title:
Oscillatory integrals with phase functions of positive real powers and
  asymptotic expansions

abstract:
  As to methods for expanding an oscillatory integral into an asymptotic series
with respect to the parameter, the method of stationary phase for the
non-degenerate phases and the method of using resolution of singularities for
degenerate phases are known. The aim of this paper is to extend the former for
degenerate phases with positive real powers without using resolution of
singularities. For this aim, we first generalize Fresnel integrals as
oscillatory integrals with phase functions of positive real powers. Next, by
using this result, we have asymptotic expansions of oscillatory integrals for
degenerate phases with positive real powers including moderate oscillations and
for a wider amplitude class in one variable. Moreover, we obtain asymptotic
expansions of oscillatory integrals for degenerate phases consisting of sums of
monomials in each variable including the types $A_{k}$, $E_6$, $E_8$ in
multivariable.


title: A Simulation Benchmark for Vision-based Autonomous Navigation

abstract:
  This work introduces a simulator benchmark for vision-based autonomous
navigation. The simulator offers control over real world variables such as the
environment, time of day, weather and traffic. The benchmark includes a modular
integration of different components of a full autonomous visual navigation
stack. In the experimental part of the paper, state-of-the-art visual
localization methods are evaluated as a part of the stack in realistic
navigation tasks. To the authors' best knowledge, the proposed benchmark is the
first to study modern visual localization methods as part of a full autonomous
visual navigation stack.


title:
Compositional Temporal Grounding with Structured Variational Cross-Graph
  Correspondence Learning

abstract:
  Temporal grounding in videos aims to localize one target video segment that
semantically corresponds to a given query sentence. Thanks to the semantic
diversity of natural language descriptions, temporal grounding allows activity
grounding beyond pre-defined classes and has received increasing attention in
recent years. The semantic diversity is rooted in the principle of
compositionality in linguistics, where novel semantics can be systematically
described by combining known words in novel ways (compositional
generalization). However, current temporal grounding datasets do not
specifically test for the compositional generalizability. To systematically
measure the compositional generalizability of temporal grounding models, we
introduce a new Compositional Temporal Grounding task and construct two new
dataset splits, i.e., Charades-CG and ActivityNet-CG. Evaluating the
state-of-the-art methods on our new dataset splits, we empirically find that
they fail to generalize to queries with novel combinations of seen words. To
tackle this challenge, we propose a variational cross-graph reasoning framework
that explicitly decomposes video and language into multiple structured
hierarchies and learns fine-grained semantic correspondence among them.
Experiments illustrate the superior compositional generalizability of our
approach. The repository of this work is at https://github.com/YYJMJC/
Compositional-Temporal-Grounding.


title:
Evolved Open-Endedness in Cultural Evolution: A New Dimension in
  Open-Ended Evolution Research

abstract:
  The goal of Artificial Life research, as articulated by Chris Langton, is "to
contribute to theoretical biology by locating life-as-we-know-it within the
larger picture of life-as-it-could-be" (1989, p.1). The study and pursuit of
open-ended evolution in artificial evolutionary systems exemplifies this goal.
However, open-ended evolution research is hampered by two fundamental issues;
the struggle to replicate open-endedness in an artificial evolutionary system,
and the fact that we only have one system (genetic evolution) from which to
draw inspiration. Here we argue that cultural evolution should be seen not only
as another real-world example of an open-ended evolutionary system, but that
the unique qualities seen in cultural evolution provide us with a new
perspective from which we can assess the fundamental properties of, and ask new
questions about, open-ended evolutionary systems, especially in regard to
evolved open-endedness and transitions from bounded to unbounded evolution.
Here we provide an overview of culture as an evolutionary system, highlight the
interesting case of human cultural evolution as an open-ended evolutionary
system, and contextualise cultural evolution under the framework of (evolved)
open-ended evolution. We go on to provide a set of new questions that can be
asked once we consider cultural evolution within the framework of open-ended
evolution, and introduce new insights that we may be able to gain about evolved
open-endedness as a result of asking these questions.


title:
Emergence of electric-field-tunable interfacial ferromagnetism in 2D
  antiferromagnet heterostructures

abstract:
  Van der Waals (vdW) magnet heterostructures have emerged as new platforms to
explore exotic magnetic orders and quantum phenomena. Here, we study
heterostructures of layered antiferromagnets, CrI3 and CrCl3, with
perpendicular and in-plane magnetic anisotropy, respectively. Using
magneto-optical Kerr effect microscopy, we demonstrate out-of-plane magnetic
order in the CrCl3 layer proximal to CrI3, with ferromagnetic interfacial
coupling between the two. Such an interlayer exchange field leads to higher
critical temperature than that of either CrI3 or CrCl3 alone. We further
demonstrate significant electric-field control of the coercivity, attributed to
the naturally broken structural inversion symmetry of the heterostructure
allowing unprecedented direct coupling between electric field and interfacial
magnetism. These findings illustrate the opportunity to explore exotic magnetic
phases and engineer spintronic devices in vdW heterostructures.


title:
Coarse-to-Fine Cascaded Networks with Smooth Predicting for Video Facial
  Expression Recognition

abstract:
  Facial expression recognition plays an important role in human-computer
interaction. In this paper, we propose the Coarse-to-Fine Cascaded network with
Smooth Predicting (CFC-SP) to improve the performance of facial expression
recognition. CFC-SP contains two core components, namely Coarse-to-Fine
Cascaded networks (CFC) and Smooth Predicting (SP). For CFC, it first groups
several similar emotions to form a rough category, and then employs a network
to conduct a coarse but accurate classification. Later, an additional network
for these grouped emotions is further used to obtain fine-grained predictions.
For SP, it improves the recognition capability of the model by capturing both
universal and unique expression features. To be specific, the universal
features denote the general characteristic of facial emotions within a period
and the unique features denote the specific characteristic at this moment.
Experiments on Aff-Wild2 show the effectiveness of the proposed CFSP. We
achieved 3rd place in the Expression Classification Challenge of the 3rd
Competition on Affective Behavior Analysis in-the-wild. The code will be
released at https://github.com/BR-IDL/PaddleViT.


title: A mean-field game of market-making against strategic traders

abstract:
  We design a market-making model \`a la Avellaneda-Stoikov in which the
market-takers act strategically, in the sense that they design their trading
strategy based on an exogenous trading signal. The market-maker chooses her
quotes based on the average market-takers' behaviour, modelled through a
mean-field interaction. We derive, up to the resolution of a coupled
HJB--Fokker--Planck system, the optimal controls of the market-maker and the
representative market-taker. This approach is flexible enough to incorporate
different behaviours for the market-takers and takes into account the impact of
their strategies on the price process.


title:
Nonthermal particle acceleration from maximum entropy in collisionless
  plasmas

abstract:
  Dissipative processes cause collisionless plasmas in many systems to develop
nonthermal particle distributions with broad power-law tails. The prevalence of
power-law energy distributions in space/astrophysical observations and kinetic
simulations of systems with a variety of acceleration and trapping (or escape)
mechanisms poses a deep mystery. We consider the possibility that such
distributions can be modeled from maximum-entropy principles, when accounting
for generalizations beyond the Boltzmann-Gibbs entropy. Using a dimensional
representation of entropy (related to the Renyi and Tsallis entropies), we
derive generalized maximum-entropy distributions with a power-law tail
determined by the energy scale at which irreversible dissipation occurs. By
assuming that particles are energized by an amount comparable to the free
energy (per particle) before equilibrating, we derive a formula for the
power-law index as a function of plasma parameters for magnetic dissipation in
systems with sufficiently complex topologies. The model reproduces several
results from kinetic simulations of relativistic turbulence and magnetic
reconnection.

categories: astro-ph.HE astro-ph.SR physics.plasm-ph physics.space-ph


title:
Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic
  Memory

abstract:
  Driving 3D characters to dance following a piece of music is highly
challenging due to the spatial constraints applied to poses by choreography
norms. In addition, the generated dance sequence also needs to maintain
temporal coherency with different music genres. To tackle these challenges, we
propose a novel music-to-dance framework, Bailando, with two powerful
components: 1) a choreographic memory that learns to summarize meaningful
dancing units from 3D pose sequence to a quantized codebook, 2) an actor-critic
Generative Pre-trained Transformer (GPT) that composes these units to a fluent
dance coherent to the music. With the learned choreographic memory, dance
generation is realized on the quantized units that meet high choreography
standards, such that the generated dancing sequences are confined within the
spatial constraints. To achieve synchronized alignment between diverse motion
tempos and music beats, we introduce an actor-critic-based reinforcement
learning scheme to the GPT with a newly-designed beat-align reward function.
Extensive experiments on the standard benchmark demonstrate that our proposed
framework achieves state-of-the-art performance both qualitatively and
quantitatively. Notably, the learned choreographic memory is shown to discover
human-interpretable dancing-style poses in an unsupervised manner.


title:
Information Preferences of Individual Agents in
  Linear-Quadratic-Gaussian Network Games

abstract:
  We consider linear-quadratic-Gaussian (LQG) network games in which agents
have quadratic payoffs that depend on their individual and neighbors' actions,
and an unknown payoff-relevant state. An information designer determines the
fidelity of information revealed to the agents about the payoff state to
maximize the social welfare. Prior results show that full information
disclosure is optimal under certain assumptions on the payoffs, i.e., it is
beneficial for the average individual. In this paper, we provide conditions
based on the strength of the dependence of payoffs on neighbors' actions, i.e.,
competition, under which a rational agent is expected to benefit, i.e., receive
higher payoffs, from full information disclosure. We find that all agents
benefit from information disclosure for the star network structure when the
game is symmetric and submodular or supermodular. We also identify that the
central agent benefits more than a peripheral agent from full information
disclosure unless the competition is strong and the number of peripheral agents
is small enough. Despite the fact that all agents expect to benefit from
information disclosure ex-ante, a central agent can be worse-off from
information disclosure in many realizations of the payoff state under strong
competition, indicating that a risk-averse central agent can prefer
uninformative signals ex-ante.

categories: cs.GT cs.MA cs.SY econ.GN eess.SY math.OC q-fin.EC


title: Logarithmic A-hypergeometric series II

abstract:
  In this paper, following [6], we continue to develop the perturbing method of
constructing logarithmic series solutions to a regular A-hypergeometric system.
Fixing a fake exponent of an A-hypergeometric system, we consider some spaces
of linear partial differential operators with constant coefficients. Comparing
these spaces, we construct a fundamental system of series solutions with the
given exponent by the perturbing method. In addition, we give a sufficient
condition for a given fake exponent to be an exponent. As important examples of
the main results, we give fundamental systems of series solutions to
Aomoto-Gel'fand systems and to Lauricella's FC systems with special parameter
vectors, respectively.


title: Metric mean dimension of flows

abstract:
  We introduce the notions of metric mean dimension of flows on the whole space
and on subsets. Firstly, we develop Lindenstrauss-Weiss's classical inequality
\cite{lw00} for mean dimension of flows, and then we establish variational
principles for metric mean dimension of general flows on subsets in terms of
lower Brin-katok local entropy. We finally establish variational principles for
metric mean dimension of uniformly Lipschitz flows on the whole phase space in
terms of R\'enyi information dimension, Shapira's entropy, upper Brin-katok
local entropy, Katok's entropy as well as general flows in terms of local
entropy function.


title: On the characteristic polynomial of an effective Hamiltonian

abstract:
  The characteristic polynomial of the effective Hamiltonian for a general
model has been discussed. It is found that, compared with the associated energy
eigenvalues, this characteristic polynomial generally has better analytical
properties and larger convergence radius when being expanded in powers of the
interaction parameter, and hence is more suitable for a perturbation
calculation. A form of effective Hamiltonian which has the same singularities
(branch points) as such characteristic polynomial has also been constructed.


title:
SwiftAgg+: Achieving Asymptotically Optimal Communication Loads in
  Secure Aggregation for Federated Learning

abstract:
  We propose SwiftAgg+, a novel secure aggregation protocol for federated
learning systems, where a central server aggregates local models of $N \in
\mathbb{N}$ distributed users, each of size $L \in \mathbb{N}$, trained on
their local data, in a privacy-preserving manner. SwiftAgg+ can significantly
reduce the communication overheads without any compromise on security, and
achieve optimal communication loads within diminishing gaps. Specifically, in
presence of at most $D$ dropout users, SwiftAgg+ achieves a per-user
communication load of $(1+\mathcal{O}(\frac{1}{N}))L$ and a server
communication load of $(1+\mathcal{O}(\frac{1}{N}))L$, with a worst-case
information-theoretic security guarantee, against any subset of up to $T$
semi-honest users who may also collude with the curious server. Moreover, the
proposed SwiftAgg+ allows for a flexible trade-off between communication loads
and the number of active communication links. In particular, for any
$K\in\mathbb{N}$, SwiftAgg+ can achieve the server communication load of
$(1+\frac{T}{K})L$, and per-user communication load of up to
$(1+\frac{T+D}{K})L$, where the number of pair-wise active connections in the
network is $\frac{N}{2}(K+T+D+1)$.


title: ACE: Towards Application-Centric Edge-Cloud Collaborative Intelligence

abstract:
  Intelligent applications based on machine learning are impacting many parts
of our lives. They are required to operate under rigorous practical constraints
in terms of service latency, network bandwidth overheads, and also privacy. Yet
current implementations running in the Cloud are unable to satisfy all these
constraints. The Edge-Cloud Collaborative Intelligence (ECCI) paradigm has
become a popular approach to address such issues, and rapidly increasing
applications are developed and deployed. However, these prototypical
implementations are developer-dependent and scenario-specific without
generality, which cannot be efficiently applied in large-scale or to general
ECC scenarios in practice, due to the lack of supports for infrastructure
management, edge-cloud collaborative service, complex intelligence workload,
and efficient performance optimization. In this article, we systematically
design and construct the first unified platform, ACE, that handles
ever-increasing edge and cloud resources, user-transparent services, and
proliferating intelligence workloads with increasing scale and complexity, to
facilitate cost-efficient and high-performing ECCI application development and
deployment. For verification, we explicitly present the construction process of
an ACE-based intelligent video query application, and demonstrate how to
achieve customizable performance optimization efficiently. Based on our initial
experience, we discuss both the limitations and vision of ACE to shed light on
promising issues to elaborate in the approaching ECCI ecosystem.


title: Light-Cone Quantization a of Scalar Field on Time-Dependent Backgrounds

abstract:
  We discuss what is light-cone quantization on a curved spacetime also without
a null Killing vector. Then we consider as an example the light-cone
quantization of a scalar field on a background with a Killing vector and the
connection with the second quantization of the particle in the same background.
It turns out that the proper way to define the light-cone quantization is to
require that the constant light-cone time hypersurface is null or,
equivalently, that the particle Hamiltonian is free of square roots. Moreover,
in order to quantize the scalar theory it is necessary to use not the original
scalar rather a scalar field density, i.e. the Schr\"odinger wave functional
depends on a scalar density and not on the original field. Finally we recover
this result as the second quantization of a particle on the same background,
where it is necessary to add as input the fact that we are dealing with a
scalar density.


title: Interplay of noise induced stability and stochastic resetting

abstract:
  Stochastic resetting and noise-enhanced stability are two phenomena which can
affect the lifetime and relaxation of nonequilibrium states. They can be
considered as measures of controlling the efficiency of the completion process
when a stochastic system has to reach a desired state. Here, we study
interaction of random (Poissonian) resetting and stochastic dynamics in
unstable potentials. Unlike noise-induced stability which increases the
relaxation time, the stochastic resetting may eliminate winding trajectories
contributing to the lifetime and accelerate the escape kinetics from unstable
states. In the paper we present a framework to analyze compromises between the
two contrasting phenomena in a noise-driven kinetics subject to random
restarts.


title:
Ensembling and Knowledge Distilling of Large Sequence Taggers for
  Grammatical Error Correction

abstract:
  In this paper, we investigate improvements to the GEC sequence tagging
architecture with a focus on ensembling of recent cutting-edge
Transformer-based encoders in Large configurations. We encourage ensembling
models by majority votes on span-level edits because this approach is tolerant
to the model architecture and vocabulary size. Our best ensemble achieves a new
SOTA result with an $F_{0.5}$ score of 76.05 on BEA-2019 (test), even without
pre-training on synthetic datasets. In addition, we perform knowledge
distillation with a trained ensemble to generate new synthetic training
datasets, "Troy-Blogs" and "Troy-1BW". Our best single sequence tagging model
that is pretrained on the generated Troy-datasets in combination with the
publicly available synthetic PIE dataset achieves a near-SOTA (To the best of
our knowledge, our best single model gives way only to much heavier T5 model
result with an $F_{0.5}$ score of 73.21 on BEA-2019 (test). The code, datasets,
and trained models are publicly available).


title:
Hyers-Ulam stability for differential systems with $2\times 2$ constant
  coefficient matrix

abstract:
  We explore the Hyers-Ulam stability of perturbations for a homogeneous linear
differential system with $2\times 2$ constant coefficient matrix. New necessary
and sufficient conditions for the linear system to be Hyers-Ulam stable are
proven, and for the first time, the best (minimal) Hyers-Ulam constant for
systems is found in some cases. Several examples are provided. Obtaining the
best Hyers-Ulam constant for second-order constant coefficient differential
equations illustrates the applicability of the strong results.


title:
Smoothing analysis of two robust multigrid methods for elliptic optimal
  control problems

abstract:
  In this paper we study and compare two multigrid relaxation schemes with
coarsening by two, three, and four for solving elliptic sparse optimal control
problems with control constraints. First, we perform a detailed local Fourier
analysis (LFA) of a well-known collective Jacobi relaxation (CJR) scheme, where
the optimal smoothing factors are derived. This insightful analysis reveals
that the optimal relaxation parameters depend on mesh size and regularization
parameters, which was not investigated in literature. Second, we propose and
analyze a new mass-based Braess-Sarazin relaxation (BSR) scheme, which is
proven to provide smaller smoothing factors than the CJR scheme when $\alpha\ge
ch^4$ for a small constant $c$. Here $\alpha$ is the regularization parameter
and $h$ is the spatial mesh step size. These schemes are successfully extended
to control-constrained cases through the semi-smooth Newton method. Coarsening
by three or four with BSR is competitive with coarsening by two. Numerical
examples are presented to validate our theoretical outcomes. The proposed
inexact BSR (IBSR) scheme, where two preconditioned conjugate gradients
iterations are applied to the Schur complement system, yields better
computational efficiency than the CJR scheme.


title:
How do dataset characteristics affect the performance of propensity
  score methods and regression for controlling confounding in observational
  studies? A simulation study

abstract:
  In observational studies, researchers must select a method to control for
confounding. Options include propensity score methods and regression. It
remains unclear how dataset characteristics (size, overlap in propensity
scores, exposure prevalence) influence the relative performance of the methods,
making it difficult to select the best method for a particular dataset.
  A simulation study to evaluate the role of dataset characteristics on the
performance of propensity score methods, compared to logistic regression, for
estimating a marginal odds ratio in the presence of confounding was conducted.
Outcomes were simulated from logistic and complementary log-log models, and
size, overlap in propensity scores, and prevalence of the exposure were varied.
  Regression showed poor coverage for small sample sizes, but with large sample
sizes it was more robust to imbalance in propensity scores and low exposure
prevalence than were propensity score methods. Propensity score methods
frequently displayed suboptimal coverage, particularly as overlap in propensity
scores decreased. These problems were exacerbated at larger sample sizes. Power
of matching methods was particularly affected by lack of overlap, low
prevalence of exposure, and small sample size. Performance of inverse
probability of treatment weighting depended heavily on dataset characteristics,
with poor coverage and bias with low overlap. The advantage of regression for
large data size was less clear in sensitivity analysis with a complementary
log-log outcome generation mechanism and unmeasured confounding, with superior
bias and error but lower coverage than nearest neighbour and caliper matching.


title: SIFT and SURF based feature extraction for the anomaly detection

abstract:
  In this paper, we suggest a way, how to use SIFT and SURF algorithms to
extract the image features for anomaly detection. We use those feature vectors
to train various classifiers on a real-world dataset in the semi -supervised
(with a small number of faulty samples) manner with a large number of
classifiers and in the one-class (with no faulty samples) manner using the SVDD
and SVM classifier. We prove, that the SIFT and SURF algorithms could be used
as feature extractors, that they could be used to train a semi-supervised and
one-class classifier with an accuracy around 89\% and that the performance of
the one-class classifier could be comparable to the semi-supervised one. We
also made our dataset and source code publicly available.


title: Game semantics of universes

abstract:
  This work extends the present author's computational game semantics of
Martin-L\"{o}f type theory to the cumulative hierarchy of universes. This
extension completes game semantics of all standard types of Martin-L\"{o}f type
theory for the first time in the 30 years history of modern game semantics. As
a result, the powerful combinatorial reasoning of game semantics becomes
available for the study of universes and types generated by them. A main
challenge in achieving game semantics of universes comes from a conflict
between identity types and universes: Naive game semantics of the encoding of
an identity type by a universe induces a decision procedure on the equality
between functions, a contradiction to a well-known fact in recursion theory. We
overcome this problem by novel games for universes that encode games for
identity types without deciding the equality.


title:
Discrete exterior calculus discretization of two-phase incompressible
  Navier-Stokes equations with a conservative phase field method

abstract:
  We present a discrete exterior calculus (DEC) based discretization scheme for
incompressible two-phase flows. Our physically-compatible exterior calculus
discretization of single phase flow is extended to simulate immiscible
two-phase flows with discontinuous changes in fluid properties such as density
and viscosity across the interface. The two-phase incompressible Navier-Stokes
equations and conservative phase field equation for interface capturing are
first transformed into the exterior calculus framework. The discrete counter
part of these smooth equations is obtained by substituting with discrete
differential forms and discrete exterior operators. We prove the boundedness of
the method for the first order Euler forward and predictor-corrector time
integration schemes in the DEC framework. With a proper choice of two free
parameters, the scheme remains phase field bounded without the requirement of
any ad hoc mass redistribution. We verify the scheme against several standard
test cases (for interface capturing) comprising not only the flat domains but
also the curved domains, leveraging the advantage that DEC operators are
independent of the coordinate system. The results show excellent properties of
boundedness, mass conservation and convergence. Moreover, we demonstrate the
ability of the scheme towards handling large density and viscosity ratios as
well as surface tension in the simulation of various two phase flow physical
phenomena on flat or curved surfaces.


title: Inner and Outer Approximations of Star-Convex Semialgebraic Sets

abstract:
  We consider the problem of approximating a semialgebraic set with a
sublevel-set of a polynomial function. In this setting, it is standard to seek
a minimum volume outer approximation and/or maximum volume inner approximation.
As there is no known relationship between the coefficients of an arbitrary
polynomial and the volume of its sublevel sets, previous works have proposed
heuristics based on the determinant and trace objectives commonly used in
ellipsoidal fitting. For the case of star-convex semialgebraic sets, we propose
a novel objective which yields both an outer and an inner approximation while
minimizing the ratio of their respective volumes. This objective is
scale-invariant and easily interpreted. Numerical examples are given which show
that the approximations obtained are often tighter than those returned by
existing heuristics. We also provide methods for establishing the
star-convexity of a semialgebraic set by finding inner and outer approximations
of its kernel.


title:
Multitask Emotion Recognition Model with Knowledge Distillation and Task
  Discriminator

abstract:
  Due to the collection of big data and the development of deep learning,
research to predict human emotions in the wild is being actively conducted. We
designed a multi-task model using ABAW dataset to predict valence-arousal,
expression, and action unit through audio data and face images at in real
world. We trained model from the incomplete label by applying the knowledge
distillation technique. The teacher model was trained as a supervised learning
method, and the student model was trained by using the output of the teacher
model as a soft label. As a result we achieved 2.40 in Multi Task Learning task
validation dataset.


title: On the Binary and Boolean Rank of Regular Matrices

abstract:
  A $0,1$ matrix is said to be regular if all of its rows and columns have the
same number of ones. We prove that for infinitely many integers $k$, there
exists a square regular $0,1$ matrix with binary rank $k$, such that the
Boolean rank of its complement is $k^{\widetilde{\Omega}(\log k)}$.
Equivalently, the ones in the matrix can be partitioned into $k$ combinatorial
rectangles, whereas the number of rectangles needed for any cover of its zeros
is $k^{\widetilde{\Omega}(\log k)}$. This settles, in a strong form, a question
of Pullman (Linear Algebra Appl. ,1988) and a conjecture of Hefner, Henson,
Lundgren, and Maybee (Congr. Numer. ,1990). The result can be viewed as a
regular analogue of a recent result of Balodis, Ben-David, G\"{o}\"{o}s, Jain,
and Kothari (FOCS, 2021), motivated by the clique vs. independent set problem
in communication complexity and by the (disproved) Alon-Saks-Seymour conjecture
in graph theory. As an application of the produced regular matrices, we obtain
regular counterexamples to the Alon-Saks-Seymour conjecture and prove that for
infinitely many integers $k$, there exists a regular graph with biclique
partition number $k$ and chromatic number $k^{\widetilde{\Omega}(\log k)}$.


title: Theory of Halo Nuclei

abstract:
  Halo nuclei are characterized by a few weakly bound halo nucleons and a more
tightly bound core. This separation of scales can be exploited in a few-body
description of halo nuclei, since the detailed structure of the core is not
resolved by the halo nucleons. We present an introduction to the effective
(field) theory for low-energy properties of halo nuclei. The focus is on halos
with S-wave interactions for which universal properties are most pronounced.
The special role of the unitary limit is illustrated using the example of
multineutron systems and the Efimov effect as a universal binding mechanism for
halo nuclei. Connections to ultracold atoms and hadron physics are highlighted
and extensions to higher partial waves, Coulomb forces and nuclear reactions
are briefly touched upon.


title: Szilard engines and information-based work extraction for active systems

abstract:
  The out of equilibrium nature of active systems can be exploited for the
design of information-based engines. We design two types of an active Szilard
engine that use a Maxwell daemon to extract work from an active bath composed
of non-interacting Active Brownian Particles (ABPs). The two engines exploit
either the quasi-static active pressure of ABPs or the long correlation time of
their velocities. For both engines the Landauer principle can be violated so
that larger work can be extracted compared to conventional Szilard engines
operating in quasi thermal equilibrium. For both of our engines,we identify the
optimal regimes at which the work extracted and the efficiency are maximized.
Finally, we we discuss them in the context of synthetic and biological active
systems.


title: Pitfalls and Potentials in Simulation Studies

abstract:
  Comparative simulation studies are workhorse tools for benchmarking
statistical methods, but if not performed transparently they may lead to
overoptimistic or misleading conclusions. The current publication requirements
adopted by statistics journals do not prevent questionable research practices
such as selective reporting. The past years have witnessed numerous suggestions
and initiatives to improve on these issues but little progress can be seen to
date. In this paper we discuss common questionable research practices which
undermine the validity of findings from comparative simulation studies. To
illustrate our point, we invent a novel prediction method with no expected
performance gain and benchmark it in a pre-registered comparative simulation
study. We show how easy it is to make the method appear superior over
well-established competitor methods if no protocol is in place and various
questionable research practices are employed. Finally, we provide researchers,
reviewers, and other academic stakeholders with concrete suggestions for
improving the methodological quality of comparative simulation studies, most
importantly the need for pre-registered simulation protocols.


title: Signatures of an energetic charge bunch moving in a plasma

abstract:
  A charge bunch moving in a plasma can excite a variety of linear and
nonlinear waves in the form of trailing wakes, fore-wake shocks and precursor
solitons. These structures can further interact with the background plasma to
create secondary effects that can serve as signatures of the passage of the
charge bunch. Using particle-in-cell simulations we investigate in detail the
dynamics of a plasma system that is being traversed by an energetic charged ion
bunch. Using two different shapes of the charge source, namely, an idealized
one dimensional line source and a two dimensional thin rectangular source we
examine the differences in the nature of the excited wave structures and their
consequent impact on the background plasma. Our simulations reveal interesting
features such as the dependence of the precursor speeds on the total charge of
the ion bunch, local particle trapping, and energization of the trapped ions in
various regions along the traversal path leading to the formation of energetic
ion beam-lets. The collective excitations and the signatures in the ambient
plasma could prove useful in practical applications such as in ion beam heating
of plasmas. They can also help in analysing the trajectories of charged objects
like space debris orbiting in the ionosphere.


title: Universal algorithms for solving inverse spectral problems

abstract:
  This paper explores the complexity associated with solving the inverse
Sturm-Liouville problem with Robin boundary conditions: given a sequence of
eigenvalues and a sequence of norming constants, how many limits does a
universal algorithm require to return the potential and boundary conditions? It
is shown that if all but finitely many of the eigenvalues and norming constants
coincide with those for the zero potential then the number of limits is zero,
i.e. it is possible to retrieve the potential and boundary conditions precisely
in finitely many steps. Otherwise, it is shown that this problem requires a
single limit; moreover, if one has a priori control over how much the
eigenvalues and norming constants differ from those of the zero-potential
problem, and one knows that the average of the potential is zero, then the
computation can be performed with complete error control. This is done in the
spirit of the Solvability Complexity Index. All algorithms are provided
explicitly along with numerical examples.


title: Learning Optimal Test Statistics in the Presence of Nuisance Parameters

abstract:
  The design of optimal test statistics is a key task in frequentist statistics
and for a number of scenarios optimal test statistics such as the
profile-likelihood ratio are known. By turning this argument around we can find
the profile likelihood ratio even in likelihood-free cases, where only samples
from a simulator are available, by optimizing a test statistic within those
scenarios. We propose a likelihood-free training algorithm that produces test
statistics that are equivalent to the profile likelihood ratios in cases where
the latter is known to be optimal.


title: Replica Symmetry Breaking in Random Non-Hermitian Systems

abstract:
  Recent studies have revealed intriguing similarities between the contribution
of wormholes to the gravitational path integral and the phenomenon of replica
symmetry breaking observed in spin glasses and other disordered systems.
Interestingly, these configurations may also be important for the explanation
of the information paradox of quantum black holes. Motivated by these
developments, we investigate the thermodynamic properties of a $PT$-symmetric
system composed of two random non-Hermitian Hamiltonians with no explicit
coupling between them. After performing ensemble averaging, we identify
numerically and analytically a robust first-order phase transition in the free
energy of two models with quantum chaotic dynamics: the elliptic Ginibre
ensemble of random matrices and a non-Hermitian Sachdev-Ye-Kitaev (SYK) model.
The free energy of the Ginibre model is temperature-independent in the
low-temperature phase. The SYK model has a similar behavior for sufficiently
low temperature, then it experiences a possible continuous phase transition to
a phase with a temperature-dependent free energy before the first-order
transition takes place at a higher temperature. We identify the order parameter
of the first-order phase transition and obtain analytical expressions for the
critical temperature. The mechanism behind the transition is the existence of
replica symmetry breaking configurations coupling Left and Right replicas that
control the low-temperature limit of the partition function. We speculate that
quantum chaos may be necessary for the observed dominance of off-diagonal
replica symmetry breaking configurations in the low-temperature limit.


title: Stochastic Gauss-Newton Algorithms for Online PCA

abstract:
  In this paper, we propose a stochastic Gauss-Newton (SGN) algorithm to study
the online principal component analysis (OPCA) problem, which is formulated by
using the symmetric low-rank product (SLRP) model for dominant eigenspace
calculation. Compared with existing OPCA solvers, SGN is of improved robustness
with respect to the varying input data and algorithm parameters. In addition,
turning to an evaluation of data stream based on approximated objective
functions, we develop a new adaptive stepsize strategy for SGN (AdaSGN) which
requires no priori knowledge of the input data, and numerically illustrate its
comparable performance with SGN adopting the manaully-tuned diminishing
stepsize. Without assuming the eigengap to be positive, we also establish the
global and optimal convergence rate of SGN with the specified stepsize using
the diffusion approximation theory.


title: H\"older regularity for fractional $p$-Laplace equations

abstract:
  We give an alternative proof for H\"older regularity for weak solutions of
nonlocal elliptic quasilinear equations modelled on the fractional p-Laplacian
where we replace the discrete De Giorgi iteration on a sequence of concentric
balls by a continuous iteration. Moreover, we also obtain a De Giorgi type
isoperimetric inequality for all $s \in (0,1)$, which partially answers a
question of M.Cozzi.


title: Behavior Trees in Robot Control Systems

abstract:
  In this paper we will give a control theoretic perspective on the research
area of behavior trees in robotics. The key idea underlying behavior trees is
to make use of modularity, hierarchies and feedback, in order to handle the
complexity of a versatile robot control system. Modularity is a well-known tool
to handle software complexity by enabling development, debugging and extension
of separate modules without having detailed knowledge of the entire system. A
hierarchy of such modules is natural, since robot tasks can often be decomposed
into a hierarchy of sub-tasks. Finally, feedback control is a fundamental tool
for handling uncertainties and disturbances in any low level control system,
but in order to enable feedback control on the higher level, where one module
decides what submodule to execute, information regarding progress and
applicability of each submodule needs to be shared in the module interfaces. We
will describe how these three concepts come to use in theoretical analysis,
practical design, as well as extensions and combinations with other ideas from
control theory and robotics.


title:
The Dutch Draw: Constructing a Universal Baseline for Binary Prediction
  Models

abstract:
  Novel prediction methods should always be compared to a baseline to know how
well they perform. Without this frame of reference, the performance score of a
model is basically meaningless. What does it mean when a model achieves an
$F_1$ of 0.8 on a test set? A proper baseline is needed to evaluate the
`goodness' of a performance score. Comparing with the latest state-of-the-art
model is usually insightful. However, being state-of-the-art can change rapidly
when newer models are developed. Contrary to an advanced model, a simple dummy
classifier could be used. However, the latter could be beaten too easily,
making the comparison less valuable. This paper presents a universal baseline
method for all binary classification models, named the Dutch Draw (DD). This
approach weighs simple classifiers and determines the best classifier to use as
a baseline. We theoretically derive the DD baseline for many commonly used
evaluation measures and show that in most situations it reduces to (almost)
always predicting either zero or one. Summarizing, the DD baseline is: (1)
general, as it is applicable to all binary classification problems; (2) simple,
as it is quickly determined without training or parameter-tuning; (3)
informative, as insightful conclusions can be drawn from the results. The DD
baseline serves two purposes. First, to enable comparisons across research
papers by this robust and universal baseline. Secondly, to provide a sanity
check during the development process of a prediction model. It is a major
warning sign when a model is outperformed by the DD baseline.

categories: cs.LG cs.PF math.ST stat.ME stat.ML stat.TH


title:
Locally Asynchronous Stochastic Gradient Descent for Decentralised Deep
  Learning

abstract:
  Distributed training algorithms of deep neural networks show impressive
convergence speedup properties on very large problems. However, they inherently
suffer from communication related slowdowns and communication topology becomes
a crucial design choice. Common approaches supported by most machine learning
frameworks are: 1) Synchronous decentralized algorithms relying on a
peer-to-peer All Reduce topology that is sensitive to stragglers and
communication delays. 2) Asynchronous centralised algorithms with a server
based topology that is prone to communication bottleneck. Researchers also
suggested asynchronous decentralized algorithms designed to avoid the
bottleneck and speedup training, however, those commonly use inexact sparse
averaging that may lead to a degradation in accuracy. In this paper, we propose
Local Asynchronous SGD (LASGD), an asynchronous decentralized algorithm that
relies on All Reduce for model synchronization.
  We empirically validate LASGD's performance on image classification tasks on
the ImageNet dataset. Our experiments demonstrate that LASGD accelerates
training compared to SGD and state of the art gossip based approaches.


title:
HiFi++: a Unified Framework for Neural Vocoding, Bandwidth Extension and
  Speech Enhancement

abstract:
  Generative adversarial networks have recently demonstrated outstanding
performance in neural vocoding outperforming best autoregressive and flow-based
models. In this paper, we show that this success can be extended to other tasks
of conditional audio generation. In particular, building upon HiFi vocoders, we
propose a novel HiFi++ general framework for neural vocoding, bandwidth
extension, and speech enhancement. We show that with the improved generator
architecture and simplified multi-discriminator training, HiFi++ performs on
par with the state-of-the-art in these tasks while spending significantly less
memory and computational resources. The effectiveness of our approach is
validated through a series of extensive experiments.


title:
Flow field disturbance due to point viscosity variations in a
  heterogeneous fluid

abstract:
  We derive the flow field disturbance produced by point viscosity variations
in a heterogeneous fluid when subject to a background flow while neglecting
fluid inertia. The disturbance flow field is found to be identical to that
generated by a force-dipole called stresslet. Using a combination of theory and
numerical simulations, we show how the hydrodynamics of an active rigid
particle is altered due to the presence of point viscosity variations, and how
this can be exploited to manipulate and steer them in microfluidic
environments.


title:
Introducing Neural Bag of Whole-Words with ColBERTer: Contextualized
  Late Interactions using Enhanced Reduction

abstract:
  Recent progress in neural information retrieval has demonstrated large gains
in effectiveness, while often sacrificing the efficiency and interpretability
of the neural model compared to classical approaches. This paper proposes
ColBERTer, a neural retrieval model using contextualized late interaction
(ColBERT) with enhanced reduction. Along the effectiveness Pareto frontier,
ColBERTer's reductions dramatically lower ColBERT's storage requirements while
simultaneously improving the interpretability of its token-matching scores. To
this end, ColBERTer fuses single-vector retrieval, multi-vector refinement, and
optional lexical matching components into one model. For its multi-vector
component, ColBERTer reduces the number of stored vectors per document by
learning unique whole-word representations for the terms in each document and
learning to identify and remove word representations that are not essential to
effective scoring. We employ an explicit multi-task, multi-stage training to
facilitate using very small vector dimensions. Results on the MS MARCO and
TREC-DL collection show that ColBERTer can reduce the storage footprint by up
to 2.5x, while maintaining effectiveness. With just one dimension per token in
its smallest setting, ColBERTer achieves index storage parity with the
plaintext size, with very strong effectiveness results. Finally, we demonstrate
ColBERTer's robustness on seven high-quality out-of-domain collections,
yielding statistically significant gains over traditional retrieval baselines.


title: The U.S. Naval Observatory VLBI Spectroscopic Catalog

abstract:
  Despite their importance for astrometry and navigation, the active galactic
nuclei (AGNs) that comprise the International Celestial Reference Frame (ICRF)
are relatively poorly understood, with key information such as their
spectroscopic redshifts, AGN spectral type, and emission/absorption line
properties generally missing from the literature. Using updated, publicly
available, state-of-the-art spectroscopic fitting code optimized for the
spectra of AGNs from low to high redshift, we present a catalog of emission
line and spectral continuum parameters for 1,014 unique ICRF3 objects with
single-fiber spectra from the Sloan Digital Sky Survey DR16. We additionally
present black hole virial mass scaling relationships that use H$\alpha$-,
H$\beta$-, Mg II-, and C IV-based line widths, all consistent with each other,
which can be used in studies of radio-loud objects across a wide range of
redshifts, and we use these scaling relationships to provide derived properties
such as black hole masses and bolometric luminosities for the catalog. We
briefly comment on these properties for the ICRF objects, as well as their
overall spectroscopic characteristics.


title:
AziNorm: Exploiting the Radial Symmetry of Point Cloud for
  Azimuth-Normalized 3D Perception

abstract:
  Studying the inherent symmetry of data is of great importance in machine
learning. Point cloud, the most important data format for 3D environmental
perception, is naturally endowed with strong radial symmetry. In this work, we
exploit this radial symmetry via a divide-and-conquer strategy to boost 3D
perception performance and ease optimization. We propose Azimuth Normalization
(AziNorm), which normalizes the point clouds along the radial direction and
eliminates the variability brought by the difference of azimuth. AziNorm can be
flexibly incorporated into most LiDAR-based perception methods. To validate its
effectiveness and generalization ability, we apply AziNorm in both object
detection and semantic segmentation. For detection, we integrate AziNorm into
two representative detection methods, the one-stage SECOND detector and the
state-of-the-art two-stage PV-RCNN detector. Experiments on Waymo Open Dataset
demonstrate that AziNorm improves SECOND and PV-RCNN by 7.03 mAPH and 3.01 mAPH
respectively. For segmentation, we integrate AziNorm into KPConv. On
SemanticKitti dataset, AziNorm improves KPConv by 1.6/1.1 mIoU on val/test set.
Besides, AziNorm remarkably improves data efficiency and accelerates
convergence, reducing the requirement of data amounts or training epochs by an
order of magnitude. SECOND w/ AziNorm can significantly outperform fully
trained vanilla SECOND, even trained with only 10% data or 10% epochs. Code and
models are available at https://github.com/hustvl/AziNorm.


title:
A moduli theoretic approach to Lagrangian subvarieties of hyperk\"ahler
  varieties: Examples

abstract:
  We propose two conjectures on a moduli theoretic approach to constructing
Lagrangian subvarieties of hyperk\"ahler varieties arising from the Kuznetsov
components of cubic fourfolds or Gushel--Mukai fourfolds. Then we verify the
conjectures in several cases, recovering classical examples. As a corollary, we
confirm a conjecture of O'Grady in several instances on the existence of
Lagrangian covering families for hyperk\"ahler varieties.


title:
Implementation of single-qubit measurement-based t-designs using IBM
  processors

abstract:
  Random unitary matrices sampled from the uniform Haar ensemble have a number
of important applications both in cryptography and in the simulation of a
variety of fundamental physical systems. Since the Haar ensemble is very
expensive to sample, pseudorandom ensembles in the form of t-designs are
frequently used as an efficient substitute, and are sufficient for most
applications. We investigate t-designs generated using a measurement-based
approach on superconducting quantum computers. In particular, we implemented an
exact single-qubit 3-design on IBM quantum processors by performing
measurements on a 6-qubit graph state. By analysing channel tomography results,
we were able to show that the ensemble of unitaries realised was a 1-design,
but not a 2-design or a 3-design under the test conditions set, which we show
to be a result of depolarising noise during the measurement-based process. We
obtained improved results for the 2-design test by implementing an approximate
2-design, in which measurements were performed on a smaller 5-qubit graph
state, but the test still did not pass for all states. This suggests that the
practical realisation of measurement-based t-designs on superconducting quantum
computers will require further work on the reduction of depolarising noise in
these devices.


title:
A Preliminary Research on Space Situational Awareness Based on Event
  Cameras

abstract:
  Event camera is a new type of sensor that is different from traditional
cameras. Each pixel is triggered asynchronously by an event. The trigger event
is the change of the brightness irradiated on the pixel. If the increment or
decrement is higher than a certain threshold, the event is output. Compared
with traditional cameras, event cameras have the advantages of high temporal
resolution, low latency, high dynamic range, low bandwidth and low power
consumption. We carried out a series of observation experiments in a simulated
space lighting environment. The experimental results show that the event camera
can give full play to the above advantages in space situational awareness. This
article first introduces the basic principles of the event camera, then
analyzes its advantages and disadvantages, then introduces the observation
experiment and analyzes the experimental results, and finally, a workflow of
space situational awareness based on event cameras is given.


title:
PREVIEW VERSION: Six Insights into 6G: Orientation and Input for
  Developing Your Strategic 6G Research Plan

abstract:
  This is the PREVIEW version of a paper about 6G.
  This paper is a summary of the findings from a series of workshops which were
held by Thinknet 6G and MUENCHNER KREIS in 2021, with the goal to provide
orientation and input for developing a strategic 6G research plan. The topics
selected for the workshops are aspects of 6G that we expect will have a
significant impact on other industries and on society:
  - 6G as both a communication infrastructure and a sensing infrastructure
  - The extensive use of artificial intelligence in 6G
  - The security and resilience of 6G
  This paper does not go into the technical details of how to develop and
implement 6G. Rather, it provides input from experts from both the wireless
industry as well as from other sectors about (mostly) non-technical topics that
will need to be addressed in parallel with the technical developments, such as
new use cases, regulation, communication with the public, and cross-industry
cooperation.
  We have identified six areas that will have a significant impact on the
development and use of 6G, and that organizations must consider as they begin
their plans and designs for 6G. Based on these six impact areas and on the
discussion in the workshops, we compiled a list of the top 10 recommendations
for specific areas where organizations should place their focus when developing
their strategic plan for 6G. In addition, for our readers who are involved in
6G research, be it at a university, at a research institute or in industrial
research, we also included a summary of the top 10 areas that require
additional research, again based on the input received in the workshops.
  A version of this paper with professional layout and graphics will be
available at www.thinknet-6g.de soon.


title: Batch Dynamic Algorithm to Find k-Cores and Hierarchies

abstract:
  Finding $k$-cores in graphs is a valuable and effective strategy for
extracting dense regions of otherwise sparse graphs. We focus on the important
problem of maintaining cores on rapidly changing dynamic graphs, where batches
of edge changes need to be processed quickly. Prior batch core algorithms have
only addressed half the problem of maintaining cores, the problem of
maintaining a core decomposition. This finds vertices that are dense, but not
regions; it misses connectivity. To address this, we bring an efficient index
from community search into the core domain, the Shell Tree Index. We develop a
novel dynamic batch algorithm to maintain it that improves efficiency over
processing edge-by-edge. We implement our algorithm and experimentally show
that with it core queries can be returned on rapidly changing graphs quickly
enough for interactive applications. For 1 million edge batches, on many graphs
we run over $100\times$ faster than processing edge-by-edge while remaining
under re-computing from scratch.


title: The essential norm of multiplication operators on $L_p(\mu)$

abstract:
  We show that the formula for the essential norm of a multiplication operator
on $L_p$, for $1<p<\infty$, also holds for $p=1$. We also provide a proof for
the formula which works simultaneously for all $p\in[1,\infty)$.


title: IA-FaceS: A Bidirectional Method for Semantic Face Editing

abstract:
  Semantic face editing has achieved substantial progress in recent years.
Known as a growingly popular method, latent space manipulation performs face
editing by changing the latent code of an input face to liberate users from
painting skills. However, previous latent space manipulation methods usually
encode an entire face into a single low-dimensional embedding, which constrains
the reconstruction capacity and the control flexibility of facial components,
such as eyes and nose. This paper proposes IA-FaceS as a bidirectional method
for disentangled face attribute manipulation as well as flexible, controllable
component editing without the need for segmentation masks or sketches in the
original image. To strike a balance between the reconstruction capacity and the
control flexibility, the encoder is designed as a multi-head structure to yield
embeddings for reconstruction and control, respectively: a high-dimensional
tensor with spatial properties for consistent reconstruction and four
low-dimensional facial component embeddings for semantic face editing.
Manipulating the separate component embeddings can help achieve disentangled
attribute manipulation and flexible control of facial components. To further
disentangle the highly-correlated components, a component adaptive modulation
(CAM) module is proposed for the decoder. The semantic single-eye editing is
developed for the first time without any input visual guidance, such as
segmentation masks or sketches. According to the experimental results, IA-FaceS
establishes a good balance between maintaining image details and performing
flexible face manipulation. Both quantitative and qualitative results indicate
that the proposed method outperforms the other techniques in reconstruction,
face attribute manipulation, and component transfer.


title: Dynamics-based machine learning of transitions in Couette flow

abstract:
  We derive low-dimensional, data-driven models for transitions among exact
coherent states (ECSs) in one of the most studied canonical shear flows, the
plane Couette flow. These one- or two-dimensional nonlinear models represent
the leading-order reduced dynamics on attracting spectral submanifolds (SSMs),
which we construct using the recently developed SSMLearn algorithm from a small
number of simulated transitions. We find that the energy input and output rates
provide efficient parametrizations for the most important SSMs. By restricting
the dynamics to these SSMs, we obtain reduced-order models that also reliably
predict nearby, off-SSM transitions that were not used in their training.


title:
Multi-species viscous models for tissue growth: incompressible limit and
  qualitative behaviour

abstract:
  We introduce two 2D mechanical models reproducing the evolution of two
viscous tissues in contact. Their main property is to model the swirling cell
motions while keeping the tissues segregated, as observed during vertebrate
embryo elongation. Segregation is encoded differently in the two models: by
passive or active segregation (based on a mechanical repulsion pressure). We
compute the incompressible limits of the two models, and obtain strictly
segregated solutions. The two models thus obtained are compared. A striking
feature in the active segregation model is the persistence of the repulsion
pressure at the limit: a ghost effect is discussed and confronted to the
biological data. Thanks to a transmission problem formulation at the
incompressible limit, we show a pressure jump at the tissues' boundaries.


title:
On Optimizing the Power Allocation and the Decoding Order in Uplink
  Cooperative NOMA

abstract:
  In this paper, we investigate for the first time the dynamic power allocation
and decoding order at the base station (BS) of two-user uplink (UL) cooperative
non-orthogonal multiple access (C-NOMA)-based cellular networks. In doing so,
we formulate a joint optimization problem aiming at maximizing the minimum user
achievable rate, which is a non-convex optimization problem and hard to be
directly solved. To tackle this issue, an iterative algorithm based on
successive convex approximation (SCA) is proposed. The numerical results reveal
that the proposed scheme provides superior performance in comparison with the
traditional UL NOMA. In addition, we demonstrated that in UL C-NOMA, decoding
the far NOMA user first at the BS provides the best performance.


title:
Some existence and regularity results for a non-local
  transport-diffusion equation with fractional derivatives in time and space

abstract:
  We study the existence of global weak solutions of a nonlinear
transport-diffusion equation with a fractional derivative in the time variable
and under some extra hypotheses, we also study some regularity properties for
this type of solutions. In the system considered here, the diffusion operator
is given by a fractional Laplacian and the nonlinear drift is assumed to be
divergence free and it is assumed to satisfy some general stability and
boundedness properties in Lebesgue spaces.


title:
Magnetic Excitations in Square Lattice Iridates: Contrast between
  Ba$_2$IrO$_4$ and Sr$_2$IrO$_4$

abstract:
  We report a resonant inelastic x-ray scattering (RIXS) investigation of
ultra-thin epitaxial films of Ba$_2$IrO$_4$, and compare their low energy
magnetic and spin-orbit excitations to those of their sister compound
Sr$_2$IrO$_4$. Due to the 180$^\circ$ Ir-O-Ir bond, the bandwidth of the magnon
and spin-orbiton is significantly larger in Ba$_2$IrO$_4$, making it difficult
to describe these two types of excitations as separate well-defined
quasiparticles. Both types of excitations are found to be quite sensitive to
the effect of epitaxial strain. In addition, we find that the d-level inversion
observed in Sr$_2$IrO$_4$ is absent in Ba$_2$IrO$_4$, as predicted in recent
theoretical studies. Our results illustrate that the magnetic properties of
Ba$_2$IrO$_4$ are substantially different from those of Sr$_2$IrO$_4$,
suggesting that these materials need to be examined more carefully with
electron itinerancy taken into account.


title: A Two-Fluid Treatment of the Effect of Neutrinos on the Matter Density

abstract:
  We study the linear effects of massive neutrinos on the matter density
fluctuations on both large and small scales by solving the fluid equations that
are obtained from Boltzmann hierarchy. These equations set the evolution of
density and velocity perturbations of cold dark matter and neutrinos after the
latter become non-relativistic. Due to the free-streaming effects, the massive
neutrinos suppress the total matter power spectrum on small scales while
leaving the large scales untouched. Approximating the neutrino distribution
function as peaked at one value gives an equation of state that can then be
used in an analytic solution of the fluid equations. We then test the validity
of fluid approximation with full Boltzmann solvers such as \textsc{class}. We
also comment on the effects of dark energy on our results.


title:
R-DFCIL: Relation-Guided Representation Learning for Data-Free Class
  Incremental Learning

abstract:
  Class-Incremental Learning (CIL) struggles with catastrophic forgetting when
learning new knowledge, and Data-Free CIL (DFCIL) is even more challenging
without access to the training data of previous classes. Though recent DFCIL
works introduce techniques such as model inversion to synthesize data for
previous classes, they fail to overcome forgetting due to the severe domain gap
between the synthetic and real data. To address this issue, this paper proposes
relation-guided representation learning (RRL) for DFCIL, dubbed R-DFCIL. In
RRL, we introduce relational knowledge distillation to flexibly transfer the
structural relation of new data from the old model to the current model. Our
RRL-boosted DFCIL can guide the current model to learn representations of new
classes better compatible with representations of previous classes, which
greatly reduces forgetting while improving plasticity. To avoid the mutual
interference between representation and classifier learning, we employ local
rather than global classification loss during RRL. After RRL, the
classification head is fine-tuned with global class-balanced classification
loss to address the data imbalance issue as well as learn the decision boundary
between new and previous classes. Extensive experiments on CIFAR100,
Tiny-ImageNet200, and ImageNet100 demonstrate that our R-DFCIL significantly
surpasses previous approaches and achieves a new state-of-the-art performance
for DFCIL.


title:
The curved Mimetic Finite Difference method: allowing grids with curved
  faces

abstract:
  We present a new mimetic finite difference method able to deal with curved
faces. Notably, it yields a symmetric discrete problem which uses only one
degree of freedom for each curved face. The principle at the core of our
construction is to abandon the standard definition of local consistency of
mimetic finite difference methods. Instead, we exploit the novel and global
concept of $P_0$-consistency. Numerical examples confirm the consistency and
the convergence of the method for real-case problems with curved boundary
geometries.


title:
An overview of new measurements of flow, chirality, and vorticity from
  STAR experiment

abstract:
  In relativistic heavy-ion collisions, the properties of quark-gluon plasma
(QGP) and complex dynamics of multi-scale processes in Quantum Chromodynamics
(QCD) are studied by analyzing the final state produced particles in a variety
of different ways. In these proceedings, we present an overview of new detailed
measurements of flow, chirality, and vorticity by the STAR experiment at RHIC.
Furthermore, STAR's future opportunities for the precision meansurements on
small systems, fixed-target (FXT) mode, and Beam Energy Scan (BES-II) program
are discussed.


title: The semileptonic $B_s$ and $\Lambda_b$ widths

abstract:
  We present estimates of the semileptonic widths of the $B_s$ and $\Lambda_b$
hadrons. We employ the latest fit to the $B$ inclusive semileptonic data, the
heavy quark expansion and lattice QCD results. Our results suffer from large
uncertainties that could be reduced with dedicated measurements and new lattice
QCD calculations.


title:
Explainable Artificial Intelligence for Exhaust Gas Temperature of
  Turbofan Engines

abstract:
  Data-driven modeling is an imperative tool in various industrial
applications, including many applications in the sectors of aeronautics and
commercial aviation. These models are in charge of providing key insights, such
as which parameters are important on a specific measured outcome or which
parameter values we should expect to observe given a set of input parameters.
At the same time, however, these models rely heavily on assumptions (e.g.,
stationarity) or are "black box" (e.g., deep neural networks), meaning that
they lack interpretability of their internal working and can be viewed only in
terms of their inputs and outputs. An interpretable alternative to the "black
box" models and with considerably less assumptions is symbolic regression (SR).
SR searches for the optimal model structure while simultaneously optimizing the
model's parameters without relying on an a-priori model structure. In this
work, we apply SR on real-life exhaust gas temperature (EGT) data, collected at
high frequencies through the entire flight, in order to uncover meaningful
algebraic relationships between the EGT and other measurable engine parameters.
The experimental results exhibit promising model accuracy, as well as
explainability returning an absolute difference of 3{\deg}C compared to the
ground truth and demonstrating consistency from an engineering perspective.


title: The Nash problem for torus actions of complexity one

abstract:
  We solve the equivariant generalized Nash problem for any non-rational normal
variety with torus action of complexity one. Namely, we give an explicit
combinatorial description of the Nash order on the set of equivariant
divisorial valuations on any such variety. Using this description, we
positively solve the classical Nash problem in this setting, showing that every
essential valuation is a Nash valuation. We also describe terminal valuations
and use our results to answer negatively a question of de Fernex and Docampo by
constructing examples of Nash valuations which are neither minimal nor
terminal, thus illustrating a striking new feature of the class of
singularities under consideration.


title:
Position Tracking using Likelihood Modeling of Channel Features with
  Gaussian Processes

abstract:
  Recent localization frameworks exploit spatial information of complex channel
measurements (CMs) to estimate accurate positions even in multipath propagation
scenarios. State-of-the art CM fingerprinting(FP)-based methods employ
convolutional neural networks (CNN) to extract the spatial information.
However, they need spatially dense data sets (associated with high acquisition
and maintenance efforts) to work well -- which is rarely the case in practical
applications. If such data is not available (or its quality is low), we cannot
compensate the performance degradation of CNN-based FP as they do not provide
statistical position estimates, which prevents a fusion with other sources of
information on the observation level.
  We propose a novel localization framework that adapts well to sparse datasets
that only contain CMs of specific areas within the environment with strong
multipath propagation. Our framework compresses CMs into informative features
to unravel spatial information. It then regresses Gaussian processes (GPs) for
each of them, which imply statistical observation models based on
distance-dependent covariance kernels. Our framework combines the trained GPs
with line-of-sight ranges and a dynamics model in a particle filter. Our
measurements show that our approach outperforms state-of-the-art CNN
fingerprinting (0.52 m vs. 1.3 m MAE) on spatially sparse data collected in a
realistic industrial indoor environment.


title: Unusually high HCO+/CO ratios in and outside supernova remnant W49B

abstract:
  Galactic supernova remnants (SNRs) and their environments provide the nearest
laboratories to study SN feedback. We performed molecular observations toward
SNR W49B -- the most luminous Galactic SNR in the X-ray band, aiming to explore
signs of multiple feedback channels of SNRs on nearby molecular clouds (MCs).
We found very broad HCO+ lines with widths of dv = 48--75 km/s in the SNR
southwest, providing strong evidence that W49B is perturbing MCs at a systemic
velocity of $V_{LSR}=61$--65 km/s, and placing W49B at a distance of $7.9\pm
0.6$ kpc. We observed unusually high intensity ratios of HCO+ J=1-0/CO J=1-0
not only at shocked regions ($1.1\pm 0.4$ and $0.70\pm 0.16$), but also in
quiescent clouds over 1 pc away from the SNR's eastern boundary (> 0.2). By
comparing with the magnetohydrodynamics (MHD) shock models, we interpret that
the high ratio in the broad-line regions can result from a cosmic-ray (CR)
induced chemistry in shocked MCs, where the CR ionization rate is enhanced to
around 10--100 times of the Galactic level. The high HCO+/CO ratio outside the
SNR is probably caused by the radiation precursor, while the luminous X-ray
emission of W49B can explain a few properties in this region. The above results
provide observational evidence that SNR can strongly influence the molecular
chemistry in and outside the shock boundary via their shocks, CRs, and
radiation. We propose that the HCO+/CO ratio is a potentially useful tool to
probe SNR's multi-channel influence on MCs.


title:
minicons: Enabling Flexible Behavioral and Representational Analyses of
  Transformer Language Models

abstract:
  We present minicons, an open source library that provides a standard API for
researchers interested in conducting behavioral and representational analyses
of transformer-based language models (LMs). Specifically, minicons enables
researchers to apply analysis methods at two levels: (1) at the prediction
level -- by providing functions to efficiently extract word/sentence level
probabilities; and (2) at the representational level -- by also facilitating
efficient extraction of word/phrase level vectors from one or more layers. In
this paper, we describe the library and apply it to two motivating case
studies: One focusing on the learning dynamics of the BERT architecture on
relative grammatical judgments, and the other on benchmarking 23 different LMs
on zero-shot abductive reasoning. minicons is available at
https://github.com/kanishkamisra/minicons


title: Estimates of Nonnegative Solutions to Semilinear Elliptic Equations

abstract:
  Let $L$ be a second order uniformly elliptic differential operator in a
domain $D$ of $\mathbb{R}^{d}$, $\psi:\mathbb{R}_+\to \mathbb{R}_+$ be a
nondecreasing continuous function and let $\xi,g:D\to\mathbb{R}_+$ be locally
bounded Borel measurable functions. Under appropriate conditions, we determine
a function $\varphi$ with values in $]0,1]$ such that for every nonnegative
solution to inequality $-Lu+\xi\psi(u) \geq g$ in $D$ and for every $x\in D$,
  $$
  u(x)\geq p(x)\,\varphi\left(\frac{G_D(\xi\psi(p))(x)}{p(x)}\right)
  $$ where $p=G_Dg$ is the Green function of $g$. The function $\varphi$ is
completely determined by $\psi$ and does not depend on $L,D,\xi$ or $g$.


title:
Coriolis mixing of the K=1 and K=0 mixed symmetry states in the well
  deformed even-even nuclei

abstract:
  The Coriolis matrix elements responsible for mixing of the $1^+ K=1$ and $1^+
K=0$ states are calculated in the framework of the Quasiparticle Phonon Model
for several Gd and Dy isotopes. In many considered cases these matrix elements
are equal to several tens of keV and are comparable with energy distances
between the mixed levels. The results obtained indicates that Gd isotopes could
be more suitable for finding deviations from Alaga rules in M1 transitions from
$1^+$ state to the states of the ground band.


title: An Intermittent Onsager Theorem

abstract:
  For any regularity exponent $\beta<\frac 12$, we construct non-conservative
weak solutions to the 3D incompressible Euler equations in the class $C^0_t
(H^{\beta} \cap L^{\frac{1}{(1-2\beta)}})$. By interpolation, such solutions
belong to $C^0_tB^{s}_{3,\infty}$ for $s$ approaching $\frac 13$ as $\beta$
approaches $\frac 12$. Hence this result provides a new proof of the flexible
side of the Onsager conjecture, which is independent from that of Isett [A
proof of Onsager's conjecture, Annals of Mathematics, 188(3):871, 2018]. Of
equal importance is that the intermittent nature of our solutions matches that
of turbulent flows, which are observed to possess an $L^2$-based regularity
index exceeding $\frac 13$. Our proof builds on the authors' previous joint
work with Buckmaster and Masmoudi [Nonconservative $H^{\frac 12-}$ weak
solutions of the incompressible 3D Euler equations, arXiv 2101.09278, 2021], in
which an intermittent convex integration scheme is developed for the 3D
incompressible Euler equations. We employ a scheme with higher-order Reynolds
stresses, which are corrected via a combinatorial placement of intermittent
pipe flows of optimal relative intermittency.


title: Egocentric Prediction of Action Target in 3D

abstract:
  We are interested in anticipating as early as possible the target location of
a person's object manipulation action in a 3D workspace from egocentric vision.
It is important in fields like human-robot collaboration, but has not yet
received enough attention from vision and learning communities. To stimulate
more research on this challenging egocentric vision task, we propose a large
multimodality dataset of more than 1 million frames of RGB-D and IMU streams,
and provide evaluation metrics based on our high-quality 2D and 3D labels from
semi-automatic annotation. Meanwhile, we design baseline methods using
recurrent neural networks and conduct various ablation studies to validate
their effectiveness. Our results demonstrate that this new task is worthy of
further study by researchers in robotics, vision, and learning communities.


title: On Benford's Law for multiplicative functions

abstract:
  We provide a criterion to determine whether a real multiplicative function is
a strong Benford sequence. The criterion implies that the $k$-divisor
functions, where $k \neq 10^j$, and Hecke eigenvalues of newforms, such as
Ramanujan tau function, are strong Benford. Moreover, we deduce from the
criterion that the collection of multiplicative functions which are not strong
Benford forms a group under pointwise multiplication. In contrast to earlier
work, our approach is based on Hal\'{a}sz's Theorem.


title: X-ray Dissectography Improves Lung Nodule Detection

abstract:
  Although radiographs are the most frequently used worldwide due to their
cost-effectiveness and widespread accessibility, the structural superposition
along the x-ray paths often renders suspicious or concerning lung nodules
difficult to detect. In this study, we apply "X-ray dissectography" to dissect
lungs digitally from a few radiographic projections, suppress the interference
of irrelevant structures, and improve lung nodule detectability. For this
purpose, a collaborative detection network is designed to localize lung nodules
in 2D dissected projections and 3D physical space. Our experimental results
show that our approach can significantly improve the average precision by 20+%
in comparison with the common baseline that detects lung nodules from original
projections using a popular detection network. Potentially, this approach could
help re-design the current X-ray imaging protocols and workflows and improve
the diagnostic performance of chest radiographs in lung diseases.


title: A GL-Equivariant Complex Inducing Character Identities for Schur Modules

abstract:
  In this paper we construct a GL-equivariant complex of Schur modules over a
ring of positive characteristic that can be used to deduce classical
alternating sum identities for Schur polynomials. This complex globalizes to a
complex of vector bundles and can also be used to give an explicit construction
of an exact sequence predicted by work of Grayson involving Adams operations
identities on the algebraic K-theory of a given scheme $X$. The more general
complex gives an explicit construction that reproves the aforementioned Adams
operations identities in full generality.


title:
Feature visualization for convolutional neural network models trained on
  neuroimaging data

abstract:
  A major prerequisite for the application of machine learning models in
clinical decision making is trust and interpretability. Current explainability
studies in the neuroimaging community have mostly focused on explaining
individual decisions of trained models, e.g. obtained by a convolutional neural
network (CNN). Using attribution methods such as layer-wise relevance
propagation or SHAP heatmaps can be created that highlight which regions of an
input are more relevant for the decision than others. While this allows the
detection of potential data set biases and can be used as a guide for a human
expert, it does not allow an understanding of the underlying principles the
model has learned. In this study, we instead show, to the best of our
knowledge, for the first time results using feature visualization of
neuroimaging CNNs. Particularly, we have trained CNNs for different tasks
including sex classification and artificial lesion classification based on
structural magnetic resonance imaging (MRI) data. We have then iteratively
generated images that maximally activate specific neurons, in order to
visualize the patterns they respond to. To improve the visualizations we
compared several regularization strategies. The resulting images reveal the
learned concepts of the artificial lesions, including their shapes, but remain
hard to interpret for abstract features in the sex classification task.


title:
Quantum Computing in the Cloud: Analyzing job and machine
  characteristics

abstract:
  As the popularity of quantum computing continues to grow, quantum machine
access over the cloud is critical to both academic and industry researchers
across the globe. And as cloud quantum computing demands increase
exponentially, the analysis of resource consumption and execution
characteristics are key to efficient management of jobs and resources at both
the vendor-end as well as the client-end. While the analysis of resource
consumption and management are popular in the classical HPC domain, it is
severely lacking for more nascent technology like quantum computing.
  This paper is a first-of-its-kind academic study, analyzing various trends in
job execution and resources consumption / utilization on quantum cloud systems.
We focus on IBM Quantum systems and analyze characteristics over a two year
period, encompassing over 6000 jobs which contain over 600,000 quantum circuit
executions and correspond to almost 10 billion "shots" or trials over 20+
quantum machines. Specifically, we analyze trends focused on, but not limited
to, execution times on quantum machines, queuing/waiting times in the cloud,
circuit compilation times, machine utilization, as well as the impact of job
and machine characteristics on all of these trends. Our analysis identifies
several similarities and differences with classical HPC cloud systems. Based on
our insights, we make recommendations and contributions to improve the
management of resources and jobs on future quantum cloud systems.


title: Moving Window Regression: A Novel Approach to Ordinal Regression

abstract:
  A novel ordinal regression algorithm, called moving window regression (MWR),
is proposed in this paper. First, we propose the notion of relative rank
($\rho$-rank), which is a new order representation scheme for input and
reference instances. Second, we develop global and local relative regressors
($\rho$-regressors) to predict $\rho$-ranks within entire and specific rank
ranges, respectively. Third, we refine an initial rank estimate iteratively by
selecting two reference instances to form a search window and then estimating
the $\rho$-rank within the window. Extensive experiments results show that the
proposed algorithm achieves the state-of-the-art performances on various
benchmark datasets for facial age estimation and historical color image
classification. The codes are available at https://github.com/nhshin-mcl/MWR.


title:
Looped Pipelines Enabling Effective 3D Qubit Lattices in a Strictly 2D
  Device

abstract:
  Many quantum computing platforms are based on a fundamentally two-dimensional
physical layout. However, there are advantages (for example in fault-tolerant
systems) to having a 3D architecture. Here we explore a concept called looped
pipelines which permits one to obtain many of the advantages of a 3D lattice
while operating a strictly 2D device. The concept leverages qubit shuttling, a
well-established feature in platforms like semiconductor spin qubits and
trapped-ion qubits. The looped pipeline architecture has similar hardware
requirements to other shuttling approaches, but can process a stack of qubit
arrays instead of just one. Simple patterns of intra- and inter-loop
interactions allow one to embody diverse schemes from NISQ-era error mitigation
through to fault-tolerant codes. For the former, protocols involving multiple
states can be implemented with a similar space-time resource as preparing one
noisy copy. For the latter, one can realise a far broader variety of code
structures; in particular, we consider a stack of 2D codes within which
transversal CNOTs are available. We find that this can achieve a cost saving of
up to a factor of $\sim 80$ in the space-time overhead for magic state
distillation (and a factor of $\sim 200$ with modest additional hardware).
Using numerical modelling and experimentally-motivated noise models we verify
that the looped pipeline approach provides these benefits without significant
reduction in the code's threshold.


title:
Universal logarithmic correction to R\'enyi (Shannon) entropy in general
  critical quadratic fermions

abstract:
  The R\'enyi (Shannon) entropy, i.e. $Re_{\alpha}(Sh)$, of the ground state of
quantum systems in local bases normally show a volume-law behavior. For a
subsystem of quantum chains at critical point there is an extra logarithmic
subleading term with a coefficient which is universal. In this paper we study
this coefficient for generic time-reversal translational invariant quadratic
critical free fermions. These models can be parameterized by a complex function
which has zeros on the unit circle. When the zeros on the unit circle do not
have degeneracy and there is no zero outside of the unit circle we are able to
classify the coefficient of the logarithm. In particular, we numerically
calculate the R\'enyi (Shannon) entropy in configuration basis for wide variety
of these models and show that there are two distinct classes. For systems with
$U(1)$ symmetry the coefficient is proportional to the central charge, i.e. one
half of the number of points that one can linearize the dispersion relation of
the system; for all the values of $\alpha$ with transition point at $\alpha=4$.
For systems without this symmetry, when $\alpha>1$ this coefficient is again
proportional to the central charge. However, the coefficient for $\alpha\leq 1$
is a new universal number. Finally, by using the discrete version of
Bisognano-Wichmann modular Hamiltonian of the Ising chain we show that these
coefficients are universal and dependent on the underlying CFT.


title:
Intelligent Systematic Investment Agent: an ensemble of deep learning
  and evolutionary strategies

abstract:
  Machine learning driven trading strategies have garnered a lot of interest
over the past few years. There is, however, limited consensus on the ideal
approach for the development of such trading strategies. Further, most
literature has focused on trading strategies for short-term trading, with
little or no focus on strategies that attempt to build long-term wealth. Our
paper proposes a new approach for developing long-term investment strategies
using an ensemble of evolutionary algorithms and a deep learning model by
taking a series of short-term purchase decisions. Our methodology focuses on
building long-term wealth by improving systematic investment planning (SIP)
decisions on Exchange Traded Funds (ETF) over a period of time. We provide
empirical evidence of superior performance (around 1% higher returns) using our
ensemble approach as compared to the traditional daily systematic investment
practice on a given ETF. Our results are based on live trading decisions made
by our algorithm and executed on the Robinhood trading platform.


title:
Local accumulation times in a diffusion-trapping model of synaptic
  receptor dynamics

abstract:
  The lateral diffusion and trapping of neurotransmitter receptors within the
postsynaptic membrane of a neuron plays a key role in determining synaptic
strength and plasticity. Trapping is mediated by the reversible binding of
receptors to scaffolding proteins (slots) within a synapse. In this paper we
introduce a new method for analyzing the transient dynamics of synapses in a
diffusion-trapping model of receptor trafficking. Given a population of
spatially distributed synapses, each of which has a fixed number of slots, we
calculate the rate of relaxation to the steady-state distribution of bound
slots (synaptic weights) in terms of a set of local accumulation times.
Assuming that the rates of exocytosis and endocytosis are sufficiently slow, we
show that the steady-state synaptic weights are independent of each other
(purely local). On the other hand, the local accumulation time of a given
synapse depends on the number of slots and the spatial location of all the
synapses, indicating a form of transient heterosynaptic plasticity. This
suggests that local accumulation time measurements could provide useful
information regarding the distribution of synaptic weights within a dendrite.


title:
Characterizing Therapist's Speaking Style in Relation to Empathy in
  Psychotherapy

abstract:
  In conversation-based psychotherapy, therapists use verbal techniques to help
clients express thoughts and feelings and change behaviors. In particular, how
well therapists convey empathy is an essential quality index of psychotherapy
sessions and is associated with psychotherapy outcome. In this paper, we
analyze the prosody of therapist speech and attempt to associate the
therapist's speaking style with subjectively perceived empathy. An automatic
speech and text processing system is developed to segment long recordings of
psychotherapy sessions into pause-delimited utterances with text
transcriptions. Data-driven clustering is applied to the utterances from
different therapists in multiple sessions. For each cluster, a typological
representation of utterance genre is derived based on quantized prosodic
feature parameters. Prominent speaking styles of the therapist can be observed
and interpreted from salient utterance genres that are correlated with empathy.
Using the salient utterance genres, an accuracy of 71% is achieved in
classifying psychotherapy sessions into "high" and "low" empathy level.
Analysis of results suggests that empathy level tends to be (1) low if
therapists speak long utterances slowly or speak short utterances quickly; and
(2) high if therapists talk to clients with a steady tone and volume.


title:
Universality, complexity and asymptotically uniformly smooth Banach
  spaces

abstract:
  For $1<p\le \infty$, we show the existence of a Banach space which is both
injectively and surjectively universal for the class of all separable Banach
spaces with an equivalent $p$-asymptotically uniformly smooth norm. We prove
that this class is analytic complete in the class of separable Banach spaces.
These results extend previous works by Kalton, Werner and Kurka in the case
$p=\infty$.


title:
Bi-level Optimization for hyperparameters in Nonnegative Matrix
  Factorizations

abstract:
  Hyperparameters (HPs) Optimization in machine learning algorithms represents
an open problem with a direct impact on algorithm performances and on the
knowledge extraction process from data. Matrix Decompositions (MDs) has
recently gained more attention in data science as mathematical techniques able
to capture latent information embedded in large datasets. MDs can be formalized
as penalized optimization problems in which the tuning of the penalization HPs
represents an issue. Current literature panorama does not provide any general
framework addressing optimally conditions for the best configuration of HPs. In
this work, we focus on the role the HPs play in the penalized Nonnegative
Matrix Factorizations (NMF) context and on the importance of their correct
selection. Using a bi-level optimization approach, HPs selection is considered
from an optimization point of view and their choice is directly incorporated in
the unsupervised algorithm as a part of the updating process. Either theorems
of existence and convergence of numerical solutions, under appropriate
assumptions, are studied and a novel algorithm, namely AltBi-Alternate bi-level
Algorithm, is proposed to tune the penalization HP in NMF problems.


title: Agile Beyond Teams and Feedback Beyond Software in Automotive Systems

abstract:
  In order to increase the ability to build complex, software-intensive
systems, as well as to decrease time-to-market for new functionality,
automotive companies aim to scale agile methods beyond individual teams. This
is challenging, given the specifics of automotive systems that are often
safety-critical and consist of software, hardware, and mechanical components.
This paper investigates the concrete reasons for scaling agility beyond teams,
the strategies that support such scaling, and foreseeable implications that
such a drastic organizational change will entail. The investigation is based on
a qualitative case study, with data from 20 semi-structured interviews with
managers and technical experts at two automotive companies. At the core of our
findings are observations about establishing an agile vehicle-level feedback
loop beyond individual teams. (I) We find that automotive OEMs aim to decrease
lead-time of development. (II) We also identify 7 strategies that aim to enable
scaled-agile beyond teams. (III) Finally, we extract 6 foreseeable implications
and side-effects of scaling agile beyond teams in automotive. By charting the
landscape of expected benefits, strategies, and implications of scaling agile
beyond teams in automotive, we enable further research and process
improvements.


title: Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors

abstract:
  Recent text-to-image generation methods provide a simple yet exciting
conversion capability between text and image domains. While these methods have
incrementally improved the generated image fidelity and text relevancy, several
pivotal gaps remain unanswered, limiting applicability and quality. We propose
a novel text-to-image method that addresses these gaps by (i) enabling a simple
control mechanism complementary to text in the form of a scene, (ii)
introducing elements that substantially improve the tokenization process by
employing domain-specific knowledge over key image regions (faces and salient
objects), and (iii) adapting classifier-free guidance for the transformer use
case. Our model achieves state-of-the-art FID and human evaluation results,
unlocking the ability to generate high fidelity images in a resolution of
512x512 pixels, significantly improving visual quality. Through scene
controllability, we introduce several new capabilities: (i) Scene editing, (ii)
text editing with anchor scenes, (iii) overcoming out-of-distribution text
prompts, and (iv) story illustration generation, as demonstrated in the story
we wrote.


title: DPST: De Novo Peptide Sequencing with Amino-Acid-Aware Transformers

abstract:
  De novo peptide sequencing aims to recover amino acid sequences of a peptide
from tandem mass spectrometry (MS) data. Existing approaches for de novo
analysis enumerate MS evidence for all amino acid classes during inference. It
leads to over-trimming on receptive fields of MS data and restricts MS evidence
associated with following undecoded amino acids. Our approach, DPST,
circumvents these limitations with two key components: (1) A confidence value
aggregation encoder to sketch spectrum representations according to
amino-acid-based connectivity among MS; (2) A global-local fusion decoder to
progressively assimilate contextualized spectrum representations with a
predefined preconception of localized MS evidence and amino acid priors. Our
components originate from a closed-form solution and selectively attend to
informative amino-acid-aware MS representations. Through extensive empirical
studies, we demonstrate the superiority of DPST, showing that it outperforms
state-of-the-art approaches by a margin of 12% - 19% peptide accuracy.


title:
Localized Wavefield Inversion (LWI): an Adaptation of Multi-Block ADMM
  for Localized FWI

abstract:
  Full-waveform inversion (FWI) is a high-resolution and computationally
intensive imaging technique to reconstruct unknown parameters in the
computational model in which the waves propagate; however, an accurate model of
only part of this medium is required for some applications. To decrease the
computational burden of such problems, target-oriented FWI was proposed where
the redatumed data on the part of the medium or localized solvers for the wave
equation are used. On the other hand, the classical formulation of FWI suffers
from non-linearity and ill-posedness, which makes FWI sensitive to the initial
model, the low-frequency content of the data, and limited illumination. In this
study, we propose a localized version of the alternating direction method of
multipliers (ADMM)-based FWI method, which was proposed to solve these problems
in classical FWI. In our localized FWI or LWI, the medium is decomposed into a
few subdomains, where some of them are updated, and the others are kept fixed
based on an adaptation of multi-block ADMM, which is a powerful algorithm for
solving inverse problems with decomposition and block separability. Numerical
tests on the Marmousi model for a time-lapse application confirm the
computational efficiency and robustness against background velocity model
errors.


title:
Bremsstrahlung in chiral medium: anomalous contribution to the
  Bethe-Heitler formula

abstract:
  We investigate photon bremsstrahlung in chiral media. The chiral medium
response to the magnetic field is described by the chiral magnetic current $ j=
b_0 B$, where $b_0$ is the chiral magnetic conductivity. This current modifies
the photon dispersion relation producing a resonance in the scattering
amplitude. We show that the resonant contribution is proportional to the
magnetic moment $\mu$ of the target nucleus. We analytically compute the
corresponding cross section. We argue that the anomalous contribution is
enhanced by a factor $b_0^2/\Gamma^2$, where $\Gamma$ is a width of the
resonance related to the chiral magnetic instability of the electromagnetic
field. The most conspicuous feature of the anomalous contribution to the photon
spectrum is the emergence of the knee-like structure at photon energies
proportional to $b_0$. We argue that the phenomenological significance of the
anomalous terms depends on the magnitude of the ratio of $b_0$ to the
projectile fermion mass.


title:
A Unified Tool for Solving Uni-Parametric Linear Programs, Convex
  Quadratic Programs, and Linear Complementarity Problems

abstract:
  We introduce a new technique for solving uni-parametric versions of linear
programs, convex quadratic programs, and linear complementarity problems in
which a single parameter is permitted to be present in any of the input data.
We demonstrate the use of our method on a small, motivating example and present
the results of a small number of computational tests demonstrating its utility
for larger scale problems.