title: Sample Efficiency of Data Augmentation Consistency Regularization

abstract:
  Data augmentation is popular in the training of large neural networks;
currently, however, there is no clear theoretical comparison between different
algorithmic choices on how to use augmented data. In this paper, we take a step
in this direction - we first present a simple and novel analysis for linear
regression, demonstrating that data augmentation consistency (DAC) is
intrinsically more efficient than empirical risk minimization on augmented data
(DA-ERM). We then propose a new theoretical framework for analyzing DAC, which
reframes DAC as a way to reduce function class complexity. The new framework
characterizes the sample efficiency of DAC for various non-linear models (e.g.,
neural networks). Further, we perform experiments that make a clean and
apples-to-apples comparison (i.e., with no extra modeling or data tweaks)
between ERM and consistency regularization using CIFAR-100 and WideResNet;
these together demonstrate the superior efficacy of DAC.


title:
Refinements of the braid arrangement and two parameter Fuss-Catalan
  numbers

abstract:
  A hyperplane arrangement in $\mathbb{R}^n$ is a finite collection of affine
hyperplanes. Counting regions of hyperplane arrangements is an active research
direction in enumerative combinatorics. In this paper, we consider the
arrangement $\mathcal{A}_n^{(m)}$ in $\mathbb{R}^n$ given by $\{x_i=0 \mid i
\in [n]\} \cup \{x_i=a^kx_j \mid k \in [-m,m], 1\leq i<j \leq n\}$ for some
fixed $a>1$. It turns out that this family of arrangements is closely related
to the well-studied extended Catalan arrangement of type $A$. We prove that the
number of regions of $\mathcal{A}_n^{(m)}$ is a certain generalization of
Catalan numbers called two parameter Fuss-Catalan numbers. We then exhibit a
bijection between these regions and certain decorated Dyck paths. We also
compute the characteristic polynomial and give a combinatorial interpretation
for its coefficients. Most of our results also generalize to sub-arrangements
of $\mathcal{A}_n^{(m)}$ by relating them to deformations of the braid
arrangement.


title: Bounding Membership Inference

abstract:
  Differential Privacy (DP) is the de facto standard for reasoning about the
privacy guarantees of a training algorithm. Despite the empirical observation
that DP reduces the vulnerability of models to existing membership inference
(MI) attacks, a theoretical underpinning as to why this is the case is largely
missing in the literature. In practice, this means that models need to be
trained with DP guarantees that greatly decrease their accuracy. In this paper,
we provide a tighter bound on the accuracy of any MI adversary when a training
algorithm provides $\epsilon$-DP. Our bound informs the design of a novel
privacy amplification scheme, where an effective training set is sub-sampled
from a larger set prior to the beginning of training, to greatly reduce the
bound on MI accuracy. As a result, our scheme enables $\epsilon$-DP users to
employ looser DP guarantees when training their model to limit the success of
any MI adversary; this ensures that the model's accuracy is less impacted by
the privacy guarantee. Finally, we discuss implications of our MI bound on the
field of machine unlearning.


title:
Automatic speaker verification spoofing and deepfake detection using
  wav2vec 2.0 and data augmentation

abstract:
  The performance of spoofing countermeasure systems depends fundamentally upon
the use of sufficiently representative training data. With this usually being
limited, current solutions typically lack generalisation to attacks encountered
in the wild. Strategies to improve reliability in the face of uncontrolled,
unpredictable attacks are hence needed. We report in this paper our efforts to
use self-supervised learning in the form of a wav2vec 2.0 front-end with fine
tuning. Despite initial base representations being learned using only bona fide
data and no spoofed data, we obtain the lowest equal error rates reported in
the literature for both the ASVspoof 2021 Logical Access and Deepfake
databases. When combined with data augmentation,these results correspond to an
improvement of almost 90% relative to our baseline system.


title: Policy Learning for Optimal Individualized Dose Intervals

abstract:
  We study the problem of learning individualized dose intervals using
observational data. There are very few previous works for policy learning with
continuous treatment, and all of them focused on recommending an optimal dose
rather than an optimal dose interval. In this paper, we propose a new method to
estimate such an optimal dose interval, named probability dose interval (PDI).
The potential outcomes for doses in the PDI are guaranteed better than a
pre-specified threshold with a given probability (e.g., 50%). The associated
nonconvex optimization problem can be efficiently solved by the
Difference-of-Convex functions (DC) algorithm. We prove that our estimated
policy is consistent, and its risk converges to that of the best-in-class
policy at a root-n rate. Numerical simulations show the advantage of the
proposed method over outcome modeling based benchmarks. We further demonstrate
the performance of our method in determining individualized Hemoglobin A1c
(HbA1c) control intervals for elderly patients with diabetes.


title:
Increasing ion yield circular dichroism in femtosecond photoionisation
  using optimal control theory

abstract:
  We investigate how optimal control theory can be used to improve Circular
Dichroism (CD) signals for A--band of fenchone measured via the photoionization
yield upon further excitation. These transitions are electric dipole forbidden
to first order, which translates into low population transfer to the excited
state (~8%) but also allows for a clearer interplay between electric and
magnetic transition dipole moments, which are of the same order of magnitude.
Using a model including the electronic ground and excited A state as well as
all permanent and transition multipole moments up to the electric quadrupole,
we find that the absolute CD signal of randomly oriented molecules can be
increased by a factor 3.5 when using shaped laser pulses, with the anisotropy
parameter g increasing from 0.06 to 1. Our insights provide additional evidence
on how optimal control can assist in amplifying chiral signatures via
interactions of permanent and transition multipole moments.


title:
Climate-mediated shifts in temperature fluctuations promote extinction
  risk

abstract:
  Climate-mediated changes in the spatiotemporal distribution of thermal stress
can destabilize animal populations and promote extinction risk. Using quantile,
spectral, and wavelet analyses of temperature projections from the latest
generation of earth system models, we show that significant regional
differences are expected to arise in the way that temperatures will increase
over time. When integrated into empirically-parameterized mathematical models
that simulate the dynamical and cumulative effects of thermal stress on the
performance of 38 global ectotherm species, the projected spatiotemporal
changes in temperature fluctuations are expected to give rise to complex
regional changes in population abundance and stability over the course of the
21st century. However, despite their idiosyncratic effects on stability,
projected temperatures universally increase extinction risk. These results show
that population changes under future climate conditions may be more extensive
and complex than the current literature suggests based on the statistical
relationship between biological performance and average temperature.


title:
A comparative study of in-air trajectories at short and long distances
  in online handwriting

abstract:
  Introduction Existing literature about online handwriting analysis to support
pathology diagnosis has taken advantage of in-air trajectories. A similar
situation occurred in biometric security applications where the goal is to
identify or verify an individual using his signature or handwriting. These
studies do not consider the distance of the pen tip to the writing surface.
This is due to the fact that current acquisition devices do not provide height
formation. However, it is quite straightforward to differentiate movements at
two different heights: a) short distance: height lower or equal to 1 cm above a
surface of digitizer, the digitizer provides x and y coordinates. b) long
distance: height exceeding 1 cm, the only information available is a time stamp
that indicates the time that a specific stroke has spent at long distance.
Although short distance has been used in several papers, long distances have
been ignored and will be investigated in this paper. Methods In this paper, we
will analyze a large set of databases (BIOSECURID, EMOTHAW, PaHaW,
Oxygen-Therapy and SALT), which contain a total amount of 663 users and 17951
files. We have specifically studied: a) the percentage of time spent
on-surface, in-air at short distance, and in-air at long distance for different
user profiles (pathological and healthy users) and different tasks; b) The
potential use of these signals to improve classification rates. Results and
conclusions Our experimental results reveal that long-distance movements
represent a very small portion of the total execution time (0.5 % in the case
of signatures and 10.4% for uppercase words of BIOSECUR-ID, which is the
largest database). In addition, significant differences have been found in the
comparison of pathological versus control group for letter l in PaHaW database
(p=0.0157) and crossed pentagons in SALT database (p=0.0122)


title:
The role of low energy resonances in the stereodynamics of cold He+D2
  collisions

abstract:
  In recent experiments using the Stark-induced Adiabatic Raman Passage (SARP)
technique, Zhou et al. measured the product's angular distribution for the
collisions between He and aligned D2 molecules at cold collision energies. The
signatures of the angular distributions were attributed to a l=2 resonance that
governs scattering at low energies. A first principles quantum mechanical
treatment of this problem is presented here using a highly accurate interaction
potential for the He-H2 system. Instead, our results predict a very intense l=1
resonance at low energies, leading to angular distributions that differ from
those measured in the experiment. A good agreement with the experiment is
achieved only when the l=1 resonance is artificially removed, for example, by
excluding the lowest energies present in the experimental velocity
distribution. Our analysis revealed that neither the position nor the intensity
of the l=1 resonance significantly changes when the interaction potential is
modified within its predicted uncertainties. Energy-resolved measurements may
help to resolve the discrepancy.


title: Noise-assisted and monitoring-enhanced quantum bath tagging

abstract:
  We analyze the capability of discriminating the statistical nature of a
thermal bath by exploiting the interaction with an additional environment. We
first shows that, at difference with the standard scenario where the additional
environment is not present, the modified evolution induced by the mere presence
of the extra bath allows to improve the discrimination task. We then also
consider the possibility of continuously monitoring the additional environment
and we discuss in detail how to obtain improved performances in the
discrimination by considering different kinds of interaction, i.e. different
jump operators, and different monitoring strategies corresponding to continuous
homodyne and photo-detection. Our strategy can be in principle implemented in a
circuit QED setup and paves the way to further developments of quantum probing
via continuous monitoring.


title: Localization and delocalization in networks with varied connectivity

abstract:
  We study the phenomenon of localization and delocalization in a circuit-QED
network with connectivity varying from finite-range to all-to-all coupling. We
find a fascinating interplay between interactions and connectivity. In
particular, we consider (i) Harmonic (ii) Jaynes-Cummings and (iii)
Bose-Hubbard networks. We start with the initial condition where one of the
nodes in the network is populated and then let it evolve in time. The time
dynamics and steady state characterize the features of localization
(self-trapping) in these large-scale networks. For the case of Harmonic
networks, exact analytical results are obtained and we demonstrate that
all-to-all connection shows self-trapping whereas the finite-ranged
connectivity shows delocalization. The interacting cases (Jaynes-Cummings,
Bose-Hubbard networks) are investigated both via exact quantum dynamics and
semi-classical approach. We obtain an interesting phase diagram when one varies
the range of connectivity and the strength of the interaction. We investigate
the consequence of imperfections in the cavity/qubit and the role of inevitable
disorder. Our results are relevant especially given recent experimental
progress in engineering systems with long-range connectivity.

categories: quant-ph cond-mat.dis-nn cond-mat.mes-hall cond-mat.stat-mech


title: A quintessence dynamical dark energy model from ratio gravity

abstract:
  Based on the work of ratio gravity developed in 2018, which postulates the
deformation of the cross ratio to associate with the physical model of gravity,
we develop a mechanism to generate dynamical dark energy - a quintessence field
coupled with gravity. Such model causes the dark energy behaving differently in
early and late time universe. In the radiation-dominated-era and
matter-dominated-era, the related analytical solutions of the quintessence
field have an interesting property - starting as a constant field, then
oscillating as the universe expands. By Markov Chain Monte Carlo search of the
parameter space with the local measurement (Type Ia supernovae) in the Bayesian
framework, the probed range of {H_0} (within 1{\sigma}) overlaps the {H_0}
value inferred from Planck CMB dataset by {\Lambda}CDM model.


title: On-line signature verification system with failure to enroll managing

abstract:
  In this paper we simulate a real biometric verification system based on
on-line signatures. For this purpose we have split the MCYT signature database
in three subsets: one for classifier training, another for system adjustment
and a third one for system testing simulating enrollment and verification. This
context corresponds to a real operation, where a new user tries to enroll an
existing system and must be automatically guided by the system in order to
detect the failure to enroll situations. The main contribution of this work is
the management of failure to enroll situations by means of a new proposal,
called intelligent enrollment, which consists of consistency checking in order
to automatically reject low quality samples. This strategy lets to enhance the
verification errors up to 22% when leaving out 8% of the users. In this
situation 8% of the people cannot be enrolled in the system and must be
verified by other biometrics or by human abilities. These people are identified
with intelligent enrollment and the situation can be thus managed. In addition
we also propose a DCT-based feature extractor with threshold coding and
discriminability criteria.


title: Flat latent manifolds for music improvisation between human and machine

abstract:
  The use of machine learning in artistic music generation leads to
controversial discussions of the quality of art, for which objective
quantification is nonsensical. We therefore consider a music-generating
algorithm as a counterpart to a human musician, in a setting where reciprocal
improvisation is to lead to new experiences, both for the musician and the
audience. To obtain this behaviour, we resort to the framework of recurrent
Variational Auto-Encoders (VAE) and learn to generate music, seeded by a human
musician. In the learned model, we generate novel musical sequences by
interpolation in latent space. Standard VAEs however do not guarantee any form
of smoothness in their latent representation. This translates into abrupt
changes in the generated music sequences. To overcome these limitations, we
regularise the decoder and endow the latent space with a flat Riemannian
manifold, i.e., a manifold that is isometric to the Euclidean space. As a
result, linearly interpolating in the latent space yields realistic and smooth
musical changes that fit the type of machine--musician interactions we aim for.
We provide empirical evidence for our method via a set of experiments on music
datasets and we deploy our model for an interactive jam session with a
professional drummer. The live performance provides qualitative evidence that
the latent representation can be intuitively interpreted and exploited by the
drummer to drive the interplay. Beyond the musical application, our approach
showcases an instance of human-centred design of machine-learning models,
driven by interpretability and the interaction with the end user.


title:
Linear canonical wavelet transform and the associated uncertainty
  principles

abstract:
  We define a novel time-frequency analyzing tool, namely linear canonical
wavelet transform (LCWT) and study some of its important properties like inner
product relation, reconstruction formula and also characterize its range. We
obtain Donoho-Stark's and Lieb's uncertainty principle for the LCWT and give a
lower bound for the measure of its essential support. We also give Shapiro's
mean dispersion theorem for the proposed LCWT.


title:
EMOTHAW: A novel database for emotional state recognition from
  handwriting

abstract:
  The detection of negative emotions through daily activities such as
handwriting is useful for promoting well-being. The spread of human-machine
interfaces such as tablets makes the collection of handwriting samples easier.
In this context, we present a first publicly available handwriting database
which relates emotional states to handwriting, that we call EMOTHAW. This
database includes samples of 129 participants whose emotional states, namely
anxiety, depression and stress, are assessed by the Depression Anxiety Stress
Scales (DASS) questionnaire. Seven tasks are recorded through a digitizing
tablet: pentagons and house drawing, words copied in handprint, circles and
clock drawing, and one sentence copied in cursive writing. Records consist in
pen positions, on-paper and in-air, time stamp, pressure, pen azimuth and
altitude. We report our analysis on this database. From collected data, we
first compute measurements related to timing and ductus. We compute separate
measurements according to the position of the writing device: on paper or
in-air. We analyse and classify this set of measurements (referred to as
features) using a random forest approach. This latter is a machine learning
method [2], based on an ensemble of decision trees, which includes a feature
ranking process. We use this ranking process to identify the features which
best reveal a targeted emotional state.
  We then build random forest classifiers associated to each emotional state.
Our results, obtained from cross-validation experiments, show that the targeted
emotional states can be identified with accuracies ranging from 60% to 71%.


title: Neural reality of argument structure constructions

abstract:
  In lexicalist linguistic theories, argument structure is assumed to be
predictable from the meaning of verbs. As a result, the verb is the primary
determinant of the meaning of a clause. In contrast, construction grammarians
propose that argument structure is encoded in constructions (or form-meaning
pairs) that are distinct from verbs. Decades of psycholinguistic research have
produced substantial empirical evidence in favor of the construction view. Here
we adapt several psycholinguistic studies to probe for the existence of
argument structure constructions (ASCs) in Transformer-based language models
(LMs). First, using a sentence sorting experiment, we find that sentences
sharing the same construction are closer in embedding space than sentences
sharing the same verb. Furthermore, LMs increasingly prefer grouping by
construction with more input data, mirroring the behaviour of non-native
language learners. Second, in a "Jabberwocky" priming-based experiment, we find
that LMs associate ASCs with meaning, even in semantically nonsensical
sentences. Our work offers the first evidence for ASCs in LMs and highlights
the potential to devise novel probing methods grounded in psycholinguistic
research.


title: Constraints on dark matter self-interaction from galactic core size

abstract:
  Self interaction of particulate dark matter may help thermalising the
galactic center and driving core formation. The core radius is expectantly
sensitive to the self interaction strength of dark matter. In this paper we
study the feasibility of constraining dark matter self interaction from the
distribution of the core radius in isolated haloes. We perform systematic
$N$-body simulations of isolated galactic haloes in the mass range of $10^{10}
$-$10^{15}M_{\odot}$ incorporating the impact of DM self interactions.
Comparing the simulated profiles with observational data provides conservative
upper limit on the self coupling cross-section $ \sigma/m_{\rm DM} < $ $ 9.8 $
$\ \rm cm^2 /\rm gm $ at $ 95 \% $ confidence level. We report significant
dependence of the derived bounds on the galactic density distribution models
assumed in the analysis.


title:
High-mobility two-dimensional electron gas in
  $\gamma$-Al$_2$O$_3$/SrTiO$_3$ heterostructures

abstract:
  The origin of the two-dimensional electron gas (2DEG) in the interface
between $\gamma$-Al$_2$O$_3$ (GAO) and SrTiO$_3$ (STO) (GAO/STO) as well as the
reason for the high mobility of the 2DEG is still in debate. In this paper, the
electronic structures of [001]-oriented GAO/STO heterostructures with and
without oxygen vacancies are investigated by first-principle calculations based
on the density functional theory. The calculation results show that the
necessary condition for the formation of 2DEG is that the GAO/STO
heterostructure has the interface composed of Al and TiO$_2$ layers. For the
heterostructure without oxygen vacancy on the GAO side, the 2DEG originates
from the polar discontinuity near the interface, and there is a critical
thickness for the GAO film, below which the 2DEG would not present and the
heterostructure exhibits insulator characteristics. For the case that only the
GAO film contains oxygen vacancies, the polar discontinuity near the interface
disappears, but the 2DEG still exists. In this situation, the critical
thickness of the GAO film for 2DEG formation does not exist either. When the
GAO film and STO substrate both contain oxygen vacancies, it is found that the
2DEG retains as long as the oxygen vacancies on the STO side are not very close
to the interface. The low-temperature mobilities of the 2DEGs in these GAO/STO
heterostructures are considered to be governed by the ionized impurity
scattering, and $\sim$3 to $\sim$11 times as large as that in
LaAlO$_3$/SrTiO$_3$ heterojunction. The high mobility of the 2DEG is mainly due
to the small electron effective mass in GAO/STO heterostructure.


title: Illuminating the nucleon spin

abstract:
  In this short note the QED extension of the nucleon spin sum rule is
considered. To this end, the leading-order (LO) QED evolution kernels for the
quark/gluon helicity and orbital-angular-momentum (OAM) distributions are
calculated, as well as their lepton and photon analogue distributions
introduced for the first time. The LO evolution kernels of the latter are also
calculated, both in QCD and QED. Putting all together, the nucleon spin sum
rule remains scale-invariant in QCD$\times$QED, as expected, which represents a
check of the newly obtained results. This theoretical development will allow in
the future the quantification of the contributions of lepton and photon
distributions to the nucleon spin, and a more precise control over the
uncertainties.


title:
BLPnet: A new DNN model and Bengali OCR engine for Automatic License
  Plate Recognition

abstract:
  The development of the Automatic License Plate Recognition (ALPR) system has
received much attention for the English license plate. However, despite being
the sixth largest population around the world, no significant progress can be
tracked in the Bengali language countries or states for the ALPR system
addressing their more alarming traffic management with inadequate road-safety
measures. This paper reports a computationally efficient and reasonably
accurate Automatic License Plate Recognition (ALPR) system for Bengali
characters with a new end-to-end DNN model that we call Bengali License Plate
Network(BLPnet). The cascaded architecture for detecting vehicle regions prior
to vehicle license plate (VLP) in the model is proposed to eliminate false
positives resulting in higher detection accuracy of VLP. Besides, a lower set
of trainable parameters is considered for reducing the computational cost
making the system faster and more compatible for a real-time application. With
a Computational Neural Network (CNN)based new Bengali OCR engine and
word-mapping process, the model is characters rotation invariant, and can
readily extract, detect and output the complete license plate number of a
vehicle. The model feeding with17 frames per second (fps) on real-time video
footage can detect a vehicle with the Mean Squared Error (MSE) of 0.0152, and
the mean license plate character recognition accuracy of 95%. While compared to
the other models, an improvement of 5% and 20% were recorded for the BLPnetover
the prominent YOLO-based ALPR model and the Tesseract model for the
number-plate detection accuracy and time requirement, respectively.


title: ISDA: Position-Aware Instance Segmentation with Deformable Attention

abstract:
  Most instance segmentation models are not end-to-end trainable due to either
the incorporation of proposal estimation (RPN) as a pre-processing or
non-maximum suppression (NMS) as a post-processing. Here we propose a novel
end-to-end instance segmentation method termed ISDA. It reshapes the task into
predicting a set of object masks, which are generated via traditional
convolution operation with learned position-aware kernels and features of
objects. Such kernels and features are learned by leveraging a deformable
attention network with multi-scale representation. Thanks to the introduced
set-prediction mechanism, the proposed method is NMS-free. Empirically, ISDA
outperforms Mask R-CNN (the strong baseline) by 2.6 points on MS-COCO, and
achieves leading performance compared with recent models. Code will be
available soon.


title:
Vaccination, life expectancy, and trust: Patterns of COVID-19
  vaccination rates around the world

abstract:
  We estimate patterns of covariation between COVID-19 vaccination rates and a
set of widely used indicators of human, social, and economic capital across 146
countries in July 2021 and February 2022. About 70% of the variability in
COVID-19 vaccination rates worldwide can be explained by differences in the
Human Development Index (HDI) and, specifically, in life expectancy at birth,
one year after the campaign debut. Trust in doctors and nurses adds predictive
value beyond the HDI, clarifying controversial discrepancies between
vaccination rates in countries with similar levels of human development and
vaccine availability. Cardiovascular disease deaths, an indicator of general
health system effectiveness, and infant measles immunization coverage, an
indicator of country-level immunization effectiveness, are also significant,
though weaker, predictors of COVID-19 vaccination success. The metrics of
economic inequality, perceived corruption, poverty, and inputs into the health
system have strong bivariate correlations with COVID-19 vaccination but no
longer remain statistically significant when controlling for the HDI. Our
analysis identified the contours of a social structure that sustains life and
is reproduced through this process. COVID-19 vaccines have proven to be part of
the Matthew effect of accumulating advantages and aggravating disadvantages
that the pandemic inflicted on societies and communities across the world. At
the same time, the remaining variability in vaccination success that cannot be
pinned down through these sets of metrics points to a considerable scope for
collective and individual agency in a time of crisis. The mobilization and
coordination in the vaccination campaigns of citizens, medical professionals,
scientists, journalists, and politicians, among others, account for at least
some of this variability in overcoming vaccine hesitancy and inequity.


title:
The correlation of WGC and Hydrodynamics bound with $R^4$ correction in
  the charged AdS$_{d+2}$ black brane

abstract:
  In this paper, we focus on the possible correlation between conjectures KSS
bound and weak gravity conjecture (WGC). The hydrodynamic values KSS bound and
weak gravity conjecture constraint the low-energy effective field theory. These
conjectures identify UV complete theories. We give four, six and eight order
derivative corrections to corresponding action and employ the hyperscaling
violating charged AdS$_{d+2}$ black brane solution. These corrections lead us
to find correlation between conjectures KSS bound and weak gravity conjecture.
We see that, with increasing perturbation correction, this correlation is more
likely to appear. We consider dynamical constant $z=1$, $d=5$ and obtain the
range of hyperscaling violation exponent $d+z-2\leq\theta\leq d+z-1$ for the
above mentioned black brane. Here, we show that higher derivative corrections
reduce the ratio of $\frac{M}{Q}$ to extremal black holes. Likewise, we also
obtain the universal relaxation bound $\tau\geq \frac{1}{\pi T}$ and KSS bound
$\frac{\eta}{s}\geq \frac{1}{4\pi}$ for our model. The results indicate that
there is a possibility of a relationship between the two conjectures. Our
studies also show the consistency of the WGC and the KSS bound conjectures for
all corrections (except curvature-cubed, $\beta_2$) in the extremal and
near-extremal condition.


title: Semiclassical theory explains stochastic ghosts scaling

abstract:
  Slowing down phenomena occur in both deterministic and stochastic dynamical
systems at the vicinity of phase transitions or bifurcations. An example is
found in systems exhibiting a saddle-node bifurcation, which undergo a dramatic
time delay towards equilibrium. Specifically the duration of the transient,
$\tau$, close to this bifurcation in deterministic systems follows scaling laws
of the form $\tau \sim |\epsilon - \epsilon_c|^{-1/2}$, where $\epsilon$ is the
bifurcation or control parameter, and $\epsilon_c$ its critical value. For
systems undergoing a saddle-node bifurcation, the mechanism involves transients
getting trapped by a so-called ghost. In a recent article we explored how
intrinsic noise affected the deterministic picture. Extensive numerical
simulations showed that, although scaling behaviour persisted in the presence
of noise, the scaling law was more complicated than a simple power law. In
order to gain deeper insight into this scaling behaviour, we resort to the WKB
asymptotic approximation of the Master Equation. According to this
approximation, the behaviour of the system is given as the weighted sum of
\emph{trajectories} within the phase space of the Hamiltonian associated to the
corresponding Hamilton-Jacobi equation. By analysing the flight time of the
Hamilton equations, we show that the statistically significant paths follow a
scaling function that exactly matches the one observed in the stochastic
simulations. We therefore put forward that the properties of the flight times
of the Hamiltonian system underpin the scaling law of the underlying stochastic
system, and that the same properties should extend in a universal way to all
stochastic systems whose associated Hamiltonian exhibits the same behaviour.


title: Exact Community Recovery over Signed Graphs

abstract:
  Signed graphs encode similarity and dissimilarity relationships among
different entities with positive and negative edges. In this paper, we study
the problem of community recovery over signed graphs generated by the signed
stochastic block model (SSBM) with two equal-sized communities. Our approach is
based on the maximum likelihood estimation (MLE) of the SSBM. Unlike many
existing approaches, our formulation reveals that the positive and negative
edges of a signed graph should be treated unequally. We then propose a simple
two-stage iterative algorithm for solving the regularized MLE. It is shown that
in the logarithmic degree regime, the proposed algorithm can exactly recover
the underlying communities in nearly-linear time at the information-theoretic
limit. Numerical results on both synthetic and real data are reported to
validate and complement our theoretical developments and demonstrate the
efficacy of the proposed method.


title:
Integration of neural network and fuzzy logic decision making compared
  with bilayered neural network in the simulation of daily dew point
  temperature

abstract:
  In this research, dew point temperature (DPT) is simulated using the
data-driven approach. Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized
as a data-driven technique to forecast this parameter at Tabriz in East
Azerbaijan. Various input patterns, namely T min, T max, and T mean, are
utilized for training the architecture whilst DPT is the model's output. The
findings indicate that, in general, ANFIS method is capable of identifying data
patterns with a high degree of accuracy. However, the approach demonstrates
that processing time and computer resources may substantially increase by
adding additional functions. Based on the results, the number of iterations and
computing resources might change dramatically if new functionalities are
included. As a result, tuning parameters have to be optimized inside the method
framework. The findings demonstrate a high agreement between results by the
data-driven technique (machine learning method) and the observed data. Using
this prediction toolkit, DPT can be adequately forecasted solely based on the
temperature distribution of Tabriz. This kind of modeling is extremely
promising for predicting DPT at various sites. Besides, this study thoroughly
compares the Bilayered Neural Network (BNN) and ANFIS models on various scales.
Whilst the ANFIS model is extremely stable for almost all numbers of membership
functions, the BNN model is highly sensitive to this scale factor to predict
DPT.


title:
A Perceptual Measure for Evaluating the Resynthesis of Automatic Music
  Transcriptions

abstract:
  This study focuses on the perception of music performances when contextual
factors, such as room acoustics and instrument, change. We propose to
distinguish the concept of "performance" from the one of "interpretation",
which expresses the "artistic intention". Towards assessing this distinction,
we carried out an experimental evaluation where 91 subjects were invited to
listen to various audio recordings created by resynthesizing MIDI data obtained
through Automatic Music Transcription (AMT) systems and a sensorized acoustic
piano. During the resynthesis, we simulated different contexts and asked
listeners to evaluate how much the interpretation changes when the context
changes. Results show that: (1) MIDI format alone is not able to completely
grasp the artistic intention of a music performance; (2) usual objective
evaluation measures based on MIDI data present low correlations with the
average subjective evaluation. To bridge this gap, we propose a novel measure
which is meaningfully correlated with the outcome of the tests. In addition, we
investigate multimodal machine learning by providing a new score-informed AMT
method and propose an approximation algorithm for the $p$-dispersion problem.


title: A Method for Waste Segregation using Convolutional Neural Networks

abstract:
  Segregation of garbage is a primary concern in many nations across the world.
Even though we are in the modern era, many people still do not know how to
distinguish between organic and recyclable waste. It is because of this that
the world is facing a major crisis of waste disposal. In this paper, we try to
use deep learning algorithms to help solve this problem of waste
classification. The waste is classified into two categories like organic and
recyclable. Our proposed model achieves an accuracy of 94.9%. Although the
other two models also show promising results, the Proposed Model stands out
with the greatest accuracy. With the help of deep learning, one of the greatest
obstacles to efficient waste management can finally be removed.


title:
Learning from the Pros: Extracting Professional Goalkeeper Technique
  from Broadcast Footage

abstract:
  As an amateur goalkeeper playing grassroots soccer, who better to learn from
than top professional goalkeepers? In this paper, we harness computer vision
and machine learning models to appraise the save technique of professionals in
a way those at lower levels can learn from. We train an unsupervised machine
learning model using 3D body pose data extracted from broadcast footage to
learn professional goalkeeper technique. Then, an "expected saves" model is
developed, from which we can identify the optimal goalkeeper technique in
different match contexts.


title:
Self-organising Urban Traffic control on micro-level using Reinforcement
  Learning and Agent-based Modelling

abstract:
  Most traffic flow control algorithms address switching cycle adaptation of
traffic signals and lights. This work addresses traffic flow optimisation by
self-organising micro-level control combining Reinforcement Learning and
rule-based agents for action selection performing long-range navigation in
urban environments. I.e., vehicles represented by agents adapt their decision
making for re-routing based on local environmental sensors. Agent-based
modelling and simulation is used to study emergence effects on urban city
traffic flows. An unified agent programming model enables simulation and
distributed data processing with possible incorporation of crowd sensing tasks
used as an additional sensor data base. Results from an agent-based simulation
of an artificial urban area show that the deployment of micro-level vehicle
navigation control just by learned individual decision making and re-routing
based on local environmental sensors can increase the efficiency of mobility in
terms of path length and travelling time.


title:
Agent-based Dynamics of a SPAHR Opioid Model on Social Network
  Structures

abstract:
  Addiction epidemiology has been an active area of mathematical research in
recent years. However, the social and mental processes involved in substance
use disorders versus contraction of a pathogenic disease have presented
challenges to advancing the epidemiological theory of substance abuse,
especially within the context of the opioids where both prescriptions and
social contagion have played a major role. In this paper, we utilize an
agent-based modeling approach on social networks to further explore these
dynamics. Using parameter estimation approaches, we compare our results to that
of the Phillips et al. SPAHR model which was previously fit to data from the
state of Tennessee. Our results show that the average path length of a social
network has a strong relationship to social contagion dynamics for drug use
initiation, while other pathways to substance use disorder should not be
constrained to social network interactions that predate the individual's drug
use.


title:
On the Omnipresence of Spurious Local Minima in Certain Neural Network
  Training Problems

abstract:
  We study the loss landscape of training problems for deep artificial neural
networks with a one-dimensional real output whose activation functions contain
an affine segment and whose hidden layers have width at least two. It is shown
that such problems possess a continuum of spurious (i.e., not globally optimal)
local minima for all target functions that are not affine. In contrast to
previous works, our analysis covers all sampling and parameterization regimes,
general differentiable loss functions, arbitrary continuous nonpolynomial
activation functions, and both the finite- and infinite-dimensional setting. It
is further shown that the appearance of the spurious local minima in the
considered training problems is a direct consequence of the universal
approximation theorem and that the underlying mechanisms also cause, e.g.,
Lp-best approximation problems to be ill-posed in the sense of Hadamard for all
networks that do not have a dense image. The latter result also holds without
the assumption of local affine linearity and without any conditions on the
hidden layers. The paper concludes with a numerical experiment which
demonstrates that spurious local minima can indeed affect the convergence
behavior of gradient-based solution algorithms in practice.


title: Effect Identification in Cluster Causal Diagrams

abstract:
  One pervasive task found throughout the empirical sciences is to determine
the effect of interventions from non-experimental data. It is well-understood
that assumptions are necessary to perform causal inferences, which are commonly
articulated through causal diagrams (Pearl, 2000). Despite the power of this
approach, there are settings where the knowledge necessary to specify a causal
diagram over all observed variables may not be available, particularly in
complex, high-dimensional domains. In this paper, we introduce a new type of
graphical model called cluster causal diagrams (for short, C-DAGs) that allows
for the partial specification of relationships among variables based on limited
prior knowledge, alleviating the stringent requirement of specifying a full
causal diagram. A C-DAG specifies relationships between clusters of variables,
while the relationships between the variables within a cluster are left
unspecified. We develop the foundations and machinery for valid causal
inferences over C-DAGs. In particular, we first define a new version of the
d-separation criterion and prove its soundness and completeness. Secondly, we
extend these new separation rules and prove the validity of the corresponding
do-calculus. Lastly, we show that a standard identification algorithm is sound
and complete to systematically compute causal effects from observational data
given a C-DAG.


title:
Understanding the Impact of the COVID-19 Pandemic on
  Transportation-related Behaviors with Human Mobility Data

abstract:
  The constrained outbreak of COVID-19 in Mainland China has recently been
regarded as a successful example of fighting this highly contagious virus. Both
the short period (in about three months) of transmission and the
sub-exponential increase of confirmed cases in Mainland China have proved that
the Chinese authorities took effective epidemic prevention measures, such as
case isolation, travel restrictions, closing recreational venues, and banning
public gatherings. These measures can, of course, effectively control the
spread of the COVID-19 pandemic. Meanwhile, they may dramatically change the
human mobility patterns, such as the daily transportation-related behaviors of
the public. To better understand the impact of COVID-19 on
transportation-related behaviors and to provide more targeted anti-epidemic
measures, we use the huge amount of human mobility data collected from Baidu
Maps, a widely-used Web mapping service in China, to look into the detail
reaction of the people there during the pandemic. To be specific, we conduct
data-driven analysis on transportation-related behaviors during the pandemic
from the perspectives of 1) means of transportation, 2) type of visited venues,
3) check-in time of venues, 4) preference on "origin-destination" distance, and
5) "origin-transportation-destination" patterns. For each topic, we also give
our specific insights and policy-making suggestions. Given that the COVID-19
pandemic is still spreading in more than 200 countries and territories
worldwide, infecting millions of people, the insights and suggestions provided
here may help fight COVID-19.


title: Clustering Edges in Directed Graphs

abstract:
  How do vertices exert influence in graph data? We develop a framework for
edge clustering, a new method for exploratory data analysis that reveals how
both vertices and edges collaboratively accomplish directed influence in
graphs, especially for directed graphs. In contrast to the ubiquitous vertex
clustering which groups vertices, edge clustering groups edges. Edges sharing a
functional affinity are assigned to the same group and form an influence
subgraph cluster. With a complexity comparable to that of vertex clustering,
this framework presents three different methods for edge spectral clustering
that reveal important influence subgraphs in graph data, with each method
providing different insight into directed influence processes. We present
several diverse examples demonstrating the potential for widespread application
of edge clustering in scientific research.


title: Generalized p-fusion frame in separable Banach space

abstract:
  Concepts of g-fusion frame and gf-Riesz basis in a Hilbert to a Banach space
is being presented. Some properties of g-fusion frame and gf-Riesz basis in
Banach space have been developed. We discuss perturbation results of g-fusion
frame in a Banach space. Finally, we construct g-p-fusion frames in Cartesian
product of Banach spaces and tensor product of Banach spaces.


title:
Inflation of test accuracy due to data leakage in deep learning-based
  classification of OCT images

abstract:
  In the application of deep learning on optical coherence tomography (OCT)
data, it is common to train classification networks using 2D images originating
from volumetric data. Given the micrometer resolution of OCT systems,
consecutive images are often very similar in both visible structures and noise.
Thus, an inappropriate data split can result in overlap between the training
and testing sets, with a large portion of the literature overlooking this
aspect. In this study, the effect of improper dataset splitting on model
evaluation is demonstrated for two classification tasks using two OCT
open-access datasets extensively used in the literature, Kermany's
ophthalmology dataset and AIIMS breast tissue dataset. Our results show that
the classification accuracy is inflated by 3.9 to 26 percentage units for
models tested on a dataset with improper splitting, highlighting the
considerable effect of dataset handling on model evaluation. This study intends
to raise awareness on the importance of dataset splitting for research on deep
learning using OCT data and volumetric data in general.


title:
Hyperacute pathophysiology of traumatic and vascular brain injury
  captured by ultrasound, photoacoustic, and magnetic resonance imaging

abstract:
  Cerebrovascular dynamics and pathomechanisms that evolve in the minutes and
hours following traumatic vascular injury in the brain remain largely unknown.
We investigated the pathophysiology evolution within the first three hours
after closed-head traumatic brain injury (TBI) and subarachnoid hemorrhage
(SAH), two common traumatic vascular injuries, in mice. We took a multi-modal
imaging approach using photoacoustic, color Doppler, and magnetic resonance
imaging (MRI) in mice. Brain oxygenation (%sO2) and velocity-weighted volume of
blood flow (VVF) values significantly decreased from baseline to fifteen
minutes after both TBI and SAH. TBI resulted in 19.2% and 41.0% ipsilateral
%sO2 and VVF reductions 15 minutes post injury while SAH resulted in 43.9% %sO2
and 85.0% VVF reduction ipsilaterally (p<0.001). We found partial recovery of
%sO2 from 15 minutes to 3-hours after injury for TBI but not SAH. Hemorrhage,
edema, reduced perfusion, and altered diffusivity were evident from MRI scans
acquired 90-150 minutes after injury in both models although the spatial
distribution was mostly focal for TBI and diffuse for SAH. The results reveal
that the cerebral %sO2 deficits immediately following injuries are reversible
for TBI and irreversible for SAH. Our findings can inform future studies on
mitigating these early responses to improve long-term recovery.


title:
Systematic review of deep learning and machine learning for building
  energy

abstract:
  The building energy (BE) management has an essential role in urban
sustainability and smart cities. Recently, the novel data science and
data-driven technologies have shown significant progress in analyzing the
energy consumption and energy demand data sets for a smarter energy management.
The machine learning (ML) and deep learning (DL) methods and applications, in
particular, have been promising for the advancement of the accurate and
high-performance energy models. The present study provides a comprehensive
review of ML and DL-based techniques applied for handling BE systems, and it
further evaluates the performance of these techniques. Through a systematic
review and a comprehensive taxonomy, the advances of ML and DL-based techniques
are carefully investigated, and the promising models are introduced. According
to the results obtained for energy demand forecasting, the hybrid and ensemble
methods are located in high robustness range, SVM-based methods are located in
good robustness limitation, ANN-based methods are located in medium robustness
limitation and linear regression models are located in low robustness
limitations. On the other hand, for energy consumption forecasting, DL-based,
hybrid, and ensemble-based models provided the highest robustness score. ANN,
SVM, and single ML models provided good and medium robustness and LR-based
models provided the lower robustness score. In addition, for energy load
forecasting, LR-based models provided the lower robustness score. The hybrid
and ensemble-based models provided a higher robustness score. The DL-based and
SVM-based techniques provided a good robustness score and ANN-based techniques
provided a medium robustness score.


title: Evaluating Feature Attribution Methods in the Image Domain

abstract:
  Feature attribution maps are a popular approach to highlight the most
important pixels in an image for a given prediction of a model. Despite a
recent growth in popularity and available methods, little attention is given to
the objective evaluation of such attribution maps. Building on previous work in
this domain, we investigate existing metrics and propose new variants of
metrics for the evaluation of attribution maps. We confirm a recent finding
that different attribution metrics seem to measure different underlying
concepts of attribution maps, and extend this finding to a larger selection of
attribution metrics. We also find that metric results on one dataset do not
necessarily generalize to other datasets, and methods with desirable
theoretical properties such as DeepSHAP do not necessarily outperform
computationally cheaper alternatives. Based on these findings, we propose a
general benchmarking approach to identify the ideal feature attribution method
for a given use case. Implementations of attribution metrics and our
experiments are available online.


title: Complex topological soliton with real energy in particle physics

abstract:
  We summarise the procedure used to find the classical masses of Higgs
particle, massive gauge boson and t'Hooft-Polyakov monopole in non-Hermitian
gauge field theory. Their physical regions are explored, and the mechanism of
the real value of the monopole solution is analysed in different physical
regions.


title:
Analyzing whether workplace smoking bans can reduce the probability of
  smoking

abstract:
  The rapid increase of smoking-related diseases and deaths globally is driving
us to find an effective approach to reduce the smoking rate. This study aims to
determine whether indoor smoking bans at workplaces can effectively reduce the
smoking rate. The Smokeban dataset used for this study is an observational
dataset that contains some socio-demographic factors, whether people smoke, and
whether smoking bans exist. Since the observational data used in the study did
not randomize people into with-smoking-bans group and without-smoking-bans
group, confounders may cause bias in the estimation of whether the smoking bans
can reduce smoking rates. The propensity score matching(PSM) method can reduce
these biases via using a logistic regression model to predict the similarities
of people in those 2 groups and using the nearest neighbour matching technique
to match people who are the most similar. After reducing the bias, another
regression model was created to interpret the relationship between the
probability of smoking and the indoor smoking bans. We conclude by arguing that
with the existence of indoor smoking bans, the probability of people who smoke
can be decreased greatly.


title: Matching Papers and Reviewers at Large Conferences

abstract:
  This paper studies a novel reviewer-paper matching approach that was recently
deployed in the 35th AAAI Conference on Artificial Intelligence (AAAI 2021),
and has since been adopted by other conferences including AAAI 2022 and ICML
2022. This approach has three main elements: (1) collecting and processing
input data to identify problematic matches and generate reviewer-paper scores;
(2) formulating and solving an optimization problem to find good reviewer-paper
matchings; and (3) the introduction of a novel, two-phase reviewing process
that shifted reviewing resources away from papers likely to be rejected and
towards papers closer to the decision boundary. This paper also describes an
evaluation of these innovations based on an extensive post-hoc analysis on real
data -- including a comparison with the matching algorithm used in AAAI's
previous (2020) iteration -- and supplements this with additional numerical
experimentation.


title:
Exploring the QGP phase above the deconfinement temperature in $pp$ and
  $A-A$ collisions at LHC energies

abstract:
  In the present work we have analyzed the transverse momentum spectra of
charged particles in high multiplicity ${\it pp}$ collisions at LHC energies
$\sqrt s = $ 5.02 and 13 TeV published by the ALICE Collaboration using the
Color String Percolation Model (CSPM). For heavy ions Pb-Pb at $\sqrt {s_{NN}}
=$ 2.76 and 5.02 TeV along with Xe-Xe at $\sqrt {s_{NN}} = $ 5.44 TeV have been
analyzed. The initial temperature is extracted both in low and high
multiplicity events in ${\it pp}$ collisions. For $A-A$ collisions the
temperature is obtained as a function of centrality. A universal scaling in the
temperature from $pp$ and $A-A$ collisions is obtained when multiplicity is
scaled by the transverse interaction area. For the higher multiplicity events
in ${\it pp}$ collisions at $\sqrt s = $ 5.02 and 13 TeV the initial
temperature is above the universal hadronization temperature and is consistent
with the creation of deconfined matter. From the measured energy density
$\varepsilon$ and the temperature the dimensionless quantity $
\varepsilon/T^{4}$ is obtained. Our results agree with the Lattice Quantum
Chromo Dynamics simulations (LQCD) up to $\sim$ 210 MeV and beyond that there
is a sharp increase in $\varepsilon/T^{4}$, reaching the ideal gas of quarks
and gluons value of $\varepsilon/T^{4} =$ 16 at temperature $\sim $ 230 MeV. It
has been argued that there exists a three-phase structure of matter in QCD :
hadronic matter, a plasma of deconfined massive quarks and a plasma of massless
quarks and gluons with full color deconfinement and chiral symmetry
restoration. Our results suggests that chiral symmetry restoration occurs at a
temperature $\sim$ 210 MeV.


title:
Partitioned Variational Inference: A Framework for Probabilistic
  Federated Learning

abstract:
  The proliferation of computing devices has brought about an opportunity to
deploy machine learning models on new problem domains using previously
inaccessible data. Traditional algorithms for training such models often
require data to be stored on a single machine with compute performed by a
single node, making them unsuitable for decentralised training on multiple
devices. This deficiency has motivated the development of federated learning
algorithms, which allow multiple data owners to train collaboratively and use a
shared model whilst keeping local data private. However, many of these
algorithms focus on obtaining point estimates of model parameters, rather than
probabilistic estimates capable of capturing model uncertainty, which is
essential in many applications. Variational inference (VI) has become the
method of choice for fitting many modern probabilistic models. In this paper we
introduce partitioned variational inference (PVI), a general framework for
performing VI in the federated setting. We develop new supporting theory for
PVI, demonstrating a number of properties that make it an attractive choice for
practitioners; use PVI to unify a wealth of fragmented, yet related literature;
and provide empirical results that showcase the effectiveness of PVI in a
variety of federated settings.


title:
On the influence of roundoff errors on the convergence of the gradient
  descent method with low-precision floating-point computation

abstract:
  The employment of stochastic rounding schemes helps prevent stagnation of
convergence, due to vanishing gradient effect when implementing the gradient
descent method in low precision. Conventional stochastic rounding achieves zero
bias by preserving small updates with probabilities proportional to their
relative magnitudes. In this study, we propose a new stochastic rounding scheme
that trades the zero bias property with a larger probability to preserve small
gradients. Our method yields a constant rounding bias that, at each iteration,
lies in a descent direction. For convex problems, we prove that the proposed
rounding method has a beneficial effect on the convergence rate of gradient
descent. We validate our theoretical analysis by comparing the performances of
various rounding schemes when optimizing a multinomial logistic regression
model and when training a simple neural network with 8-bit floating-point
format.


title: Solving optimization problems with Blackwell approachability

abstract:
  We introduce the Conic Blackwell Algorithm$^+$ (CBA$^+$) regret minimizer, a
new parameter- and scale-free regret minimizer for general convex sets. CBA$^+$
is based on Blackwell approachability and attains $O(\sqrt{T})$ regret. We show
how to efficiently instantiate CBA$^+$ for many decision sets of interest,
including the simplex, $\ell_{p}$ norm balls, and ellipsoidal confidence
regions in the simplex. Based on CBA$^+$, we introduce SP-CBA$^+$, a new
parameter-free algorithm for solving convex-concave saddle-point problems,
which achieves a $O(1/\sqrt{T})$ ergodic rate of convergence. In our
simulations, we demonstrate the wide applicability of SP-CBA$^+$ on several
standard saddle-point problems, including matrix games, extensive-form games,
distributionally robust logistic regression, and Markov decision processes. In
each setting, SP-CBA$^+$ achieves state-of-the-art numerical performance, and
outperforms classical methods, without the need for any choice of step sizes or
other algorithmic parameters.


title: Learning Stochastic Dynamics with Statistics-Informed Neural Network

abstract:
  We introduce a machine-learning framework named statistics-informed neural
network (SINN) for learning stochastic dynamics from data. This new
architecture was theoretically inspired by a universal approximation theorem
for stochastic systems introduced in this paper and the projection-operator
formalism for stochastic modeling. We devise mechanisms for training the neural
network model to reproduce the correct \emph{statistical} behavior of a target
stochastic process. Numerical simulation results demonstrate that a
well-trained SINN can reliably approximate both Markovian and non-Markovian
stochastic dynamics. We demonstrate the applicability of SINN to model
transition dynamics. Furthermore, we show that the obtained reduced-order model
can be trained on temporally coarse-grained data and hence is well suited for
rare-event simulations.

categories: cs.LG cond-mat.stat-mech cs.AI math.DS physics.comp-ph


title: Bohmian mechanics is not deterministic

abstract:
  The aim of this paper is to argue that Bohmian mechanics (or any similar
pilot-wave theory) cannot reasonably be claimed to be a deterministic theory.
In fact, even if one simply assumes the "quantum equilibrium distribution" in
order to correctly reproduce the (Born) statistics of experimental outcomes,
Bohmian mechanics requires something like an external stochastic oracle that
samples this distribution. Taken by itself, Bohmian mechanics is unable to
assign some value of its hidden variable (i.e. position) to a given experiment
in a long run of repeated identical experiments, and hence it cannot predict
its result (as might be expected of a deterministic hidden variable theory).
Thus the advantages of Bohmian mechanics over other interpretations of quantum
mechanics, if any, must lie either at an ontological level, or in the potential
ability to explain the quantum equilibrium distribution and hence the Born
rule.


title:
Tactile Materials in Practice: Understanding the Experiences of Teachers
  of the Visually Impaired

abstract:
  Teachers of the visually impaired (TVIs) regularly present tactile materials
(tactile graphics, 3D models, and real objects) to students with vision
impairments. Researchers have been increasingly interested in designing tools
to support the use of tactile materials, but we still lack an in-depth
understanding of how tactile materials are created and used in practice today.
To address this gap, we conducted interviews with 21 TVIs and a 3-week diary
study with eight of them. We found that tactile materials were regularly used
for academic as well as non-academic concepts like tactile literacy, motor
ability, and spatial awareness. Real objects and 3D models served as "stepping
stones" to tactile graphics and our participants preferred to teach with 3D
models, despite finding them difficult to create, obtain, and modify. Use of
certain materials also carried social implications; participants selected
materials that fostered student independence and allow classroom inclusion. We
contribute design considerations, encouraging future work on tactile materials
to enable student and TVI co-creation, facilitate rapid prototyping, and
promote movement and spatial awareness. To support future research in this
area, our paper provides a fundamental understanding of current practices. We
bridge these practices to established pedagogical approaches and highlight
opportunities for growth regarding this important genre of educational
materials.


title: Network analysis approach to Likert-style surveys

abstract:
  Likert-style surveys are a widely used research instrument to assess
respondents' preferences, beliefs, or experiences. In this paper, we propose
and demonstrate how network analysis (NA) can be employed to model and evaluate
the interconnectedness of items in Likert-style surveys. We explore the
advantages of this approach by applying the methodology to the Aspects of
Student Experience Scale (ASES) data set and compare the results to the
principal component analysis. We find that we are able to successfully create a
meaningful network based on survey item response similarity and use modular
analysis of the network to identify larger themes built from the connections of
particular aspects. The modular NA of the network of survey items identifies
important themes that highlight differences in students' overall experiences.
We also show how the resulting themes can be used to explore meaningful
differences in respondents' experiences based on demographics, such as gender.
Our proposed methodology is widely applicable and provides a new way to
investigate phenomena assessed by Likert-style surveys.


title: The branched deformations of the special Lagrangian submanifolds

abstract:
  The branched deformations of immersed compact special Lagrangian submanifolds
are studied in this paper. If there exists a nondegenerate $\mathbb{Z}_2$
harmonic 1-form over a special Lagrangian submanifold $L$, we construct a
family of immersed special Lagrangian submanifolds $\tilde{L}_t$, that are
diffeomorphic to a branched covering of $L$ and $\tilde{L}_t$ convergence to
$2L$ as current. This answers a question suggested by Donaldson. Combining with
the work of Abouzaid and Imagi, we obtain constraints for the existence of
nondegenerate $\mathbb{Z}_2$ harmonic 1-forms.


title:
Existence of nondegenerate $\mathbb{Z}_2$ harmonic 1-forms via
  $\mathbb{Z}_3$ symmetry

abstract:
  Using $\mathbb{Z}_3$ symmetry, we present a topological condition for the
existence of the $\mathbb{Z}_2$ harmonic 1-forms over Riemannian manifold. As a
corollary, if $L$ is an oriented link on $S^3$ with determinant zero, then
there exists a nondegenerate $\mathbb{Z}_2$ harmonic 1-form over the 3-cyclic
branched covering of $L$. Furthermore, we found infinite number of rational
homology 3-spheres that admit a nondegenerate $\mathbb{Z}_2$ harmonic 1-form.


title: Noisy Group Testing with Side Information

abstract:
  Group testing has recently attracted significant attention from the research
community due to its applications in diagnostic virology. An instance of the
group testing problem includes a ground set of individuals which includes a
small subset of infected individuals. The group testing procedure consists of a
number of tests, such that each test indicates whether or not a given subset of
individuals includes one or more infected individuals. The goal of the group
testing procedure is to identify the subset of infected individuals with the
minimum number of tests. Motivated by practical scenarios, such as testing for
viral diseases, this paper focuses on the following group testing settings: (i)
the group testing procedure is noisy, i.e., the outcome of the group testing
procedure can be flipped with a certain probability; (ii) there is a certain
amount of side information on the distribution of the infected individuals
available to the group testing algorithm. The paper makes the following
contributions. First, we propose a probabilistic model, referred to as an
interaction model, that captures the side information about the probability
distribution of the infected individuals. Next, we present a decoding scheme,
based on the belief propagation, that leverages the interaction model to
improve the decoding accuracy. Our results indicate that the proposed algorithm
achieves higher success probability and lower false-negative and false-positive
rates when compared to the traditional belief propagation especially in the
high noise regime.


title: Improved Spectral Representations of Neutron-Star Equations of State

abstract:
  Spectral representations have been shown to provide an efficient way to
represent the poorly understood high-density portion of the neutron-star
equation of state. This paper shows how the efficiency and accuracy of those
representations can be improved by a very simple change.


title: ALP induced polarization effects on photons from galaxy clusters

abstract:
  Many extensions of the Standard Model of particle physics such as superstring
and superbrane theories predict the existence of axion-like particles (ALPs).
ALPs are very elusive particles, which primarily interact with photons and in
the presence of an external magnetic field two effects are produced: (i)
photon-ALP oscillations, (ii) change of the polarization state of photons. The
astrophysical context represents the best possibility to obtain indirect
evidence for the ALP existence thanks to the various effects that photon-ALP
interaction produces. Great attention has been paid so far to the former
effect: photon-ALP interaction modifies the transparency of the crossed media
and final spectra are altered presenting either a flux excess or irregularities
or both these features. Two hints at ALP existence have also been discovered.
However, less interest has been attracted by the latter main ALP effect: the
modification of photon polarization. In this paper we want to address it both
in the X-ray and in the high-energy (HE) bands. In particular, we analyze the
photon degree of linear polarization and the polarization angle when photon-ALP
interaction is present concerning photons generated in the central region of
galaxy clusters: we study Perseus and Coma. We observe that photon-ALP
interaction substantially changes the photon polarization expected from
standard physics and that it shows interesting features both in the X-ray and
in the HE band. We conclude that the ALP-induced polarization effects are more
likely detectable from the proposed missions like COSI, e-ASTROGAM and AMEGO in
the HE range.


title: Davydov--Yetter cohomology and relative homological algebra

abstract:
  Davydov--Yetter (DY) cohomology classifies infinitesimal deformations of the
monoidal structure of tensor functors and tensor categories. We consider such
deformations of finite tensor categories and exact tensor functors between
them. In arXiv:1910.06094, DY cohomology with coefficients was introduced and
related to the comonad cohomology for a certain adjunction; in the case of
tensor categories it reduces to the adjunction for the forgetful functor of the
Drinfeld center. We first prove that the DY cohomology groups are isomorphic to
the relative Ext groups for this adjunction. From this, we derive the following
main results: the vanishing of the first DY cohomology group, long exact
sequences of DY cohomology groups which allow to express the groups in terms of
Hom spaces, and the existence of a Yoneda-type product on DY cocycles. We apply
these results to the category of finite-dimensional modules over a
finite-dimensional Hopf algebra and provide a method to compute explicit DY
cocycles. We study in detail the examples of the bosonization of exterior
algebras $\Lambda\mathbb{C}^k \rtimes \mathbb{C}[\mathbb{Z}_2]$, the Taft
algebras and the restricted quantum group of $\mathfrak{sl}_2$ at a fourth root
of unity $\bar U_{\mathbf{i}}(\mathfrak{sl}_2)$.


title: Toward More Meaningful Resources for Lower-resourced Languages

abstract:
  In this position paper, we describe our perspective on how meaningful
resources for lower-resourced languages should be developed in connection with
the speakers of those languages. We first examine two massively multilingual
resources in detail. We explore the contents of the names stored in Wikidata
for a few lower-resourced languages and find that many of them are not in fact
in the languages they claim to be and require non-trivial effort to correct. We
discuss quality issues present in WikiAnn and evaluate whether it is a useful
supplement to hand annotated data. We then discuss the importance of creating
annotation for lower-resourced languages in a thoughtful and ethical way that
includes the languages' speakers as part of the development process. We
conclude with recommended guidelines for resource development.


title: Squeezed Knots

abstract:
  Squeezed knots are those knots that appear as slices of genus-minimizing
oriented smooth cobordisms between positive and negative torus knots. We show
that this class of knots is large and discuss how to obstruct squeezedness. The
most effective obstructions appear to come from quantum knot invariants,
notably including refinements of the Rasmussen invariant due to Lipshitz-Sarkar
and Sarkar-Scaduto-Stoffregen involving stable cohomology operations on
Khovanov homology.


title:
Kinematics of Parsec-Scale Jets of Gamma-Ray Bright Blazars at 43 GHz
  during Ten Years of the VLBA-BU-BLAZAR Program

abstract:
  We analyze the parsec-scale jet kinematics from 2007 June to 2018 December of
a sample of $\gamma$-ray bright blazars monitored roughly monthly with the Very
Long Baseline Array at 43 GHz under the VLBA-BU-BLAZAR program. We implement a
novel piece-wise linear fitting method to derive the kinematics of 521 distinct
emission knots from a total of 3705 total intensity images in 22 quasars, 13 BL
Lacertae objects, and 3 radio galaxies. Apparent speeds of these components
range from $0.01c$ to $78c$, and 18.6\% of knots (other than the "core") are
quasi-stationary. One-fifth of moving knots exhibit non-ballistic motion, with
acceleration along the jet within 5 pc of the core (projected) and deceleration
farther out. These accelerations occur mainly at locations coincident with
quasi-stationary features. We calculate the physical parameters of 273 knots
with statistically significant motion, including their Doppler factors, Lorentz
factors, and viewing angles. We determine the typical values of these
parameters for each jet and the average for each subclass of active galactic
nuclei. We investigate the variability of the position angle of each jet over
the ten years of monitoring. The fluctuations in position of the
quasi-stationary components in radio galaxies tend to be parallel to the jet,
while no directional preference is seen in the components of quasars and BL
Lacertae objects. We find a connection between $\gamma$-ray states of blazars
and their parsec-scale jet properties, with blazars with brighter 43 GHz cores
typically reaching higher $\gamma$-ray maxima during flares.


title: Parametric Amplification of an Optomechanical Quantum Interconnect

abstract:
  Connecting microwave quantum computing technologies with optical fiber
necessitates the conversion of microwave photons to optical photons mediated by
strong non-linear coupling between microwave and optical modes. Modern
experimental demonstrations exhibit strong coupling between a microwave
resonator and an optical cavity mediated through phononic modes in a mechanical
oscillator. This paradigmatic transduction experiment is bounded by theoretical
efficiency bounds with constant driving amplitudes on the microwave resonator
and optical cavity. By adding a parametric drive to the microwave resonator and
optical cavity we can amplify the phononic occupation and amplify the converted
signal through the quantum transducer. We present a theoretical framework for
time-dependent control of the driving lasers based on the input-output
formalism of quantum optics, and solve the linear time-varying equation of
motion to obtain an analytic expression for the transfer function. The transfer
function with the parametric drive results in a simple expression for
efficiency gain in a transducer. We illustrate our analytic solution in varying
parameter regimes relevant to current transduction experiments using numerical
simulation.


title: Bridging Level-K to Nash Equilibrium

abstract:
  We introduce NLK, a model that connects the Nash equilibrium (NE) and
Level-K. It allows a player in a game to believe that her opponent may be
either less or as sophisticated as, she is, a view supported in psychology. We
apply NLK to data from five published papers on static, dynamic, and auction
games. NLK provides different predictions than those of the NE and Level-K;
moreover, a simple version of NLK explains the experimental data better in many
cases, with the same or lower number of parameters. We discuss extensions to
games with more than two players and heterogeneous beliefs.


title: Planarizing Graphs and their Drawings by Vertex Splitting

abstract:
  The splitting number of a graph $G=(V,E)$ is the minimum number of vertex
splits required to turn $G$ into a planar graph, where a vertex split removes a
vertex $v \in V$, introduces two new vertices $v_1, v_2$, and distributes the
edges formerly incident to $v$ among its two split copies $v_1, v_2$. The
splitting number problem is known to be NP-complete. In this paper we shift
focus to the splitting number of graph drawings in $\mathbb R^2$, where the new
vertices resulting from vertex splits can be re-embedded into the existing
drawing of the remaining graph. We first provide a non-uniform fixed-parameter
tractable (FPT) algorithm for the splitting number problem (without drawings).
Then we show the NP-completeness of the splitting number problem for graph
drawings, even for its two subproblems of (1) selecting a minimum subset of
vertices to split and (2) for re-embedding a minimum number of copies of a
given set of vertices. For the latter problem we present an FPT algorithm
parameterized by the number of vertex splits. This algorithm reduces to a
bounded outerplanarity case and uses an intricate dynamic program on a
sphere-cut decomposition.


title: The Low Energy Spectrum of Trapped Bosons in the Gross-Pitaevskii Regime

abstract:
  Bogoliubov Theory provides important predictions for the low energy
properties of the weakly interacting Bose gas. Recently, Bogoliubov's
predictions could be justified rigorously in \cite{BBCS3} for translation
invariant systems in the Gross-Pitaveskii regime, where $N$ bosons in $\Lambda
= [0;1]^3\subset \mathbb{R}^3$ interact through a potential whose scattering
length is of size $ N^{-1}$. In this note, we review recent results from
\cite{BSS2}, a joint work with B. Schlein and S. Schraven, which extends the
analysis of \cite{BBCS3} to systems of bosons in $\mathbb{R}^3$ that are
trapped by an external potential.


title:
Factorizer: A Scalable Interpretable Approach to Context Modeling for
  Medical Image Segmentation

abstract:
  Convolutional Neural Networks (CNNs) with U-shaped architectures have
dominated medical image segmentation, which is crucial for various clinical
purposes. However, the inherent locality of convolution makes CNNs fail to
fully exploit global context, essential for better recognition of some
structures, e.g., brain lesions. Transformers have recently proved promising
performance on vision tasks, including semantic segmentation, mainly due to
their capability of modeling long-range dependencies. Nevertheless, the
quadratic complexity of attention makes existing Transformer-based models use
self-attention layers only after somehow reducing the image resolution, which
limits the ability to capture global contexts present at higher resolutions.
Therefore, this work introduces a family of models, dubbed Factorizer, which
leverages the power of low-rank matrix factorization for constructing an
end-to-end segmentation model. Specifically, we propose a linearly scalable
approach to context modeling, formulating Nonnegative Matrix Factorization
(NMF) as a differentiable layer integrated into a U-shaped architecture. The
shifted window technique is also utilized in combination with NMF to
effectively aggregate local information. Factorizers compete favorably with
CNNs and Transformers in terms of accuracy, scalability, and interpretability,
achieving state-of-the-art results on the BraTS dataset for brain tumor
segmentation, with Dice scores of 79.33%, 83.14%, and 90.16% for enhancing
tumor, tumor core, and whole tumor, respectively. Highly meaningful NMF
components give an additional interpretability advantage to Factorizers over
CNNs and Transformers. Moreover, our ablation studies reveal a distinctive
feature of Factorizers that enables a significant speed-up in inference for a
trained Factorizer without any extra steps and without sacrificing much
accuracy.


title: Feynman Polytopes and the Tropical Geometry of UV and IR Divergences

abstract:
  We introduce a class of polytopes that concisely capture the structure of UV
and IR divergences of general Feynman integrals in Schwinger parameter space,
treating them in a unified way as worldline segments shrinking and expanding at
different relative rates. While these polytopes conventionally arise as convex
hulls - via Newton polytopes of Symanzik polynomials - we show that they also
have a remarkably simple dual description as cut out by linear inequalities
defining the facets. It is this dual definition that makes it possible to
transparently understand and efficiently compute leading UV and IR divergences
for any Feynman integral. In the case of the UV, this provides a transparent
geometric understanding of the familiar nested and overlapping divergences. In
the IR, the polytope exposes a new perspective on soft/collinear singularities
and their intricate generalizations. Tropical geometry furnishes a simple
framework for calculating the leading UV/IR divergences of any Feynman
integral, associating them with the volumes of certain dual cones. As concrete
applications, we generalize Weinberg's theorem to include a characterization of
IR divergences, and classify space-time dimensions in which general IR
divergences (logarithmic as well as power-law) can occur. We also compute the
leading IR divergence of rectangular fishnet diagrams at all loop orders, which
turn out to have a surprisingly simple combinatorial description.


title: Embedded Ensembles: Infinite Width Limit and Operating Regimes

abstract:
  A memory efficient approach to ensembling neural networks is to share most
weights among the ensembled models by means of a single reference network. We
refer to this strategy as Embedded Ensembling (EE); its particular examples are
BatchEnsembles and Monte-Carlo dropout ensembles. In this paper we perform a
systematic theoretical and empirical analysis of embedded ensembles with
different number of models. Theoretically, we use a Neural-Tangent-Kernel-based
approach to derive the wide network limit of the gradient descent dynamics. In
this limit, we identify two ensemble regimes - independent and collective -
depending on the architecture and initialization strategy of ensemble models.
We prove that in the independent regime the embedded ensemble behaves as an
ensemble of independent models. We confirm our theoretical prediction with a
wide range of experiments with finite networks, and further study empirically
various effects such as transition between the two regimes, scaling of ensemble
performance with the network width and number of models, and dependence of
performance on a number of architecture and hyperparameter choices.


title:
Towards Low-distortion Multi-channel Speech Enhancement: The ESPNet-SE
  Submission to The L3DAS22 Challenge

abstract:
  This paper describes our submission to the L3DAS22 Challenge Task 1, which
consists of speech enhancement with 3D Ambisonic microphones. The core of our
approach combines Deep Neural Network (DNN) driven complex spectral mapping
with linear beamformers such as the multi-frame multi-channel Wiener filter.
Our proposed system has two DNNs and a linear beamformer in between. Both DNNs
are trained to perform complex spectral mapping, using a combination of
waveform and magnitude spectrum losses. The estimated signal from the first DNN
is used to drive a linear beamformer, and the beamforming result, together with
this enhanced signal, are used as extra inputs for the second DNN which refines
the estimation. Then, from this new estimated signal, the linear beamformer and
second DNN are run iteratively. The proposed method was ranked first in the
challenge, achieving, on the evaluation set, a ranking metric of 0.984, versus
0.833 of the challenge baseline.


title: Capturing Failures of Large Language Models via Human Cognitive Biases

abstract:
  Large language models generate complex, open-ended outputs: instead of
outputting a single class, they can write summaries, generate dialogue, and
produce working code. In order to study the reliability of these open-ended
systems, we must understand not just when they fail, but also how they fail. To
approach this, we draw inspiration from human cognitive biases -- systematic
patterns of deviation from rational judgement. Specifically, we use cognitive
biases to (i) identify inputs that models are likely to err on, and (ii)
develop tests to qualitatively characterize their errors on these inputs. Using
code generation as a case study, we find that OpenAI's Codex errs predictably
based on how the input prompt is framed, adjusts outputs towards anchors, and
is biased towards outputs that mimic frequent training examples. We then use
our framework to uncover high-impact errors such as incorrectly deleting files.
Our experiments suggest that cognitive science can be a useful jumping-off
point to better understand how contemporary machine learning systems behave.


title:
Can the clocks tick together despite the noise? Stochastic simulations
  and analysis

abstract:
  The suprachiasmatic nucleus (SCN), also known as the circadian master clock,
consists of a large population of oscillator neurons. Together, these neurons
produce a coherent signal that drives the body's circadian rhythms. What
properties of the cell-to-cell communication allow the synchronization of these
neurons, despite a wide range of environmental challenges such as fluctuations
in photoperiods? To answer that question, we present a mean-field description
of globally coupled neurons modeled as Goodwin oscillators with standard
Gaussian noise. Provided that the initial conditions of all neurons are
independent and identically distributed, any finite number of neurons becomes
independent and has the same probability distribution in the mean-field limit,
a phenomenon called propagation of chaos. This probability distribution is a
solution to a Vlasov-Fokker-Planck type equation, which can be obtained from
the stochastic particle model. We study, using the macroscopic description, how
the interaction between external noise and intercellular coupling affects the
dynamics of the collective rhythm, and we provide a numerical description of
the bifurcations resulting from the noise-induced transitions. Our numerical
simulations show a noise-induced rhythm generation at low noise intensities,
while the SCN clock is arrhythmic in the high noise setting. Notably, coupling
induces resonance-like behavior at low noise intensities, and varying coupling
strength can cause period locking and variance dissipation even in the presence
of noise.


title:
Channel capacity of relativistic quantum communication with rapid
  interaction

abstract:
  In this work we study nonperturbatively the transmission of classical and
quantum information in globally hyperbolic spacetimes, where the communication
channel is between two qubit detectors interacting with a quantized massless
scalar field via delta-coupling interaction. This interaction approximates very
rapid detector-field interaction, effectively occurring at a single instant in
time for each detector. We show that when both detectors interact via
delta-coupling, one can arrange and tune the detectors so that the channel
capacity is (at least) as good as the quantum channel constructed
nonperturbatively using \textit{gapless detectors} by Landulfo [PRD 93,
104019]. Furthermore, we prove that this channel capacity is in fact optimal,
i.e., both nonperturbative methods give essentially the same channel capacity,
thus there is a sense in which the two methods can be regarded as equivalent as
far as relativistic quantum communication is concerned.


title: Probing New Gauge Forces with a High-Energy Muon Beam Dump

abstract:
  We propose a new beam dump experiment at a future TeV-scale muon collider. A
beam dump would be an economical and effective way to increase the discovery
potential of the collider complex in a complementary regime. In this work we
consider vector models such as the dark photon and $L_\mu-L_\tau$ gauge boson
as new physics candidates and explore which novel regions of parameter space
can be probed with a muon beam dump. We find that for the dark photon model, we
gain sensitivity in the moderate mass (MeV - GeV) range at both higher and
lower couplings compared to existing and proposed experiments, and gain access
to previously untouched areas of parameter space of the $L_\mu-L_\tau$ model.


title:
The Combined Effects of Two-Body Relaxation Processes and the Eccentric
  Kozai-Lidov Mechanism on the EMRI Rate

abstract:
  Gravitational wave (GW) emissions from extreme-mass-ratio inspirals (EMRIs)
are promising sources for low-frequency GW-detectors. They result from a
compact object, such as a stellar-mass black-hole (BH), captured by a
supermassive black hole (SMBH). Several physical processes have been proposed
to form EMRIs. In particular, weak two-body interactions over a long time scale
(i.e., relaxation processes) have been proposed as a likely mechanism to drive
the BH orbit to high eccentricity. Consequently, it is captured by the SMBH and
becomes an EMRI. Here we demonstrate that EMRIs are naturally formed in SMBH
binaries. Gravitational perturbations from an SMBH companion, known as the
eccentric Kozai-Lidov (EKL) mechanism, combined with relaxation processes,
yield a significantly more enhanced rate than any of these processes operating
alone. Since EKL is sensitive to the orbital configuration, two-body relaxation
can alter the orbital parameters, rendering the system in a more EKL-favorable
regime. As SMBH binaries are expected to be prevalent in the Universe, this
process predicts a substantially high EMRI rate.


title:
The resolved chemical abundance properties within the interstellar
  medium of star-forming galaxies at $\mathbf{ \textit{z} \approx 1.5}$

abstract:
  We exploit the unprecedented depth of integral field data from the KMOS
Ultra-deep Rotational Velocity Survey (KURVS) to analyse the strong (H$\alpha$)
and forbidden ([NII], [SII]) emission line ratios in 22 main-sequence galaxies
at $z\approx1.5$. Using the [NII]/H$\alpha$ emission-line ratio we confirm the
presence of the stellar mass $-$ gas-phase metallicity relation at this epoch,
with galaxies exhibiting on average 0.13$\pm$0.04 dex lower gas-phase
metallicity (12+log(O/H)$_{\rm M13}$=8.40$\pm$0.03) for a given stellar mass
($\log_{10}$($M_{\rm *}$[$M_{\odot}$]=10.1$\pm$0.1) than local main-sequence
galaxies. We determine the galaxy-integrated [SII] doublet ratio, with a median
value of [SII]$\lambda$6716/$\lambda$6731=1.26$\pm$0.14 equivalent to an
electron density of log$_{10}$($n_{\rm e}$[cm$^{-3}$])=1.95$\pm$0.12. Utilising
CANDELS $HST$ multi-band imaging we define the pixel surface-mass and
star-formation rate density in each galaxy and spatially resolve the
fundamental metallicity relation at $z\approx1.5$, finding an evolution of
0.05$\pm$0.01 dex compared to the local relation. We quantify the intrinsic
gas-phase metallicity gradient within the galaxies using the [NII]/H$\alpha$
calibration, finding a median annuli-based gradient of
$\Delta$Z/$\Delta$R=$-$0.015$\pm$0.005 dex kpc$^{-1}$. Finally we examine the
azimuthal variations in gas-phase metallicity, which show a negative
correlation with the galaxy integrated star-formation rate surface density
($r_{\rm s}$=$-$0.40, $p_{\rm s}$=0.07) but no connection to the galaxies
kinematic or morphological properties nor radial variations in stellar mass
surface density or star formation rate surface density. This suggests both the
radial and azimuthal variations in interstellar medium properties are connected
to the galaxy integrated density of recent star formation.


title: The width difference in \bbm\ at order $\alpha_s$ and beyond

abstract:
  We complete the calculation of the element $\Gamma_{12}^q$ of the decay
matrix in $B_q-\bar{B}_q$ mixing, $q=d,s$, to order $\alpha_s$ in the leading
power of the Heavy Quark Expansion. To this end we compute one- and two-loop
contributions involving two four-quark penguin operators. Furthermore, we
present two-loop QCD corrections involving a chromomagnetic operator and either
a current-current or four-quark penguin operator. Such contributions are of
order $\alpha_s^2$, i.e. next-to-next-to-leading-order. We also present
one-loop and two-loop results involving two chromomagnetic operators which are
formally of next-to-next-to-leading and next-to-next-to-next-to-leading-order,
respectively. With our new corrections we obtain the Standard-Model prediction
$\Delta\Gamma_s/\Delta M_s= (5.20\pm 0.69)\cdot 10^{-3}$ if $\Gamma_{12}^s$ is
expressed in terms of the $\overline{\rm MS}$ b-quark mass, while we find
$\Delta \Gamma_s/\Delta M_s= (4.70\pm 0.96)\cdot 10^{-3}$ instead for the use
of the pole mass.


title:
Emergent quantum state designs and biunitarity in dual-unitary circuit
  dynamics

abstract:
  Recent works have investigated the emergence of a new kind of random matrix
behaviour in unitary dynamics following a quantum quench. Starting from a
time-evolved state, an ensemble of pure states supported on a small subsystem
can be generated by performing projective measurements on the remainder of the
system, leading to a projected ensemble. In chaotic quantum systems it was
conjectured that such projected ensembles become indistinguishable from the
uniform Haar-random ensemble and lead to a quantum state design. Exact results
were recently presented by Ho and Choi [Phys. Rev. Lett. 128, 060601 (2022)]
for the kicked Ising model at the self-dual point. We provide an alternative
construction that can be extended to general chaotic dual-unitary circuits with
solvable initial states and measurements, highlighting the role of the
underlying dual-unitarity and further showing how dual-unitary circuit models
exhibit both exact solvability and random matrix behaviour. Building on results
from biunitary connections, we show how complex Hadamard matrices and unitary
error bases both lead to solvable measurement schemes.

categories: quant-ph cond-mat.stat-mech cond-mat.str-el nlin.CD


title:
Retriever: Learning Content-Style Representation as a Token-Level
  Bipartite Graph

abstract:
  This paper addresses the unsupervised learning of content-style decomposed
representation. We first give a definition of style and then model the
content-style representation as a token-level bipartite graph. An unsupervised
framework, named Retriever, is proposed to learn such representations. First, a
cross-attention module is employed to retrieve permutation invariant (P.I.)
information, defined as style, from the input data. Second, a vector
quantization (VQ) module is used, together with man-induced constraints, to
produce interpretable content tokens. Last, an innovative link attention module
serves as the decoder to reconstruct data from the decomposed content and
style, with the help of the linking keys. Being modal-agnostic, the proposed
Retriever is evaluated in both speech and image domains. The state-of-the-art
zero-shot voice conversion performance confirms the disentangling ability of
our framework. Top performance is also achieved in the part discovery task for
images, verifying the interpretability of our representation. In addition, the
vivid part-based style transfer quality demonstrates the potential of Retriever
to support various fascinating generative tasks. Project page at
https://ydcustc.github.io/retriever-demo/.


title: Impact of cosmic rays on the global 21-cm signal during cosmic dawn

abstract:
  It is extremely important to understand the processes through which the
thermal state of the inter-galactic medium (IGM) evolved in the early universe
in order to study the evolution of HI 21-cm signal during cosmic dawn. Here, we
consider the heating of the IGM due to cosmic ray protons generated by the
supernovae from both early Pop III and Pop II stars. The low energy cosmic ray
protons from Pop III supernovae can escape from minihalos and heat the IGM via
collision and ionization of hydrogen. Furthermore, high energy protons
generated in Pop II supernovae can escape the hosting halos and heat the IGM
via magnetosonic Alfv\'en waves. We show that the heating due to these cosmic
ray particles can significantly impact the IGM temperature and hence the global
21-cm signal at $z\sim 14-18$. The depth, location, and duration of the 21-cm
absorption profile are highly dependent on the efficiencies of cosmic ray
heating. In particular, the EDGES signal can be well fitted by the cosmic ray
heating along with the Lyman-$\alpha$ coupling, and the dark matter-baryon
interaction that we consider to achieve a `colder IGM background'. Further, we
argue that the properties of cosmic rays and the nature of first generation of
stars could be constrained by accurately measuring the global 21-cm absorption
signal during the cosmic dawn.


title:
Parthenon -- a performance portable block-structured adaptive mesh
  refinement framework

abstract:
  On the path to exascale the landscape of computer device architectures and
corresponding programming models has become much more diverse. While various
low-level performance portable programming models are available, support at the
application level lacks behind. To address this issue, we present the
performance portable block-structured adaptive mesh refinement (AMR) framework
Parthenon, derived from the well-tested and widely used Athena++ astrophysical
magnetohydrodynamics code, but generalized to serve as the foundation for a
variety of downstream multi-physics codes. Parthenon adopts the Kokkos
programming model, and provides various levels of abstractions from
multi-dimensional variables, to packages defining and separating components, to
launching of parallel compute kernels. Parthenon allocates all data in device
memory to reduce data movement, supports the logical packing of variables and
mesh blocks to reduce kernel launch overhead, and employs one-sided,
asynchronous MPI calls to reduce communication overhead in multi-node
simulations. Using a hydrodynamics miniapp, we demonstrate weak and strong
scaling on various architectures including AMD and NVIDIA GPUs, Intel and AMD
x86 CPUs, as well as Fujitsu A64FX CPUs. At the largest scale, the miniapp
reaches a total of 3.5x10^12 zone-cycles/s on 4096 Summit nodes (24576 GPUs) at
~55% weak scaling parallel efficiency (starting from a single node). In
combination with being an open, collaborative project, this makes Parthenon an
ideal framework to target exascale simulations in which the downstream
developers can focus on their specific application rather than on the
complexity of handling massively-parallel, device-accelerated AMR.