$. At
z=0 with $u$=354.61km/s, $a_0$=$1.2\times10^{-10}m/s^2$ can be obtained. For
given particle velocity $v_p$, maximum entropy distributions developed from
mass/energy cascade lead to a particle kinetic energy $\epsilon_k\propto v_p$
at small acceleration $<a_0$ and $\epsilon_k\propto v_p^2$ for large
acceleration $>a_0$. Combining this with the constant rate of energy transfer
$\epsilon_u$, both regular Newtonian dynamics and deep-MOND behavior can be
fully recovered.


title:
In-depth characterization and analysis of simple shear flows over
  regularly arranged micro pillars, I. Effect of fluid inertia

abstract:
  Through high-fidelity numerical simulation, the simple shear flow over
regularly arranged micro pillars has been investigated. The essential issues to
be addressed include the characteristics of a simple shear flow over
quadrilateral array of micro pillars, the effect of fluid inertia on the basic
flow pattern, and the decomposition of the complex surface friction. The
results show that the flow is characterized by a series of microscale
recirculating eddies in the gaps between the streamwise neighboring pillars.
The recirculation of the micro eddies and the oscillation of the overhead flow
climbing over the pillar tips create a local flow advection. At smaller
Reynolds number, the fluid inertia is weak and the flow patterns are
symmetrical about the pillar center. When the Reynolds number is sufficiently
large, the fluid inertia takes effect and breaks the symmetrical patterns. The
overhead flow tilts downward, forming a spiral long-range advection between the
fluid flow above pillar array and the flow in the spaces among micro pillars.
The local advection and long-range advection constitute the transport mechanism
in wall-normal direction. On micro-structured walls, the total friction
includes the reaction forces of micro pillars due to flow shear and flow
pressure at pillar surfaces and the reaction force of bottom plane due to flow
shear on bottom surface. For larger Reynolds numbers, fluid inertia prevents
the fluid from flowing along the curved surface of micro pillars and reduces
the equivalent shear stress of the pillar reaction force due to flow shear. At
the same time, the fluid inertia makes the overhead flow impact the windward
side of micro pillars more strongly and therefore increases the equivalent
shear stress of the pillar reaction force due to flow pressure.


title:
A modular and flexible data acquisition system for a cosmic rays
  detector network

abstract:
  In this paper, we describe a modular data acquisition system developed as the
foundation of a cosmic ray detector network. Each detector setup (henceforth
referred as a station) is composed of an independent hardware device that can
be controlled and read-out through the Internet. This device is designed to
acquire and process the signal of up to eight different detector planes. Each
of these detector planes uses plastic scintillator slabs that are optically
coupled to silicon photomultipliers (SiPM). Within a single station, different
geometries and plane orientations are possible using the same baseline design.
The main readout is based on a programmable system-on-a-chip (PSoC), a flexible
and re-configurable commodity hardware that is used to implement the trigger
and timing logic. A Time to Digital Converter (TDC) is used to determine the
precise timing of the event relative to a GPS timing signal and to estimate the
signal amplitude through the Time-over-Threshold (ToT) method. An auxiliary set
of sensors provide environmental information and station detector planes
orientation that, together with other operation data, are periodically sent to
a server using the MQTT protocol. Data is cached using an in-memory database
for online monitoring and further persisted into a SQL database for offline
analysis. The server framework is based in software application containers
allowing easy replication of the server infrastructure.


title: Locally compact models for approximate rings

abstract:
  By an approximate subring of a ring we mean an additively symmetric subset
$X$ such that $X\cdot X \cup (X +X)$ is covered by finitely many additive
translates of $X$. We prove that each approximate subring $X$ of a ring has a
locally compact model, i.e. a ring homomorphism $f \colon \langle X \rangle \to
S$ for some locally compact ring $S$ such that $f[X]$ is relatively compact in
$S$ and there is a neighborhood $U$ of $0$ in $S$ with $f^{-1}[U] \subseteq 4X
+ X \cdot 4X$ (where $4X:=X+X+X+X$). This $S$ is obtained as the quotient of
the ring $\langle X \rangle$ interpreted in a sufficiently saturated model by
its type-definable ring connected component. The main point is to prove that
this component always exists. In order to do that, we extend the basic theory
of model-theoretic connected components of definable rings (developed in
[GJK20] and [KR20]) to the case of rings generated by definable approximate
subgrings and we answer a question from [KR20] in the more general context of
approximate subrings. Namely, let $X$ be a definable (in a structure $M$)
approximate subring of a ring and $R:=\langle X \rangle$. Let $\bar X$ be the
interpretation of $X$ in a sufficiently saturated elementary extension and
$\bar R := \langle \bar X \rangle$. It follows from [MW15] that there exists
the smallest $M$-type-definable subgroup of $(\bar R,+)$ of bounded index,
which is denoted by $(\bar R,+)^{00}_M$. We prove that $(\bar R,+)^{00}_M +
\bar R \cdot (\bar R,+)^{00}_M$ is the smallest $M$-type-definable two-sided
ideal of $\bar R$ of bounded index, which we denote by $\bar R^{00}_M$. Then
$S$ in the first sentence of the abstract is just $\bar R/\bar R^{00}_M$ and
$f: R \to \bar R/\bar R^{00}_M$ is the quotient map. In fact, $f$ is the
universal "definable" (in a suitable sense) locally compact model.


title:
Parallel inexact Newton-Krylov and quasi-Newton solvers for nonlinear
  elasticity

abstract:
  In this work, we address the implementation and performance of inexact
Newton-Krylov and quasi-Newton algorithms, more specifically the BFGS method,
for the solution of the nonlinear elasticity equations, and compare them to a
standard Newton-Krylov method. This is done through a systematic analysis of
the performance of the solvers with respect to the problem size, the magnitude
of the data and the number of processors in both almost incompressible and
incompressible mechanics. We consider three test cases: Cook's membrane
(static, almost incompressible), a twist test (static, incompressible) and a
cardiac model (complex material, time dependent, almost incompressible). Our
results suggest that quasi-Newton methods should be preferred for compressible
mechanics, whereas inexact Newton-Krylov methods should be preferred for
incompressible problems. We show that these claims are also backed up by the
convergence analysis of the methods. In any case, all methods present adequate
performance, and provide a significant speed-up over the standard Newton-Krylov
method, with a CPU time reduction exceeding 50% in the best cases.


title:
Correlating exciton coherence length, localization, and its optical
  lineshape. I. a finite temperature solution of the Davydov soliton model

abstract:
  The lineshape of spectroscopic transitions offer windows into the local
environment of a system. Here, we present a novel approach for connecting the
lineshape of a molecular exciton to finite-temperature lattice vibrations
within the context of the Davydov soliton model (A. S. Davydov and N. I.
Kislukha, Phys. Stat. Sol. {\bf 59},465(1973)). Our results are based upon a
numerically exact, self-consistent treatment of the model in which thermal
effects are introduced as fluctuations about the zero-temperature localized
soliton state. We find that both the energy fluctuations and the localization
can be described in terms of a parameter-free, reduced description by
introducing a critical temperature below which exciton self-trapping is
expected to be stable. Above this temperature, the self-consistent ansatz
relating the lattice distortion to the exciton wavefunction breaks down. Our
theoretical model coorelates well with both experimental observations on
molecular J-aggregate and resolves one of the critical issues concerning the
finite temperture stability of soliton states in alpha-helices and protein
peptide chains.


title:
City-wide Street-to-Satellite Image Geolocalization of a Mobile Ground
  Agent

abstract:
  Cross-view image geolocalization provides an estimate of an agent's global
position by matching a local ground image to an overhead satellite image
without the need for GPS. It is challenging to reliably match a ground image to
the correct satellite image since the images have significant viewpoint
differences. Existing works have demonstrated localization in constrained
scenarios over small areas but have not demonstrated wider-scale localization.
Our approach, called Wide-Area Geolocalization (WAG), combines a neural network
with a particle filter to achieve global position estimates for agents moving
in GPS-denied environments, scaling efficiently to city-scale regions. WAG
introduces a trinomial loss function for a Siamese network to robustly match
non-centered image pairs and thus enables the generation of a smaller satellite
image database by coarsely discretizing the search area. A modified particle
filter weighting scheme is also presented to improve localization accuracy and
convergence. Taken together, WAG's network training and particle filter
weighting approach achieves city-scale position estimation accuracies on the
order of 20 meters, a 98% reduction compared to a baseline training and
weighting approach. Applied to a smaller-scale testing area, WAG reduces the
final position estimation error by 64% compared to a state-of-the-art baseline
from the literature. WAG's search space discretization additionally
significantly reduces storage and processing requirements.


title:
Extending the spin excitation lifetime of a magnetic molecule on a
  proximitized superconductor

abstract:
  Magnetic molecules deposited on surfaces are a promising platform to
individually address and manipulate spins. Long spin excitation lifetimes are
necessary to utilize them in quantum information processing and data storage.
Normally, coupling of the molecular spin with the conduction electrons of
metallic surfaces causes fast relaxation of spin excitations into the ground
state. However, the presence of superconducting paring effects in the substrate
can protect the excited spin from decaying. In this work, we show that a
proximity-induced superconducting gold film can sustain spin excitations of a
FeTPP-Cl molecule for more than 80ns. This long value was determined by
studying inelastic spin excitations of the S=5/2 multiplet of FeTPP-Cl on Au
films over V(100) using scanning tunneling spectroscopy. The spin lifetime
decreases with increasing film thickness, in apparent connection with the
gradual gap-closing of a pair of de Gennes-Saint James resonances found inside
the superconducting gap. Our results elucidate the use of proximitized gold
electrodes for addressing quantum spins on surfaces, envisioning new routes for
tuning the value of their spin lifetime.


title:
The MUSE Extremely Deep Field: a first panoramic view of an Mg II
  emitting intragroup medium

abstract:
  Using the exquisite MUSE eXtremely Deep Field data, we report the discovery
of an MgII emission nebula with an area above a 2$\sigma$ significance level of
1000 proper kpc$^2$, providing the first panoramic view of the spatial
distribution of magnesium in the intragroup medium of a low mass group of five
star-forming galaxies at z=1.31. The galaxy group members are separated by less
than 50 physical kpc in projection and $\approx$120 km/s in velocity space. The
most massive galaxy has a stellar mass of 10$^{9.35}$ M$_\odot$ and shows an
MgII P-Cygni line profile indicating the presence of an outflow, which is
consistent with the spatially resolved spectral analysis showing $\approx+$120
km/s shift of the MgII emission lines with respect to the systemic redshift.
The other galaxies are less massive and only show MgII in emission. The
detected MgII nebula has a maximal projected extent of $\approx$70 kpc
including a low surface brightness (2 $\times$ 10$^{-19}$
erg/s/cm$^{2}$/arcsec$^{2}$) gaseous bridge between two subgroups of galaxies.
The presence of absorption features in the spectrum of a background galaxy
located at an impact parameter of 19 kpc from the closest galaxy of the group
indicates the presence of gas enriched in magnesium even beyond the detected
nebula seen in emission, suggesting that we are observing the tip of a larger
intragroup medium. The observed MgII velocity gradient suggests an overall
rotation of the structure along the major axis of the most massive galaxy. Our
MUSE data also reveal extended Fe II* emission in the vicinity of the most
massive galaxy, aligned with its minor axis. Extended [OII] emission is found
around the galaxy group members and at the location of the MgII bridge. Our
results suggest that both tidal stripping effects from galaxy interactions and
outflows are enriching the intragroup medium of this system.


title: Was Ulam right? II: Small width and general ideals

abstract:
  We continue our study of Sierpinski-type colourings. In contrast to the
prequel paper, we focus here on colourings for ideals stratified by their
completeness degree. In particular, improving upon Ulam's theorem and its
extension by Hajnal, it is proved that if $\kappa$ is a regular uncountable
cardinal that is not weakly compact in L, then there is a universal witness for
non-weak-saturation of $\kappa$-complete ideals. Specifically, there are
$\kappa$-many decompositions of $\kappa$ such that, for every $\kappa$-complete
ideal $J$ over $\kappa$, and every $B\in J^+$, one of the decompositions
shatters $B$ into $\kappa$-many $J^+$-sets.
  A second focus here is the feature of narrowness of colourings, one already
present in the theorem of Sierpinski. This feature ensures that a colouring
suitable for an ideal is also suitable for all superideals possessing the
requisite completeness degree. It is proved that unlike successors of regulars,
every successor of a singular cardinal admits such a narrow colouring.


title: Cosmologia com aglomerados de gal\'axias

abstract:
  Galaxy clusters are important cosmological probes since their abundance and
spatial distribution are directly linked to structure formation on large
scales. The principal uncertainty source on the cosmological parameter
constraints concerns the cluster mass estimation from mass proxies. In
addition, future surveys will provide a large amount of data, requiring an
improvement in the accuracy of other elements used in the construction of
cluster likelihoods. Therefore, accurate modeling of the mass-observable
relations and reducing the effect of different systematic errors are
fundamental steps for the success of cluster cosmology. In this work, we
briefly review the abundance of galaxy clusters and discuss many sources of
uncertainty.
  Os aglomerados de gal\'axias s\~ao importantes sondas cosmol\'ogicas, j\'a
que a abund\^ancia e a distribui\c{c}\~ao espacial desses objetos est\~ao
diretamente ligadas \`a forma\c{c}\~ao de estruturas em grandes escalas. A
maior fonte de incerteza nas restri\c{c}\~oes de par\^ametros cosmol\'ogicos
\'e origin\'aria das estimativas das massas dos aglomerados a partir da
rela\c{c}\~ao massa-observ\'avel. Al\'em disso, os pr\'oximos grandes
levantamentos fornecer\~ao uma grande quantidade de dados, requerendo uma
melhoria na precis\~ao de outros elementos utilizados na constru\c{c}\~ao das
verossimilhan\c{c}as de aglomerados. Portanto, uma modelagem precisa da
rela\c{c}\~ao massa-observ\'avel e diminuir o efeito dos diferentes erros
sistem\'aticos s\~ao passos fundamentais para o sucesso da cosmologia com
aglomerados. Neste trabalho, fazemos uma breve revis\~ao da abund\^ancia de
aglomerados de gal\'axias, e discuss\~ao de diferentes fontes de incerteza.


title: Discovery of a new molecular ion, HC7NH+, in TMC-1

abstract:
  We report the detection of the protonated form of HC7N in TMC-1. The
discovery of the cation HC7NH+ was carried out via the observation of nine
harmonically related lines in the Q-band using the Yebes 40m radiotelescope.
The observed frequencies allowed us to obtain the rotational constants
B_0=553.938802(160)MHz and D_0=3.6292(705) Hz. The identification of HC7NH+ is
further supported by accurate ab initio calculations. We derived a column
density of (5.5+/-0.7)e10 cm-2, which constitutes another piece of evidence for
the identification of the carrier. In addition, we revised the HC7N column
density and we derived a new value of (2.1+/-0.2)e13cm-2. Hence, the abundance
ratio HC7N/HC7NH+ is 380, while those for HC3N/HC3NH+ and HC5N/HC5NH+ are 230
and 240, respectively. Here, we discuss these results within the framework of a
chemical model for protonated molecules in cold dense clouds.


title:
A Linearithmic Time Locally Optimal Algorithm for the Multiway Number
  Partition Optimization

abstract:
  We study the problem of multiway number partition optimization, which has a
myriad of applications in the decision, learning and optimization literature.
Even though the original multiway partitioning problem is NP-hard and requires
exponential time complexity algorithms; we formulate an easier optimization
problem, where our goal is to find a solution that is locally optimal. We
propose a linearithmic time complexity $O(N\log N)$ algorithm that can produce
such a locally optimal solution. Our method is robust against the input and
requires neither positive nor integer inputs.


title: Quantum SMEFT tomography: top quark pair production at the LHC

abstract:
  Quantum information observables, such as entanglement measures, provide a
powerful way to characterize the properties of quantum states. We propose to
use them to probe the structure of fundamental interactions and to search for
new physics at high energy. Inspired by recent proposals to measure
entanglement of top quark pairs produced at the LHC, we examine how
higher-dimensional operators in the framework of the SMEFT modify the Standard
Model expectations. We explore two regions of interest in the phase space where
the Standard Model produces maximally entangled states: at threshold and in the
high-energy limit. We unveil a non-trivial pattern of effects, which depend on
the initial state partons, $q\bar q$ or $gg$, on whether only linear or up to
quadratic SMEFT contributions are included, and on the phase space region. In
general, we find that higher-dimensional effects lower the entanglement
predicted in the Standard Model.


title:
KDSource, a tool for the generation of Monte Carlo particle sources
  using kernel density estimation

abstract:
  Monte Carlo radiation transport simulations have clearly contributed to
improve the design of nuclear systems. When performing in-beam or shielding
simulations a complexity arises due to the fact that particles must be tracked
to regions far from the original source or behind the shielding, often lacking
sufficient statistics. Different possibilities to overcome this problem such as
using particle lists or generating synthetic sources have already been
reported. In this work we present a new approach by using the adaptive
multivariate kernel density estimator (KDE) method. This concept was
implemented in KDSource, a general tool for modelling, optimizing and sampling
KDE sources, which provides a convenient user interface. The basic properties
of the method were studied in an analytical problem with a known density
distribution. Furthermore, the tool was used in two Monte Carlo simulations
that modelled neutron beams, which showed good agreement with experimental
results.


title:
Disadvantaged students increase their academic performance through
  collective intelligence exposure in emergency remote learning due to COVID 19

abstract:
  During the COVID-19 crisis, educational institutions worldwide shifted from
face-to-face instruction to emergency remote teaching (ERT) modalities. In this
forced and sudden transition, teachers and students did not have the
opportunity to acquire the knowledge or skills necessary for online learning
modalities implemented through a learning management system (LMS). Therefore,
undergraduate teachers tend to mainly use an LMS as an information repository
and rarely promote virtual interactions among students, thus limiting the
benefits of collective intelligence for students. We analyzed data on 7,528
undergraduate students and found that cooperative and consensus dynamics among
university students in discussion forums positively affect their final GPA,
with a steeper effect for students with low academic performance during high
school. These results hold above and beyond socioeconomic and other LMS
activity confounders. Furthermore, using natural language processing, we show
that first-year students with low academic performance during high school are
exposed to more content-intensive posts in discussion forums, leading to
significantly higher university GPAs than their low-performance peers in high
school. We expect these results to motivate higher education teachers worldwide
to promote cooperative and consensus dynamics among students using tools such
as forum discussions in their classes to reap the benefits of social learning
and collective intelligence.


title: MS2M: A message-based approach for live stateful microservices migration

abstract:
  In the last few years, the proliferation of edge and cloud computing
infrastructures as well as the increasing number of mobile devices has
facilitated the emergence of many novel applications. However, that increase of
complexities also creates novel challenges for service providers, for example,
the efficient management of interdependent services during runtime. One
strategy is to reallocate services dynamically by migrating them to suitable
servers. However, not every microservice can be deployed as stateless
instances, which leads to suboptimal performance of live migration techniques.
In this work, we propose a novel live migration scheme focusing on stateful
microservices in edge and cloud environments by utilizing the underlying
messaging infrastructure to reconstruct the service's state. Not only can this
approach be applied in various microservice deployment scenarios, experimental
evaluation results also show a reduction of 19.92% downtime compared to the
stop-and-copy migration method.


title: GPU Acceleration of Large-Scale Full-Frequency GW Calculations

abstract:
  Many-body perturbation theory is a powerful method to simulate electronic
excitations in molecules and materials starting from the output of density
functional theory calculations. By implementing the theory efficiently so as to
run at scale on the latest leadership high-performance computing systems it is
possible to extend the scope of GW calculations. We present a GPU acceleration
study of the full-frequency GW method as implemented in the WEST code.
Excellent performance is achieved through the use of (i) optimized GPU
libraries, e.g., cuFFT and cuBLAS, (ii) a hierarchical parallelization strategy
that minimizes CPU-CPU, CPU-GPU, and GPU-GPU data transfer operations, (iii)
asynchronous MPI communications that overlap with GPU computations, and (iv)
mixed-precision in selected portions of the code. A series of performance
benchmarks have been carried out on leadership high-performance computing
systems, showing a substantial speedup of the GPU-accelerated version of WEST
with respect to its CPU version. Good strong and weak scaling is demonstrated
using up to 25920 GPUs. Finally, we showcase the capability of the GPU version
of WEST for large-scale, full-frequency GW calculations of realistic systems,
e.g., a nanostructure, an interface, and a defect, comprising up to 10368
valence electrons.


title: Interconnectedness in Education Systems

abstract:
  Traditional methods used in education sciences often disregard the underlying
complexity of the educational system and consequently its emergence phenomena.
Underlying complex systems, there is a rich web of interconnected components
that determine the relational properties of the system. Here, we argue that an
interconnected vision of educational systems -- from classrooms to an
organizational level -- is key to improving learning, social integration,
well-being, and decision making, all fundamental aspects of the educational
experience. Hence, understanding the education system as an interconnected
network of people, degree programs, and/or institutions requires methods and
concepts from computational social sciences. Thus, we can leverage
institutional records and experimental designs to elicit the relational maps of
key players in education and derive their implications in their functioning at
all scales. Here, in different settings, from elementary classrooms to higher
education programs, we show how mapping the network relationships between
entities can lead to the inference of novel insights about education systems
and the development of solutions with societal implications.


title:
PETR: Position Embedding Transformation for Multi-View 3D Object
  Detection

abstract:
  In this paper, we develop position embedding transformation (PETR) for
multi-view 3D object detection. PETR encodes the position information of 3D
coordinates into image features, producing the 3D position-aware features.
Object query can perceive the 3D position-aware features and perform end-to-end
object detection. PETR achieves state-of-the-art performance (50.4% NDS and
44.1% mAP) on standard nuScenes dataset and ranks 1st place on the benchmark.
It can serve as a simple yet strong baseline for future research.


title:
Vecchia Likelihood Approximation for Accurate and Fast Inference in
  Intractable Spatial Extremes Models

abstract:
  Max-stable processes are the most popular models for high-impact spatial
extreme events, as they arise as the only possible limits of spatially-indexed
block maxima. However, likelihood inference for such models suffers severely
from the curse of dimensionality, since the likelihood function involves a
combinatorially exploding number of terms. In this paper, we propose using the
Vecchia approximation, which conveniently decomposes the full joint density
into a linear number of low-dimensional conditional density terms based on
well-chosen conditioning sets designed to improve and accelerate inference in
high dimensions. Theoretical asymptotic relative efficiencies in the Gaussian
setting and simulation experiments in the max-stable setting show significant
efficiency gains and computational savings using the Vecchia likelihood
approximation method compared to traditional composite likelihoods. Our
application to extreme sea surface temperature data at more than a thousand
sites across the entire Red Sea further demonstrates the superiority of the
Vecchia likelihood approximation for fitting complex models with intractable
likelihoods, delivering significantly better results than traditional composite
likelihoods, and accurately capturing the extremal dependence structure at
lower computational cost.


title: Quantifying Collective Memories

abstract:
  Collective memory is a common representation of the past shared by a group of
people that modulates its identity. Recent literature on computational social
science quantifies collective memories using expressions of those memories
operationalized as the amount of collective attention focused on specific
cultural icons, either artifacts or people. We model the temporal dynamics of
collective memory using a two-step decay process, characterized by a
short-lived and intense initial stage followed by a long-lived and milder
decline of collective attention. Different collective memory mechanisms sustain
the two-step process. The first is communicative memory, which corresponds to
all memories supported by socializing acts. The second is cultural memory,
which corresponds to all memories sustained by accessing records. Thus, this
model predicts a transition time when cultural memory overcomes communicative
memory. It has practical consequences related to shrinking or extending the
time a specific topic is part of a community's conversation, i.e.,
communicative memory, empowering policymakers with valuable time to generate
solutions and compromises for contingent and public problems.


title: Fragmentation instability in aggregating systems

abstract:
  The inclusion of a fragmentation mechanism in population balance equations
introduces complex interactions that make the analytical or even computational
treatment much more difficult than for the pure aggregation case. This is
specially true when variable sized fragments are allowed, because of the
exponential growth in fragments size combinations with the number of monomers
in the exchanges. In this contribution we present a new model that incorporates
an instability threshold in the clusters, which induces arbitrary losses or
gains of particles by fracture with a substantial simplification of the
combinatorics of the process. The model exhibits two different regimes.


title:
Formation of Dust Rings and Gaps in Non-ideal MHD Disks Through
  Meridional Gas Flows

abstract:
  Rings and gaps are commonly observed in the dust continuum emission of young
stellar disks. Previous studies have shown that substructures naturally develop
in the weakly ionized gas of magnetized, non-ideal MHD disks. The gas rings are
expected to trap large mm/cm-sized grains through pressure gradient-induced
radial dust-gas drift. Using 2D (axisymmetric) MHD simulations that include
ambipolar diffusion and dust grains of three representative sizes (1~mm,
3.3~mm, and 1~cm), we show that the grains indeed tend to drift radially
relative to the gas towards the centers of the gas rings, at speeds much higher
than in a smooth disk because of steeper pressure gradients. However, their
spatial distribution is primarily controlled by meridional gas motions, which
are typically much faster than the dust-gas drift. In particular, the grains
that have settled near the midplane are carried rapidly inwards by a fast
accretion stream to the inner edges of the gas rings, where they are lifted up
by the gas flows diverted away from the midplane by a strong poloidal magnetic
field. The flow pattern in our simulation provides an attractive explanation
for the meridional flows recently inferred in HD 163296 and other disks,
including both "collapsing" regions where the gas near the disk surface
converges towards the midplane and a disk wind. Our study highlights the
prevalence of the potentially observable meridional flows associated with the
gas substructure formation in non-ideal MHD disks and their crucial role in
generating rings and gaps in dust.


title: PLATO: Predicting Latent Affordances Through Object-Centric Play

abstract:
  Constructing a diverse repertoire of manipulation skills in a scalable
fashion remains an unsolved challenge in robotics. One way to address this
challenge is with unstructured human play, where humans operate freely in an
environment to reach unspecified goals. Play is a simple and cheap method for
collecting diverse user demonstrations with broad state and goal coverage over
an environment. Due to this diverse coverage, existing approaches for learning
from play are more robust to online policy deviations from the offline data
distribution. However, these methods often struggle to learn under scene
variation and on challenging manipulation primitives, due in part to improperly
associating complex behaviors to the scene changes they induce. Our insight is
that an object-centric view of play data can help link human behaviors and the
resulting changes in the environment, and thus improve multi-task policy
learning. In this work, we construct a latent space to model object affordances
-- properties of an object that define its uses -- in the environment, and then
learn a policy to achieve the desired affordances. By modeling and predicting
the desired affordance across variable horizon tasks, our method, Predicting
Latent Affordances Through Object-Centric Play (PLATO), outperforms existing
methods on complex manipulation tasks in both 2D and 3D object manipulation
simulation environments for diverse types of interactions. Videos can be found
on our website: https://tinyurl.com/2m8wn449


title:
On the general family of third-order shape-invariant Hamiltonians
  related to generalized Hermite polynomials

abstract:
  This work reports and classifies the most general construction of rational
quantum potentials in terms of the generalized Hermite polynomials. This is
achieved by exploiting the intrinsic relation between third-order
shape-invariant Hamiltonians and the fourth Painlev\'e equation, such that the
generalized Hermite polynomials emerge from the $-1/x$ and $-2x$ hierarchies of
rational solutions. Such a relation unequivocally establishes the discrete
spectrum structure, which, in general, is composed as the union of a finite-
and infinite-dimensional sequence of equidistant eigenvalues separated by a
gap. The two indices of the generalized Hermite polynomials determine the
dimension of the finite sequence and the gap. Likewise, the complete set of
eigensolutions can be decomposed into two disjoint subsets. In this form, the
eigensolutions within each set are written as the product of a weight function
defined on the real line times a polynomial. These polynomials fulfill a
second-order differential equation and are alternatively determined from a
three-term recurrence relation (second-order difference equation), the initial
conditions of which are also fixed in terms of generalized Hermite polynomials.


title:
Stochastic evaluation of four-component relativistic second-order
  many-body perturbation energies: A potentially quadratic-scaling correlation
  method

abstract:
  A second-order many-body perturbation correction to the relativistic
Dirac-Hartree-Fock energy is evaluated stochastically by integrating
13-dimensional products of four-component spinors and Coulomb potentials. The
integration in the real space of electron coordinates is carried out by the
Monte Carlo (MC) method with the Metropolis sampling, whereas the MC
integration in the imaginary-time domain is performed by the inverse-CDF
(cumulative distribution function) method. The computational cost to reach a
given relative statistical error for spatially compact but heavy molecules is
observed to be no worse than cubic and possibly quadratic with the number of
electrons or basis functions. This is a vast improvement over the quintic
scaling of the conventional, deterministic second-order many-body perturbation
method. The algorithm is also easily and efficiently parallelized with
demonstrated 92% strong scalability going from 64 to 4096 processors for a
fixed job size.


title:
Optical Design of the Orbiting Astronomical Satellite for Investigating
  Stellar Systems (OASIS)

abstract:
  The Orbiting Astronomical Satellite for Investigating Stellar Systems (OASIS)
is a proposed space telescope with a 14 m inflatable primary reflector that
will perform high spectral resolution observations at terahertz frequencies
with heterodyne receivers. The telescope consists of an inflatable metallized
polymer membrane that serves as the primary antenna, followed by aberration
correction optics, and a scanner that enables a 0.1 degrees Field of Regards
while achieving diffraction limited performance over wavelength range from 63
to 660 {\mu}m. Here the parametric solution space of the OASIS inflatable
telescope design is systematically investigated by establishing analytical
relations among figure of merits including 1st order geometrical photon
collection area and the size of correction optics. The 1st order solution was
further optimized by ray-trace code by incorporating numerically calculated
mirror shape with pre-formed membrane gores. Design study shows that a
space-based telescope with an effective photon collection area of over 90m2 can
be achieved utilizing a 14m inflatable aperture.


title:
Toward Smaller and Lower-Cost 5G Devices with Longer Battery Life: An
  Overview of 3GPP Release 17 RedCap

abstract:
  The fifth generation (5G) wireless technology is primarily developed to
support three classes of use cases, namely, enhanced mobile broadband (eMBB),
ultra-reliable and low-latency communication (URLLC), and massive machine-type
communication (mMTC), with significantly different requirements in terms of
data rate, latency, connection density and power consumption. Meanwhile, there
are several key use cases, such as industrial wireless sensor networks, video
surveillance, and wearables, whose requirements fall in-between those of eMBB,
URLLC, and mMTC. In this regard, 5G can be further optimized to efficiently
support such mid-range use cases. Therefore, in Release 17, the 3rd generation
partnership project (3GPP) developed the essential features to support a new
device type enabling reduced capability (RedCap) NR devices aiming at lower
cost/complexity, smaller physical size, and longer battery life compared to
regular 5G NR devices. In this paper, we provide a comprehensive overview of
3GPP Release 17 RedCap while describing newly introduced features, cost
reduction and power saving gains, and performance and coexistence impacts.
Moreover, we present key design guidelines, fundamental tradeoffs, and future
outlook for RedCap evolution.


title: Compact perturbations of operator semigroups

abstract:
  We study lifting problems for operator semigroups in the Calkin algebra
$\mathscr{Q}(\mathcal{H})$, our approach being mainly based on the
Brown--Douglas--Fillmore theory. With any normal $C_0$-semigroup $(q(t))_{t\geq
0}$ in $\mathscr{Q}(\mathcal{H})$ we associate an extension
$\Gamma\in\mathrm{Ext}(\Delta)$, where $\Delta$ is the inverse limit of certain
compact metric spaces defined purely in terms of the spectrum $\sigma(A)$ of
the generator of $(q(t))_{t\geq 0}$. By using Milnor's exact sequence, we show
that if each $q(t)$ has a normal lift, then the question whether $\Gamma$ is
trivial reduces to the question whether the corresponding first derived functor
vanishes. With the aid of the CRISP property and Kasparov's Technical Theorem,
we provide geometric conditions on $\sigma(A)$ which guarantee splitting of
$\Gamma$. If $\Delta$ is a perfect compact metric space, we obtain in this way
a $C_0$-semigroup $(Q(t))_{t\geq 0}$ which lifts $(q(t))_{t\geq 0}$ on dyadic
rationals.


title:
Carbon Abundances in Compact Galactic Planetary Nebulae: An Ultraviolet
  spectroscopic study with the Space Telescope Imaging Spectrograph (STIS)

abstract:
  We surveyed a sample of compact Galactic planetary nebulae (PNe) with the
Space Telescope Imaging Spectrograph on the Hubble Space Telescope (HST/STIS)
to determine their gas-phase carbon abundances. Carbon abundances in PNe
constrain the nature of their asymptotic giant branch (AGB) progenitors, as
well as cosmic recycling. We measured carbon abundances, or limits thereof, of
11 compact Galactic PNe, notably increasing the sample of Galactic PNe whose
carbon abundance based on HST ultraviolet spectra is available. Dust content of
most targets has been studied elsewhere from Spitzer spectroscopy; given the
compact nature of the nebulae, both UV and IR spectra can be directly compared
to study gas- and dust-phase carbon. We found that carbon-poor (C/O<1) compact
Galactic PNe have oxygen-rich dust type (ORD), while their carbon-enhanced
counterparts (C/O>1) have carbon-rich dust (CRD), confirming the correlation
between gas- and dust-phase carbon content which was known for Magellanic Cloud
PNe. Based on models of expected final yields from AGB evolution we interpret
the majority of the carbon-poor PNe in this study as the progeny of ~1.1-1.2
M$_{\odot}$ stars that experienced some extra-mixing on the red giant branch
(RGB), they went through the AGB but did not go through the carbon star phase.
Most PNe in this group have bipolar morphology, possibly due to the presence of
a sub-solar companion. Carbon-enhanced PNe in our sample could be the progeny
of stars in the ~1.5-2.5 M$_{\odot}$ range, depending on their original
metallicity.


title: Major and Minor Flares on Cygnus X-3 Revisited

abstract:
  Intense flares at cm-wavelengths reaching levels of tens of Jy have been
observed from Cygnus X-3 for many years. This active high mass X-ray binary
also has periods of quenching before major outbursts, and has minor flares at
levels of a few hundred mJy. In this paper we show that the minor flares have
much shorter rise times and durations suggesting more rapid expansion of the
synchrotron radiation emitting material than in the strong flares. They also
appear closer to the binary, whereas the large flares form a more developed
jet. Calculations of physical conditions show that the minor out-bursts have
lower minimum power but have larger magnetic fields and energy densities than
the major flares. Minor flares can occur while a major flare is in progress,
suggesting an indirect coupling between them. The spectral evolution of the
minor flares can be explained by either an expanding synchrotron source or a
shock model. The possibility that there is a brightening zone as in SS433 is
explored.


title:
Long-term collaboration with strong friendship ties improves academic
  performance in remote and hybrid teaching modalities in high school physics

abstract:
  Collaboration among students is fundamental for knowledge building and
competency development. Yet, the effectiveness of student collaboration depends
on the extent that these interactions occur under conditions that favor
commitment, trust, and decision-making among those who interact. The sanitary
situation and the transition to emergency remote teaching have added new
challenges for collaboration, mainly because now, Information and Communication
Technologies mediate students' interactions. This study first explores the
effectiveness of different collaborative relationships on physics from a sample
of secondary students from two schools located in rural and urban areas in
southern Chile. We used Social Network Analysis to map students' academic
prestige, collaboration, and friendship ties. We define a strong association if
two students are declared mutually as friends. Using multiple linear regression
models, we found positive effects of collaboration on grades, particularly
among students working with friends (strong ties). Then, the second study
corresponds to a quasi-experiment on four classes at the analyzed urban school
during the following academic year. Here, students attended hybrid sessions to
test whether the effects of collaboration found on the first study were stable
throughout two semesters. Following the same procedures as in the exploratory
phase, we found that the initial effects of collaboration on grades are
negative for all collaborative variables, while strong working ties afford
academic gains later into the year. These results contribute to the literature
of collaboration in physics education and its effectiveness, taking into
account social relationships and the needed time for developing beneficial
collective processes among students in the classroom.


title: Maximal Operators of Walsh-N\"orlund means On the Dyadic Hardy Spaces

abstract:
  The presented paper will be proved the necessary and sufficient conditions in
order maximal operator of Walsh-N\"orlund means with non-increasing weights to
be bounded from the dyadic Hardy space $H_{p}(\mathbb{I})$\ to the space $%
L_{p}(\mathbb{I})$.


title: High Definition, Inexpensive, Underwater Mapping

abstract:
  In this paper we present a complete framework for Underwater SLAM utilizing a
single inexpensive sensor. Over the recent years, imaging technology of action
cameras is producing stunning results even under the challenging conditions of
the underwater domain. The GoPro 9 camera provides high definition video in
synchronization with an Inertial Measurement Unit (IMU) data stream encoded in
a single mp4 file. The visual inertial SLAM framework is augmented to adjust
the map after each loop closure. Data collected at an artificial wreck of the
coast of South Carolina and in caverns and caves in Florida demonstrate the
robustness of the proposed approach in a variety of conditions.


title:
Three-Way Serpentine Slow-Wave Structures with Stationary Inflection
  Point and Enhanced Interaction Impedance

abstract:
  We introduce two novel variants of the serpentine waveguide slow-wave
structure (SWS), often utilized in millimeter-wave traveling-wave tubes (TWTs),
with an enhanced interaction impedance. Using dispersion engineering in
conjunction with transfer matrix methods, we tune the guided wavenumber
dispersion relation to exhibit stationary inflection points (SIPs), and also
non-stationary, or tilted inflection points (TIPs), within the dominant TE10
mode of a rectangular waveguide. The degeneracy is found below the first upper
band-edge associated with the bandgap where neighboring spatial harmonics meet
in the dispersion of the serpentine waveguide (SWG) which is threaded by a beam
tunnel.
  The structure geometries are optimized to be able to achieve an SIP which
allows for three-mode synchronism with an electron beam over a specified
wavenumber interval in the desired Brillouin zone. Full-wave simulations are
used to obtain and verify the existence of the SIP in the three-way coupled
waveguide and fine-tune the geometry such that a beam would be in synchronism
at or near the SIP. The three-way waveguide SWS exhibits a moderately high
Pierce impedance in the vicinity of a nearly-stationary inflection point,
making the SWS geometry potentially useful for improving the power gain and
basic extraction efficiency of millimeter-wave TWTs. Additionally, the
introduced SWS geometries have directional coupler-like behavior, which enables
distributed power extraction at frequencies near the SIP frequency.


title: Parameter-Free Attentive Scoring for Speaker Verification

abstract:
  This paper presents a novel study of parameter-free attentive scoring for
speaker verification. Parameter-free scoring provides the flexibility of
comparing speaker representations without the need of an accompanying
parametric scoring model. Inspired by the attention component in Transformer
neural networks, we propose a variant of the scaled dot product attention
mechanism to compare enrollment and test segment representations. In addition,
this work explores the effect on performance of (i) different types of
normalization, (ii) independent versus tied query/key estimation, (iii) varying
the number of key-value pairs and (iv) pooling multiple enrollment utterance
statistics. Experimental results for a 4 task average show that a simple
parameter-free attentive scoring mechanism can improve the average EER by 10%
over the best cosine similarity baseline.


title:
Controlling Transaction Rate in Tangle Ledger: A Principal Agent Problem
  Approach

abstract:
  Tangle is a distributed ledger technology that stores data as a directed
acyclic graph (DAG). Unlike blockchain, Tangle does not require dedicated
miners for its operation. This makes Tangle suitable for Internet of Things
(IoT) applications. To prevent congestion and spamming, distributed ledgers
have a built-in transaction rate control mechanism. This is typically achieved
by increasing or decreasing the proof of work (PoW) difficulty level;
unfortunately, this simplistic mechanism gives an unfair advantage to users
with high computational power. This paper proposes a principal-agent problem
(PAP) framework from microeconomics to control the transaction rate in Tangle.
With users as the agents and the transaction rate controller as the principal,
we design a truth-telling mechanism to assign PoW difficulty levels to agents
as a function of their computational power. The solution of the PAP is achieved
by compensating a higher PoW difficulty level with a larger weight/reputation
for the transaction. The solution of PAP is obtained by solving a mixed-integer
optimization problem. Moreover, we show that the decision variables have useful
structures: the optimal solution of the PAP increases with the computational
power of the agents. We also show that the optimal PoW increases with the
number of agents. The structural result reduces the search space of the
mixed-integer program and enables efficient computation of the optimal
mechanism. Finally, via numerical examples, we illustrate the transaction rate
control mechanism and study its impact on the dynamics of Tangle.


title: Active nematic flows on curved surfaces

abstract:
  Cell monolayers are a central model system to tissue biophysics. In vivo,
epithelial tissues are curved on the scale of microns, and curvature's role in
the onset of spontaneous tissue flows is still not well-understood. Here, we
present a hydrodynamic theory for an apical-basal asymmetric active nematic gel
on a curved strip. We show that surface curvature qualitatively changes
monolayer motion compared to flat space: the resulting flows can be
thresholdless, and the transition to motion may change from continuous to
discontinuous. Surface curvature, friction and active tractions are all shown
to control the flow pattern selected: from simple shear to vortex chains.


title:
Millimeter-scale topography enables coral larval settlement in
  wave-driven oscillatory flow

abstract:
  Larval settlement in wave-dominated, nearshore environments is the most
critical life stage for a vast array of marine invertebrates, yet it is poorly
understood and virtually impossible to observe in situ. Using a custom-built
flume tank that mimics the oscillatory fluid flow over a shallow coral reef, we
show that millimeter-scale benthic topography increases the settlement of
slow-swimming coral larvae by an order of magnitude relative to flat
substrates. Particle tracking velocimetry of flow fields revealed that
millimeter-scale ridges introduced regions of flow recirculation that
redirected larvae toward the substrate surface and decreased the local fluid
speed, effectively increasing the window of time for larvae to settle. In
agreement with experiments, computational fluid dynamics modeling and
agent-based larval simulations also showed significantly higher settlement on
ridged substrates. These findings highlight how physics-based substrate design
can create new opportunities to increase larval recruitment for ecosystem
restoration.


title:
Koopman Methods for Estimation of Animal Motions over Unknown, Regularly
  Embedded Submanifolds

abstract:
  This paper introduces a data-dependent approximation of the forward
kinematics map for certain types of animal motion models. It is assumed that
motions are supported on a low-dimensional, unknown configuration manifold $Q$
that is regularly embedded in high dimensional Euclidean space
$X:=\mathbb{R}^d$. This paper introduces a method to estimate forward
kinematics from the unknown configuration submanifold $Q$ to an $n$-dimensional
Euclidean space $Y:=\mathbb{R}^n$ of observations. A known reproducing kernel
Hilbert space (RKHS) is defined over the ambient space $X$ in terms of a known
kernel function, and computations are performed using the known kernel defined
on the ambient space $X$. Estimates are constructed using a certain
data-dependent approximation of the Koopman operator defined in terms of the
known kernel on $X$. However, the rate of convergence of approximations is
studied in the space of restrictions to the unknown manifold $Q$. Strong rates
of convergence are derived in terms of the fill distance of samples in the
unknown configuration manifold, provided that a novel regularity result holds
for the Koopman operator. Additionally, we show that the derived rates of
convergence can be applied in some cases to estimates generated by the extended
dynamic mode decomposition (EDMD) method. We illustrate characteristics of the
estimates for simulated data as well as samples collected during motion capture
experiments.


title:
Excited states and precision results for nucleon charges and form
  factors

abstract:
  The exponentially falling signal-to-noise ratio in all nucleon correlation
functions, and the presence of towers of multihadron excited states with
relatively small mass gaps makes extraction of matrix elements of various
operators within the ground state nucleon challenging. Theoretically, the
allowed positive parity states with the smallest mass gaps are the $N(\bm
p)\pi(-\bm p)$, $N(\bm 0)\pi(\bm 0)\pi(\bm 0)$, $N(\bm p)\pi(\bm 0)$, $N(\bm
0)\pi(\bm p),\ \ldots$, states. A priori, the contribution of these states
arises at one loop in chiral perturbation theory ($\chi$PT), however, in many
cases the contributions are enhanced. In this talk, I will review four such
cases: the correlation functions from which the axial form factors, electric
and magnetic form factors, the $\Theta$-term contribution to neutron electric
dipole moment (nEDM), and the pion-nucleon sigma term are extracted. Including
appropriate multihadron states in the analysis can lead to significantly
different results compared to standard analyses with the mass gaps taken from
fits to 2-point functions. The $\chi$PT case for $N \pi$ states is the most
clear in the axial/pseudoscalar form factors which need to satisfy the PCAC
relation between them. Our analyses, supported by $\chi$PT, suggests similarly
large effects in the calculations of the $\Theta$-term and the pion-nucleon
sigma term that have significant phenomenological implications.


title:
Contextualized Sensorimotor Norms: multi-dimensional measures of
  sensorimotor strength for ambiguous English words, in context

abstract:
  Most large language models are trained on linguistic input alone, yet humans
appear to ground their understanding of words in sensorimotor experience. A
natural solution is to augment LM representations with human judgments of a
word's sensorimotor associations (e.g., the Lancaster Sensorimotor Norms), but
this raises another challenge: most words are ambiguous, and judgments of words
in isolation fail to account for this multiplicity of meaning (e.g., "wooden
table" vs. "data table"). We attempted to address this problem by building a
new lexical resource of contextualized sensorimotor judgments for 112 English
words, each rated in four different contexts (448 sentences total). We show
that these ratings encode overlapping but distinct information from the
Lancaster Sensorimotor Norms, and that they also predict other measures of
interest (e.g., relatedness), above and beyond measures derived from BERT.
Beyond shedding light on theoretical questions, we suggest that these ratings
could be of use as a "challenge set" for researchers building grounded language
models.


title:
Clocked Quantum-dot Cellular Automata Circuits Tolerate Unwanted
  External Electric Fields

abstract:
  Quantum-dot cellular automata (QCA) may provide low-power, general-purpose
computing in the post-CMOS era. A molecular implementation of QCA features
nanometer-scale devices and may support THz switching speeds at
room-temperature. Here, we explore the ability of molecular QCA circuits to
tolerate unwanted applied electric fields, which may come from a variety of
sources. One likely source of strong unwanted electric fields may be electrodes
recently proposed for the write-in of classical bits to molecular QCA input
circuits. Previous models have shown that the input circuits are sensitive to
the applied field, and a coupled QCA wire can successfully transfer the input
bit to downstream circuits despite strong applied fields. However, the ability
of other QCA circuits to tolerate an applied field has not yet been
demonstrated. Here we study the robustness of various QCA circuits by
calculating their ground state responses in the presence an applied field. To
do this, a circuit is built from several QCA molecules, each described as a
two-state system. A circuit Hamiltonian is formed and diagonalized. All
pairwise interactions between cells are considered, along with all
correlations. An examination of the ground state shows that these QCA circuits
may indeed tolerate strong unwanted electric fields. We also show that circuit
immunity to the dominant unwanted field component may be obtained by choosing
the orientation of constituent molecules. This suggests that relatively large
electrodes used for bit write-in to molecular QCA need not disrupt the
operation of nearby QCA circuits. The circuits may tolerate significant
electric fields from other sources, as well.


title:
Superconducting dome by tuning through a Van Hove singularity in a
  two-dimensional metal

abstract:
  Chemical substitution is a promising route for the exploration of a rich
variety of doping- and/or disorder-dependent collective phenomena in
low-dimensional quantum materials. Here we show that transition metal
dichalcogenide alloys are ideal platforms to this purpose. In particular, we
demonstrate the emergence of superconductivity in the otherwise metallic
single-layer TaSe$_{2}$ by minute electron doping provided by substitutional W
atoms. We investigate the temperature- and magnetic field-dependence of the
superconducting state of Ta$_{1-\delta}$W$_\delta$Se$_2$ with electron doping
($\delta$) using variable temperature (0.34 K - 4.2 K) scanning tunneling
spectroscopy (STS). We unveil the emergence of a superconducting dome spanning
0.003 < $\delta$ < 0.03 with a maximized critical temperature of 0.85 K, a
significant increase from that of bulk TaSe$_{2}$ (T$_C$ = 0.14 K).
Superconductivity emerges from an increase of the density of states (DOS) as
the Fermi surface approaches a van Hove singularity due to doping. Once the
singularity is reached, however, the DOS decreases with $\delta$, which
gradually weakens the superconducting state, thus shaping the superconducting
dome. Lastly, our doping-dependent measurements allow us to unambiguously track
the development of a Coulomb glass phase triggered by disorder due to W
dopants.

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


title:
BASIL: Balanced Active Semi-supervised Learning for Class Imbalanced
  Datasets

abstract:
  Current semi-supervised learning (SSL) methods assume a balance between the
number of data points available for each class in both the labeled and the
unlabeled data sets. However, there naturally exists a class imbalance in most
real-world datasets. It is known that training models on such imbalanced
datasets leads to biased models, which in turn lead to biased predictions
towards the more frequent classes. This issue is further pronounced in SSL
methods, as they would use this biased model to obtain psuedo-labels (on the
unlabeled data) during training. In this paper, we tackle this problem by
attempting to select a balanced labeled dataset for SSL that would result in an
unbiased model. Unfortunately, acquiring a balanced labeled dataset from a
class imbalanced distribution in one shot is challenging. We propose BASIL
(Balanced Active Semi-supervIsed Learning), a novel algorithm that optimizes
the submodular mutual information (SMI) functions in a per-class fashion to
gradually select a balanced dataset in an active learning loop. Importantly,
our technique can be efficiently used to improve the performance of any SSL
method. Our experiments on Path-MNIST and Organ-MNIST medical datasets for a
wide array of SSL methods show the effectiveness of Basil. Furthermore, we
observe that Basil outperforms the state-of-the-art diversity and uncertainty
based active learning methods since the SMI functions select a more balanced
dataset.


title:
Gamma-ray Eclipses and Orbital Modulation Transitions in the Candidate
  Redback 4FGL J1702.7-5655

abstract:
  Observations with the Fermi Large Area Telescope (LAT) of the gamma-ray
source 4FGL J1702.7-5655, previously classified as a candidate millisecond
pulsar, show highly-significant modulation at a period of 0.2438033 days (~
5.85 hours). Further examination of the folded light curve indicates the
presence of narrow eclipses, suggesting this is a redback binary system. An
examination of the long-term properties of the modulation over 13 years of LAT
observations indicates that the orbital modulation of the gamma-rays changed
from a simple eclipse before early 2013, to a broader, more easily detected,
quasi-sinusoidal modulation. In addition, the time of the eclipse shifts to
~0.05 later in phase. This change in the orbital modulation properties is,
however, not accompanied by a significant overall change in gamma-ray flux or
spectrum. The quasi-sinusoidal component peaks ~0.5 out of phase with the
eclipse, which would indicate inferior conjunction of the compact object in the
system. Swift X-ray Telescope observations reveal a possible X-ray counterpart
within the LAT error ellipse. However, radio observations obtained with the
Australia Telescope Compact Array do not detect a source in the region. 4FGL
J1702.7-5655 appears to have changed its state in 2013, perhaps related to
changes in the intrabinary shock in the system. We discuss how the properties
of 4FGL J1702.7-5655 compare to other binary millisecond pulsars that have
exhibited orbital modulation in gamma rays.


title:
Attack Analysis of Face Recognition Authentication Systems Using Fast
  Gradient Sign Method

abstract:
  Biometric authentication methods, representing the "something you are"
scheme, are considered the most secure approach for gaining access to protected
resources. Recent attacks using Machine Learning techniques demand a serious
systematic reevaluation of biometric authentication. This paper analyzes and
presents the Fast Gradient Sign Method (FGSM) attack using face recognition for
biometric authentication. Machine Learning techniques have been used to train
and test the model, which can classify and identify different people's faces
and which will be used as a target for carrying out the attack. Furthermore,
the case study will analyze the implementation of the FGSM and the level of
performance reduction that the model will have by applying this method in
attacking. The test results were performed with the change of parameters both
in terms of training and attacking the model, thus showing the efficiency of
applying the FGSM.


title: CALiPPSO: A Linear Programming Algorithm for Jamming Hard Spheres

abstract:
  The jamming transition is ubiquitous. It is present in granular matter,
foams, colloids, structural glasses, and many other systems. Yet, it defines a
critical point whose properties still need to be fully understood. Recently, a
major breakthrough came about when the replica formalism was extended to build
a mean-field theory that provides an exact description of the jamming
transition of spherical particles in the infinite-dimensional limit. While such
theory explains the jamming critical behavior of both soft and hard spheres
(HS), investigating these two kinds of particles in finite dimensions poses
very different problems, particularly from the numerical point of view. Soft
particles are modeled by a continuous potential; thus, their jamming point can
be reached through efficient energy minimization algorithms. In contrast, such
methods are inapplicable in HS systems because the interaction energy among the
particles is always zero by construction. To overcome this difficulty, here we
recast HS jamming as a constrained optimization problem and introduce the
CALiPPSO algorithm, capable of readily producing jammed packings of HS without
including any effective potential. This algorithm brings a HS configuration of
arbitrary dimensions to its jamming point by solving a chain of linear
optimization problems. We show that there is a strict correspondence between
the force balance conditions of jammed packings and the properties of the
optimal solutions of CALiPPSO, whence we prove analytically that our packings
are always isostatic and in mechanical equilibrium. Furthermore, using
extensive numerical simulations, we show that our algorithm is able to probe
the complex structure of the free energy landscape, finding qualitative
agreement with mean-field predictions. We also characterize the algorithmic
complexity of CALiPPSO and provide an open-source implementation of it.


title:
Bipolaronic nature of the pseudogap in (TaSe4)2I revealed via weak
  photoexcitation

abstract:
  The origin of the pseudogap in many strongly correlated materials has been a
longstanding puzzle. Here, we uncover which many-body interactions underlie the
pseudogap in quasi-one-dimensional (quasi-1D) material (TaSe4)2I by weak
photo-excitation of the material to partially melt the ground state order and
thereby reveal the underlying states in the gap. We observe the appearance of
both dispersive and flat bands by using time- and angle-resolved photoemission
spectroscopy. We assign the dispersive band to a single-particle bare band,
while the flat band to a collection of single-polaron sub-bands. Our results
provide direct experimental evidence that many-body interactions among small
Holstein polarons i.e., the formation of bipolarons, are primarily responsible
for the pseudogap in (TaSe4)2I. Recent theoretical studies of the Holstein
model support the presence of such a bipolaron-to-polaron crossover. We also
observe dramatically different relaxation times for the excited in-gap states
in (TaSe4)2I (~600 fs) compared with another quasi-1D material Rb0.3MoO3 (~60
fs), which provides a new method for distinguishing between pseudogaps induced
by polaronic or Luttinger-liquid many-body interactions.