title:
The role of core and strahlo electrons properties on the whistler
  heat-flux instability thresholds in the solar wind

abstract:
  There is wide observational evidence that electron velocity distribution
functions (eVDF) observed in the solar wind generally present enhanced tails
and field-aligned skewness. These properties may induce the excitation of
electromagnetic perturbations through the whistler heat-flux instability
(WHFI), that may contribute to a non-collisional regulation of the electron
heat-flux values observed in the solar wind via wave-particle interactions.
Recently, a new way to model the solar wind eVDF has been proposed: the
core-strahlo model. This representation consist in a bi-Maxwellian core plus a
Skew-Kappa distribution, representing the halo and strahl electrons as a single
skewed distribution. The core-strahlo model is able to reproduce the main
features of the eVDF in the solar wind (thermal core, enhanced tails, and
skewness), with the advantage that the asymmetry is controlled by only one
parameter. In this work we use linear kinetic theory to analyze the effect of
solar wind electrons described by the core-strahlo model, over the excitation
of the parallel propagating WHFI. We use parameters relevant to the solar wind
and focus our attention on the effect on the linear stability introduced by
different values of the core-to-strahlo density and temperature ratios, which
are known to vary throughout the Heliosphere. We also obtain the stability
threshold for this instability as a function of the electron beta and the
skewness parameter, which is a better indicator of instability than the
heat-flux macroscopic moment, and present a threshold conditions for the
instability that can be compared with observational data.


title:
An averaged space-time discretization of the stochastic $p$-Laplace
  system

abstract:
  We study the stochastic $p$-Laplace system in a bounded domain. We propose
two new space-time discretizations based on the approximation of time-averaged
values. We establish linear convergence in space and $1/2$ convergence in time.
Additionally, we provide a sampling algorithm to construct the necessary random
input in an efficient way. The theoretical error analysis is complemented by
numerical experiments.


title: Lower bounds for the Tur\'an densities of daisies

abstract:
  For integers $r \geq 3$ and $t \geq 2$, an $r$-uniform $t$-daisy
$\mathcal{D}^t_r$ is a family of $\binom{2t}{t}$ $r$-element sets of the form
$$\{S \cup T \ : T\subset U, \ |T|=t \}$$ for some sets $S,U$ with $|S|=r-t$,
$|U|=2t$ and $S \cap U = \emptyset$. It was conjectured by Bollob\'as, Leader
and Malvenuto (and independently Bukh) that the Tur\'an densities of
$t$-daisies satisfy $\lim\limits_{r \to \infty} \pi(\mathcal{D}_r^t) = 0$ for
all $t \geq 2$; this has become a well-known problem, and it is still open for
all values of $t$. In this paper, we give lower bounds for the Tur\'an
densities of $r$-uniform $t$-daisies. To do so, we introduce (and make some
progress on) the following natural problem in additive combinatorics: for
integers $m \geq 2t \geq 4$, what is the maximum cardinality $g(m,t)$ of a
subset $R$ of $\mathbb{Z}/m\mathbb{Z}$ such that for any $x \in
\mathbb{Z}/m\mathbb{Z}$ and any $2t$-element subset $X$ of
$\mathbb{Z}/m\mathbb{Z}$, there are $t$ distinct elements of $X$ whose sum is
not in the translate $x+R$? This is a slice-analogue of the extremal Hilbert
cube problem considered by Gunderson and R\"odl and its generalization studied
by Cilleruelo and Tesoro.


title: Effective Field Theories and Cosmological Scattering Equations

abstract:
  We propose worldsheet formulae for correlators of the massive non-linear
sigma model (NLSM), scalar Dirac-Born-Infeld (DBI), and special Galileon (sGal)
theories in de Sitter momentum space in terms of the recently proposed
cosmological scattering equations constructed from conformal generators in the
future boundary. The four-point integrands are constructed from simple building
blocks and we propose a generalised double copy which maps the NLSM correlator
to the DBI and sGal correlators, including mass deformations and curvature
corrections. Finally, we compute the soft limits of these correlators and find
that they can also be written in terms of boundary conformal generators acting
on contact diagrams.


title:
Learning to Imagine: Diversify Memory for Incremental Learning using
  Unlabeled Data

abstract:
  Deep neural network (DNN) suffers from catastrophic forgetting when learning
incrementally, which greatly limits its applications. Although maintaining a
handful of samples (called `exemplars`) of each task could alleviate forgetting
to some extent, existing methods are still limited by the small number of
exemplars since these exemplars are too few to carry enough task-specific
knowledge, and therefore the forgetting remains. To overcome this problem, we
propose to `imagine` diverse counterparts of given exemplars referring to the
abundant semantic-irrelevant information from unlabeled data. Specifically, we
develop a learnable feature generator to diversify exemplars by adaptively
generating diverse counterparts of exemplars based on semantic information from
exemplars and semantically-irrelevant information from unlabeled data. We
introduce semantic contrastive learning to enforce the generated samples to be
semantic consistent with exemplars and perform semanticdecoupling contrastive
learning to encourage diversity of generated samples. The diverse generated
samples could effectively prevent DNN from forgetting when learning new tasks.
Our method does not bring any extra inference cost and outperforms
state-of-the-art methods on two benchmarks CIFAR-100 and ImageNet-Subset by a
clear margin.


title: Gauge Field Theory Vacuum and Cosmological Inflation

abstract:
  The deep interrelation between elementary particle physics and cosmology
manifests itself when one considers the contribution of quantum fluctuations of
vacuum fields to the dark energy and the effective cosmological constant. The
contribution of zero-point energy exceeds by many orders of magnitude the
observational cosmological upper bound on the energy density of the universe.
Therefore it seems natural to expect that vacuum fluctuations of the
fundamental fields would influence the cosmological evolution in any way. Our
aim in this review article is to describe a recent investigation of the
influence of the Yang-Mills vacuum polarisation and of the chromomagnetic
condensation on the evolution of Friedmann cosmology, on inflation and on
primordial gravitational waves. We derive the quantum energy-momentum tensor
and the corresponding quantum equation of state for gauge field theory using
the effective Lagrangian approach. The energy-momentum tensor has a term
proportional to the space-time metric and provides a finite non-diverging
contribution to the effective cosmological constant. This allows to investigate
the influence of the gauge field theory vacuum polarisation on the evolution of
Friedmann cosmology, inflation and primordial gravitational waves. The Type
I-IV solutions of the Friedmann equations induced by the gauge field theory
vacuum polarisation provide an alternative inflationary mechanism and a
possibility for late-time acceleration. The Type II solution of the Friedmann
equations generates the initial exponential expansion of the universe of finite
duration and the Type IV solution demonstrates late-time acceleration. The
solutions fulfil the necessary conditions for the amplification of primordial
gravitational waves.


title:
$\varphi-$fixed point theorems in $C^{\ast}-$algebra valued partial
  metric spaces

abstract:
  In this present article, we etablish some existence results of
$\varphi-$fixed point of a mapping in a $C^{\ast}$-algebra valued metric spaces
and we deduce some fixed point theorems in $C^{\ast}$-algebra valued partial
metric spaces. Non-trivial examples are further provided to support the
hypotheses of our results.


title: On Xing Tian and the Perseverance of Anti-China Sentiment Online

abstract:
  Sinophobia, anti-Chinese sentiment, has existed on the Web for a long time.
The outbreak of COVID-19 and the extended quarantine has further amplified it.
However, we lack a quantitative understanding of the cause of Sinophobia as
well as how it evolves over time. In this paper, we conduct a large-scale
longitudinal measurement of Sinophobia, between 2016 and 2021, on two
mainstream and fringe Web communities. By analyzing 8B posts from Reddit and
206M posts from 4chan's /pol/, we investigate the origins, evolution, and
content of Sinophobia. We find that, anti-Chinese content may be evoked by
political events not directly related to China, e.g., the U.S. withdrawal from
the Paris Agreement. And during the COVID-19 pandemic, daily usage of
Sinophobic slurs has significantly increased even with the hate-speech ban
policy. We also show that the semantic meaning of the words "China" and
"Chinese" are shifting towards Sinophobic slurs with the rise of COVID-19 and
remain the same in the pandemic period. We further use topic modeling to show
the topics of Sinophobic discussion are pretty diverse and broad. We find that
both Web communities share some common Sinophobic topics like ethnics,
economics and commerce, weapons and military, foreign relations, etc. However,
compared to 4chan's /pol/, more daily life-related topics including food, game,
and stock are found in Reddit. Our finding also reveals that the topics related
to COVID-19 and blaming the Chinese government are more prevalent in the
pandemic period. To the best of our knowledge, this paper is the longest
quantitative measurement of Sinophobia.


title:
On permutations avoiding partially ordered patterns defined by bipartite
  graphs

abstract:
  Partially ordered patterns (POPs) generalize the notion of classical patterns
studied in the literature in the context of permutations, words, compositions
and partitions. In this paper, we give a number of general, and specific
enumerative results for POPs in permutations defined by bipartite graphs,
substantially extending the list of known results in this direction. In
particular, we completely characterize the Wilf-equivalence for patterns
defined by the N-shape posets.


title:
On the structure of isothermal acoustic shocks under classical and
  artificial viscosity laws: Selected case studies

abstract:
  Assuming Newton's law of cooling, the propagation and structure of isothermal
acoustic shocks are studied under four different viscosity laws. Employing both
analytical and numerical methods, 1D traveling wave solutions for the velocity
and density fields are derived and analyzed. For each viscosity law considered,
expressions for both the shock thickness and the asymmetry metric are
determined. And, to ensure that isothermal flow is achievable, upper bounds on
the associated Mach number values are derived/computed using the isothermal
version of the energy equation.


title: Learning heuristics for A*

abstract:
  Path finding in graphs is one of the most studied classes of problems in
computer science. In this context, search algorithms are often extended with
heuristics for a more efficient search of target nodes. In this work we combine
recent advancements in Neural Algorithmic Reasoning to learn efficient
heuristic functions for path finding problems on graphs. At training time, we
exploit multi-task learning to learn jointly the Dijkstra's algorithm and a
consistent heuristic function for the A* search algorithm. At inference time,
we plug our learnt heuristics into the A* algorithm. Results show that running
A* over the learnt heuristics value can greatly speed up target node searching
compared to Dijkstra, while still finding minimal-cost paths.


title:
Deep learning-based surrogate model for 3-D patient-specific
  computational fluid dynamics

abstract:
  Optimization and uncertainty quantification have been playing an increasingly
important role in computational hemodynamics. However, existing methods based
on principled modeling and classic numerical techniques have faced significant
challenges, particularly when it comes to complex 3D patient-specific shapes in
the real world. First, it is notoriously challenging to parameterize the input
space of arbitrarily complex 3-D geometries. Second, the process often involves
massive forward simulations, which are extremely computationally demanding or
even infeasible. We propose a novel deep learning surrogate modeling solution
to address these challenges and enable rapid hemodynamic predictions.
Specifically, a statistical generative model for 3-D patient-specific shapes is
developed based on a small set of baseline patient-specific geometries. An
unsupervised shape correspondence solution is used to enable geometric morphing
and scalable shape synthesis statistically. Moreover, a simulation routine is
developed for automatic data generation by automatic meshing, boundary setting,
simulation, and post-processing. An efficient supervised learning solution is
proposed to map the geometric inputs to the hemodynamics predictions in latent
spaces. Numerical studies on aortic flows are conducted to demonstrate the
effectiveness and merit of the proposed techniques.

categories: physics.med-ph cs.CV eess.IV physics.flu-dyn


title: Reducing the Depth of Quantum FLT-Based Inversion Circuit

abstract:
  Works on quantum computing and cryptanalysis has increased significantly in
the past few years. Various constructions of quantum arithmetic circuits, as
one of the essential components in the field, has also been proposed. However,
there has only been a few studies on finite field inversion despite its
essential use in realizing quantum algorithms, such as in Shor's algorithm for
Elliptic Curve Discrete Logarith Problem (ECDLP). In this study, we propose to
reduce the depth of the existing quantum Fermat's Little Theorem (FLT)-based
inversion circuit for binary finite field. In particular, we propose follow a
complete waterfall approach to translate the Itoh-Tsujii's variant of FLT to
the corresponding quantum circuit and remove the inverse squaring operations
employed in the previous work by Banegas et al., lowering the number of CNOT
gates (CNOT count), which contributes to reduced overall depth and gate count.
Furthermore, compare the cost by firstly constructing our method and previous
work's in Qiskit quantum computer simulator and perform the resource analysis.
Our approach can serve as an alternative for a time-efficient implementation.


title: CodexDB: Generating Code for Processing SQL Queries using GPT-3 Codex

abstract:
  CodexDB is an SQL processing engine whose internals can be customized via
natural language instructions. CodexDB is based on OpenAI's GPT-3 Codex model
which translates text into code. It is a framework on top of GPT-3 Codex that
decomposes complex SQL queries into a series of simple processing steps,
described in natural language. Processing steps are enriched with user-provided
instructions and descriptions of database properties. Codex translates the
resulting text into query processing code. An early prototype of CodexDB is
able to generate correct code for a majority of queries of the WikiSQL
benchmark and can be customized in various ways.


title: The Binary Rank of Circulant Block Matrices

abstract:
  The binary rank of a $0,1$ matrix is the smallest size of a partition of its
ones into monochromatic combinatorial rectangles. A matrix $M$ is called $(k_1,
\ldots, k_m ; n_1, \ldots, n_m)$ circulant block diagonal if it is a block
matrix with $m$ diagonal blocks, such that for each $i \in [m]$, the $i$th
diagonal block of $M$ is the circulant matrix whose first row has $k_i$ ones
followed by $n_i-k_i$ zeros, and all of whose other entries are zeros. In this
work, we study the binary rank of these matrices and of their complement. In
particular, we compare the binary rank of these matrices to their rank over the
reals, which forms a lower bound on the former.
  We present a general method for proving upper bounds on the binary rank of
block matrices that have diagonal blocks of some specified structure and ones
elsewhere. Using this method, we prove that the binary rank of the complement
of a $(k_1, \ldots, k_m ; n_1, \ldots, n_m)$ circulant block diagonal matrix
for integers satisfying $n_i>k_i>0$ for each $i \in [m]$ exceeds its real rank
by no more than the maximum of $\gcd(n_i,k_i)-1$ over all $i \in [m]$. We
further present several sufficient conditions for the binary rank of these
matrices to strictly exceed their real rank. By combining the upper and lower
bounds, we determine the exact binary rank of various families of matrices and,
in addition, significantly generalize a result of Gregory.
  Motivated by a question of Pullman, we study the binary rank of $k$-regular
$0,1$ matrices and of their complement. As an application of our results on
circulant block diagonal matrices, we show that for every $k \geq 2$, there
exist $k$-regular $0,1$ matrices whose binary rank is strictly larger than that
of their complement. Furthermore, we exactly determine for every integer $r$,
the smallest possible binary rank of the complement of a $2$-regular $0,1$
matrix with binary rank $r$.


title: Physics in the tau-charm Region at BESIII

abstract:
  The Beijing Spectrometer (BESIII) collaboration uses $e^+e^-$ collisions in
the tau-charm energy region to study a broad spectrum of topics. These include
studies of light mesons and light baryons, studies of charmonium, including
exotic mesons and baryons containing charmonium, studies of charmed mesons and
baryons, studies of QCD and tau physics, as well as searches for new physics.
The following is a Snowmass white paper that outlines the BESIII
accomplishments and potential in each of these areas.


title: Mass-loss rates of cool evolved stars in M33 galaxy

abstract:
  We have conducted a near-infrared monitoring campaign at the UK InfraRed
Telescope (UKIRT), of the Local Group spiral galaxy M33 (Triangulum). In this
paper, we present the dust and gas mass-loss rates by the pulsating Asymptotic
Giant Branch (AGB) stars and red supergiants (RSGs) across the stellar disc of
M33.


title: Missingness Bias in Model Debugging

abstract:
  Missingness, or the absence of features from an input, is a concept
fundamental to many model debugging tools. However, in computer vision, pixels
cannot simply be removed from an image. One thus tends to resort to heuristics
such as blacking out pixels, which may in turn introduce bias into the
debugging process. We study such biases and, in particular, show how
transformer-based architectures can enable a more natural implementation of
missingness, which side-steps these issues and improves the reliability of
model debugging in practice. Our code is available at
https://github.com/madrylab/missingness


title:
Liquid-Hexatic-Solid phases in active and passive Brownian particles
  determined by stochastic birth and death events

abstract:
  We study the effects of stochastic birth and death processes on the
structural phases of systems of active and passive Brownian particles subject
to volume exclusion. The total number of particles in the system is a
fluctuating quantity, determined by the birth and death parameters, and on the
activity of the particles. As the birth and death parameters are varied we find
liquid, hexatic and solid phases. For passive particles these phases are found
to be spatially homogeneous. For active particles motility-induced phase
separation (co-existing hexatic and liquid phases) occurs for large activity
and sufficiently small birth rates. We also observe a re-entrant transition to
the hexatic phase when the birth rate is increased. This results from a balance
of an increasing number of particles filling the system, and a larger number of
defects resulting from the birth and death dynamics.


title: Unbounded $\mathfrak{sl}_3$-laminations and their shear coordinates

abstract:
  We study the space $\mathcal{L}_{\mathfrak{sl}_3}^x(\Sigma,\mathbb{Q})$ of
rational unbounded $\mathfrak{sl}_3$-laminations on a marked surface $\Sigma$.
We introduce an $\mathfrak{sl}_3$-analogue of the Thurston's shear coordinates
associated with any decorated triangulation, which gives rise to a natural
identification $\mathcal{L}_{\mathfrak{sl}_3}^x(\Sigma,\mathbb{Q}) \cong
\mathcal{X}_{\mathfrak{sl}_3,\Sigma}^\mathrm{uf}(\mathbb{Q}^{\mathrm{trop}})$.
We also introduce the space $\mathcal{L}^p(\Sigma,\mathbb{Q})$ of rational
unbounded $\mathfrak{sl}_3$-laminations with pinnings, which possesses the
frozen coordinates as well. Then we give a tropical anologue of the
amalgamation maps [FG06b] between them, which is indeed a procedure of gluing
$\mathfrak{sl}_3$-laminations with "shearings". We also investigate a relation
to the graphical basis of the $\mathfrak{sl}_3$-skein algebra [IY21], which
conjecturally leads to a quantum duality map.


title:
On the space-like analyticity in the extension problem for nonlocal
  parabolic equations

abstract:
  In this note we give an elementary proof of the space-like real analyticity
of solutions to a degenerate evolution problem that arises in the study of
fractional parabolic operators of the type $(\partial_t -
div_x(B(x)\nabla_x))^s$, $0<s<1$. Our primary interest is in the so-called
\emph{extension variable}. We show that weak solutions that are even in such
variable, are in fact real-analytic in the totality of the space variables. As
an application of this result we prove the weak unique continuation property
for nonlocal parabolic operators of the type above, where $B(x)$ is a uniformly
elliptic matrix-valued function with real-analytic entries.


title:
Second order linear differential equations with a basis of solutions
  having only real zeros

abstract:
  Let $A$ be a transcendental entire function of finite order. We show that if
the differential equation $w''+Aw=0$ has two linearly independent solutions
with only real zeros, then the order of $A$ must be an odd integer or one half
of an odd integer. Moreover, $A$ has completely regular growth in the sense of
Levin and Pfluger. These results follow from a more general geometric theorem,
which classifies symmetric local homeomorphisms from the plane to the sphere
for which all zeros and poles lie on the real axis, and which have only
finitely many singularities over finite non-zero values.


title:
Extreme temporal intermittency in the linear Sobolev transport: almost
  smooth nonunique solutions

abstract:
  In this paper, we revisit the notion of temporal intermittency to obtain
sharp nonuniqueness results for linear transport equations. We construct
divergence-free vector fields with sharp Sobolev regularity $L^1_t W^{1,p}$ for
all $p<\infty$ in space dimensions $d\geq 2$ whose transport equations admit
nonunique weak solutions belonging to $L^p_tC^k$ for all $p<\infty$ and $k\in
\mathbb{N}$. In particular, our result shows that the time-integrability
assumption in the uniqueness of the DiPerna-Lions theory is sharp. The same
result also holds for transport-diffusion equations with diffusion operators of
arbitrarily large order in any dimensions $d \geq 2$.


title: Seculator: A Fast and Secure Neural Processing Unit

abstract:
  Securing deep neural networks (DNNs) is a problem of significant interest
since an ML model incorporates high-quality intellectual property, features of
data sets painstakingly collated by mechanical turks, and novel methods of
training on large cluster computers. Sadly, attacks to extract model parameters
are on the rise, and thus designers are being forced to create architectures
for securing such models. State-of-the-art proposals in this field take the
deterministic memory access patterns of such networks into cognizance (albeit
partially), group a set of memory blocks into a tile, and maintain state at the
level of tiles (to reduce storage space). For providing integrity guarantees
(tamper avoidance), they don't propose any significant optimizations, and still
maintain block-level state.
  We observe that it is possible to exploit the deterministic memory access
patterns of DNNs even further, and maintain state information for only the
current tile and current layer, which may comprise a large number of tiles.
This reduces the storage space, reduces the number of memory accesses,
increases performance, and simplifies the design without sacrificing any
security guarantees. The key techniques in our proposed accelerator
architecture, Seculator, are to encode memory access patterns to create a small
HW-based tile version number generator for a given layer, and to store
layer-level MACs. We completely eliminate the need for having a MAC cache and a
tile version number store (as used in related work). We show that using
intelligently-designed mathematical operations, these structures are not
required. By reducing such overheads, we show a speedup of 16% over the closest
competing work.


title:
Retrieval Enhanced Data Augmentation for Question Answering on Privacy
  Policies

abstract:
  Prior studies in privacy policies frame the question answering (QA) tasks as
identifying the most relevant text segment or a list of sentences from the
policy document for a user query. However, annotating such a dataset is
challenging as it requires specific domain expertise (e.g., law academics).
Even if we manage a small-scale one, a bottleneck that remains is that the
labeled data are heavily imbalanced (only a few segments are relevant)
--limiting the gain in this domain. Therefore, in this paper, we develop a
novel data augmentation framework based on ensembling retriever models that
captures the relevant text segments from unlabeled policy documents and expand
the positive examples in the training set. In addition, to improve the
diversity and quality of the augmented data, we leverage multiple pre-trained
language models (LMs) and cascaded them with noise reduction oracles. Using our
augmented data on the PrivacyQA benchmark, we elevate the existing baseline by
a large margin (10% F1) and achieve a new state-of-the-art F1 score of 50%. Our
ablation studies provide further insights into the effectiveness of our
approach.


title: Self-propulsion of chemically-active droplets

abstract:
  Microscopic active droplets are able to swim autonomously in viscous flows:
this puzzling feature stems from solute exchanges with the surrounding fluid
via surface reactions or their spontaneous solubilisation, and the interfacial
flows resulting from these solutes' gradients. Contrary to asymmetric active
colloids, these isotropic droplets swim spontaneously by exploiting the
nonlinear coupling of solute transport with self-generated Marangoni flows,
which is also responsible for secondary transitions to more complex individual
and collective dynamics. Thanks to their simple design and their sensitivity to
physico-chemical signals, they are fascinating physicists, chemists, biologists
and fluid dynamicists alike to analyse viscous self-propulsion and collective
dynamics in active matter systems, to develop synthetic cellular models or to
perform targeted biomedical or engineering applications. I review here the most
recent and significant developments of this rapidly-growing field, focusing on
the mathematical and physical modelling of these intringuing droplets, together
with its experimental design and characterisation.


title:
Revisiting Vicinal Risk Minimization for Partially Supervised
  Multi-Label Classification Under Data Scarcity

abstract:
  Due to the high human cost of annotation, it is non-trivial to curate a
large-scale medical dataset that is fully labeled for all classes of interest.
Instead, it would be convenient to collect multiple small partially labeled
datasets from different matching sources, where the medical images may have
only been annotated for a subset of classes of interest. This paper offers an
empirical understanding of an under-explored problem, namely partially
supervised multi-label classification (PSMLC), where a multi-label classifier
is trained with only partially labeled medical images. In contrast to the fully
supervised counterpart, the partial supervision caused by medical data scarcity
has non-trivial negative impacts on the model performance. A potential remedy
could be augmenting the partial labels. Though vicinal risk minimization (VRM)
has been a promising solution to improve the generalization ability of the
model, its application to PSMLC remains an open question. To bridge the
methodological gap, we provide the first VRM-based solution to PSMLC. The
empirical results also provide insights into future research directions on
partially supervised learning under data scarcity.


title: Chaotic Behaviour of the Earth System in the Anthropocene

abstract:
  It is shown that the Earth System (ES) can, due to the impact of human
activities, behave in a chaotic fashion. Our arguments are based on the
assumption that the ES can be described by a Landau-Ginzburg model, which on
its own allows for predicting that the ES evolves, through regular trajectories
in the phase space, towards a Hothouse Earth scenario for a finite amount of
human-driven impact. Furthermore, we find that the equilibrium point for
temperature fluctuations can exhibit bifurcations and a chaotic pattern if the
human impact follows a logistic map.


title:
The cross section of the process $e^+e^- \to \Lambda\bar{\Lambda}$ in
  the vicinity of charmonium $\psi(3770)$ including three-gluon and $D$-meson
  loop contributions

abstract:
  The total cross section of the process $e^+e^- \to \Lambda\bar{\Lambda}$ is
calculated within the energy range close to the mass of $\psi(3770)$ charmonium
state. Two different contributions were considered: the $D$-meson loop and the
three gluon charmonium annihilation one. Both of them contribute noticeably and
in sum fairly reproduce the data. Large relative phase for these contributions
are generated with respect to the pure electromagnetic mechanism. As a by
product the fit for the electromagnetic form factor of $\Lambda$-hyperon is
obtained for the large momentum transferred region.


title:
COptiDICE: Offline Constrained Reinforcement Learning via Stationary
  Distribution Correction Estimation

abstract:
  We consider the offline constrained reinforcement learning (RL) problem, in
which the agent aims to compute a policy that maximizes expected return while
satisfying given cost constraints, learning only from a pre-collected dataset.
This problem setting is appealing in many real-world scenarios, where direct
interaction with the environment is costly or risky, and where the resulting
policy should comply with safety constraints. However, it is challenging to
compute a policy that guarantees satisfying the cost constraints in the offline
RL setting, since the off-policy evaluation inherently has an estimation error.
In this paper, we present an offline constrained RL algorithm that optimizes
the policy in the space of the stationary distribution. Our algorithm,
COptiDICE, directly estimates the stationary distribution corrections of the
optimal policy with respect to returns, while constraining the cost upper
bound, with the goal of yielding a cost-conservative policy for actual
constraint satisfaction. Experimental results show that COptiDICE attains
better policies in terms of constraint satisfaction and return-maximization,
outperforming baseline algorithms.


title:
MANIQA: Multi-dimension Attention Network for No-Reference Image Quality
  Assessment

abstract:
  No-Reference Image Quality Assessment (NR-IQA) aims to assess the perceptual
quality of images in accordance with human subjective perception.
Unfortunately, existing NR-IQA methods are far from meeting the needs of
predicting accurate quality scores on GAN-based distortion images. To this end,
we propose Multi-dimension Attention Network for no-reference Image Quality
Assessment (MANIQA) to improve the performance on GAN-based distortion. We
firstly extract features via ViT, then to strengthen global and local
interactions, we propose the Transposed Attention Block (TAB) and the Scale
Swin Transformer Block (SSTB). These two modules apply attention mechanisms
across the channel and spatial dimension, respectively. In this
multi-dimensional manner, the modules cooperatively increase the interaction
among different regions of images globally and locally. Finally, a dual branch
structure for patch-weighted quality prediction is applied to predict the final
score depending on the weight of each patch's score. Experimental results
demonstrate that MANIQA outperforms state-of-the-art methods on four standard
datasets (LIVE, TID2013, CSIQ, and KADID-10K) by a large margin. Besides, our
method ranked first place in the final testing phase of the NTIRE 2022
Perceptual Image Quality Assessment Challenge Track 2: No-Reference. Codes and
models are available at https://github.com/IIGROUP/MANIQA.


title:
Generalized stability theory of polydisperse particle-laden flows.
  Part1. Channel flow

abstract:
  We present a generalized hydrodynamic stability theory for interacting
particles in polydisperse particle-laden flows. The addition of dispersed
particulate matter to a clean flow can either stabilize or destabilize the
flow, depending on the particles' relaxation time-scale relative to the carrier
flow time scales and the particle loading. To study the effects of
polydispersity and particle interactions on the hydrodynamic stability of shear
flows, we propose a new mathematical framework by combining a linear stability
analysis and a discrete Eulerian sectional formulation to describe the flow and
the dispersed particulate matter. In this formulation, multiple momentum and
transport equations are written for each size-section of the dispersed phase,
where interphase and inter-particle mass and momentum transfer are modelled as
source terms in the governing equations. A new modal linear stability framework
is derived by linearizing the coupled equations. Using this approach,
particle-flow interactions, such as polydispersity, droplet vaporization,
condensation, and coalescence, may be modelled. The method is validated with
linear stability analyses of clean and monodisperse particle-laden flows. We
show that the stability characteristics of a channel flow laden with particles
drastically change due to polydispersity. While relatively large monodisperse
particles tend to stabilize the flow, adding a second size section of a very
small mass fraction of low-to-moderate Stokes number particles may
significantly increase the growth rates, and for high-Reynolds numbers may
destabilize flows that might have been regarded as linearly stable in the
monodisperse case. These findings may apply to a vast number of fluid mechanics
applications involving particle-laden flows such as atmospheric flows,
environmental flows, medical applications, propulsion, and energy systems.


title: Building Odia Shallow Parser

abstract:
  Shallow parsing is an essential task for many NLP applications like machine
translation, summarization, sentiment analysis, aspect identification and many
more. Quality annotated corpora is critical for building accurate shallow
parsers. Many Indian languages are resource poor with respect to the
availability of corpora in general. So, this paper is an attempt towards
creating quality corpora for shallow parsers. The contribution of this paper is
two folds: creation pos and chunk annotated corpora for Odia and development of
baseline systems for pos tagging and chunking in Odia.


title: Optimal Layered Defense For Site Protection

abstract:
  We present a model for layered security with applications to the protection
of sites such as stadiums or large gathering places. We formulate the problem
as one of maximizing the capture of illegal contraband. The objective function
is indefinite and only limited information can be gained when the problem is
solved by standard convex optimization methods. In order to solve the model, we
develop a dynamic programming approach, and study its convergence properties.
Additionally, we formulate a version of the problem aimed at addressing
intelligent adversaries who can adjust their direction of attack as they
observe changes in the site security. Furthermore, we also develop a method for
the solution of the latter model. Finally, we perform computational experiments
to demonstrate the use of our methods.


title: CEDR -- A Compiler-integrated, Extensible DSSoC Runtime

abstract:
  In this work, we present CEDR, a Compiler-integrated, Extensible Domain
Specific System on Chip Runtime ecosystem to facilitate research towards
addressing the challenges of architecture, system software and application
development with distinct plug-and-play integration points in a unified compile
time and run time workflow. We demonstrate the utility of CEDR on the Xilinx
Zynq MPSoC-ZCU102 for evaluating performance of pre-silicon hardware in the
trade space of SoC configuration, scheduling policy and workload complexity
based on dynamically arriving workload scenarios composed of real-life signal
processing applications scaling to thousands of application instances with FFT
and matrix multiply accelerators. We provide insights into the trade-offs
present in this design space through a number of distinct case studies. CEDR is
portable and has been deployed and validated on Odroid-XU3, X86 and Nvidia
Jetson Xavier based SoC platforms. Taken together, CEDR is a capable
environment for enabling research in exploring the boundaries of productive
application development, resource management heuristic development, and
hardware configuration analysis for heterogeneous architectures.


title: Rapid screening of high-throughput ground state predictions

abstract:
  High-through computational thermodynamic approaches are becoming an
increasingly popular tool to uncover novel compounds. However, traditional
methods tend to be limited to stability predictions of stoichiometric phases at
absolute zero. Such methods thus carry the risk of identifying an excess of
possible phases that do not survive to temperatures of practical relevance. We
demonstrate how the Calphad formalism, informed by simple first-principles
input can be simply used to overcome this problem at a low computational cost
and deliver quantitatively useful phase diagram predictions at all
temperatures. We illustrate the method by re-assessing prior compound formation
predictions and reconcile these findings with long-standing experimental
evidence to the contrary.


title:
Adaptive measurement filter: efficient strategy for optimal estimation
  of quantum Markov chains

abstract:
  Continuous-time measurements are instrumental for a multitude of tasks in
quantum engineering and quantum control, including the estimation of dynamical
parameters of open quantum systems monitored through the environment. However,
such measurements do not extract the maximum amount of information available in
the output state, so finding alternative optimal measurement strategies is a
major open problem.
  In this paper we solve this problem in the setting of discrete-time
input-output quantum Markov chains. We present an efficient algorithm for
optimal estimation of one-dimensional dynamical parameters which consists of an
iterative procedure for updating a `measurement filter' operator and
determining successive measurement bases for the output units. A key ingredient
of the scheme is the use of a coherent quantum absorber as a way to
post-process the output after the interaction with the system. This is designed
adaptively such that the joint system and absorber stationary state is pure at
a reference parameter value. The scheme offers an exciting prospect for optimal
continuous-time adaptive measurements, but more work is needed to find
realistic practical implementations.

categories: quant-ph math-ph math.MP math.ST stat.TH


title: Per Clip Lagrangian Multiplier Optimisation for HEVC

abstract:
  The majority of internet traffic is video content. This drives the demand for
video compression in order to deliver high quality video at low target
bitrates. This paper investigates the impact of adjusting the rate distortion
equation on compression performance. An constant of proportionality, k, is used
to modify the Lagrange multiplier used in H.265 (HEVC). Direct optimisation
methods are deployed to maximise BD-Rate improvement for a particular clip.
This leads to up to 21% BD-Rate improvement for an individual clip. Furthermore
we use a more realistic corpus of material provided by YouTube. The results
show that direct optimisation using BD-rate as the objective function can lead
to further gains in bitrate savings that are not available with previous
approaches.


title:
Per-clip adaptive Lagrangian multiplier optimisation with low-resolution
  proxies

abstract:
  This work focuses on reducing the computational cost of repeated video
encodes by using a lower resolution clip as a proxy. Features extracted from
the low resolution clip are used to learn an optimal lagrange multiplier for
rate control on the original resolution clip. In addition to reducing the
computational cost and encode time by using lower resolution clips, we also
investigate the use of older, but faster codecs such as H.264 to create
proxies. This work shows that the computational load is reduced by 22 times
using 144p proxies. Our tests are based on the YouTube UGC dataset, hence our
results are based on a practical instance of the adaptive bitrate encoding
problem. Further improvements are possible, by optimising the placement and
sparsity of operating points required for the rate distortion curves.


title: When Is Partially Observable Reinforcement Learning Not Scary?

abstract:
  Applications of Reinforcement Learning (RL), in which agents learn to make a
sequence of decisions despite lacking complete information about the latent
states of the controlled system, that is, they act under partial observability
of the states, are ubiquitous. Partially observable RL can be notoriously
difficult -- well-known information-theoretic results show that learning
partially observable Markov decision processes (POMDPs) requires an exponential
number of samples in the worst case. Yet, this does not rule out the existence
of large subclasses of POMDPs over which learning is tractable.
  In this paper we identify such a subclass, which we call weakly revealing
POMDPs. This family rules out the pathological instances of POMDPs where
observations are uninformative to a degree that makes learning hard. We prove
that for weakly revealing POMDPs, a simple algorithm combining optimism and
Maximum Likelihood Estimation (MLE) is sufficient to guarantee polynomial
sample complexity. To the best of our knowledge, this is the first provably
sample-efficient result for learning from interactions in overcomplete POMDPs,
where the number of latent states can be larger than the number of
observations.


title: A general descent principle for compact support extensions of functors

abstract:
  A characteristic property of compact support cohomology is the long exact
sequence that connects the compact support cohomology groups of a space, an
open subspace and its complement. Given an arbitrary cohomology theory of
algebraic varieties, one can ask whether a compact support version exists,
satisfying such a long exact sequence. This is the case whenever the cohomology
theory satisfies descent for abstract blowups (also known as proper cdh
descent). We make this precise by proving an equivalence between certain
categories of hypersheaves. We show how several classical and non-trivial
results, such as the existence of a unique weight filtration on compact support
cohomology, can be derived from this theorem.


title: Couplings and primitives on topological spaces

abstract:
  For an open covering $\mathcal U$ of a topological space and a map
$d\colon\mathcal I\to\mathbb{K}$, where $\mathcal I:=\bigl\{(U,V)\in\mathcal
U\times\mathcal U;\ U\cap V\ne\varnothing\bigr\}$, we present a context for the
existence of a map $C\colon\mathcal U\to\mathbb{K}$ satisfying $C_V-C_U=d_{UV}$
for all $(U,V)\in\mathcal I$. The result is applied to a Poincar\'e type
theorem concerning distributional potentials.


title: Rendering Nighttime Image Via Cascaded Color and Brightness Compensation

abstract:
  Image signal processing (ISP) is crucial for camera imaging, and neural
networks (NN) solutions are extensively deployed for daytime scenes. The lack
of sufficient nighttime image dataset and insights on nighttime illumination
characteristics poses a great challenge for high-quality rendering using
existing NN ISPs. To tackle it, we first built a high-resolution nighttime
RAW-RGB (NR2R) dataset with white balance and tone mapping annotated by expert
professionals. Meanwhile, to best capture the characteristics of nighttime
illumination light sources, we develop the CBUnet, a two-stage NN ISP to
cascade the compensation of color and brightness attributes. Experiments show
that our method has better visual quality compared to traditional ISP pipeline,
and is ranked at the second place in the NTIRE 2022 Night Photography Rendering
Challenge for two tracks by respective People's and Professional Photographer's
choices. The code and relevant materials are avaiable on our website:
https://njuvision.github.io/CBUnet.


title: Prime factors of $\Phi_3(x)$ of the same form

abstract:
  We parameterize solutions to the equality
$\Phi_3(x)=\Phi_3(a_1)\Phi_3(a_2)\cdots\Phi_3(a_n)$ when each $\Phi_3(a_i)$ is
prime. Our focus is on the special cases when $n=2,3,4$, as this analysis
simplifies and extends bounds on the total number of prime factors of an odd
perfect number.


title: Shallow camera pipeline for night photography rendering

abstract:
  We introduce a camera pipeline for rendering visually pleasing photographs in
low light conditions, as part of the NTIRE2022 Night Photography Rendering
challenge. Given the nature of the task, where the objective is verbally
defined by an expert photographer instead of relying on explicit ground truth
images, we design an handcrafted solution, characterized by a shallow structure
and by a low parameter count. Our pipeline exploits a local light enhancer as a
form of high dynamic range correction, followed by a global adjustment of the
image histogram to prevent washed-out results. We proportionally apply image
denoising to darker regions, where it is more easily perceived, without losing
details on brighter regions. The solution reached the fifth place in the
competition, with a preference vote count comparable to those of other entries,
based on deep convolutional neural networks. Code is available at
www.github.com/AvailableAfterAcceptance.


title: The [CII] and FIR properties of z>6 radio-loud quasars

abstract:
  There are only five radio-loud quasars currently known within 1 Gyr from the
Big Bang ($z>6$) and the properties of their host galaxies have not been
explored in detail. We present a NOrthern Extended Millimeter Array (NOEMA)
survey of [CII] (158 $\mu$m) and underlying continuum emission of four $z>6$
radio-loud quasars, revealing their diverse properties. J0309+2717 ($z=6.10$)
has a bright [CII] line and underlying continuum, implying a starburst with a
star-formation rate SFR=$340-1200$ $M_\odot$ yr$^{-1}$. J1429+5447 ($z=6.18$)
has a SFR=$520-870$ $M_{\odot}$yr$^{-1}$ and its [CII] profile is consistent
with two Gaussians, which could be interpreted as a galaxy merger. J1427+3312
($z=6.12$) has a moderate SFR=$30-90$ $M_\odot$ yr$^{-1}$. Notably, this is a
broad absorption line quasar and we searched for the presence of high-velocity
outflows in the host galaxy. Although the NOEMA data reveal a tentative broad
component of the [CII] line as wide as $\sim$1400~km~s$^{-1}$, the sensitivity
of our current data is not sufficient to confirm it. Finally, P172+18
($z=6.82$) is undetected in both [CII] and continuum, implying a SFR$<22-40$
$M_{\odot}$yr$^{-1}$. The broad range of SFRs is similar to what is observed in
radio-quiet quasars at similar redshifts. If radio jets do not significantly
contribute to both [CII] and IR luminosities, this suggest no feedback from the
jet on the star formation in the host galaxy.


title:
A comparison of different atmospheric turbulence simulation methods for
  image restoration

abstract:
  Atmospheric turbulence deteriorates the quality of images captured by
long-range imaging systems by introducing blur and geometric distortions to the
captured scene. This leads to a drastic drop in performance when computer
vision algorithms like object/face recognition and detection are performed on
these images. In recent years, various deep learning-based atmospheric
turbulence mitigation methods have been proposed in the literature. These
methods are often trained using synthetically generated images and tested on
real-world images. Hence, the performance of these restoration methods depends
on the type of simulation used for training the network. In this paper, we
systematically evaluate the effectiveness of various turbulence simulation
methods on image restoration. In particular, we evaluate the performance of two
state-or-the-art restoration networks using six simulations method on a
real-world LRFID dataset consisting of face images degraded by turbulence. This
paper will provide guidance to the researchers and practitioners working in
this field to choose the suitable data generation models for training deep
models for turbulence mitigation. The implementation codes for the simulation
methods, source codes for the networks, and the pre-trained models will be
publicly made available.


title: Detecting Text Formality: A Study of Text Classification Approaches

abstract:
  Formality is an important characteristic of text documents. The automatic
detection of the formality level of a text is potentially beneficial for
various natural language processing tasks, such as retrieval of texts with a
desired formality level, integration in language learning and document editing
platforms, or evaluating the desired conversation tone by chatbots. Recently
two large-scale datasets were introduced for multiple languages featuring
formality annotation. However, they were primarily used for the training of
style transfer models. However, detection text formality on its own may also be
a useful application. This work proposes the first systematic study of
formality detection methods based on current (and more classic) machine
learning methods and delivers the best-performing models for public usage. We
conducted three types of experiments -- monolingual, multilingual, and
cross-lingual. The study shows the overcome of BiLSTM-based models over
transformer-based ones for the formality classification task. We release
formality detection models for several languages yielding state of the art
results and possessing tested cross-lingual capabilities.


title:
HMT: A Hardware-Centric Hybrid Bonsai Merkle Tree Algorithm for
  High-Performance Authentication

abstract:
  Bonsai Merkle tree (BMT) is a widely used data structure for authenticating
data/metadata in a secure computing system. However, the predominantly
recursive andsequential nature of traditional BMT algorithms make them
challenging to implement with Field-Programmable Gate Array (FPGA) in modern
heterogeneous computing platforms. In this work, we introduce HMT, a
hardware-friendly implementation methodology for BMT that enables the
verification and update processes to function independently, as well as saves
additional write-backs by making the update conditions more flexible compared
to previous algorithms. The methodology of HMT contributes both novel algorithm
revisions and innovative hardware techniques to implementing BMT. Our empirical
performance measurements have demonstrated that HMT can achieve up to 7x
improvement in bandwidth and 4.5x reduction in latency over the baseline.


title:
Disappeared Command: Spoofing Attack On Automatic Speech Recognition
  Systems with Sound Masking

abstract:
  The development of deep learning technology has greatly promoted the
performance improvement of automatic speech recognition (ASR) technology, which
has demonstrated an ability comparable to human hearing in many tasks. Voice
interfaces are becoming more and more widely used as input for many
applications and smart devices. However, existing research has shown that DNN
is easily disturbed by slight disturbances and makes false recognition, which
is extremely dangerous for intelligent voice applications controlled by voice.


title: Real-Time Face Recognition System

abstract:
  Over the past few decades, interest in algorithms for face recognition has
been growing rapidly and has even surpassed human-level performance. Despite
their accomplishments, their practical integration with a real-time
performance-hungry system is not feasible due to high computational costs. So
in this paper, we explore the recent, fast, and accurate face recognition
system that can be easily integrated with real-time devices, and tested the
algorithms on robot hardware platforms to confirm their robustness and speed.


title: Treating random sequential addition via the replica method

abstract:
  While many physical processes are non-equilibrium in nature, the theory and
modeling of such phenomena lag behind theoretical treatments of equilibrium
systems. The diversity of powerful theoretical tools available to describe
equilibrium systems has inspired strategies that map non-equilibrium systems
onto equivalent equilibrium analogs so that interrogation with standard
statistical mechanical approaches is possible. In this work, we revisit the
mapping from the non-equilibrium random sequential addition process onto an
equilibrium multi-component mixture via the replica method, allowing for
theoretical predictions of non-equilibrium structural quantities. We validate
the above approach by comparing the theoretical predictions to numerical
simulations of random sequential addition.


title:
Optimal Control for Kinematic Bicycle Model with Continuous-time Safety
  Guarantees: A Sequential Second-order Cone Programming Approach

abstract:
  The optimal control problem for the kinematic bicycle model is considered
where the trajectories are required to satisfy the safety constraints in the
continuous-time sense. Based on the differential flatness property of the
model, necessary and sufficient conditions in the flat space are provided to
guarantee safety in the state space. The optimal control problem is relaxed to
the problem of solving three second-order cone programs (SOCPs) sequentially,
which find the safe path, the trajectory duration, and the speed profile,
respectively. Solutions of the three SOCPs together provide a sub-optimal
solution to the original optimal control problem. Simulation examples and
comparisons with state-of-the-art optimal control solvers are presented to
demonstrate the effectiveness of the proposed approach.


title:
Finding an almost perfect matching in a hypergraph avoiding forbidden
  submatchings

abstract:
  In 1973, Erd\H{o}s conjectured the existence of high girth $(n,3,2)$-Steiner
systems. Recently, Glock, K\"{u}hn, Lo, and Osthus and independently Bohman and
Warnke proved the approximate version of Erd\H{o}s' conjecture. Just this year,
Kwan, Sah, Sawhney, and Simkin proved Erd\H{o}s' conjecture. As for Steiner
systems with more general parameters, Glock, K\"{u}hn, Lo, and Osthus
conjectured the existence of high girth $(n,q,r)$-Steiner systems. We prove the
approximate version of their conjecture.
  This result follows from our general main results which concern finding
perfect or almost perfect matchings in a hypergraph $G$ avoiding a given set of
submatchings (which we view as a hypergraph $H$ where $V(H)=E(G)$). Our first
main result is a common generalization of the classical theorems of Pippenger
(for finding an almost perfect matching) and Ajtai, Koml\'os, Pintz, Spencer,
and Szemer\'edi (for finding an independent set in girth five hypergraphs).
More generally, we prove this for coloring and even list coloring, and also
generalize this further to when $H$ is a hypergraph with small codegrees (for
which high girth designs is a specific instance). Indeed, the coloring version
of our result even yields an almost partition of $K_n^r$ into approximate high
girth $(n,q,r)$-Steiner systems.
  Our main results also imply the existence of a perfect matching in a
bipartite hypergraph where the parts have slightly unbalanced degrees. This has
a number of applications; for example, it proves the existence of $\Delta$
pairwise disjoint list colorings in the setting of Kahn's theorem; it also
proves asymptotic versions of various rainbow matching results in the sparse
setting (where the number of times a color appears could be much smaller than
the number of colors) and even the existence of many pairwise disjoint rainbow
matchings in such circumstances.


title: A ${\mathbb N}$atural Avenue

abstract:
  We consider an infinite sequence of rooted trees naturally emerging in a
number-theoretical context. We advance some ideas on its structure by
discussing some elementary properties. Some of those properties are shown to be
related to classical results or conjectures in number theory.


title: Transforming U.S. Particle Physics Education: A Snowmass 2021 Study

abstract:
  The pursuit of knowledge in particle physics requires constant learning. As
new tools become available, new theories are developed, and physicists search
for new answers with ever-evolving methods. However, it is the case that formal
educational systems serve as the primary training grounds for particle
physicists. Graduate school (and undergraduate school to a lesser extent) is
where researchers learn most of the technical skills required for research,
develop scientific problem-solving abilities, learn how to establish themselves
in their field, and begin developing their career. It is unfortunate, then,
that the skills gained by physicists during their formal education are often
mismatched with the skills actually required for a successful career in
physics. We performed a survey of the U.S. particle physics community to
determine the missing elements of graduate and undergraduate education and to
gauge how to bridge these gaps. In this contributed paper, part of the 2021-22
Snowmass Community Planning Exercise, we report the results of this survey. We
also recommend several specific community actions to improve the quality of
particle physics education; the "community" here refers to physics departments,
national labs, professional societies, funding agencies, and individual
physicists.


title:
A warm super-Neptune around the G-dwarf star TOI-1710 revealed with
  TESS, SOPHIE and HARPS-N

abstract:
  We report the discovery and characterization of the transiting extrasolar
planet TOI-1710$\:$b. It was first identified as a promising candidate by the
Transiting Exoplanet Survey Satellite (TESS). Its planetary nature was then
established with SOPHIE and HARPS-N spectroscopic observations via the RV
method. The stellar parameters for the host star are derived from the spectra
and a joint Markov chain Monte-Carlo (MCMC) adjustment of the spectral energy
distribution and evolutionary tracks of TOI-1710. A joint MCMC analysis of the
TESS light curve and the RV evolution allows us to determine the planetary
system properties. From our analysis, TOI-1710$\:$b is found to be a massive
warm super-Neptune ($M_{\rm p}=28.3\:\pm\:4.7\:{\rm M}_{\rm Earth}$ and $R_{\rm
p}=5.34\:\pm\:0.11\:{\rm R}_{\rm Earth}$) orbiting a G5V dwarf star ($T_{\rm
eff}=5665\pm~55\mathrm{K}$) on a nearly circular 24.3-day orbit
($e=0.16\:\pm\:0.08$). The orbital period of this planet is close to the
estimated rotation period of its host star $P_{\rm
rot}=22.5\pm2.0~\mathrm{days}$ and it has a low Keplerian semi-amplitude
$K=6.4\pm1.0~\mathrm{m\:s^{-1}}$; we thus performed additional analyses to show
the robustness of the retrieved planetary parameters. With a low bulk density
of $1.03\pm0.23~\mathrm{g\:cm^{-3}}$ and orbiting a bright host star ($J=8.3$,
$V=9.6$), TOI-1710$\:$b is one of the best targets in this mass-radius range
(near the Neptunian desert) for atmospheric characterization via transmission
spectroscopy, a key measurement in constraining planet formation and
evolutionary models of sub-Jovian planets.


abstract:
  This work proposes an extension to Structured Grammatical Evolution (SGE)
called Co-evolutionary Probabilistic Structured Grammatical Evolution
(Co-PSGE). In Co-PSGE each individual in the population is composed by a
grammar and a genotype, which is a list of dynamic lists, each corresponding to
a non-terminal of the grammar containing real numbers that correspond to the
probability of choosing a derivation rule. Each individual uses its own grammar
to map the genotype into a program. During the evolutionary process, both the
grammar and the genotype are subject to variation operators. The performance of
the proposed approach is compared to 3 different methods, namely, Grammatical
Evolution (GE), Probabilistic Grammatical Evolution (PGE), and SGE on four
different benchmark problems. The results show the effectiveness of the
approach since Co-PSGE is able to outperform all the methods with statistically
significant differences in the majority of the problems.


title: The 2020 Census Disclosure Avoidance System TopDown Algorithm

abstract:
  The Census TopDown Algorithm (TDA) is a disclosure avoidance system using
differential privacy for privacy-loss accounting. The algorithm ingests the
final, edited version of the 2020 Census data and the final tabulation
geographic definitions. The algorithm then creates noisy versions of key
queries on the data, referred to as measurements, using zero-Concentrated
Differential Privacy. Another key aspect of the TDA are invariants, statistics
that the Census Bureau has determined, as matter of policy, to exclude from the
privacy-loss accounting. The TDA post-processes the measurements together with
the invariants to produce a Microdata Detail File (MDF) that contains one
record for each person and one record for each housing unit enumerated in the
2020 Census. The MDF is passed to the 2020 Census tabulation system to produce
the 2020 Census Redistricting Data (P.L. 94-171) Summary File. This paper
describes the mathematics and testing of the TDA for this purpose.


title:
Deep learning based closed-loop optimization of geothermal reservoir
  production

abstract:
  To maximize the economic benefits of geothermal energy production, it is
essential to optimize geothermal reservoir management strategies, in which
geologic uncertainty should be considered. In this work, we propose a
closed-loop optimization framework, based on deep learning surrogates, for the
well control optimization of geothermal reservoirs. In this framework, we
construct a hybrid convolution-recurrent neural network surrogate, which
combines the convolution neural network (CNN) and long short-term memory (LSTM)
recurrent network. The convolution structure can extract spatial information of
geologic parameter fields and the recurrent structure can approximate
sequence-to-sequence mapping. The trained model can predict time-varying
production responses (rate, temperature, etc.) for cases with different
permeability fields and well control sequences. In the closed-loop optimization
framework, production optimization based on the differential evolution (DE)
algorithm, and data assimilation based on the iterative ensemble smoother
(IES), are performed alternately to achieve real-time well control optimization
and geologic parameter estimation as the production proceeds. In addition, the
averaged objective function over the ensemble of geologic parameter estimations
is adopted to consider geologic uncertainty in the optimization process.
Several geothermal reservoir development cases are designed to test the
performance of the proposed production optimization framework. The results show
that the proposed framework can achieve efficient and effective real-time
optimization and data assimilation in the geothermal reservoir production
process.


title:
CPU- and GPU-based Distributed Sampling in Dirichlet Process Mixtures
  for Large-scale Analysis

abstract:
  In the realm of unsupervised learning, Bayesian nonparametric mixture models,
exemplified by the Dirichlet Process Mixture Model (DPMM), provide a principled
approach for adapting the complexity of the model to the data. Such models are
particularly useful in clustering tasks where the number of clusters is
unknown. Despite their potential and mathematical elegance, however, DPMMs have
yet to become a mainstream tool widely adopted by practitioners. This is
arguably due to a misconception that these models scale poorly as well as the
lack of high-performance (and user-friendly) software tools that can handle
large datasets efficiently. In this paper we bridge this practical gap by
proposing a new, easy-to-use, statistical software package for scalable DPMM
inference. More concretely, we provide efficient and easily-modifiable
implementations for high-performance distributed sampling-based inference in
DPMMs where the user is free to choose between either a multiple-machine,
multiple-core, CPU implementation (written in Julia) and a multiple-stream GPU
implementation (written in CUDA/C++). Both the CPU and GPU implementations come
with a common (and optional) python wrapper, providing the user with a single
point of entry with the same interface. On the algorithmic side, our
implementations leverage a leading DPMM sampler from (Chang and Fisher III,
2013). While Chang and Fisher III's implementation (written in MATLAB/C++) used
only CPU and was designed for a single multi-core machine, the packages we
proposed here distribute the computations efficiently across either multiple
multi-core machines or across mutiple GPU streams. This leads to speedups,
alleviates memory and storage limitations, and lets us fit DPMMs to
significantly larger datasets and of higher dimensionality than was possible
previously by either (Chang and Fisher III, 2013) or other DPMM methods.


title:
Efficient Deep Learning-based Estimation of the Vital Signs on
  Smartphones

abstract:
  Nowadays, due to the widespread use of smartphones in everyday life and the
improvement of computational capabilities of these devices, many complex tasks
can now be deployed on them. Concerning the need for continuous monitoring of
vital signs, especially for the elderly or those with certain types of
diseases, the development of algorithms that can estimate vital signs using
smartphones has attracted researchers worldwide. Such algorithms estimate vital
signs (heart rate and oxygen saturation level) by processing an input PPG
signal. These methods often apply multiple pre-processing steps to the input
signal before the prediction step. This can increase the computational
complexity of these methods, meaning only a limited number of mobile devices
can run them. Furthermore, multiple pre-processing steps also require the
design of a couple of hand-crafted stages to obtain an optimal result. This
research proposes a novel end-to-end solution to mobile-based vital sign
estimation by deep learning. The proposed method does not require any
pre-processing. Due to the use of fully convolutional architecture, the
parameter count of our proposed model is, on average, a quarter of the ordinary
architectures that use fully-connected layers as the prediction heads. As a
result, the proposed model has less over-fitting chance and computational
complexity. A public dataset for vital sign estimation, including 62 videos
collected from 35 men and 27 women, is also provided. The experimental results
demonstrate state-of-the-art estimation accuracy.


title:
Study of Robust Sparsity-Aware RLS algorithms with Jointly-Optimized
  Parameters for Impulsive Noise Environments

abstract:
  This paper proposes a unified sparsity-aware robust recursive least-squares
RLS (S-RRLS) algorithm for the identification of sparse systems under impulsive
noise. The proposed algorithm generalizes multiple algorithms only by replacing
the specified criterion of robustness and sparsity-aware penalty. Furthermore,
by jointly optimizing the forgetting factor and the sparsity penalty parameter,
we develop the jointly-optimized S-RRLS (JO-S-RRLS) algorithm, which not only
exhibits low misadjustment but also can track well sudden changes of a sparse
system. Simulations in impulsive noise scenarios demonstrate that the proposed
S-RRLS and JO-S-RRLS algorithms outperform existing techniques.


title: Mathematics of Floating 3D Printed Objects

abstract:
  We explore the stability of floating objects through mathematical modeling
and experimentation. Our models are based on standard ideas of center of
gravity, center of buoyancy, and Archimedes' Principle. We investigate a
variety of floating shapes with two-dimensional cross sections and identify
analytically and/or computationally a potential energy landscape that helps
identify stable and unstable floating orientations. We compare our analyses and
computations to experiments on floating objects designed and created through 3D
printing. In addition to our results, we provide code for testing the floating
configurations for new shapes, as well as giving details of the methods for 3D
printing the objects. The paper includes conjectures and open problems for
further study.


title: Unit-Disk Range Searching and Applications

abstract:
  Given a set $P$ of $n$ points in the plane, we consider the problem of
computing the number of points of $P$ in a query unit disk (i.e., all query
disks have the same radius). We show that the main techniques for simplex range
searching in the plane can be adapted to this problem. For example, by adapting
Matou\v{s}ek's results, we can build a data structure of $O(n)$ space so that
each query can be answered in $O(\sqrt{n})$ time. Our techniques lead to
improvements for several other classical problems, such as batched range
searching, counting/reporting intersecting pairs of unit circles, distance
selection, discrete 2-center, etc. For example, given a set of $n$ unit disks
and a set of $n$ points in the plane, the batched range searching problem is to
compute for each disk the number of points in it. Previous work [Katz and
Sharir, 1997] solved the problem in $O(n^{4/3}\log n)$ time while our new
algorithm runs in $O(n^{4/3})$ time.


title: Metappearance: Meta-Learning for Visual Appearance Reproduction

abstract:
  There currently are two main approaches to reproducing visual appearance
using Machine Learning (ML): The first is training models that generalize over
different instances of a problem, e.g., different images from a dataset. Such
models learn priors over the data corpus and use this knowledge to provide fast
inference with little input, often as a one-shot operation. However, this
generality comes at the cost of fidelity, as such methods often struggle to
achieve the final quality required. The second approach does not train a model
that generalizes across the data, but overfits to a single instance of a
problem, e.g., a flash image of a material. This produces detailed and
high-quality results, but requires time-consuming training and is, as mere
non-linear function fitting, unable to exploit previous experience. Techniques
such as fine-tuning or auto-decoders combine both approaches but are sequential
and rely on per-exemplar optimization. We suggest to combine both techniques
end-to-end using meta-learning: We over-fit onto a single problem instance in
an inner loop, while also learning how to do so efficiently in an outer-loop
that builds intuition over many optimization runs. We demonstrate this concept
to be versatile and efficient, applying it to RGB textures, Bi-directional
Reflectance Distribution Functions (BRDFs), or Spatially-varying BRDFs
(svBRDFs).


title:
Calculate the Optimum Threshold for Double Energy Detection Technique in
  Cognitive Radio Networks (CRNs)

abstract:
  One of the most important technical challenges when designing a Cognitive
Radio Networks (CRNs) is spectrum sensing, which has the responsibility of
recognizing the presence or absence of the primary users in the frequency
bands. A common technique used for spectrum sensing is double energy detection
since it can operate without any prior information regarding the
characteristics of the primary user signals. A double threshold energy
detection algorithm is based on the use of two thresholds, to check the energy
of the received signals and decided whether the spectrum is occupied or not.
Furthermore, thresholds play a key role in the energy detection algorithm, by
considering the stochastic features of noise in this model, as a result
calculating the optimal threshold is a crucial task. In this paper, the
Bi-Section algorithm was used to detect the optimum energy level in the fuzzy
region which is an area between the low and high energy threshold. For this
purpose, the decision threshold was determined by the use of the Bisection
function for cognitive users. Numerical simulations show that the proposed
method achieves better detection performance than the conventional
double-threshold energy-sensing schemes. Moreover, the presented technique has
advantages such as increasing the probability of detection of primary users and
decreasing the probability of Collison between primary and secondary users.


title: Evaluating Digital Sine wave Generator Using Analog Metrics

abstract:
  This work aims to relate comparison metrics for both Direct Digital
Synthesizers (DDS) and their analog counterparts. The proposed metrics are
Total Harmonic Distortion (THD) and maximum absolute error. Error is
theoretically formulated into closed forms for known systematic parameters of
DDS, sample rate, and bit counts. By the use of Matlab scripting, the system
model is simulated for a wide range of parameters sweep.


title: Time Difference on Arrival Extraction from Two-Way Ranging

abstract:
  Two-Way Ranging enables the distance estimation between two active parties
and allows time of flight measurements despite relative clock offset and drift.
Limited by the number of messages, scalable solutions build on Time Difference
on Arrival to infer timing information at passive listeners. However, the
demand for accurate distance estimates dictates a tight bound on the time
synchronization, thus limiting scalability to the localization of passive tags
relative to static, synchronized anchors. This work describes the extraction of
Time Difference on Arrival information from a Two-Way Ranging process, enabling
the extraction of distance information on passive listeners and further
allowing scalable tag localization without the need for static or synchronized
anchors. The expected error is formally deducted. The extension allows the
extraction of the timing difference despite relative clock offset and drift for
the Double-Sided Two-Way Ranging and Single-Sided Two-Way Ranging with
additional carrier frequency offset estimation.


title:
A Benchmark for Automatic Medical Consultation System: Frameworks, Tasks
  and Datasets

abstract:
  In recent years, interest has arisen in using machine learning to improve the
efficiency of automatic medical consultation and enhance patient experience. In
this paper, we propose two frameworks to support automatic medical
consultation, namely doctor-patient dialogue understanding and task-oriented
interaction. A new large medical dialogue dataset with multi-level fine-grained
annotations is introduced and five independent tasks are established, including
named entity recognition, dialogue act classification, symptom label inference,
medical report generation and diagnosis-oriented dialogue policy. We report a
set of benchmark results for each task, which shows the usability of the
dataset and sets a baseline for future studies.


title: Optimal Power Flow Schedules with Reduced Low-Frequency Oscillations

abstract:
  The dynamic response of power grids to small events or persistent stochastic
disturbances influences their stable operation. Low-frequency inter-area
oscillations are of particular concern due to insufficient damping. This paper
studies the effect of the operating point on the linear time-invariant dynamics
of power networks. A pertinent metric based on the frequency response of grid
dynamics is proposed to quantify power system's stability against inter-area
oscillations. We further put forth an optimal power flow formulation to yield a
grid dispatch that optimizes this novel stability metric. A semidefinite
program (SDP) relaxation is employed to yield a computationally tractable
convex problem. Numerical tests on the IEEE-39 bus system demonstrate that the
SDP relaxation is exact yielding a rank-1 solution. The relative trade-off of
the proposed small-signal stability metric versus the generation cost is also
studied.


title: STPA-driven Multilevel Runtime Monitoring for In-time Hazard Detection

abstract:
  Runtime verification or runtime monitoring equips safety-critical
cyber-physical systems to augment design assurance measures and ensure
operational safety and security. Cyber-physical systems have interaction
failures, attack surfaces, and attack vectors resulting in unanticipated
hazards and loss scenarios. These interaction failures pose challenges to
runtime verification regarding monitoring specifications and monitoring
placements for in-time detection of hazards. We develop a well-formed workflow
model that connects system theoretic process analysis, commonly referred to as
STPA, hazard causation information to lower-level runtime monitoring to detect
hazards at the operational phase. Specifically, our model follows the DepDevOps
paradigm to provide evidence and insights to runtime monitoring on what to
monitor, where to monitor, and the monitoring context. We demonstrate and
evaluate the value of multilevel monitors by injecting hazards on an autonomous
emergency braking system model.


title:
Neutron star radii, deformabilities, and moments of inertia from
  experimental and ab initio theory constraints on the 208Pb neutron skin
  thickness

abstract:
  Recent experimental and ab initio theory investigations of the 208Pb neutron
skin thickness are sufficiently precise to inform the neutron star equation of
state. In particular, the strong correlation between the 208Pb neutron skin
thickness and the pressure of neutron matter at normal nuclear densities leads
to modified predictions for the radii, tidal deformabilities, and moments of
inertia of typical 1.4 solar-mass neutron stars. In the present work, we study
the relative impact of these recent analyses of the 208Pb neutron skin
thickness on bulk properties of neutron stars within a Bayesian statistical
analysis. Two models for the equation of state prior are employed in order to
study the role of the highly uncertain high-density equation of state. From our
combined Bayesian analysis of nuclear theory, nuclear experiment, and
observational constraints on the dense matter equation of state, we find at the
90% credibility level $R_{1.4}=12.36^{+0.38}_{-0.73}$ km for the radius of a
1.4 solar-mass neutron star, $R_{2.0}=11.96^{+0.94}_{-0.71}$ km for the radius
of a 2.0 solar-mass neutron star, $\Lambda_{1.4}=440^{+103}_{-144}$ for the
tidal deformability of a 1.4 solar-mass neutron star, and
$I_{1.338}=1.425^{+0.074}_{-0.146}\, \times 10^{45}\,\rm{g\,cm}^{2}$ for the
moment of inertia of PSR J0737-3039A whose mass is 1.338 solar masses.


title: $W$-Boson Mass Anomaly from Scale Invariant 2HDM

abstract:
  The recently reported measurement of the $W$-boson mass by CDF-II
collaboration is significantly heavier than that of the Standard Model
prediction. We study this anomaly in the scale invariant
Two-Higgs-Doublet-Model (2HDM) with a $\mathbb{Z}_2$ symmetry to avoid the
flavor-changing-neutral-current (FCNC). In this scenario the Higgs particle is
the classically massless scalon in the scale invariant 2HDM gaining its mass by
radiative corrections, hence being naturally light. Moreover, because of the
scale symmetry the model is more predictive respect to the generic 2HDM. We
show that the oblique parameters depending on the masses of the charged and
CP-even (CP-odd) neutral scalar components of the 2HDM denoted respectively by
$m_{H^\pm}$ and $m_h$ ($m_A$), are large enough to accommodate the $W$-boson
mass anomaly in the model. There are as well viable regions in the mass
spectrum of the scale invariant 2HDM that evade the experimental bounds from
colliders on the charged Higgs.


title: On existence of hypersurfaces translating by powers of Gauss curvature

abstract:
  In this paper we construct complete convex hypersurfaces in $\mathbb R^{n+1}$
which translate under the flow by powers $\alpha \in (0, \frac1{n+2})$ of the
Gauss curvature. The level set of each solution is asymptotic to a shrinking
soliton for the flow by power $\frac \alpha {1-\alpha}$ of the Gauss curvature
in $\mathbb R^n$. For example, our construction reveals the existence of
translators whose level set converges to the sphere, simplex, hypercube and so
on. The translating solitons exist as a family whose parameters correspond to
Jacobi fields, solutions to linearized equation around the asymptotic profile.


title: Depinning and flow of a vortex line in an uniaxial random medium

abstract:
  We study numerically and analytically the dynamics of a single directed
elastic string driven through a 3-dimensional disordered medium. In the
quasistatic limit the string is super-rough in the driving direction, with
roughness exponent $\zeta_{\parallel} = 1.25\pm 0.01$, dynamic exponent
$z_{\parallel}= 1.43 \pm 0.01$, correlation-length exponent $\nu= 1.33 \pm
0.02$, depinning exponent $\beta = 0.24\pm 0.01$, and avalanche-size exponent
$\tau_{\parallel} = 1.09 \pm 0.03$. In the transverse direction we find
$\zeta_{\perp} = 0.5 \pm 0.01$, $z_{\perp} = 2.27 \pm 0.05$, and $\tau_{\perp}
=1.17\pm 0.06$. We show that transverse fluctuations do not alter the critical
exponents in the driving direction, as predicted by the planar approximation
(PA) proposed in 1996 by Ertas and Kardar (EK). We check the PA for the
force-force correlator, comparing to the functional renormalization group and
simulations. Both Random-Bond (RB) and Random-Field (RF) disorder yield a
single universality class, indistinguishable from the one of an elastic string
in a 2-dimensional random medium. While $z_{\perp}=z_{\parallel}+1/\nu$ and
$\nu=1/(2-\zeta_{\parallel})$ of EK are satisfied, the transversal movement is
that of a Brownian, with a clock set locally by the forward movement. This
implies $\zeta_\perp = (2-d)/2$, distinct from EK. At small driving velocities
the distribution of local parallel displacements has a negative skewness, while
in the transverse direction it is a Gaussian. For large scales, the system can
be described by anisotropic effective temperatures. In the fast-flow regime the
local displacement distributions become Gaussian in both directions and the
effective temperatures vanish as $T^{\perp}_{\tt eff}\sim 1/v$ and
$T^{\parallel}_{\tt eff}\sim 1/v^3$ for RB and as $T^{\perp}_{\tt eff} \approx
T^{\parallel}_{\tt eff} \sim 1/v$ for RF.


title: Regulation of stem cell dynamics through volume exclusion

abstract:
  Maintenance and regeneration of adult tissues rely on the self-renewal of
stem cells. Regeneration without over-proliferation requires precise regulation
of the stem cell proliferation and differentiation rates. The nature of such
regulatory mechanisms in different tissues, and how to incorporate them in
models of stem cell population dynamics, is incompletely understood. The
critical birth-death (CBD) process is widely used to model stem cell
populations, capturing key phenomena, such as population asymmetry and scaling
laws in clone size distributions. However, the CBD process neglects regulatory
mechanisms. Here, we propose the birth-death process with volume exclusion
(vBD), a variation of the birth-death process that takes into account crowding
effects, such as may arise due to limited space in a stem cell niche. While the
deterministic rate equations predict a single non-trivial attracting steady
state, the master equation predicts extinction and a transient distribution of
stem cell numbers that can be bimodal. We investigate the accuracy of the
system-size expansion (including finite size corrections to the linear-noise
approximation), the quasi-steady state approximation, and the WKB method to
approximate the probability distribution solution of the vBD master equation,
as well as the mean extinction time. Our study suggests that the size
distribution of a stem cell population and its extinction dynamics bear
signatures that may be useful to detect negative feedback mediated via volume
exclusion.


title:
Discovery prospects of a vectorlike top partner decaying to a singlet
  boson

abstract:
  The possibility of a vectorlike top partner decaying to a new colourless
weak-singlet scalar or pseudoscalar has attracted some attention in the
literature. We investigate the production of a weak-singlet charge-$2/3$ $T$
quark that can decay to a spinless boson ($\Phi$) and a top quark at the LHC.
Earlier, in 2203.13753, we have shown that in a large part of the parameter
space, the $T\to t\Phi$ and the loop-induced $\Phi \to gg$ decays become the
dominant decay modes for these particles. Here, we investigate the discovery
prospects of the $T$ quark in this region through the above decays. In
particular, we focus on the $pp\to TT\to (t\Phi)(t\Phi)\to (t(gg))\,(t(gg))$
channel. Separating this signal from the huge Standard Model background is a
challenging task, forcing us to employ a multivariate machine-learning
technique. We find that the above channel can be a discovery channel of the top
partner in the large part of the parameter space where the above decay chain
dominates. Our analysis is largely model-independent, and hence our results
would be useful in a broad class of new physics models.


title:
Geodesic stability and quasinormal modes of non-commutative
  Schwarzschild black hole employing Lyapunov exponent

abstract:
  We study the dynamics of test particle and stability of circular geodesics in
the gravitational field of a non-commutative geometry inspired Schwarzschild
black hole spacetime (NCSBH). The coordinate time Lyapunov exponent
($\lambda_{c}$) is crucial to investigate the stability of equatorial circular
geodesics of massive and massless test particles. The stability or instability
of circular orbits are discussed by analysing the variation of Lyapunov
exponent with radius of these orbits for different values of non-commutative
parameter ($\alpha$). In the case of null circular orbits, the instability
exponent is calculated and presented to discuss the instability of null
circular orbits. Further, by relating parameters corresponding to null circular
geodesics (i.e. angular frequency and Lyapunov exponent), the quasinormal modes
(QNMs) for a massless scalar field perturbation in the eikonal approximation
are evaluated, and also visualised by relating the real and imaginary parts.
The nature of scalar field potential, by varying the non-commutative parameter
($\alpha$) and angular momentum of perturbation ($l$), are also observed and
discussed.


title: Opal: Multimodal Image Generation for News Illustration

abstract:
  Multimodal AI advancements have presented people with powerful ways to create
images from text. Recent work has shown that text-to-image generations are able
to represent a broad range of subjects and artistic styles. However,
translating text prompts into visual messages is difficult. In this paper, we
address this challenge with Opal, a system that produces text-to-image
generations for editorial illustration. Given an article text, Opal guides
users through a structured search for visual concepts and provides pipelines
allowing users to illustrate based on an article's tone, subjects, and intended
illustration style. Our evaluation shows that Opal efficiently generates
diverse sets of editorial illustrations, graphic assets, and concept ideas.
Users with Opal were more efficient at generation and generated over two times
more usable results than users without. We conclude on a discussion of how
structured and rapid exploration can help users better understand the
capabilities of human AI co-creative systems.


title: Dynamically vanishing Dirac neutrino mass from quantum scale symmetry

abstract:
  We present a mechanism which drives Dirac neutrino masses to tiny values
along the Renormalization Group flow, starting from an asymptotically safe
ultraviolet completion of the third generation of the Standard Model including
quantum gravity. At the same time, the mechanism produces a mass-splitting
between the neutrino and the quark sector and also generates the mass splitting
between top and bottom quark. The mechanism hinges on the hypercharges of the
fermions and produces a tiny neutrino Yukawa coupling, because the right-handed
neutrino is sterile and does not carry hypercharge.


title: A Fixed-Parameter Algorithm for the Schrijver Problem

abstract:
  The Schrijver graph $S(n,k)$ is defined for integers $n$ and $k$ with $n \geq
2k$ as the graph whose vertices are all the $k$-subsets of $\{1,2,\ldots,n\}$
that do not include two consecutive elements modulo $n$, where two such sets
are adjacent if they are disjoint. A result of Schrijver asserts that the
chromatic number of $S(n,k)$ is $n-2k+2$ (Nieuw Arch. Wiskd., 1978). In the
computational Schrijver problem, we are given an access to a coloring of the
vertices of $S(n,k)$ with $n-2k+1$ colors, and the goal is to find a
monochromatic edge. The Schrijver problem is known to be complete in the
complexity class $\mathsf{PPA}$. We prove that it can be solved by a randomized
algorithm with running time $n^{O(1)} \cdot k^{O(k)}$, hence it is
fixed-parameter tractable with respect to the parameter $k$.


title: Schwarzian for colored Jackiw-Teitelboim gravity

abstract:
  We study the boundary effective action of the colored version of the
Jackiw-Teitelboim (JT) gravity. We derive the boundary action, which is the
color generalization of the Schwarzian action, from the $su(N,N)$ BF
formulation of the colored JT gravity. Using different types of the $SU(N,N)$
group decompositions both the zero and finite temperature cases are elaborated.
We provide the semi-classical perturbative analysis of the boundary action and
discuss the instability of the spin-1 mode and its implication for the quantum
chaos. A rainbow-AdS$_2$ geometry is introduced where the color gauge symmetry
is spontaneously broken.


title: Cascading traffic jamming in a two-dimensional Motter and Lai model

abstract:
  We study the cascading traffic jamming on a two-dimensional random geometric
graph using the Motter and Lai model. The traffic jam is caused by a localized
attack incapacitating circular region or a line of a certain size, as well as a
dispersed attack on an equal number of randomly selected nodes. We investigate
if there is a critical size of the attack above which the network becomes
completely jammed due to cascading jamming, and how this critical size depends
on the average degree $\langle k\rangle$ of the graph, on the number of nodes
$N$ in the system, and the tolerance parameter $\alpha$ of the Motter and Lai
model.


title: Going beyond Soft plus Virtual

abstract:
  We present a formalism that sums up the soft-virtual (SV) and next-to-SV
(NSV) diagonal contributions to inclusive colorless productions in hadron
colliders to all orders in perturbative QCD. Using factorisation theorem,
renormalisation group invariance and employing the transcendental structure of
perturbative results, we show the exponential behavior of soft-collinear
function. This allows us to predict certain SV and NSV terms to all orders from
lower order information. We also present an integral representation for the
coefficient functions which is suitable for Mellin $N$-space resummation.


title:
A Pattern Avoidance Characterization for Smoothness of Positroid
  Varieties

abstract:
  Positroids are certain representable matroids originally studied by Postnikov
in connection with the totally nonnegative Grassmannian and now used widely in
algebraic combinatorics. The positroids give rise to determinantal equations
defining positroid varieties as subvarieties of the Grassmannian variety.
Rietsch, Knutson-Lam-Speyer and Pawlowski studied geometric and cohomological
properties of these varieties. In this paper, we continue the study of the
geometric properties of positroid varieties by establishing several equivalent
conditions characterizing smooth positroid varieties using a variation of
pattern avoidance defined on decorated permutations, which are in bijection
with positroids. Furthermore, we give a combinatorial method for determining
the dimension of the tangent space of a positroid variety at key points using
an induced subgraph of the Johnson graph. We also give a Bruhat interval
characterization of positroids.


title:
Leveraging space-based data from the nearest Solar-type star to better
  understand stellar activity signatures in radial velocity data

abstract:
  Stellar variability is a key obstacle in reaching the sensitivity required to
recover Earth-like exoplanetary signals using the radial velocity (RV)
detection method. To explore activity signatures in Sun-like stars, we present
SolAster, a publicly-distributed analysis pipeline that allows for comparison
of space-based measurements with ground-based disk-integrated RVs. Using high
spatial resolution Dopplergrams, magnetograms, and continuum filtergrams from
the Helioseismic and Magnetic Imager (HMI) aboard the Solar Dynamics
Observatory (SDO), we estimate 'Sun-as-a-star' disk-integrated RVs due to
rotationally modulated flux imbalances and convective blueshift suppression, as
well as other observables such as unsigned magnetic flux. Comparing these
measurements with ground-based RVs from the NEID instrument, which observes the
Sun daily using an automated solar telescope, we find a strong relationship
between magnetic activity indicators and RV variation, supporting efforts to
examine unsigned magnetic flux as a proxy for stellar activity in slowly
rotating stars. Detrending against measured unsigned magnetic flux allows us to
improve the NEID RV measurements by ~20\% (~50 cm/s in a quadrature sum),
yielding an RMS scatter of ~60 cm/s over five months. We also explore
correlations between individual and averaged spectral line shapes in the NEID
spectra and SDO-derived magnetic activity indicators, motivating future studies
of these observables. Finally, applying SolAster to archival planetary transits
of Venus and Mercury, we demonstrate the ability to recover small amplitude (<
50 cm/s) RV variations in the SDO data by directly measuring the
Rossiter-McLaughlin (RM) signals.


title: Dual-Domain Image Synthesis using Segmentation-Guided GAN

abstract:
  We introduce a segmentation-guided approach to synthesise images that
integrate features from two distinct domains. Images synthesised by our
dual-domain model belong to one domain within the semantic mask, and to another
in the rest of the image - smoothly integrated. We build on the successes of
few-shot StyleGAN and single-shot semantic segmentation to minimise the amount
of training required in utilising two domains. The method combines a few-shot
cross-domain StyleGAN with a latent optimiser to achieve images containing
features of two distinct domains. We use a segmentation-guided perceptual loss,
which compares both pixel-level and activations between domain-specific and
dual-domain synthetic images. Results demonstrate qualitatively and
quantitatively that our model is capable of synthesising dual-domain images on
a variety of objects (faces, horses, cats, cars), domains (natural, caricature,
sketches) and part-based masks (eyes, nose, mouth, hair, car bonnet). The code
is publicly available at:
https://github.com/denabazazian/Dual-Domain-Synthesis.


title: Benchmarking Domain Generalization on EEG-based Emotion Recognition

abstract:
  Electroencephalography (EEG) based emotion recognition has demonstrated
tremendous improvement in recent years. Specifically, numerous domain
adaptation (DA) algorithms have been exploited in the past five years to
enhance the generalization of emotion recognition models across subjects. The
DA methods assume that calibration data (although unlabeled) exists in the
target domain (new user). However, this assumption conflicts with the
application scenario that the model should be deployed without the
time-consuming calibration experiments. We argue that domain generalization
(DG) is more reasonable than DA in these applications. DG learns how to
generalize to unseen target domains by leveraging knowledge from multiple
source domains, which provides a new possibility to train general models. In
this paper, we for the first time benchmark state-of-the-art DG algorithms on
EEG-based emotion recognition. Since convolutional neural network (CNN), deep
brief network (DBN) and multilayer perceptron (MLP) have been proved to be
effective emotion recognition models, we use these three models as solid
baselines. Experimental results show that DG achieves an accuracy of up to
79.41\% on the SEED dataset for recognizing three emotions, indicting the
potential of DG in zero-training emotion recognition when multiple sources are
available.


title:
Short time quaternion quadratic phase Fourier transform and its
  uncertainty principles

abstract:
  In this paper, we extend the quadratic phase Fourier transform of a complex
valued functions to that of the quaternion valued functions of two variables.
We call it the quaternion quadratic phase Fourier transform (QQPFT). Based on
the relation between the QQPFT and the quaternion Fourier transform (QFT) we
obtain the sharp Hausdorff-Young inequality for QQPFT. We define the short time
quaternion quadratic phase Fourier transform (STQQPFT) and explore some of its
properties including inner product relation and inversion formula. We find its
relation with that of the 2D quaternion ambiguity function and the quaternion
Wigner-Ville distribution associated with QQPFT and obtain the Lieb's
uncertainty and entropy uncertainty principles for these three transforms.