title:
Conceptualizing World-Earth System resilience: Exploring transformation
  pathways towards a safe and just operating space for humanity

abstract:
  We develop a framework within which to conceptualize World-Earth System
resilience. Our notion of World-Earth System resilience emphasizes the need to
move beyond the basin of attraction notion of resilience as we are not in a
basin we can stay in. We are on a trajectory to a new basin and we have to
avoid falling into undesirable basins. We thus focus on `pathway resilience',
i.e. the relative number of paths that allow us to move from the transitional
operating space we occupy now as we leave the Holocene basin to a safe and just
operating space in the Anthropocene. We develop a mathematical model to
formalize this conceptualization and demonstrate how interactions between earth
system resilience (biophysical processes) and world system resilience (social
processes) impact pathway resilience. Our findings show that building earth
system resilience is probably our only chance to reach a safe and just
operating space. We also illustrate the importance of world system dynamics by
showing how the notion of fairness coupled with regional inequality affects
pathway resilience.


title:
A BAT-based Exact-Solution Algorithm for the Series-Parallel Redundancy
  Allocation Problem with Mixed Components

abstract:
  The series-parallel (active) redundancy allocation problem with mixed
components (RAP) involves setting reliable objectives for components or
subsystems to meet the resource consumption constraint, e.g., the total cost.
RAP has been an active research area for the past four decades. The NP-hard
difficulties confronted by RAP are maintaining feasibility with respect to two
constraints: cost and weight. A novel algorithm called the bound-rule-BAT (BRB)
based on the binary-addition-tree algorithm (BAT), the dominance rule, and
dynamic bounds are proposed to solve the exact solutions of the most famous RAP
benchmark problems called the (33-variation) Fyffe RAP. From the experiments,
the proposed BRB can solve the Fyffe RAP correctly under the assumption that
the maximal number of components of each subsystem is eight, and this is the
first exact-solution algorithm that can solve the Fyffe RAP within 8 seconds
and 60 seconds if no reliability lower bound is used.


title:
White dwarfs in a uniform sphere approximation, with general relativity
  effects taken into account

abstract:
  The limiting mass of cold white dwarfs was first calculated by E. Stoner in
an approximate model of a uniform star and was soon reduced by ~20% in papers
by S. Chandrasekhar and L. D. Landau based on an exact solution of the
equations for the stellar equilibrium. Here we examine uniform models of white
dwarfs taking general relativity effects and the influence of finite
temperature into account. Solutions are obtained in the form of finite analytic
formulas and, for masses differing by no more than ~20% from the exact
solutions, found by numerical integration of the differential equations for the
stellar equilibrium.


title:
Algebraic number fields generated by an infinite family of monogenic
  trinomials

abstract:
  For an infinite family of monogenic trinomials $P(X) = X^3\pm 3rbX-b$ in
$\mathbb{Z}\lbrack X\rbrack$, arithmetical invariants of the cubic number field
$L = \mathbb{Q}(\theta)$, generated by a zero $\theta$ of $P(X)$, and of its
Galois closure $N = L(\sqrt{d(L)})$ are determined. The conductor $f$ of the
cyclic cubic relative extension $N/K$, where $K = \mathbb{Q}(\sqrt{d(L)})$
denotes the unique quadratic subfield of $N$, is proved to be of the form
$3^eb$ with $e\in\lbrace 1,2\rbrace$, which admits statements concerning
primitive ambiguous principal ideals, lattice minima, and independent units in
$L$. The number $m$ of non-isomorphic cubic fields $L_1,\ldots,L_m$ sharing a
common discriminant $d(L_i) = d(L)$ with $L$ is determined.


title: Adaptive Sparse Sampling for Quasiparticle Interference Imaging

abstract:
  Quasiparticle interference imaging (QPI) offers insight into the band
structure of quantum materials from the Fourier transform of local density of
states (LDOS) maps. Their acquisition with a scanning tunneling microscope is
traditionally tedious due to the large number of required measurements that may
take several days to complete. The recent demonstration of sparse sampling for
QPI imaging showed how the effective measurement time could be fundamentally
reduced by only sampling a small and random subset of the total LDOS. However,
the amount of required sub-sampling to faithfully recover the QPI image
remained a recurring question. Here we introduce an adaptive sparse sampling
(ASS) approach in which we gradually accumulate sparsely sampled LDOS
measurements until a desired quality level is achieved via compressive sensing
recovery. The iteratively measured random subset of the LDOS can be interleaved
with regular topographic images that are used for image registry and drift
correction. These reference topographies also allow to resume interrupted
measurements to further improve the QPI quality. Our ASS approach is a
convenient extension to quasiparticle interference imaging that should remove
further hesitation in the implementation of sparse sampling mapping schemes.


title:
High-dimensional Asymptotics of Langevin Dynamics in Spiked Matrix
  Models

abstract:
  We study Langevin dynamics for recovering the planted signal in the spiked
matrix model. We provide a "path-wise" characterization of the overlap between
the output of the Langevin algorithm and the planted signal. This overlap is
characterized in terms of a self-consistent system of integro-differential
equations, usually referred to as the
Crisanti-Horner-Sommers-Cugliandolo-Kurchan (CHSCK) equations in the spin glass
literature. As a second contribution, we derive an explicit formula for the
limiting overlap in terms of the signal-to-noise ratio and the injected noise
in the diffusion. This uncovers a sharp phase transition -- in one regime, the
limiting overlap is strictly positive, while in the other, the injected noise
overcomes the signal, and the limiting overlap is zero.


title: FoundationLayerNorm: Scaling BERT and GPT to 1,000 Layers

abstract:
  The mainstream BERT/GPT model contains only 10 to 20 layers, and there is
little literature to discuss the training of deep BERT/GPT. This paper proposes
a simple yet effective method to stabilize BERT and GPT training. We
successfully scale up BERT and GPT to 1,000 layers, which is an order of
magnitude deeper than previous BERT and GPT. The proposed method
FoundationLayerNormalization enables efficient training of deep neural networks
and is validated at the 1000-layer scale.


title:
Spotlight on Charge-Transfer Excitons in Crystalline Textured n-Alkyl
  Anilino Squaraine Thin Films

abstract:
  Prototypical n-alkyl terminated anilino squaraines for photovoltaic
applications show characteristic double-hump absorption features peaking in the
green and deep-red spectral range. These signatures result from coupling of an
intramolecular Frenkel exciton and an intermolecular charge transfer exciton.
Crystalline, textured thin films suitable for polarized spectro-microscopy have
been obtained for compounds with n-hexyl (nHSQ) and n-octyl (nOSQ) terminal
alkyl chains. The here released triclinic crystal structure of nOSQ is similar
to the known nHSQ crystal structure. Consequently, crystallites from both
compounds show equal pronounced linear dichroism with two distinct polarization
directions. The difference in polarization angle between the two absorbance
maxima cannot be derived by spatial considerations from the crystal structure
alone but requires theoretical modeling. Using an essential state model, the
observed polarization behavior was discovered to depend on the relative
contributions of the intramolecular Frenkel exciton and the intermolecular
charge transfer exciton to the total transition dipole moment. For both nHSQ
and nOSQ, the contribution of the charge transfer exciton to the total
transition dipole moment was found to be small compared to the intramolecular
Frenkel exciton. Therefore, the net transition dipole moment is largely
determined by the intramolecular component resulting in a relatively small
mutual difference between the polarization angles. Ultimately, the molecular
alignment within the micro-textured crystallites can be deduced and, with that,
the excited state transitions can be spotted.


title: On local antimagic vertex coloring for complete full $t$-ary trees

abstract:
  Let $G = (V, E)$ be a finite simple undirected graph without $K_2$
components. A bijection $f : E \rightarrow \{1, 2,\cdots, |E|\}$ is called a
local antimagic labeling if for any two adjacent vertices $u$ and $v$, they
have different vertex sums, i.e., $w(u) \neq w(v)$, where the vertex sum $w(u)
= \sum_{e \in E(u)} f(e)$, and $E(u)$ is the set of edges incident to $u$. Thus
any local antimagic labeling induces a proper vertex coloring of $G$ where the
vertex $v$ is assigned the color (vertex sum) $w(v)$. The local antimagic
chromatic number $\chi_{la}(G)$ is the minimum number of colors taken over all
colorings induced by local antimagic labelings of $G$. It was conjectured
\cite{Aru-Wang} that for every tree $T$ the local antimagic chromatic number
$l+ 1 \leq \chi_{la} ( T )\leq l+2$, where $l$ is the number of leaves of $T$.
In this article we verify the above conjecture for complete full $t$-ary trees,
for $t \geq 2$. A complete full $t$-ary tree is a rooted tree in which all
nodes have exactly $t$ children except leaves and every leaf is of the same
depth. In particular we obtain that the exact value for the local antimagic
chromatic number of all complete full $t$-ary trees is $ l+1$ for odd $t$.


title:
Every countable group admits amenable actions on stably finite simple
  C*-algebras

abstract:
  We give the first examples of (non-amenable group) amenable actions on stably
finite simple C*-algebras. More precisely, we give such actions for any
countable group in an explicit way. The main ingredients of our construction
are the full Fock space of the regular representation and a trace-scaling
automorphism.


title:
KUCST@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media
  Text

abstract:
  In this paper we present our approach for detecting signs of depression from
social media text. Our model relies on word unigrams, part-of-speech tags,
readabilitiy measures and the use of first, second or third person and the
number of words. Our best model obtained a macro F1-score of 0.439 and ranked
25th, out of 31 teams. We further take advantage of the interpretability of the
Logistic Regression model and we make an attempt to interpret the model
coefficients with the hope that these will be useful for further research on
the topic.


title: The Almost Schur Lemma in Quaternionic Contact Geometry

abstract:
  We establish quaternionic contact (qc) versions of the so called Almost Schur
Lemma, which give estimations of the qc scalar curvature on a compact qc
manifold to be a constant in terms of the norm of the $[-1]$-component and the
norm of the trace-free part of the $[3]$-component of the horizontal qc Ricci
tensor and the torsion endomorphism, under certain positivity conditions.


title:
Why did I fail? A Causal-based Method to Find Explanations for Robot
  Failures

abstract:
  Robot failures in human-centered environments are inevitable. Therefore, the
ability of robots to explain such failures is paramount for interacting with
humans to increase trust and transparency. To achieve this skill, the main
challenges addressed in this paper are I) acquiring enough data to learn a
cause-effect model of the environment and II) generating causal explanations
based on that model. We address I) by learning a causal Bayesian network from
simulation data. Concerning II), we propose a novel method that enables robots
to generate contrastive explanations upon task failures. The explanation is
based on setting the failure state in contrast with the closest state that
would have allowed for successful execution, which is found through
breadth-first search and is based on success predictions from the learned
causal model. We assess the sim2real transferability of the causal model on a
cube stacking scenario. Based on real-world experiments with two differently
embodied robots, we achieve a sim2real accuracy of 70% without any adaptation
or retraining. Our method thus allowed real robots to give failure explanations
like, 'the upper cube was dropped too high and too far to the right of the
lower cube.'


title:
Data-analysis software framework 2DMAT and its application to
  experimental measurements for two-dimensional material structures

abstract:
  An open-source data-analysis framework 2DMAT has been developed for
experimental measurements of two-dimensional material structures. 2DMAT| offers
five analysis methods: (i) Nelder-Mead optimization, (ii) grid search, (iii)
Bayesian optimization, (iv) replica exchange Monte Carlo method, and (v)
population-annealing Monte Carlo method. Methods (ii) through (v) are
implemented by parallel computation, which is efficient not only for personal
computers but also for supercomputers. The current version of 2DMAT is
applicable to total-reflection high-energy positron diffraction (TRHEPD),
surface X-ray diffraction (SXRD), and low-energy electron diffraction (LEED)
experiments by installing corresponding forward problem solvers that generate
diffraction intensity data from a given dataset of the atomic positions. The
analysis methods are general and can be applied also to other experiments and
phenomena.


title:
Isospin dependent properties of the isotopic chain of Scandium and
  Titanium nuclei within the relativistic mean-field formalism

abstract:
  The density-dependent nuclear symmetry energy is directly related to the
isospin asymmetry for finite and infinite nuclear systems. It is critical to
determine the coefficients of symmetry energy and its related observables as it
holds great importance in different areas of nuclear physics, such as analyzing
the structure of ground state exotic nuclei and neutron star study. The ground
state bulk properties such as nuclear binding energy, quadrupole deformation,
two-neutron separation energy, the differential variation of two-neutron
separation energy, and root-mean-square charge radius for Scandium (Z = 21),
and Titanium (Z = 22) nuclei are calculated. The coherent density fluctuation
model is used to estimate the isospin-dependent properties of finite nuclei
such as symmetry energy and its surface and volume components from its
corresponding value in infinite nuclear matter system. Finally, we correlate
the neutron-skin thickness with the coefficient of symmetry energy and its
related observables corresponding to these isotopic chains of nuclei. The
relativistic mean-field formalism with non-linear NL3 and
Relativistic-Hartree-Bogoliubov with density-dependent DD-ME2 interaction
parameters are employed in the present analysis. A shape transition is observed
from spherical to prolate near N $\geq$ 44 and N $\geq$ 40 for Sc- and Ti-
isotopic chain, respectively. Notable signatures of shell and/or sub-shell
closures have been found for the magic neutron numbers at N = 20 and 28 for
both the isotopic chain using the nuclear bulk and isospin quantities. In
addition to these, a few signatures of shell/sub-shell closure are observed
near the drip-line region, at N = 34 and 50 by following the surface/isospin
dependent observables, namely symmetry energy and its component for both the
isotopic chain of odd-A Sc- and even-even Ti- nuclei.


title: Boson stars, primary photons and phase transitions

abstract:
  We analyse the possibility that the dark matter candidate is from the
approximate scale symmetry theory of the hidden scalar sector. The study
includes the warm dark matter scenario and the Bose-Einstein condensation which
may lead to massive dark scalar boson stars giving rise to direct detection
through observation of the primary (direct) photons.


title:
Informativeness and Invariance: Two Perspectives on Spurious
  Correlations in Natural Language

abstract:
  Spurious correlations are a threat to the trustworthiness of natural language
processing systems, motivating research into methods for identifying and
eliminating them. However, addressing the problem of spurious correlations
requires more clarity on what they are and how they arise in language data.
Gardner et al (2021) argue that the compositional nature of language implies
that \emph{all} correlations between labels and individual "input features" are
spurious. This paper analyzes this proposal in the context of a toy example,
demonstrating three distinct conditions that can give rise to feature-label
correlations in a simple PCFG. Linking the toy example to a structured causal
model shows that (1) feature-label correlations can arise even when the label
is invariant to interventions on the feature, and (2) feature-label
correlations may be absent even when the label is sensitive to interventions on
the feature. Because input features will be individually correlated with labels
in all but very rare circumstances, domain knowledge must be applied to
identify spurious correlations that pose genuine robustness threats.


title:
Comparison of EEG based epilepsy diagnosis using neural networks and
  wavelet transform

abstract:
  Epilepsy is one of the common neurological disorders characterized by
recurrent and uncontrollable seizures, which seriously affect the life of
patients. In many cases, electroencephalograms signal can provide important
physiological information about the activity of the human brain which can be
used to diagnose epilepsy. However, visual inspection of a large number of
electroencephalogram signals is very time-consuming and can often lead to
inconsistencies in physicians' diagnoses. Quantification of abnormalities in
brain signals can indicate brain conditions and pathology so the
electroencephalogram (EEG) signal plays a key role in the diagnosis of
epilepsy. In this article, an attempt has been made to create a single
instruction for diagnosing epilepsy, which consists of two steps. In the first
step, a low-pass filter was used to preprocess the data and three separate
mid-pass filters for different frequency bands and a multilayer neural network
were designed. In the second step, the wavelet transform technique was used to
process data. In particular, this paper proposes a multilayer perceptron neural
network classifier for the diagnosis of epilepsy, that requires normal data and
epilepsy data for education, but this classifier can recognize normal
disorders, epilepsy, and even other disorders taught in educational examples.
Also, the value of using electroencephalogram signal has been evaluated in two
ways: using wavelet transform and non-using wavelet transform. Finally, the
evaluation results indicate a relatively uniform impact factor on the use or
non-use of wavelet transform on the improvement of epilepsy data functions, but
in the end, it was shown that the use of perceptron multilayer neural network
can provide a higher accuracy coefficient for experts.


title: ShorTor: Improving Tor Network Latency via Multi-hop Overlay Routing

abstract:
  We present ShorTor, a protocol for reducing latency on the Tor network.
ShorTor uses multi-hop overlay routing, a technique typically employed by
content delivery networks, to influence the route Tor traffic takes across the
internet. ShorTor functions as an overlay on top of onion routing-Tor's
existing routing protocol and is run by Tor relays, making it independent of
the path selection performed by Tor clients. As such, ShorTor reduces latency
while preserving Tor's existing security properties. Specifically, the routes
taken in ShorTor are in no way correlated to either the Tor user or their
destination, including the geographic location of either party. We analyze the
security of ShorTor using the AnoA framework, showing that ShorTor maintains
all of Tor's anonymity guarantees. We augment our theoretical claims with an
empirical analysis. To evaluate ShorTor's performance, we collect a real-world
dataset of over 400,000 latency measurements between the 1,000 most popular Tor
relays, which collectively see the vast majority of Tor traffic. With this
data, we identify pairs of relays that could benefit from ShorTor: that is, two
relays where introducing an additional intermediate network hop results in
lower latency than the direct route between them. We use our measurement
dataset to simulate the impact on end users by applying ShorTor to two million
Tor circuits chosen according to Tor's specification. ShorTor reduces the
latency for the 99th percentile of relay pairs in Tor by 148 ms. Similarly,
ShorTor reduces the latency of Tor circuits by 122 ms at the 99th percentile.
In practice, this translates to ShorTor truncating tail latencies for Tor which
has a direct impact on page load times and, consequently, user experience on
the Tor browser.


title:
First-passage process in degree space for the time-dependent
  Erd\H{o}s-R\'enyi and Watts-Strogatz models

abstract:
  In this work, we investigate the temporal evolution of the degree of a given
vertex in a network by mapping the dynamics into a random walk problem in
degree space. We analyze when the degree approximates a pre-established value
through a parallel with the first-passage problem of random walks. The method
is illustrated on the time-dependent versions of the Erd\H{o}s-R\'enyi and
Watts-Strogatz models, which originally were formulated as static networks. We
have succeeded in obtaining an analytic form for the first and the second
moments of the first-passage time and showing how they depend on the size of
the network. The dominant contribution for large networks with $N$ vertices
indicates that these quantities scale on the ratio $N/p$, where $p$ is the
linking probability.


title:
Bayesian evidence-driven likelihood selection for sky-averaged 21-cm
  signal extraction

abstract:
  We demonstrate that the Bayesian evidence can be used to find a good
approximation of the true likelihood function of a dataset, a goal of the
likelihood-free inference (LFI) paradigm. As a concrete example, we use forward
modelled sky-averaged 21-cm signal antenna temperature datasets where we
artificially inject noise structures of various physically motivated forms. We
find that the Gaussian likelihood performs poorly when the noise distribution
deviates from the Gaussian case e.g. heteroscedastic radiometric or
heavy-tailed noise. For these non-Gaussian noise structures, we show that the
generalised normal likelihood is on a similar Bayesian evidence scale with
comparable sky-averaged 21-cm signal recovery as the true likelihood function
of our injected noise. We therefore propose the generalised normal likelihood
function as a good approximation of the true likelihood function if the noise
structure is a priori unknown.


title: S4OD: Semi-Supervised learning for Single-Stage Object Detection

abstract:
  Single-stage detectors suffer from extreme foreground-background class
imbalance, while two-stage detectors do not. Therefore, in semi-supervised
object detection, two-stage detectors can deliver remarkable performance by
only selecting high-quality pseudo labels based on classification scores.
However, directly applying this strategy to single-stage detectors would
aggravate the class imbalance with fewer positive samples. Thus, single-stage
detectors have to consider both quality and quantity of pseudo labels
simultaneously. In this paper, we design a dynamic self-adaptive threshold
(DSAT) strategy in classification branch, which can automatically select pseudo
labels to achieve an optimal trade-off between quality and quantity. Besides,
to assess the regression quality of pseudo labels in single-stage detectors, we
propose a module to compute the regression uncertainty of boxes based on
Non-Maximum Suppression. By leveraging only 10% labeled data from COCO, our
method achieves 35.0% AP on anchor-free detector (FCOS) and 32.9% on
anchor-based detector (RetinaNet).


title: Entanglement-invertible channels

abstract:
  In a well-known result \cite{Werner2001}, Werner classified all \emph{tight}
quantum teleportation and dense coding schemes, showing that they correspond to
\emph{unitary error bases}. Here tightness is a certain dimensional
restriction: the quantum system to be teleported and the entangled resource
must be of dimension d, and the measurement must have d^2 outcomes.
  In this work we generalise this classification so as to remove the
dimensional restriction altogether, thereby resolving an open problem raised in
that work. In fact, we classify not just teleportation and dense coding
schemes, but \emph{entanglement-reversible channels}. These are channels
between finite-dimensional C^*-algebras which are reversible with the aid of an
entangled resource state, generalising ordinary reversibility of a channel.
  In Werner's classification, a bijective correspondence between tight
teleportation and dense coding schemes was shown: swapping Alice and Bob's
operations turns a teleportation scheme into a dense coding scheme and vice
versa. We observe that this property generalises ordinary invertibility of a
channel; we call it \emph{entanglement-invertibility}. We show that
entanglement-invertible channels are precisely the \emph{quantum bijections}
previously studied in the setting of quantum combinatorics \cite{Musto2018},
which are classified in terms of the representation theory of the quantum
permutation group.


title:
DeepLIIF: An Online Platform for Quantification of Clinical Pathology
  Slides

abstract:
  In the clinic, resected tissue samples are stained with Hematoxylin-and-Eosin
(H&E) and/or Immunhistochemistry (IHC) stains and presented to the pathologists
on glass slides or as digital scans for diagnosis and assessment of disease
progression. Cell-level quantification, e.g. in IHC protein expression scoring,
can be extremely inefficient and subjective. We present DeepLIIF
(https://deepliif.org), a first free online platform for efficient and
reproducible IHC scoring. DeepLIIF outperforms current state-of-the-art
approaches (relying on manual error-prone annotations) by virtually restaining
clinical IHC slides with more informative multiplex immunofluorescence
staining. Our DeepLIIF cloud-native platform supports (1) more than 150
proprietary/non-proprietary input formats via the Bio-Formats standard, (2)
interactive adjustment, visualization, and downloading of the IHC
quantification results and the accompanying restained images, (3) consumption
of an exposed workflow API programmatically or through interactive plugins for
open source whole slide image viewers such as QuPath/ImageJ, and (4) auto
scaling to efficiently scale GPU resources based on user demand.


title:
Directly determining orbital angular momentum of ultrashort
  Laguerre-Gauss pulses via autocorrelation measurement

abstract:
  Autocorrelation measurement based on second-harmonic generation (SHG), the
best-known technique for measuring the temporal duration of ultrashort pulses,
could date back to the birth of ultrafast lasers. Here, we propose and
experimentally demonstrate that such well-established technique can also be
used to measure the orbital angular momentum of ultrashort Laguerre-Gauss (LG)
pulses. By analysing the far-field pattern of the SHG signal, the full spatial
structure of ultrashort LG pulses, including both azimuthal and radial indices,
are unambiguously determined. Our results provide an important advancement for
the well-established autocorrelation technique by extending it to reach its
full potential in laser characterization, especially for structured ultrashort
pulses.


title: Finding Nonlinear Production -- Consumption Equilibrium

abstract:
  We introduce and study nonlinear production - consumption equilibrium (NPCE).
The NPCE is a combination and generalization of both classical linear
programming (LP) and classical input-output (IO) models. In contrast to LP and
IO the NPCE has both production and consumption components. Moreover, the
production cost, the consumption and the factors (resources) availability are
not fixed. Instead they are corespondent functions of the production output,
prices of goods and prices of factors. At the NPCE the total production cost
reaches its minimum, while the total consumption, without factors expences,
reaches its maximum. At the same time the production cost is consistent with
the production output, the consumption is consistent with the prices for goods
and the factors availability is consistent with prices for factors. Finding
NPCE is equivalent to solving a variational inequality (VI) with a particular
nonlinear operator and a simple feasible set. Under natural assumptions on the
production, consumption and factor operators the NPCE exists and it is unique.
Projection on the feasible set {\Omega} is a low cost operation, therefore for
solving the VI we use two projection methods. Each of them requires at each
step few matrix by vector multiplications and allows, along with convergence
and convergence rate, establish complexity bound. The methods decompose the
problem, so both the primal and the dual variables are computed simultaneously.
On the other hand, both methods are pricing mechanisms for establishing NPCE,
which is a generalization of Walras - Wald equilibrium (see [12]) in few
directions.


title: IDPG: An Instance-Dependent Prompt Generation Method

abstract:
  Prompt tuning is a new, efficient NLP transfer learning paradigm that adds a
task-specific prompt in each input instance during the model training stage. It
freezes the pre-trained language model and only optimizes a few task-specific
prompts. In this paper, we propose a conditional prompt generation method to
generate prompts for each input instance, referred to as the Instance-Dependent
Prompt Generation (IDPG). Unlike traditional prompt tuning methods that use a
fixed prompt, IDPG introduces a lightweight and trainable component to generate
prompts based on each input sentence. Extensive experiments on ten natural
language understanding (NLU) tasks show that the proposed strategy consistently
outperforms various prompt tuning baselines and is on par with other efficient
transfer learning methods such as Compacter while tuning far fewer model
parameters.


title:
A unified weighted inequality for fourth-order partial differential
  operators and applications

abstract:
  In this paper, we establish a fundamental inequality for fourth order partial
differential operator $\cal P=\alpha\partial_s+\beta\partial_{ss}+\Delta^2$
($\alpha, \beta\in\mathbb{R}$) with an abstract exponential-type weight
function. Such kind of weight functions including not only the regular weight
functions but also the singular weight functions. Using this inequality we are
able to prove some Carleman estimates for the operator $\cal P$ with some
suitable boundary conditions in the case of $\beta<0$ or $\alpha\neq 0,
\beta=0$. As application, we obtain a resolvent estimate for $\cal P$, which
can imply a log-type stabilization result for the plate equation with clamped
boundary conditions or hinged boundary conditions.


title:
Rheological model for the alpha relaxation of glass-forming liquids and
  its comparison to data for DC704 and DC705

abstract:
  Dynamic shear-modulus data are presented for the two silicone oils DC704 and
DC705 for frequencies between 1 mHz and 10 kHz at temperatures covering more
than five decades of relaxation-time variation. The data are fitted to the
alpha part of a phenomenological model previously shown to describe well the
dynamic shear modulus of squalane, which has a large beta process [Hecksher
\textit{et al.}, J. Chem. Phys. \textbf{146}, 154504 (2017)]; that model is
characterized by additivity of the alpha and beta shear compliance and by a
high-frequency decay of the alpha process in proportion to $\omega^{-1/2}$ in
which $\omega$ is the angular frequency. The fits of the alpha part of this
model to the DC704 and DC705 data are compared to fits by a Havriliak-Negami
type model, the Barlow-Erginsav-Lamb model, and a Cole-Davidson type model. At
all temperatures the best fit is obtained by the alpha part of the squalane
model. This strengthens the conjecture that so-called $\sqrt{t}$-relaxation,
leading to high-frequency decays proportional to $\omega^{-1/2}$, is a general
characteristic of the alpha relaxation of supercooled liquids [Dyre, Phys. Rev.
E {\bf 74}, 021502 (2006); Nielsen \textit{et al.}, J. Chem. Phys.
\textbf{130}, 154508 (2009); Pabst \textit{et al.}, J. Phys. Chem. Lett.
\textbf{12}, 3685 (2021)].


title: Faster Min-Plus Product for Monotone Instances

abstract:
  In this paper, we show that the time complexity of monotone min-plus product
of two $n\times n$ matrices is
$\tilde{O}(n^{(3+\omega)/2})=\tilde{O}(n^{2.687})$, where $\omega < 2.373$ is
the fast matrix multiplication exponent [Alman and Vassilevska Williams 2021].
That is, when $A$ is an arbitrary integer matrix and $B$ is either row-monotone
or column-monotone with integer elements bounded by $O(n)$, computing the
min-plus product $C$ where $C_{i,j}=\min_k\{A_{i,k}+B_{k,j}\}$ takes
$\tilde{O}(n^{(3+\omega)/2})$ time, which greatly improves the previous time
bound of $\tilde{O}(n^{(12+\omega)/5})=\tilde{O}(n^{2.875})$ [Gu, Polak,
Vassilevska Williams and Xu 2021]. Then by simple reductions, this means the
following problems also have $\tilde{O}(n^{(3+\omega)/2})$ time algorithms:
  (1) $A$ and $B$ are both bounded-difference, that is, the difference between
any two adjacent entries is a constant. The previous results give time
complexities of $\tilde{O}(n^{2.824})$ [Bringmann, Grandoni, Saha and
Vassilevska Williams 2016] and $\tilde{O}(n^{2.779})$ [Chi, Duan and Xie 2022].
  (2) $A$ is arbitrary and the columns or rows of $B$ are bounded-difference.
Previous result gives time complexity of $\tilde{O}(n^{2.922})$ [Bringmann,
Grandoni, Saha and Vassilevska Williams 2016].
  (3) The problems reducible to these problems, such as language edit distance,
RNA-folding, scored parsing problem on BD grammars. [Bringmann, Grandoni, Saha
and Vassilevska Williams 2016].
  Finally, we also consider the problem of min-plus convolution between two
integral sequences which are monotone and bounded by $O(n)$, and achieve a
running time upper bound of $\tilde{O}(n^{1.5})$. Previously, this task
requires running time $\tilde{O}(n^{(9+\sqrt{177})/12}) = O(n^{1.859})$ [Chan
and Lewenstein 2015].


title: Explain yourself! Effects of Explanations in Human-Robot Interaction

abstract:
  Recent developments in explainable artificial intelligence promise the
potential to transform human-robot interaction: Explanations of robot decisions
could affect user perceptions, justify their reliability, and increase trust.
However, the effects on human perceptions of robots that explain their
decisions have not been studied thoroughly. To analyze the effect of
explainable robots, we conduct a study in which two simulated robots play a
competitive board game. While one robot explains its moves, the other robot
only announces them. Providing explanations for its actions was not sufficient
to change the perceived competence, intelligence, likeability or safety ratings
of the robot. However, the results show that the robot that explains its moves
is perceived as more lively and human-like. This study demonstrates the need
for and potential of explainable human-robot interaction and the wider
assessment of its effects as a novel research direction.


title:
Predicting aggregation of sequence-defined macromolecules with Recurrent
  Neural Networks

abstract:
  Self-assembly of dilute sequence-defined macromolecules is a complex
phenomenon in which the local arrangement of chemical moieties leads to the
formation of a long-range structure. The dependence of this structure on the
sequence necessarily implies that a mapping between the two exists, yet it has
been difficult to model so far. Predicting the aggregation behavior of these
macromolecules is challenging due to the lack of effective order parameters, a
vast design space, inherent variability, and high computational costs
associated with currently available simulation techniques. Here, we accurately
predict the morphology of aggregates self-assembled from sequence-defined
macromolecules using supervised machine learning. We find that regression
models with implicit representation learning perform significantly better than
those based on engineered features such as $k$-mer counting, and a
Recurrent-Neural-Network-based regressor performs the best. Further, we
demonstrate the high-throughput screening of monomer sequences using the
regression model to identify candidates for self-assembly into selected
morphologies. Our strategy is shown to successfully identify multiple suitable
sequences in every test we performed, so we hope the insights gained here can
be extended to other increasingly complex design scenarios in the future, such
as the design of sequences under polydispersity and at varying environmental
conditions.

categories: cond-mat.soft cond-mat.dis-nn cond-mat.mtrl-sci cond-mat.other


title:
Role of substrate surface morphology on the performance of graphene inks
  for flexible electronics

abstract:
  Two-dimensional (2D) materials, such as graphene, are seen as potential
candidates for fabricating electronic devices and circuits on flexible
substrates. Inks or dispersions of 2D materials can be deposited on flexible
substrates by large-scale coating techniques, such as inkjet printing and spray
coating. One of the main issues in coating processes is nonuniform deposition
of inks, which may lead to large variations of properties across the
substrates. Here, we investigate the role of surface morphology on the
performance of graphene ink deposited on different paper substrates with
specific top coatings. Substrates with good wetting properties result in
reproducible thin films and electrical properties with low sheet resistance.
The correct choice of surface morphology enables high-performance films without
postdeposition annealing or treatment. Scanning terahertz time-domain
spectroscopy (THz-TDS) is introduced to evaluate both the uniformity and the
local conductivity of graphene inks on paper. A paper-based strain gauge is
demonstrated and a variable resistor acts as an on-off switch for operating an
LED. Customized surfaces can thus help in unleashing the full potential of
ink-based 2D materials.


title:
TANet: Thread-Aware Pretraining for Abstractive Conversational
  Summarization

abstract:
  Although pre-trained language models (PLMs) have achieved great success and
become a milestone in NLP, abstractive conversational summarization remains a
challenging but less studied task. The difficulty lies in two aspects. One is
the lack of large-scale conversational summary data. Another is that applying
the existing pre-trained models to this task is tricky because of the
structural dependence within the conversation and its informal expression, etc.
In this work, we first build a large-scale (11M) pretraining dataset called
RCS, based on the multi-person discussions in the Reddit community. We then
present TANet, a thread-aware Transformer-based network. Unlike the existing
pre-trained models that treat a conversation as a sequence of sentences, we
argue that the inherent contextual dependency among the utterances plays an
essential role in understanding the entire conversation and thus propose two
new techniques to incorporate the structural information into our model. The
first is thread-aware attention which is computed by taking into account the
contextual dependency within utterances. Second, we apply thread prediction
loss to predict the relations between utterances. We evaluate our model on four
datasets of real conversations, covering types of meeting transcripts,
customer-service records, and forum threads. Experimental results demonstrate
that TANET achieves a new state-of-the-art in terms of both automatic
evaluation and human judgment.


title:
Kolmogorov turbulence co-exists with pseudo-turbulence in
  buoyancy-driven bubbly flows

abstract:
  We investigate spectral properties of buoyancy driven bubbly flows. Using
high-resolution numerical simulations and phenomenology of homogeneous
turbulence, we identify the relevant energy transfer mechanisms. We find: (a)
At high enough Galilei number (ratio of the buoyancy to viscous forces) the
kinetic energy spectrum shows the Kolmogorov scaling with a power law exponent
$-5/3$ for the range of scales between the bubble diameter and the dissipation
scale ($\eta$). (b) For scales smaller than $\eta$, the physics of
pseudo-turbulence is recovered.


title:
Dark energy and accelerating cosmological evolution from osculating
  Barthel-Kropina geometry

abstract:
  Finsler geometry is an important extension of Riemann geometry, in which to
each point of the spacetime manifold an arbitrary internal variable is
associated. Interesting Finsler geometries, with many physical applications,
are the Randers and Kropina type geometries, respectively. A subclass of
Finsler geometries is represented by the osculating Finsler spaces, in which
the internal variable is a function of the base manifold coordinates only. In
an osculating Finsler geometry one introduces the Barthel connection, which has
the remarkable property that it is the Levi-Civita connection of a Riemannian
metric. In the present work we consider the gravitational and cosmological
implications of a Barthel-Kropina type geometry. We assume that in this
geometry the Ricci type curvatures are related to the matter energy-momentum
tensor by the standard Einstein equations. The generalized Friedmann equations
in the Barthel-Kropina geometry are obtained by considering that the background
Riemannian metric is of Friedmann-Lemaitre-Robertson-Walker type. The matter
energy balance equation is also derived. The cosmological properties of the
model are investigated in detail, and it is shown that the model admits a de
Sitter type solution, and that an effective dark energy component can also be
generated. Several cosmological solutions are also obtained by numerically
integrating the generalized Friedmann equations. A comparison of two specific
classes of models with the observational data and with the standard
$\Lambda$CDM model is also performed, and it turns out that the Barthel-Kropina
type models give a satisfactory description of the observations.


title:
MR-iNet Gym: Framework for Edge Deployment of Deep Reinforcement
  Learning on Embedded Software Defined Radio

abstract:
  Dynamic resource allocation plays a critical role in the next generation of
intelligent wireless communication systems. Machine learning has been leveraged
as a powerful tool to make strides in this domain. In most cases, the progress
has been limited to simulations due to the challenging nature of hardware
deployment of these solutions. In this paper, for the first time, we design and
deploy deep reinforcement learning (DRL)-based power control agents on the GPU
embedded software defined radios (SDRs). To this end, we propose an end-to-end
framework (MR-iNet Gym) where the simulation suite and the embedded SDR
development work cohesively to overcome real-world implementation hurdles. To
prove feasibility, we consider the problem of distributed power control for
code-division multiple access (DS-CDMA)-based LPI/D transceivers. We first
build a DS-CDMA ns3 module that interacts with the OpenAI Gym environment.
Next, we train the power control DRL agents in this ns3-gym simulation
environment in a scenario that replicates our hardware testbed. Next, for edge
(embedded on-device) deployment, the trained models are optimized for real-time
operation without loss of performance. Hardware-based evaluation verifies the
efficiency of DRL agents over traditional distributed constrained power control
(DCPC) algorithm. More significantly, as the primary goal, this is the first
work that has established the feasibility of deploying DRL to provide optimized
distributed resource allocation for next-generation of GPU-embedded radios.


title:
Finding the Right Place: Sensor Placement for UWB Time Difference of
  Arrival Localization in Cluttered Indoor Environments

abstract:
  Ultra-wideband (UWB) time difference of arrival (TDOA)-based localization has
recently emerged as a promising indoor positioning solution. However, in
cluttered environments, both the UWB radio positions and the obstacle-induced
non-line-of-sight (NLOS) measurement biases significantly impact the quality of
the position estimate. Consequently, the placement of the UWB radios must be
carefully designed to provide satisfactory localization accuracy for a region
of interest. In this work, we propose a novel algorithm that optimizes the UWB
radio positions for a pre-defined region of interest in the presence of
obstacles. The mean-squared error (MSE) metric is used to formulate an
optimization problem that balances the influence of the geometry of the radio
positions and the NLOS effects. We further apply the proposed algorithm to
compute a minimal number of UWB radios required for a desired localization
accuracy and their corresponding positions. In a real-world cluttered
environment, we show that the designed UWB radio placements provide 47% and 76%
localization root-mean-squared error (RMSE) reduction in 2D and 3D experiments,
respectively, when compared against trivial placements.


title:
Graphene coating of Nafion$^{(R)}$ membranes for enhanced fuel cell
  performance

abstract:
  Electrochemically exfoliated graphene (e-G) thin films on Nafion$^{(R)}$
membranes exhibit a selective barrier effect against undesirable fuel
crossover. The approach combines the high proton conductivity of
state-of-the-art Nafion$^{(R)}$ and the ability of e-G layers to effectively
block the transport of methanol and hydrogen. Nafion$^{(R)}$ membranes are
coated with aqueous dispersions of e-G on the anode side, making use of a
facile and scalable spray process. Scanning transmission electron microscopy
(STEM) and electron energy-loss spectroscopy (EELS) confirm the formation of a
dense percolated graphene flake network which acts as diffusion barrier. The
maximum power density in direct methanol fuel cell (DMFC) operation with e-G
coated Nafion$^{(R)}$ N115 is 3.9 times higher than the Nafion$^{(R)}$ N115
reference (39 vs. 10 mW cm$^{-2}$ @ 0.3 V) at 5M methanol feed concentration.
This suggests the application of e-G coated Nafion$^{(R)}$ membranes for
portable DMFCs, where the use of highly concentrated methanol is desirable.


title:
Efficient Representation Learning of Subgraphs by Subgraph-To-Node
  Translation

abstract:
  A subgraph is a data structure that can represent various real-world
problems. We propose Subgraph-To-Node (S2N) translation, which is a novel
formulation to efficiently learn representations of subgraphs. Specifically,
given a set of subgraphs in the global graph, we construct a new graph by
coarsely transforming subgraphs into nodes. We perform subgraph-level tasks as
node-level tasks through this translation. By doing so, we can significantly
reduce the memory and computational costs in both training and inference. We
conduct experiments on four real-world datasets to evaluate performance and
efficiency. Our experiments demonstrate that models with S2N translation are
more efficient than state-of-the-art models without substantial performance
decrease.


title:
FuNNscope: Visual microscope for interactively exploring the loss
  landscape of fully connected neural networks

abstract:
  Despite their effective use in various fields, many aspects of neural
networks are poorly understood. One important way to investigate the
characteristics of neural networks is to explore the loss landscape. However,
most models produce a high-dimensional non-convex landscape which is difficult
to visualize. We discuss and extend existing visualization methods based on 1D-
and 2D slicing with a novel method that approximates the actual loss landscape
geometry by using charts with interpretable axes. Based on the assumption that
observations on small neural networks can generalize to more complex systems
and provide us with helpful insights, we focus on small models in the range of
a few dozen weights, which enables computationally cheap experiments and the
use of an interactive dashboard. We observe symmetries around the zero vector,
the influence of different layers on the global landscape, the different weight
sensitivities around a minimizer, and how gradient descent navigates high-loss
obstacles. The user study resulted in an average SUS (System Usability Scale)
score with suggestions for improvement and opened up a number of possible
application scenarios, such as autoencoders and ensemble networks.


title: Spectral bounds of the $\varepsilon$-entropy of kernel classes

abstract:
  We develop new upper and lower bounds on the $\varepsilon$-entropy of a unit
ball in a reproducing kernel Hilbert space induced by some Mercer kernel $K$.
Our bounds are based on the behaviour of eigenvalues of a corresponding
integral operator. In our approach we exploit an ellipsoidal structure of a
unit ball in RKHS and a previous work on covering numbers of an ellipsoid in
the euclidean space obtained by Dumer, Pinsker and Prelov.
  We present a number of applications of our main bound, such as its tightness
for a practically important case of the Gaussian kernel. Further, we develop a
series of lower bounds on the $\varepsilon$-entropy that can be established
from a connection between covering numbers of a ball in RKHS and a quantization
of a Gaussian Random Field that corresponds to the kernel $K$ by the
Kosambi-Karhunen-Lo\`eve transform.


title: On the Exploitation of Deepfake Model Recognition

abstract:
  Despite recent advances in Generative Adversarial Networks (GANs), with
special focus to the Deepfake phenomenon there is no a clear understanding
neither in terms of explainability nor of recognition of the involved models.
In particular, the recognition of a specific GAN model that generated the
deepfake image compared to many other possible models created by the same
generative architecture (e.g. StyleGAN) is a task not yet completely addressed
in the state-of-the-art. In this work, a robust processing pipeline to evaluate
the possibility to point-out analytic fingerprints for Deepfake model
recognition is presented. After exploiting the latent space of 50 slightly
different models through an in-depth analysis on the generated images, a proper
encoder was trained to discriminate among these models obtaining a
classification accuracy of over 96%. Once demonstrated the possibility to
discriminate extremely similar images, a dedicated metric exploiting the
insights discovered in the latent space was introduced. By achieving a final
accuracy of more than 94% for the Model Recognition task on images generated by
models not employed in the training phase, this study takes an important step
in countering the Deepfake phenomenon introducing a sort of signature in some
sense similar to those employed in the multimedia forensics field (e.g. for
camera source identification task, image ballistics task, etc).


title: The W boson mass weighs in on the non-standard Higgs

abstract:
  We consider the implications of the CDF collaboration high-precision
measurement of the W boson mass on models with a non-standard Higgs. We show
that this requires an enhancement of 3-10% in the non-standard Higgs coupling
to the gauge bosons. This is naturally accommodated in dynamical models such as
the dilaton Higgs, the Technicolor and glueball Higgs. The needed composite
scale between 2 and 3 TeV can also explain the muon g-2 anomaly, as well as
possible violations of lepton flavour universality.


title:
Applying machine learning to predict behavior of bus transport in
  Warsaw, Poland

abstract:
  Nowadays, it is possible to collect precise data describing movements of
public transport. Specifically, for each bus (or tram) geoposition data can be
regularly collected. This includes data for all buses in Warsaw, Poland.
Moreover, this data can be downloaded and analyzed. In this context, one of the
simplest questions is: can a model be build to represent behavior of busses,
and predict their delays. This work provides initial results of our attempt to
answer this question.


title:
Uncertainty-Informed Deep Learning Models Enable High-Confidence
  Predictions for Digital Histopathology

abstract:
  A model's ability to express its own predictive uncertainty is an essential
attribute for maintaining clinical user confidence as computational biomarkers
are deployed into real-world medical settings. In the domain of cancer digital
histopathology, we describe a novel, clinically-oriented approach to
uncertainty quantification (UQ) for whole-slide images, estimating uncertainty
using dropout and calculating thresholds on training data to establish cutoffs
for low- and high-confidence predictions. We train models to identify lung
adenocarcinoma vs. squamous cell carcinoma and show that high-confidence
predictions outperform predictions without UQ, in both cross-validation and
testing on two large external datasets spanning multiple institutions. Our
testing strategy closely approximates real-world application, with predictions
generated on unsupervised, unannotated slides using predetermined thresholds.
Furthermore, we show that UQ thresholding remains reliable in the setting of
domain shift, with accurate high-confidence predictions of adenocarcinoma vs.
squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts.


title: The spin-one Motzkin chain is gapped for any area weight $t<1$

abstract:
  We prove a conjecture by Zhang, Ahmadain, and Klich that the spin-$1$ Motzkin
chain is gapped for any area weight $t<1$. Existence of a finite spectral gap
is necessary for the Motzkin Hamiltonian to belong to the Haldane phase, which
has been argued to potentially be the case in recent work of Barbiero,
Dell'Anna, Trombettoni, and Korepin. Our proof rests on the combinatorial
structure of the ground space and the analytical verification of a finite-size
criterion.

categories: quant-ph cond-mat.stat-mech math-ph math.MP


title: Attention U-Net as a surrogate model for groundwater prediction

abstract:
  Numerical simulations of groundwater flow are used to analyze and predict the
response of an aquifer system to its change in state by approximating the
solution of the fundamental groundwater physical equations. The most used and
classical methodologies, such as Finite Difference (FD) and Finite Element (FE)
Methods, use iterative solvers which are associated with high computational
cost. This study proposes a physics-based convolutional encoder-decoder neural
network as a surrogate model to quickly calculate the response of the
groundwater system. Holding strong promise in cross-domain mappings,
encoder-decoder networks are applicable for learning complex input-output
mappings of physical systems. This manuscript presents an Attention U-Net model
that attempts to capture the fundamental input-output relations of the
groundwater system and generates solutions of hydraulic head in the whole
domain given a set of physical parameters and boundary conditions. The model
accurately predicts the steady state response of a highly heterogeneous
groundwater system given the locations and piezometric head of up to 3 wells as
input. The network learns to pay attention only in the relevant parts of the
domain and the generated hydraulic head field corresponds to the target samples
in great detail. Even relative to coarse finite difference approximations the
proposed model is shown to be significantly faster than a comparative
state-of-the-art numerical solver, thus providing a base for further
development of the presented networks as surrogate models for groundwater
prediction.


title:
Deflection and Gravitational lensing of null and timelike signals in the
  Kiselev black hole spacetime

abstract:
  In this work we study the deflection and gravitational lensing of null and
timelike signals in the Kiselev spacetime in the weak field limit, to
investigate the effects of the equation of state parameter $\omega$ and the
matter amount parameter $\alpha$. In doing this, we extend a perturbative
method previously developed for asymptotically flat spacetimes whose metric
functions have integer-power asymptotic expansions to the case that may or may
not be asymptotically flat but with non-integer power expansions. It is found
that in the asymptotically flat case ($-1/3<\omega<0$) the deflection angles
are expressable as quasi-power series of the dimensionless quantities
$M/b,~b/r_{s,d}$ and $\alpha/M^{1+3\omega}$ where $M,~b,~r_{s,d}$ are
respectively the lens mass, impact parameter and source/detector radius. A
similar series exist for the non-asymptotically flat case of
($-1<\omega<-1/3$), but with the closest radius $r_0$ replacing $b$. In the
asymptotically flat (or non-flat) case, the increase of $\alpha$ or decrease of
$\omega$ will increase (or increase) the deflection angle. Since the obtained
deflection angles naturally take into account the finite distance effect of the
source and the detector, we can establish an exact gravitational lensing
equation, from which the apparent angles of the images and their magnifications
are solved. It is found that generally for asymptotically flat case, increasing
$\alpha$ or decreasing $\omega$ will increase the apparent angles of the
images. While for non-asymptotically flat case, increasing $\alpha$ or $\omega$
will both lead to smaller apparent angles.


title:
A generalized Hopfield model to store and retrieve mismatched memory
  patterns

abstract:
  We introduce a class of Hopfield models where the memories are represented by
a mixture of Gaussian and bimodal variables and the neurons are Ising spins. We
study the properties of this family of models as the relative weight of the two
kinds of variables in the patterns varies. Spinodal and first order recovery
transition lines in the phase diagram are squeezed towards zero storage
capacity as the memory patterns contain a larger fraction of continuous
variables. As the memory is purely Gaussian retrieval is lost for any non zero
storage capacity. It is shown that this comes about because of the spherical
symmetry of the free energy in the Gaussian case. Introducing two different
memory pattern overlaps between spin configurations and each contribution to
the pattern from the two kinds of variables one can observe that the Gaussian
parts of the patterns act as a noise, making retrieval more difficult. The
basins of attraction of the states, the accuracy of the retrieval and the
storage capacity are studied by means of Monte Carlo numerical simulations.


title:
Benchmarking for Public Health Surveillance tasks on Social Media with a
  Domain-Specific Pretrained Language Model

abstract:
  A user-generated text on social media enables health workers to keep track of
information, identify possible outbreaks, forecast disease trends, monitor
emergency cases, and ascertain disease awareness and response to official
health correspondence. This exchange of health information on social media has
been regarded as an attempt to enhance public health surveillance (PHS).
Despite its potential, the technology is still in its early stages and is not
ready for widespread application. Advancements in pretrained language models
(PLMs) have facilitated the development of several domain-specific PLMs and a
variety of downstream applications. However, there are no PLMs for social media
tasks involving PHS. We present and release PHS-BERT, a transformer-based PLM,
to identify tasks related to public health surveillance on social media. We
compared and benchmarked the performance of PHS-BERT on 25 datasets from
different social medial platforms related to 7 different PHS tasks. Compared
with existing PLMs that are mainly evaluated on limited tasks, PHS-BERT
achieved state-of-the-art performance on all 25 tested datasets, showing that
our PLM is robust and generalizable in the common PHS tasks. By making PHS-BERT
available, we aim to facilitate the community to reduce the computational cost
and introduce new baselines for future works across various PHS-related tasks.


title:
Knowledge-Free Black-Box Watermark and Ownership Proof for Image
  Classification Neural Networks

abstract:
  Watermarking has become a plausible candidate for ownership verification and
intellectual property protection of deep neural networks. Regarding image
classification neural networks, current watermarking schemes uniformly resort
to backdoor triggers. However, injecting a backdoor into a neural network
requires knowledge of the training dataset, which is usually unavailable in the
real-world commercialization. Meanwhile, established watermarking schemes
oversight the potential damage of exposed evidence during ownership
verification and the watermarking algorithms themselves. Those concerns decline
current watermarking schemes from industrial applications. To confront these
challenges, we propose a knowledge-free black-box watermarking scheme for image
classification neural networks. The image generator obtained from a data-free
distillation process is leveraged to stabilize the network's performance during
the backdoor injection. A delicate encoding and verification protocol is
designed to ensure the scheme's security against knowledgable adversaries. We
also give a pioneering analysis of the capacity of the watermarking scheme.
Experiment results proved the functionality-preserving capability and security
of the proposed watermarking scheme.


title:
Search for continuous gravitational wave emission from the Milky Way
  center in O3 LIGO--Virgo data

abstract:
  We present a directed search for continuous gravitational wave (CW) signals
emitted by spinning neutron stars located in the inner parsecs of the Galactic
Center (GC). Compelling evidence for the presence of a numerous population of
neutron stars has been reported in the literature, turning this region into a
very interesting place to look for CWs. In this search, data from the full O3
LIGO--Virgo run in the detector frequency band $[10,2000]\rm~Hz$ have been
used. No significant detection was found and 95$\%$ confidence level upper
limits on the signal strain amplitude were computed, over the full search band,
with the deepest limit of about $7.6\times 10^{-26}$ at $\simeq 142\rm~Hz$.
These results are significantly more constraining than those reported in
previous searches. We use these limits to put constraints on the fiducial
neutron star ellipticity and r-mode amplitude. These limits can be also
translated into constraints in the black hole mass -- boson mass plane for a
hypothetical population of boson clouds around spinning black holes located in
the GC.


title: Asymptotics of eigenvalue sums when some turning points are complex

abstract:
  Recent work has shown a deep connection between semilocal approximations in
density functional theory and the asymptotics of the sum of the WKB
semiclassical expansion for the eigenvalues. However, all examples studied to
date have potentials with only real classical turning points. But systems with
complex turning points generate subdominant terms beyond those in the WKB
series. The simplest case is a pure quartic oscillator. We show how to
generalize the asymptotics of eigenvalue sums to include subdominant
contributions to the sums, if they are known for the eigenvalues. These
corrections to WKB greatly improve accuracy for eigenvalue sums, especially for
many levels. We obtain further improvements to the sums through
hyperasymptotics. For the lowest level, our summation method has error below $2
\times 10^{-4}$. For the sum of the lowest 10 levels, our error is less than
$10^{-22}$. We report all results to many digits and include copious details of
the asymptotic expansions and their derivation.

categories: quant-ph math-ph math.MP physics.chem-ph


title: Crystallographic design of intercalation materials

abstract:
  Intercalation materials are promising candidates for reversible energy
storage and are, for example, used as lithium-battery electrodes,
hydrogen-storage compounds, and electrochromic materials. An important issue
preventing the more widespread use of these materials is that they undergo
structural transformations (of up to ~10% lattice strains) during
intercalation, which expand the material, nucleate microcracks, and,
ultimately, lead to material failure. Besides the structural transformation of
lattices, the crystallographic texture of the intercalation material plays a
key role in governing ion-transport properties, generating phase separation
microstructures, and elastically interacting with crystal defects. In this
review, I provide an overview of how the structural transformation of lattices,
phase transformation microstructures, and crystallographic defects affect the
chemo-mechanical properties of intercalation materials. In each section, I
identify the key challenges and opportunities to crystallographically design
intercalation compounds to improve their properties and lifespans. I
predominantly cite examples from the literature of intercalation cathodes used
in rechargeable batteries, however, the identified challenges and opportunities
are transferable to a broader range of intercalation compounds.


title: Oligomorphic groups and tensor categories

abstract:
  Given an oligomorphic group $G$ and a measure $\mu$ for $G$ (in a sense that
we introduce), we define a rigid tensor category $\underline{\mathrm{Perm}}(G;
\mu)$ of "permutation modules," and, in certain cases, an abelian envelope
$\underline{\mathrm{Rep}}(G; \mu)$ of this category. When $G$ is the infinite
symmetric group, this recovers Deligne's interpolation category, while other
choices for $G$ lead to fundamentally new tensor categories. In particular, we
give the first example (so far as we know) of a $\mathbf{C}$-linear rigid
tensor category that bears no obvious relation to finite dimensional
representations. One interesting aspect of our construction is that, unlike
much previous work in this direction, our categories are concrete: the objects
are modules over a ring, and the tensor product receives a universal bi-linear
map. Central to our constructions is a novel theory of integration on
oligomorphic groups, which could be of more general interest. Classifying the
measures on an oligomorphic group appears to be a difficult problem, which we
solve in only a few cases.


title: Prognostic classification based on random convolutional kernel

abstract:
  Assessing the health status (HS) of system/component has long been a
challenging task in the prognostic and health management (PHM) study. Differed
from other regression based prognostic task such as predicting the remaining
useful life, the HS assessment is essentially a multi class classificatIon
problem. To address this issue, we introduced the random convolutional
kernel-based approach, the RandOm Convolutional KErnel Transforms (ROCKET) and
its latest variant MiniROCKET, in the paper. We implement ROCKET and MiniROCKET
on the NASA's CMPASS dataset and assess the turbine fan engine's HS with the
multi-sensor time-series data. Both methods show great accuracy when tackling
the HS assessment task. More importantly, they demonstrate considerably
efficiency especially compare with the deep learning-based method. We further
reveal that the feature generated by random convolutional kernel can be
combined with other classifiers such as support vector machine (SVM) and linear
discriminant analysis (LDA). The newly constructed method maintains the high
efficiency and outperforms all the other deop neutal network models in
classification accuracy.


title: Taylor rolls on tour

abstract:
  Turbulent flows exhibit wide ranges of time and length scales. Their
small-scale dynamics is well understood, but less is known about the mechanisms
governing the dynamics of large-scale coherent motions in the flow field. We
perform direct numerical simulations of axisymmetric Taylor--Couette flow and
show that beyond a critical domain size, the largest structures (turbulent
Taylor rolls) undergo erratic drifts evolving on a viscous time scale. We
estimate a diffusion coefficient for the drift and compare the dynamics to
analogous motions in Rayleigh--B\'enard convection and Poiseuille flow. We
argue that viscous processes govern the lateral displacement of large coherent
structures in wall-bounded turbulent flows.


title:
Learning hyperelastic anisotropy from data via a tensor basis neural
  network

abstract:
  Anisotropy in the mechanical response of materials with microstructure is
common and yet is difficult to assess and model. To construct accurate response
models given only stress-strain data, we employ classical representation
theory, novel neural network layers, and L1 regularization. The proposed
tensor-basis neural network can discover both the type and orientation of the
anisotropy and provide an accurate model of the stress response. The method is
demonstrated with data from hyperelastic materials with off-axis transverse
isotropy and orthotropy, as well as materials with less well-defined symmetries
induced by fibers or spherical inclusions. Both plain feed-forward neural
networks and input-convex neural network formulations are developed and tested.
Using the latter, a polyconvex potential can be established, which, by
satisfying the growth condition can guarantee the existence of boundary value
problem solutions.


title: Spectra of magnetic turbulence in a relativistic plasma

abstract:
  We present a phenomenological and numerical study of strong Alfv\'enic
turbulence in a magnetically dominated collisionless relativistic plasma with a
strong background magnetic field. In contrast with the non-relativistic case,
the energy in such turbulence is contained in magnetic and electric
fluctuations. We argue that such turbulence is analogous to turbulence in a
strongly magnetized non-relativistic plasma in the regime of broken
quasi-neutrality. Our 2D particle-in-cell numerical simulations of turbulence
in a relativistic pair plasma find that the spectrum of the total energy has
the scaling $k^{-3/2}$, while the difference between the magnetic and electric
energies, the so-called residual energy, has the scaling $k^{-2.4}$. The
electric and magnetic fluctuations at scale $\ell$ exhibit dynamic alignment
with the alignment-angle scaling close to $\cos\phi_\ell\propto \ell^{1/4}$. At
scales smaller than the (relativistic) plasma inertial scale, the energy
spectrum of relativistic inertial Alfv\'en turbulence steepens to $k^{-3.5}$.


title:
Enhanced and stable spin Hall conductivity in a disordered time-reversal
  and inversion symmetry broken topological insulator thin film

abstract:
  We consider a disordered topological insulator thin film placed on the top of
a ferromagnetic insulator with a perpendicular exchange field $M$ and subjected
to a perpendicular electric field. The presence of ferromagnetic insulator
causes that bottom surface states of the topological insulator thin film become
spin polarized and the electric field provides a potential difference $V$
between the two surface states, resulting in breaking of time-reversal and
inversion symmetry in the system. Using Kubo formalism and employing the first
Born approximation as well as the self-consistent Born approximation, we
calculate the spin Hall conductivity. We find that for small values of $V$, a
large spin conductivity can be generated through large values of $M$ away from
the charge neutrality point. But for large values of $V$, the spin conductivity
can be promoted even with small values of $M$ around the charge neutrality
point. The effect of vertex corrections and the stability of the obtained large
spin conductivity against disorders are also examined.


title: The entropy cones of $W_N$ states

abstract:
  The quantum entropy cones (QEC) for $W_N$ states of qubits are computed.
These cones emerge as symmetrized quantum entropy cones (SQEC) for arbitrary N
with the number of parties $\le$ N, and party N the purifier. A directed graph
model is presented which describes the SQEC for $W_N$ states. Monogamous mutual
information (MMI) is violated for all $N>3$.


title:
Motion Artifacts Correction from Single-Channel EEG and fNIRS Signals
  using Novel Wavelet Packet Decomposition in Combination with Canonical
  Correlation Analysis

abstract:
  The electroencephalogram (EEG) and functional near-infrared spectroscopy
(fNIRS) signals, highly non-stationary in nature, greatly suffers from motion
artifacts while recorded using wearable sensors. This paper proposes two robust
methods: i) Wavelet packet decomposition (WPD), and ii) WPD in combination with
canonical correlation analysis (WPD-CCA), for motion artifact correction from
single-channel EEG and fNIRS signals. The efficacy of these proposed techniques
is tested using a benchmark dataset and the performance of the proposed methods
is measured using two well-established performance matrices: i) Difference in
the signal to noise ratio ({\Delta}SNR) and ii) Percentage reduction in motion
artifacts ({\eta}). The proposed WPD-based single-stage motion artifacts
correction technique produces the highest average {\Delta}SNR (29.44 dB) when
db2 wavelet packet is incorporated whereas the greatest average {\eta} (53.48%)
is obtained using db1 wavelet packet for all the available 23 EEG recordings.
Our proposed two-stage motion artifacts correction technique i.e. the WPD-CCA
method utilizing db1 wavelet packet has shown the best denoising performance
producing an average {\Delta}SNR and {\eta} values of 30.76 dB and 59.51%,
respectively for all the EEG recordings. On the other hand, the two-stage
motion artifacts removal technique i.e. WPD-CCA has produced the best average
{\Delta}SNR (16.55 dB, utilizing db1 wavelet packet) and largest average {\eta}
(41.40%, using fk8 wavelet packet). The highest average {\Delta}SNR and {\eta}
using single-stage artifacts removal techniques (WPD) are found as 16.11 dB and
26.40%, respectively for all the fNIRS signals using fk4 wavelet packet. In
both EEG and fNIRS modalities, the percentage reduction in motion artifacts
increases by 11.28% and 56.82%, respectively when two-stage WPD-CCA techniques
are employed.


title:
Extending the Scope of Out-of-Domain: Examining QA models in multiple
  subdomains

abstract:
  Past works that investigate out-of-domain performance of QA systems have
mainly focused on general domains (e.g. news domain, wikipedia domain),
underestimating the importance of subdomains defined by the internal
characteristics of QA datasets. In this paper, we extend the scope of
"out-of-domain" by splitting QA examples into different subdomains according to
their several internal characteristics including question type, text length,
answer position. We then examine the performance of QA systems trained on the
data from different subdomains. Experimental results show that the performance
of QA systems can be significantly reduced when the train data and test data
come from different subdomains. These results question the generalizability of
current QA systems in multiple subdomains, suggesting the need to combat the
bias introduced by the internal characteristics of QA datasets.


title: Fibonacci-Catalan Series

abstract:
  We study certain series with Catalan numbers and reciprocal Catalan numbers,
respectively, and provide seemingly new closed form evaluations of these series
with Fibonacci (Lucas) entries. In addition, we state some combinatorial sums
that can be inferred from the series.


title:
Electrical breakdown of a dielectric for the formation of a
  superconducting nanocontact

abstract:
  Electrical breakdown of the dielectric nanolayer between film electrodes of
niobium and an alloy of 50% indium and 50% tin forms a bridge of this alloy
between the electrodes. The bridge resistance depends on the breakdown current.
The length of the bridge is equal to the thickness of the dielectric (30 nm),
and its diameter is 25 nm. The calculated coherence length of the alloy at 0 K
is close to the length of the bridge. The calculated critical current of a
bridge with a resistance of 1 {\Omega} at a temperature of 0 K is 2 mA. It is
concluded that such a bridge should have the properties of a Josephson contact
at a temperature lower than the critical temperature of the alloy (6.5 K).


title:
Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF
  Signals Using Deep Learning

abstract:
  Recently, super-resolution ultrasound imaging with ultrasound localization
microscopy (ULM) has received much attention. However, ULM relies on low
concentrations of microbubbles in the blood vessels, ultimately resulting in
long acquisition times. Here, we present an alternative super-resolution
approach, based on direct deconvolution of single-channel ultrasound
radio-frequency (RF) signals with a one-dimensional dilated convolutional
neural network (CNN). This work focuses on low-frequency ultrasound (1.7 MHz)
for deep imaging (10 cm) of a dense cloud of monodisperse microbubbles (up to
1000 microbubbles in the measurement volume, corresponding to an average echo
overlap of 94%). Data are generated with a simulator that uses a large range of
acoustic pressures (5-250 kPa) and captures the full, nonlinear response of
resonant, lipid-coated microbubbles. The network is trained with a novel
dual-loss function, which features elements of both a classification loss and a
regression loss and improves the detection-localization characteristics of the
output. Whereas imposing a localization tolerance of 0 yields poor detection
metrics, imposing a localization tolerance corresponding to 4% of the
wavelength yields a precision and recall of both 0.90. Furthermore, the
detection improves with increasing acoustic pressure and deteriorates with
increasing microbubble density. The potential of the presented approach to
super-resolution ultrasound imaging is demonstrated with a delay-and-sum
reconstruction with deconvolved element data. The resulting image shows an
order-of-magnitude gain in axial resolution compared to a delay-and-sum
reconstruction with unprocessed element data.


title: New results in AdS/CFT in low dimensions from massive Type IIA

abstract:
  We review recent developments in the study of the AdS/CFT correspondence in
low dimensions, focusing on the construction of AdS$_3$/CFT$_2$ and
AdS$_2$/CFT$_1$ dual pairs in massive Type IIA string theory. We start
discussing the $\text{AdS}_3\times S^2\times \text{CY}_2\times I$ solutions to
massive IIA supergravity with $\mathcal{N}=(0,4)$ supersymmetry constructed in
1908.09851. We review the 2d CFTs dual to these solutions, together with their
defect interpretation as surface defects within 5d fixed point theories living
in D4-D8 bound states. Next, we discuss the $\text{AdS}_2\times S^3\times
\text{CY}_2\times I$ solutions with $\mathcal{N}=4$ supersymmetry constructed
in 2011.13932. We discuss the superconformal quantum mechanics dual to these
solutions, that we interpret in terms of line defects realised as D0-D4 baryon
vertices in 5d Sp(N) fixed point theories. We review a particular example in
this class of solutions, constructed through non-Abelian T-duality with respect
to a non-compact isometry group, and discuss the possible embedding of a 3d
black hole geometry constructed long ago via non-Abelian T-duality within this
solution.


title: Testability in group theory

abstract:
  This paper is a journal counterpart to our FOCS 2021 paper, in which we
initiate the study of property testing problems concerning a finite system of
relations $E$ between permutations, generalizing the study of stability in
permutations. To every such system $E$, a group $\Gamma=\Gamma_E$ is associated
and the testability of $E$ depends only on $\Gamma$ (just like in Galois
theory, where the solvability of a polynomial is determined by the solvability
of the associated group). This leads to the notion of testable groups, and,
more generally, Benjamini-Schramm rigid groups. The paper presents an ensemble
of tools to check if a given group $\Gamma$ is testable/BS-rigid or not.


title:
Peekaboo: A Hub-Based Approach to Enable Transparency in Data Processing
  within Smart Homes (Extended Technical Report)

abstract:
  We present Peekaboo, a new privacy-sensitive architecture for smart homes
that leverages an in-home hub to pre-process and minimize outgoing data in a
structured and enforceable manner before sending it to external cloud servers.
Peekaboo's key innovations are (1) abstracting common data pre-processing
functionality into a small and fixed set of chainable operators, and (2)
requiring that developers explicitly declare desired data collection behaviors
(e.g., data granularity, destinations, conditions) in an application manifest,
which also specifies how the operators are chained together. Given a manifest,
Peekaboo assembles and executes a pre-processing pipeline using operators
pre-loaded on the hub. In doing so, developers can collect smart home data on a
need-to-know basis; third-party auditors can verify data collection behaviors;
and the hub itself can offer a number of centralized privacy features to users
across apps and devices, without additional effort from app developers. We
present the design and implementation of Peekaboo, along with an evaluation of
its coverage of smart home scenarios, system performance, data minimization,
and example built-in privacy features.


title: KOBEST: Korean Balanced Evaluation of Significant Tasks

abstract:
  A well-formulated benchmark plays a critical role in spurring advancements in
the natural language processing (NLP) field, as it allows objective and precise
evaluation of diverse models. As modern language models (LMs) have become more
elaborate and sophisticated, more difficult benchmarks that require linguistic
knowledge and reasoning have been proposed. However, most of these benchmarks
only support English, and great effort is necessary to construct benchmarks for
other low resource languages. To this end, we propose a new benchmark named
Korean balanced evaluation of significant tasks (KoBEST), which consists of
five Korean-language downstream tasks. Professional Korean linguists designed
the tasks that require advanced Korean linguistic knowledge. Moreover, our data
is purely annotated by humans and thoroughly reviewed to guarantee high data
quality. We also provide baseline models and human performance results. Our
dataset is available on the Huggingface.


title:
Survival Seq2Seq: A Survival Model based on Sequence to Sequence
  Architecture

abstract:
  This paper introduces a novel non-parametric deep model for estimating
time-to-event (survival analysis) in presence of censored data and competing
risks. The model is designed based on the sequence-to-sequence (Seq2Seq)
architecture, therefore we name it Survival Seq2Seq. The first recurrent neural
network (RNN) layer of the encoder of our model is made up of Gated Recurrent
Unit with Decay (GRU-D) cells. These cells have the ability to effectively
impute not-missing-at-random values of longitudinal datasets with very high
missing rates, such as electronic health records (EHRs). The decoder of
Survival Seq2Seq generates a probability distribution function (PDF) for each
competing risk without assuming any prior distribution for the risks. Taking
advantage of RNN cells, the decoder is able to generate smooth and virtually
spike-free PDFs. This is beyond the capability of existing non-parametric deep
models for survival analysis. Training results on synthetic and medical
datasets prove that Survival Seq2Seq surpasses other existing deep survival
models in terms of the accuracy of predictions and the quality of generated
PDFs.


title: Eventually fixed points of endomorphisms of virtually free groups

abstract:
  We consider the subgroup of points of finite orbit through the action of an
endomorphism of a virtually free group, with particular emphasis on the
subgroup of eventually fixed points, EvFix($\varphi$): points whose orbit
contains a fixed point. We provide an algorithm to compute the subgroup of
fixed points of an endomorphism of a finitely generated virtually free group
and prove that finite orbits have cardinality bounded by a computable constant,
which allows us to solve several algorithmic problems: deciding if $\varphi$ is
a finite order element of End($G$), if $\varphi$ is aperiodic, if
EvFix($\varphi$) is finitely generated and, in the free group case, whether
EvFix($\varphi$) is a normal subgroup of $F_n$ or not. We also present a bound
for the rank of EvFix($\varphi$) in case it is finitely generated.


title:
Efficient Extraction of Pathologies from C-Spine Radiology Reports using
  Multi-Task Learning

abstract:
  Pretrained Transformer based models finetuned on domain specific corpora have
changed the landscape of NLP. Generally, if one has multiple tasks on a given
dataset, one may finetune different models or use task specific adapters. In
this work, we show that a multi-task model can beat or achieve the performance
of multiple BERT-based models finetuned on various tasks and various task
specific adapter augmented BERT-based models. We validate our method on our
internal radiologist's report dataset on cervical spine. We hypothesize that
the tasks are semantically close and related and thus multitask learners are
powerful classifiers. Our work opens the scope of using our method to
radiologist's reports on various body parts.


title:
Self-Labeling Refinement for Robust Representation Learning with
  Bootstrap Your Own Latent

abstract:
  In this work, we have worked towards two major goals. Firstly, we have
investigated the importance of Batch Normalisation (BN) layers in a
non-contrastive representation learning framework called Bootstrap Your Own
Latent (BYOL). We conducted several experiments to conclude that BN layers are
not necessary for representation learning in BYOL. Moreover, BYOL only learns
from the positive pairs of images but ignores other semantically similar images
in the same input batch. For the second goal, we have introduced two new loss
functions to determine the semantically similar pairs in the same input batch
of images and reduce the distance between their representations. These loss
functions are Cross-Cosine Similarity Loss (CCSL) and Cross-Sigmoid Similarity
Loss (CSSL). Using the proposed loss functions, we are able to surpass the
performance of Vanilla BYOL (71.04%) by training the BYOL framework using CCSL
loss (76.87%) on the STL10 dataset. BYOL trained using CSSL loss performs
comparably with Vanilla BYOL.


title: Adaptive search area for fast motion estimation

abstract:
  This paper suggests a new method for determining the search area for a motion
estimation algorithm based on block matching. The search area is adaptively
found in the proposed method for each frame block. This search area is similar
to that of the full search (FS) algorithm but smaller for most blocks of a
frame. Therefore, the proposed algorithm is analogous to FS in terms of
regularity but has much less computational complexity. The temporal and spatial
correlations among the motion vectors of blocks are used to find the search
area. The matched block is chosen from a rectangular area that the prediction
vectors set out. Simulation results indicate that the speed of the proposed
algorithm is at least seven times better than the FS algorithm.


title: Small polygons with large area

abstract:
  A polygon is \textit{small} if it has unit diameter. The maximal area of a
small polygon with a fixed number of sides $n$ is not known when $n$ is even
and $n\geq14$. We determine an improved lower bound for the maximal area of a
small $n$-gon for this case. The improvement affects the $1/n^3$ term of an
asymptotic expansion; prior advances affected less significant terms. This
bound cannot be improved by more than $O(1/n^3)$. For $n=6$, $8$, $10$, and
$12$, the polygon we construct has maximal area.


title: Instantaneous blowup and singular potentials on Heisenberg groups

abstract:
  In this paper we generalize the instantaneous blowup result from [3] and [15]
to the heat equation perturbed by singular potentials on the Heisenberg group.


title: Maxwell Matter Waves

abstract:
  Maxwell matter waves emerge from a perspective, complementary to de
Broglie's, that matter is fundamentally a wave phenomenon whose particle
aspects are revealed by quantum mechanics. Their quantum mechanical description
is derived through the introduction of a matter vector potential, having
frequency $\omega_0$, to Schrodinger's equation for a massive particle. Maxwell
matter waves are then seen to be coherent excitations of a single-mode of the
matter-wave field. In the classical regime, their mechanics is captured by a
matter analog of Maxwell's equations for the electromagnetic field. As such,
Maxwell matter waves enable a spectrum of systems that have useful optical
analogs, such as resonant matter-wave interferometric sensors and matter-wave
parametric oscillators. These waves are associated with a wavelength that is
tied to the drive frequency $\omega_0$ rather than to the massive particle's
energy, as is ordinarily the case with de Broglie matter waves. As a result,
simple interferometric measurements lead to different outcomes for the two
types of waves. While their apparent departure from de Broglie character is
surprising, Maxwell matter waves are wholly consistent with quantum mechanics.


title: Embedding Learning in Hybrid Quantum-Classical Neural Networks

abstract:
  Quantum embedding learning is an important step in the application of quantum
machine learning to classical data. In this paper we propose a quantum few-shot
embedding learning paradigm, which learns embeddings useful for training
downstream quantum machine learning tasks. Crucially, we identify the circuit
bypass problem in hybrid neural networks, where learned classical parameters do
not utilize the Hilbert space efficiently. We observe that the few-shot learned
embeddings generalize to unseen classes and suffer less from the circuit bypass
problem compared with other approaches.


title: The $\kappa$-nullity of Riemannian manifolds and their splitting tensors

abstract:
  We consider Riemannian $n$-manifolds $M$ with nontrivial $\kappa$-nullity
"distribution" of the curvature tensor $R$, namely, the variable rank
distribution of tangent subspaces to $M$ where $R$ coincides with the curvature
tensor of a space of constant curvature $\kappa$ ($\kappa\in\mathbb R$) is
nontrivial. We obtain classification theorems under diferent additional
assumptions, in terms of low nullity/conullity, controlled scalar curvature or
existence of quotients of finite volume. We prove new results, but also revisit
previous ones.


title: Magnetized granular particles running and tumbling on $S^{1}$

abstract:
  It has been shown that a nonvibrated magnetic granular system, when it is
feeded by means an altenating magnetic field, behaves with most of the
distinctive physical features of active matter systems. In this work we focus
our attention on the simplest granular system composed by a single magnetized
spherical particle allocated in a quasi one-dimensional circular channel that
receives energy from a magnetic field reservoir and transduces it into a
running and tumbling motion. The theoretical analysis based on the run and
tumble model on a circle of radius R forecasts the existence of a dynamical
phase transition between an erratic motion (disordered phase) when the
characteristic persistence length of the run and tumble motion, $\ell_{c} <
R/2$, to a persistent motion (ordered phase) when $\ell_{c}> R/2$. It is found
that the limiting behaviours of these phases correspond to a Brownian motion on
the circle and a simple uniform circular motion, respectively. It is
qualitatively shown that the lower magnetization of a particle, the larger
persistence lenght is. It is so at least within the experimental limit of
validity of our experiments. Our results show a very good agreement between
theory and experiment.


title: Residues of connections and the Chevalley-Weil formula for curves

abstract:
  Given a finite group of automorphisms of a compact Riemann surface, the
Chevalley-Weil formula computes the character valued Euler characteristic of an
equivariant line bundle. The goal of this article is to give a proof by
computing using residues of a Gauss-Manin connection.


title: Simple, Exact Asymptotic Density Functional Theory

abstract:
  An exact, computationally efficient density functional theory (DFT) ansatz is
derived using the asymptotic limits of large and small interatomic separations.
A generalization of the Anderson-Weeks-Adams-Gilbert chemical pseudopotential
theory of localized atomic states, asymptotic density functional theory (ADFT)
recasts the Kohn-Sham equations in a simple, self-consistent Huckel form that
features nonempirical Hamiltonian matrix elements and explicit
exchange-correlation (XC), electrostatic, and orthogonalization effects. The
theory provides fundamental justification for several characteristics of
successful tight binding models, most notably the atomic additivity of electron
density and the potential and the pairwise additivity of interatomic XC,
electrostatic, and Pauli repulsion effects. The Sanderson electronegativity
equalization principle and the charge equilibration mechanism resembling the
Rappe-Goddard scheme naturally follow. ADFT offers a bridge between molecular
orbital, valence bond, and DFT theories and holds promise to accelerate
accurate quantum chemical calculations.


title:
Trust-based Rate-Tunable Control Barrier Functions for Non-Cooperative
  Multi-Agent Systems

abstract:
  For efficient and robust task accomplishment in multi-agent systems, an agent
must be able to distinguish cooperative agents from non-cooperative agents,
i.e., uncooperative and adversarial agents. Task descriptions capturing safety
and collaboration can often be encoded as Control Barrier Functions (CBFs). In
this work, we first develop a trust metric that each agent uses to form its own
belief of how cooperative other agents are. The metric is used to adjust the
rate at which the CBFs allow the system trajectories to approach the boundaries
of the safe region. Then, based on the presented notion of trust, we propose a
Rate-Tunable CBF framework that leads to less conservative performance compared
to an identity-agnostic implementation, where cooperative and non-cooperative
agents are treated similarly. Finally, in presence of non-cooperating agents,
we show the application of our control algorithm to heterogeneous multi-agent
system through simulations.


title: On the GKZ discriminant locus

abstract:
  The "stringy K\"ahler moduli space" of a (Calabi-Yau) toric variety is
conjecturally given by a quotient of the complement of the "principal
$A$-determinant" locus. The latter is the union of the closures of those
discriminant loci of the Laurent polynomials associated to the faces of $A$
that are hypersurfaces. In this short note we show that it is also the
straightforward union of all the discriminant loci, i.e. we may include those
of higher codimension, and there is no need to take closures. This answers a
question by Kite and Segal.


title: ROOT for the HL-LHC: data format

abstract:
  This document discusses the state, roadmap, and risks of the foundational
components of ROOT with respect to the experiments at the HL-LHC (Run 4 and
beyond). As foundational components, the document considers in particular the
ROOT input/output (I/O) subsystem. The current HEP I/O is based on the TFile
container file format and the TTree binary event data format. The work going
into the new RNTuple event data format aims at superseding TTree, to make
RNTuple the production ROOT event data I/O that meets the requirements of Run 4
and beyond.


title:
Trajectory Optimization Using Neural Network Gradients of Learned
  Dynamics

abstract:
  Trajectory optimization methods have achieved an exceptional level of
performance on real-world robots in recent years. These methods heavily rely on
accurate physics simulators, yet some aspects of the physical world, such as
friction, can only be captured to a limited extent by most simulators. The goal
of this paper is to leverage trajectory optimization for performing highly
dynamic and complex tasks with robotic systems in absence of an accurate
physics simulator. This is achieved by applying machine learning techniques to
learn a differentiable dynamics model of the system from data. On the example
of a RC car, we show that from data collected in only 15 minutes of
human-operated interactions with the car, a neural network is able to model
highly nonlinear behaviors such as loss of traction and drifting. Furthermore,
we use the analytical gradients of the neural network to perform gradient-based
trajectory optimization, both in an offline and online setting. We find that
our learned model is able to represent complex physical behavior, like drifting
and gives unprecedented performance in combination with trajectory optimization
methods.


title: Right-handed neutrinos and the CDF II anomaly

abstract:
  We point out that right-handed neutrinos can resolve the tension between the
latest CDF II measurement of the $W$-boson mass, $M_W$, and the standard model.
Integrating out the new states yields a single $d=6$ operator, which induces a
deviation from unitarity in the PMNS matrix. This alters the extraction of the
Fermi constant from muon decay and increases the prediction for $M_W$, in line
with the CDF II result. Non-unitarity of the PMNS matrix would also affect
beta, meson, and tau decays. We find that the CDF II value for $M_W$ can be
explained without conflicting with lepton flavour universality constraints or
the invisible decay width of the $Z$. However, the so-called Cabibbo angle
anomaly is worsened if right-handed neutrinos are the origin of the $d=6$
operator. The situation improves if the operator coefficient is left
unconstrained, implying additional sources of new physics, but a common
explanation of both anomalies is in tension with universality bounds.