title: An intuitive picture of the Casimir effect

abstract:
  The Casimir effect, which predicts the emergence of an attractive force
between two parallel, highly reflecting plates in vacuum, plays a vital role in
various fields of physics, from quantum field theory and cosmology to
nanophotonics and condensed matter physics. Nevertheless, Casimir forces still
lack an intuitive explanation and current derivations rely on regularisation
procedures to remove infinities. Starting from special relativity and treating
space and time coordinates equivalently, this paper overcomes no-go theorems of
quantum electrodynamics and obtains a local relativistic quantum description of
the electromagnetic field in free space. When extended to cavities, our
approach can be used to calculate Casimir forces directly in position space
without the introduction of cut-off frequencies.


title:
Velocity continuation with Fourier neural operators for accelerated
  uncertainty quantification

abstract:
  Seismic imaging is an ill-posed inverse problem that is challenged by noisy
data and modeling inaccuracies -- due to errors in the background
squared-slowness model. Uncertainty quantification is essential for determining
how variability in the background models affects seismic imaging. Due to the
costs associated with the forward Born modeling operator as well as the high
dimensionality of seismic images, quantification of uncertainty is
computationally expensive. As such, the main contribution of this work is a
survey-specific Fourier neural operator surrogate to velocity continuation that
maps seismic images associated with one background model to another virtually
for free. While being trained with only 200 background and seismic image pairs,
this surrogate is able to accurately predict seismic images associated with new
background models, thus accelerating seismic imaging uncertainty
quantification. We support our method with a realistic data example in which we
quantify seismic imaging uncertainties using a Fourier neural operator
surrogate, illustrating how variations in background models affect the position
of reflectors in a seismic image.


title: Towards Domain Generalization in Object Detection

abstract:
  Despite the striking performance achieved by modern detectors when training
and test data are sampled from the same or similar distribution, the
generalization ability of detectors under unknown distribution shifts remains
hardly studied. Recently several works discussed the detectors' adaptation
ability to a specific target domain which are not readily applicable in
real-world applications since detectors may encounter various environments or
situations while pre-collecting all of them before training is inconceivable.
In this paper, we study the critical problem, domain generalization in object
detection (DGOD), where detectors are trained with source domains and evaluated
on unknown target domains. To thoroughly evaluate detectors under unknown
distribution shifts, we formulate the DGOD problem and propose a comprehensive
evaluation benchmark to fill the vacancy. Moreover, we propose a novel method
named Region Aware Proposal reweighTing (RAPT) to eliminate dependence within
RoI features. Extensive experiments demonstrate that current DG methods fail to
address the DGOD problem and our method outperforms other state-of-the-art
counterparts.


title: Spontaneous Scalarization of Extremal Black Holes

abstract:
  We study spontaneous scalarization of electrically charged extremal black
holes in $D\geq 4$ spacetime dimensions. Such a phenomenon is caused by the
symmetry breaking due to quartic interactions of the scalar -- Higgs potential
and Stueckelberg interaction with electromagnetic and gravitational fields,
characterized by the couplings $a$ and $b$, respectively. We use the entropy
representation of the states in the vicinity of the horizon, apply the inverse
attractor mechanism for the scalar field, and analyze analytically the
thermodynamic stability of the system using the laws of thermodynamics. As a
result, we obtain that the scalar field condensates on the horizon only in
spacetimes which are asymptotically non-flat, $\Lambda \neq 0$ (dS or AdS), and
whose extremal black holes have non-planar horizons $k=\pm 1$, provided that
the mass $m$ of the scalar field belongs to a mass interval (area code)
different for each set of the boundary conditions specified by $(\Lambda ,k)$.
A process of scalarization describes a second order phase transition of the
black hole, from the extremal Reissner-Nordstr\"{o}m (A)dS one, to the
corresponding extremal hairy one. Furthermore, for the transition to happen,
the interaction has to be strong enough, and all physical quantities on the
horizon depend at most on the effective Higgs-Stueckelberg interaction
$am^2-2b$. Most of our results are general, valid for any parameter and any
spacetime dimension.


title:
Low power In Memory Computation with Reciprocal Ferromagnet/Topological
  Insulator Heterostructures

abstract:
  The surface state of a 3D topological insulator (3DTI) is a spin-momentum
locked conductive state, whose large spin hall angle can be used for the
energy-efficient spin orbit torque based switching of an overlying ferromagnet
(FM). Conversely, the gated switching of the magnetization of a separate FM in
or out of the TI surface plane, can turn on and off the TI surface current. The
gate tunability of the TI Dirac cone gap helps reduce its sub-threshold swing.
By exploiting this reciprocal behaviour, we can use two FM/3DTI
heterostructures to design a 1-Transistor 1-magnetic tunnel junction random
access memory unit (1T1MTJ RAM) for an ultra low power Processing-in-Memory
(PiM) architecture. Our calculation involves combining the Fokker-Planck
equation with the Non-equilibrium Green Function (NEGF) based flow of
conduction electrons and Landau-Lifshitz-Gilbert (LLG) based dynamics of
magnetization. Our combined approach allows us to connect device performance
metrics with underlying material parameters, which can guide proposed
experimental and fabrication efforts.


title: Existence of Life in Lenia

abstract:
  Lenia is a continuous generalization of Conway's Game of Life. Bert Wang-Chak
Chan has discovered and published many seemingly organic dynamics in his Lenia
simulations since 2019. These simulations follow the Euler curve algorithm
starting from function space initial conditions. The Picard-Lindel\"of Theorem
for the existence of integral curves to Lipschitz vector fields on Banach
spaces fails to guarantee solutions, because the vector field associated with
the integro-differential equation defining Lenia is discontinuous. However, we
demonstrate the dynamic Chan is using to generate simulations is actually an
arc field and not the traditional Euler method for the vector field derived
from the integro-differential equation. Using arc field theory we prove the
Euler curves converge to a unique flow which solves the original
integro-differential equation. Extensions are explored and the modeling of
entropy is discussed.
  Keywords: arc fields; discontinuous vector fields; integro-differential
equations; entropy models


title: Bailey pairs and strange identities

abstract:
  Zagier introduced the term "strange identity" to describe an asymptotic
relation between a certain $q$-hypergeometric series and a partial theta
function at roots of unity. We show that behind Zagier's strange identity lies
a statement about Bailey pairs. Using the iterative machinery of Bailey pairs
then leads to many families of multisum strange identities, including Hikami's
generalization of Zagier's identity.


title: Towards physiology-informed data augmentation for EEG-based BCIs

abstract:
  Most EEG-based Brain-Computer Interfaces (BCIs) require a considerable amount
of training data to calibrate the classification model, owing to the high
variability in the EEG data, which manifests itself between participants, but
also within participants from session to session (and, of course, from trial to
trial). In general, the more complex the model, the more data for training is
needed. We suggest a novel technique for augmenting the training data by
generating new data from the data set at hand. Different from existing
techniques, our method uses backward and forward projection using source
localization and a head model to modify the current source dipoles of the
model, thereby generating inter-participant variability in a physiologically
meaningful way. In this manuscript, we explain the method and show first
preliminary results for participant-independent motor-imagery classification.
The accuracy was increased when using the proposed method of data augmentation
by 13, 6 and 2 percentage points when using a deep neural network, a shallow
neural network and LDA, respectively.


title:
Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data
  Products and a Bidirectional LSTM Network

abstract:
  Solar energetic particles (SEPs) are an essential source of space radiation,
which are hazards for humans in space, spacecraft, and technology in general.
In this paper we propose a deep learning method, specifically a bidirectional
long short-term memory (biLSTM) network, to predict if an active region (AR)
would produce an SEP event given that (i) the AR will produce an M- or X-class
flare and a coronal mass ejection (CME) associated with the flare, or (ii) the
AR will produce an M- or X-class flare regardless of whether or not the flare
is associated with a CME. The data samples used in this study are collected
from the Geostationary Operational Environmental Satellite's X-ray flare
catalogs provided by the National Centers for Environmental Information. We
select M- and X-class flares with identified ARs in the catalogs for the period
between 2010 and 2021, and find the associations of flares, CMEs and SEPs in
the Space Weather Database of Notifications, Knowledge, Information during the
same period. Each data sample contains physical parameters collected from the
Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory.
Experimental results based on different performance metrics demonstrate that
the proposed biLSTM network is better than related machine learning algorithms
for the two SEP prediction tasks studied here. We also discuss extensions of
our approach for probabilistic forecasting and calibration with empirical
evaluation.


title: Tightness for Thick Points in two dimensions

abstract:
  Let $W_{t}$ be Brownian motion in the plane started at the origin and let $
\theta$ be the first exit time of the unit disk $D_{1}$. Let
  \[\mu_{ \theta } ( x,\epsilon) =\frac{1}{\pi\epsilon^{ 2} }\int_{0}^{ \theta
}1_{\{ B( x,\epsilon)\}}( W_{t})\,dt,\] and set $\mu^{ \ast}_{ \theta }
(\epsilon)=\sup_{x\in D_{1}}\mu_{ \theta } ( x,\epsilon)$. We show that
\[\sqrt{\mu^{\ast}_{\theta} (\epsilon)}-\sqrt{2/\pi} \left(\log \epsilon^{-1}-
\frac{1}{2}\log\log \epsilon^{-1}\right)\] is tight.


title: Single-Stream Multi-Level Alignment for Vision-Language Pretraining

abstract:
  Recent progress in large-scale vision-language pre-training has shown the
importance of aligning the visual and text modalities for downstream
vision-language tasks. Many methods use a dual-stream architecture that fuses
visual tokens and language tokens after representation learning, which aligns
only at a global level and cannot extract finer-scale semantics. In contrast,
we propose a single stream model that aligns the modalities at multiple levels:
i) instance level, ii) fine-grained patch level, iii) conceptual semantic
level. We achieve this using two novel tasks: symmetric cross-modality
reconstruction and a pseudo-labeled key word prediction. In the former part, we
mask the input tokens from one of the modalities and use the cross-modal
information to reconstruct the masked token, thus improving fine-grained
alignment between the two modalities. In the latter part, we parse the caption
to select a few key words and feed it together with the momentum encoder pseudo
signal to self-supervise the visual encoder, enforcing it to learn rich
semantic concepts that are essential for grounding a textual token to an image
region. We demonstrate top performance on a set of Vision-Language downstream
tasks such as zero-shot/fine-tuned image/text retrieval, referring expression,
and VQA. We also demonstrate how the proposed models can align the modalities
at multiple levels.


title:
Learned coupled inversion for carbon sequestration monitoring and
  forecasting with Fourier neural operators

abstract:
  Seismic monitoring of carbon storage sequestration is a challenging problem
involving both fluid-flow physics and wave physics. Additionally, monitoring
usually requires the solvers for these physics to be coupled and differentiable
to effectively invert for the subsurface properties of interest. To drastically
reduce the computational cost, we introduce a learned coupled inversion
framework based on the wave modeling operator, rock property conversion and a
proxy fluid-flow simulator. We show that we can accurately use a Fourier neural
operator as a proxy for the fluid-flow simulator for a fraction of the
computational cost. We demonstrate the efficacy of our proposed method by means
of a synthetic experiment. Finally, our framework is extended to carbon
sequestration forecasting, where we effectively use the surrogate Fourier
neural operator to forecast the CO2 plume in the future at near-zero additional
cost.


title: Parallel InAs nanowires for Cooper pair splitters with Coulomb repulsion

abstract:
  Hybrid nanostructures consisting of two parallel InAs nanowires connected by
an epitaxially grown superconductor (SC) shell recently became available. Due
to the defect-free SC-semiconductor interface and the two quasi-one-dimensional
channels being close by, these novel platforms can be utilized to spatially
separate entangled pairs of electrons by using quantum dots (QD) in the
so-called Cooper pair splitting (CPS) process. The minimized distance between
the QDs overcomes the limitations of single-wire-based geometries and can boost
the splitting efficiency. Here we investigate CPS in such a device, for the
first time, where strong inter-dot Coulomb repulsion is also present and
studied thoroughly. We analyze theoretically the slight reduction of the CPS
efficiency imposed by the Coulomb interaction and compare it to the
experiments. Despite the competition between crossed Andreev reflection (CAR)
and inter-wire capacitance, a significant CPS signal is observed indicating the
dominance of the superconducting coupling. Our results demonstrate that the
application of parallel InAs nanowires with epitaxial SC is a promising route
for the realization of parafermionic states relying on enhanced CAR between the
wires.


title:
Investigation of bi-particle states in gate-array-controlled quantum-dot
  systems aided by machine learning techniques

abstract:
  Quantum computing architectures require an accurate and efficient description
in terms of many-electron states. Recent implementations include quantum dot
arrays, where the ground state of a multi q-bit system can be altered by
voltages applied to the top gates. An extensive investigation concerning the
spectra of the many-electron systems under multiple operation conditions set by
external voltages typically requires a relatively large number of Hamiltonian
diagonalizations, where the Coulomb interaction is considered in an exact
manner. Instead of making exhaustive calculations using high throughput
computing, we approach this problem by augmenting numerical diagonalizations
with machine learning techniques designed to predict the many-electron
eigenvalues and eigenfunctions. To this end, we employ and compare the results
from linear regression methods such as multivariate least squares (MLS) as well
as non-linear techniques based on kernel ridge regression (KRR), Gaussian
process regression (GPR) and artificial neural networks (ANNs). The input
feature vectors are assembled from readily available information comprised from
a binary representation of the potential and the strength of the Coulomb
interaction. Furthermore, employing a linear classifier, we establish a rule
for detecting a singlet-triplet transition which may arise for certain
potential configurations.


title: Nonextensive It\^o-Langevin Dynamics

abstract:
  We study nonextensive generalizations of It\^o-Langevin dynamics and their
connection with power-law nonlinear Fokker-Planck equations describing
correlated anomalous diffusion. A generalized central limit theorem is proposed
in order to demonstrate how such equations emerge as a limit of correlated
random variables. In doing so, we connect microscopic and macroscopic
descriptions of correlated anomalous diffusion in a mathematically sound way
and shed some light in explaining why q-Gaussian distributions appear quite
often in nature.


title: First-Passage-Driven Boundary Recession

abstract:
  We investigate a moving boundary problem for a Brownian particle on the
semi-infinite line in which the boundary moves by a distance proportional to
the time between successive collisions of the particle and the boundary.
Phenomenologically rich dynamics arises. In particular, the probability for the
particle to first reach the moving boundary for the $n^\text{th}$ time
asymptotically scales as $t^{-(1+2^{-n})}$. Because the tail of this
distribution becomes progressively fatter, the typical time between successive
first passages systematically gets longer. We also find that the number of
collisions between the particle and the boundary scales as $\ln\ln t$, while
the time dependence of the boundary position varies as $t/\ln t$.


title:
FingerPrint: A 3-D Printed Soft Monolithic 4-Degree-of-Freedom Fingertip
  Haptic Device with Embedded Actuation

abstract:
  Wearable fingertip haptic interfaces provide tactile stimuli on the
fingerpads by applying skin pressure, linear and rotational shear, and
vibration. Designing and fabricating a compact, multi-degree-of-freedom, and
forceful fingertip haptic interface is challenging due to trade-offs among
miniaturization, multifunctionality, and manufacturability. Downsizing
electromagnetic actuators that produce high torques is infeasible, and
integrating multiple actuators, links, joints, and transmission elements
increases device size and weight. 3-D printing enables rapid manufacturing of
complex devices with minimal assembly in large batches. However, it requires a
careful arrangement of material properties, geometry, scale, and printer
capabilities. Here we present a fully 3-D printed, soft, monolithic fingertip
haptic device based on an origami pattern known as the "waterbomb" base that
embeds foldable vacuum actuation and produces 4-DoF of motion on the fingerpad
with tunable haptic forces (up to 1.3 N shear and 7 N normal) and torque (up to
25 N-mm). Including the thimble mounting, the compact device is 40 mm long and
20 mm wide. This demonstrates the efficacy of origami design and soft material
3D printing for designing and rapidly fabricating miniature yet complex
wearable mechanisms with force output appropriate for haptic interaction.


title:
UV Volumes for Real-time Rendering of Editable Free-view Human
  Performance

abstract:
  Neural volume rendering has been proven to be a promising method for
efficient and photo-realistic rendering of a human performer in free-view, a
critical task in many immersive VR/AR applications. However, existing
approaches are severely limited by their high computational cost in the
rendering process. To solve this problem, we propose the UV Volumes, an
approach that can render an editable free-view video of a human performer in
real-time. It is achieved by removing the high-frequency (i.e., non-smooth)
human textures from the 3D volume and encoding them into a 2D neural texture
stack (NTS). The smooth UV volume allows us to employ a much smaller and
shallower structure for 3D CNN and MLP, to obtain the density and texture
coordinates without losing image details. Meanwhile, the NTS only needs to be
queried once for each pixel in the UV image to retrieve its RGB value. For
editability, the 3D CNN and MLP decoder can easily fit the function that maps
the input structured-and-posed latent codes to the relatively smooth densities
and texture coordinates. It gives our model a better generalization ability to
handle novel poses and shapes. Furthermore, the use of NST enables new
applications, e.g., retexturing. Extensive experiments on CMU Panoptic, ZJU
Mocap, and H36M datasets show that our model can render 900 * 500 images in 40
fps on average with comparable photorealism to state-of-the-art methods. The
project and supplementary materials are available at
https://fanegg.github.io/UV-Volumes.


title:
Improvement on the blow-up for a weakly coupled wave equations with
  scale-invariant damping and mass and time derivative nonlinearity

abstract:
  An improvement of [18] on the blow-up region and the lifespan estimate of a
weakly coupled system of wave equations with damping and mass in the
scale-invariant case and with time-derivative nonlinearity is obtained in this
article. Indeed, thanks to a better understanding of the dynamics of the
solutions, we give here a better characterization of the blow-up region.
Furthermore, the techniques used in this article may be extended to other
systems and interestingly they simplify the proof of the blow-up result in [3]
which is concerned with the single wave equation in the same context as in the
present work.


title:
Code Review Practices for Refactoring Changes: An Empirical Study on
  OpenStack

abstract:
  Modern code review is a widely used technique employed in both industrial and
open-source projects to improve software quality, share knowledge, and ensure
adherence to coding standards and guidelines. During code review, developers
may discuss refactoring activities before merging code changes in the code
base. To date, code review has been extensively studied to explore its general
challenges, best practices and outcomes, and socio-technical aspects. However,
little is known about how refactoring is being reviewed and what developers
care about when they review refactored code. Hence, in this work, we present a
quantitative and qualitative study to understand what are the main criteria
developers rely on to develop a decision about accepting or rejecting a
submitted refactored code, and what makes this process challenging. Through a
case study of 11,010 refactoring and non-refactoring reviews spread across
OpenStack open-source projects, we find that refactoring-related code reviews
take significantly longer to be resolved in terms of code review efforts.
Moreover, upon performing a thematic analysis on a significant sample of the
refactoring code review discussions, we built a comprehensive taxonomy
consisting of 28 refactoring review criteria. We envision our findings
reaffirming the necessity of developing accurate and efficient tools and
techniques that can assist developers in the review process in the presence of
refactorings.


title:
Abstract argumentation and answer set programming: two faces of Nelson's
  logic

abstract:
  In this work, we show that both logic programming and abstract argumentation
frameworks can be interpreted in terms of Nelson's constructive logic N4. We do
so by formalizing, in this logic, two principles that we call non-contradictory
inference and strengthened closed world assumption: the first states that no
belief can be held based on contradictory evidence while the latter forces both
unknown and contradictory evidence to be regarded as false. Using these
principles, both logic programming and abstract argumentation frameworks are
translated into constructive logic in a modular way and using the object
language. Logic programming implication and abstract argumentation supports
become, in the translation, a new implication connective following the
non-contradictory inference principle. Attacks are then represented by
combining this new implication with strong negation. Under consideration in
Theory and Practice of Logic Programming (TPLP).


title: Active Phase for Activated Random Walk on $\mathbb Z^2$

abstract:
  We show that for small enough sleep rate, the critical density of the
symmetric Activated Random Walk model on $\mathbb Z^2$ is strictly less than
one.


title: Conormal Varieties of covexillary Schubert Varieties

abstract:
  A permutation is called covexillary if it avoids the pattern $3412$. We
construct an open embedding of a covexillary matrix Schubert variety into a
Grassmannian Schubert variety. As applications of this embedding, we show that
the characteristic cycles of covexillary Schubert varieties are irreducible,
and provide a new proof of Lascoux's model computing Kazhdan-Lusztig
polynomials of vexillary permutations. Combining the above embedding with
earlier work of the author on the conormal varieties of Grassmannian Schubert
varieties, we develop an algebraic criterion identifying the conormal varieties
of covexillary Schubert and matrix Schubert varieties as subvarieties of the
respective cotangent bundles.


title: Control-Oriented Modeling of Pipe Flow in Gas Processing Facilities

abstract:
  Pipe flow models are developed with a focus on their eventual use for
feedback control design at the process control level, as opposed to the unit
level, in gas processing facilities. Accordingly, linearized facility-scale
models are generated to describe pressures, mass flows and temperatures based
on sets of nonlinear partial differential equations from fluid dynamics and
thermodynamics together with constraints associated with their interconnection.
As part of the treatment, the divergence of these simplified models from
physics is assessed, since robustness to these errors will be an objective for
the eventual control system. The approach commences with a thorough analysis of
pipe flow models and then proceeds to study their automated interconnection
into network models which subsume the algebraic constraints of bond graph or
standard fluid modeling. The models are validated and their errors quantified
by referring them to operational data from a commercial gas compressor test
facility. For linear time-invariant models, the interconnection method to
generate network models is shown to coincide with automation of Mason's Gain
{Formula}. These pipe network models based on engineering data are the first
part of the development of general facility process control tools.


title: SMP-PHAT: Lightweight DoA Estimation by Merging Microphone Pairs

abstract:
  This paper introduces SMP-PHAT, which performs direction of arrival (DoA) of
sound estimation with a microphone array by merging pairs of microphones that
are parallel in space. This approach reduces the number of pairwise
cross-correlation computations, and brings down the number of flops and memory
lookups when searching for DoA. Experiments on low-cost hardware with commonly
used microphone arrays show that the proposed method provides the same accuracy
as the former SRP-PHAT approach, while reducing the computational load by 39%
in some cases.


title: The linearized 3D Euler equations with inflow, outflow

abstract:
  In 1983, Antontsev, Kazhikhov, and Monakhov published a proof of the
existence and uniqueness of solutions to the 3D Euler equations in which on
certain inflow boundary components fluid is forced into the domain while on
other outflow components fluid is drawn out of the domain. A key tool they used
was the linearized Euler equations in vorticity form. We extend their result on
the linearized problem to multiply connected domains and establish
compatibility conditions on the initial data that allow higher regularity
solutions.


title: Random Graphs by Product Random Measures

abstract:
  A natural representation of random graphs is the random measure. The
collection of product random measures, their transformations, and non-negative
test functions forms a general representation of the collection of non-negative
weighted random graphs, directed or undirected, labeled or unlabeled, where (i)
the composition of the test function and transformation is a non-negative edge
weight function, (ii) the mean measures encode edge density/weight and vertex
degree density/weight, and (iii) the mean edge weight, when square-integrable,
encodes generalized spectral and Sobol representations. We develop a number of
properties of these random graphs, and we give simple examples of some of their
possible applications.


title: iPLAN: Interactive and Procedural Layout Planning

abstract:
  Layout design is ubiquitous in many applications, e.g. architecture/urban
planning, etc, which involves a lengthy iterative design process. Recently,
deep learning has been leveraged to automatically generate layouts via image
generation, showing a huge potential to free designers from laborious routines.
While automatic generation can greatly boost productivity, designer input is
undoubtedly crucial. An ideal AI-aided design tool should automate repetitive
routines, and meanwhile accept human guidance and provide smart/proactive
suggestions. However, the capability of involving humans into the loop has been
largely ignored in existing methods which are mostly end-to-end approaches. To
this end, we propose a new human-in-the-loop generative model, iPLAN, which is
capable of automatically generating layouts, but also interacting with
designers throughout the whole procedure, enabling humans and AI to co-evolve a
sketchy idea gradually into the final design. iPLAN is evaluated on diverse
datasets and compared with existing methods. The results show that iPLAN has
high fidelity in producing similar layouts to those from human designers, great
flexibility in accepting designer inputs and providing design suggestions
accordingly, and strong generalizability when facing unseen design tasks and
limited training data.


title: Realizing Finite Groups as Automizers

abstract:
  It is shown that any finite group $A$ is realizable as the automizer in a
finite perfect group $G$ of an abelian subgroup whose conjugates generate $G$.
The construction uses techniques from fusion systems on arbitrary finite
groups, most notably certain realization results for fusion systems of the type
studied originally by Park.


title:
Framework for a simulation-based aerodynamic shape optimization of
  bridge decks for different limit state phenomena

abstract:
  Wind-induced response governs the design of the long-span bridges. The shape
of the deck is one of the most important factors that not only affects the
mechanical properties but greatly influences the aerodynamic performance of the
bridge. An efficient framework is proposed to perform aerodynamic shape
optimization of a bridge deck using Computational Fluid Dynamics (CFD)
simulations and response surface strategies considering aeroelastic phenomena
such as flutter, buffeting and Vortex-induced Vibrations (VIV). A parameterized
cross section of the deck has been utilized ensuring required carriageway width
and structural demand. The Particle swarm optimization algorithm has been
employed which leads to an optimized shape that performs better as compared to
the reference section. The presented aerodynamic shape optimization framework
has a great potential to be used in the design of long-span bridges.


title: Mugs: A Multi-Granular Self-Supervised Learning Framework

abstract:
  In self-supervised learning, multi-granular features are heavily desired
though rarely investigated, as different downstream tasks (e.g., general and
fine-grained classification) often require different or multi-granular
features, e.g.~fine- or coarse-grained one or their mixture. In this work, for
the first time, we propose an effective MUlti-Granular Self-supervised learning
(Mugs) framework to explicitly learn multi-granular visual features. Mugs has
three complementary granular supervisions: 1) an instance discrimination
supervision (IDS), 2) a novel local-group discrimination supervision (LGDS),
and 3) a group discrimination supervision (GDS). IDS distinguishes different
instances to learn instance-level fine-grained features. LGDS aggregates
features of an image and its neighbors into a local-group feature, and pulls
local-group features from different crops of the same image together and push
them away for others. It provides complementary instance supervision to IDS via
an extra alignment on local neighbors, and scatters different local-groups
separately to increase discriminability. Accordingly, it helps learn high-level
fine-grained features at a local-group level. Finally, to prevent similar
local-groups from being scattered randomly or far away, GDS brings similar
samples close and thus pulls similar local-groups together, capturing
coarse-grained features at a (semantic) group level. Consequently, Mugs can
capture three granular features that often enjoy higher generality on diverse
downstream tasks over single-granular features, e.g.~instance-level
fine-grained features in contrastive learning. By only pretraining on
ImageNet-1K, Mugs sets new SoTA linear probing accuracy 82.1$\%$ on ImageNet-1K
and improves previous SoTA by $1.1\%$. It also surpasses SoTAs on other tasks,
e.g. transfer learning, detection and segmentation.


title:
Bunched LPCNet2: Efficient Neural Vocoders Covering Devices from Cloud
  to Edge

abstract:
  Text-to-Speech (TTS) services that run on edge devices have many advantages
compared to cloud TTS, e.g., latency and privacy issues. However, neural
vocoders with a low complexity and small model footprint inevitably generate
annoying sounds. This study proposes a Bunched LPCNet2, an improved LPCNet
architecture that provides highly efficient performance in high-quality for
cloud servers and in a low-complexity for low-resource edge devices. Single
logistic distribution achieves computational efficiency, and insightful tricks
reduce the model footprint while maintaining speech quality. A DualRate
architecture, which generates a lower sampling rate from a prosody model, is
also proposed to reduce maintenance costs. The experiments demonstrate that
Bunched LPCNet2 generates satisfactory speech quality with a model footprint of
1.1MB while operating faster than real-time on a RPi 3B. Our audio samples are
available at https://srtts.github.io/bunchedLPCNet2.


title:
Dynamical large deviations for the boundary driven symmetric exclusion
  process with Robin boundary conditions

abstract:
  In this article, we consider a one-dimensional symmetric exclusion process in
weak contact with reservoirs at the boundary. In the diffusive time-scaling the
empirical measure evolves according to the heat equation with Robin boundary
conditions. We prove the associated dynamical large deviations principle.


title: The Cayley hyperbolic space and volume entropy rigidity

abstract:
  Let $M$ be a Riemannian manifold with dimension greater or equal to $3$ which
admits a complete, finite-volume Riemannian metric $g_0$ locally isometric to a
rank-1 symmetric space of non-compact type. The volume entropy rigidity theorem
asserts that $g_0$ minimizes a normalized volume growth entropy among all
complete, finite-volume, Riemannian metric on $M$. We will repair a gap in the
proof when $g_0$ is locally isometric to the Cayley hyperbolic space.


title:
Localized Energy States Induced by Atomic-Level Interfacial Broadening
  in Heterostructures

abstract:
  A theoretical framework incorporating atomic-level interfacial details is
derived to include the electronic structure of buried interfaces and describe
the behavior of charge carriers in heterostructures in the presence of finite
interfacial broadening. Applying this model to ultrathin heteroepitaxial
(SiGe)m/(Si)m superlattices predicts the existence of localized energy levels
in the band structure induced by sub-nanometer broadening, which provides
additional paths for hole-electron recombination. These predicted interfacial
electronic transitions and the associated absorptive effects are confirmed
experimentally at variable superlattice thickness and periodicity. By mapping
the energy of the critical points, the optical transitions are identified
between 2 and 2.5 eV thus extending the optical absorption to lower energies.
This phenomenon enables a straightforward and non-destructive probe of the
atomic-level broadening in heterostructures.


title: Generalized Dedekind's theorem and integer group determinants

abstract:
  In this paper, we give a refinement of a generalized Dedekind's theorem,
which is a generalization of Laquer's theorem. In addition, we show that all
possible values of an integer group determinant are also possible values of the
integer group determinant of any its abelian subgroup.


title: Dark Andreev States in Superconductors

abstract:
  The conventional Bardeen-Cooper-Schrieffer (BCS) model of superconductivity
assumes a frequency-independent order parameter, which allows a relatively
simple description of the superconducting state. In particular, its excitation
spectrum readily follows from the Bogoliubov-de-Gennes (BdG) equations. A more
realistic description of a superconductor is the Migdal-Eliashberg theory,
where the pairing interaction, the order parameter, and electronic self-energy
are strongly frequency dependent. This work combines these ingredients of
phonon-mediated superconductivity with the standard BdG approach. Surprisingly,
we find qualitatively new features such as the emergence of a shadow
superconducting gap in the quasiparticle spectrum at energies close to the
Debye energy. We show how these features reveal themselves in standard
tunneling experiments. Finally, we also predict the existence of additional
high-energy bound states, which we dub "dark Andreev states."


title:
Principal Specialization of Monomial Symmetric Polynomials and Group
  Determinants of Cyclic Groups

abstract:
  We give explicit expressions of some special values for the monomial
symmetric polynomials as applications of symmetric functions and group
determinants. We also prove some vanishing or non-vanishing properties of these
special values.


title:
Simulating Araponga - The High Resolution Diffractometer of Brazilian
  Multipurpose Reactor

abstract:
  Neutron diffraction is in the hall of state-of-the-art techniques for
analyzing materials at the mesoscopic scale. A high-resolution neutron
diffractometer, called Araponga, is foreseen in the Brazilian Multipurpose
Reactor (RMB) project, a modern 30 MW research nuclear reactor, to be built in
the city of Ipero-SP, Brazil. In this work, we simulate the Araponga instrument
through the MCSTAS software, a computational environment based on the Monte
Carlo method. The results for the neutron fluxes at the sample position are
promising and compatible with the best instruments from international
facilities.


title:
Detectable Gravitational Waves from (P)-reheating probes non-thermal
  Dark Matter

abstract:
  We show that the stochastic Gravitational Waves (GW) signal generated during
the (p)reheating era can act as a novel probe of the non-thermal dark matter
production in the early Universe. Such scenarios are of utmost interest when no
other interaction between the visible and dark sectors is present, therefore
having no other detectability prospects. We consider the remnant energy in the
coherently oscillating inflaton zeroth mode to contribute as the observed relic
dark matter density in the Universe. To fully capture the nonlinear dynamics
and the effects of back-reactions during the oscillation, we resort to fully
nonlinear lattice simulation with pseudo-spectral methods to eliminate the
differencing noises. We investigate for models whose behavior during the
reheating era is of quadratic ($m_{\Phi}^2\Phi^2$ type) and find the typical
primordial stochastic GW background (SGWB) spectrum from scatterings among
highly populated inflaton modes behaving like matter, as expected, during this
nonlinear phase. We predict the detectable dark matter mass ranges within the
future GW detectors such as BBO, DECIGO, PTA, AION-MAGIS, and CE to be (MeV -
TeV) ranges. We conclude that with the current and future GW detectors
operating at high frequencies one will be able to probe non-thermal dark matter
which is otherwise impossible to test at laboratories and astrophysical
searches due to feeble or no interaction among the visible and dark sectors.


title:
Geometric singular perturbation analysis to a perturbed (1 +
  1)-dimensional dispersive long wave equation

abstract:
  The existence of the solitary wave and the nonexistence of kink (anti-kink)
wave solutions are studied for a perturbed $(1 + 1)$-dimensional dispersive
long wave equation. The methods are based on the geometric singular
perturbation {(GSP, for short)} approach, Melnikov method and bifurcation
analysis. The results show that the solitary wave solution with a suitable wave
speed $c$ and parameter ${\kappa}$ exists under the small singular
perturbation. Interestingly, unlike solitary wave solutions, the kink
(anti-kink) wave solution doesn't persist because the corresponding Melnikov
function has no zeros. Further, numerical simulations are utilized to verify
the correctness of our analytical results.


title: An Information Centric Framework for Weather Sensing Data

abstract:
  Weather sensing and forecasting has become increasingly accurate in the last
decade thanks to high-resolution radars, efficient computational algorithms,
and high-performance computing facilities. Through a distributed and federated
network of radars, scientists can make high-resolution observations of the
weather conditions on a scale that benefits public safety, commerce,
transportation, and other fields. While weather radars are critical
infrastructure, they are often located in remote areas with poor network
connectivity. Data retrieved from these radars are often delayed or lost, or
even lack proper synchronization, resulting in sub-optimal weather prediction.
This work applies Named Data Networking (NDN) to a federation of weather
sensing radars for efficient content addressing and retrieval. We identify
weather data based on a hierarchical naming scheme that allows us to explicitly
access desired files. We demonstrate that compared to the window-based
mechanism in TCP/IP, an NDN based mechanism improves data quality, reduces
uncertainty, and enhances weather prediction. Our evaluation demonstrates that
this naming scheme enables effective data retrieval, while compared to the
window-based mechanism in TCP/IP, an NDN based mechanism improves data quality,
reduces uncertainty, and enhances weather prediction.


title:
Strong ergodicity for the Gamma-jump operator and for actions of Polish
  wreath products

abstract:
  Let $\Gamma$ and $\Delta$ be sufficiently distinct countable groups. We show
that there is an orbit equivalence relation $E$, induced by an action of the
Polish wreath product group $\Gamma\wr\Gamma$, so that $E$ is generically
$F$-ergodic for any orbit equivalence relation $F$ induced by an action of
$\Delta\wr\Delta$. More generally, we establish generic ergodicity between
$\Gamma$-jumps and the iterated $\Delta$-jumps, answering a question of Clemens
and Coskey. The proofs follow a translation between Borel homomorphisms and
definable pins.


title:
Relaxation Labeling Meets GANs: Solving Jigsaw Puzzles with Missing
  Borders

abstract:
  This paper proposes JiGAN, a GAN-based method for solving Jigsaw puzzles with
eroded or missing borders. Missing borders is a common real-world situation,
for example, when dealing with the reconstruction of broken artifacts or ruined
frescoes. In this particular condition, the puzzle's pieces do not align
perfectly due to the borders' gaps; in this situation, the patches' direct
match is unfeasible due to the lack of color and line continuations. JiGAN, is
a two-steps procedure that tackles this issue: first, we repair the eroded
borders with a GAN-based image extension model and measure the alignment
affinity between pieces; then, we solve the puzzle with the relaxation labeling
algorithm to enforce consistency in pieces positioning, hence, reconstructing
the puzzle. We test the method on a large dataset of small puzzles and on three
commonly used benchmark datasets to demonstrate the feasibility of the proposed
approach.


title:
Role of the chiral spin configuration in field-free spin-orbit
  torque-induced magnetization switching by a locally injected spin current

abstract:
  For deterministic magnetization switching by spin-orbit torque (SOT) in a
perpendicular magnetic anisotropy system, an additional in-plane direction
magnetic field is essential for deterministic switching by breaking the
magnetization symmetry. Realizing chirality in a magnetic ordering system can
be one approach for achieving asymmetry in the lateral direction for field-free
magnetization switching. However, systematic analysis of the influence of the
chiral spin system on deterministic switching is still scarce. In this report,
the achievement of field-free SOT-induced magnetization switching by using a
chiral spin configuration is investigated by experiments and micromagnetic
simulations. We designed a system in which only part of the ferromagnetic layer
overlaps with the heavy metal layer in the Pt/Co/MgO structure. Therefore, a
spin current is exerted only on a local area of the ferromagnetic layer, which
results in a N\'eel-type chiral spin configuration. The induced chiral spin
configuration can be stabilized (or destabilized) depending on the sign of the
interfacial Dzyaloshinskii-Moriya interaction of the system. The stabilized
spin configuration plays a crucial role in the deterministic switching in zero
field. We expect our findings to widen the perspective on chirality-based
all-electrical SOT device fabrication.


title:
Integrated Home Care Staffing and Capacity Planning: Stochastic
  Optimization Approaches

abstract:
  We propose stochastic optimization methodologies for a staffing and capacity
planning problem arising from home care practice. Specifically, we consider the
perspective of a home care agency that must decide the number of caregivers to
hire (staffing) and the allocation of hired caregivers to different types of
services (capacity planning) in each day within a specified planning horizon.
The objective is to minimize the total cost associated with staffing (i.e.,
employment), capacity allocation, over-staffing, and under-staffing. We propose
two-stage stochastic programming (SP) and distributionally robust optimization
(DRO) approaches to model and solve this problem considering two types of
decision-makers, namely an everything in advance decision-maker (EA) and a
flexible adjustment decision-maker (FA). In the EA models, we determine the
staffing and capacity allocation decisions in the first stage before observing
the demand. In the FA models, we decide the staffing decisions in the first
stage. Then, we determine the capacity allocation decisions based on demand
realizations in the second stage. We derive equivalent mixed-integer linear
programming (MILP) reformulations of the proposed nonlinear DRO model for the
EA decision-maker that can be implemented and efficiently solved using
off-the-shelf optimization software. We propose a computationally efficient
column-and-constraint generation algorithm with valid inequalities to solve the
proposed DRO model for the FA decision-maker. Finally, we conduct extensive
numerical experiments comparing the operational and computational performance
of the proposed approaches and discuss insights and implications for home care
staffing and capacity planning.


title: Misiurewicz polynomials and dynamical units, part II

abstract:
  Fix an integer $d\geq 2$. The parameters $c_0\in \bar{\mathbb{Q}}$ for which
the unicritical polynomial $f_{d,c}(z)=z^d+c\in \mathbb{C}[z]$ has finite
postcritical orbit, also known as Misiurewicz parameters, play a significant
role in complex dynamics. Recent work of Buff, Epstein, and Koch proved the
first known cases of a long-standing dynamical conjecture of Milnor using their
arithmetic properties, about which relatively little is otherwise known.
Continuing our work in a companion paper, we address further arithmetic
properties of Misiurewicz parameters, especially the nature of the algebraic
integers obtained by evaluating the polynomial defining one such parameter at a
different Misiurewicz parameter. In the most challenging such combinations, we
describe a connection between such algebraic integers and the multipliers of
associated periodic points. As part of our considerations, we also introduce a
new class of polynomials we call $p$-special, which may be of independent
number theoretic interest.


title:
Encoding trade-offs and design toolkits in quantum algorithms for
  discrete optimization: coloring, routing, scheduling, and other problems

abstract:
  Challenging combinatorial optimization problems are ubiquitous in science and
engineering. Several quantum methods for optimization have recently been
developed, in different settings including both exact and approximate solvers.
Addressing this field of research, this manuscript has three distinct purposes.
First, we present an intuitive method for synthesizing and analyzing discrete
(i.e., integer-based) optimization problems, wherein the problem and
corresponding algorithmic primitives are expressed using a discrete quantum
intermediate representation (DQIR) that is encoding-independent. This compact
representation often allows for more efficient problem compilation, automated
analyses of different encoding choices, easier interpretability, more complex
runtime procedures, and richer programmability, as compared to previous
approaches, which we demonstrate with a number of examples. Second, we perform
numerical studies comparing several qubit encodings; the results exhibit a
number of preliminary trends that help guide the choice of encoding for a
particular set of hardware and a particular problem and algorithm. Our study
includes problems related to graph coloring, the traveling salesperson problem,
factory/machine scheduling, financial portfolio rebalancing, and integer linear
programming. Third, we design low-depth graph-derived partial mixers (GDPMs) up
to 16-level quantum variables, demonstrating that compact (binary) encodings
are more amenable to QAOA than previously understood. We expect this toolkit of
programming abstractions and low-level building blocks to aid in designing
quantum algorithms for discrete combinatorial problems.


title:
Emergence of electronic modes by doping Kondo insulators in the Kondo
  lattice and periodic Anderson models

abstract:
  Heavy-fermion or Kondo lattice materials are considered to be typical
strongly correlated systems, for which mean-field approximations have shown
that the Coulomb interaction increases the effective mass and narrows the band
gap. In this paper, to clarify interaction effects on the electronic
excitation, the spectral function of the Kondo lattice and periodic Anderson
models is studied around Kondo insulators in the strong-coupling regime, by
using the non-Abelian dynamical density-matrix renormalization group method and
perturbation theory. Upon doping a Kondo insulator, an electronic mode emerges
in the Kondo insulating gap, exhibiting the momentum-shifted magnetic
dispersion relation. Although the ground-state properties are similar to those
of a doped band insulator, the emergence of the electronic mode reflecting
spin-charge separation of the Kondo insulator is a crucial interaction effect
that allows us to regard the Kondo-insulator--metal transition as a type of
Mott transition. In addition, electronic modes emerge even in the high-energy
regime by doping in the periodic Anderson model. These strong-correlation
effects have not been expected in conventional mean-field approximations and
would bring a different perspective on heavy-fermion or Kondo lattice systems.


title: Risk regularization through bidirectional dispersion

abstract:
  Many alternative notions of "risk" (e.g., CVaR, entropic risk, DRO risk) have
been proposed and studied, but these risks are all at least as sensitive as the
mean to loss tails on the upside, and tend to ignore deviations on the
downside. In this work, we study a complementary new risk class that penalizes
loss deviations in a bidirectional manner, while having more flexibility in
terms of tail sensitivity than is offered by classical mean-variance, without
sacrificing computational or analytical tractability.


title:
On the gauge invariance of the locally averaged Friedmann Universe and
  the Hubble tension

abstract:
  The Hubble tension cast a blight on the standard cosmology. As a possible
attitude to the problem, the local variation of the expansion rate in an
inhomogeneous cosmology has been proposed where the spatial averaging over a
finite domain was introduced in order to construct local Friedmann spacetime.
However, it is not clear that the concept of the spatial averaging itself is
gauge invariant or not. Namely the conclusion obtained by the averaging in a
particular gauge is physically meaningful or not. In this paper we address this
question, namely the gauge invariance of the spatial averaging in cosmology .
We show that the answer is positive by studying the spatial average in the
gauge-invariant cosmological perturbation theory.


title: Crosscap Contribution to Late-Time Two-Point Correlators

abstract:
  We show that in Jackiw-Teitelboim (JT) gravity, late-time two-point functions
can get a leading non-decaying contribution from a spacetime with the topology
of a Mobius strip (a disk with one crosscap). There is an interesting interplay
between this contribution and the standard "plateau". The two can add together
or cancel, depending on topological weighting factors. We match this behavior
to Random Matrix Theory (RMT) and the N mod 8 periodicity of Sachdev-Kitaev-Ye
(SYK) results.


title: Individual and Team Trust Preferences for Robotic Swarm Behaviors

abstract:
  Trust between humans and multi-agent robotic swarms may be analyzed using
human preferences. These preferences are expressed by an individual as a
sequence of ordered comparisons between pairs of swarm behaviors. An
individual's preference graph can be formed from this sequence. In addition,
swarm behaviors may be mapped to a feature vector space. We formulate a linear
optimization problem to locate a trusted behavior in the feature space.
Extending to human teams, we define a novel distinctiveness metric using a
sparse optimization formulation to cluster similar individuals from a
collection of individuals' labeled pairwise preferences. The case of anonymized
unlabeled pairwise preferences is also examined to find the average trusted
behavior and minimum covariance bound, providing insights into group cohesion.
A user study was conducted, with results suggesting that individuals with
similar trust profiles can be clustered to facilitate human-swarm teaming.


title: Optimal Correction Cost for Object Detection Evaluation

abstract:
  Mean Average Precision (mAP) is the primary evaluation measure for object
detection. Although object detection has a broad range of applications, mAP
evaluates detectors in terms of the performance of ranked instance retrieval.
Such the assumption for the evaluation task does not suit some downstream
tasks. To alleviate the gap between downstream tasks and the evaluation
scenario, we propose Optimal Correction Cost (OC-cost), which assesses
detection accuracy at image level. OC-cost computes the cost of correcting
detections to ground truths as a measure of accuracy. The cost is obtained by
solving an optimal transportation problem between the detections and the ground
truths. Unlike mAP, OC-cost is designed to penalize false positive and false
negative detections properly, and every image in a dataset is treated equally.
Our experimental result validates that OC-cost has better agreement with human
preference than a ranking-based measure, i.e., mAP for a single image. We also
show that detectors' rankings by OC-cost are more consistent on different data
splits than mAP. Our goal is not to replace mAP with OC-cost but provide an
additional tool to evaluate detectors from another aspect. To help future
researchers and developers choose a target measure, we provide a series of
experiments to clarify how mAP and OC-cost differ.


title:
Fractional structures on bundle gerbe modules and fractional classifying
  spaces

abstract:
  We study the homotopy aspects of the twisted Chern classes of torsion bundle
gerbe modules. Using Sullivan's rational homotopy theory, we realize the
twisted Chern classes at the level of classifying spaces. The construction
suggests a notion, which we call fractional U-structure serving as a universal
framework to study the twisted Chern classes of torsion bundle gerbe modules
from the perspective of classifying spaces. Based on this, we introduce and
study higher fractional structures on torsion bundle gerbe modules parallel to
the higher structures on ordinary vector bundles.


title:
Mass formulas and stringy point-count for semi-direct products of tame
  abelian groups and wild symmetric or cyclic groups in characteristic three

abstract:
  In positive characteristic, there exist counterexamples to the statement
corresponding to Batyrev's theorem concerning the McKay correspondence. In this
paper, we give another computation of the counterexamples by using
stringy-point count for some sequences of quotient varieties. There is a proof
of the Serre-Bhagava's mass formula, which is important formula in the number
theory, from the computation of stringy-point count of a quotient variety
associated to a representation of symmetric group. Our computation of the
stringy-point count in this paper is regarded as a variation of this proof to
give different versions of mass formula.


title: An Interactive Image-based Modeling System

abstract:
  This paper propose a interactive 3D modeling method and corresponding system
based on single or multiple uncalibrated images. The main feature of this
method is that, according to the modeling habits of ordinary people, the 3D
model of the target is reconstructed from coarse to fine images. On the basis
of determining the approximate shape, the user adds or modify projection
constraints and spatial constraints, and apply topology modification, gradually
realize camera calibration, refine rough model, and finally complete the
reconstruction of objects with arbitrary geometry and topology. During the
interactive process, the geometric parameters and camera projection matrix are
solved in real time, and the reconstruction results are displayed in a 3D
window.


title:
Three-Dimensional stochastic Navier-Stokes equations with Markov
  switching

abstract:
  A finite-state Markov chain is introduced in the noise terms of the
three-dimensional stochastic Navier-Stokes equations in order to allow for
transitions between two types of multiplicative noises. We call such systems as
stochastic Navier-Stokes equations with Markov switching. To solve such a
system, a family of regularized stochastic systems is introduced. For each such
regularized system, the existence of a unique strong solution (in the sense of
stochastic analysis) is established by the method of martingale problems and
pathwise uniqueness. The regularization is removed in the limit by obtaining a
weakly convergent sequence from the family of regularized solutions, and
identifying the limit as a solution of the three-dimensional stochastic
Navier-Stokes equation with Markov switching.


title:
Constraints on light Dark Matter evaporated from Primordial Black Hole
  through electron targets

abstract:
  Light sub-GeV dark matter (DM) particles in the Milky Way or macroscopic
objects such as primordial black holes (PBHs) become attractive DM candidates
due to null results of WIMP from direct detection experiments. We explore the
possibility in which the present PBHs play as a novel source to produce light
boosted DM and confine light PBHs with current and future terrestrial
facilities. We study the electron elastic scattering data and obtain the
current constraints from Super-Kamiokande and XENON1T on the boosted DM from
PBH evaporation. The prospective bounds on the sub-GeV DM-electron scattering
cross section and the fraction of DM composed of PBHs $f_{\rm PBH}$ are also
imposed for future Xenon experiments.


title:
The fate of particles in the dynamical environment around Kuiper Belt
  object (486958) Arrokoth

abstract:
  The contact binary Kuiper Belt object (486958) Arrokoth, targeted by New
Horizons mission, has a unique slope pattern, which is a result of its
irregular bilobate surface shape and high spin period. Thus, some peculiar
topographic regions on its surface are predisposed to lose or accumulate
material, as a long circular depression feature, an impact crater called
Maryland, on its small lobe. The equilibrium points of Arrokoth are also
directly related to the structure of the environment near these surface
features. In this work, we performed numerical simulations around Arrokoth to
explore the fate of particles close to equilibrium points and their dynamical
connection with its surface features. Our results suggest that most of these
particles in a ring inside the Arrokoth's rotational Roche lobe fall near the
equatorial region of the Maryland impact crater or close to the Bright spots
area on the large lobe. Also, particles in a spherical cloud orbiting Arrokoth
accumulate preferentially near low-mid-latitudes regions close to the
longitudes of Maryland crater and Bright spots area. In contrast, a few
particles will fall in regions diametrically opposite to them, as in the
LL_Term boundary on the large lobe. High-latitudes are those more empty of
impacts, as in polar sites. In addition, particles larger than a couple of
microns are not significantly perturbed by solar radiation pressure in the
environment around Arrokoth.


title: Mapping Long-Term Natural Orbits about Titania, a Satellite of Uranus

abstract:
  Close polar and circular orbits are of great interest for the exploration of
natural satellites. There are still no studies in the literature investigating
orbits around Titania, the largest satellite of Uranus. In this work, we
present results of a set of numerical simulations carried out to obtain
long-duration orbits for a probe around Titania. Through an expansion of the
gravitational potential up to second order, the asymmetry of the gravitational
field due to Titania's coefficient $C_{22}$, the zonal coefficient $J_2$, and
the gravitational perturbation of Uranus is considered. The analysis of
lifetime sensitivity due to possible errors in the values of $J_2$ and $C_{22}$
is investigated using multiple regression models. Simulations were performed
for different eccentricity values, and lifetime maps were constructed. The
results show that low-altitude and near-circular orbits have longer lifetimes
due to the balance between the disturbance of Uranus and the gravitational
coefficients of Titania. The results also show that non-zero values of the
longitude of periapsis ($\omega$) and longitude of the ascending node
($\Omega$) are essential to increase the lifetime up to eight times compared to
cases where $\omega= \Omega=0^\circ$. We also show that an orbit with
eccentricity $10^{-3}$ is the most affected by errors in the values of $J_2$
and $C_{22}$.


title: Weak decays of triply heavy baryons in light front approach

abstract:
  We analyze weak decays of triply heavy baryon $\Omega_{ccc}^{++}$ and
calculate the form factors for $\Omega_{ccc}^{++}\to
\Xi_{cc}^{++}/\Omega_{cc}^+$ transitions in a light front quark model. The
momentum distributions in form factors are accessed via the pole-model
parametrization. Using the results for form factors, we predict decay branching
fractions for semi-leptonic decays, and obtain
$\mathcal{B}(\Omega_{ccc}^{++}\to\Xi_{cc}^+e^+\nu_e)=0.069\%$,
$\mathcal{B}(\Omega_{ccc}^{++}\to\Omega_{cc}^+e^+\nu_e)=1.93\%$.
Forward-backward asymmetry and angular distributions are then investigated in
this work. We also estimate branching fractions for a few color-allowed
processes and find that the $\Omega_{ccc}^{++}\to\Omega_{cc}^+\pi^+$ has a
sizable branching fraction:
$\mathcal{B}(\Omega_{ccc}^{++}\to\Omega_{cc}^+\pi^+)=1.37\%$. We point out that
the $\Omega_{ccc}^{++}\to\Omega_{cc}^+\pi^+$ is a golden channel for the
discovery of the $\Omega_{ccc}^{++}$. This analysis can provide a useful
reference for future experimental search in future.


title: Chiral sum rule on the light front

abstract:
  We obtain a sum rule for the valence sector light-front wave function of the
pion from analyzing the conserved axial-vector current and the general
structures of the wave functions. This chiral sum rule provides a synergy
between the quark-antiquark bound-state structure of the lightest hadron and
its role as the Goldstone boson. Using an analytic model motivated by
holography, we show this sum rule is consistent with requirements of chiral
symmetry breaking in AdS/QCD. Within the same model, we find some remarkable
feature of the pion: it is mostly uniform within.


title:
Decoupled Multi-task Learning with Cyclical Self-Regulation for Face
  Parsing

abstract:
  This paper probes intrinsic factors behind typical failure cases (e.g.
spatial inconsistency and boundary confusion) produced by the existing
state-of-the-art method in face parsing. To tackle these problems, we propose a
novel Decoupled Multi-task Learning with Cyclical Self-Regulation (DML-CSR) for
face parsing. Specifically, DML-CSR designs a multi-task model which comprises
face parsing, binary edge, and category edge detection. These tasks only share
low-level encoder weights without high-level interactions between each other,
enabling to decouple auxiliary modules from the whole network at the inference
stage. To address spatial inconsistency, we develop a dynamic dual graph
convolutional network to capture global contextual information without using
any extra pooling operation. To handle boundary confusion in both single and
multiple face scenarios, we exploit binary and category edge detection to
jointly obtain generic geometric structure and fine-grained semantic clues of
human faces. Besides, to prevent noisy labels from degrading model
generalization during training, cyclical self-regulation is proposed to
self-ensemble several model instances to get a new model and the resulting
model then is used to self-distill subsequent models, through alternating
iterations. Experiments show that our method achieves the new state-of-the-art
performance on the Helen, CelebAMask-HQ, and Lapa datasets. The source code is
available at
https://github.com/deepinsight/insightface/tree/master/parsing/dml_csr.


title: Reduction of General One-loop Integrals Using Auxiliary Vector

abstract:
  As a key method to deal with loop integrals, Integration-By-Parts (IBP)
method can be used to do reduction as well as establish the differential
equations for master integrals. However, when talking about tensor reduction,
the Passarino-Veltman (PV) reduction method is also widely used for one-loop
integrals. Recently, we have proposed an improved PV reduction method, i.e.,
the PV reduction method with auxiliary vector $R$, which can easily give
analytical reduction results for any tensor rank. However, our results are only
for integrals with propagators with power one. In this paper, we generalize our
method to one-loop integrals with general tensor structures and propagators
with general powers. Our ideas are simple. We solve the generalised reduction
problem by combining differentiation over masses and proper limit of reduction
with power-one propagators. Finally, we demonstrate our method with several
examples. With the result in this paper, we have shown that our improved
PV-reduction method with auxiliary vector is a self-completed reduction method
for one-loop integrals.


title: Hyperbolic L-space knots and their formal semigroups

abstract:
  For an L-space knot, the formal semigroup is defined from its Alexander
polynomial. It is not necessarily a semigroup. That is, it may not be closed
under addition. There exists an infinite family of hyperbolic L-space knots
whose formal semigroups are semigroups generated by three elements. In this
paper, we give the first infinite family of hyperbolic L-space knots whose
formal semigroups are semigroups generated by five elements.


title:
A quantum algorithm for solving eigenproblem of the Laplacian matrix of
  a fully connected graph

abstract:
  Solving eigenproblem of the Laplacian matrix of a fully connected graph has
wide applications in data science, machine learning, and image processing, etc.
However, it is very challenging because it involves expensive matrix
operations. Here, we propose an efficient quantum algorithm to solve it based
on a general assumption that the element of each vertex and its norms can be
accessed via a quantum random access data structure. Specifically, we propose
in detail how to construct the block-encodings of operators containing the
information of the weight matrix and the degree matrix respectively, and
further obtain the block-encoding of the Laplacian matrix. This enables us to
implement the quantum simulation of the Laplacian matrix by employing the
optimal Hamiltonian simulation technique based on the block-encoding framework.
Afterwards, the eigenvalues and eigenvectors of the Laplacian matrix are
extracted by the quantum phase estimation algorithm. Compared with its
classical counterpart, our algorithm has a polynomial speedup on the number of
vertices and an exponential speedup on the dimension of each vertex over its
classical counterpart. We also show that our algorithm can be extended to solve
the eigenproblem of symmetric (non-symmetric) normalized Laplacian matrix.


title:
Optimisation-free Classification and Density Estimation with Quantum
  Circuits

abstract:
  We demonstrate the implementation of a novel machine learning framework for
probability density estimation and classification using quantum circuits. The
framework maps a training data set or a single data sample to the quantum state
of a physical system through quantum feature maps. The quantum state of the
arbitrarily large training data set summarises its probability distribution in
a finite-dimensional quantum wave function. By projecting the quantum state of
a new data sample onto the quantum state of the training data set, one can
derive statistics to classify or estimate the density of the new data sample.
Remarkably, the implementation of our framework on a real quantum device does
not require any optimisation of quantum circuit parameters. Nonetheless, we
discuss a variational quantum circuit approach that could leverage quantum
advantage for our framework.


title:
SC^2-PCR: A Second Order Spatial Compatibility for Efficient and Robust
  Point Cloud Registration

abstract:
  In this paper, we present a second order spatial compatibility (SC^2) measure
based method for efficient and robust point cloud registration (PCR), called
SC^2-PCR. Firstly, we propose a second order spatial compatibility (SC^2)
measure to compute the similarity between correspondences. It considers the
global compatibility instead of local consistency, allowing for more
distinctive clustering between inliers and outliers at early stage. Based on
this measure, our registration pipeline employs a global spectral technique to
find some reliable seeds from the initial correspondences. Then we design a
two-stage strategy to expand each seed to a consensus set based on the SC^2
measure matrix. Finally, we feed each consensus set to a weighted SVD algorithm
to generate a candidate rigid transformation and select the best model as the
final result. Our method can guarantee to find a certain number of outlier-free
consensus sets using fewer samplings, making the model estimation more
efficient and robust. In addition, the proposed SC^2 measure is general and can
be easily plugged into deep learning based frameworks. Extensive experiments
are carried out to investigate the performance of our method. Code will be
available at \url{https://github.com/ZhiChen902/SC2-PCR}.


title: Topological charge Fano effect in multi-Weyl semimetals

abstract:
  We theoretically analyze the Fano interference in a single impurity
multi-Weyl semimetal hybrid system and show the emergence of the topological
charge Fano effect in the bulk local density of states. In multi-Weyl
semimetals, the number of Fermi arcs at the system boundaries is determined by
the topological charge $J$, a direct consequence of the "bulk-boundary"
correspondence principle. Analogously, we find that $J$ also modulates the bulk
Fano profile of the system with an embedded quantum impurity. Thus, by
increasing $J$, the Fano lineshape evolves from resonant, typical for $J=1$
(single Weyl), towards antiresonant, extrapolating to the so-called hyper Weyl
semimetals with $J\gg1$. Specially for the maximum case protected by the
rotational symmetry $C_{2J=6}$, namely the $J=3$ (triple Weyl), which acquires
asymmetric Fano profile, the Fano parameter absolute value is predicted to be
$\tan(C_{2J=6})$, where $C_{2J}\equiv(360^{\circ}/2J)$ defines the rotational
angle. Hence, the Fano discretization in the $J$ term introduces the
topological charge Fano effect in multi-Weyl semimetals.


title:
Self-Propelled Soft Everting Toroidal Robot for Navigation and Climbing
  in Confined Spaces

abstract:
  There are many spaces inaccessible to humans where robots could help deliver
sensors and equipment. Many of these spaces contain three-dimensional
passageways and uneven terrain that pose challenges for robot design and
control. Everting toroidal robots, which move via simultaneous eversion and
inversion of their body material, are promising for navigation in these types
of spaces. We present a novel soft everting toroidal robot that propels itself
using a motorized device inside an air-filled membrane. Our robot requires only
a single control signal to move, can conform to its environment, and can climb
vertically with a motor torque that is independent of the force used to brace
the robot against its environment. We derive and validate models of the forces
involved in its motion, and we demonstrate the robot's ability to navigate a
maze and climb a pipe.


title:
3MASSIV: Multilingual, Multimodal and Multi-Aspect dataset of Social
  Media Short Videos

abstract:
  We present 3MASSIV, a multilingual, multimodal and multi-aspect,
expertly-annotated dataset of diverse short videos extracted from short-video
social media platform - Moj. 3MASSIV comprises of 50k short videos (20 seconds
average duration) and 100K unlabeled videos in 11 different languages and
captures popular short video trends like pranks, fails, romance, comedy
expressed via unique audio-visual formats like self-shot videos, reaction
videos, lip-synching, self-sung songs, etc. 3MASSIV presents an opportunity for
multimodal and multilingual semantic understanding on these unique videos by
annotating them for concepts, affective states, media types, and audio
language. We present a thorough analysis of 3MASSIV and highlight the variety
and unique aspects of our dataset compared to other contemporary popular
datasets with strong baselines. We also show how the social media content in
3MASSIV is dynamic and temporal in nature, which can be used for semantic
understanding tasks and cross-lingual analysis.


title:
PAEDID: Patch Autoencoder Based Deep Image Decomposition For Pixel-level
  Defective Region Segmentation

abstract:
  Unsupervised pixel-level defective region segmentation is an important task
in image-based anomaly detection for various industrial applications. The
state-of-the-art methods have their own advantages and limitations:
matrix-decomposition-based methods are robust to noise but lack complex
background image modeling capability; representation-based methods are good at
defective region localization but lack accuracy in defective region shape
contour extraction; reconstruction-based methods detected defective region
match well with the ground truth defective region shape contour but are noisy.
To combine the best of both worlds, we present an unsupervised patch
autoencoder based deep image decomposition (PAEDID) method for defective region
segmentation. In the training stage, we learn the common background as a deep
image prior by a patch autoencoder (PAE) network. In the inference stage, we
formulate anomaly detection as an image decomposition problem with the deep
image prior and domain-specific regularizations. By adopting the proposed
approach, the defective regions in the image can be accurately extracted in an
unsupervised fashion. We demonstrate the effectiveness of the PAEDID method in
simulation studies and an industrial dataset in the case study.


title:
Moments of Dirichlet $L$-functions to a fixed modulus over function
  fields

abstract:
  In this paper, we establish the expected order of magnitude of the
$k$th-moment of central values of the family of Dirichlet $L$-functions to a
fixed prime modulus over function fields for all real $k \geq 0$.


title:
Radiation from cold molecular clouds and Sun chromosphere produced by
  anti-quark nugget dark matter

abstract:
  We study astrophysical implications of the quark nugget model of dark matter
and propose observational techniques for detecting anti-Quark Nuggets
(anti-QNs) with modern telescopes. Anti-QNs are compact composite objects of
antiquark matter with a typical radius $R\sim 10^{-5}$ cm and density exceeding
that of nuclear matter. Atoms and molecules of interstellar medium collide with
anti-quark nuggets and annihilate. We estimate thermal radiation from anti-QNs
in cold molecular clouds in our galaxy and show that this radiation appears
sufficiently strong to be observed in infrared and visible spectra. Proton
annihilation on anti-QNs produces $\gamma$-photons with energies in the range
100-400 MeV which may be detected by telescopes such as Fermi-LAT. We have
found that anti-QN radiation inside the solar corona is too weak to produce a
significant plasma heating or any other observable effects, while the radiation
of $\gamma$-photons from the chromosphere may be observable. We also address
the problem of survival of anti-quark nuggets in the early universe.

categories: astro-ph.GA astro-ph.HE astro-ph.SR hep-ph


title:
The balanced superelliptic mapping class groups are generated by three
  elements

abstract:
  The balanced superelliptic mapping class group is the normalizer of the
transformation group of the balanced superelliptic covering in the mapping
class group of the total surface. We prove that the balanced superelliptic
mapping class groups with either one marked point, one boundary component, or
no marked points and boundary are generated by three elements. To prove this,
we also show that its liftable mapping class groups are also generated by three
elements. These generating sets are minimal except for several no marked points
and boundary cases.


title:
OTFace: Hard Samples Guided Optimal Transport Loss for Deep Face
  Representation

abstract:
  Face representation in the wild is extremely hard due to the large scale face
variations. To this end, some deep convolutional neural networks (CNNs) have
been developed to learn discriminative feature by designing properly
margin-based losses, which perform well on easy samples but fail on hard
samples. Based on this, some methods mainly adjust the weights of hard samples
in training stage to improve the feature discrimination. However, these methods
overlook the feature distribution property which may lead to better results
since the miss-classified hard samples may be corrected by using the
distribution metric. This paper proposes the hard samples guided optimal
transport (OT) loss for deep face representation, OTFace for short. OTFace aims
to enhance the performance of hard samples by introducing the feature
distribution discrepancy while maintain the performance on easy samples.
Specifically, we embrace triplet scheme to indicate hard sample groups in one
mini-batch during training. OT is then used to characterize the distribution
differences of features from the high level convolutional layer. Finally, we
integrate the margin-based-softmax (e.g. ArcFace or AM-Softmax) and OT to guide
deep CNN learning. Extensive experiments are conducted on several benchmark
databases. The quantitative results demonstrate the advantages of the proposed
OTFace over state-of-the-art methods.


title:
Stationary martingale solution for the 2D stochastic Gross-Pitaevskii
  equation

abstract:
  In this short report we give a proof of the existence of a stationary
solution to the Gross-Pitaevskii equation in $2d$ driven by a space-time white
noise.


title: Large-scale Bilingual Language-Image Contrastive Learning

abstract:
  This paper is a technical report to share our experience and findings
building a Korean and English bilingual multimodal model. While many of the
multimodal datasets focus on English and multilingual multimodal research uses
machine-translated texts, employing such machine-translated texts is limited to
describing unique expressions, cultural information, and proper noun in
languages other than English. In this work, we collect 1.1 billion image-text
pairs (708 million Korean and 476 million English) and train a bilingual
multimodal model named KELIP. We introduce simple yet effective training
schemes, including MAE pre-training and multi-crop augmentation. Extensive
experiments demonstrate that a model trained with such training schemes shows
competitive performance in both languages. Moreover, we discuss
multimodal-related research questions: 1) strong augmentation-based methods can
distract the model from learning proper multimodal relations; 2) training
multimodal model without cross-lingual relation can learn the relation via
visual semantics; 3) our bilingual KELIP can capture cultural differences of
visual semantics for the same meaning of words; 4) a large-scale multimodal
model can be used for multimodal feature analogy. We hope that this work will
provide helpful experience and findings for future research. We provide an
open-source pre-trained KELIP.


title: Barrow holographic dark energy with Granda-Oliveros cut-off

abstract:
  A study on the effects of implementing the Granda-Oliveros infrared cutoff in
the recently introduced Barrow Holographic Dark Energy model is presented, and
its cosmological evolution is investigated. We find how the deformation
parameter, $\Delta$, affects the values of $H(z)$, and find that from this
model it is possible to obtain an accelerated expansion regime of the universe
at late times. We also obtain that increasing $\Delta$ causes the EoS parameter
to transition from quintessence to phantom. In addition, we show that the model
can be used to describe the know eras of dominance. Finally, after studying the
stability of the proposed model, a fit of the corresponding parameters is
preformed, utilizing the measurements of the expansion rate of the universe,
$H(z)$. The best fit of the parameters is found to be $(\alpha,\, \beta,\,
\Delta) =
(1.00^{+0.02}_{-0.02},\,0.69^{+0.03}_{-0.02},\,0.000^{+0.004}_{-0.000})$ at
$1\sigma$ C.L, for which the Bekenstein-Hawking relation is favored.


title: STaR: Bootstrapping Reasoning With Reasoning

abstract:
  Generating step-by-step "chain-of-thought" rationales improves language model
performance on complex reasoning tasks like mathematics or commonsense
question-answering. However, inducing language model rationale generation
currently requires either constructing massive rationale datasets or
sacrificing accuracy by using only few-shot inference. We propose a technique
to iteratively leverage a small number of rationale examples and a large
dataset without rationales, to bootstrap the ability to perform successively
more complex reasoning. This technique, the "Self-Taught Reasoner" (STaR),
relies on a simple loop: generate rationales to answer many questions, prompted
with a few rationale examples; if the generated answers are wrong, try again to
generate a rationale given the correct answer; fine-tune on all the rationales
that ultimately yielded correct answers; repeat. We show that STaR
significantly improves performance on multiple datasets compared to a model
fine-tuned to directly predict final answers, and performs comparably to
fine-tuning a 30$\times$ larger state-of-the-art language model on
CommensenseQA. Thus, STaR lets a model improve itself by learning from its own
generated reasoning.


title: Multi-model Ensemble Learning Method for Human Expression Recognition

abstract:
  Analysis of human affect plays a vital role in human-computer interaction
(HCI) systems. Due to the difficulty in capturing large amounts of real-life
data, most of the current methods have mainly focused on controlled
environments, which limit their application scenarios. To tackle this problem,
we propose our solution based on the ensemble learning method. Specifically, we
formulate the problem as a classification task, and then train several
expression classification models with different types of backbones--ResNet,
EfficientNet and InceptionNet. After that, the outputs of several models are
fused via model ensemble method to predict the final results. Moreover, we
introduce the multi-fold ensemble method to train and ensemble several models
with the same architecture but different data distributions to enhance the
performance of our solution. We conduct many experiments on the AffWild2
dataset of the ABAW2022 Challenge, and the results demonstrate the
effectiveness of our solution.


title:
Temperature-resilient anapole modes associated with TE polarization in
  semiconductor nanowire

abstract:
  Polarization-dependent scattering anisotropy of cylindrical nanowires has
numerous potential applications in, for example, nanoantennas, photothermal
therapy, thermophotovoltaics, catalysis, sensing, optical filters and switches.
In all these applications, temperature-dependent material properties play an
important role and often adversely impact performance depending on the
dominance of either radiative or dissipative damping. Here, we employ numerical
modeling based on Mie scattering theory to investigate and compare the
temperature and polarization-dependent optical anisotropy of metallic (gold,
Au) nanowires with indirect (silicon, Si) and direct (gallium arsenide, GaAs)
bandgap semiconducting nanowires. Results indicate that plasmonic scattering
resonances in semiconductors, within the absorption band, deteriorate with an
increase in temperature whereas those occurring away from the absorption band
strengthen as a result of the increase in phononic contribution.
Indirect-bandgap thin ($20 \,\mathrm{nm}$) Si nanowires present low absorption
efficiencies for both the transverse electric (TE, $E_{\perp}$) and magnetic
(TM, $E_{\parallel}$) modes, and high scattering efficiencies for the TM mode
at shorter wavelengths making them suitable as highly efficient scatterers.
Temperature-resilient higher-order anapole modes with their characteristic high
absorption and low scattering efficiencies are also observed in the
semiconductor nanowires ($r \! = \! 125 \! - \! 130$ nm) for the TE
polarization. Herein, the GaAs nanowires present $3 \! - \! 7$ times greater
absorption efficiencies compared to the Si nanowires making them especially
suitable for temperature-resilient applications such as scanning near-field
optical microscopy (SNOM), localized heating, non-invasive sensing or detection
that require strong localization of energy in the near field.

categories: cond-mat.mes-hall cond-mat.mtrl-sci physics.comp-ph physics.optics


title:
Validating continuous gravitational-wave candidates based on Doppler
  modulation

abstract:
  Following up large numbers of candidates in continuous gravitational wave
searches presents a challenge, particularly in regard to computational power
and the time required to manually scrutinize each of the candidates. It is
important to design and test good follow-up procedures that are safe (i.e.,
minimize false dismissals) and computationally efficient across many search
configurations. We investigate two existing follow-up procedures, or "vetoes,"
both of which exploit the Doppler modulation predicted in astrophysical
signals. We take advantage of a well-established semicoherent search algorithm
based on a hidden Markov model to study a wide variety of search configurations
and to generalize the veto criteria by considering the overall veto performance
in terms of efficiency and safety. The results can serve as a guideline for
follow-up studies in future continuous gravitational wave searches using a
hidden Markov model algorithm. The results also apply qualitatively to other
semicoherent search algorithms.


title:
Integrating Physiological Time Series and Clinical Notes with
  Transformer for Early Prediction of Sepsis

abstract:
  Sepsis is a leading cause of death in the Intensive Care Units (ICU). Early
detection of sepsis is critical for patient survival. In this paper, we propose
a multimodal Transformer model for early sepsis prediction, using the
physiological time series data and clinical notes for each patient within $36$
hours of ICU admission. Specifically, we aim to predict sepsis using only the
first 12, 18, 24, 30 and 36 hours of laboratory measurements, vital signs,
patient demographics, and clinical notes. We evaluate our model on two large
critical care datasets: MIMIC-III and eICU-CRD. The proposed method is compared
with six baselines. In addition, ablation analysis and case studies are
conducted to study the influence of each individual component of the model and
the contribution of each data modality for early sepsis prediction.
Experimental results demonstrate the effectiveness of our method, which
outperforms competitive baselines on all metrics.


title:
Soft robotic hand with finger-bending/friction-reduction switching
  mechanism through 1-degree-of-freedom flow control

abstract:
  This paper proposes a novel pneumatic soft robotic hand that incorporates a
mechanism that can switch the airflow path using a single airflow control. The
developed hand can control the finger motion and operate the surface friction
variable mechanism. In the friction variable mechanism, a lubricant is injected
onto the high-friction finger surface to reduce surface friction. To inject the
lubrication using a positive-pressure airflow, the Venturi effect is applied.
The design and evaluation of the airflow-path switching and friction variable
mechanisms are described. Moreover, the entire design of a soft robotic hand
equipped with these mechanisms is presented. The performance was validated
through grasping, placing, and manipulation tests.


title:
UTIL: An Ultra-wideband Time-difference-of-arrival Indoor Localization
  Dataset

abstract:
  This paper presents an ultra-wideband (UWB) time-difference-of-arrival (TDOA)
dataset collected from a quadrotor for research purposes. The dataset consists
of low-level signal information from static experiments and UWB TDOA
measurements and additional onboard sensor data from flight experiments on a
quadrotor. The data collection process is discussed in detail, including the
equipment used, measurement collection procedure, and the calibration of the
quadrotor platform. All the data is made available as plain text files and we
provide both Matlab and Python scripts to parse and analyze the data. We
provide a thorough description of the data format and some pointers on the
potential usage of each sub-dataset. The dataset is available for download at
https://utiasdsl.github.io/util-uwb-dataset/. We hope this dataset will help
researchers develop and compare reliable estimation methods for the emerging
UWB TDOA-based indoor localization technology.


title:
Enhancing Transformer Efficiency for Multivariate Time Series
  Classification

abstract:
  Most current multivariate time series (MTS) classification algorithms focus
on improving the predictive accuracy. However, for large-scale (either
high-dimensional or long-sequential) time series (TS) datasets, there is an
additional consideration: to design an efficient network architecture to reduce
computational costs such as training time and memory footprint. In this work we
propose a methodology based on module-wise pruning and Pareto analysis to
investigate the relationship between model efficiency and accuracy, as well as
its complexity. Comprehensive experiments on benchmark MTS datasets illustrate
the effectiveness of our method.


title: Towards Dynamic and Safe Configuration Tuning for Cloud Databases

abstract:
  Configuration knobs of database systems are essential to achieve high
throughput and low latency. Recently, automatic tuning systems using machine
learning methods (ML) have shown to find better configurations compared to
experienced database administrators (DBAs). However, there are still gaps to
apply the existing systems in production environments, especially in the cloud.
First, they conduct tuning for a given workload within a limited time window
and ignore the dynamicity of workloads and data. Second, they rely on a copied
instance and do not consider the availability of the database when sampling
configurations, making the tuning expensive, delayed, and unsafe. To fill these
gaps, we propose OnlineTune, which tunes the online databases safely in
changing cloud environments. To accommodate the dynamicity, OnlineTune embeds
the environmental factors as context feature and adopts contextual Bayesian
Optimization with context space partition to optimize the database adaptively
and scalably. To pursue safety during tuning, we leverage the black-box and the
white-box knowledge to evaluate the safety of configurations and propose a safe
exploration strategy via subspace adaptation.%, greatly decreasing the risks of
applying bad configurations. We conduct evaluations on dynamic workloads from
benchmarks and real-world workloads. Compared with the state-of-the-art
methods, OnlineTune achieves 14.4%~165.3% improvement on cumulative performance
while reducing 91.0%~99.5% unsafe configuration recommendations.


title:
Interpretable Research Replication Prediction via Variational Contextual
  Consistency Sentence Masking

abstract:
  Research Replication Prediction (RRP) is the task of predicting whether a
published research result can be replicated or not. Building an interpretable
neural text classifier for RRP promotes the understanding of why a research
paper is predicted as replicable or non-replicable and therefore makes its
real-world application more reliable and trustworthy. However, the prior works
on model interpretation mainly focused on improving the model interpretability
at the word/phrase level, which are insufficient especially for long research
papers in RRP. Furthermore, the existing methods cannot utilize a large size of
unlabeled dataset to further improve the model interpretability. To address
these limitations, we aim to build an interpretable neural model which can
provide sentence-level explanations and apply weakly supervised approach to
further leverage the large corpus of unlabeled datasets to boost the
interpretability in addition to improving prediction performance as existing
works have done. In this work, we propose the Variational Contextual
Consistency Sentence Masking (VCCSM) method to automatically extract key
sentences based on the context in the classifier, using both labeled and
unlabeled datasets. Results of our experiments on RRP along with European
Convention of Human Rights (ECHR) datasets demonstrate that VCCSM is able to
improve the model interpretability for the long document classification tasks
using the area over the perturbation curve and post-hoc accuracy as evaluation
metrics.


title:
Zwicky Transient Facility and Globular Clusters: The RR Lyrae gri-Band
  Period-Luminosity-Metallicity and Period-Wesenheit-Metallicity Relations

abstract:
  Based on time-series observations collected from Zwicky Transient Facility
(ZTF), we derived period-luminosity-metallicity (PLZ) and
period-Wesenheit-metallicity (PWZ) relations for RR Lyrae located in globular
clusters. We have applied various selection criteria to exclude RR Lyrae with
problematic or spurious light curves. These selection criteria utilized
information on the number of data points per light curve, amplitudes, colors,
and residuals on the period-luminosity and/or period-Wesenheit relations. Due
to blending, a number of RR Lyrae in globular clusters were found to be
anomalously bright and have small amplitudes of their ZTF light curves. We used
our final sample of ~750 RR Lyrae in 46 globular clusters covering a wide
metallicity range (-2.36 < [Fe/H] < -0.54 dex) to derive PLZ and PWZ relations
in gri bands. In addition, we have also derived the period-color-metallicity
(PCZ) and for the first time, the PQZ relations where the Q-index is
extinction-free by construction. We have compared our various relations to
empirical and theoretical relations available in literature, and found a good
agreement with most studies. Finally, we applied our derived PLZ relation to a
dwarf galaxy, Crater II, and found its true distance modulus should be larger
than the most recent determination.


title:
A Novel Remote Sensing Approach to Recognize and Monitor Red Palm Weevil
  in Date Palm Trees

abstract:
  The spread of the Red Pal Weevil (RPW) has become an existential threat for
palm trees around the world. In the Middle East, RPW is causing wide-spread
damage to date palm Phoenix dactylifera L., having both agricultural impacts on
the palm production and environmental impacts. Early detection of RPW is very
challenging, especially at large scale. This research proposes a novel remote
sensing approach to recognize and monitor red palm weevil in date palm trees,
using a combination of vegetation indices, object detection and semantic
segmentation techniques. The study area consists of date palm trees with three
classes, including healthy palms, smallish palms and severely infected palms.
This proposed method achieved a promising 0.947 F1 score on test data set. This
work paves the way for deploying artificial intelligence approaches to monitor
RPW in large-scale as well as provide guidance for practitioners.