title:
Dynamic Magnetic Response of Quark-Gluon Plasma to Electromagnetic
  Fields

abstract:
  We investigate the electromagnetic response of a viscous quark-gluon plasma
in the framework of the relativistic Boltzmann equation with current conserving
collision term. Our formalism incorporates dissipative effects at all orders in
linear response to the electromagnetic field while accounting for the full
space- and time-dependence of the perturbing fields. As an example, we consider
the collision of two nuclei in a stationary, homogeneous quark-gluon plasma. We
show that for large collision energies the induced magnetic fields are governed
by the response of quark-gluon plasma along the light-cone. In this limit we
derive an analytic expression for the magnetic field along the beam axis
between the receding nuclei and show that its strength varies only weakly with
collision energy for $\sqrt{s_\mathrm{NN}} \ge 30\,$GeV.


title: Randomized Smoothing under Attack: How Good is it in Pratice?

abstract:
  Randomized smoothing is a recent and celebrated solution to certify the
robustness of any classifier. While it indeed provides a theoretical robustness
against adversarial attacks, the dimensionality of current classifiers
necessarily imposes Monte Carlo approaches for its application in practice.
This paper questions the effectiveness of randomized smoothing as a defense,
against state of the art black-box attacks. This is a novel perspective, as
previous research works considered the certification as an unquestionable
guarantee. We first formally highlight the mismatch between a theoretical
certification and the practice of attacks on classifiers. We then perform
attacks on randomized smoothing as a defense. Our main observation is that
there is a major mismatch in the settings of the RS for obtaining high
certified robustness or when defeating black box attacks while preserving the
classifier accuracy.


title: On a problem of von Renteln

abstract:
  We discuss the solution of a harmonic analysis problem proposed by M. von
Renteln in 1980.


title: Oracle Guided Image Synthesis with Relative Queries

abstract:
  Isolating and controlling specific features in the outputs of generative
models in a user-friendly way is a difficult and open-ended problem. We develop
techniques that allow an oracle user to generate an image they are envisioning
in their head by answering a sequence of relative queries of the form
\textit{"do you prefer image $a$ or image $b$?"} Our framework consists of a
Conditional VAE that uses the collected relative queries to partition the
latent space into preference-relevant features and non-preference-relevant
features. We then use the user's responses to relative queries to determine the
preference-relevant features that correspond to their envisioned output image.
Additionally, we develop techniques for modeling the uncertainty in images'
predicted preference-relevant features, allowing our framework to generalize to
scenarios in which the relative query training set contains noise.


title: Revisiting the secondary eclipses of KELT-1b using TESS observations

abstract:
  We present the characterization of the transiting planet KELT-1b % ($R_p
\simeq 1.15 R_J, M_p \simeq 27.23 M_J $) using data from the Transiting
Exoplanet Survey Satellite (TESS). % during cycle $2$ and sector $17$. Our
light curve model includes primary transit and secondary eclipse. Here, we
model the systematic noise using Gaussian processes (GPs) and fit it to the
data using the Markov Chain Monte Carlo (MCMC) method. Modelling of the TESS
light curve returns a planet-to-star radius ratio, $p =$
$0.07652_{-0.00028}^{+0.00029}$ and a relatively large secondary eclipse depth
of $388_{-13}^{+12}$ ppm. The transit ephemeris of KELT-1b is updated using the
MCMC method. Finally, we complement our work by searching for transit timing
variations (TTVs) for KELT-1b. We do not find significant variations from the
constant-period models in our transit time data.


title: FindFacts: A Scalable Theorem Search

abstract:
  The Isabelle Archive of Formal Proofs (AFP) has grown to over 500 articles in
late 2019. Meanwhile, finding formalizations in it has not exactly become
easier. At the time of writing, the site-specific AFP google search and the
Isabelle find_theories resp. find_consts commands (that only work on imported
theories) are still the only tools readily available to find formalizations in
Isabelle. We present FindFacts, a novel domain-specific search tool for formal
Isabelle theory content. Instead of utilizing term unification, we solve the
problem with a classical drill-down search engine. We put special emphasis on
scalability of the search system, so that the whole AFP can be searched
interactively.


title: Human's Role in-the-Loop

abstract:
  Data integration has been recently challenged by the need to handle large
volumes of data, arriving at high velocity from a variety of sources, which
demonstrate varying levels of veracity. This challenging setting, often
referred to as big data, renders many of the existing techniques, especially
those that are human-intensive, obsolete. Big data also produces technological
advancements such as Internet of things, cloud computing, and deep learning,
and accordingly, provides a new, exciting, and challenging research agenda.
Given the availability of data and the improvement of machine learning
techniques, this blog discusses the respective roles of humans and machines in
achieving cognitive tasks in matching, aiming to determine whether traditional
roles of humans and machines are subject to change. Such investigation, we
believe, will pave a way to better utilize both human and machine resources in
new and innovative manners. We shall discuss two possible modes of change,
namely humans out and humans in. Humans out aim at exploring out-of-the-box
latent matching reasoning using machine learning algorithms when attempting to
overpower human matcher performance. Pursuing out-of-the-box thinking, machine
and deep learning can be involved in matching. Humans in explores how to better
involve humans in the matching loop by assigning human matchers with a
symmetric role to algorithmic matcher in the matching process.


title:
Complex-Valued Frequency Selective Extrapolation for Fast Image and
  Video Signal Extrapolation

abstract:
  Signal extrapolation tasks arise in miscellaneous manners in the field of
image and video signal processing. But, due to the widespread use of low-power
and mobile devices, the computational complexity of an algorithm plays a
crucial role in selecting an algorithm for a given problem. Within the scope of
this contribution, we introduce the complex-valued Frequency Selective
Extrapolation for fast image and video signal extrapolation. This algorithm
iteratively generates a generic complex-valued model of the signal to be
extrapolated as weighted superposition of Fourier basis functions. We further
show that this algorithm is up to 10 times faster than the existent real-valued
Frequency Selective Extrapolation that takes the real-valued nature of the
input signals into account during the model generation. At the same time, the
quality which is achievable by the complex-valued model generation is similar
to the quality of the real-valued model generation.


title:
A Fast Algorithm for Selective Signal Extrapolation with Arbitrary Basis
  Functions

abstract:
  Signal extrapolation is an important task in digital signal processing for
extending known signals into unknown areas. The Selective Extrapolation is a
very effective algorithm to achieve this. Thereby, the extrapolation is
obtained by generating a model of the signal to be extrapolated as weighted
superposition of basis functions. Unfortunately, this algorithm is
computationally very expensive and, up to now, efficient implementations exist
only for basis function sets that emanate from discrete transforms. Within the
scope of this contribution, a novel efficient solution for Selective
Extrapolation is presented for utilization with arbitrary basis functions. The
proposed algorithm mathematically behaves identically to the original Selective
Extrapolation, but is several decades faster. Furthermore, it is able to
outperform existent fast transform domain algorithms which are limited to basis
function sets that belong to the corresponding transform. With that, the novel
algorithm allows for an efficient use of arbitrary basis functions, even if
they are only numerically defined.


title: Improving Transferability for Domain Adaptive Detection Transformers

abstract:
  DETR-style detectors stand out amongst in-domain scenarios, but their
properties in domain shift settings are under-explored. This paper aims to
build a simple but effective baseline with a DETR-style detector on domain
shift settings based on two findings. For one, mitigating the domain shift on
the backbone and the decoder output features excels in getting favorable
results. For another, advanced domain alignment methods in both parts further
enhance the performance. Thus, we propose the Object-Aware Alignment (OAA)
module and the Optimal Transport based Alignment (OTA) module to achieve
comprehensive domain alignment on the outputs of the backbone and the detector.
The OAA module aligns the foreground regions identified by pseudo-labels in the
backbone outputs, leading to domain-invariant based features. The OTA module
utilizes sliced Wasserstein distance to maximize the retention of location
information while minimizing the domain gap in the decoder outputs. We
implement the findings and the alignment modules into our adaptation method,
and it benchmarks the DETR-style detector on the domain shift settings.
Experiments on various domain adaptive scenarios validate the effectiveness of
our method.


title: Hybrid subconvexity and the partition function

abstract:
  We give an upper bound for the error term in the Hardy-Ramanujan-Rademacher
formula for the partition function. The main input is a new hybrid subconvexity
bound for the central value $L(\tfrac 12,f\times (\tfrac{q}{\cdot}))$ in the
$q$ and spectral parameter aspects, where $f$ is a Hecke-Maass cusp form for
$\Gamma_0(N)$ and $q$ is a fundamental discriminant.


title:
An isogeometric finite element formulation for frictionless contact of
  Cosserat rods with unconstrained directors

abstract:
  This paper presents an isogeometric finite element formulation for nonlinear
beams with impenetrability constraints, based on the kinematics of Cosserat
rods with unconstrained directors. The beam cross-sectional deformation is
represented by director vectors of an arbitrary order. For the frictionless
lateral beam-to-beam contact, a surface-to-surface contact algorithm combined
with an active set strategy and a penalty method is employed. The lateral
boundary surface of the beam is parameterized by its axis and cross-sectional
boundary curves with NURBS basis functions having at least $C^2$-continuity,
which yields a continuous surface metric and curvature for the closest point
projection. Three-dimensional constitutive laws of hyperelastic materials are
considered. Several numerical examples verify the accuracy and efficiency of
the proposed beam contact formulation in comparison to brick element solutions.
The lateral contact pressure distribution of the beam formulation is in
excellent agreement with the contact pressure of the brick element formulation
while using much less degrees-of-freedom.


title: Flamingo: a Visual Language Model for Few-Shot Learning

abstract:
  Building models that can be rapidly adapted to numerous tasks using only a
handful of annotated examples is an open challenge for multimodal machine
learning research. We introduce Flamingo, a family of Visual Language Models
(VLM) with this ability. Flamingo models include key architectural innovations
to: (i) bridge powerful pretrained vision-only and language-only models, (ii)
handle sequences of arbitrarily interleaved visual and textual data, and (iii)
seamlessly ingest images or videos as inputs. Thanks to their flexibility,
Flamingo models can be trained on large-scale multimodal web corpora containing
arbitrarily interleaved text and images, which is key to endow them with
in-context few-shot learning capabilities. We perform a thorough evaluation of
the proposed Flamingo models, exploring and measuring their ability to rapidly
adapt to a variety of image and video understanding benchmarks. These include
open-ended tasks such as visual question-answering, where the model is prompted
with a question which it has to answer, captioning tasks, which evaluate the
ability to describe a scene or an event, and close-ended tasks such as multiple
choice visual question-answering. For tasks lying anywhere on this spectrum, we
demonstrate that a single Flamingo model can achieve a new state of the art for
few-shot learning, simply by prompting the model with task-specific examples.
On many of these benchmarks, Flamingo actually surpasses the performance of
models that are fine-tuned on thousands of times more task-specific data.


title:
Preoperative brain tumor imaging: models and software for segmentation
  and standardized reporting

abstract:
  For patients suffering from brain tumor, prognosis estimation and treatment
decisions are made by a multidisciplinary team based on a set of preoperative
MR scans. Currently, the lack of standardized and automatic methods for tumor
detection and generation of clinical reports represents a major hurdle. In this
study, we investigate glioblastomas, lower grade gliomas, meningiomas, and
metastases, through four cohorts of up to 4000 patients. Tumor segmentation
models were trained using the AGU-Net architecture with different preprocessing
steps and protocols. Segmentation performances were assessed in-depth using a
wide-range of voxel and patient-wise metrics covering volume, distance, and
probabilistic aspects. Finally, two software solutions have been developed,
enabling an easy use of the trained models and standardized generation of
clinical reports: Raidionics and Raidionics-Slicer. Segmentation performances
were quite homogeneous across the four different brain tumor types, with an
average true positive Dice ranging between 80% and 90%, patient-wise recall
between 88% and 98%, and patient-wise precision around 95%. With our Raidionics
software, running on a desktop computer with CPU support, tumor segmentation
can be performed in 16 to 54 seconds depending on the dimensions of the MRI
volume. For the generation of a standardized clinical report, including the
tumor segmentation and features computation, 5 to 15 minutes are necessary. All
trained models have been made open-access together with the source code for
both software solutions and validation metrics computation. In the future, an
automatic classification of the brain tumor type would be necessary to replace
manual user input. Finally, the inclusion of post-operative segmentation in
both software solutions will be key for generating complete post-operative
standardized clinical reports.


title:
Magnetic domains in 2D moir\'e lattices with square and hexagonal
  symmetry

abstract:
  We report the persistence of magnetic domains lying in moir\'e patterns with
square and hexagonal symmetries. Our investigation is based on the dynamical
description of two magnetic domains represented by a two species bosonic
mixture of $^{87}$Rb ultracold atoms, being each specie initially localized in
the left and right halves of a moir\'e lattice defined by a specific angle
$\theta$. To demonstrate the persistence of such initial domains, we follow the
time evolution of the superfluid spin texture, and in particular, the
magnetization on each halve. The two-component superfluid, confined in the
moir\'e pattern plus a harmonic trap, was described through the time dependent
Gross-Pitaevskii coupled equations for moir\'e lattices having $90 \times 90$
sites. Results showed the existence of rotation-angle-dependent structures for
which the initial magnetic domain is preserved for both, square and hexagonal
moir\'e patterns; above $\theta >10^\circ$ the initial magnetic domain is never
destroyed. Stationary magnetic states for a single component Bose condensate
allowed us to identify the lattice parameter associated with moir\'e crystals
that emerge for twisting angles belonging to the intervals $\theta \in
\left(0^\circ ,30^\circ \right)$ and $\theta \in \left(0^\circ ,45^\circ
\right)$ for hexagonal and square geometries respectively.


title:
Impact of helium ion implantation dose and annealing on dense
  near-surface layers of NV centers

abstract:
  Implantation of diamonds with helium ions becomes a common method to create
hundreds-nanometers-thick near-surface layers of NV centers for
high-sensitivity sensing and imaging applications. However, optimal
implantation dose and annealing temperature is still a matter of discussion. In
this study, we irradiated HPHT diamonds with an initial nitrogen concentration
of 100 ppm using different implantation doses of helium ions to create 200-nm
thick NV layers. We compare a previously considered optimal implantation dose
of $\sim10^{12}$ to double and triple doses by measuring fluorescence
intensity, contrast, and linewidth of magnetic resonances, as well as
longitudinal and transversal relaxation times $T_1$ and $T_2$. From these
direct measurements we also estimate concentrations of P1 and NV centers. In
addition, we compare the three diamond samples that underwent three consequent
annealing steps to quantify the impact of processing at 1100 $^{\circ}$C, which
follows initial annealing at 800 $^{\circ}$C. By tripling the implantation dose
we have increased the magnetic sensitivity of our sensors by $28\pm5$ %. By
projecting our results to higher implantation doses we show that a further
improvement of up to 70 % may be achieved. At the same time, additional
annealing steps at 1100 $^{\circ}$C improve the sensitivity only by 6.6 $\pm$
2.7 %.

categories: quant-ph cond-mat.mes-hall cond-mat.mtrl-sci physics.ins-det


title:
THz displacement current in tunneling devices with coherent
  electron-photon interaction

abstract:
  For frequencies comparable to the inverse of the electron transit time, the
measured total current in electron devices needs to include not only the
particle but also the displacement current. Typically, in most quantum
transport simulators, the displacement current is ignored, or simply computed
from Gauss' law involving only the longitudinal (zero rotational) electric
field. In this work, the role of the transverse (zero divergence)
electromagnetic field in the evaluation of the displacement current in resonant
tunneling diodes is analyzed within either classical or quantum models of such
fields. In the quantum case, the peaks of the original transmission
coefficient, without interaction with photons, split into two new peaks due to
the resonant electron-photon interaction leading to Rabi oscillations. Such
phenomenon, that mimics known effects predicted by a Jaynes-Cummings model in
closed systems, exemplifies how a full quantum treatment of electrons and
electromagnetic fields opens unexplored paths for engineering new THz electron
devices. The computational burden involved in the multi-time measurements of
THz currents is minimized by invoking a Bohmian description of the light-matter
interaction. As an additional result, we show that the traditional transmission
coefficient used to characterize DC quantum electron devices has to be
substituted by a new displacement current coefficient in high frequency AC
scenarios.


title: A Testing Time for Anti-matter

abstract:
  Precision measurement of anti-protonic Helium provides a test of physical
laws.


title:
3T-1R Analog Write and Digital Read of MRAM for RNG and Low Power Memory
  Application

abstract:
  This work represents integration of MTJ with 30nm FinFET for low voltage
analog write operations and readout optimization for the p-bit or true random
number generator (TRNG), where the induced p-bit, the probabilistic state of
the magnetic tunnel junction (MTJ), is detected within only a single
computational period. The period contains two sub-cycles: write and joined read
& reset cycles. The operation with MTJ becomes stochastic, independent after
calibrating at the desired working point against the factors, which can induce
the signal deviations, e.g. temperature, material degradation or external
magnetic field.


title: Symbolic synthesis of Clifford circuits and beyond

abstract:
  Path sums are a convenient symbolic formalism for quantum operations with
applications to the simulation, optimization, and verification of quantum
protocols. Unlike quantum circuits, path sums are not limited to unitary
operations, but can express arbitrary linear ones. Two problems, therefore,
naturally arise in the study of path sums: the unitarity problem and the
extraction problem. The former is the problem of deciding whether a given path
sum represents a unitary operator. The latter is the problem of constructing a
quantum circuit, given a path sum promised to represent a unitary operator.
  We show that the unitarity problem is co-NP-hard in general, but that it is
in P when restricted to Clifford path sums. We then provide an algorithm to
synthesize a Clifford circuit from a unitary Clifford path sum. The circuits
produced by our extraction algorithm are of the form $C_1HC_2$, where $C_1$ and
$C_2$ are Hadamard-free circuits and $H$ is a layer of Hadamard gates. We also
provide a heuristic generalization of our extraction algorithm to arbitrary
path sums. While this algorithm is not guaranteed to succeed, it often succeeds
and typically produces natural looking circuits. Alongside applications to the
optimization and decompilation of quantum circuits, we demonstrate the
capability of our algorithm by synthesizing the standard quantum Fourier
transform directly from a path sum.


title: Double scaling limits of Dirac ensembles and Liouville quantum gravity

abstract:
  In this paper we study ensembles of finite real spectral triples equipped
with a path integral over the space of possible Dirac operators. In the
noncommutative geometric setting of spectral triples, Dirac operators take the
center stage as a replacement for a metric on a manifold. Thus, this path
integral serves as a noncommutative analogue of integration over metrics, a key
feature of a theory of quantum gravity. From these integrals in the so-called
double scaling limit we derive critical exponents of minimal models from
Liouville conformal field theory coupled with gravity. Additionally, the
asymptotics of the partition function of these models satisfy differential
equations such as Painlev\'e I, as a reduction of the KDV hierarchy, which is
predicted by conformal field theory. This is all proven using well-established
and rigorous techniques from random matrix theory.


title:
Triaxial shape and rotation phases and transitions predicted by cranking
  models for light nuclei

abstract:
  Using Hartree-Fock variational method, we derive a quantal, microscopic
cranking model Hamiltonian for triaxial rotation (MSCRM3) from the application
of a rotation operator (with appropriately-chosen rotation angles) to a
deformed nuclear ground-state wavefunction. The derived MSCRM3 Hamiltonian is
identical in form to that of the conventional cranking model (CCRM3) except for
residual correction terms, some of them are negligibly small. In the MSCRM3,
the angular velocity is not a constant parameter, but is microscopically
determined resulting in a self-consistent, and time-reversal and D2 invariant
Hamiltonian. The governing equations of the MSCRM3/CCRM3 are determined in
closed algebraic forms, and solved iteratively using the MSCRM3/CCRM3 Newton's
equations of motion and Feynman's theorem. The MSCRM3/CCRM3 are used to
investigate the stability of the rotational states and nuclear shape and
rotation mode transitions in the light nuclei 20Ne, 24Mg, and 28Si. The impact
of spin-orbit and residuals of the square of the angular momentum and
quadrupole-quadrupole interaction is examined. It is shown that the MSCRM3
predicts rotational relaxation of the intrinsic system, uniform rotation,
triaxial rotation of a triaxial nucleus, observed reduced
rotational-energy-level spacing due to wobbling quenching, and nutating,
figure-skater's slow-fast spinning, and tumbling rotations, whereas CCRM3 does
not, uniform and figure-skating cases excepted. The MSCRM3 rotational states
are found to be more unstable than those of the CCRM3. The MSCRM3 also rules
out a free self-sustaining rotation.


title: Diophantine triples with the property $D(n)$ for distinct $n$

abstract:
  We prove that for every integer $n$, there exist infinitely many
$D(n)$-triples which are also $D(t)$-triples for $t\in\mathbb{Z}$ with $n\ne
t$. We also prove that there are infinitely many triples with the property
$D(-1)$ in $\mathbb{Z}[i]$ which are also $D(n)$-triple in $\mathbb{Z}[i]$ for
two distinct $n$'s other than $n = -1$ and these triples are not equivalent to
any triple with the property $D(1)$.


title:
Site-specific electronic and magnetic excitations of the skyrmion
  material Cu$_2$OSeO$_3$

abstract:
  The manifestation of skyrmions in the Mott-insulator Cu$_2$OSeO$_3$
originates from a delicate balance between magnetic and electronic energy
scales. As a result of these intertwined couplings, the two
symmetry-inequivalent magnetic ions, Cu-I and Cu-II, bond into a spin S=1
entangled tetrahedron. However, conceptualizing the unconventional properties
of this material and the energy of the competing interactions is a challenging
task due the complexity of this system. Here we combine X-ray Absorption
Spectroscopy and Resonant Inelastic X-ray Scattering to uncover the electronic
and magnetic excitations of Cu$_2$OSeO$_3$ with site-specificity. We quantify
the energies of the 3d crystal-field splitting for both Cu-I and Cu-II,
fundamental to optimize model Hamiltonians. Additionally, we unveil a
site-specific magnetic mode, indicating that individual spin character is
preserved within the entangled-tetrahedron picture. Our results thus provide
experimental constraint for validating theories that describe the interactions
of Cu$_2$OSeO$_3$, highlighting the site-selective capabilities of resonant
spectroscopies


title:
The Analysis of a Proposed Experiment to Measure the Speed of Gravity in
  Short Distances

abstract:
  In order to investigate the speed of gravitational signals travelling in air
or through a different medium two experiments were designed. One of the
experiments contains 2 masses rotating at very high speed and in the other
experiment a sapphire bar will vibrate, in both cases they will emit a periodic
tidal gravitational signal and one sapphire device that behaves as a detector,
which are suspended in vacuum and cooled down to 4.2 K will act as a detector.
The vibrational amplitude of the sapphire detector device is measured by an
microwave signal with ultralow phase-noise that uses resonance in the
whispering gallery modes inside the detector device. Sapphire has a quite high
mechanical Q and electrical Q which implies a very narrow detection band thus
reducing the detection sensitivity. A new detector shape for the detector
device is presented in this work, yielding a detection band of about half of
the device vibrational frequency. With the aid of a Finite Element Program the
normal mode frequencies of the detector can be calculated with high precision.
The results show a similar expected sensitivity between the two experimental
setup, but the experiment with the vibration masses is more stable in frequency
then it is chosen for the experimental setup to measure the speed of gravity in
short distances. Then a more precise analysis is made with this experiment
reaching a signal-noise ratio of 10 at a frequency of 5000 Hz.


title:
TemporalWiki: A Lifelong Benchmark for Training and Evaluating
  Ever-Evolving Language Models

abstract:
  Language Models (LMs) become outdated as the world changes; they often fail
to perform tasks requiring recent factual information which was absent or
different during training, a phenomenon called temporal misalignment. This is
especially a challenging problem because the research community still lacks a
coherent dataset for assessing the adaptability of LMs to frequently-updated
knowledge corpus such as Wikipedia. To this end, we introduce TemporalWiki, a
lifelong benchmark for ever-evolving LMs that utilizes the difference between
consecutive snapshots of English Wikipedia and English Wikidata for training
and evaluation, respectively. The benchmark hence allows researchers to
periodically track an LM's ability to retain previous knowledge and acquire
updated/new knowledge at each point in time. We also find that training an LM
on the diff data through continual learning methods achieves similar or better
perplexity than on the entire snapshot in our benchmark with 12 times less
computational cost, which verifies that factual knowledge in LMs can be safely
updated with minimal training data via continual learning. The dataset and the
code are available at https://github.com/joeljang/temporalwiki .


title:
Acyclic Coloring of Products of Digraphs and of Digraphs with Bounded
  Treewidth

abstract:
  The dichromatic number of a digraph $G$ is the smallest integer $\chi_a(G)$
such that the vertex set of $G$ can be partitioned into $\chi_a(G)$ sets, each
of which induces an acyclic subdigraph. This is a generalization of the classic
chromatic number of graphs. Here, we investigate the dichromatic number of the
cartesian, direct, strong and lexicographic products, giving generalizations of
some classic results on the chromatic number of products. More specifically, we
prove that the following inequalities, known to hold for the chromatic number
of graphs, still hold for the dichromatic number of digraphs: $\chi_a(G\square
H)=\max\{\chi_a(G),\chi_a(H)\}$; $\chi_a(G\times H)\le
\min\{\chi_a(G),\chi_a(H)\}$; and $\chi_a(G[H]) =
\chi_a(G[\overset{\leftrightarrow}{K}_k])$, where $k =\chi_a(H)$ and
$\overset{\leftrightarrow}{K}_k$ denotes the complete digraph on $k$ vertices.
In addition, we investigate the products of directed cycles, giving exact
values for $\chi_a(\overset{\rightarrow}{C}_n\times
\overset{\rightarrow}{C}_m)$ and $\chi_a(\overset{\rightarrow}{C}_n\boxtimes
\overset{\rightarrow}{C}_m)$ for every $n,m$, and for
$\chi_a(\overset{\rightarrow}{C}_n[H])$ for every positive integer $n$. This
latter result generalizes a result given in \cite{PP.16}, where they give exact
values when $n>\chi_a(H)$. We also provide a upper-bound to the dichromatic
number of a digraph $G$ as a function of the treewidth of its underlying graph
and we present an {\FPT}-time algorithm that computes the dichromatic number of
$G$, when parameterized by treewidth of the underlying graph of $G$.


title: Modular Domain Adaptation

abstract:
  Off-the-shelf models are widely used by computational social science
researchers to measure properties of text, such as sentiment. However, without
access to source data it is difficult to account for domain shift, which
represents a threat to validity. Here, we treat domain adaptation as a modular
process that involves separate model producers and model consumers, and show
how they can independently cooperate to facilitate more accurate measurements
of text. We introduce two lightweight techniques for this scenario, and
demonstrate that they reliably increase out-of-domain accuracy on four
multi-domain text classification datasets when used with linear and contextual
embedding models. We conclude with recommendations for model producers and
consumers, and release models and replication code to accompany this paper.


title:
Incorporating Actor Heterogeneity into Large Network Models through
  Variational Approximations

abstract:
  The analysis of network data has gained considerable interest in the recent
years. This also includes the analysis of large, high dimensional networks with
hundreds and thousands of nodes. While Exponential Random Graph Models (ERGMs)
serve as workhorse for network data analyses, their applicability to very large
networks is problematic via classical inference such as maximum likelihood or
fully Bayesian estimation due to scaling and instability issues. The latter
trace from the fact, that classical network statistics consider nodes as
exchangeable, i.e. actors in the network are assumed to be homogeneous. This is
often questionable and one way to circumvent the restrictive assumption is to
include actor specific random effects which account for unobservable
heterogeneity. This in turn however increases the number of unknowns
considerably making the model highly-parameterized. As a solution even for very
large networks we propose a scalable approach based on variational
approximations, which not only leads to numerically stable estimation but is
also applicable to high-dimensional directed as well as undirected networks. We
furthermore show that including node specific covariates can reduce node
heterogeneity which we facilitate through versatile prior formulations. We
demonstrate the procedure in two complex examples, namely Facebook data and
data from an international arms trading network.


title:
A numerical approach using a finite element model to constrain the
  possible interior layout of (16) Psyche

abstract:
  Asteroid (16) Psyche is notable for the largest M-type asteroid and has the
high radar albedo among the main-belt asteroids. The object is likely a mixture
of metal and silicates because of its lower bulk density than metallic
materials and observations inferring the existence of silicate materials on the
surface. Here, we numerically investigate the interior layout when the
structure of Psyche consists of a spherical iron core and two types of
silicate-rich layers (compressed and uncompressed ones resulting from the
compaction process. We develop an inverse problem algorithm to determine the
layout distribution by combining a Finite Element Model approach that accounts
for density variations and constrains pressure-based crushing conditions. The
results show that given the crushing limit of 10 MPa the smallest core size
likely reaches 72 km in radius, and the silicate-rich layer, consisting of both
compressed and uncompressed regions, has a thickness ranging up to 68 km. To
support the localized metal concentration at the crater-like region detected in
the recent radar observation, we give more constraints on the minimum core size
which takes up to 34 - 40 % of the entire size of Psyche. Our study also
addresses that the ferrovolcanic surface eruptions could still be a source of
metal-rich materials. Finally, while the differentiated structure having a
spherical core condition is just part of potential scenarios, the present study
infers that the core and compressed layer conditions likely control the surface
condition.


title: From Noncommutative Geometry to Random Matrix Theory

abstract:
  We review recent progress in the analytic study of random matrix models
suggested by noncommutative geometry. One considers finite real spectral
triples where the space of possible Dirac operators is assigned a probability
distribution. These Dirac ensembles are constructed as toy models of Euclidean
quantum gravity on finite noncommutative spaces and display many interesting
properties. Near phase transitions they exhibit manifold like behavior and in
certain cases one can recover Liouville quantum gravity in the double scaling
limit. We highlight examples where bootstrap techniques, Coulomb gas methods,
and topological recursion are applicable.


title:
CogView2: Faster and Better Text-to-Image Generation via Hierarchical
  Transformers

abstract:
  The development of the transformer-based text-to-image models are impeded by
its slow generation and complexity for high-resolution images. In this work, we
put forward a solution based on hierarchical transformers and local parallel
auto-regressive generation. We pretrain a 6B-parameter transformer with a
simple and flexible self-supervised task, Cross-modal general language model
(CogLM), and finetune it for fast super-resolution. The new text-to-image
system, CogView2, shows very competitive generation compared to concurrent
state-of-the-art DALL-E-2, and naturally supports interactive text-guided
editing on images.


title:
An Interactive Polymer Building Toolkit for Molecular Dynamics
  Simulations: PolyMAPS

abstract:
  PolyMAPS is an open-source library that helps researchers to initialize
LAMMPS molecular dynamics simulations. It introduces an integrated workflow by
combining preparation, launching, visualization, and analysis into a single
Jupyter notebook. PolyMAPS enables users to build small or polymeric molecules
in a user-friendly interactive 3D plotting system that supports reading and
writing systems in LAMMPS data file format. Hence, PolyMAPS demonstrates the
potential of reducing the learning difficulties of new users of the LAMMPS
software.


title:
Coordinated Charging Station Search in Stochastic Environments: A
  Multi-Agent Approach

abstract:
  Range and charge anxiety remain essential barriers to a faster electric
vehicle market diffusion. To this end, quickly and reliably finding suitable
charging stations may foster an electric vehicle uptake by mitigating drivers'
anxieties. Here, existing commercial services help drivers to find available
stations based on real-time availability data but struggle with data
inaccuracy, e.g., due to conventional vehicles blocking the access to public
charging stations. In this context, recent works have studied stochastic search
methods to account for availability uncertainty in order to minimize a driver's
detour until reaching an available charging station. So far, both practical and
theoretical approaches ignore driver coordination enabled by charging requests
centralization or sharing of data, e.g., sharing observations of charging
stations' availability or visit intentions between drivers. Against this
background, we study coordinated stochastic search algorithms, which help to
reduce station visit conflicts and improve the drivers' charging experience. We
model a multi-agent stochastic charging station search problem as a
finite-horizon Markov decision process and introduce an online solution
framework applicable to static and dynamic policies. In contrast to static
policies, dynamic policies account for information updates during policy
planning and execution. We present a hierarchical implementation of a
single-agent heuristic for decentralized decision making and a rollout
algorithm for centralized decision making. Extensive numerical studies show
that compared to an uncoordinated setting, a decentralized setting with
visit-intentions sharing decreases the system cost by 26%, which is nearly as
good as the 28% cost decrease achieved in a centralized setting, and saves up
to 23% of a driver's search time while increasing her search reliability.


title: A Rickard equivalence for hopfological homotopy categories

abstract:
  In his paper [Ric89], Rickard presents the stable module category of a
self-injective algebra as a Verdier quotient of its derived category by perfect
complexes. We present a similar realization of the homotopy category in
hopfological algebra as such a Verdier quotient.


title:
Reduction of Stratified Axi-Symmetric Euler-Poisson Equations Under
  Symmetry

abstract:
  The paper considers Euler-Poisson equations which govern the steady state of
a self gravitating, rotating, axi-symmetric fluid under the additional
assumption that it is incompressible and stratified. In this setting we show
that the original system of six nonlinear partial differential equations can be
reduced to two equations, one for the mass density and the other for
gravitational field. This reduction is carried out in cylindrical coordinates.
As a result we are able to derive also expressions for the pressure as a
function of the density. The resulting equations are then solved analytically.
These analytic solutions are used then to determine the shape of the rotating
star (or interstellar cloud) by applying the boundary condition that the
pressure is zero at the boundary.


title: Periodic signature change in spacetimes of embedding class one

abstract:
  The idea of an oscillating Universe has remained a topic of interest even
after the discovery of dark energy. This paper confirms this idea by means of
another well-established theory in general relativity, the embedding of curved
spacetimes in higher-dimensional flat spacetimes: an $n$-dimensional Riemannian
space is said to be of embedding class $m$ if $m+n$ is the lowest dimension of
the flat space in which the given space can be embedded; here
$m=\frac{1}{2}n(n+1)$. So a four-dimensional Riemannian space is of class two
and can therefore be embedded in a six-dimensional flat space. A line element
of class two can be reduced to a line element of class one by a suitable
coordinate transformation. The extra dimension can be either spacelike or
timelike, leading to accelerating and decelerating expansions, respectively.
Accordingly, it is proposed in this paper that the free parameter occurring in
the transformation be a periodic function of time. The result is a mathematical
model that can be interpreted as a periodic change in the signature of the
embedding space. This signature change may be the best model for an oscillating
Universe and complements various models proposed in the literature.


title: Dataset of Multi-aspect Integrated Migration Indicators

abstract:
  Nowadays, new branches of research are proposing the use of non-traditional
data sources for the study of migration trends in order to find an original
methodology to answer open questions about the human mobility framework. In
this context we presents the Multi-aspect Integrated Migration Indicators
(MIMI) dataset, an new dataset of migration drivers, resulting from the process
of acquisition, transformation and merge of both official data about
international flows and stocks and original indicators not typically used in
migration studies, such as online social networks. This work describes the
process of gathering, embedding and merging traditional and novel features,
resulting in this new multidisciplinary dataset that we believe could
significantly contribute to nowcast to forecast both present and future
bilateral migration trends.


title:
Application of machine learning methods to detect and classify Core
  images using GAN and texture recognition

abstract:
  During exploration campaigns, oil companies rely heavily on drill core
samples as they provide valuable geological information that helps them find
important oil deposits. Traditional core logging techniques are laborious and
subjective. Core imaging, a new technique in the oil industry, is used to
supplement analysis by rapidly characterising large quantities of drill cores
in a nondestructive and noninvasive manner. In this paper, we will present the
problem of core detection and classification. The first problem is detecting
the cores and segmenting the holes in images by using Faster RCNN and Mask RCNN
models respectively. The second problem is filling the hole in the core image
by applying the Generative adversarial network(GAN) technique and using
Contextual Residual Aggregation(CRA) which creates high frequency residual for
missing contents in images. And finally applying Texture recognition models for
the classification of core images.


title:
Curl and gradient of divergence operators in Spaces $ \mathbf{W}^{m}$
  and $\mathbf{A}^{2k}$ vortex and potential fields and in the classes
  $\mathbf{C}(2k, m)$

abstract:
  The properties of the curl and the gradient of divergence operators ( $
\text{rot}$ and $\nabla\text{div}$ ) are studied in the space $ \mathbf {L}_{2}
(G) $ in a bounded domain $ G \subset \textrm {R}^3 $ with a smooth boundary $
\Gamma$ and in the classes $ \mathbf{C}(2k, m)(G)\equiv \mathbf{A}^{2k}(G)
\oplus \mathbf{W}^m(G)$. The space $ \mathbf {L}_{2} (G) $ is decomposed into
orthogonal subspaces $ \mathcal{A} $ and $ \mathcal {B} $:
$\mathbf{L}_{2}(G)=\mathcal{A}\oplus \mathcal{B}$. In turn, $ \mathcal{A}=
\mathcal{A}_H\oplus \mathbf{A}^0$ and $\mathcal{B}=\mathcal{B}_H \oplus
\mathbf{V}^0$, where $\mathcal{A}_H $ and $\mathcal{B}_H $ are null spaces of
operators $\nabla \text{div}$ and $ \text{rot}$ in $\mathcal{A}$ and
$\mathcal{B}$; the dimensions of $\mathcal{A}_H $ and $\mathcal{B}_H $ are
finite and determined by the topology of the boundary; $\mathcal{A}_H=\emptyset
$ and $\mathcal{B}_H= \emptyset $ if the domain $\Omega $ is a ball. The
orthonormal basis are constructed in the class $ \mathbf{A}^0$ (resp., In
$\mathbf{V}^0$ ) by eigenfields $\mathbf{q}_{j}(\mathbf{x})$ of $\nabla
\text{div}$ operator (resp., $\mathbf{q}^{\pm }_{j}(\mathbf{x})$ of $
\text{rot}$ operator) with nonzero eigenvalues $\mu_{j}$ (resp., $\pm
\lambda_{j}$ ). The operators $\nabla\mathrm{div}$ and $\mathrm{rot}$ cancel
each other out and project $\mathbf{L}_{2}(G) $ onto $ \mathcal {A} $ and $
\mathcal { B} $, and $ \mathrm {rot} \, \mathbf {u} = 0 $ for $ \mathbf {u} \in
\mathcal {A} $, and $ \nabla \mathrm div \mathbf {v} = 0 $ for $ \mathbf {v}
\in \mathcal {B} $ \cite{hw}. Laplace matrix operator expressed through them:
$\mathrm{\Delta} \mathbf {v} \equiv \nabla \mathrm{div}\,\mathbf {v}
-(\mathrm{rot})^2\, \mathbf {v}$.


title:
Recommendations on test datasets for evaluating AI solutions in
  pathology

abstract:
  Artificial intelligence (AI) solutions that automatically extract information
from digital histology images have shown great promise for improving
pathological diagnosis. Prior to routine use, it is important to evaluate their
predictive performance and obtain regulatory approval. This assessment requires
appropriate test datasets. However, compiling such datasets is challenging and
specific recommendations are missing.
  A committee of various stakeholders, including commercial AI developers,
pathologists, and researchers, discussed key aspects and conducted extensive
literature reviews on test datasets in pathology. Here, we summarize the
results and derive general recommendations for the collection of test datasets.
  We address several questions: Which and how many images are needed? How to
deal with low-prevalence subsets? How can potential bias be detected? How
should datasets be reported? What are the regulatory requirements in different
countries?
  The recommendations are intended to help AI developers demonstrate the
utility of their products and to help regulatory agencies and end users verify
reported performance measures. Further research is needed to formulate criteria
for sufficiently representative test datasets so that AI solutions can operate
with less user intervention and better support diagnostic workflows in the
future.

categories: eess.IV cs.AI cs.CV cs.LG physics.med-ph


title: The "teapot in a city": a paradigm shift in urban climate modeling

abstract:
  Urban areas are a high-stake target of climate change mitigation and
adaptation measures. To understand, predict and improve the energy performance
of cities, the scientific community develops numerical models that describe how
they interact with the atmosphere through heat and moisture exchanges at all
scales. In this review, we present recent advances that are at the origin of
last decade's revolution in computer graphics, and recent breakthroughs in
statistical physics that extend well established path-integral formulations to
non-linear coupled models. We argue that this rare conjunction of scientific
advances in mathematics, physics, computer and engineering sciences opens
promising avenues for urban climate modeling and illustrate this with coupled
heat transfer simulations in complex urban geometries under complex atmospheric
conditions. We highlight the potential of these approaches beyond urban climate
modeling, for the necessary appropriation of the issues at the heart of the
energy transition by societies.

categories: physics.soc-ph physics.ao-ph physics.comp-ph physics.data-an


title:
Hardware Trojan Detection Using Unsupervised Deep Learning on Quantum
  Diamond Microscope Magnetic Field Images

abstract:
  This paper presents a method for hardware trojan detection in integrated
circuits. Unsupervised deep learning is used to classify wide field-of-view
(4x4 mm$^2$), high spatial resolution magnetic field images taken using a
Quantum Diamond Microscope (QDM). QDM magnetic imaging is enhanced using
quantum control techniques and improved diamond material to increase magnetic
field sensitivity by a factor of 4 and measurement speed by a factor of 16 over
previous demonstrations. These upgrades facilitate the first demonstration of
QDM magnetic field measurement for hardware trojan detection. Unsupervised
convolutional neural networks and clustering are used to infer trojan presence
from unlabeled data sets of 600x600 pixel magnetic field images without human
bias. This analysis is shown to be more accurate than principal component
analysis for distinguishing between field programmable gate arrays configured
with trojan free and trojan inserted logic. This framework is tested on a set
of scalable trojans that we developed and measured with the QDM. Scalable and
TrustHub trojans are detectable down to a minimum trojan trigger size of 0.5%
of the total logic. The trojan detection framework can be used for golden-chip
free detection, since knowledge of the chips' identities is only used to
evaluate detection accuracy


title:
Computing Pareto-Optimal and Almost Envy-Free Allocations of Indivisible
  Goods

abstract:
  We study the problem of fair and efficient allocation of a set of indivisible
goods to agents with additive valuations using the popular fairness notions of
envy-freeness up to one good (EF1) and equitability up to one good (EQ1) in
conjunction with Pareto-optimality (PO). There exists a pseudo-polynomial time
algorithm to compute an EF1+PO allocation, and a non-constructive proof of
existence of allocations that are both EF1 and fractionally Pareto-optimal
(fPO), which is a stronger notion than PO. We present a pseudo-polynomial time
algorithm to compute an EF1+fPO allocation, thereby improving the earlier
results. Our techniques also enable us to show that an EQ1+fPO allocation
always exists when the values are positive, and that it can be computed in
pseudo-polynomial time.
  We also consider the class of $k$-ary instances where $k$ is a constant,
i.e., each agent has at most $k$ different values for the goods. We show that
for such instances an EF1+fPO allocation can be computed in strongly polynomial
time. When all values are positive, we show that an EQ1+fPO allocation for such
instances can be computed in strongly polynomial time. Next, we consider
instances where the number of agents is constant, and show that an EF1+PO (also
EQ1+PO) allocation can be computed in polynomial time. These results
significantly extend the polynomial-time computability beyond the known cases
of binary or identical valuations.
  Further, we show that the problem of computing an EF1+PO allocation
polynomial-time reduces to a problem in the complexity class PLS. We also
design a polynomial-time algorithm that computes a Nash welfare maximizing
allocation when there are constantly many agents with constant many different
values for the goods.


title: Cohomological boundedness for flat bundles on surfaces and applications

abstract:
  This paper explores the cohomological consequences of the existence of moduli
spaces for flat bundles with bounded rank and irregularity at infinity and
gives unconditional proofs in the surface case. Namely, we prove the existence
of a universal bound for the dimension of the De Rham cohomology of flat
bundles with bounded rank and irregularity. We also prove a Lefschetz
recognition principle stating the existence of a finite union of hyperplane
sections distinguishing flat bundles with bounded rank and irregularity after
restriction. In higher dimension, we deduce a universal bound for the degrees
of the turning loci of flat bundles with bounded rank and irregularity. Along
the way, we introduce a new operation on the group of b-divisors on a smooth
surface (the partial discrepancy) and prove a closed formula for the
characteristic cycles of flat bundles on surfaces in terms of the partial
discrepancy of the irregularity b-divisor attached to any flat bundle by
Kedlaya.


title: Scalar-Fermion Interaction as the Driver of Cosmic Acceleration

abstract:
  We argue that an interacting scalar-fermion distribution can be used to
demonstrate the cosmic acceleration in General Relativity. The interaction is
of Yukawa nature and it drives the fermion density to decay with cosmic time.
The consistency of the model is established through, (a) a generalization of
the Om(z) parameter, the present matter density contrast of the universe, and
(b) a comparison of theoretical results with observational data, using a Markov
Chain Monte Carlo code. A simple model of unified cosmic expansion history is
also discussed. There are more than one instances where the universe goes
through a transition between subsequent epochs with different patterns of
cosmic expansion. These patterns impose some constraints on the scalar-fermion
interaction profile and on the overall cosmological dynamics.


title: Panoptic: a perpetual, oracle-free options protocol

abstract:
  Panoptic is a perpetual, oracle-free, instant-settlement options trading
protocol on the Ethereum blockchain. Panoptic enables the permissionless
trading of options on top of any asset pool in the Uniswap v3 ecosystem and
seeks to develop a trustless, permissionless, and composable options product,
i.e., do for decentralized options markets what x*y=k automated market maker
protocols did for spot trading.


title: Generalized Buzano's Inequality

abstract:
  The paper deals with the Buzano's inequality on a complex Hilbert space H.
Mainly, we obtain generalizations of such classical inequality, which hold for
certain families of bounded linear operators defined on H. In addition, several
new inequalities involving the norm and numerical radius of an operator are
established.


title: Vietoris--Rips Complexes of Metric Spaces Near a Metric Graph

abstract:
  For a sufficiently small scale $\beta>0$, the Vietoris--Rips complex
$\mathcal{R}_\beta(S)$ of a metric space $S$ with a small Gromov--Hausdorff
distance to a closed Riemannian manifold $M$ has been already known to recover
$M$ up to homotopy type. While the qualitative result is remarkable and
generalizes naturally to the recovery of spaces beyond Riemannian manifolds -
such as geodesic metric spaces with a positive convexity radius - the
generality comes at a cost. Although the scale parameter $\beta$ is known to
depend only on the geometric properties of the geodesic space, how to
quantitatively choose such a $\beta$ for a given geodesic space is still
elusive. In this work, we focus on the topological recovery of a special type
of geodesic space, called a metric graph. For an abstract metric graph
$\mathcal{G}$ and a (sample) metric space $S$ with a small Gromov--Hausdorff
distance to it, we provide a description of $\beta$ based on the convexity
radius of $\mathcal{G}$ in order for $\mathcal{R}_\beta(S)$ to be homotopy
equivalent to $\mathcal{G}$. Our investigation also extends to the study of the
Vietoris--Rips complexes of a Euclidean subset $S\subset\mathbb{R}^d$ with a
small Hausdorff distance to an embedded metric graph
$\mathcal{G}\subset\mathbb{R}^d$. From the pairwise Euclidean distances of
points of $S$, we introduce a family (parametrized by $\varepsilon$) of
path-based Vietoris--Rips complexes $\mathcal{R}^\varepsilon_\beta(S)$ for a
scale $\beta>0$. Based on the convexity radius and distortion of the embedding
of $\mathcal{G}$, we show how to choose a suitable parameter $\varepsilon$ and
a scale $\beta$ such that $\mathcal{R}^\varepsilon_\beta(S)$ is homotopy
equivalent to $\mathcal{G}$.


title:
Permuting the roots of univariate polynomials whose coefficients depend
  on parameters

abstract:
  We address two interrelated problems concerning permutation of roots of
univariate polynomials whose coefficients depend on parameters. First, we
compute the Galois group of polynomials
$\varphi(x)\in\mathbb{C}[t_1,\cdots,t_k][x]$ over $\mathbb{C}(t_1,\cdots,t_k)$.
Provided that the corresponding multivariate polynomial
$\varphi(x,t_1,\cdots,t_k)$ is generic with respect to its support set
$A\subset \mathbb{Z}^{k+1}$, we determine the latter Galois group for any $A$.
Second, we determine the Galois group of systems of polynomial equations of the
form $p(x,t)=q(t)=0 $ where $p$ and $q$ have prescribed support sets
$A_1\subset \mathbb{Z}^2$ and $A_2\subset \{0\}\times \mathbb{Z}$ respectively.
For each problem, we determine the image of an appropriate braid monodromy map
in order to compute the sought Galois group. Among other topics, the present
results apply to the computation of the Galois group of some general
enumerative problems, to Zariski's theorem on the fundamental group of the
complement to projective hypersurfaces and to the determination of the kernel
as well as the isomonodromy loci of the braid monodromy maps involved.


title:
Lipschitz-based Surrogate Model for High-dimensional Computationally
  Expensive Problems

abstract:
  Standard evolutionary optimization algorithms assume that the evaluation of
the objective and constraint functions is straightforward and computationally
cheap. However, in many real-world optimization problems, the computations of
the objective function or constraints involve computationally expensive
numerical simulations or physical experiments. Surrogate-assisted evolutionary
algorithms (SAEAs) have recently gained increased attention because of their
search capabilities for solving these computationally expensive optimization
problems. The main idea of SAEAs is the integration of an evolutionary
algorithm with a selected surrogate model. In this paper, we propose a novel
surrogate model based on a Lipschitz underestimation of the
expensive-to-compute objective function. We also develop a differential
evolution-based algorithm, that utilizes the Lipschitz-based surrogate model,
along with a standard radial basis function surrogate model and a local search
procedure. This algorithm, called Lipschitz Surrogate-assisted Differential
Evolution (LSADE), is designed for high-dimensional computationally expensive
problems. The experimental results on seven benchmark functions of dimensions
30, 50, 100, and 200 show that the proposed method utilizing the
Lipschitz-based surrogate model is competitive compared with the
state-of-the-art algorithms under a limited computational budget, being
especially effective for the very complicated benchmark functions in high
dimensions.


title:
Riesz-Kolmogorov type compactness criteria in function spaces with
  applications

abstract:
  We present forms of the classical Riesz-Kolmogorov theorem for compactness
that are applicable in a wide variety of settings. In particular, our theorems
apply to classify the precompact subsets of the Lebesgue space $L^2$,
Paley-Wiener spaces, weighted Bargmann-Fock spaces, and a scale of weighted
Besov-Sobolev spaces of holomorphic functions that includes weighted Bergman
spaces of general domains as well as the Hardy space and the Dirichlet space.
We apply the compactness criteria to characterize the compact Toeplitz
operators on the Bergman space, deduce the compactness of Hankel operators on
the Hardy space, and obtain general umbrella theorems.


title:
Crystallographic effects on transgranular chloride-induced stress
  corrosion crack propagation of arc welded austenitic stainless steel

abstract:
  The effect of crystallography on transgranular chloride-induced stress
corrosion cracking (TGCISCC) of arc welded 304L austenitic stainless steel is
studied on >300 grains along crack paths. Schmid and Taylor factor mismatches
across grain boundaries (GBs) reveal that cracks propagate either from a hard
to soft grain, which can be explained merely by mechanical arguments, or soft
to hard grain. In the latter case, finite element analysis reveals that TGCISCC
will arrest at GBs without sufficient mechanical stress, favorable
crystallographic orientations, or crack tip corrosion. GB type does not play a
significant role in determining TGCISCC cracking behavior nor susceptibility.
TGCISCC crack behaviors at GBs are discussed in the context of the competition
between mechanical, crystallographic, and corrosion factors.


title: Non-local microwave electrodynamics in ultra-pure PdCoO$_{2}$

abstract:
  The motion of electrons in the vast majority of conductors is diffusive,
obeying Ohm's law. However, the recent discovery and growth of high-purity
materials with extremely long electronic mean free paths has sparked interest
in non-ohmic alternatives, including viscous and ballistic flow. Although
non-ohmic transport regimes have been discovered across a range of materials,
including two-dimensional electron gases, graphene, topological semimetals, and
the delafossite metals, determining their nature has proved to be challenging.
Here, we report on a new approach to the problem, employing broadband microwave
spectroscopy of the delafossite metal PdCoO$_{2}$ in three distinct sample
geometries that would be identical for diffusive transport. The observed
differences, which go as far as differing power laws, take advantage of the
hexagonal symmetry of PdCoO$_{2}$. This permits a particularly elegant
symmetry-based diagnostic for non-local electrodynamics, with the result
favouring ballistic over strictly hydrodynamic flow. Furthermore, it uncovers a
new effect for ballistic electron flow, owing to the highly facetted shape of
the hexagonal Fermi surface. We combine our extensive dataset with an analysis
of the Boltzmann equation to characterize the non-local regime in PdCoO$_{2}$.
More broadly, our results highlight the potential of broadband microwave
spectroscopy to play a central role in investigating exotic transport regimes
in the new generation of ultra-high conductivity materials.


title: EndoMapper dataset of complete calibrated endoscopy procedures

abstract:
  Computer-assisted systems are becoming broadly used in medicine. In
endoscopy, most research focuses on automatic detection of polyps or other
pathologies, but localization and navigation of the endoscope is completely
performed manually by physicians. To broaden this research and bring spatial
Artificial Intelligence to endoscopies, data from complete procedures are
needed. This data will be used to build a 3D mapping and localization systems
that can perform special task like, for example, detect blind zones during
exploration, provide automatic polyp measurements, guide doctors to a polyp
found in a previous exploration and retrieve previous images of the same area
aligning them for easy comparison. These systems will provide an improvement in
the quality and precision of the procedures while lowering the burden on the
physicians. This paper introduces the Endomapper dataset, the first collection
of complete endoscopy sequences acquired during regular medical practice,
including slow and careful screening explorations, making secondary use of
medical data. Its original purpose is to facilitate the development and
evaluation of VSLAM (Visual Simultaneous Localization and Mapping) methods in
real endoscopy data. The first release of the dataset is composed of 59
sequences with more than 15 hours of video. It is also the first endoscopic
dataset that includes both the computed geometric and photometric endoscope
calibration with the original calibration videos. Meta-data and annotations
associated to the dataset varies from anatomical landmark and description of
the procedure labeling, tools segmentation masks, COLMAP 3D reconstructions,
simulated sequences with groundtruth and meta-data related to special cases,
such as sequences from the same patient. This information will improve the
research in endoscopic VSLAM, as well as other research lines, and create new
research lines.


title:
Non-Hertz-Millis scaling of the antiferromagnetic quantum critical metal
  via scalable Hybrid Monte Carlo

abstract:
  We numerically study the $\mathrm{O}(3)$ spin-fermion model, a minimal model
of the onset of antiferromagnetic spin-density wave (SDW) order in a
two-dimensional metal. We employ a Hybrid Monte Carlo (HMC) algorithm with a
novel auto-tuning procedure, which learns the optimal HMC hyperparameters in an
initial warmup phase. This allows us to study unprecedentedly large systems,
even at criticality. At the quantum critical point, we find a critical scaling
of the dynamical spin susceptibility $\chi(\omega, \vec q)$ that strongly
violates the Hertz-Millis form, which is the first demonstrated instance of
such a phenomenon in this model. The form that we do observe provides strong
evidence that the universal scaling is actually governed by the fixed point
near perfect hot-spot nesting of Schlief, Lunts, and Lee [Phys. Rev. X 7,
021010 (2017)], even away from perfect nesting. Our work provides a concrete
link between controlled calculations of SDW metallic criticality in the
long-wavelength and small nesting angle limits and a microscopic finite-size
model at realistic appreciable values of the nesting angle. Additionally, the
HMC method we introduce is generic and can be used to study other fermionic
models of quantum criticality, where there is a strong need to simulate large
systems.


title: Analytical Performance Estimation during Code Generation on Modern GPUs

abstract:
  Automatic code generation is frequently used to create implementations of
algorithms specifically tuned to particular hardware and application
parameters. The code generation process involves the selection of adequate code
transformations, tuning parameters, and parallelization strategies. We propose
an alternative to time-intensive autotuning, scenario-specific performance
models, or black-box machine learning to select the best-performing
configuration.
  This paper identifies the relevant performance-defining mechanisms for
memory-intensive GPU applications through a performance model coupled with an
analytic hardware metric estimator. This enables a quick exploration of large
configuration spaces to identify highly efficient code candidates with high
accuracy.
  We examine the changes of the A100 GPU architecture compared to the
predecessor V100 and address the challenges of how to model the data transfer
volumes through the new memory hierarchy.
  We show how our method can be coupled to the pystencils stencil code
generator, which is used to generate kernels for a range-four 3D-25pt stencil
and a complex two-phase fluid solver based on the Lattice Boltzmann Method. For
both, it delivers a ranking that can be used to select the best-performing
candidate.
  The method is not limited to stencil kernels but can be integrated into any
code generator that can generate the required address expressions.


title: Training Naturalized Semantic Parsers with Very Little Data

abstract:
  Semantic parsing is an important NLP problem, particularly for voice
assistants such as Alexa and Google Assistant. State-of-the-art (SOTA) semantic
parsers are seq2seq architectures based on large language models that have been
pretrained on vast amounts of text. To better leverage that pretraining, recent
work has explored a reformulation of semantic parsing whereby the output
sequences are themselves natural language sentences, but in a controlled
fragment of natural language. This approach delivers strong results,
particularly for few-shot semantic parsing, which is of key importance in
practice and the focus of our paper. We push this line of work forward by
introducing an automated methodology that delivers very significant additional
improvements by utilizing modest amounts of unannotated data, which is
typically easy to obtain. Our method is based on a novel synthesis of four
techniques: joint training with auxiliary unsupervised tasks; constrained
decoding; self-training; and paraphrasing. We show that this method delivers
new SOTA few-shot performance on the Overnight dataset, particularly in very
low-resource settings, and very compelling few-shot results on a new semantic
parsing dataset.


title: CLIP-Art: Contrastive Pre-training for Fine-Grained Art Classification

abstract:
  Existing computer vision research in artwork struggles with artwork's
fine-grained attributes recognition and lack of curated annotated datasets due
to their costly creation. To the best of our knowledge, we are one of the first
methods to use CLIP (Contrastive Language-Image Pre-Training) to train a neural
network on a variety of artwork images and text descriptions pairs. CLIP is
able to learn directly from free-form art descriptions, or, if available,
curated fine-grained labels. Model's zero-shot capability allows predicting
accurate natural language description for a given image, without directly
optimizing for the task. Our approach aims to solve 2 challenges: instance
retrieval and fine-grained artwork attribute recognition. We use the iMet
Dataset, which we consider the largest annotated artwork dataset. In this
benchmark we achieved competitive results using only self-supervision.


title:
Implementation of FAIR principles in the IPCC: The WGI AR6 Atlas
  repository

abstract:
  The Sixth Assessment Report (AR6) of the Intergovernmental Panel on Climate
Change (IPCC) has adopted the FAIR Guiding Principles. The Atlas chapter of
Working Group I (WGI) is presented as a test case. Here, we describe the
application of these principles in the Atlas, the challenges faced during its
implementation, and those that remain for the future. We present the open
source repository resulting from this process, which collects the code
(including annotated Jupyter notebooks), data provenance, and some aggregated
datasets underpinning the key figures in the Atlas chapter and its interactive
companion (the Interactive Atlas), open to scrutiny by the scientific community
and the general public. We describe the informal pilot review conducted on this
repository to gather recommendations that led to significant improvements.
Finally, a working example illustrates the use of the repository to produce
customized regional information, extending the Interactive Atlas products and
running the code interactively in a web browser using Jupyter notebooks. Atlas
repository: doi:10.5281/zenodo.5171760.


title: The fast neutron induced fission of $^{240}$Pu and $^{242}$Pu

abstract:
  We report the measurement of the TKE release in the fast neutron induced
fission of $^{240}$Pu and $^{242}$Pu. The results are compared to the
predictions of the GEF model, the CGMF model, and the model of Denisov and
Sedykh as well as previous experimental work on these reactions. Our absolute
measurements of the TKE release are in good agreement with the previous
measurements of Nethaway \textit{et al.} for the interaction of 14.8 MeV
neutrons with $^{240}$Pu \cite{Nethaway} and of Winkelmann and Aumann for the
interaction of 15 MeV neutrons with $^{242}$Pu \cite{Winkelmann}. The general
trends of the measured TKE values agree with phenomenological models but the
variances of the TKE distributions are significantly less than predicted by
various models. The mean post neutron emission TKE release decreases non-
linearly with increasing neutron energy and can be represented as TKE(MeV) =
175.8 $\pm$ 0.3 - (2. 4 $\pm$ 0.8)log$_{10}$E$_n$ - (1.4
$\pm$0.4)log$_{10}$E$_{n}^2$ for $^{240}$Pu and TKE(MeV) = 177.1 $\pm$ 0.3 -
(1.2 $\pm$ 0.9)log$_{10}$E$_n$ - (1.8 $\pm$0.5)log$_{10}$E$_{n}^2$ for
$^{242}$Pu.


title:
Breaking the Linear Error Barrier in Differentially Private Graph
  Distance Release

abstract:
  Releasing all pairwise shortest path (APSP) distances between vertices on
general graphs under weight Differential Privacy (DP) is known as a challenging
task. In the previous attempt of (Sealfon 2016}, by adding Laplace noise to
each edge weight or to each output distance, to achieve DP with some fixed
budget, with high probability the maximal absolute error among all published
pairwise distances is roughly $O(n)$ where $n$ is the number of nodes. It was
shown that this error could be reduced for some special graphs, which, however,
is hard for general graphs. Therefore, whether the approximation error can be
reduced to sublinear in $n$ is posted as an interesting open problem.
  We break the linear barrier on the distance approximation error of previous
result, by proposing an algorithm that releases a constructed synthetic graph
privately. Computing all pairwise distances on the constructed graph only
introduces $\tilde O(n^{1/2})$ error in answering all pairwise shortest path
distances for fixed privacy parameter. Our method is based on a novel graph
diameter (link length) augmentation via constructing "shortcuts" for the paths.
By adding a set of shortcut edges to the original graph, we show that any node
pair has a shortest path with link length $\tilde O(n^{1/2})$. Then by adding
noises with some positive mean to the edge weights, we show that the new graph
is differentially private and can be published to answer all pairwise shortest
path distances with $\tilde O(n^{1/2})$ approximation error using standard APSP
computation.
  Additionally, we consider the graph with small feedback vertex set number. A
feedback vertex set (FVS) of a graph is a set of vertices whose removal leaves
a graph without cycles, and the feedback vertex set number of a graph, $k$, is
the size of a smallest feedback vertex set. We propose a DP algorithm with
error rate $\tilde O(k)$.


title:
Magnetic properties of disordered polycrystalline bulk Sm$ _{2} $NiMnO$
  _{6} $ double perovskite

abstract:
  The structural, electronic and magnetic properties of anti-site disordered
Sm$ _{2} $NiMnO$ _{6} $ double perovskite has been studied. RE$_{2}$NiMnO$_{6}$
(RE: rare-earth) ordered double perovskite is commonly believed to show two
distinct magnetic phase transitions viz, paramagnetic to ferromagnetic (FM)
transition at T = T$ _{C} $ due to Ni-O-Mn super exchange interaction and
another transition at T = T$ _{d} $ due to coupling of RE spins with Ni-Mn
network. In our present study, we have observed that the presence of intrinsic
B-site disorder results in an additional antiferromagnetic (AFM) coupling,
mediated via Ni-O-Ni and Mn-O-Mn local bond pairs. As a consequence, the
magnetic behavior of SNMO comprises of co-existing FM-AFM phases, which are
respectively governed by the anti-site ordered and disordered structures. Field
dependent inverted cusp like trend in M(T) and two step reversible loop
behavior in M(H) measurements indicate the presence of competing FM-AFM phases
over a wide range of temperature values (T$ _{d} < $ T $ < $ T$ _{C} $).


title: OSSGAN: Open-Set Semi-Supervised Image Generation

abstract:
  We introduce a challenging training scheme of conditional GANs, called
open-set semi-supervised image generation, where the training dataset consists
of two parts: (i) labeled data and (ii) unlabeled data with samples belonging
to one of the labeled data classes, namely, a closed-set, and samples not
belonging to any of the labeled data classes, namely, an open-set. Unlike the
existing semi-supervised image generation task, where unlabeled data only
contain closed-set samples, our task is more general and lowers the data
collection cost in practice by allowing open-set samples to appear. Thanks to
entropy regularization, the classifier that is trained on labeled data is able
to quantify sample-wise importance to the training of cGAN as confidence,
allowing us to use all samples in unlabeled data. We design OSSGAN, which
provides decision clues to the discriminator on the basis of whether an
unlabeled image belongs to one or none of the classes of interest, smoothly
integrating labeled and unlabeled data during training. The results of
experiments on Tiny ImageNet and ImageNet show notable improvements over
supervised BigGAN and semi-supervised methods. Our code is available at
https://github.com/raven38/OSSGAN.


title:
Collision Risk and Operational Impact of Speed Change Advisories as
  Aircraft Collision Avoidance Maneuvers

abstract:
  Aircraft collision avoidance systems have long been a key factor in keeping
our airspace safe. Over the past decade, the FAA has supported the development
of a new family of collision avoidance systems called the Airborne Collision
Avoidance System X (ACAS X), which model the collision avoidance problem as a
Markov decision process (MDP). Variants of ACAS X have been created for both
manned (ACAS Xa) and unmanned aircraft (ACAS Xu and ACAS sXu). The variants
primarily differ in the types of collision avoidance maneuvers they issue. For
example, ACAS Xa issues vertical collision avoidance advisories, while ACAS Xu
and ACAS sXu allow for horizontal advisories due to reduced aircraft
performance capabilities. Currently, a new variant of ACAS X, called ACAS Xr,
is being developed to provide collision avoidance capability to rotorcraft and
Advanced Air Mobility (AAM) vehicles. Due to the desire to minimize deviation
from the prescribed flight path of these aircraft, speed adjustments have been
proposed as a potential collision avoidance maneuver for aircraft using ACAS
Xr. In this work, we investigate the effect of speed change advisories on the
safety and operational efficiency of collision avoidance systems. We develop an
MDP-based collision avoidance logic that issues speed advisories and compare
its performance to that of horizontal and vertical logics through Monte Carlo
simulation on existing airspace encounter models. Our results show that while
speed advisories are able to reduce collision risk, they are neither as safe
nor as efficient as their horizontal and vertical counterparts.


title:
Analyzing Strategies for Dynamical Decoupling Insertion on IBM Quantum
  Computer

abstract:
  Near-term quantum devices are subject to errors and decoherence error is one
of the non-negligible sources. Dynamical decoupling (DD) is a well-known
technique to protect idle qubits from decoherence error. However, the optimal
approach to inserting DD sequences still remains unclear. In this paper, we
identify different conditions that lead to idle qubits and evaluate strategies
for DD insertion under these specific conditions. Specifically, we divide the
idle qubit into crosstalk-idle or idle-idle qubit depending on its coupling
with other qubits and report the DD insertion strategies for the two types of
idle qubits. We also perform Ramsey experiment to understand the reasons behind
the strategy choice. Finally, we provide design guidelines for DD insertion for
small circuits and insights for large-scale circuit design.


title:
Analytical techniques in single and multi-parameter quantum estimation
  theory: a focused review

abstract:
  As we enter the era of quantum technologies, quantum estimation theory
provides an operationally motivating framework for determining high precision
devices in modern technological applications. The aim of any estimation process
is to extract information from an unknown parameter embedded in a physical
system such as the estimation converges to the true value of the parameter.
According to the Cram\'er-Rao inequality in mathematical statistics, the Fisher
information in the case of single-parameter estimation procedures, and the
Fisher information matrix in the case of multi-parameter estimation, are the
key quantities representing the ultimate precision of the parameters specifying
a given statistical model. In quantum estimation strategies, it is usually
difficult to derive the analytical expressions of such quantities in a given
quantum state. This review provides comprehensive techniques on the analytical
calculation of the quantum Fisher information as well as the quantum Fisher
information matrix in various scenarios and via several methods. Furthermore,
it provides a mathematical transition from classical to quantum estimation
theory applied to many freedom quantum systems. To clarify these results, we
examine these developments using some examples. Other challenges, including
their links to quantum correlations and saturating the quantum Cram\'er-Rao
bound, are also addressed.


title:
Reconstruction of the collapse of the 'Azure Window' natural arch via
  photogrammetry

abstract:
  The Azure Window was a natural arch situated in the west coast of Gozo
(Maltese Archipelago) that collapsed in March 2017. We employ a Diver
Propulsion Vehicle-mounted camera system to capture data for the 3D-modelling
of this collapsed arch via photogrammetry. We demonstrate use of this method to
document complex underwater geomorphology spread across a large area, and draw
up a geomorphic assessment of the site and collapse event on the basis of this
3D-model. The methodology enables a reconstruction and understanding of the
collapse event. On account of the high-resolution attained, we are able to
cross-match the principal submerged components with their pre-collapse
location; this enables an understanding of the dynamics of the collapse,
confirmation of rock break-up along existing joints, and mapping the
distribution of the rock and debris from the collapse event. We conclude that
the key stages in the collapse of the Azure Window entailed erosion at the base
of the pillar, leading to the latter's collapse in the southwest direction,
breaking into two main sections that separated along the lithological boundary.
We also find clear evidence that separation of some sections of the pillar
followed pre-existing joints. The bridge collapsed vertically upon loss of
support from the pillar, breaking into two main components and many other
fragments. We also document further changes at the site post-collapse. We show
that this approach can be utilised to understand and characterise such events
even when significant time has elapsed since collapse, and rocks have already
undergone erosion and significant marine growth.


title: Flexible domains for minimal surfaces in Euclidean spaces

abstract:
  In this paper we introduce and investigate a new notion of flexibility for
domains in Euclidean spaces $\mathbb R^n$ for $n\ge 3$ in terms of minimal
surfaces which they contain. A domain $\Omega$ in $\mathbb R^n$ is said to be
flexible if every conformal minimal immersion $U\to\Omega$ from a Runge domain
$U$ in an open conformal surface $M$ can be approximated uniformly on compacts,
with interpolation on any given finite set, by conformal minimal immersion
$M\to \Omega$. Together with hyperbolicity phenomena considered in recent
works, this extends the dichotomy between flexibility and rigidity from complex
analysis to minimal surface theory.


title:
Human-in-the-loop online multi-agent approach to increase
  trustworthiness in ML models through trust scores and data augmentation

abstract:
  Increasing a ML model accuracy is not enough, we must also increase its
trustworthiness. This is an important step for building resilient AI systems
for safety-critical applications such as automotive, finance, and healthcare.
For that purpose, we propose a multi-agent system that combines both machine
and human agents. In this system, a checker agent calculates a trust score of
each instance (which penalizes overconfidence and overcautiousness in
predictions) using an agreement-based method and ranks it; then an improver
agent filters the anomalous instances based on a human rule-based procedure
(which is considered safe), gets the human labels, applies geometric data
augmentation, and retrains with the augmented data using transfer learning. We
evaluate the system on corrupted versions of the MNIST and FashionMNIST
datasets. We get an improvement in accuracy and trust score with just few
additional labels compared to a baseline approach.


title: Handling and Presenting Harmful Text

abstract:
  Textual data can pose a risk of serious harm. These harms can be categorised
along three axes: (1) the harm type (e.g. misinformation, hate speech or racial
stereotypes) (2) whether it is \textit{elicited} as a feature of the research
design from directly studying harmful content (e.g. training a hate speech
classifier or auditing unfiltered large-scale datasets) versus
\textit{spuriously} invoked from working on unrelated problems (e.g. language
generation or part of speech tagging) but with datasets that nonetheless
contain harmful content, and (3) who it affects, from the humans
(mis)represented in the data to those handling or labelling the data to readers
and reviewers of publications produced from the data. It is an unsolved problem
in NLP as to how textual harms should be handled, presented, and discussed;
but, stopping work on content which poses a risk of harm is untenable.
Accordingly, we provide practical advice and introduce \textsc{HarmCheck}, a
resource for reflecting on research into textual harms. We hope our work
encourages ethical, responsible, and respectful research in the NLP community.


title: Electronic-photonic circuit crossings

abstract:
  Electrical control of light in integrated photonics is central to a wide
range of research and applications. It is conventionally achieved with
thermo-optic tuning, but this suffers from high energy consumption and
crosstalk. These challenges could be resolved with nanoelectromechanical
photonics but scalable platforms were so far not demonstrated because of
incompatibility with conventional multilayer metal architectures and because
conventional approaches do not allow crossings of electrical wires and photonic
waveguides. Here we use topology optimization to devise a single-layer
electronic-photonic circuit crossing with up to 99.8% optical transmission
across an isolation trench with excellent electrical isolation. We use the
crossing to demonstrate a monolithic silicon nano-electro-mechanical add-drop
switch in which the flow of photons, electrons, and mechanical motions are
fully integrated within the same layer. Our work enables hitherto impossible
opto-electro-mechanical topologies and may lead to new functionalities in
nano-opto-electro-mechanical systems, optomechanics, and integrated quantum
photonics.


title: Study of low-lying orbital excited $B_c$ mesons

abstract:
  In this paper, we study the mass spectra and the decays of low-lying orbital
excited $B_c$ mesons. We predict the mass spectra of the high excited $B_c$
states using Cornell potential model taking into account the screening effect.
We adopt a wave function which is expanded with a set of complete Simple
Harmonic Oscillator (SHO) bases to calculate the radiative decay width of the
$B_c$ mesons. Furthermore, the widths of the two-body strong decays of $B_c$
mesons are calculated by the quark pair creation(QPC) model, and the branching
ratio are given accordingly. We expect that our research have reference value
for searching for the other $B_c$ mesons that have not been observed and
establishing the $B_c$ meson family in the future.


title:
Mathematical encoding within multi-resonant planetary systems as SETI
  beacons

abstract:
  How might an advanced alien civilization manipulate the orbits within a
planetary system to create a durable signpost that communicates its existence?
While it is still debated whether such a purposeful advertisement would be
prudent and wise, we propose that mean-motion resonances between neighboring
planets -- with orbital periods that form integer ratios -- could in principle
be used to encode simple sequences that one would not expect to form in nature.
In this Letter we build four multi-resonant planetary systems and test their
long-term orbital stability. The four systems each contain 6 or 7 planets and
consist of: (i) consecutive integers from 1 to 6; (ii) prime numbers from 2 to
11; (iii) the Fibonacci sequence from 1 to 13; and (iv) the Lazy Caterer
sequence from 1 to 16. We built each system using N-body simulations with
artificial migration forces. We evaluated the stability of each system over the
full 10 Gyr integration of the Sun's main sequence phase. We then tested the
stability of these systems for an additional 10 Gyr, during and after post-main
sequence evolution of the central stars (assumed to be Sun-like) to their
final, white dwarf phase. The only system that was destabilized was the
consecutive integer sequence (system i). The other three sequences therefore
represent potential SETI beacons.


title:
Effective electronic forces and potentials from ab initio path integral
  Monte Carlo simulations

abstract:
  The rigorous description of correlated quantum many-body systems constitutes
one of the most challenging tasks in contemporary physics and related
disciplines. In this context, a particularly useful tool is the concept of
effective pair potentials that take into account the effects of the complex
many-body medium consistently. In this work, we present extensive, highly
accurate \emph{ab initio} path integral Monte Carlo (PIMC) results for the
effective interaction and the effective force between two electrons in the
presence of the uniform electron gas (UEG). This gives us a direct insight into
finite-size effects, thereby opening up the possibility for novel domain
decompositions and methodological advances. In addition, we present
unassailable numerical proof for an effective attraction between two electrons
under moderate coupling conditions, without the mediation of an underlying
ionic structure. Finally, we compare our exact PIMC results to effective
potentials from linear-response theory, and we demonstrate their usefulness for
the description of the dynamic structure factor. All PIMC results are made
freely available online and can be used as a thorough benchmark for new
developments and approximations.


title: Finite Entailment of UCRPQs over ALC Ontologies

abstract:
  We investigate the problem of finite entailment of ontology-mediated queries.
We consider the expressive query language, unions of conjunctive regular path
queries (UCRPQs), extending the well-known class of union of conjunctive
queries, with regular expressions over roles. We look at ontologies formulated
using the description logic ALC, and show a tight 2EXPTIME upper bound for
entailment of UCRPQs. At the core of our decision procedure, there is a novel
automata-based technique introducing a stratification of interpretations
induced by the deterministic finite automaton underlying the input UCRPQ


title:
Realization of the electric-field driven "one-material"-based magnetic
  tunnel junction using van der Waals antiferromagnetic MnPX3 (X: S, Se)

abstract:
  Presently a lot of efforts are devoted to the investigation of new
two-dimensional magnetic materials, which are considered as promising for the
realization of the future electronics and spintronics devices. However, the
utilization of these materials in different junctions requires complicated
processing that in many cases leads to unwanted parasitic effects influencing
the performance of the junctions. Here, we propose the new elegant approach for
the realization of the "one-material"-based magnetic tunnel junction. The
several layers of 2D van der Waals MnPX3 (X: S, Se), which is insulating
antiferromagnet in its ground state, are used and the effect of the applied
external electric filed leads to the half-metallic ferromagnetic states for the
outermost layers of the MnPX3 stack. The rich states diagram of such magnetic
tunnel junction permits to precisely control its tunneling conductivity. The
realized "one-material"-based magnetic tunnel junction allows to avoid all
effects connected with the lattice mismatches and carriers scattering effects
at the materials interfaces, giving high perspectives for the application of
such systems in electronics and spintronics.


title:
Long-term evolution of the neutron rate at the Canfranc Underground
  Laboratory

abstract:
  We report results on the long-term variation of the neutron rate at the
Canfranc Underground Laboratory, of importance for the rare event searches
located there. The measurement campaign was performed employing the High
Efficiency Neutron Spectrometry Array (HENSA) mounted in Hall A and lasted 412
live days. The present study is the first long-term measurement of the neutron
rate with sensitivity over a wide range of neutron energies (from thermal to 10
GeV) carried on in any underground laboratory so far. Data on the environmental
variables inside the experimental hall (radon concentration, air temperature,
pressure and humidity) were also acquired during all the measurement campaign.
We have investigated for the first time the evolution of the neutron rate for
different energies of the neutrons and its connection with the ambient
variables.


title: Polyglot Prompt: Multilingual Multitask PrompTraining

abstract:
  This paper aims for a potential architectural breakthrough for multilingual
learning and asks: could different tasks from different languages be modeled in
a monolithic framework (without any task/language-specific module)? The benefit
of achieving this is not only that systems trained on low resources scenario
can be assisted by more other languages and tasks, but opening new doors for
future multilingual research. We approach this goal by developing a learning
framework Polyglot Prompt, where prompting methods are introduced to learn a
unified semantic space for different languages and tasks after proper
multilingual prompt engineering. Experimentally, we perform a comprehensive
evaluation on 6 tasks (topic classification, sentiment classification, named
entity recognition, question answering, natural language inference,
summarization), 24 datasets, and 49 languages, which shows the efficacy of
multilingual multitask prompting training and suggests several interesting
observations. e.g., English prompts are polyglots since directly applying them
to task samples in other languages could result in a better improvement. We
also present an interpretable multilingual evaluation methodology and show how
the proposed framework, multilingual multitask prompt training, works. We
release all datasets prompted in the best setting and will release our code
soon.


title: Taiwan Axion Search Experiment with Haloscope: CD102 Analysis Details

abstract:
  This paper presents the analysis of the data acquired during the first
physics run of the Taiwan Axion Search Experiment with Haloscope (TASEH), a
search for axions using a microwave cavity at frequencies between 4.70750 and
4.79815 GHz. The data were collected from October 13, 2021 to November 15,
2021, and are referred to as the CD102 data. The analysis of the TASEH CD102
data excludes models with the axion-two-photon coupling
$\left|g_{a\gamma\gamma}\right| \gtrsim 8.2\times 10^{-14}$ GeV$^{-1}$, a
factor of eleven above the benchmark KSVZ model for the mass range 19.4687 < ma
< 19.8436 $\mu$eV.


title:
Left or right centralizers on $ \star $-algebras through orthogonal
  elements

abstract:
  In this paper we consider the problem of characterizing linear maps on
special $ \star $-algebras behaving like left or right centralizers at
orthogonal elements and obtain some results in this regard.


title: A Grammar for Hypothesis-Driven Visual Analysis

abstract:
  A hallmark of visual analytics is its ability to support users in translating
broad, open-ended analytic questions (e.g., "is our company succeeding?") into
verifiable hypotheses that can be tested from the available data (e.g., "our
total revenue increased this past quarter"). However, the process of converting
open-ended analytic questions into testable hypotheses is complex and often
ill-defined. Beyond high-level descriptions, the visual analytics literature
lacks a formalization that can be operationalized for analysis and automation.
In this paper, we propose a novel grammar to express hypothesis-based analytic
questions for visual analysis. Drawing from prior work in science and
education, our grammar defines a formal way to express sets of verifiable
hypotheses as a "hypothesis space". Our proposed use of hypothesis spaces
contributes a new lens to unify concepts of user goals, the capabilities of a
dataset, visual analysis, and testable hypotheses. As a result, we can
reformulate abstract classes of visual analysis goals, such as analytic and
data-related tasks, in a way that is suitable for analysis and automation. We
demonstrate use cases of our grammar in real-world analytic applications
including VAST challenges, Kaggle competitions, and pre-existing task
taxonomies. Finally, we provide design opportunities in which our grammar can
be operationalized to articulate analysis tasks, evaluate visualization
systems, and support hypothesis-based reasoning in visual analytic tools.


title:
How Robust is Neural Machine Translation to Language Imbalance in
  Multilingual Tokenizer Training?

abstract:
  A multilingual tokenizer is a fundamental component of multilingual neural
machine translation. It is trained from a multilingual corpus. Since a skewed
data distribution is considered to be harmful, a sampling strategy is usually
used to balance languages in the corpus. However, few works have systematically
answered how language imbalance in tokenizer training affects downstream
performance. In this work, we analyze how translation performance changes as
the data ratios among languages vary in the tokenizer training corpus. We find
that while relatively better performance is often observed when languages are
more equally sampled, the downstream performance is more robust to language
imbalance than we usually expected. Two features, UNK rate and closeness to the
character level, can warn of poor downstream performance before performing the
task. We also distinguish language sampling for tokenizer training from
sampling for model training and show that the model is more sensitive to the
latter.


title:
Generation of Josephson vortices in stacked toroidal Bose-Einstein
  condensates

abstract:
  Coupled coaxially stacked toroidal condensates with persistent currents
suggest an appealing physical platform for the investigation of various
phenomena related to interacting superflows from Josephson effects in the
regime of weak interactions to the quantum Kelvin-Helmholtz instability for
merging rings. We suggest experimentally accessible methods to prepare states
with different topological charges in two coupled coaxial ring shaped atomic
Bose Einstein condensates. Our results open up the way to direct observation of
rotational Josephson vortices in atomic Bose Einstein condensates.