title:
Exploiting Session Information in BERT-based Session-aware Sequential
  Recommendation

abstract:
  In recommendation systems, utilizing the user interaction history as
sequential information has resulted in great performance improvement. However,
in many online services, user interactions are commonly grouped by sessions
that presumably share preferences, which requires a different approach from
ordinary sequence representation techniques. To this end, sequence
representation models with a hierarchical structure or various viewpoints have
been developed but with a rather complex network structure. In this paper, we
propose three methods to improve recommendation performance by exploiting
session information while minimizing additional parameters in a BERT-based
sequential recommendation model: using session tokens, adding session segment
embeddings, and a time-aware self-attention. We demonstrate the feasibility of
the proposed methods through experiments on widely used recommendation
datasets.


title:
A Generalization of Ripley's K Function for the Detection of Spatial
  Clustering in Areal Data

abstract:
  Spatial clustering detection has a variety of applications in diverse fields,
including identifying infectious disease outbreaks, assessing land use
patterns, pinpointing crime hotspots, and identifying clusters of neurons in
brain imaging applications. While performing spatial clustering analysis on
point process data is common, applications to areal data are frequently of
interest. For example, researchers might wish to know if census tracts with a
case of a rare medical condition or an outbreak of an infectious disease tend
to cluster together spatially. Since few spatial clustering methods are
designed for areal data, researchers often reduce the areal data to point
process data (e.g., using the centroid of each areal unit) and apply methods
designed for point process data, such as Ripley's K function or the average
nearest neighbor method. However, since these methods were not designed for
areal data, a number of issues can arise. For example, we show that they can
result in loss of power and/or a significantly inflated type I error rate. To
address these issues, we propose a generalization of Ripley's K function
designed specifically to detect spatial clustering in areal data. We compare
its performance to that of the traditional Ripley's K function, the average
nearest neighbor method, and the spatial scan statistic with an extensive
simulation study. We then evaluate the real world performance of the method by
using it to detect spatial clustering in land parcels containing conservation
easements and US counties with high pediatric overweight/obesity rates.


title:
An active approach towards monitoring and enhancing drivers'
  capabilities -- the ADAM cogtec solution

abstract:
  Driver's cognitive ability at a given moment is the most elusive variable in
assessing driver's safety. In contrast to other physical conditions, such as
short-sight, or manual disability cognitive ability is transient. Safety
regulations attempt to reduce risk related to driver's cognitive ability by
removing risk factors such as alcohol or drug consumption, forbidding secondary
tasks such as texting, and urging drivers to take breaks when feeling tired.
However, one cannot regulate all factors that affect driver's cognition,
furthermore, the driver's momentary cognitive ability in most cases is covert
even to driver.
  Here, we introduce an active approach aiming at monitoring a specific
cognitive process that is affected by all these forementioned causes and
directly affects the driver's performance in the driving task. We lean on the
scientific approach that was framed by Karl Friston (Friston, 2010). We
developed a closed loop-method in which driver's ocular responses to visual
probing were recorded. Machine-learning-algorithms were trained on ocular
responses of vigilant condition and were able to detect decrease in capability
due fatigue and substance abuse. Our results show that we manage to correctly
classify subjects with impaired and unimpaired cognitive process regardless of
the cause of impairment (77% accuracy, 5% false alarms).


title: Delivering data differently

abstract:
  Human-computer interaction relies on mouse/touchpad, keyboard, and screen,
but tools have recently been developed that engage sound, smell, touch,
muscular resistance, voice dialogue, balance, and multiple senses at once. How
might these improvements impact upon the practice of statistics and data
science? People with low vision may be better able to grasp and explore data.
More generally, methods developed to enable this have the potential to allow
sighted people to use more senses and become better analysts. We would like to
adapt some of the wide range of available computer and sensory input/output
technologies to transform data science workflows. Here is a vision of what this
synthesis might accomplish.


title:
ParticLS: Object-oriented software for discrete element methods and
  peridynamics

abstract:
  ParticLS (\emph{Partic}le \emph{L}evel \emph{S}ets) is a software library
that implements the discrete element method (DEM) and meshfree methods.
ParticLS tracks the interaction between individual particles whose geometries
are defined by level sets capable of capturing complex shapes. These particles
either represent rigid bodies or material points within a continuum.
Particle-particle interactions using various contact laws numerically
approximate solutions to energy and mass conservation equations, simulating
rigid body dynamics or deformation/fracture. By leveraging multiple contact
laws, ParticLS can simulate interacting bodies that deform, fracture, and are
composed of many particles. In the continuum setting, we numerically solve the
peridynamic equations -- integro-differential equations capable of modeling
objects with discontinuous displacement fields and complex fracture dynamics.
We show that the discretized peridynamic equations can be solved using the same
software infrastructure that implements the DEM. Therefore, we design a unique
software library where users can easily add particles with arbitrary geometries
and new contact laws that model either rigid-body interaction or peridynamic
constitutive relationships. We demonstrate ParticLS' versatility on test
problems meant to showcase features applicable to a broad selection of fields
such as tectonics, granular media, multiscale simulations, glacier calving, and
sea ice.


title:
New Core-Guided and Hitting Set Algorithms for Multi-Objective
  Combinatorial Optimization

abstract:
  In the last decade, a plethora of algorithms for single-objective Boolean
optimization has been proposed that rely on the iterative usage of a highly
effective Propositional Satisfiability (SAT) solver. But the use of SAT solvers
in Multi-Objective Combinatorial Optimization (MOCO) algorithms is still
scarce. Due to this shortage of efficient tools for MOCO, many real-world
applications formulated as multi-objective are simplified to single-objective,
using either a linear combination or a lexicographic ordering of the objective
functions to optimize. In this paper, we extend the state of the art of MOCO
solvers with two novel unsatisfiability-based algorithms. The first is a
core-guided MOCO solver. The second is a hitting set-based MOCO solver.
Experimental results obtained in a wide range of benchmark instances show that
our new unsatisfiability-based algorithms can outperform state-of-the-art
SAT-based algorithms for MOCO.


title:
Blockchain and Cryptocurrency in Human Computer Interaction: A
  Systematic Literature Review and Research Agenda

abstract:
  We present a systematic literature review of cryptocurrency and blockchain
research in Human-Computer Interaction (HCI) published between 2014 and 2021.
We aim to provide an overview of the field, consolidate existing knowledge, and
chart paths for future research. Our analysis of 99 articles identifies six
major themes: (1) the role of trust, (2) understanding motivation, risk, and
perception of cryptocurrencies, (3) cryptocurrency wallets, (4) engaging users
with blockchain, (5) using blockchain for application-specific use cases, and
(6) support tools for blockchain. We discuss the focus of the existing research
body and juxtapose it to the changing landscape of emerging blockchain
technologies to highlight future research avenues for HCI and interaction
design. With this review, we identify key aspects where interaction design is
critical for the adoption of blockchain systems. Doing so, we provide a
starting point for new scholars and designers and help them position future
contributions.


title:
Designing Trustworthy User Interfaces for the Voluntary Carbon Market: A
  Randomized Online Experiment

abstract:
  The voluntary carbon market is an important building block in the fight
against climate change. However, it is not trivial for consumers to verify
whether carbon offset projects deliver what they promise. While technical
solutions for measuring their impact are emerging, there is a lack of
understanding of how to translate this data into interface designs that mediate
the establishment of trust. With interaction between users and offset projects
mainly happening online, it is critical to meet this design challenge. To this
end, we designed and evaluated interfaces with varying trust cues for carbon
offset projects in a randomized online experiment (n=244). Our results show
that content design, particularly financial and forest-related quantitative
data presented at the right detail level, increases the perceived
trustworthiness, while images have no significant effect. We contribute the
first specific guidance for interface designers for carbon offsets and discuss
implications for interaction design.


title:
Bayesian Data Analysis for Sky-averaged 21-cm Experiments in the
  Presence of Ionospheric Effects

abstract:
  The ionosphere introduces chromatic distortions on low frequency radio waves,
and thus poses a hurdle for 21-cm cosmology. In this paper we introduce
time-varying chromatic ionospheric effects on simulated antenna temperature
data of a global 21-cm data analysis pipeline, and try to detect the injected
global signal. We demonstrate that given turbulent ionospheric conditions, more
than 5\% error in our knowledge of the ionospheric parameters could lead to
comparatively low evidence and high root-mean-square error (RMSE), suggesting a
false or null detection. When using a constant antenna beam for cases that
include data at different times, the significance of the detection lowers as
the number of time samples increases. It is also shown that for observations
that include data at different times, readjusting beam configurations according
to the time-varying ionospheric conditions should greatly improve the
significance of a detection, yielding higher evidences and lower RMSE, and that
it is a necessary procedure for a successful detection when the ionospheric
conditions are not ideal.


title: On Quantum and Classical Treatments of Radiative Recombination

abstract:
  A quantum-mechanical solution to the problem of radiative recombination of an
electron in the Coulomb field has long been known. However, in astrophysics,
the classical approach is sometimes used to describe similar processes in
systems of magnetic monopoles or interacting dark matter particles. The
importance of such problems is determined by the fact that recombination
processes play a decisive role in the evolution of the large-scale structure of
the Universe. It is shown that the applicability of the quantum and classical
approaches to radiative recombination is closely related to the magnitude of
the radiated angular momentum and its quantization. For situations where the
classical approach is not suitable, a semiclassical approach based on
consideration of the quantization of the angular momentum is proposed. It can
be useful for an approximate description of radiative recombination in a
variety of systems.


title: Generalizations Of Graded Prime Ideals Over Graded Near Rings

abstract:
  This paper considers graded near-rings over a monoid G as a generalizations
of the graded rings over groups, introduce certain innovative graded weakly
prime ideals and graded almost prime ideals as a generalizations of graded
prime ideals over graded near-rings, and explore their various properties and
their generalizations in graded near-rings.


title: The Hilbert space basis and Hilbert's eighth problem

abstract:
  The paper considers the Hilbert space $\hat{H}_r$ of real functions summable
with the square $L^2(a,b)_r$ on any interval $\{(a,b)_r\}_{r=1}^{\infty}\in
\mathbb{R}$. It is shown on the basis of the theorem on zeros of real
orthogonal polynomials if in $\hat{H}_r$ there exists a complete orthonormal
basis $\{f(x)_k\}_{k=1}^{\infty}$ and the function
$f(x)\in\{f(x)_k\}_{k=1}^{\infty}$ has zeros, then these zeros are simple and
real. The generalized Hardy function
$Z(\sigma,t)=\Re\zeta(\sigma+it)e^{i\theta(t)}$ is considered. It is shown that
in the Hilbert space $\hat{H}_r$ there exists a complete basis
$\{Z(\lambda_k,t\}_{k=1}^{\infty}$ where $\lambda_k\in\mathbb{Q}$ and
$Z(t)\in\{Z(\lambda_k,t\}_{k=1}^{\infty}$ when $\lambda_k=1/2$, hence the Hardy
function $Z(t)=\zeta(1/2+it)e^{i\theta(t)}$ has all simple and real zeros.


title: Two New Methods for Counting and Tracking the Evolution of Polar Faculae

abstract:
  Polar faculae (PFe) are the footpoints of magnetic field lines near the Sun's
poles that are seen as bright regions along the edges of granules. The time
variation in the number of PFe has been shown to correlate with the strength of
the polar magnetic field and to be a predictor of the subsequent solar cycle.
Due to the small size and transient nature of these features, combined with
different techniques and observational factors, previous counts of PFe differ
in magnitude. Further, there were no scalable techniques to measure the
statistical properties of faculae. Using data from the Helioseismic and
Magnetic Imager (HMI) onboard the Solar Dynamics Observatory (SDO), we present
two new methods for tracking faculae and measuring their properties. In the
first, we calculate the standard deviation of the HMI images over one day,
visualizing the faculae as streaks. The facular lifetime is found by dividing
the angular streak length by the latitude-dependent rotation rate. We apply
this method to 134 days of data over 11 years. The distribution of facular
lifetimes has a mean of 6.0 hours, a FWHM of 5.4 hours, and a skew towards
longer lifetimes, with some lasting up to 1 day. In the second method, we
overlay images of the progressive standard deviation with the HMI magnetogram
to show the close relationship between facular candidates and the magnetic
field. The results allow us to distinguish between motion due to the Sun's
rotation and "proper motion" due to faculae moving across the Sun's surface,
confirming that PFe participate in convective motions at the poles. Counts of
PFe using both methods agree with previous counts in their variation with the
solar cycle and the polar magnetic field. These methods can be extended to
automate the identification and measurement of other properties of of PFe,
which would allow for daily measurements of all faculae since SDO began
operation in 2010.


title: A Kilonova Following a Long-Duration Gamma-Ray Burst at 350 Mpc

abstract:
  Here, we report the discovery of a kilonova associated with the nearby (350
Mpc) minute-duration GRB 211211A. In tandem with deep optical limits that rule
out the presence of an accompanying supernova to $M_I > -13$ mag at 17.7 days
post-burst, the identification of a kilonova confirms that this burst's
progenitor was a compact object merger. While the spectrally softer tail in GRB
211211A's gamma-ray light curve is reminiscent of previous extended emission
short GRBs (EE-SGRBs), its prompt, bright spikes last $\gtrsim 12$ s,
separating it from past EE-SGRBs. GRB 211211A's kilonova has a similar
luminosity, duration and color to AT2017gfo, the kilonova found in association
with the gravitational wave (GW)-detected binary neutron star (BNS) merger
GW170817. We find that the merger ejected $\approx 0.04 M_{\odot}$ of
r-process-rich material, and is consistent with the merger of two neutron stars
(NSs) with masses close to the canonical $1.4 M_{\odot}$. This discovery
implies that GRBs with long, complex light curves can be spawned from compact
object merger events and that a population of kilonovae following GRBs with
durations $\gg 2$ s should be accounted for in calculations of the NS merger
r-process contribution and rate. At 350 Mpc, the current network of GW
interferometers at design sensitivity would have detected the merger
precipitating GRB 211211A, had it been operating at the time of the event.
Further searches for GW signals coincident with long GRBs are therefore a
promising route for future multi-messenger astronomy.


title:
Continuous Mott transition in moir\'e semiconductors: role of
  long-wavelength inhomogeneities

abstract:
  Recent experiments in moir\'{e} transition metal dichalcogenide materials
have reported the observation of a continuous bandwidth-tuned transition from a
metal to a paramagnetic Mott insulator at a fixed filling of one electron per
moir\'{e} unit cell. The electrical transport measurements reveal a number of
puzzling features that are seemingly at odds with the theoretical expectations
of an interaction induced, but disorder-free, bandwidth-tuned metal-insulator
transition. In this work, we include the effects of long-wavelength
inhomogeneities, building on the results for a continuous metal-insulator
transition at fixed filling in the clean limit. We examine the effects of
meso-scale inhomogeneities near the critical point on transport using the
framework of random resistor networks, highlighting the salient differences
from a simple percolation-based picture. We place our results in the context of
recent and ongoing experiments.


title:
Measurements of the Hubble Constant with a Two Rung Distance Ladder: Two
  Out of Three Ain't Bad

abstract:
  The three rung distance ladder, which calibrates Type Ia supernovae through
stellar distances linked to geometric measurements, provides the highest
precision direct measurement of the Hubble constant. In light of the Hubble
tension, it is important to test the individual components of the distance
ladder. For this purpose, we report a measurement of the Hubble constant from
35 extragalactic Cepheid hosts measured by the SH0ES team, using their
distances and redshifts at cz < 3300 km /s , instead of any, more distant Type
Ia supernovae, to measure the Hubble flow. The Cepheid distances are calibrated
geometrically in the Milky Way, NGC 4258, and the Large Magellanic Cloud.
Peculiar velocities are a significant source of systematic uncertainty at z
$\sim$ 0.01, and we present a formalism for both mitigating and quantifying
their effects, making use of external reconstructions of the density and
velocity fields in the nearby universe. We identify a significant source of
uncertainty originating from different assumptions about the selection criteria
of this sample, whether distance or redshift limited, as it was assembled over
three decades. Modeling these assumptions yields central values ranging from H0
= 71.8 to 77.0 km/s/Mpc. Combining the four best fitting selection models
yields H0 = 73.1 (+2.6/-2.3) km/s/Mpc as a fiducial result, at $2.6\sigma$
tension with Planck. While Type Ia supernovae are essential for a precise
measurement of H0, unknown systematics in these supernovae are unlikely to be
the source of the Hubble tension


title:
3D intrinsic shapes of quiescent galaxies in observations and
  simulations

abstract:
  We study the intrinsic 3D shapes of quiescent galaxies over the last half of
cosmic history based on their axial ratio distribution. To this end, we
construct a sample of unprecedented size, exploiting multi-wavelength
$u$-to-$K_s$ photometry from the deep wide area surveys KiDS+VIKING paired with
high-quality $i$-band imaging from HSC-SSP. Dependencies of the shapes on mass,
redshift, photometric bulge prominence and environment are considered. For
comparison, the intrinsic shapes of quenched galaxies in the IllustrisTNG
simulations are analyzed and contrasted to their formation history. We find
that over the full $0<z<0.9$ range, and in both simulations and observations,
spheroidal 3D shapes become more abundant at $M_* > 10^{11}\ M_{\odot}$, with
the effect being most pronounced at lower redshifts. In TNG, the most massive
galaxies feature the highest ex-situ stellar mass fractions, pointing to
violent relaxation via mergers as the mechanism responsible for their 3D shape
transformation. Larger differences between observed and simulated shapes are
found at low to intermediate masses. At any mass, the most spheroidal quiescent
galaxies in TNG feature the highest bulge mass fractions, and conversely
observed quiescent galaxies with the highest bulge-to-total ratios are found to
be intrinsically the roundest. Finally, we detect an environmental influence on
galaxy shape, at least at the highest masses, such that at fixed mass and
redshift quiescent galaxies tend to be rounder in denser environments.


title:
The problem with Proca: ghost instabilities in self-interacting vector
  fields

abstract:
  Massive vector fields feature in several areas of particle physics, e.g., as
carriers of weak interactions, dark matter candidates, or as an effective
description of photons in a plasma. Here we investigate vector fields with
self-interactions by replacing the mass term in the Proca equation with a
general potential. We show that this seemingly benign modification inevitably
introduces ghost instabilities of the same kind as those recently identified
for vector-tensor theories of modified gravity (but in this simpler, minimally
coupled theory). It has been suggested that nonperturbative dynamics may drive
systems away from such instabilities. We demonstrate that this is not the case
by evolving a self-interacting Proca field on a black hole background, where it
grows due to the superradiant instability. The system initially evolves as in
the massive case, but instabilities are triggered in a finite time once the
self-interaction becomes significant. These instabilities have implications for
the formation of condensates of massive, self-interacting vector bosons, the
possibility of spin-one bosenovae, vector dark matter models, and effective
models for interacting photons in a plasma.


title: Identity Preserving Loss for Learned Image Compression

abstract:
  Deep learning model inference on embedded devices is challenging due to the
limited availability of computation resources. A popular alternative is to
perform model inference on the cloud, which requires transmitting images from
the embedded device to the cloud. Image compression techniques are commonly
employed in such cloud-based architectures to reduce transmission latency over
low bandwidth networks. This work proposes an end-to-end image compression
framework that learns domain-specific features to achieve higher compression
ratios than standard HEVC/JPEG compression techniques while maintaining
accuracy on downstream tasks (e.g., recognition). Our framework does not
require fine-tuning of the downstream task, which allows us to drop-in any
off-the-shelf downstream task model without retraining. We choose faces as an
application domain due to the ready availability of datasets and off-the-shelf
recognition models as representative downstream tasks. We present a novel
Identity Preserving Reconstruction (IPR) loss function which achieves
Bits-Per-Pixel (BPP) values that are ~38% and ~42% of CRF-23 HEVC compression
for LFW (low-resolution) and CelebA-HQ (high-resolution) datasets,
respectively, while maintaining parity in recognition accuracy. The superior
compression ratio is achieved as the model learns to retain the domain-specific
features (e.g., facial features) while sacrificing details in the background.
Furthermore, images reconstructed by our proposed compression model are robust
to changes in downstream model architectures. We show at-par recognition
performance on the LFW dataset with an unseen recognition model while retaining
a lower BPP value of ~38% of CRF-23 HEVC compression.


title:
On the relative importance of AGN winds for the evolution of exoplanet
  atmospheres

abstract:
  Recent work investigating the impact of winds and outflows from active
galactic nuclei (AGN) on the habitability of exoplanets suggests that such
activity could be deleterious for the long-term survival of planetary
atmospheres and the habitability of planets subject to such winds. Here, we
discuss the relative importance of the effect of AGN winds compared to stellar
winds and the effect of the planet's magnetosphere and stellar irradiation and
conclude that AGN winds are not likely to play a significant role in the
evolution of atmospheric conditions in planets under conditions otherwise
favorable for habitability.


title: An Improved GPU-Based Ray-Shooting Code For Gravitational Microlensing

abstract:
  We present an improved inverse ray-shooting code based on GPUs for generating
microlensing magnification maps. In addition to introducing GPUs for
acceleration, we put the efforts in two aspects: (i) A standard circular lens
plane is replaced by a rectangular one to reduce the number of unnecessary
lenses as a result of an extremely prolate rectangular image plane. (ii)
Interpolation method is applied in our implementation which has achieved an
significant acceleration when dealing with large number of lenses and light
rays required by high resolution maps. With these applications, we have greatly
reduced the running time while maintaining high accuracy: the speed has been
increased by about 100 times compared with ordinary GPU based IRS code and
GPU-D code when handling large number of lenses. If encountered the high
resolution situation up to $10000^2$ pixels, resulting in almost $10^{11}$
light rays, the running time can also be reduced by two orders of magnitude.


title:
Learning-to-Rank at the Speed of Sampling: Plackett-Luce Gradient
  Estimation With Minimal Computational Complexity

abstract:
  Plackett-Luce gradient estimation enables the optimization of stochastic
ranking models within feasible time constraints through sampling techniques.
Unfortunately, the computational complexity of existing methods does not scale
well with the length of the rankings, i.e. the ranking cutoff, nor with the
item collection size. In this paper, we introduce the novel PL-Rank-3 algorithm
that performs unbiased gradient estimation with a computational complexity
comparable to the best sorting algorithms. As a result, our novel
learning-to-rank method is applicable in any scenario where standard sorting is
feasible in reasonable time. Our experimental results indicate large gains in
the time required for optimization, without any loss in performance. For the
field, our contribution could potentially allow state-of-the-art
learning-to-rank methods to be applied to much larger scales than previously
feasible.


title:
Rheology of Pseudomonas fluorescens biofilms: from experiments to DPD
  mesoscopic modelling

abstract:
  The presence of bacterial biofilms in clinical and industrial settings is a
major issue worldwide. A biofilm is a viscoelastic matrix, composed of bacteria
producing a network of Extracellular Polymeric Substances (EPS) to which
bacteria crosslink. Modelling of complex biofilms is relevant to provide
accurate descriptions and predictions that include parameters as hydrodynamics,
dynamics of the bacterial population and solute mass transport. However,
up-to-date numerical modelling, even at a coarse-grained level, is not
satisfactorily. In this work, we present a numerical coarse-grain model of a
bacterial biofilm, consisting of bacteria immersed in an aqueous matrix of a
polymer network, that allows to study rheological properties of a biofilm. We
study its viscoelastic modulus, varying topology and composition (such as the
number of crosslinks between EPS polymers, the number of bacteria and the
amount of solvent), and compare the numerical results with experimental
rheological data of Pseudomonas fluorescens biofilms grown under static and
shaking conditions, as previously described by Jara et al, Frontiers in
Microbiology (2021).


title:
Quantum-classical correspondence in spin-boson equilibrium states at
  arbitrary coupling

abstract:
  It is known that the equilibrium properties of nanoscale systems can deviate
significantly from standard thermodynamics due to their coupling to an
environment. For the generalized $\theta$-angled spin-boson model, here we
derive an explicit form of the classical mean force equilibrium state. Taking
the large spin limit of the quantum spin-boson model, we demonstrate that the
quantum-classical correspondence is maintained at arbitrary coupling strength.
This correspondence gives insight into the conditions for a quantum system to
be well-approximated by its classical counterpart. We further demonstrate that,
counterintuitively, previously identified environment-induced 'coherences' in
the equilibrium state of weakly coupled quantum spins, do not disappear in the
classical case. Finally, we categorise various coupling regimes, from
ultra-weak to ultra-strong, and find that the same value of coupling strength
can either be 'weak' or 'strong', depending on whether the system is quantum or
classical. Our results shed light on the interplay of quantum and mean force
corrections in equilibrium states of the spin-boson model, and will help draw
the quantum to classical boundary in a range of fields, such as magnetism and
exciton dynamics.


title:
Resistive instabilities in sinusoidal shear flows with a streamwise
  magnetic field

abstract:
  We investigate the linear stability of a sinusoidal shear flow with an
initially uniform streamwise magnetic field in the framework of incompressible
magnetohydrodynamics (MHD) with finite resistivity and viscosity. This flow is
known to be unstable to the Kelvin-Helmholtz instability in the hydrodynamic
case. The same is true in ideal MHD, where dissipation is neglected, provided
the magnetic field strength does not exceed a critical threshold beyond which
magnetic tension stabilizes the flow. Here, we demonstrate that including
viscosity and resistivity introduces two new modes of instability. One of these
modes, which we call a resistively-unstable Alfv\'en wave due to its connection
to shear Alfv\'en waves, exists for any nonzero magnetic field strength as long
as the magnetic Prandtl number $Pm < 1$. We present a reduced model for this
instability that reveals its excitation mechanism to be the negative eddy
viscosity of periodic shear flows described by Dubrulle & Frisch (1991).
Finally, we demonstrate numerically that this mode saturates in a
quasi-stationary state dominated by counter-propagating solitons.

categories: physics.flu-dyn astro-ph.EP astro-ph.SR physics.plasm-ph


title:
Convergence of Lagrange Finite Element Methods for Maxwell Eigenvalue
  Problem in 3D

abstract:
  We prove convergence of the Maxwell eigenvalue problem using quadratic or
higher Lagrange finite elements on Worsey-Farin splits in three dimensions. To
do this, we construct two Fortin-like operators to prove uniform convergence of
the corresponding source problem. We present numerical experiments to
illustrate the theoretical results.


title: Quantum Fault Trees

abstract:
  Fault tree analysis is a technique widely used in risk and reliability
analysis of complex engineering systems given its deductive nature and
relatively simple interpretation. In a fault tree, events are usually
represented by a binary variable that indicates whether an event occurs or not,
traditionally associated with the values 1 and 0, respectively. Different
events are linked together using logical gates, modelling the dependencies that
a subsystem or system may have over its basic components. In this study,
quantum computing is leveraged to propose a novel approach to encode a
traditional fault tree into a quantum algorithm. This quantum fault tree method
uses quantum bits to represent basic events, effectively encoding the original
fault tree into a quantum circuit. The execution of the resulting quantum
circuit represents a full simulation of the fault tree, and multiple executions
can be utilized to compute the failure probability of the whole system. The
proposed approach is tested on a case study portraying a dynamic positioning
system. Results verify that the quantum-based proposed approach is able to
effectively obtain the dynamic positioning failure probability through
simulation, opening promising opportunities for future investigations in the
area.


title: ChapterBreak: A Challenge Dataset for Long-Range Language Models

abstract:
  While numerous architectures for long-range language models (LRLMs) have
recently been proposed, a meaningful evaluation of their discourse-level
language understanding capabilities has not yet followed. To this end, we
introduce ChapterBreak, a challenge dataset that provides an LRLM with a long
segment from a narrative that ends at a chapter boundary and asks it to
distinguish the beginning of the ground-truth next chapter from a set of
negative segments sampled from the same narrative. A fine-grained human
annotation reveals that our dataset contains many complex types of chapter
transitions (e.g., parallel narratives, cliffhanger endings) that require
processing global context to comprehend. Experiments on ChapterBreak show that
existing LRLMs fail to effectively leverage long-range context, substantially
underperforming a segment-level model trained directly for this task. We
publicly release our ChapterBreak dataset to spur more principled future
research into LRLMs.


title:
Event-by-event anti-deuteron multiplicity fluctuation in Pb+Pb
  collisions at $\sqrt{s_{NN}}=5.02$ TeV

abstract:
  Using the nucleon coalescence model based on kinetic freeze-out nucleons from
the hybrid model of MUSIC hydrodynamics and UrQMD hadronic transport, we study
the production of anti-deuteron and its event-by-event fluctuation in Pb+Pb
collisions at $\sqrt{s_{NN}}=5.02$ TeV. We find a clear suppression of the
anti-deuteron to antiproton yield ratio in peripheral collisions, which is in
accordance with the measurements from the ALICE Collaboration. Also found is a
Poissonian event-by-event fluctuation of the anti-deuteron multiplicity
distribution in all collision centralities, which is different from the
prediction of a simple coalescence model calculation that assumes all
antiproton and antineutron pairs to have the same probability to form
anti-deuterons. We further find a small negative correlation between the
anti-deuteron and antiproton multiplicity distributions as a result of the
baryon conservation.


title: Backflow in relativistic wave equations

abstract:
  We show that, contrary to the statements made by many authors, the backflow
is not a nonclassical effect. The backflow is a characteristic feature of
solutions of the wave equations: quantum and classical. We present simple
solutions of the Dirac equation, Maxwell equations and equations of linearized
gravity where the backflow phenomenon is clearly seen. In this work we describe
backflow in relativistic theories but this phenomenon can occur in the
solutions of all kinds of wave equations: quantum and classical.


title:
A Ihara-Bass Formula for Non-Boolean Matrices and Strong Refutations of
  Random CSPs

abstract:
  We define a notion of "non-backtracking" matrix associated to any symmetric
matrix, and we prove a "Ihara-Bass" type formula for it. Previously, these
notions were known only for symmetric 0/1 matrices.
  We use this theory to prove new results on polynomial-time strong refutations
of random constraint satisfaction problems with $k$ variables per constraints
(k-CSPs). For a random k-CSP instance constructed out of a constraint that is
satisfied by a $p$ fraction of assignments, if the instance contains $n$
variables and $n^{k/2} / \epsilon^2$ constraints, we can efficiently compute a
certificate that the optimum satisfies at most a $p+O_k(\epsilon)$ fraction of
constraints.
  Previously, this was known for even $k$, but for odd $k$ one needed $n^{k/2}
(\log n)^{O(1)} / \epsilon^2$ random constraints to achieve the same
conclusion.
  Although the improvement is only polylogarithmic, it overcomes a significant
barrier to these types of results. Strong refutation results based on current
approaches construct a certificate that a certain matrix associated to the
k-CSP instance is quasirandom. Such certificate can come from a Feige-Ofek type
argument, from an application of Grothendieck's inequality, or from a spectral
bound obtained with a trace argument. The first two approaches require a union
bound that cannot work when the number of constraints is $o(n^{\lceil k/2
\rceil})$ and the third one cannot work when the number of constraints is
$o(n^{k/2} \sqrt{\log n})$.


title:
Cluster Detection Capabilities of the Average Nearest Neighbor Ratio and
  Ripley's K Function on Areal Data: an Empirical Assessment

abstract:
  Spatial clustering detection methods are widely used in many fields of
research including epidemiology, ecology, biology, physics, and sociology. In
these fields, areal data is often of interest; such data may result from
spatial aggregation (e.g. the number disease cases in a county) or may be
inherent attributes of the areal unit as a whole (e.g. the habitat suitability
of conserved land parcel). This study aims to assess the performance of two
spatial clustering detection methods on areal data: the average nearest
neighbor (ANN) ratio and Ripley's K function. These methods are designed for
point process data, but their ease of implementation in GIS software and the
lack of analogous methods for areal data have contributed to their use for
areal data. Despite the popularity of applying these methods to areal data,
little research has explored their properties in the areal data context. In
this paper we conduct a simulation study to evaluate the performance of each
method for areal data under different types of spatial dependence and different
areal structures. The results shows that the empirical type I error rates are
inflated for the ANN ratio and Ripley's K function, rendering the methods
unreliable for areal data.


title: A Survey of Traversability Estimation for Mobile Robots

abstract:
  Traversability illustrates the difficulty of driving through a specific
region and encompasses the suitability of the terrain for traverse based on its
physical properties, such as slope and roughness, surface condition, etc. In
this survey we highlight the merits and limitations of all the major steps in
the evolution of traversability estimation techniques, covering both
non-trainable and machine-learning methods, leading up to the recent
proliferation of deep learning literature. We discuss how the nascence of Deep
Learning has created an opportunity for radical improvement in traversability
estimation. Finally, we discuss how self-supervised learning can help satisfy
deep methods' increased need for (challenging to acquire and label) large-scale
datasets.


title:
Three favorite dimensions of Andrzej Kossakowski: along his path to a
  scientific discovery

abstract:
  Some achievements of the late Andrzej Kossakowski in the field of statistical
physics and quantum theory are presented. We recall his attempt to find an
analytical solution of the 3-dimensional Ising model and present a problem
concerning the set of positive maps.


title: Random walks on networks with stochastic reset to multiple nodes

abstract:
  In the previous chapters, we explored the effects of resetting on networks
considering one and two nodes. In this chapter, we will describe a
generalization of random walks with resetting to an arbitrary number of nodes
$\mathcal{M}$. In order to make the equations clear and understandable, it is
necessary to introduce a more compact notation. Once the theory is fully
explained and implemented, we will apply this generalization to more complex
structures than the simple ring, particularly to Cayley trees, random
distribution of points in a continuous space, and interacting cycles. For this
last type of networks, we apply the Google search strategy where the dynamics
resets to all nodes.
  We introduce the total resetting probability $\beta$ and the global mean
first passage time $\mathcal{T}$, which is the average of the MFPT over all the
target and source nodes, consequently its value does not depend on the initial
condition of the random walker. The main objective is to show how the parameter
$\beta$ affects $\mathcal{T}$ and in some cases optimizes it.


title:
Energetics of quantum vacuum friction. II: Dipole fluctuations and field
  fluctuations

abstract:
  As a second paper in series with arXiv:2108.01539, we discuss here quantum
vacuum friction on an intrinsically dissipative particle. The friction arises
not only from the field fluctuations but also from the dipole fluctuations
intrinsic to the particle. As a result, the dissipative particle can be out of
the nonequilibrium steady state (NESS), where it loses or gains internal
energy. Only if the temperature of the particle equals a special NESS
temperature will the particle be in NESS. We first derive the NESS conditions
which give the NESS temperature of the particle as a function of the radiation
temperature and the velocity of the particle. Imposing the NESS conditions, we
then obtain an expression for the quantum vacuum friction in NESS. The NESS
quantum vacuum friction is shown to be always negative definite, therefore a
true drag. The NESS temperature and quantum vacuum friction are calculated
numerically for various models. Out of NESS, even though the quantum vacuum
frictional force no longer has a definite sign in the rest frame of the
radiation, we find the external force needed to keep the particle moving must
be in the same direction as the motion of the particle. This then excludes the
possibility of making a perpetual motion machine, which could convert the
vacuum energy into useful mechanical work. In addition, we find that the
deviation of the temperature of the particle from its NESS temperature causes
the particle to lose or gain internal energy in such a way that the particle
would return to NESS after deviating from it. This enables experimental
measurements of the NESS temperature of the particle to serve as a feasible
signature for these quantum vacuum frictional effects.

categories: hep-th cond-mat.mes-hall hep-ph physics.atom-ph quant-ph


title: Truth-relevant Logic, Propositional Calculus

abstract:
  The thesis of this paper is that truth-relevant logic is a better foundation
for mathematics than classical logic. It is a system proposed by Richard Diaz
in 1981. In a certain sense t-relevant logic is based on Kleene strong tables.
These define a system with three values: true, false, unknown. It turns out
that there exist tautologies with the following property: there exists a proper
subset of propositional variables (t-relevant variables) such that for all
combinations true/false the tautology will be true, that is, the rest of the
variables (redundant variables) occurring in the tautology can be unknown. We
consider such compound sentences as neither true nor false. Philosophical
justification is provided. Proof system based on tableaux is proposed. The
following theorem is proved: tautology L = (R1 v ~R1) & (R2 v ~R2) & ... & (Rm
v ~Rm), where {R1, R2, ... Rm} is a subset of t-relevant propositional
variables, and = is equivalence according to Kleene strong tables.


title:
Compressibility: Power of PCA in Clustering Problems Beyond
  Dimensionality Reduction

abstract:
  In this paper we take a step towards understanding the impact of principle
component analysis (PCA) in the context of unsupervised clustering beyond a
dimensionality reduction tool. We explore another property of PCA in vector
clustering problems, which we call compressibility. This phenomenon shows that
PCA significantly reduces the distance of data points belonging to the same
clusters, while reducing inter-cluster distances relatively mildly. This gap
explains many empirical observations found in practice. For example, in
single-cell RNA-sequencing analysis, which is an application of vector
clustering in biology, it has been observed that applying PCA on datasets
significantly improves the accuracy of classical clustering algorithms such as
K-means.
  We study this compression gap in both theory and practice. On the theoretical
side, we analyze PCA in a fairly general probabilistic setup, which we call the
random vector model. In terms of practice, we verify the compressibility of PCA
on multiple single-cell RNA-seq datasets.


title:
In situ nonlinear Rayleigh wave technique to characterize the tensile
  plastic deformation of stainless steel 316L

abstract:
  The acoustic nonlinearity parameter(beta) is sensitive to dislocation
parameters, which continuously change during plastic deformation.
Dislocation-based damage in structures/components is the source of the failure;
thus, beta has been studied as a metric for non-destructive evaluation. This
work consists of two parts: the development of an in situ experimental setup
for nonlinear Rayleigh wave measurements, and characterization of the
dependence of beta on applied stress at different levels of initial plastic
strain. First, we introduce an experimental setup and methods for repeatable in
situ nonlinear ultrasonic measurements. Details on design considerations and
measurement schemes are provided. In the second part, beta was measured in situ
during an incremental monotonic tensile test. The measured \beta monotonically
decreases with plastic strain, but it is relatively insensitive to the applied
stress during elastic deformation. This result highlights three aspects of the
evolution of beta, which have not been sufficiently emphasized in prior work:
the apparent insensitivity of beta to the applied stress during elastic
deformation, decreasing beta with plastic deformation, and the saturation of
beta. We attribute the trend of decreasing beta to a scaling of beta with
monopole loop length during plastic deformation, which depends on initial
microstructure. The saturation of beta at 1.8% coincides with a planar-to-wavy
transition of dislocation structures. The in situ nonlinear ultrasonic
experimental method presented in this work is significant as the in situ
results can provide broader insights on beta and dislocation-based damage
evolution than ex situ measurements alone.


title:
A New Lagrangian Problem Crossover: A Systematic Review and
  Meta-Analysis of Crossover Standards

abstract:
  The performance of most evolutionary metaheuristic algorithms depends on
various operators. The crossover operator is one of them and is mainly
classified into two standards; application-dependent crossover operators and
application-independent crossover operators. These standards always help to
choose the best-fitted point in the evolutionary algorithm process. The high
efficiency of crossover operators enables minimizing the error that occurred in
engineering application optimization within a short time and cost. There are
two crucial objectives behind this paper; at first, it is an overview of
crossover standards classification that has been used by researchers for
solving engineering operations and problem representation. The second objective
of this paper; The significance of novel standard crossover is proposed
depending on Lagrangian Dual Function (LDF) to progress the formulation of the
Lagrangian Problem Crossover (LPX) as a new systematic standard operator. The
results of the proposed crossover standards for 100 generations of parent
chromosomes are compared to the BX and SBX standards, which are the communal
real-coded crossover standards. The accuracy and performance of the proposed
standard have evaluated by three unimodal test functions. Besides, the proposed
standard results are statistically demonstrated and proved that it has an
excessive ability to generate and enhance the novel optimization algorithm
compared to BX and SBX.


title: Generative sampling in tractography using autoencoders (GESTA)

abstract:
  Current tractography methods use the local orientation information to
propagate streamlines from seed locations. Many such seeds provide streamlines
that stop prematurely or fail to map the true pathways because some white
matter bundles are "harder-to-track" than others. This results in tractography
reconstructions with poor white and gray matter spatial coverage. In this work,
we propose a generative, autoencoder-based method, named GESTA (Generative
Sampling in Tractography using Autoencoders), that produces streamlines with
better spatial coverage. Compared to other deep learning methods, our
autoencoder-based framework is not constrained by any prior or a fixed set of
bundles. GESTA produces new and complete streamlines for any white matter
bundle. GESTA is shown to be effective on both synthetic and human brain in
vivo data. Our streamline evaluation framework ensures that the streamlines
produced by GESTA are anatomically plausible and fit well to the local
diffusion signal. The streamline evaluation criteria assess anatomy (white
matter coverage), local orientation alignment (direction), geometry features of
streamlines, and optionally, gray matter connectivity. The GESTA framework
offers considerable gains in bundle coverage using a reduced set of seeding
streamlines with a 1.5x improvement for the "Fiber Cup", and 6x for the ISMRM
2015 Tractography Challenge datasets. Similarly, it provides a 4x white matter
volume increase on the BIL&GIN callosal homotopic dataset. It also successfully
generates new streamlines in poorly populated bundles, such as the fornix and
other hard-to-track bundles, on in vivo data. GESTA is thus the first deep
tractography generative method that can improve white matter reconstruction of
hard-to-track bundles.


title: 3D shape explains star formation mystery of California and Orion A

abstract:
  The new Gaia data release (EDR3) with improved astrometry has opened a new
era in studying our Milky Way in fine detail. We use Gaia EDR3 astrometry
together with 2MASS and WISE photometry to study two of the most massive
molecular clouds in the solar vicinity: Orion A and California. Despite having
remarkable similarities in the plane of the sky in terms of shape, size, and
extinction, California has an order of magnitude lower star formation
efficiency. We use our state-of-the-art dust mapping technique to derive the
detailed three-dimensional (3D) structure of the two clouds, taking into
account both distance and extinction uncertainties, and a full 3D spatial
correlation between neighbouring points. We discover that, despite the apparent
filamentary structure in the plane of the sky, California is a flat 120-pc-long
sheet extending from 410 to 530 $pc$. We show that not only Orion A and
California differ substantially in their 3D shapes, but also Orion A has
considerably higher density substructures in 3D than California. This result
presents a compelling reason why the two clouds have different star formation
activities. We also demonstrate how the viewing angle of California can
substantially change the cloud's position in the Kennicutt-Schmidt relation.
This underlines the importance of 3D information in interpreting star formation
relations and challenges studies that rely solely on the column density
thresholds to determine star formation activities in molecular clouds. Finally,
we provide accurate distance estimates to multiple lines of sight towards
various parts of the two clouds.


title:
Locally Aggregated Feature Attribution on Natural Language Model
  Understanding

abstract:
  With the growing popularity of deep-learning models, model understanding
becomes more important. Much effort has been devoted to demystify deep neural
networks for better interpretability. Some feature attribution methods have
shown promising results in computer vision, especially the gradient-based
methods where effectively smoothing the gradients with reference data is key to
a robust and faithful result. However, direct application of these
gradient-based methods to NLP tasks is not trivial due to the fact that the
input consists of discrete tokens and the "reference" tokens are not explicitly
defined. In this work, we propose Locally Aggregated Feature Attribution
(LAFA), a novel gradient-based feature attribution method for NLP models.
Instead of relying on obscure reference tokens, it smooths gradients by
aggregating similar reference texts derived from language model embeddings. For
evaluation purpose, we also design experiments on different NLP tasks including
Entity Recognition and Sentiment Analysis on public datasets as well as key
feature detection on a constructed Amazon catalogue dataset. The superior
performance of the proposed method is demonstrated through experiments.


title: Quantum Control Noise Spectroscopy with Optimal Suppression of Dephasing

abstract:
  We extend quantum noise spectroscopy (QNS) of amplitude control noise to
settings where dephasing noise or detuning errors make significant
contributions to qubit dynamics. Previous approaches to characterize amplitude
noise are limited by their vulnerability to low-frequency dephasing noise and
static detuning errors, which can overwhelm the target control noise signal and
introduce bias into estimates of the amplitude noise spectrum. To overcome this
problem, we leverage optimal control to identify a family of amplitude control
waveforms that optimally suppress low-frequency dephasing noise and detuning
errors, while maintaining the spectral concentration in the amplitude filter
essential for spectral estimation. The waveforms found via numerical
optimization have surprisingly simple analytic forms, consisting of oscillating
sine waves obeying particular amplitude and frequency constraints. In
numerically simulated QNS experiments, these waveforms demonstrate superior
robustness, enabling accurate estimation of the amplitude noise spectrum in
regimes where existing approaches are biased by low-frequency dephasing noise
and detuning errors.


title: Hole-initiated melting process of thin films

abstract:
  It is generally believed that melting of a thin film starts from a small
defect, e.g., a hole on the film that acts as the nucleation site. We show in
this study that when the contribution of the capillary surface (liquid/air
interface) is taken into consideration, the melting process is non-trivial: 1)
There may be multiple melting pathways for one system, and hence different "de
facto" melting points. 2) Melting of a thin film may start from the long,
straight edge rather than the small holes if the edge facet wets better than
the substrate. This is verified by experimental data from melting alkane layers
between silica and air. Our findings provides a general approach for solving
the non-trivial melting scenarios in systems with capillary surfaces.


title:
Efficient dynamical low-rank approximation for the
  Vlasov-Amp\`{e}re-Fokker-Planck system

abstract:
  Kinetic equations are difficult to solve numerically due to their high
dimensionality. A promising approach for reducing computational cost is the
dynamical low-rank algorithm, which decouples the dimensions of the phase space
by proposing an ansatz as the sum of separable (rank-1) functions in position
and velocity respectively. The fluid asymptotic limit of collisional kinetic
equations, obtained in the small-Knudsen number limit, admits a low-rank
representation when written as $f = Mg$, where $M$ is the local Maxwellian, and
$g$ is low-rank. We apply this decomposition to the
Vlasov-Amp\`{e}re-Fokker-Planck equation of plasma dynamics, considering the
asymptotic limit of strong collisions and electric field. We implement our
proposed algorithm and demonstrate the expected improvement in computation time
by comparison to an implementation that evolves the full solution tensor $f$.
We also demonstrate that our algorithm can capture dynamics in both the kinetic
regime, and in the fluid regime with relatively lower computational effort,
thereby efficiently capturing the asymptotic fluid limit.


title: Welfare effects of strategic voting under scoring rules

abstract:
  Strategic voting, or manipulation, is the process by which a voter
misrepresents his preferences in an attempt to elect an outcome that he
considers preferable to the outcome under sincere voting. It is generally
agreed that manipulation is a negative feature of elections, and much effort
has been spent on gauging the vulnerability of voting rules to manipulation.
However, the question of why manipulation is actually bad is less commonly
asked. One way to measure the effect of manipulation on an outcome is by
comparing a numeric measure of social welfare under sincere behaviour to that
in the presence of a manipulator. In this paper we conduct numeric experiments
to assess the effects of manipulation on social welfare under scoring rules. We
find that manipulation is usually negative, and in most cases the optimum rule
with a manipulator is different to the one with sincere voters.


title:
Roofline Model for UAVs: A Bottleneck Analysis Tool for Onboard Compute
  Characterization of Autonomous Unmanned Aerial Vehicles

abstract:
  We introduce an early-phase bottleneck analysis and characterization model
called the F-1 for designing computing systems that target autonomous Unmanned
Aerial Vehicles (UAVs). The model provides insights by exploiting the
fundamental relationships between various components in the autonomous UAV,
such as sensor, compute, and body dynamics. To guarantee safe operation while
maximizing the performance (e.g., velocity) of the UAV, the compute, sensor,
and other mechanical properties must be carefully selected or designed. The F-1
model provides visual insights that can aid a system architect in understanding
the optimal compute design or selection for autonomous UAVs. The model is
experimentally validated using real UAVs, and the error is between 5.1\% to
9.5\% compared to real-world flight tests. An interactive web-based tool for
the F-1 model called Skyline is available for free of cost use at:
~\url{https://bit.ly/skyline-tool}


title:
Comparative Study of Machine Learning Test Case Prioritization for
  Continuous Integration Testing

abstract:
  There is a growing body of research indicating the potential of machine
learning to tackle complex software testing challenges. One such challenge
pertains to continuous integration testing, which is highly time-constrained,
and generates a large amount of data coming from iterative code commits and
test runs. In such a setting, we can use plentiful test data for training
machine learning predictors to identify test cases able to speed up the
detection of regression bugs introduced during code integration. However,
different machine learning models can have different fault prediction
performance depending on the context and the parameters of continuous
integration testing, for example variable time budget available for continuous
integration cycles, or the size of test execution history used for learning to
prioritize failing test cases. Existing studies on test case prioritization
rarely study both of these factors, which are essential for the continuous
integration practice. In this study we perform a comprehensive comparison of
the fault prediction performance of machine learning approaches that have shown
the best performance on test case prioritization tasks in the literature. We
evaluate the accuracy of the classifiers in predicting fault-detecting tests
for different values of the continuous integration time budget and with
different length of test history used for training the classifiers. In
evaluation, we use real-world industrial datasets from a continuous integration
practice. The results show that different machine learning models have
different performance for different size of test history used for model
training and for different time budget available for test case execution. Our
results imply that machine learning approaches for test prioritization in
continuous integration testing should be carefully configured to achieve
optimal performance.


title:
Exponential dichotomy for dynamically defined matrix-valued Jacobi
  operators

abstract:
  We present in this work a proof of the exponential dichotomy for dynamically
defined matrix-valued Jacobi operators in $(\mathbb{C}^{l})^{\mathbb{Z}}$,
given for each $\omega \in \Omega$ by the law $[H_{\omega} \textbf{u}]_{n} :=
D(T^{n - 1}\omega) \textbf{u}_{n - 1} + D(T^{n}\omega) \textbf{u}_{n + 1} +
V(T^{n}\omega) \textbf{u}_{n}$, where $\Omega$ is a compact metric space, $T:
\Omega \rightarrow \Omega$ is a minimal homeomorphism and $D, V: \Omega
\rightarrow M(l, \mathbb{R})$ are continuous maps with $D(\omega)$ invertible
for each $\omega\in\Omega$. Namely, we show that for each $\omega\in\Omega$,
  \[\rho(H_{\omega})=\{z \in \mathbb{C}\mid (T, A_z)\;\mathrm{is\; uniformly\;
hyperbolic}\}, \] where $\rho(H_{\omega})$ is the resolvent set of $H_\omega$
and $(T, A_z)$ is the $SL(2l,\mathbb{C})$-cocycle induced by the eigenvalue
equation $[H_\omega u]_n=zu_n$ at $z\in\mathbb{C}$.


title: Nested Papercrafts for Anatomical and Biological Edutainment

abstract:
  In this paper, we present a new workflow for the computer-aided generation of
physicalizations, addressing nested configurations in anatomical and biological
structures. Physicalizations are an important component of anatomical and
biological education and edutainment. However, existing approaches have mainly
revolved around creating data sculptures through digital fabrication. Only a
few recent works proposed computer-aided pipelines for generating sculptures,
such as papercrafts, with affordable and readily available materials.
Papercraft generation remains a challenging topic by itself. Yet, anatomical
and biological applications pose additional challenges, such as reconstruction
complexity and insufficiency to account for multiple, nested structures--often
present in anatomical and biological structures. Our workflow comprises the
following steps: (i) define the nested configuration of the model and detect
its levels, (ii) calculate the viewpoint that provides optimal, unobstructed
views on inner levels, (iii) perform cuts on the outer levels to reveal the
inner ones based on the viewpoint selection, (iv) estimate the stability of the
cut papercraft to ensure a reliable outcome, (v) generate textures at each
level, as a smart visibility mechanism that provides additional information on
the inner structures, and (vi) unfold each textured mesh guaranteeing
reconstruction. Our novel approach exploits the interactivity of nested
papercraft models for edutainment purposes.


title:
Transfer Learning from Synthetic In-vitro Soybean Pods Dataset for
  In-situ Segmentation of On-branch Soybean Pod

abstract:
  The mature soybean plants are of complex architecture with pods frequently
touching each other, posing a challenge for in-situ segmentation of on-branch
soybean pods. Deep learning-based methods can achieve accurate training and
strong generalization capabilities, but it demands massive labeled data, which
is often a limitation, especially for agricultural applications. As lacking the
labeled data to train an in-situ segmentation model for on-branch soybean pods,
we propose a transfer learning from synthetic in-vitro soybean pods. First, we
present a novel automated image generation method to rapidly generate a
synthetic in-vitro soybean pods dataset with plenty of annotated samples. The
in-vitro soybean pods samples are overlapped to simulate the frequently
physically touching of on-branch soybean pods. Then, we design a two-step
transfer learning. In the first step, we finetune an instance segmentation
network pretrained by a source domain (MS COCO dataset) with a synthetic target
domain (in-vitro soybean pods dataset). In the second step, transferring from
simulation to reality is performed by finetuning on a few real-world mature
soybean plant samples. The experimental results show the effectiveness of the
proposed two-step transfer learning method, such that AP$_{50}$ was 0.80 for
the real-world mature soybean plant test dataset, which is higher than that of
direct adaptation and its AP$_{50}$ was 0.77. Furthermore, the visualizations
of in-situ segmentation results of on-branch soybean pods show that our method
performs better than other methods, especially when soybean pods overlap
densely.


title: Chiral magnetic effect in heavy ion collisions and beyond

abstract:
  Chirality is a ubiquitous concept in modern science, from particle physics to
biology. In quantum physics, chirality of fermions is linked to topology of
gauge fields by the chiral anomaly. While the chiral anomaly is usually
associated with the short-distance behavior in field theory, in recent years it
has been realized that it also affects the macroscopic behavior of systems with
chiral fermions. In particular, the local imbalance between left- and
right-handed fermions in the presence of a magnetic field induces
non-dissipative transport of electric charge ("the Chiral Magnetic Effect",
CME). In heavy ion collisions, there is an ongoing search for this effect at
Relativistic Heavy Ion Collider, with results from a dedicated isobar run
presented very recently. An observation of CME in heavy ion collisions could
shed light on the mechanism of baryon asymmetry generation in the Early
Universe. Recently, the CME has been discovered in Dirac and Weyl semimetals
possessing chiral quasi-particles. This observation opens a path towards
quantum sensors, and potentially a new kind of quantum computers.


title: Neural-Network Decoders for Measurement Induced Phase Transitions

abstract:
  Open quantum systems have been shown to host a plethora of exotic dynamical
phases. Measurement-induced entanglement phase transitions in monitored quantum
systems are a striking example of this phenomena. However, naive realizations
of such phase transitions requires an exponential number of repetitions of the
experiment which is practically unfeasible on large systems. Recently, it has
been proposed that these phase transitions can be probed locally via entangling
reference qubits and studying their purification dynamics. In this work, we
leverage modern machine learning tools to devise a neural network decoder to
determine the state of the reference qubits conditioned on the measurement
outcomes. We show that the entanglement phase transition manifests itself as a
stark change in the learnability of the decoder function. We study the
complexity and scalability of this approach and discuss how it can be utilized
to detect entanglement phase transitions in generic experiments.

categories: quant-ph cond-mat.dis-nn cond-mat.stat-mech cond-mat.str-el


title:
Label a Herd in Minutes: Individual Holstein-Friesian Cattle
  Identification

abstract:
  We describe a practically evaluated approach for training visual cattle ID
systems for a whole farm requiring only ten minutes of labelling effort. In
particular, for the task of automatic identification of individual
Holstein-Friesians in real-world farm CCTV, we show that self-supervision,
metric learning, cluster analysis, and active learning can complement each
other to significantly reduce the annotation requirements usually needed to
train cattle identification frameworks. Evaluating the approach on the test
portion of the publicly available Cows2021 dataset, for training we use 23,350
frames across 435 single individual tracklets generated by automated oriented
cattle detection and tracking in operational farm footage. Self-supervised
metric learning is first employed to initialise a candidate identity space
where each tracklet is considered a distinct entity. Grouping entities into
equivalence classes representing cattle identities is then performed by
automated merging via cluster analysis and active learning. Critically, we
identify the inflection point at which automated choices cannot replicate
improvements based on human intervention to reduce annotation to a minimum.
Experimental results show that cluster analysis and a few minutes of labelling
after automated self-supervision can improve the test identification accuracy
of 153 identities to 92.44% (ARI=0.93) from the 74.9% (ARI=0.754) obtained by
self-supervision only. These promising results indicate that a tailored
combination of human and machine reasoning in visual cattle ID pipelines can be
highly effective whilst requiring only minimal labelling effort. We provide all
key source code and network weights with this paper for easy result
reproduction.


title: Improved Network Calculus Delay Bounds in Time-Sensitive Networks

abstract:
  In time-sensitive networks, bounds on worst-case delays are typically
obtained by using network calculus and assuming that flows are constrained by
bit-level arrival curves. However, in IEEE TSN or IETF DetNet, source flows are
constrained on the number of packets rather than bits. A common approach to
obtain a delay bound is to derive a bit-level arrival curve from a packet-level
arrival curve. However, such a method is not tight: we show that better bounds
can be obtained by directly exploiting the arrival curves expressed at the
packet level. Our analysis method also obtains better bounds when flows are
constrained with g-regulation, such as the recently proposed Length-Rate
Quotient rule. It can also be used to generalize some recently proposed
network-calculus delay-bounds for a service curve element with known
transmission rate.


title: Mapping cell cortex rheology to tissue rheology, and vice-versa

abstract:
  The mechanics of biological tissues mainly proceeds from the cell cortex
rheology. A direct, explicit link between cortex rheology and tissue rheology
remains lacking, yet would be instrumental in understanding how modulations of
cortical mechanics may impact tissue mechanical behaviour. Using an ordered
geometry built on 3D hexagonal, incompressible cells, we build a mapping
relating the cortical rheology to the monolayer tissue rheology. Our approach
shows that the tissue low frequency elastic modulus is proportional to the rest
tension of the cortex, as expected from the physics of liquid foams. In
agreement with recent experimental observations, we show that a fractional
visco-elastic monolayer rheology may emerge from a fractional visco-contractile
cortex rheology. In particular cases, the mapping may be inverted, allowing to
derive from a given tissue rheology the underlying cortex rheology.
Interestingly, applying the same approach to a 2D hexagonal tiling fails, which
suggests that the 2D character of planar cell cortex-based models may be
unsuitable to account for realistic monolayer rheologies. We provide
quantitative predictions, amenable to experimental tests through standard
perturbation assays of cortex constituents, and hope to foster new, challenging
mechanical experiments on cell monolayers.


title: Birefringent Rydberg Dark Matter from Cosmic Dust

abstract:
  A recently published study of dark matter in distant galaxies has found a
direct interaction between unknown dark matter particles and ordinary galactic
baryonic matter. It is proposed here that the dark matter is Rydberg Matter
composed of water nanoclusters ejected from abundant amorphous water-ice-coated
cosmic dust, leading to non-gravitational interactions with ordinary matter.
This may also clarify recently observed CMB birefringence, suggesting possible
new physics such as quintessence.


title: Error-in-variables modelling for operator learning

abstract:
  Deep operator learning has emerged as a promising tool for reduced-order
modelling and PDE model discovery. Leveraging the expressive power of deep
neural networks, especially in high dimensions, such methods learn the mapping
between functional state variables. While proposed methods have assumed noise
only in the dependent variables, experimental and numerical data for operator
learning typically exhibit noise in the independent variables as well, since
both variables represent signals that are subject to measurement error. In
regression on scalar data, failure to account for noisy independent variables
can lead to biased parameter estimates. With noisy independent variables,
linear models fitted via ordinary least squares (OLS) will show attenuation
bias, wherein the slope will be underestimated. In this work, we derive an
analogue of attenuation bias for linear operator regression with white noise in
both the independent and dependent variables. In the nonlinear setting, we
computationally demonstrate underprediction of the action of the Burgers
operator in the presence of noise in the independent variable. We propose
error-in-variables (EiV) models for two operator regression methods,
MOR-Physics and DeepONet, and demonstrate that these new models reduce bias in
the presence of noisy independent variables for a variety of operator learning
problems. Considering the Burgers operator in 1D and 2D, we demonstrate that
EiV operator learning robustly recovers operators in high-noise regimes that
defeat OLS operator learning. We also introduce an EiV model for time-evolving
PDE discovery and show that OLS and EiV perform similarly in learning the
Kuramoto-Sivashinsky evolution operator from corrupted data, suggesting that
the effect of bias in OLS operator learning depends on the regularity of the
target operator.


title:
Evaluating the jet/accretion coupling of Aql X-1: probing the
  contribution of accretion flow spectral components

abstract:
  The coupling between radio and X-ray luminosity is an important diagnostic
tool to study the connection between the accretion inflow and jet outflow for
low-mass X-ray binaries (LMXBs). When comparing NS- and BH-LMXBs, we find that
the radio/X-ray correlation for individual NS-LMXBs is scattered, whereas for
individual BH-LMXBs a more consistent correlation is generally found.
Furthermore, we observe jet quenching for both types of LMXBs, but it is
unclear what exactly causes this, and if jets in NS-LMXBs quench as strongly as
those in BH-LMXBs. While additional soft X-ray spectral components can be
present for NS-LMXBs due to the presence of the neutron star's surface,
disentangling the individual X-ray spectral components has thus far not been
considered when studying the radio/X-ray coupling. Here we present eleven
epochs of Swift/XRT observations matched with quasi-simultaneous archival radio
observations of the 2009 November outburst of Aql X-1. We decompose the thermal
and Comptonised spectral components in the Swift/XRT spectra, with the aim of
studying whether the presence of additional thermal emission affects the
coupling of the radio/X-ray luminosity. We find that there is no evidence of a
significant thermal contribution in Swift/XRT spectra that could cause scatter
in the radio/X-ray coupling. However, a noteworthy finding is that the X-ray
observation with the strongest statistically significant thermal component
occurs around the same epoch as a bright radio detection. Follow-up research
using more sensitive X-ray observations combined with densely sampled
near-simultaneous radio observations is required to explore the radio/X-ray
coupling for NS-LMXBs in more detail.


title:
Die Einfl\"usse von Arbeitsbelastung auf die Arbeitsqualit\"at agiler
  Software-Entwicklungsteams

abstract:
  Due to the Covid 19 pandemic and the associated effects on the world of work,
the burden on employees has been brought into focus. This fact also applies to
agile software development teams in many companies due to the extensive switch
to remote work. Too high a workload can lead to various negative effects, such
as increased sick leave, the well-being of employees, or reduced productivity.
It is also known that the workload in knowledge work impacts the quality of the
work results. This research article identifies potential factors of the
workload of the agile software development team members at Otto GmbH & Co KG.
Based on the factors, we present measures to reduce workload and explain our
findings, which we have validated in an experiment. Our results show that even
small-scale actions, such as the introduction of rest work phases during the
working day, lead to positive effects, for example, increased ability to
concentrate and how these affect the quality of the work results.


title:
Robustness-by-Construction Synthesis: Adapting to the Environment at
  Runtime

abstract:
  While most of the current synthesis algorithms only focus on
correctness-by-construction, ensuring robustness has remained a challenge.
Hence, in this paper, we address the robust-by-construction synthesis problem
by considering the specifications to be expressed by a robust version of Linear
Temporal Logic (LTL), called robust LTL (rLTL). rLTL has a many-valued
semantics to capture different degrees of satisfaction of a specification,
i.e., satisfaction is a quantitative notion.
  We argue that the current algorithms for rLTL synthesis do not compute
optimal strategies in a non-antagonistic setting. So, a natural question is
whether there is a way of satisfying the specification "better" if the
environment is indeed not antagonistic. We address this question by developing
two new notions of strategies. The first notion is that of adaptive strategies,
which, in response to the opponent's non-antagonistic moves, maximize the
degree of satisfaction. The idea is to monitor non-optimal moves of the
opponent at runtime using multiple parity automata and adaptively change the
system strategy to ensure optimality. The second notion is that of strongly
adaptive strategies, which is a further refinement of the first notion. These
strategies also maximize the opportunities for the opponent to make non-optimal
moves. We show that computing such strategies for rLTL specifications is not
harder than the standard synthesis problem, e.g., computing strategies with LTL
specifications, and takes doubly-exponential time.


title:
Adaptive robust electric vehicle routing under energy consumption
  uncertainty

abstract:
  Electric vehicles (EVs) have been highly favoured as a future transportation
mode in the transportation section in recent years. EVs have many advantages
compared to traditional transportation, especially the environmental aspect.
However, despite many EVs' benefits, operating EVs has limitations in their
usage. One of the significant issues is the uncertainty in their driving range.
The driving range of EVs is closely related to their energy consumption, which
is highly affected by exogenous and endogenous factors. Since those factors are
unpredictable, uncertainty in EVs' energy consumption should be considered for
efficient operation. This paper proposes an adaptive robust optimization
framework for the electric vehicle routing problem. The objective is to
minimize the worst-case energy consumption while guaranteeing that services are
delivered at the appointed time windows without battery level deficiency. We
postulate that EVs can be recharged en route, and the charging amount can be
adjusted depending on the circumstance. The proposed problem is formulated as a
two-stage adaptive robust problem. A column-and-constraint generation based
heuristic algorithm, which is cooperated with variable neighborhood search and
alternating direction algorithm, is proposed to solve the proposed model. The
computational results show the economic efficiency and robustness of the
proposed model, and that there is a tradeoff between the total required energy
and the risk of failing to satisfy all customers' demand.


title:
Latency Analysis of Vehicle-to-Pedestrian C-V2X Communications at Urban
  Street Intersections

abstract:
  Cellular Vehicle-to-Everything (C-V2X) technology promises to provide
ultra-reliable low latency communication (URLLC) framework for connected
vehicles. Connected vehicles can help us improve traffic safety, congestion and
reduce fatal accidents. C-V2X promises new levels of connectivity and
intelligence providing numerous features like infotainment, always-on
telematics, real-time navigation, etc. with heterogeneous network architecture.
C-V2X leverages existing cellular architecture and is on the way to replace
DSRC/WAVE for vehicular communication. There are still some challenges which
needs to be tackled before C-V2X can be deployed for efficient on-road V2X. In
this paper, we look at end-to-end latency for Vehicle-to-Pedestrian (V2P) which
demands ultra low latencies with reliable packet delivery ratio. To evaluate
the LTE-V2P performance, we used network simulator for an urban intersection
scenario where vulnerable road-side users (VRUs) are trying to cross the street
and they communicate with moving vehicles. We evaluated the end-to-end latency
and throughput for the given scenario and concluded that with the existing
network architecture the latency is high. By utilizing multi-edge access
computing (MEC) servers latency can be reduce drastically and hence can be made
feasible for cellular V2P communication.


title: On Extrapolation of Carleson Measures

abstract:
  We give an alternate proof of three versions of the theorem on extrapolation
of Carleson measures.


title: Revealing Occlusions with 4D Neural Fields

abstract:
  For computer vision systems to operate in dynamic situations, they need to be
able to represent and reason about object permanence. We introduce a framework
for learning to estimate 4D visual representations from monocular RGB-D, which
is able to persist objects, even once they become obstructed by occlusions.
Unlike traditional video representations, we encode point clouds into a
continuous representation, which permits the model to attend across the
spatiotemporal context to resolve occlusions. On two large video datasets that
we release along with this paper, our experiments show that the representation
is able to successfully reveal occlusions for several tasks, without any
architectural changes. Visualizations show that the attention mechanism
automatically learns to follow occluded objects. Since our approach can be
trained end-to-end and is easily adaptable, we believe it will be useful for
handling occlusions in many video understanding tasks. Data, code, and models
are available at https://occlusions.cs.columbia.edu/.


title: Coefficient of restitution of a linear dashpot on a rigid surface

abstract:
  The linear dashpot model is applied to a single ball bouncing on a rigid
surface. It is shown that when gravity is included the coefficient of
restitution depends on impact velocity, in contrast to previous work that
ignored the effects of gravity. This velocity dependence is most pronounced at
low impact velocities and high damping. Previous work has considered the ball
to be in contact with the floor when the compression is nonzero, while other
analysis terminates the collision earlier, to prevent an attractive force. We
compare these models and propose a hybrid between the two. The hybrid model is
successful in reproducing experimental results for a cart bouncing repeatedly
on a spring.


title: Extensivity of categories of relational structures

abstract:
  We prove that the category of models of any relational Horn theory satisfying
a mild syntactic condition is infinitely extensive. Central examples of such
categories include the categories of preordered sets and partially ordered
sets, and the categories of small $\mathscr{V}$-categories, (symmetric)
pseudo-$\mathscr{V}$-metric spaces, and (symmetric) $\mathscr{V}$-metric spaces
for a commutative unital quantale $\mathscr{V}$. We also explicitly
characterize initial sources and final sinks in such categories, and in
particular embeddings and quotients.


title: The Outer Edge of the Venus Zone Around Main-Sequence Stars

abstract:
  A key item of interest for planetary scientists and astronomers is the
habitable zone, or the distance from a host star where a terrestrial planet can
maintain necessary temperatures in order to retain liquid water on its surface.
However, when observing a system's habitable zone, it is possible that one may
instead observe a Venus-like planet. We define "Venus-like" as
greenhouse-gas-dominated atmosphere occurring when incoming solar radiation
exceeds infrared radiation emitted from the planet at the top of the
atmosphere, resulting in a runaway greenhouse. Our definition of Venus-like
includes both incipient and post-runaway greenhouse states. Both the
possibility of observing a Venus-like world and the possibility that Venus
could represent an end-state of evolution for habitable worlds, requires an
improved understanding of the Venus-like planet; specifically, the distances
where these planets can exist. Understanding this helps us define a "Venus
zone", or the region in which Venus-like planets could exist, and assess the
overlap with the aforementioned "Habitable Zone". In this study, we use a 1D
radiative-convective climate model to determine the outer edge of the Venus
zone for F0V, G2V, K5V, and M3V and M5V stellar spectral types. Our results
show that the outer edge of the Venus zone resides at 3.01, 1.36, 0.68, 0.23,
and 0.1 AU, respectively. These correspond to incident stellar fluxes of 0.8,
0.55, 0.38, 0.32, and 0.3 S, respectively, where stellar flux is relative to
Earth (1.0). These results indicate that there may be considerable overlap
between the habitable zone and the Venus zone.


title:
Your Echos are Heard: Tracking, Profiling, and Ad Targeting in the
  Amazon Smart Speaker Ecosystem

abstract:
  Smart speakers collect voice input that can be used to infer sensitive
information about users. Given a number of egregious privacy breaches, there is
a clear unmet need for greater transparency and control over data collection,
sharing, and use by smart speaker platforms as well as third party skills
supported on them. To bridge the gap, we build an auditing framework that
leverages online advertising to measure data collection, its usage, and its
sharing by the smart speaker platforms. We evaluate our framework on the Amazon
smart speaker ecosystem. Our results show that Amazon and third parties
(including advertising and tracking services) collect smart speaker interaction
data. We find that Amazon processes voice data to infer user interests and uses
it to serve targeted ads on-platform (Echo devices) as well as off-platform
(web). Smart speaker interaction leads to as much as 30X higher ad bids from
advertisers. Finally, we find that Amazon's and skills' operational practices
are often not clearly disclosed in their privacy policies.


title:
Subscriptions and external links help drive resentful users to
  alternative and extremist YouTube videos

abstract:
  Do online platforms facilitate the consumption of potentially harmful
content? Despite widespread concerns that YouTube's algorithms send people down
"rabbit holes" with recommendations to extremist videos, little systematic
evidence exists to support this conjecture. Using paired behavioral and survey
data provided by participants recruited from a representative sample (n=1,181),
we show that exposure to alternative and extremist channel videos on YouTube is
heavily concentrated among a small group of people with high prior levels of
gender and racial resentment. These viewers typically subscribe to these
channels (causing YouTube to recommend their videos more often) and often
follow external links to them. Contrary to the "rabbit holes" narrative,
non-subscribers are rarely recommended videos from alternative and extremist
channels and seldom follow such recommendations when offered.


title:
Investigating $R_D$ and $R_{D^*}$ anomalies in a Left-Right model with
  an Inverse Seesaw

abstract:
  We investigate $R_D$ and $R_{D^*}$ anomalies in a low scale left-right
symmetric model based on $SU(3)_C\times SU(2)_L\times SU(2)_R\times
U(1)_{B-L}\times Z_2$ with a simplified Higgs sector consisting of only one
bidoublet and one $SU(2)_R$ doublet. The Wilson coefficients relevant to the
transition $b\to c\tau \nu$ are derived by integrating out the charged Higgs
$H^\pm$ boson, which gives the dominant contributions. We emphasize that the
charged Higgs effects, with the complex right-handed quark mixing matrix, can
account for both $R_D$ and $R_{D^*}$ anomalies simultaneously, while adhering
to the constraints from ${\rm BR}(B^-_c \to \tau^- \bar{\nu}_\tau) $.


title: You Only Linearize Once: Tangents Transpose to Gradients

abstract:
  Automatic differentiation (AD) is conventionally understood as a family of
distinct algorithms, rooted in two "modes" -- forward and reverse -- which are
typically presented (and implemented) separately. Can there be only one?
Following up on the AD systems developed in the JAX and Dex projects, we
formalize a decomposition of reverse-mode AD into (i) forward-mode AD followed
by (ii) unzipping the linear and non-linear parts and then (iii) transposition
of the linear part.
  To that end, we use the technology of linear types to formalize a notion of
structurally linear functions, which are then also algebraically linear. Our
main results are that forward-mode AD produces structurally linear functions,
and that we can unzip and transpose any structurally linear function,
conserving cost, size, and structural linearity. Composing these three
transformations recovers reverse-mode AD. This decomposition also sheds light
on checkpointing, which emerges naturally from a free choice in unzipping let
expressions. As a corollary, checkpointing techniques are applicable to
general-purpose partial evaluation, not just AD.
  We hope that our formalization will lead to a deeper understanding of
automatic differentiation and that it will simplify implementations, by
separating the concerns of differentiation proper from the concerns of gaining
efficiency (namely, separating the derivative computation from the act of
running it backward).


title:
Reflection theorems of Ohno-Nakagawa type for quartic rings and pairs of
  $n$-ary quadratic forms

abstract:
  We prove a reflection theorem, conjectured by Nakagawa and Ohno, for the
number of quartic rings, or pairs of ternary quadratic forms, with a given
cubic resolvent. Over $\mathbb{Z}$, our results are unconditional; we also
allow the base to be the ring of integers of a general number field,
conditional on some algebraic identities that are Monte Carlo verified. We also
establish a reflection theorem for quartic $11111$-forms and $48441$-forms that
relates them to the number of $3\times 3$ symmetric matrices with given
characteristic polynomial. Along the way, we find elegant new results on Igusa
zeta functions of conics and the average value of a quadratic character over a
box in a local field.
  We conjecture that a reflection theorem holds for pairs of $n$-ary quadratic
forms for any odd $n$, and we prove this for odd cubefree discriminant. This
furnishes a more satisfactory answer for a question raised by Cohen, Diaz y
Diaz, and Olivier, namely whether there exist an infinite family of reflection
theorems of Ohno-Nakagawa type.


title:
Superconductivity and phase diagrams of
  CaK(Fe$_{1-x}$Mn$_{x}$)$_{4}$As$_{4}$ single crystals

abstract:
  Members of the CaK(Fe$_{1-x}$Mn$_{x}$)$_{4}$As$_{4}$ series have been
synthesized in single crystalline form and characterized by elemental analysis,
thermodynamic and transport measurements. These measurements show that the
superconducting transition temperature decreases monotonically and is finally
suppressed below 1.8 K. For $x$-values greater than 0.016, signatures of a
magnetic transition can be detected in both thermodynamic and transport
measurements in which kink-like features allow for the determination of the
transition temperature, $T^*$, that increases as Mn substitution increases. A
temperature-composition ($T$-$x$) phase diagram is constructed, revealing a
half-dome of superconductivity with the magnetic transition temperature, $T^*$,
appearing near 26 K for $x$ $\sim$ 0.017 and rising slowly up to 33 K for $x$
$\sim$ 0.036. Specific heat data are used to track the jump in specific heat at
$T_c$; The CaK(Fe$_{1-x}$Mn$_x$)$_4$As$_4$ data does not follow the scaling of
$\Delta$$C_{p}$ with $T_{c}^3$ as many of the other Fe-based superconducting
systems do. Elastoresistivity coefficients, $2m_{66}$ and $m_{11}-m_{12}$, as a
function of temperature are also measured. $2m_{66}$ and $m_{11}-m_{12}$ are
qualitatively similar to CaK(Fe$_{1-x}$Ni$_x$)$_4$As$_4$. This may indicate
that the magnetic order in Mn substituted system may be still the same as
CaK(Fe$_{1-x}$Ni$_x$)$_4$As$_4$. A clear change in $H^\prime_{c2}$($T$)/$T_c$,
where $H^\prime_{c2}$($T$) is d$H_{c2}$($T$)/d$T$, at $x$ $\sim$ 0.015 is
observed and probably is related to change of the Fermi surface due to magnetic
order. Coherence lengths and the London penetration depths are also calculated
based on $H_{c1}$ and $H_{c2}$ data. Coherence lengths as the function of $x$
also shows the changes near $x$ = 0.015.


title:
SegDiscover: Visual Concept Discovery via Unsupervised Semantic
  Segmentation

abstract:
  Visual concept discovery has long been deemed important to improve
interpretability of neural networks, because a bank of semantically meaningful
concepts would provide us with a starting point for building machine learning
models that exhibit intelligible reasoning process. Previous methods have
disadvantages: either they rely on labelled support sets that incorporate human
biases for objects that are "useful," or they fail to identify multiple
concepts that occur within a single image. We reframe the concept discovery
task as an unsupervised semantic segmentation problem, and present SegDiscover,
a novel framework that discovers semantically meaningful visual concepts from
imagery datasets with complex scenes without supervision. Our method contains
three important pieces: generating concept primitives from raw images,
discovering concepts by clustering in the latent space of a self-supervised
pretrained encoder, and concept refinement via neural network smoothing.
Experimental results provide evidence that our method can discover multiple
concepts within a single image and outperforms state-of-the-art unsupervised
methods on complex datasets such as Cityscapes and COCO-Stuff. Our method can
be further used as a neural network explanation tool by comparing results
obtained by different encoders.


title:
A spintronic analog of the Landauer residual resistivity dipole on the
  surface of a disordered topological insulator

abstract:
  The Landauer "residual resistivity dipole" is a well-known concept in
electron transport through a disordered medium. It is formed when a
defect/scatterer reflects an impinging electron causing negative charges to
build up on one side of the scatterer and positive charges on the other. This
results in the formation of a microscopic electric dipole that affects the
resistivity of the medium. Here, we show that an equivalent entity forms in
spin polarized electron transport through the surface of a disordered
topological insulator (TI). When electrons reflect from a scatterer on the TI
surface, a spin imbalance forms around the scatterer, resulting in a spin
current that flows either in the same or the opposite direction as the injected
spin current and hence either increases or decreases the spin resistivity. It
also destroys spin-momentum locking and produces a magnetic field around the
scatterer. The latter will cause transiting spins to precess as they pass the
scatterer, thereby radiating electromagnetic waves and implementing an
oscillator. If an alternating current is passed through the TI instead of a
static current, the magnetic field will oscillate with the frequency of the
current and radiate electromagnetic waves of the same frequency, thus making
the scatterer act as a miniature antenna.


title:
One-transmitter Multiple-receiver Wireless Power Transfer System Using
  an Exceptional Point of Degeneracy

abstract:
  Robust transfer efficiency against the various operating conditions in a
wireless power transfer system remains a fundamentally important challenge.
This challenge becomes even more critical when transferring power to groups of
inductively coupled receivers. We propose a method for efficient wireless power
transfer to multiple receivers exploiting the concept of exceptional points of
degeneracy (EPD). In previous studies based on PT symmetry, a receiver's
operation has been divided into two strong and weak coupling regimes, and the
power transfer efficiency is constant in the strong coupling regime when
varying the coupling factor.Here the concept of strong and weak coupling and
constant power efficiency is extended to a system of multiple receivers that do
not follow PT symmetry. We show that the important feature to have a roughly
constant power efficiency, independently of the positions of the receivers, is
the existence of an EPD that separates the weak and strong regimes. Our
proposed method demonstrates a system with less sensitivity to the coupling
change than a conventional system without EPD when the receivers and their
couplings to the transmitter are not necessarily identical.


title: Bayesian Spatiotemporal Modeling for Inverse Problems

abstract:
  Inverse problems with spatiotemporal observations are ubiquitous in
scientific studies and engineering applications. In these spatiotemporal
inverse problems, observed multivariate time series are used to infer
parameters of physical or biological interests. Traditional solutions for these
problems often ignore the spatial or temporal correlations in the data (static
model), or simply model the data summarized over time (time-averaged model). In
either case, the data information that contains the spatiotemporal interactions
is not fully utilized for parameter learning, which leads to insufficient
modeling in these problems. In this paper, we apply Bayesian models based on
spatiotemporal Gaussian processes (STGP) to the inverse problems with
spatiotemporal data and show that the spatial and temporal information provides
more effective parameter estimation and uncertainty quantification (UQ). We
demonstrate the merit of Bayesian spatiotemporal modeling for inverse problems
compared with traditional static and time-averaged approaches using a
time-dependent advection-diffusion partial different equation (PDE) and three
chaotic ordinary differential equations (ODE). We also provide theoretic
justification for the superiority of spatiotemporal modeling to fit the
trajectories even it appears cumbersome (e.g. for chaotic dynamics).