title:
Permutation Jensen-Shannon distance: A versatile and fast symbolic tool
  for complex time series analysis

abstract:
  The main motivation of this paper is to introduce the permutation
Jensen-Shannon distance, a symbolic tool able to quantify the degree of
similarity between two arbitrary time series. This quantifier results from the
fusion of two concepts, the Jensen-Shannon divergence and the encoding scheme
based on the sequential ordering of the elements in the data series. The
versatility and robustness of this ordinal symbolic distance for characterizing
and discriminating different dynamics are illustrated through several numerical
and experimental applications. Results obtained allow us to be optimistic about
its usefulness in the field of complex time series analysis. Moreover, thanks
to its simplicity, low computational cost, wide applicability and less
susceptibility to outliers and artifacts, this ordinal measure can efficiently
handle large amounts of data and help to tackle the current big data
challenges.


title:
Efficient, ever-ready quantum memory at room temperature for single
  photons

abstract:
  Efficient quantum memories will be an essential building block of large scale
networked quantum systems and provide a link between flying photonic qubits and
atomic or quasi-atomic local quantum processors. To provide a path to
scalability avoidance of bulky, difficult to maintain systems such as high
vacuum and low temperature cryogenics is imperative. Memory efficiencies above
50% are required to be operating above the quantum no-cloning limit. Such high
efficiencies have only been achieved in systems with photon sources tailored to
the memory bandwidth. In this paper we explore the combination of an ultralow
spectral bandwidth source of single photons from cavity-enhanced spontaneous
parametric down-conversion with a gas-ensemble atomic memory. Our rubidium
vapour gradient echo memory achieves 84$\pm$3% recall efficiency of single
photons: a record for an always-ready, hot, and vacuum system free optical
memory.


title:
Enhanced Low-Energy Supernova Burst Detection in Large Liquid Argon Time
  Projection Chambers Enabled by Q-Pix

abstract:
  The detection of neutrinos from core-collapse supernovae may reveal important
process features as well as neutrino properties. The detection of supernova
neutrinos is one of the main science drivers for future kiloton-scale neutrino
detectors based on liquid argon. Here we show that for such detectors the
intrinsically 3D readout in Q-Pix offers numerous advantages relative to a
wire-based readout, such as higher reconstruction efficiency, lower energy
threshold, considerably lower data rates, and potential pointing information.


title: A time-resolved picture of our Milky Way's early formation history

abstract:
  The formation of our Milky Way can be parsed qualitatively into different
phases that resulted in its structurally different stellar populations: the
halo and the disk components. Revealing a quantitative overall picture of the
Galactic assembly awaits a large sample of stars with very precise ages. Here
we report an analysis of such a sample using subgiant stars. We find that the
stellar age-metallicity distribution p(age, metallicity) splits into two almost
disjoint parts, separated at 8 Gyr. The younger reflecting a late phase of
quiescent Galactic disk formation with manifest evidence for stellar radial
orbit migration; the other reflecting the earlier phase, when the stellar halo
and the old alpha-process-enhanced (thick) disk formed. Our results indicate
that the formation of the Galactic old (thick) disk started 13 Gyr ago, only
0.8 Gyr after the Big Bang, and two Gigayears earlier than the final assembly
of the inner Galactic halo. Most of these stars formed 11 Gyr ago, when the
Gaia-Sausage-Enceladus satellite merged with our Galaxy. Over the next 5--6
Gyr, the Galaxy experienced continuous chemical element enrichment, ultimately
by a factor of 10, while the star-forming gas managed to stay well-mixed.


title:
Muscle Vision: Real Time Keypoint Based Pose Classification of Physical
  Exercises

abstract:
  Recent advances in machine learning technology have enabled highly portable
and performant models for many common tasks, especially in image recognition.
One emerging field, 3D human pose recognition extrapolated from video, has now
advanced to the point of enabling real-time software applications with robust
enough output to support downstream machine learning tasks. In this work we
propose a new machine learning pipeline and web interface that performs human
pose recognition on a live video feed to detect when common exercises are
performed and classify them accordingly. We present a model interface capable
of webcam input with live display of classification results. Our main
contributions include a keypoint and time series based lightweight approach for
classifying a selected set of fitness exercises and a web-based software
application for obtaining and visualizing the results in real time.


title:
Lymphocyte Classification in Hyperspectral Images of Ovarian Cancer
  Tissue Biopsy Samples

abstract:
  Current methods for diagnosing the progression of multiple types of cancer
within patients rely on interpreting stained needle biopsies. This process is
time-consuming and susceptible to error throughout the paraffinization,
Hematoxylin and Eosin (H&E) staining, deparaffinization, and annotation stages.
Fourier Transform Infrared (FTIR) imaging has been shown to be a promising
alternative to staining for appropriately annotating biopsy cores without the
need for deparaffinization or H&E staining with the use of Fourier Transform
Infrared (FTIR) images when combined with machine learning to interpret the
dense spectral information. We present a machine learning pipeline to segment
white blood cell (lymphocyte) pixels in hyperspectral images of biopsy cores.
These cells are clinically important for diagnosis, but some prior work has
struggled to incorporate them due to difficulty obtaining precise pixel labels.
Evaluated methods include Support Vector Machine (SVM), Gaussian Naive Bayes,
and Multilayer Perceptron (MLP), as well as analyzing the comparatively modern
convolutional neural network (CNN).


title:
Influence of the Endothelial Surface Layer on the Wall-induced Migration
  of Red Blood Cells

abstract:
  The endothelial lining of blood vessels presents a large surface area for
exchanging materials between blood and tissues. The endothelial surface layer
(ESL) plays a critical role in regulating vascular permeability, hindering
leukocyte adhesion as well as inhibiting coagulation during inflammation.
Changes in the ESL structure are believed to cause vascular hyperpermeability
and induce thrombus formation during sepsis. In addition, ESL topography is
relevant for the interactions between red blood cells (RBCs) and the vessel
wall, including the wall-induced migration of RBCs and formation of a cell-free
layer. To investigate the influence of the ESL on the motion of RBCs, we
construct two models to represent the ESL using the immersed boundary method.
In particular, we use simulations to study how lift force and drag force change
over time when a RBC is placed close to the ESL as the thickness, roughness,
and permeability of the ESL vary. We find that roughness has a significant
effect on the wall-induced migration of the RBC when the ESL is highly
permeable and that the wall-induced migration can be significantly inhibited by
the presence of a thick ESL.


title: An Optical Controlling Environment and Reinforcement Learning Benchmarks

abstract:
  Deep reinforcement learning has the potential to address various scientific
problems. In this paper, we implement an optics simulation environment for
reinforcement learning based controllers. The environment incorporates
nonconvex and nonlinear optical phenomena as well as more realistic
time-dependent noise. Then we provide the benchmark results of several
state-of-the-art reinforcement learning algorithms on the proposed simulation
environment. In the end, we discuss the difficulty of controlling the
real-world optical environment with reinforcement learning algorithms.


title:
Imaging current paths in silicon photovoltaic devices with a quantum
  diamond microscope

abstract:
  Magnetic imaging with nitrogen-vacancy centers in diamond, also known as
quantum diamond microscopy, has emerged as a useful technique for the spatial
mapping of charge currents in solid-state devices. In this work, we investigate
an application to photovoltaic (PV) devices, where the currents are induced by
light. We develop a widefield nitrogen-vacancy microscope that allows
independent stimulus and measurement of the PV device, and test our system on a
range of prototype crystalline silicon PV devices. We first demonstrate
micrometer-scale vector magnetic field imaging of custom PV devices illuminated
by a focused laser spot, revealing the internal current paths in both
short-circuit and open-circuit conditions. We then demonstrate time-resolved
imaging of photocurrents in an interdigitated back-contact solar cell,
detecting current build-up and subsequent decay near the illumination point
with microsecond resolution. This work presents a versatile and accessible
analysis platform that may find distinct application in research on emerging PV
technologies.


title: GOSS: Towards Generalized Open-set Semantic Segmentation

abstract:
  In this paper, we present and study a new image segmentation task, called
Generalized Open-set Semantic Segmentation (GOSS). Previously, with the
well-known open-set semantic segmentation (OSS), the intelligent agent only
detects the unknown regions without further processing, limiting their
perception of the environment. It stands to reason that a further analysis of
the detected unknown pixels would be beneficial. Therefore, we propose GOSS,
which unifies the abilities of two well-defined segmentation tasks, OSS and
generic segmentation (GS), in a holistic way. Specifically, GOSS classifies
pixels as belonging to known classes, and clusters (or groups) of pixels of
unknown class are labelled as such. To evaluate this new expanded task, we
further propose a metric which balances the pixel classification and clustering
aspects. Moreover, we build benchmark tests on top of existing datasets and
propose a simple neural architecture as a baseline, which jointly predicts
pixel classification and clustering under open-set settings. Our experiments on
multiple benchmarks demonstrate the effectiveness of our baseline. We believe
our new GOSS task can produce an expressive image understanding for future
research. Code will be made available.


title: NovGrid: A Flexible Grid World for Evaluating Agent Response to Novelty

abstract:
  A robust body of reinforcement learning techniques have been developed to
solve complex sequential decision making problems. However, these methods
assume that train and evaluation tasks come from similarly or identically
distributed environments. This assumption does not hold in real life where
small novel changes to the environment can make a previously learned policy
fail or introduce simpler solutions that might never be found. To that end we
explore the concept of {\em novelty}, defined in this work as the sudden change
to the mechanics or properties of environment. We provide an ontology of for
novelties most relevant to sequential decision making, which distinguishes
between novelties that affect objects versus actions, unary properties versus
non-unary relations, and the distribution of solutions to a task. We introduce
NovGrid, a novelty generation framework built on MiniGrid, acting as a toolkit
for rapidly developing and evaluating novelty-adaptation-enabled reinforcement
learning techniques. Along with the core NovGrid we provide exemplar novelties
aligned with our ontology and instantiate them as novelty templates that can be
applied to many MiniGrid-compliant environments. Finally, we present a set of
metrics built into our framework for the evaluation of
novelty-adaptation-enabled machine-learning techniques, and show
characteristics of a baseline RL model using these metrics.


title:
R&D works for Superconducting Magnet for Future Accelerator Applications
  in Japan

abstract:
  KEK cryogenics science center is developing superconducting magnet technology
for future accelerator science. Three major technological categories are
focused; 1) high precision 3D magnetic field technology based on the g-2/EDM
magnet developments, 2) rad-hard superconducting magnet technology based on the
COMET magnet developments, and 3) high magnetic field superconducting magnet
technology for future colliders based on the LHC MQXA and HL-LHC D1 magnet
developments. Extensive studies including Nb3Sn conductor and magnet
developments for high field magnets and HTS for rad-hard superconducting
magnets are ongoing with various collaboration programs such as the US-Japan
research collaboration.


title: Visual Prompt Tuning

abstract:
  The current modus operandi in adapting pre-trained models involves updating
all the backbone parameters, ie, full fine-tuning. This paper introduces Visual
Prompt Tuning (VPT) as an efficient and effective alternative to full
fine-tuning for large-scale Transformer models in vision. Taking inspiration
from recent advances in efficiently tuning large language models, VPT
introduces only a small amount (less than 1% of model parameters) of trainable
parameters in the input space while keeping the model backbone frozen. Via
extensive experiments on a wide variety of downstream recognition tasks, we
show that VPT achieves significant performance gains compared to other
parameter efficient tuning protocols. Most importantly, VPT even outperforms
full fine-tuning in many cases across model capacities and training data
scales, while reducing per-task storage cost.


title: Matrix Completion with Heterogonous Cost

abstract:
  The matrix completion problem has been studied broadly under many underlying
conditions. The problem has been explored under adaptive or non-adaptive, exact
or estimation, single-phase or multi-phase, and many other categories. In most
of these cases, the observation cost of each entry is uniform and has the same
cost across the columns. However, in many real-life scenarios, we could expect
elements from distinct columns or distinct positions to have a different cost.
In this paper, we explore this generalization under adaptive conditions. We
approach the problem under two different cost models. The first one is that
entries from different columns have different observation costs, but, within
the same column, each entry has a uniform cost. The second one is any two entry
has different observation cost, despite being the same or different columns. We
provide complexity analysis of our algorithms and provide tightness guarantees.


title:
Contrastive Transformer-based Multiple Instance Learning for Weakly
  Supervised Polyp Frame Detection

abstract:
  Current polyp detection methods from colonoscopy videos use exclusively
normal (i.e., healthy) training images, which i) ignore the importance of
temporal information in consecutive video frames, and ii) lack knowledge about
the polyps. Consequently, they often have high detection errors, especially on
challenging polyp cases (e.g., small, flat, or partially visible polyps). In
this work, we formulate polyp detection as a weakly-supervised anomaly
detection task that uses video-level labelled training data to detect
frame-level polyps. In particular, we propose a novel convolutional
transformer-based multiple instance learning method designed to identify
abnormal frames (i.e., frames with polyps) from anomalous videos (i.e., videos
containing at least one frame with polyp). In our method, local and global
temporal dependencies are seamlessly captured while we simultaneously optimise
video and snippet-level anomaly scores. A contrastive snippet mining method is
also proposed to enable an effective modelling of the challenging polyp cases.
The resulting method achieves a detection accuracy that is substantially better
than current state-of-the-art approaches on a new large-scale colonoscopy video
dataset introduced in this work.


title: On Adversarial Robustness of Large-scale Audio Visual Learning

abstract:
  As audio-visual systems are being deployed for safety-critical tasks such as
surveillance and malicious content filtering, their robustness remains an
under-studied area. Existing published work on robustness either does not scale
to large-scale dataset, or does not deal with multiple modalities. This work
aims to study several key questions related to multi-modal learning through the
lens of robustness: 1) Are multi-modal models necessarily more robust than
uni-modal models? 2) How to efficiently measure the robustness of multi-modal
learning? 3) How to fuse different modalities to achieve a more robust
multi-modal model? To understand the robustness of the multi-modal model in a
large-scale setting, we propose a density-based metric, and a convexity metric
to efficiently measure the distribution of each modality in high-dimensional
latent space. Our work provides a theoretical intuition together with empirical
evidence showing how multi-modal fusion affects adversarial robustness through
these metrics. We further devise a mix-up strategy based on our metrics to
improve the robustness of the trained model. Our experiments on AudioSet and
Kinetics-Sounds verify our hypothesis that multi-modal models are not
necessarily more robust than their uni-modal counterparts in the face of
adversarial examples. We also observe our mix-up trained method could achieve
as much protection as traditional adversarial training, offering a
computationally cheap alternative. Implementation:
https://github.com/lijuncheng16/AudioSetDoneRight


title: Mixing Constant Sum and Constant Product Market Makers

abstract:
  Two popular forms of automated market makers are constant sum and constant
product (CSMM and CPMM respectively). Each has its advantages and
disadvantages: CSMMs have stable exchange rates but are vulnerable to arbitrage
and can sometimes fail to provide liquidity, while a CPMM can have large
impermanent loss due to exchange rate changes but are always able to provide
liquidity to participants.
  A significant amount of work has been done in order to get the best of both
constant sum and constant product characteristics. Perhaps most the relevant to
this paper is Stableswap, which has an "amplification coefficient" parameter
controlling the balance between the two types of behavior [Ego19]. Alternative
approaches, such as in [AEC21], involve constructing AMMs using portfolio value
functions. However, there is still much work to be done on these fronts. This
paper presents multiple novel methods for mixing market makers and demonstrates
new tools for designing markets with specific features.


title:
Structure of test magnetic fields and charged particle motion around the
  Hayward spacetime

abstract:
  A configuration of a test magnetic field in Hayward spacetime is obtained by
solving Maxwell's equation with the Hayward metric as the background. The
magnetic field lines show a dipole loop-like configuration in the regular
Hayward interior, and tends to an asymptotically uniform structure away from
the cylindrical axis. The motion of charged particles is then studied in this
spacetime. The parameters and stability of circular orbits on the equatorial
plane are studied. Aspects of non-equatorial motion are also studied.


title:
Ultraviolet-to-far-infra-red self-consistent analysis of the stellar
  populations of massive starburst galaxies at intermediate redshifts

abstract:
  We study in detail the properties of the stellar populations of 111 massive
($\log(M_{\star}/\mathrm{M}_\odot) \ge 10)$ dusty (FIR-selected) starburst
($SFR/SFR_\mathrm{MS}>2$) galaxies at $0.7<z<1.2$. For that purpose, we use
self-consistent methods that analyse the UV-to-FIR broadband observations in
terms of the stellar light and dust re-emission with energy-balance techniques.
We find that the emission of our starburst galaxies can be interpreted as a
recent star formation episode superimposed on a more evolved stellar
population. On average, the burst age is $\sim 80$ Myr and its attenuation
$\sim 2.4$ mag. Assuming our starburst galaxies at half their lifetimes, we
infer a duration of the starburst phase of $\sim 160$ Myr. The median stellar
mass and SFR are $\log(M_\star/\mathrm{M}_\odot)\sim 10.6$ and
$\sim220~\mathrm{M}_\odot~$yr$^{-1}$. Assuming this SFR and the inferred
duration of the starburst phase, the stellar mass added during this phase
corresponds to $\sim 40$ per cent the median stellar mass of our sample. The
young-population age determines the position of our galaxies in the
$M_{\star}-SFR$ plane. Galaxies located at the largest distances of the MS
present shorter young-population ages. The properties of the underlying stellar
population cannot be constrained accurately with our broadband data. We also
discuss the impact of including the FIR data and energy-balance techniques in
the analysis of the properties of the stellar populations in starburst
galaxies.


title: Immersed Boundary Double Layer Method

abstract:
  The Immersed Boundary (IB) method of Peskin (J. Comput. Phys., 1977) is
useful for problems involving fluid-structure interactions or complex
geometries. By making use of a regular Cartesian grid that is independent of
the geometry, the IB framework yields a robust numerical scheme that can
efficiently handle immersed deformable structures. Additionally, the IB method
has been adapted to problems with prescribed motion and other PDEs with given
boundary data. IB methods for these problems traditionally involve penalty
forces which only approximately satisfy boundary conditions, or they are
formulated as constraint problems. In the latter approach, one must find the
unknown forces by solving an equation that corresponds to a poorly conditioned
first-kind integral equation. This operation can require a large number of
iterations of a Krylov method, and since a time-dependent problem requires this
solve at each time step, this method can be prohibitively inefficient without
preconditioning. In this work, we introduce a new, well-conditioned IB
formulation for boundary value problems, which we call the Immersed Boundary
Double Layer (IBDL) method. We present the method as it applies to Poisson and
Helmholtz problems to demonstrate its efficiency over the original constraint
method. In this double layer formulation, the equation for the unknown boundary
distribution corresponds to a well-conditioned second-kind integral equation
that can be solved efficiently with a small number of iterations of a Krylov
method. Furthermore, the iteration count is independent of both the mesh size
and immersed boundary point spacing. The method converges away from the
boundary, and when combined with a local interpolation, it converges in the
entire PDE domain. Additionally, while the original constraint method applies
only to Dirichlet problems, the IBDL formulation can also be used for Neumann
conditions.


title: Analog Quantum Simulation of the Dynamics of Open Quantum Systems

abstract:
  We introduce a general setup for the analog quantum simulation of the
dynamics of open quantum systems based on semiconductor quantum dots
electrically connected to a chain of quantum $RLC$ electronic circuits. The
dots are chosen to be in the regime of spin-charge hybridization to enhance
their sensitivity to the $RLC$ circuits while mitigating the detrimental
effects of unwanted noise. In this context, we establish an experimentally
realizable map between the hybrid system and a qubit coupled to thermal
harmonic environments of arbitrary complexity that enables the analog quantum
simulation of open quantum systems. We assess the utility of the simulator by
numerically exact emulations that indicate that the experimental setup can
faithfully mimic the intended target. We further provide a detailed analysis of
the physical requirements on the quantum dots and the $RLC$ circuits needed to
experimentally realize this proposal that indicates that the simulator can be
created with existing technology. The approach can capture the effects of
highly structured non-Markovian quantum environments typical of photosynthesis
and chemical dynamics, and offer clear potential advantages over conventional
and even quantum computation. The proposal opens a general path for effective
quantum dynamics simulations based on semiconductor quantum dots.


title: Quantitative marked length spectrum rigidity

abstract:
  We consider a closed Riemannian manifold $M$ of negative curvature and
dimension at least 3 with marked length spectrum sufficiently close
(multiplicatively) to that of a locally symmetric space $N$. Using the methods
of Hamenst\"adt, we show the volumes of $M$ and $N$ are approximately equal. We
then show the Besson-Courtois-Gallot map $F: M \to N$ is a diffeomorphism with
derivative bounds close to 1 and depending on the ratio of the two marked
length spectrum functions. Thus, we refine the results of Hamenst\"adt and
Besson-Courtois-Gallot, which show $M$ and $N$ are isometric if their marked
length spectra are equal. We also prove a similar result for compact negatively
curved surfaces using the methods of Otal together with a version of the Gromov
compactness theorem due to Greene--Wu and Pugh.


title:
$\textit{Herschel}$ discovery of far-infrared emission from the hot spot
  D in the radio galaxy Cygnus A

abstract:
  The far infrared counterpart of hot spot D, the terminal hot spot of the
eastern jet hosted by the radio galaxy Cygnus A, is detected with
\textit{Herschel} Aperture photometery of the source performed in 5 photometric
bands covering the wavelength range of $70\unicode{x2013}350$ $\mathrm{\mu m}$.
After removing the contamination from another nearby hot spot, E, the
far-infrared intensity of hot spot D is derived as $83\pm13$ and $269\pm66$ mJy
at $160$ and $350$ $\mathrm{\mu m}$, respectively. Since the far-infrared
spectrum of the object smoothly connects to the radio one, the far-infrared
emission is attributed to the synchrotron radiation from the radio-emitting
electron population. The radio-to-near-infrared spectrum is confirmed to
exhibit a far-infrared break feature at the frequency of
$\nu_\mathrm{br}=2.0^{+1.2}_{-0.8} \times10^{12}$ Hz. The change in energy
index at the break ($\Delta\alpha=0.5$) is interpreted as the impact of
radiative cooling on an electron distribution sustained by continuous injection
from diffusive shock acceleration. By ascribing the derived break to this
cooling break, the magnetic field, $B$, in the hot spot is determined as a
function of its radius, $R$ within a uniform one-zone model combined with the
strong relativistic shock condition. An independent $B$-$R$ constraint is
obtained by assuming the X-ray spectrum is wholly due to
synchrotron-self-Compton emission. By combining these conditions, the two
parameters are tightly determined as $B=120\unicode{x2013}150$ $\mathrm{\mu G}$
and $R=1.3\unicode{x2013}1.6$ kpc. A further investigation into the two
conditions indicates the observed X-ray flux is highly dominated by the
synchrotron-self-Compton emission.


title: Pixel VQ-VAEs for Improved Pixel Art Representation

abstract:
  Machine learning has had a great deal of success in image processing.
However, the focus of this work has largely been on realistic images, ignoring
more niche art styles such as pixel art. Additionally, many traditional machine
learning models that focus on groups of pixels do not work well with pixel art,
where individual pixels are important. We propose the Pixel VQ-VAE, a
specialized VQ-VAE model that learns representations of pixel art. We show that
it outperforms other models in both the quality of embeddings as well as
performance on downstream tasks.


title: Should Machine Learning Models Report to Us When They Are Clueless?

abstract:
  The right to AI explainability has consolidated as a consensus in the
research community and policy-making. However, a key component of
explainability has been missing: extrapolation, which describes the extent to
which AI models can be clueless when they encounter unfamiliar samples (i.e.,
samples outside the convex hull of their training sets, as we will explain). We
report that AI models extrapolate outside their range of familiar data,
frequently and without notifying the users and stakeholders. Knowing whether a
model has extrapolated or not is a fundamental insight that should be included
in explaining AI models in favor of transparency and accountability. Instead of
dwelling on the negatives, we offer ways to clear the roadblocks in promoting
AI transparency. Our analysis commentary accompanying practical clauses useful
to include in AI regulations such as the National AI Initiative Act in the US
and the AI Act by the European Commission.


title: Runtime Software Patching: Taxonomy, Survey and Future Directions

abstract:
  Runtime software patching aims to minimize or eliminate service downtime,
user interruptions and potential data losses while deploying a patch. Due to
modern software systems' high variance and heterogeneity, no universal
solutions are available or proposed to deploy and execute patches at runtime.
Existing runtime software patching solutions focus on specific cases,
scenarios, programming languages and operating systems. This paper aims to
identify, investigate and synthesize state-of-the-art runtime software patching
approaches and gives an overview of currently unsolved challenges. It further
provides insights on multiple aspects of runtime patching approaches such as
patch scales, general strategies and responsibilities. This study identifies
seven levels of granularity, two key strategies providing a conceptual model of
three responsible entities and four capabilities of runtime patching solutions.
Through the analysis of the existing literature, this research also reveals
open issues hindering more comprehensive adoption of runtime patching in
practice. Finally, it proposes several crucial future directions that require
further attention from both researchers and practitioners.


title:
Congestion-aware motion planning game with Markov decision process
  dynamics

abstract:
  To model a competitive multi-agent motion planning scenario for heterogeneous
players with distinct dynamics and objectives, we propose a novel atomic
congestion game framework with Markov decision process (MDP) dynamics. We
assume that all players share the state-action space but have unique costs and
transition dynamics. Each player's cost is a function of the joint state-action
density, and thus is implicitly coupled to the opponents' policies. With proper
cost design, the resulting Nash equilibrium avoids congesting the state-action
space and each player optimally achieves its individual objective with respect
to the presence of its opponents. For a class of player cost functions, we
demonstrate equivalence between players' Q-value functions and the KKT point of
a potential minimization problem, and derive sufficient conditions for the
existence of a unique Nash equilibrium. Finally, we outline a learning
algorithm for finding the Nash equilibrium that extends single-agent
MDP/reinforcement learning algorithms and has linear complexity in the number
of players. Throughout the paper, we provide examples for multi-agent motion
planning, accumulating in a multi-robot warehouse demonstration, in which
robots autonomously retrieve and deliver packages while avoiding collisions.


title: Orientable maps and polynomial invariants of free-by-cyclic groups

abstract:
  We relate the McMullen polynomial of a free-by-cyclic group to its Alexander
polynomial. To do so, we introduce the notion of an orientable fully
irreducible outer automorphism $\varphi$ and use it to characterize when the
homological stretch factor of $\varphi$ is equal to its geometric stretch
factor.


title:
ALT: um software para an\'alise de legibilidade de textos em L\'ingua
  Portuguesa

abstract:
  In the initial stage of human life, communication, seen as a process of
social interaction, was always the best way to reach consensus between the
parties. Understanding and credibility in this process are essential for the
mutual agreement to be validated. But, how to do it so that this communication
reaches the great mass? This is the main challenge when what is sought is the
dissemination of information and its approval. In this context, this study
presents the ALT software, developed from original readability metrics adapted
to the Portuguese language, available on the web, to reduce communication
difficulties. The development of the software was motivated by the theory of
communicative action of Habermas, which uses a multidisciplinary style to
measure the credibility of the discourse in the communication channels used to
build and maintain a safe and healthy relationship with the public.
  --
  No est\'agio inicial da vida humana a comunica\c{c}\~ao, vista como um
processo de intera\c{c}\~ao social, foi sempre o melhor caminho para o consenso
entre as partes. O entendimento e a credibilidade nesse processo s\~ao
fundamentais para que o acordo m\'utuo seja validado. Mas, como faz\^e-lo de
forma que essa comunica\c{c}\~ao alcance a grande massa? Esse \'e o principal
desafio quando o que se busca \'e a difus\~ao da informa\c{c}\~ao e a sua
aprova\c{c}\~ao. Nesse contexto, este estudo apresenta o software ALT,
desenvolvido a partir de m\'etricas de legibilidade originais adaptadas para a
L\'ingua Portuguesa, dispon\'ivel na web, para reduzir as dificuldades na
comunica\c{c}\~ao. O desenvolvimento do software foi motivado pela teoria do
agir comunicativo de Habermas, que faz uso de um estilo multidisciplinar para
medir a credibilidade do discurso nos canais de comunica\c{c}\~ao utilizados
para construir e manter uma rela\c{c}\~ao segura e saud\'avel com o p\'ublico.


title:
Wasserstein Distributionally Robust Optimization via Wasserstein
  Barycenters

abstract:
  In many applications in statistics and machine learning, the availability of
data samples from multiple sources has become increasingly prevalent. On the
other hand, in distributionally robust optimization, we seek data-driven
decisions which perform well under the most adverse distribution from a nominal
distribution constructed from data samples within a certain distance of
probability distributions. However, it remains unclear how to achieve such
distributional robustness when data samples from multiple sources are
available. In this paper, we propose constructing the nominal distribution in
Wasserstein distributionally robust optimization problems through the notion of
Wasserstein barycenter as an aggregation of data samples from multiple sources.
Under specific choices of the loss function, the proposed formulation admits a
tractable reformulation as a finite convex program, with powerful finite-sample
and asymptotic guarantees. We illustrate our proposed method through concrete
examples with nominal distributions of location-scatter families and
distributionally robust maximum likelihood estimation.


title: A disk instability model for the quasi-periodic eruptions of GSN 069

abstract:
  GSN 069 is a recently discovered QPE (Quasi-periodic eruptions) source
recurring about every 9 hours. The mechanism for the QPEs of GSN 069 is still
unclear so far. In this work, a disk instability model is constructed to
explain GSN 069 based on Pan et al. (2021) (PLC21), where the authors proposed
a toy model for the repeating changing-look (CL) active galactic nuclei (AGN).
We improve the work of PLC21 by including a non-zero viscous torque condition
on the inner boundary of disk and adopting a general form for the viscous
stress torque in Kerr metric. It is found that the 0.4-2 keV light curves, the
light curves at different energy bands and the phase-resolved X-ray spectrum of
GSN 069 can all be qualitatively reproduced by our model. Furthermore, the
profiles of light curve in QPEs can be significantly changed by the parameter
\mu in viscous torque equation, which implies that our model may also be
applied to other QPEs.


title:
A Search-Based Framework for Automatic Generation of Testing
  Environments for Cyber-Physical Systems

abstract:
  Many modern cyber physical systems incorporate computer vision technologies,
complex sensors and advanced control software, allowing them to interact with
the environment autonomously. Testing such systems poses numerous challenges:
not only should the system inputs be varied, but also the surrounding
environment should be accounted for. A number of tools have been developed to
test the system model for the possible inputs falsifying its requirements.
However, they are not directly applicable to autonomous cyber physical systems,
as the inputs to their models are generated while operating in a virtual
environment. In this paper, we aim to design a search based framework, named
AmbieGen, for generating diverse fault revealing test scenarios for autonomous
cyber physical systems. The scenarios represent an environment in which an
autonomous agent operates. The framework should be applicable to generating
different types of environments. To generate the test scenarios, we leverage
the NSGA II algorithm with two objectives. The first objective evaluates the
deviation of the observed system behaviour from its expected behaviour. The
second objective is the test case diversity, calculated as a Jaccard distance
with a reference test case. We evaluate AmbieGen on three scenario generation
case studies, namely a smart-thermostat, a robot obstacle avoidance system, and
a vehicle lane keeping assist system. We compared three configurations of
AmbieGen: based on a single objective genetic algorithm, multi objective, and
random search. Both single and multi objective configurations outperform the
random search. Multi objective configuration can find the individuals of the
same quality as the single objective, producing more unique test scenarios in
the same time budget.


title: Approximate Inference for Stochastic Planning in Factored Spaces

abstract:
  The paper explores the use of approximate inference techniques as solution
methods for stochastic planning problems with discrete factored spaces. While
much prior work exists on this topic, subtle variations hinder a global
understanding of different approaches for their differences and potential
advantages. Here we abstract a simple framework that captures and connects
prior work along two dimensions, direction of information flow, i.e., forward
vs backward inference, and the type of approximation used, e.g., Belief
Propagation (BP) vs mean field variational inference (MFVI). Through this
analysis we also propose a novel algorithm, CSVI, which provides a tighter
variational approximation compared to prior work. An extensive experimental
evaluation compares algorithms from different branches of the framework,
showing that methods based on BP are generally better than methods based on
MFVI, that CSVI is competitive with BP algorithms, and that while inference
direction does not show a significant effect for VI methods, forward inference
provides stronger performance with BP.


title: Balanced systems for $\mathrm{Hom}$

abstract:
  From the notion of (co)generator in relative homological algebra, we present
the concept of finite balanced system $[(\mathcal{X} , \omega ); (\nu,
\mathcal{Y})]$ as a tool to induce balanced pairs $(\mathcal{X} , \mathcal{Y}
)$ for the $\mathrm{Hom}$ functor with domain determined by the finiteness of
homological dimensions relative to $\mathcal{X}$ and $\mathcal{Y}$.
  This approach to balance will cover several well known ambients where right
derived functors of $\mathrm{Hom}$ are obtained relative to certain classes of
objects in an abelian category, such as Gorenstein projective and injective
modules and chain complexes, Gorenstein modules relative to Auslander and Bass
classes, among others.


title: Online Encrypted Skype Identification Based on an Updating Mechanism

abstract:
  The machine learning algorithm is gaining prominence in traffic
identification research as it offers a way to overcome the shortcomings of
port-based and deep packet inspection, especially for P2P-based Skype.
However,recent studies have focused mainly on traffic identification based on a
full-packet dataset, which poses great challenges to identifying online network
traffic. This study aims to provide a new flow identification algorithm by
taking the sampled flow records as the object. The study constructs flow
records from a Skype set as the dataset, considers the inherent NETFLOW and
extended flow metrics as features, and uses a fast correlation-based filter
algorithm to select highly correlated features. The study also proposes a new
NFI method that adopts a Bayesian updating mechanism to improve the classifier
model. The experimental results show that the proposed scheme can achieve much
better identification performance than existing state-of-the-art traffic
identification methods, and a typical feature metric is analyzed in the
sampling environment. The NFI method improves identification accuracy and
reduces false positives and false negatives compared to other methods.


title:
Proceedings of the 13th International Workshop on Programming Language
  Approaches to Concurrency and Communication-cEntric Software

abstract:
  The increasingly concurrent and parallel landscape of hardware and software
infrastructures demands the exploration and understanding of a wide variety of
foundational and practical ideas. The International Workshop on Programming
Language Approaches to Concurrency and Communication-cEntric Software (PLACES)
is dedicated to work in this area. The workshop offers a forum for researchers
from different fields to exchange new ideas about these challenges to modern
and future programming, where concurrency and distribution are the norm rather
than a marginal concern. The topic is central and combines well with the main
conferences of ETAPS, in particular with ESOP.


title: On the global dynamics of Yang-Mills-Higgs equations

abstract:
  We study solutions to the Yang-Mills-Higgs equations on the maximal Cauchy
development of the data given on a ball of radius $R$ in $\mathbb{R}^3$. The
energy of the data could be infinite and the solution grows at most inverse
polynomially in $R-t$ as $t\rightarrow R$. As applications, we derive pointwise
decay estimates for Yang-Mills-Higgs fields in the future of a hyperboloid or
in the Minkowski space $\mathbb{R}^{1+3}$ for data bounded in the weighted
energy space with weights $|x|^{1+\epsilon}$. Moreover, for the abelian case of
Maxwell-Klein-Gordon system, we extend the small data result of Lindblad and
Sterbenz to general large data (under same assumptions but without any
smallness). The proof is gauge independent and it is based on the framework of
Eardley and Moncrief together with the geometric Kirchhoff-Sobolev parametrix
constructed by Klainerman and Rodnianski. The new ingredient is a class of
weighted energy estimates through backward light cones adapted to the initial
data.


title:
Precise sinusoidal signal extraction from noisy waveform in vibration
  calibration

abstract:
  Precise extraction of sinusoidal vibration parameters is essential for the
dynamic calibration of vibration sensors, such as accelerometers. However,
several standard methods have not yet been optimized for large background
noise. In this work, signal processing methods to extract small vibration
signals from noisy data in the case of accelerometer calibration is discussed.
The results show that spectral leakage degrades calibration accuracy. Three
methods based on the use of a filter, window function, and numerical
differentiation are investigated with theoretical calculations, simulations,
and experiments. These methods can effectively reduce the contribution of the
calibration system noise. The uncertainty of micro vibration calibration in the
National Metrology Institute of Japan is reduced by two orders of magnitudes
using the proposed methods. The theoretical analyses in this work can lay the
foundation for the optimization of signal processing in vibration calibration,
and can be applied to other dynamic calibration fields.


title:
Out of Distribution Detection, Generalization, and Robustness Triangle
  with Maximum Probability Theorem

abstract:
  Maximum Probability Framework, powered by Maximum Probability Theorem, is a
recent theoretical development, aiming to formally define probabilistic models,
guiding development of objective functions, and regularization of probabilistic
models. MPT uses the probability distribution that the models assume on random
variables to provide an upper bound on probability of the model. We apply MPT
to challenging out-of-distribution (OOD) detection problems in computer vision
by incorporating MPT as a regularization scheme in training of CNNs and their
energy based variants. We demonstrate the effectiveness of the proposed method
on 1080 trained models, with varying hyperparameters, and conclude that MPT
based regularization strategy both stabilizes and improves the generalization
and robustness of base models in addition to improved OOD performance on
CIFAR10, CIFAR100 and MNIST datasets.


title:
Role of Functionalized Graphene Quantum Dots in Hydrogen Evolution
  Reaction: A Density Functional Theory Study

abstract:
  Density functional theory (DFT) can be quite advantageous in advancing the
field of catalysis because of the microscopic insights it provides, and thus
can guide experimental searches of novel catalysts. Several recent works have
demonstrated that low-dimensional materials can be very efficient catalysts.
Graphene quantum dots (GQDs) have gained much attention in past years due to
their unique properties like low toxicity, chemical inertness,
biocompatibility, crystallinity, etc. These properties of GQDs which are due to
quantum confinement and edge effects facilitate their applications in various
fields like sensing, photoelectronics, catalysis, and many more. Furthermore,
the properties of GQDs can be enhanced by doping and functionalization. In
order to understand the effects of functionalization by oxygen and boron based
groups on the catalytic properties relevant to the hydrogen-evolution reaction
(HER), we perform a systematic study of GQDs functionalized with the oxygen
(O), borinic acid (BC$_2$O), and boronic acid (BCO$_2$ ). All calculations that
included geometry optimization, electronic and adsorption mechanism, were
carried out using the Gaussian16 package, employing the hybrid functional
B3LYP, and the basis set 6-31G(d,p). With the variation in functionalization
groups in GQDs, we observe significant changes in their electronic properties.
The adsorption energy E$_{ads}$ of hydrogen over O-GQD, BC$_2$O-GQD, and
BCO$_2$-GQD is -0.059 eV, -0.031 eV and -0.032 eV respectively. Accordingly,
Gibbs free energy ($\Delta G$) of hydrogen adsorption is extraordinarily near
the ideal value (0 eV) for all the three types of functionalized GQDs. Thus,
the present work suggests pathways for experimental realization of low-cost and
multifunctional GQDs based catalysts for clean and renewable hydrogen energy
production.


title: 3D-EDM: Early Detection Model for 3D-Printer Faults

abstract:
  With the advent of 3D printers in different price ranges and sizes, they are
no longer just for professionals. However, it is still challenging to use a 3D
printer perfectly. Especially, in the case of the Fused Deposition Method, it
is very difficult to perform with accurate calibration. Previous studies have
suggested that these problems can be detected using sensor data and image data
with machine learning methods. However, there are difficulties to apply the
proposed method due to extra installation of additional sensors. Considering
actual use in the future, we focus on generating the lightweight early
detection model with easily collectable data. Proposed early detection model
through Convolutional Neural Network shows significant fault classification
accuracy with 96.72% for the binary classification task, and 93.38% for
multi-classification task respectively. By this research, we hope that general
users of 3D printers can use the printer accurately.


title:
Efficient modeling of laser wakefield accelerators using the
  ponderomotive guiding center algorithm in an azimuthal mode decomposition
  based quasi-static particle-in-cell code

abstract:
  High-fidelity modeling of plasma-based acceleration (PBA) requires the use of
3D fully nonlinear and kinetic descriptions based on the particle-in-cell (PIC)
method. Three-dimensional PIC algorithms based on the quasi-static
approximation (QSA) have been successfully applied to efficiently model the
beam-plasma interaction. In a QSA-PIC algorithm, the plasma response to a
charged particle beam or laser driver is calculated based on self-consistent
forces from the QSA form of Maxwell's equations. These fields are then used to
advance the charged particle beam or laser forward by a large time step. Since
the time step is not limited by the regular Courant-Friedrichs-Lewy condition
that constrains a standard 3D fully electromagnetic PIC code, a 3D QSA-PIC code
can achieve orders of magnitude speedup in performance. Recently, a new hybrid
QSA-PIC algorithm that combines another speedup technique known as azimuthal
Fourier decomposition has been proposed and implemented. This hybrid algorithm
decomposes the electromagnetic fields, charge and current density into
azimuthal harmonics and only the Fourier coefficients need to be updated, which
can significantly reduce the algorithmic complexity. Modeling the laser-plasma
interaction in a full 3D PIC algorithm is very computationally expensive due to
the enormous disparity of physical scales to be resolved. In the QSA the laser
is modeled using the ponderomotive guiding center (PGC) approach. We describe
how to implement a PGC algorithm compatible with the QSA PIC algorithms based
on the azimuthal mode expansion. This algorithm permits time steps orders of
magnitude larger than the cell size and it can be asynchronously parallelized.
Details on how this is implemented into the QSA PIC code that utilizes an
azimuthal mode expansion, QPAD, are also described.


title: On generalized quasi-cyclic codes over $\mathbb{Z}_4$

abstract:
  Based on good algebraic structures and practicabilities, generalized
quasi-cyclic (GQC) codes play important role in coding theory. In this paper,
we study some results on GQC codes over $\mathbb{Z}_4$ including the normalized
generating set, the minimum generating set and the normalized generating set of
their dual codes. As an application, new $\mathbb{Z}_4$-linear codes and good
nonlinear binary codes are constructed from GQC codes over $\mathbb{Z}_4$.


title:
New existence results for prescribed fractional $Q$-curvatures problem
  on $\mathbb{S}^n$ under pinching conditions

abstract:
  In this paper we study the prescribed fractional $Q$-curvatures problem of
order $2 \sigma$ on the $n$-dimensional standard sphere $(\mathbb{S}^{n},
g_0)$, where $n\geq3$, $\sigma\in(0,\frac{n-2}{2})$. By combining critical
points at infinity approach with Morse theory we obtain new existence results
under suitable pinching conditions.


title: Semi-Supervised Hybrid Spine Network for Segmentation of Spine MR Images

abstract:
  Automatic segmentation of vertebral bodies (VBs) and intervertebral discs
(IVDs) in 3D magnetic resonance (MR) images is vital in diagnosing and treating
spinal diseases. However, segmenting the VBs and IVDs simultaneously is not
trivial. Moreover, problems exist, including blurry segmentation caused by
anisotropy resolution, high computational cost, inter-class similarity and
intra-class variability, and data imbalances. We proposed a two-stage
algorithm, named semi-supervised hybrid spine network (SSHSNet), to address
these problems by achieving accurate simultaneous VB and IVD segmentation. In
the first stage, we constructed a 2D semi-supervised DeepLabv3+ by using cross
pseudo supervision to obtain intra-slice features and coarse segmentation. In
the second stage, a 3D full-resolution patch-based DeepLabv3+ was built. This
model can be used to extract inter-slice information and combine the coarse
segmentation and intra-slice features provided from the first stage. Moreover,
a cross tri-attention module was applied to compensate for the loss of
inter-slice and intra-slice information separately generated from 2D and 3D
networks, thereby improving feature representation ability and achieving
satisfactory segmentation results. The proposed SSHSNet was validated on a
publicly available spine MR image dataset, and remarkable segmentation
performance was achieved. Moreover, results show that the proposed method has
great potential in dealing with the data imbalance problem. Based on previous
reports, few studies have incorporated a semi-supervised learning strategy with
a cross attention mechanism for spine segmentation. Therefore, the proposed
method may provide a useful tool for spine segmentation and aid clinically in
spinal disease diagnoses and treatments. Codes are publicly available at:
https://github.com/Meiyan88/SSHSNet.


title:
Direct Search for Dark Matter Axions Excluding ALP Cogenesis in the
  63-67 micro-eV Range, with The ORGAN Experiment

abstract:
  The standard model axion seesaw Higgs portal inflation (SMASH) model is a
well motivated, self-contained description of particle physics over a range of
energy scales that predicts axion dark matter particles to exist within the
mass range of $50-200\,\mu$eV. To scan these masses an axion haloscope under a
strong constant magnetic field must operate between 12 to 48 GHz. The ORGAN
experiment (situated in Perth, Australia) is a microwave cavity axion haloscope
that aims to search the majority of the mass range predicted by the SMASH
model. Here we present results of Phase 1a, the first experiment to scan and
search for axions in the microwave Ku Band. Our initial scan sets a new limit
on the coupling of axions to two photons of $g_{a\gamma\gamma}\geq 3\times
10^{-12}\, \textrm{GeV}^{-1}$ over the mass range $63.2$ to $67.1~\mu$eV with
$95\%$ confidence. This result is the most sensitive to date in this mass
range, sufficient to exclude the well motivated ALP (Axion Like Particle)
cogenesis model for dark matter, which adds ALPs to the standard model in the
early universe to simultaneously explain the observed baryon and dark matter
densities. To attain this level of sensitivity we utilised a TM$_{010}$
cylindrical cavity resonator, scanned between 15.28 to 16.23 GHz through the
utilisation of a tuning rod. Measurements were performed over a duration of 3.5
weeks with a $74\%$ duty cycle, with the resonator coupled to a low noise HEMT
amplifier and placed inside a superconducting solenoidal electromagnet of 11.5
Tesla in magnetic field strength.

categories: hep-ex astro-ph.IM gr-qc physics.ins-det


title:
Inelastic heavy quarkonium photoproduction in ultrarelativistic
  heavy-ion collisions

abstract:
  The inelastic charmonium ($J/\psi$, $\psi(2S)$, $\eta_{c}$, $h_{c}$ and
$\chi_{cJ}$) and bottomonium ($\Upsilon(nS)$, $\eta_{b}$, $h_{b}$, and
$\chi_{bJ}$) photoproductions in ultrarelativistic heavy-ion collisions at LHC
energies are studied, where the fragmentation processes are included. Based on
the factorization formalism of non-relativistic QCD, an exact treatment is
developed, which can weight the contribution from different channels and
recovers the Weizs\"{a}cker-Williams approximation (WWA) near the region
$Q^{2}\sim0$. The relevant kinematical relations are also achieved. We present
a comprehensive analysis for the properties of WWA in heavy-ion collisions, and
discuss the contribution of inelastic photoproduction processes to the heavy
quarkonium production. The $Q^{2}$-, $y$-, $\sqrt{s}$-, and $p_{T}$-dependent
cross sections, and the total cross sections are estimated. It is shown that
the inelastic photoproduction and fragmentation processes can provide the
evident modification to the heavy quarkonium production in $p$-$p$ and
$Pb$-$Pb$ collisions at LHC energies, where the ultra-incoherent photon
emission plays a very important role. Moreover, the WWA is only effective in
very restricted domains, and the exact treatment can naturally avoid double
counting and WWA errors.


title:
Estimating trans-ancestry genetic correlation with unbalanced data
  resources

abstract:
  The aim of this paper is to propose a novel estimation method of using
genetic-predicted observations to estimate trans-ancestry genetic correlations,
which describes how genetic architecture of complex traits varies among
populations, in genome-wide association studies (GWAS). Our new estimator
corrects for prediction errors caused by high-dimensional weak GWAS signals,
while addressing the heterogeneity of GWAS data across ethnicities, such as
linkage disequilibrium (LD) differences, which can lead to biased findings in
homogeneity-agnostic analyses. Moreover, our estimator only requires one
population to have a large GWAS sample size, and the second population can only
have a much smaller number of participants (for example, hundreds). It is
designed to specifically address the unbalanced data resources such that the
GWAS sample size for European populations is usually larger than that of
non-European ancestry groups. Extensive simulations and real data analyses of
30 complex traits in the UK Biobank study show that our method is capable of
providing reliable estimates of a wide range of complex traits. Our results
provide deep insights into the transferability of population-specific genetic
findings.


title: Square function estimates for conical regions

abstract:
  We prove square function estimates for certain conical regions. Specifically,
let $\{\Delta_j\}$ be regions of the unit sphere $\mathbb{S}^{n-1}$ and let
$S_j f$ be the smooth Fourier restriction of $f$ to the conical region
$\{\xi\in\mathbb{R}^n:\xi/|\xi|\in\Delta_j\}$. We are interested in the
following estimate
  $$\Big\|(\sum_j|S_jf|^2)^{1/2}\Big\|_p\lesssim_\epsilon
\delta^{-\epsilon}\|f\|_p.$$
  The first result is: when $\{\Delta_j\}$ is a set of disjoint $\delta$-balls,
then the estimate holds for $p=4$. The second result is: In $\mathbb{R}^3$,
when $\{\Delta_j\}$ is a set of disjoint $\delta\times\delta^{1/2}$-rectangles
contained in the band $\mathbb{S}^2\cap N_\delta(\{\xi_1^2+\xi_2^2=\xi_3^2\})$
and ${\rm{supp}}\widehat f\subset
\{\xi\in\mathbb{R}^3:\xi/|\xi|\in\mathbb{S}^2\cap
N_\delta(\{\xi_1^2+\xi_2^2=\xi_3^2\})\}$, then the estimate holds for $p=8$.
The two estimates are sharp.


title: Accessible hyperbolic components in anti-holomorphic dynamics

abstract:
  The tricorn, the connectedness locus of anti-holomorphic quadratic family is
known to be non-locally connected. The boundary of every hyperbolic components
of odd periods has inaccessible arcs from the complement of the tricorn. As the
period increases, the decorations become more and more complicated and it seems
natural to think that every hyperbolic component of sufficiently large and odd
period is inaccessible. Contrary to such an anticipation, we show that there
exists infinitely many accessible hyperbolic components for the tricorn.


title: Refined applications of Kato's Euler systems for modular forms

abstract:
  We discuss refined applications of Kato's Euler systems for modular forms of
higher weight at good primes (with more emphasis on the non-ordinary ones)
beyond the one-sided divisibility of the main conjecture and the finiteness of
Selmer groups. These include a proof of the Mazur-Tate conjecture on Fitting
ideals of Selmer groups over $p$-cyclotomic extensions and a new interpretation
of the Iwasawa main conjecture with structural applications.


title: On endomorphisms and automorphisms of $G$-sets

abstract:
  For a group $G$ acting on a set $X$, consider the monoid $\text{End}_G(X)$
consisting of all $G$-equivariant transformations, or $G$-endomorphisms, of
$X$. We study various algebraic aspects of $\text{End}_G(X)$ and its group of
units $\text{Aut}_G(X)$. First, when the action is transitive, we show that
$\text{Aut}_G(X) = \text{End}_G(X)$ if and only if the stabilizer of a point in
$X$ is not properly contained in any of its conjugates. For an arbitrary
action, we use the Imprimitive Wreath Product Embedding Theorem and the Axiom
of Choice to decompose $\text{Aut}_G(X)$ as a direct product of wreath products
of certain symmetric groups. Finally, when $G$ and $X$ are both finite, we
determine the smallest cardinality of a set $W \subseteq \text{End}_G(X)$ such
that $W \cup \text{Aut}_G(X)$ generates $\text{End}_G(X)$, which is known in
semigroup theory as the relative rank of $\text{End}_G(X)$ modulo
$\text{Aut}_G(X)$.


title:
The structure of Selmer groups and the Iwasawa main conjecture for
  elliptic curves

abstract:
  We reveal a new and refined application of (a weaker statement than) the
Iwasawa main conjecture for elliptic curves to the structure of Selmer groups
of elliptic curves of arbitrary rank. For a large class of elliptic curves, we
obtain the following arithmetic consequences.
  1. Kato's Kolyvagin systems is non-trivial. It is the cyclotomic analogue of
the Kolyvagin conjecture.
  2. The structure of Selmer groups of elliptic curves over the rationals is
completely determined in terms of certain modular symbols. It is a structural
refinement of Birch and Swinnerton-Dyer conjecture.
  3. The rank zero $p$-converse, the $p$-parity conjecture, and a new upper
bound of the ranks of elliptic curves are obtained.
  4. The conjecture of Kurihara on the semi-local description of mod $p$ Selmer
groups is confirmed.
  5. An application of the $p$-adic Birch and Swinnerton-Dyer conjecture to the
structure of Iwasawa modules is discussed.


title: An Emulation Framework for Fire Front Spread

abstract:
  Forecasting bushfire spread is an important element in fire prevention and
response efforts. Empirical observations of bushfire spread can be used to
estimate fire response under certain conditions. These observations form
rate-of-spread models, which can be used to generate simulations. We use
machine learning to drive the emulation approach for bushfires and show that
emulation has the capacity to closely reproduce simulated fire-front data. We
present a preliminary emulator approach with the capacity for fast emulation of
complex simulations. Large numbers of predictions can then be generated as part
of ensemble estimation techniques, which provide more robust and reliable
forecasts of stochastic systems.


title: A higher Gross-Zagier formula and the structure of Selmer groups

abstract:
  We establish a structural refinement of Gross-Zagier formula by comparing
Heegner point Kolyvagin systems with Kurihara numbers when the root number of a
rational elliptic curve $E$ over an imaginary quadratic field $K$ is $-1$. When
the root number of $E$ over $K$ is 1, we first prove the structure theorem of
the Selmer group of $E$ over $K$ and also obtain a structural refinement of
Waldspurger formula by comparing bipartite Euler systems with Kurihara numbers.
No low rank assumption is imposed in both refinements.
  We also discuss how the non-triviality of various "Kolyvagin systems" can be
directly obtained from the corresponding main conjectures.


title: Numerical radius inequalities for tensor product of operators

abstract:
  The two well-known numerical radius inequalities for the tensor product $A
\otimes B$ acting on $\mathbb{H} \otimes \mathbb{K}$, where $A$ and $B$ are
bounded linear operators defined on complex Hilbert spaces $\mathbb{H} $ and $
\mathbb{K},$ respectively are, $ \frac{1}{2} \|A\|\|B\| \leq w(A \otimes B)
\leq \|A\|\|B\| $ and $w(A)w(B) \leq w(A \otimes B) \leq \min \{ w(A) \|B\|,
w(B) \|A\| \}. $ In this article we develop new lower and upper bounds for the
numerical radius $w(A \otimes B)$ of the tensor product $A \otimes B $ and
study the equality conditions for those bounds.


title: Adaptive Aggregation For Federated Learning

abstract:
  Advances in federated learning (FL) algorithms,along with technologies like
differential privacy and homomorphic encryption, have led to FL being
increasingly adopted and used in many application domains. This increasing
adoption has led to rapid growth in the number, size (number of
participants/parties) and diversity (intermittent vs. active parties) of FL
jobs. Many existing FL systems, based on centralized (often single) model
aggregators are unable to scale to handle large FL jobs and adapt to parties'
behavior.
  In this paper, we present a new scalable and adaptive architecture for FL
aggregation. First, we demonstrate how traditional tree overlay based
aggregation techniques (from P2P, publish-subscribe and stream processing
research) can help FL aggregation scale, but are ineffective from a resource
utilization and cost standpoint. Next, we present the design and implementation
of AdaFed, which uses serverless/cloud functions to adaptively scale
aggregation in a resource efficient and fault tolerant manner. We describe how
AdaFed enables FL aggregation to be dynamically deployed only when necessary,
elastically scaled to handle participant joins/leaves and is fault tolerant
with minimal effort required on the (aggregation) programmer side. We also
demonstrate that our prototype based on Ray scales to thousands of
participants, and is able to achieve a >90% reduction in resource requirements
and cost, with minimal impact on aggregation latency.


title:
Application of Co-Kriging and Ordinary Kriging for Selecting Additional
  Well Locations

abstract:
  Well performance index (WPI), which is an indicator on how much producing
potential a well has, is proposed for Cana Field using the available
information from the completion database. I used ordinary kriging and
co-kriging to create interpolation maps for WPI across the region. The
interpolation results can be used to predict WPI values for locations that have
not gone through drilling programs, thus guiding operator to find the next
drilling locations. Different kriging models' performance are compared using
cross-validation. It is shown co-kriging with clean fluid volume has the best
performance. Recommendations are given regarding new well locations.


title:
Comprehensive Benchmark Datasets for Amharic Scene Text Detection and
  Recognition

abstract:
  Ethiopic/Amharic script is one of the oldest African writing systems, which
serves at least 23 languages (e.g., Amharic, Tigrinya) in East Africa for more
than 120 million people. The Amharic writing system, Abugida, has 282
syllables, 15 punctuation marks, and 20 numerals. The Amharic syllabic matrix
is derived from 34 base graphemes/consonants by adding up to 12 appropriate
diacritics or vocalic markers to the characters. The syllables with a common
consonant or vocalic markers are likely to be visually similar and challenge
text recognition tasks. In this work, we presented the first comprehensive
public datasets named HUST-ART, HUST-AST, ABE, and Tana for Amharic script
detection and recognition in the natural scene. We have also conducted
extensive experiments to evaluate the performance of the state of art methods
in detecting and recognizing Amharic scene text on our datasets. The evaluation
results demonstrate the robustness of our datasets for benchmarking and its
potential of promoting the development of robust Amharic script detection and
recognition algorithms. Consequently, the outcome will benefit people in East
Africa, including diplomats from several countries and international
communities.


title: A Repulsive Casimir Effect for Circular Geometries

abstract:
  Using numerical analytic continuation, we compute the Zeta function for the
Casimir Effect for circular geometries in 2+1 dimensions. After subtraction of
the simple pole of the zeta function, essentially MS renormalization, we find
the Casimir force is repulsive. Implications for the stability of 2+1
dimensional domain walls and Axion membranes in 3+1 dimensions are discussed,
especially in the context of strongly coupled underlying physics.


title: Effects of Self-gravity on Mass-loss of the Post-impact Super-Earths

abstract:
  Kepler's observations show most of the exoplanets are super-Earths. The
formation of super-Earth is generally related to the atmospheric mass loss that
is crucial in the planetary structure and evolution. The shock driven by the
giant impact will heat the planet, resulting in the atmosphere escape. We focus
on whether self-gravity changes the efficiency of mass loss. Without
self-gravity, if the impactor mass is comparable to the envelope mass, there is
a significant mass-loss. The radiative-convective boundary will shift inward by
self-gravity. As the temperature and envelope mass increase, the situation
becomes more prominent, resulting in a heavier envelope. Therefore, the
impactor mass will increase to motivate the significant mass loss, as the
self-gravity is included. With the increase of envelope mass, the self-gravity
is particularly important.


title: Averages of exponential twists of the von Mangoldt function

abstract:
  In this paper, we obtain some improved results for the exponential sum
$\sum_{x<n\leq 2x}\Lambda(n)e(\alpha k n^{\theta})$ with $\theta\in(0,5/12),$
where $\Lambda(n)$ is the von Mangoldt function. Such exponential sums have
relations with the so-called quasi-Riemann hypothesis and were considered by
Vinogradov \cite{Va} and Murty-Srinivas \cite{Mu}.


title:
Neutrinoless Double-Beta Decay: A Roadmap for Matching Theory to
  Experiment

abstract:
  The observation of neutrino oscillations and hence non-zero neutrino masses
provided a milestone in the search for physics beyond the Standard Model. But
even though we now know that neutrinos are massive, the nature of neutrino
masses, i.e., whether they are Dirac or Majorana, remains an open question. A
smoking-gun signature of Majorana neutrinos is the observation of neutrinoless
double-beta decay, a process that violates the lepton-number conservation of
the Standard Model. This white paper focuses on the theoretical aspects of the
neutrinoless double-beta decay program and lays out a roadmap for future
developments. The roadmap is a multi-scale path starting from high-energy
models of neutrinoless double-beta decay all the way to the low-energy nuclear
many-body problem that needs to be solved to supplement measurements of the
decay rate. The path goes through a systematic effective-field-theory
description of the underlying processes at various scales and needs to be
supplemented by lattice quantum chromodynamics input. The white paper also
discusses the interplay between neutrinoless double-beta decay, experiments at
the Large Hadron Collider and results from astrophysics and cosmology in
probing simplified models of lepton-number violation at the TeV scale, and the
generation of the matter-antimatter asymmetry via leptogenesis. This white
paper is prepared for the topical groups TF11 (Theory of Neutrino Physics),
TF05 (Lattice Gauge Theory), RF04 (Baryon and Lepton Number Violating
Processes), NF03 (Beyond the Standard Model) and NF05 (Neutrino Properties)
within the Theory Frontier, Rare Processes and Precision Frontier, and Neutrino
Physics Frontier of the U.S. Community Study on the Future of Particle Physics
(Snowmass 2021).


title:
Joint Covering Congestion Rents in Multi-area Power Systems Considering
  Loop Flow Effects

abstract:
  We consider the problem of how multiple areas should jointly cover congestion
rents of internal and tie-lines in an interconnected power system. A key issue
of our concern is the loop flow problem, which represents discrepancies between
scheduled and actual power flow distributions because electric power does not
always flow along the most direct paths of transactions. We employ generalized
coordinated transaction scheduling (GCTS) for interchange scheduling, which can
eliminate dispatch errors caused by loop flow effects and asymptotically
converge to the joint economic dispatch (JED) under ideal assumptions.
Subsequently, distributed algorithms are proposed for each area to recover
multipliers of the global GCTS model, as well as quantifying and pricing its
contribution to line congestions. Thereby, all areas and interface bids can
jointly cover congestion rents of internal and tie-lines with their merchandise
surpluses and profits. Simulations demonstrate the effectiveness of the
proposed approach of LMP recovery and joint covering congestion rents.


title: An Empirical Study of Memorization in NLP

abstract:
  A recent study by Feldman (2020) proposed a long-tail theory to explain the
memorization behavior of deep learning models. However, memorization has not
been empirically verified in the context of NLP, a gap addressed by this work.
In this paper, we use three different NLP tasks to check if the long-tail
theory holds. Our experiments demonstrate that top-ranked memorized training
instances are likely atypical, and removing the top-memorized training
instances leads to a more serious drop in test accuracy compared with removing
training instances randomly. Furthermore, we develop an attribution method to
better understand why a training instance is memorized. We empirically show
that our memorization attribution method is faithful, and share our interesting
finding that the top-memorized parts of a training instance tend to be features
negatively correlated with the class label.


title: Seebeck-driven transverse thermoelectric generation in on-chip devices

abstract:
  An unconventional approach to enhance the transverse thermopower by combining
magnetic and thermoelectric materials, namely the Seebeck-driven transverse
thermoelectric generation (STTG), has been proposed and demonstrated recently.
Here, we improve on the previously used sample structure and achieve large
transverse thermopower over 40 $\mu$V K$^{-1}$ due to STTG in on-chip devices.
We deposited polycrystalline Fe-Ga alloy films directly on n-type Si
substrates, where Fe-Ga and Si serve as the magnetic and thermoelectric
materials, respectively. Using microfabrication, contact holes were created
through the SiO$_\text{x}$ layer at the top of Si to electrically connect the
Fe-Ga film with the Si substrate. These thin devices with simple structure
clearly exhibited enhancement of transverse thermopower due to STTG, and the
obtained values agreed well with the estimation over a wide range of the size
ratio between the Fe-Ga film and the Si substrate.


title: The Impact of Geopolitical Conflicts on Trade, Growth, and Innovation

abstract:
  Geopolitical conflicts have increasingly been a driver of trade policy. We
study the potential effects of global and persistent geopolitical conflicts on
trade, technological innovation, and economic growth. In conventional trade
models the welfare costs of such conflicts are modest. We build a multi-sector
multi-region general equilibrium model with dynamic sector-specific knowledge
diffusion, which magnifies welfare losses of trade conflicts. Idea diffusion is
mediated by the input-output structure of production, such that both sector
cost shares and import trade shares characterize the source distribution of
ideas. Using this framework, we explore the potential impact of a "decoupling
of the global economy," a hypothetical scenario under which technology systems
would diverge in the global economy. We divide the global economy into two
geopolitical blocs -- East and West -- based on foreign policy similarity and
model decoupling through an increase in iceberg trade costs (full decoupling)
or tariffs (tariff decoupling). Results yield three main insights. First, the
projected welfare losses for the global economy of a decoupling scenario can be
drastic, as large as 15% in some regions and are largest in the lower income
regions as they would benefit less from technology spillovers from richer
areas. Second, the described size and pattern of welfare effects are specific
to the model with diffusion of ideas. Without diffusion of ideas the size and
variation across regions of the welfare losses would be substantially smaller.
Third, a multi-sector framework exacerbates diffusion inefficiencies induced by
trade costs relative to a single-sector one.


title:
Post-Hopf algebras, relative Rota-Baxter operators and solutions of the
  Yang-Baxter equation

abstract:
  In this paper, first we introduce the notion of a post-Hopf algebra, which
gives rise to a post-Lie algebra on the space of primitive elements and there
is naturally a post-Hopf algebra structure on the universal enveloping algebra
of a post-Lie algebra. A novel property is that a cocommutative post-Hopf
algebra gives rise to a generalized Grossman-Larsson product, which leads to a
subadjacent Hopf algebra and can be used to construct solutions of the
Yang-Baxter equation. Then we introduce the notion of relative Rota-Baxter
operators on Hopf algebras. A cocommutative post-Hopf algebra gives rise to a
relative Rota-Baxter operator on its subadjacent Hopf algebra. Conversely, a
relative Rota-Baxter operator also induces a post-Hopf algebra. Then we show
that relative Rota-Baxter operators give rise to matched pairs of Hopf
algebras. Consequently, post-Hopf algebras and relative Rota-Baxter operators
give solutions of the Yang-Baxter equation in certain cocommutative Hopf
algebras. Finally we characterize relative Rota-Baxter operators on Hopf
algebras using relative Rota-Baxter operators on the Lie algebra of primitive
elements, graphs and module bialgebra structures.


title:
Adaptive Transformers for Robust Few-shot Cross-domain Face
  Anti-spoofing

abstract:
  While recent face anti-spoofing methods perform well under the intra-domain
setups, an effective approach needs to account for much larger appearance
variations of images acquired in complex scenes with different sensors for
robust performance. In this paper, we present adaptive vision transformers
(ViT) for robust cross-domain face anti-spoofing. Specifically, we adopt ViT as
a backbone to exploit its strength to account for long-range dependencies among
pixels. We further introduce the ensemble adapters module and feature-wise
transformation layers in the ViT to adapt to different domains for robust
performance with a few samples. Experiments on several benchmark datasets show
that the proposed models achieve both robust and competitive performance
against the state-of-the-art methods.


abstract:
  The Baxter permuton is a random probability measure on the unit square which
describes the scaling limit of uniform Baxter permutations. We find an explict
formula for the expectation of the Baxter permuton, i.e.\ the density of its
intensity measure. This answers a question of Dokos and Pak (2014).
  We also prove that all pattern densities of the Baxter permuton are strictly
positive, distinguishing it from other permutons arising as scaling limits of
pattern-avoiding permutations. Our proofs rely on a recent connection between
the Baxter permuton and Liouville quantum gravity (LQG) coupled with the
Schramm-Loewner evolution (SLE). The method works equally well for a
two-parameter generalization of the Baxter permuton recently introduced by the
first author, except that the density is not as explicit. This new family of
permutons, called \emph{skew Brownian permuton}, describes the scaling limit of
a number of random constrained permutations. We finally observe that in the
LQG/SLE framework, the expected proportion of inversions in a skew Brownian
permuton equals $\frac{\pi-2\theta}{2\pi}$ where $\theta$ is the so-called
imaginary geometry angle between a certain pair of SLE curves.


title:
A radiation hydrodynamics scheme on adaptive meshes using the Variable
  Eddington Tensor (VET) closure

abstract:
  We present a new algorithm to solve the equations of radiation hydrodynamics
(RHD) in a frequency-integrated, two-moment formulation. Novel features of the
algorithm include i) the adoption of a non-local Variable Eddington Tensor
(VET) closure for the radiation moment equations, computed with a ray-tracing
method, ii) support for adaptive mesh refinement (AMR), iii) use of a
time-implicit Godunov method for the hyperbolic transport of radiation, and iv)
a fixed-point Picard iteration scheme to accurately handle the stiff nonlinear
gas-radiation energy exchange. Tests demonstrate that our scheme works
correctly, yields accurate rates of energy and momentum transfer between gas
and radiation, and obtains the correct radiation field distribution even in
situations where more commonly used -- but less accurate -- closure relations
like the Flux-limited Diffusion and Moment-1 approximations fail. Our scheme
presents an important step towards performing RHD simulations with increasing
spatial and directional accuracy, effectively improving their predictive
capabilities.


title: Unifying Motion Deblurring and Frame Interpolation with Events

abstract:
  Slow shutter speed and long exposure time of frame-based cameras often cause
visual blur and loss of inter-frame information, degenerating the overall
quality of captured videos. To this end, we present a unified framework of
event-based motion deblurring and frame interpolation for blurry video
enhancement, where the extremely low latency of events is leveraged to
alleviate motion blur and facilitate intermediate frame prediction.
Specifically, the mapping relation between blurry frames and sharp latent
images is first predicted by a learnable double integral network, and a fusion
network is then proposed to refine the coarse results via utilizing the
information from consecutive blurry inputs and the concurrent events. By
exploring the mutual constraints among blurry frames, latent images, and event
streams, we further propose a self-supervised learning framework to enable
network training with real-world blurry videos and events. Extensive
experiments demonstrate that our method compares favorably against the
state-of-the-art approaches and achieves remarkable performance on both
synthetic and real-world datasets.


title: Targeted Function Balancing

abstract:
  This paper introduces Targeted Function Balancing (TFB), a covariate
balancing framework for estimating the causal effect of a binary intervention
on an outcome. TFB first regresses the outcome on observed covariates, and then
selects weights that balance functions (of the covariates) that are
probabilistically near the resulting regression function. This yields balance
in the predicted values of the regression function and the covariates, with the
regression function's estimated variance determining how much balance in the
covariates is sufficient. Notably, TFB demonstrates that intentionally leaving
imbalance in some covariates can increase efficiency without introducing bias,
challenging traditions that warn against imbalance in any variable. TFB is
shown here to be useful in high-dimensional settings, with particular focus
given to kernel regularized least squares and the LASSO as potential regression
estimators. With the former, TFB contributes to the literature of kernel-based
weights. As for the LASSO, TFB uses the regression function's estimated
variance to prioritize balance in certain dimensions of the covariates, a
feature that can be exploited to great effects by choosing a sparse regression
estimator. Most appealing is that TFB is entirely defined by the choice of
regression estimator and an estimator for the resulting function's variance,
turning the problem of how best to balance the covariates into how best to
model the outcome. Additionally, this paper introduces a balance diagnostic,
Targeted Function Imbalance, which TFB minimizes subject to constraints on its
weights' variance, and may have other applications.


title: Preconditioned Least-Squares Petrov-Galerkin Reduced Order Models

abstract:
  This paper introduces a methodology for improving the accuracy and efficiency
of reduced order models (ROMs) constructed using the least-squares
Petrov-Galerkin (LSPG) projection method through the introduction of
preconditioning. Unlike prior related work, which focuses on preconditioning
the linear systems arising within the ROM numerical solution procedure to
improve linear solver performance, our approach leverages a preconditioning
matrix directly within the LSPG minimization problem. Applying preconditioning
in this way can improve ROM accuracy for several reasons. First,
preconditioning the LSPG formulation changes the norm defining the residual
minimization, which can improve the residual-based stability constant bounding
the ROM solution's error. The incorporation of a preconditioner into the LSPG
formulation can have the additional effect of scaling the components of the
residual being minimized, which can be beneficial for problems with disparate
scales. Importantly, we demonstrate that an 'ideal preconditioned' LSPG ROM (a
ROM preconditioned with the inverse of the Jacobian of its corresponding full
order model, or FOM) emulates projection of the FOM solution increment onto the
reduced basis, a lower bound on the ROM solution error for a given reduced
basis. By designing preconditioners that approximate the Jacobian inverse, a
ROM whose error approaches this lower bound can be obtained. The proposed
approach is evaluated in the predictive regime on several mechanical and
thermo-mechanical problems within the Albany HPC code. We demonstrate
numerically that the introduction of simple Jacobi, Gauss-Seidel and ILU
preconditioners into the Proper Orthogonal Decomposition/LSPG formulation
reduces significantly the ROM solution error, the reduced Jacobian condition
number, the number of nonlinear iterations required to reach convergence, and
the wall time.


title: Dark matter stars

abstract:
  The dark matter in the CGF cosmology is a cosmological SU(2)-weak gauge field
(the CGF). The TOV stellar structure equations are solved numerically for stars
composed of this dark matter. The star mass M can take any value up to a
maximum $9.14 \times 10^{-6}$ M_sun. For each value of M the star radius R lies
between 5.23cm and 13.6cm. More than one value of R is possible when M > $5.09
\times 10^{-6}$ M_sun. For those stars, a transition from larger to smaller R
would release gravitational energy on the order of $10^{41}$J in a time on the
order of $10^{-10}$s.


title:
Rhombic Fermi surfaces in a ferromagnetic MnGa thin film with
  perpendicular magnetic anisotropy

abstract:
  Mn$_{1-x}$Ga$_x$ (MnGa) with the $L1_0$ structure is a ferromagnetic material
with strong perpendicular magneto-crystalline anisotropy. Although MnGa thin
films have been successfully grown epitaxially and studied for various
spintronics devices, fundamental understandings of its electronic structure are
still lacking. To address this issue, we have investigated $L1_0$-MnGa thin
films using angle-resolved photoemission spectroscopy (ARPES). We have observed
a large Fermi surface with a rhombic shape in the $k_x$-$k_y$ plane overlapping
neighboring Fermi surfaces. The $k_z$ dependence of the band structure suggests
that the band dispersion observed by ARPES comes from the three-dimensional
band structure of MnGa folded by a $\sqrt{2} \times \sqrt{2}$ reconstruction.
The band dispersion across the corner of the rhombic Fermi surface forms an
electron pocket with a weak $k_z$ dependence. The effective mass and the
mobility of the bands crossing the Fermi level near the corner are estimated
from the ARPES images. Based on the experimental findings, the relationship
between the observed band structure and the spin-dependent properties in
MnGa-based heterostructures is discussed.


title:
A stochastic Hamiltonian formulation applied to dissipative particle
  dynamics

abstract:
  In this paper, a stochastic Hamiltonian formulation (SHF) is proposed and
applied to dissipative particle dynamics (DPD) simulations. As an extension of
Hamiltonian dynamics to stochastic dissipative systems, the SHF provides
necessary foundations and great convenience for constructing efficient
numerical integrators. As a first attempt, we develop the St\"ormer--Verlet
type of schemes based on the SHF, which are structure-preserving for
deterministic Hamiltonian systems without external forces, the dissipative
forces in DPD. Long-time behaviour of the schemes is shown numerically by
studying the damped Kubo oscillator. In particular, the proposed schemes
include the conventional Groot-Warren's modified velocity-Verlet method and a
modified version of Gibson-Chen-Chynoweth as special cases. The schemes are
applied to DPD simulations and analysed numerically.


title: A Theoretically Grounded Benchmark for Evaluating Machine Commonsense

abstract:
  Programming machines with commonsense reasoning (CSR) abilities is a
longstanding challenge in the Artificial Intelligence community. Current CSR
benchmarks use multiple-choice (and in relatively fewer cases, generative)
question-answering instances to evaluate machine commonsense. Recent progress
in transformer-based language representation models suggest that considerable
progress has been made on existing benchmarks. However, although tens of CSR
benchmarks currently exist, and are growing, it is not evident that the full
suite of commonsense capabilities have been systematically evaluated.
Furthermore, there are doubts about whether language models are 'fitting' to a
benchmark dataset's training partition by picking up on subtle, but normatively
irrelevant (at least for CSR), statistical features to achieve good performance
on the testing partition. To address these challenges, we propose a benchmark
called Theoretically-Grounded Commonsense Reasoning (TG-CSR) that is also based
on discriminative question answering, but with questions designed to evaluate
diverse aspects of commonsense, such as space, time, and world states. TG-CSR
is based on a subset of commonsense categories first proposed as a viable
theory of commonsense by Gordon and Hobbs. The benchmark is also designed to be
few-shot (and in the future, zero-shot), with only a few training and
validation examples provided. This report discusses the structure and
construction of the benchmark. Preliminary results suggest that the benchmark
is challenging even for advanced language representation models designed for
discriminative CSR question answering tasks.
  Benchmark access and leaderboard:
https://codalab.lisn.upsaclay.fr/competitions/3080 Benchmark website:
https://usc-isi-i2.github.io/TGCSR/


title:
Application of Projection Operator Method to Coarse-Grained Dynamics
  with Transient Potential

abstract:
  We show that the coarse-grained dynamics model with the time-dependent and
fluctuating potential (transient potential) can be derived from the microscopic
Hamiltonian dynamics. The concept of the transient potential was first
introduced rather phenomenologically, and its relation to the underlying
microscopic dynamics has not been clarified yet. This is in contrast to the
generalized Langevin equation, of which relation to the microscopic dynamics is
well-established. In this work, we show that the dynamic equations with the
transient potential can be derived for the coupled oscillator model, without
any approximations. It is known that the dynamics of the coupled oscillator
model can be exactly described by the generalized Langevin type equations. This
fact implies that the dynamic equations with the transient potential can be
utilized as a coarse-grained dynamics model in a similar way to the generalized
Langevin equation. Then we show that the dynamic equations for the transient
potential can be also formally derived for the microscopic Hamiltonian
dynamics, without any approximations. We use the projection operator method for
the coarse-grained variables and transient potential. The dynamic equations for
the coarse-grained positions and momenta are similar to those in the
Hamiltonian dynamics, but the interaction potential is replaced by the
transient potential. The dynamic equation for the transient potential is the
generalized Langevin equation with the memory effect. Our result justifies the
use of the transient potential to describe the coarse-grained dynamics. We
propose several approximations to obtain simplified dynamics model. We show
that, under several approximations, the dynamic equation for the transient
potential reduces to the relatively simple Markovian dynamic equation for the
potential parameters.


title: AbductionRules: Training Transformers to Explain Unexpected Inputs

abstract:
  Transformers have recently been shown to be capable of reliably performing
logical reasoning over facts and rules expressed in natural language, but
abductive reasoning - inference to the best explanation of an unexpected
observation - has been underexplored despite significant applications to
scientific discovery, common-sense reasoning, and model interpretability.
  We present AbductionRules, a group of natural language datasets designed to
train and test generalisable abduction over natural-language knowledge bases.
We use these datasets to finetune pretrained Transformers and discuss their
performance, finding that our models learned generalisable abductive techniques
but also learned to exploit the structure of our data. Finally, we discuss the
viability of this approach to abductive reasoning and ways in which it may be
improved in future work.


title: Converse: A Tree-Based Modular Task-Oriented Dialogue System

abstract:
  Creating a system that can have meaningful conversations with humans to help
accomplish tasks is one of the ultimate goals of Artificial Intelligence (AI).
It has defined the meaning of AI since the beginning. A lot has been
accomplished in this area recently, with voice assistant products entering our
daily lives and chat bot systems becoming commonplace in customer service. At
first glance there seems to be no shortage of options for dialogue systems.
However, the frequently deployed dialogue systems today seem to all struggle
with a critical weakness - they are hard to build and harder to maintain. At
the core of the struggle is the need to script every single turn of
interactions between the bot and the human user. This makes the dialogue
systems more difficult to maintain as the tasks become more complex and more
tasks are added to the system. In this paper, we propose Converse, a flexible
tree-based modular task-oriented dialogue system. Converse uses an and-or tree
structure to represent tasks and offers powerful multi-task dialogue
management. Converse supports task dependency and task switching, which are
unique features compared to other open-source dialogue frameworks. At the same
time, Converse aims to make the bot building process easy and simple, for both
professional and non-professional software developers. The code is available at
https://github.com/salesforce/Converse.


title:
FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for
  Speech Enhancement

abstract:
  Previously proposed FullSubNet has achieved outstanding performance in Deep
Noise Suppression (DNS) Challenge and attracted much attention. However, it
still encounters issues such as input-output mismatch and coarse processing for
frequency bands. In this paper, we propose an extended single-channel real-time
speech enhancement framework called FullSubNet+ with following significant
improvements. First, we design a lightweight multi-scale time sensitive channel
attention (MulCA) module which adopts multi-scale convolution and channel
attention mechanism to help the network focus on more discriminative frequency
bands for noise reduction. Then, to make full use of the phase information in
noisy speech, our model takes all the magnitude, real and imaginary
spectrograms as inputs. Moreover, by replacing the long short-term memory
(LSTM) layers in original full-band model with stacked temporal convolutional
network (TCN) blocks, we design a more efficient full-band module called
full-band extractor. The experimental results in DNS Challenge dataset show the
superior performance of our FullSubNet+, which reaches the state-of-the-art
(SOTA) performance and outperforms other existing speech enhancement
approaches.


title:
A density description of a bounded-confidence model of opinion dynamics
  on hypergraphs

abstract:
  Models of opinion dynamics on graphs allow one to study how the opinions of
agents change due to dyadic interactions between them. It is useful to extend
such models to hypergraphs to examine how opinions change when social
interactions occur between three or more agents at once. In this paper, we
consider a bounded-confidence model, in which opinions take continuous values
and interacting agents comprise their opinions if they are close enough to each
other. We propose a density description to study the Deffuant--Weisbuch
bounded-confidence model on hypergraphs. We derive a mean-field rate equation
as the number of agents in a network becomes infinite, and we prove that the
rate equation yields a probability density that converges to noninteracting
opinion clusters. Using numerical simulations, we examine bifurcations of the
density model's steady-state opinion clusters and demonstrate that the
agent-based model converges to the density model as the number of agents
becomes infinite.

categories: cs.SI cs.SY eess.SY math.DS math.PR physics.soc-ph


title:
Improved Gradual Resistive Switching Range and 1000x On/Off Ratio in
  HfOx RRAM Achieved with a $Ge_2Sb_2Te_5$ Thermal Barrier

abstract:
  Gradual switching between multiple resistance levels is desirable for analog
in-memory computing using resistive random-access memory (RRAM). However, the
filamentary switching of $HfO_x$-based conventional RRAM often yields only two
stable memory states instead of gradual switching between multiple resistance
states. Here, we demonstrate that a thermal barrier of $Ge_2Sb_2Te_5$ (GST)
between $HfO_x$ and the bottom electrode (TiN) enables wider and weaker
filaments, by promoting heat spreading laterally inside the $HfO_x$. Scanning
thermal microscopy suggests that $HfO_x+GST$ devices have a wider heating
region than control devices with only $HfO_x$, indicating the formation of a
wider filament. Such wider filaments can have multiple stable conduction paths,
resulting in a memory device with more gradual and linear switching. The
thermally-enhanced $HfO_x+GST$ devices also have higher on/off ratio ($>10^3$)
than control devices ($<10^2$), and a median set voltage lower by approximately
1 V (~35%), with a corresponding reduction of the switching power. Our
$HfO_x+GST$ RRAM shows 2x gradual switching range using fast (~ns) identical
pulse trains with amplitude less than 2 V.