title: Stereo Neural Vernier Caliper

abstract:
  We propose a new object-centric framework for learning-based stereo 3D object
detection. Previous studies build scene-centric representations that do not
consider the significant variation among outdoor instances and thus lack the
flexibility and functionalities that an instance-level model can offer. We
build such an instance-level model by formulating and tackling a local update
problem, i.e., how to predict a refined update given an initial 3D cuboid
guess. We demonstrate how solving this problem can complement scene-centric
approaches in (i) building a coarse-to-fine multi-resolution system, (ii)
performing model-agnostic object location refinement, and (iii) conducting
stereo 3D tracking-by-detection. Extensive experiments demonstrate the
effectiveness of our approach, which achieves state-of-the-art performance on
the KITTI benchmark. Code and pre-trained models are available at
https://github.com/Nicholasli1995/SNVC.


title: Quantum boomerang effect in systems without time reversal symmetry

abstract:
  In an Anderson localized system, a quantum particle with a nonzero initial
velocity returns, on average, to its origin. This recently discovered behavior
is known as the quantum boomerang effect. Time reversal invariance was
initially thought to be a necessary condition for the existence of this
phenomenon. We theoretically analyze the impact of the symmetry breaking on the
phenomenon using a one-dimensional system with a spin-orbit coupling and show
that the time reversal invariance is not necessary for the boomerang effect to
occur.


title:
White Dwarf Photospheric Abundances in Cataclysmic Variables -- II.
  White Dwarfs With and Without a Mask

abstract:
  Taking advantage of the now available Gaia EDR3 parallaxes, we carry out an
archival {\it Hubble Space Telescope} (HST) far ultraviolet spectroscopic
analysis of 10 cataclysmic variable systems, including 5 carefully selected
eclipsing systems. We obtain accurate white dwarf (WD) masses and temperatures,
in excellent agreement with the masses for 4 of the eclipsing systems. For
three systems in our sample, BD Pav, HS 2214, and TT Crt, we report the first
robust masses for their WDs. We modeled the absorption lines to derive the WD
chemical abundances and rotational velocities for each of the ten systems. As
expected, for five higher inclination ($i \gtrsim 75^{\circ}$) systems, the
model fits are improved with the inclusion of a cold absorbing slab (an iron
curtain masking the WD) with $N_{\rm H} \approx 10^{20}-10^{22}$cm$^{-2}$.
Modeling of the metal lines in the HST spectra reveals that 7 of the 10 systems
have significant subsolar carbon abundance, and six have subsolar silicon
abundance, thereby providing further evidence that CV WDs exhibit subsolar
abundances of carbon and silicon. We suggest that strong aluminum absorption
lines (and iron absorption features) in the spectra of some CV WDs (such as IR
Com) may be due to the presence of a {\it thin} iron curtain ($N_{\rm H}\approx
10^{19}$cm$^{-2}$) rather than to suprasolar aluminum and iron abundances in
the WD photosphere. The derived WD (projected) rotational velocities all fall
in the range $\approx 100-400$~km/s, all sub-Keplerian similar to the values
obtained in earlier studies.


title: High-harmonic generation in a strongly overdriven regime

abstract:
  High-harmonic generation (HHG) normally requires a careful adjustment of the
driving laser intensity (typically $10^{14} - 10^{15}$ W/cm$^2$) and gas medium
parameters to enable good phase matching conditions. In contrast with
conventional wisdom, we present experimental results indicating phase-matched
HHG in all rare gases, using a high-density medium and a driver laser intensity
of around $10^{16}$ W/cm$^2$. The experimental results are corroborated by
theoretical simulations, which indicate that ionization-induced self-phase
modulation and plasma defocusing self-regulate the driver laser intensity to a
level that is appropriate for good phase matching. A ten-fold broadening of the
NIR spectrum is observed, which results in the generation of continuous spectra
from $18-140$ eV in spite of using 50-fs-long driving pulses. The presented
scheme represents a simple and versatile concept for the generation of XUV and
soft X-ray continua, which are ideally suited for transient absorption and
reflection spectroscopy.


title:
Voltage-Dependent Synaptic Plasticity (VDSP): Unsupervised probabilistic
  Hebbian plasticity rule based on neurons membrane potential

abstract:
  This study proposes voltage-dependent-synaptic plasticity (VDSP), a novel
brain-inspired unsupervised local learning rule for the online implementation
of Hebb's plasticity mechanism on neuromorphic hardware. The proposed VDSP
learning rule updates the synaptic conductance on the spike of the postsynaptic
neuron only, which reduces by a factor of two the number of updates with
respect to standard spike-timing-dependent plasticity (STDP). This update is
dependent on the membrane potential of the presynaptic neuron, which is readily
available as part of neuron implementation and hence does not require
additional memory for storage. Moreover, the update is also regularized on
synaptic weight and prevents explosion or vanishing of weights on repeated
stimulation. Rigorous mathematical analysis is performed to draw an equivalence
between VDSP and STDP. To validate the system-level performance of VDSP, we
train a single-layer spiking neural network (SNN) for the recognition of
handwritten digits. We report 85.01 $ \pm $ 0.76% (Mean $ \pm $ S.D.) accuracy
for a network of 100 output neurons on the MNIST dataset. The performance
improves when scaling the network size (89.93 $ \pm $ 0.41% for 400 output
neurons, 90.56 $ \pm $ 0.27 for 500 neurons), which validates the applicability
of the proposed learning rule for large-scale computer vision tasks.
Interestingly, the learning rule better adapts than STDP to the frequency of
input signal and does not require hand-tuning of hyperparameters.


title: Formal multiparameter quantum groups, deformations and specializations

abstract:
  We introduce the notion of formal multiparameter quantum universal enveloping
algebras -- in short FoMpQUEA -- as a straightforward generalization of
Drinfeld's quantum group. Then we show that the class of FoMpQUEA's is closed
under deformations by twists and deformations by 2-cocycles: as a consequence,
all "multiparameter formal QUEA's" considered so far are recoverd, as falling
within this class. In particular, up to technicalities we prove that any
FoMpQUEA is isomorphic to a suitable deformation, by twist or by 2-cocycle, of
Drinfeld's standard QUEA. We introduce also multiparameter Lie bialgebras (in
short, MpLbA's), and we consider their deformations, by twist and by
2-cocycles. The semiclassical limit of every FoMpQUEA is a suitable MpLbA, and
conversely each MpLbA can be quantized to a suitable FoMpQUEA. In the end, we
prove that, roughly speaking, the two processes of "specialization" (of
FoMpQUEA to a MpLbA) and of "deformation (by toral twist or toral 2-cocycle)"
-- at the level of FoMpQUEA's or of MpLbA's -- do commute with each other.


title: Multi-View Dreaming: Multi-View World Model with Contrastive Learning

abstract:
  In this paper, we propose Multi-View Dreaming, a novel reinforcement learning
agent for integrated recognition and control from multi-view observations by
extending Dreaming. Most current reinforcement learning method assumes a
single-view observation space, and this imposes limitations on the observed
data, such as lack of spatial information and occlusions. This makes obtaining
ideal observational information from the environment difficult and is a
bottleneck for real-world robotics applications. In this paper, we use
contrastive learning to train a shared latent space between different
viewpoints, and show how the Products of Experts approach can be used to
integrate and control the probability distributions of latent states for
multiple viewpoints. We also propose Multi-View DreamingV2, a variant of
Multi-View Dreaming that uses a categorical distribution to model the latent
state instead of the Gaussian distribution. Experiments show that the proposed
method outperforms simple extensions of existing methods in a realistic robot
control task.


title:
Multigrid-augmented deep learning preconditioners for the Helmholtz
  equation

abstract:
  In this paper, we present a data-driven approach to iteratively solve the
discrete heterogeneous Helmholtz equation at high wavenumbers. In our approach,
we combine classical iterative solvers with convolutional neural networks
(CNNs) to form a preconditioner which is applied within a Krylov solver. For
the preconditioner, we use a CNN of type U-Net that operates in conjunction
with multigrid ingredients. Two types of preconditioners are proposed 1) U-Net
as a coarse grid solver, and 2) U-Net as a deflation operator with shifted
Laplacian V-cycles. Following our training scheme and data-augmentation, our
CNN preconditioner can generalize over residuals and a relatively general set
of wave slowness models. On top of that, we also offer an encoder-solver
framework where an "encoder" network generalizes over the medium and sends
context vectors to another "solver" network, which generalizes over the
right-hand-sides. We show that this option is more robust and efficient than
the stand-alone variant. Lastly, we also offer a mini-retraining procedure, to
improve the solver after the model is known. This option is beneficial when
solving multiple right-hand-sides, like in inverse problems. We demonstrate the
efficiency and generalization abilities of our approach on a variety of 2D
problems.


title:
An Introduction to Matrix factorization and Factorization Machines in
  Recommendation System, and Beyond

abstract:
  This paper aims at a better understanding of matrix factorization (MF),
factorization machines (FM), and their combination with deep algorithms'
application in recommendation systems. Specifically, this paper will focus on
Singular Value Decomposition (SVD) and its derivations, e.g Funk-SVD, SVD++,
etc. Step-by-step formula calculation and explainable pictures are displayed.
What's more, we explain the DeepFM model in which FM is assisted by deep
learning. Through numerical examples, we attempt to tie the theory to
real-world problems.


title: Reasoning over Public and Private Data in Retrieval-Based Systems

abstract:
  Users and organizations are generating ever-increasing amounts of private
data from a wide range of sources. Incorporating private data is important to
personalize open-domain applications such as question-answering, fact-checking,
and personal assistants. State-of-the-art systems for these tasks explicitly
retrieve relevant information to a user question from a background corpus
before producing an answer. While today's retrieval systems assume the corpus
is fully accessible, users are often unable or unwilling to expose their
private data to entities hosting public data. We first define the
PUBLIC-PRIVATE AUTOREGRESSIVE INFORMATION RETRIEVAL (PAIR) privacy framework
for the novel retrieval setting over multiple privacy scopes. We then argue
that an adequate benchmark is missing to study PAIR since existing textual
benchmarks require retrieving from a single data distribution. However, public
and private data intuitively reflect different distributions, motivating us to
create ConcurrentQA, the first textual QA benchmark to require concurrent
retrieval over multiple data-distributions. Finally, we show that existing
systems face large privacy vs. performance tradeoffs when applied to our
proposed retrieval setting and investigate how to mitigate these tradeoffs.


title: DSP-Packing: Squeezing Low-precision Arithmetic into FPGA DSP Blocks

abstract:
  The number of Digital Signal Processor (DSP) resources available in Field
Programmable Gate Arrays (FPGAs) is often quite limited. Therefore, full
utilization of available DSP resources for the computationally intensive parts
of an algorithm is paramount for optimizing the non-functional properties of an
implementation (i.e., performance, power, and area). The DSPs available in
Xilinx devices implement large bit width operators (i.e. a 48-bit accumulator
or a $18 \times 27$ multiplier). However, using such a DSP for low-precision
quantized data (as is common in image processing or machine learning
applications) leaves the DSP resources underutilized. As a remedy, A method has
been proposed to pack and compute four 4-bit multiplications on a single DSP in
a single clock cycle. This paper presents a generalization of this scheme to
arbitrary bit widths and number of multiplications. We also demonstrate that
the previously proposed approach leads to errors (Mean Absolute Error (MAE) =
0.37). Furthermore, we explain where these errors come from and how they can be
corrected. On top, we introduce a novel approximate method called "Overpacking"
which allows to squeeze even more multiplications into a single DSP at the cost
of small errors (MAE = 0.47). Overpacking allows to squeeze six 4-bit
multiplications into a single DSP compared to just four in the literature.
Finally, we introduce an alternative method for packing multiple small-bit
width additions into a single 48-bit accumulator for use in applications such
as Spiking Neural Networks.


title:
The effects of a far-infrared photon cavity field on the magnetization
  of a square quantum dot array

abstract:
  The orbital and spin magnetization of a cavity-embedded quantum dot array
defined in a GaAs heterostructure are calculated within
quantum-electrodynamical density-functional theory (QEDFT). To this end a
gradient-based exchange-correlation functional recently employed for atomic
systems is adapted to the hosting two-dimensional electron gas (2DEG) submitted
to an external perpendicular homogeneous magnetic field. Numerical results
reveal the polarizing effects of the cavity photon field on the electron charge
distribution and nontrivial changes of the orbital magnetization. We discuss
its intertwined dependence on the electron number in each dot, and on the
electron-photon coupling strength. In particular, the calculated dispersion of
the photon-dressed electron states around the Fermi energy as a function of the
electron-photon coupling strength indicates the formation of
magnetoplasmon-polaritons in the dots.


title:
The evolution of coherent vortical structures in increasingly turbulent
  stratified shear layers

abstract:
  We study the morphology of Eulerian vortical structures and their interaction
with density interfaces in increasingly turbulent stably-stratified shear
layers. We analyse the three-dimensional, simultaneous velocity and density
fields obtained in the stratified inclined duct laboratory experiment. We
track, across 15 datasets, the evolution of coherent structures from
pre-turbulent Holmboe waves, through intermittent turbulence, to full
turbulence and mixing. We use the Rortex--Shear decomposition of the vorticity
field into a pure rotational part (the rortex vector), and a non-rotational
part (the shear vector). We describe the morphology of ubiquitous hairpin-like
vortical structures (revealed by the rortex), similar to those commonly
observed in boundary-layer turbulence. These are born as relatively weak
vortices around the strong three-dimensional shearing structures of confined
Holmboe waves, and gradually strengthen and deform under increasing turbulence,
transforming into pairs of upward- and downward-pointing hairpins propagating
in opposite directions on the top and bottom edge of the shear layer. Each
hairpin's pair of legs are counter-rotating and entrain fluid laterally and
vertically, and their arched-up `heads', which are transverse vortices, entrain
fluid vertically. We then elucidate how this large-scale vortex morphology
stirs and mixes the density field. Essentially, vortices located at the sharp
density interface on either edge of the mixing layer (mostly hairpin heads)
engulf blobs of unmixed fluid into the mixing layer, while vortices inside the
mixing layer (mostly hairpin legs) further stir it, generating strong,
small-scale shear, enhancing mixing. These findings provide new insights into
the role of turbulent coherent structures in shear-driven stratified mixing.


title: Finite temperature applications in G\"odel space-time

abstract:
  Temperature effects in a scalar field non-minimally coupled to gravity are
investigated. The Thermo Field Dynamics formalism is used. This is a
topological field theory that allows us to calculate different effects, such as
the Stefan-Boltzmann law and the Casimir effect, on an equal footing. These
phenomena are calculated assuming the G\"odel space-time as a gravitational
background. A possible implication of these results at the beginning of the
universe is discussed.


title:
Long-Range Order and Quantum Criticality in Antiferromagnetic Chains
  with Long-Range Staggered Interactions

abstract:
  We study quantum phase transitions in Heisenberg antiferromagnetic chains
with a staggered power-law decaying long-range interactions. Employing the
density-matrix renormalization group (DMRG) algorithm and the fidelity
susceptibility as the criticality measure, we establish more accurate values of
quantum critical points than the results obtained from the spin-wave
approximation, quantum Monte Carlo and DMRG in literatures. The deviation is
especially evident for strong long-range interactions. We extend isotropic
long-range interactions to the anisotropic cases and find that kaleidoscope of
quantum phases emerge from the interplay of anisotropy of the long-range
exchange interaction and symmetry breaking. We demonstrate nonfrustrating
long-range interactions induce the true long-range order in Heisenberg
antiferromagnetic chains with a continuous symmetry breaking, lifting the
restrictions imposed by the Mermin-Wagner theorem.


title:
Lithography Free Process for the Fabrication of Periodic Silicon
  Micro/Nano-Wire Arrays and Its Light-trapping Properties

abstract:
  Vertically aligned silicon micro/nanowire arrays of different sizes have been
synthesized by combining the modified metal-assisted chemical etching (MACE)
and reactive ion etching (RIE) methods. This is a novel lithography-free method
to fabricate silicon micro/nanowire arrays. The size of micro/nanowire arrays
is controlled by controlling the etching rate and diameter of silica particles.
The silicon micro/nanowire geometry can utilize for efficient collection of
photo-generated charge carriers from impure silicon wafers, which have a short
minority carrier diffusion length also act as a self-antireflection coating
layer. For micro/nanowire having average diameters of 40 nm, 330 nm and 950 nm
and their corresponding average length 1.12 micron, 1.1 micron, and 1 micron,
respectively, the observed average reflectance was 0.22, 0.6 and 0.33 percent
at 45-degree incident angle, while the average reflectance was increased up to
4.2, 9.2, and 11 percent, respectively at 75-degree incident angle in the broad
range of 300 - 1200 nm of the solar spectrum. The measured average reflectance
for these samples is quite low compared to the planar silicon wafer. Thus this
geometry is a promising candidate for fabricating low-cost and highly efficient
radial junction silicon micro/nanowire arrays based solar cells.


title: A new perspective on probabilistic image modeling

abstract:
  We present the Deep Convolutional Gaussian Mixture Model (DCGMM), a new
probabilistic approach for image modeling capable of density estimation,
sampling and tractable inference. DCGMM instances exhibit a CNN-like layered
structure, in which the principal building blocks are convolutional Gaussian
Mixture (cGMM) layers. A key innovation w.r.t. related models like sum-product
networks (SPNs) and probabilistic circuits (PCs) is that each cGMM layer
optimizes an independent loss function and therefore has an independent
probabilistic interpretation. This modular approach permits intervening
transformation layers to harness the full spectrum of (potentially
non-invertible) mappings available to CNNs, e.g., max-pooling or
half-convolutions. DCGMM sampling and inference are realized by a deep chain of
hierarchical priors, where a sample generated by a given cGMM layer defines the
parameters of sampling in the next-lower cGMM layer. For sampling through
non-invertible transformation layers, we introduce a new gradient-based
sharpening technique that exploits redundancy (overlap) in, e.g.,
half-convolutions. DCGMMs can be trained end-to-end by SGD from random initial
conditions, much like CNNs. We show that DCGMMs compare favorably to several
recent PC and SPN models in terms of inference, classification and sampling,
the latter particularly for challenging datasets such as SVHN. We provide a
public TF2 implementation.


title:
Continuous Flow Model of a Historical Battle: A Fresh Look at Pickett's
  charge

abstract:
  A continuous flow model of infantry behavior, based on conservation of
individuals and tracking of subunit identity, has been developed in sufficient
detail that it can now be applied to a realistic simulation of a historical
battle. Pickett's charge during the 1863 Battle of Gettysburg, Pennsylvania in
the U.S. Civil War was chosen as an initial application of the model. This
scenario is a good test of the current mathematical model because many modern
military tactics were employed, in a context where the action took place on
foot or horseback, and the historical map and troop numbers are available.
Compared to a discrete agent model, the flow model was found to better capture
the interaction of the forces with the terrain and each other. A brigade-level
simulation, faithful to the details of the historical events, was performed.
The main source of asymmetry in the numbers of casualties was found to be the
inability of the Confederate forces to use effective ranged fire while they
were moving. Comparison of simulations with and without terrain effects showed
that they slow the pace of battle and favor the defenders, exposing the
attackers to heavy ranged fire for an extended period. A statistical analysis
of possible outcomes for an ensemble of 1000 randomized perturbations of the
baseline brigade-level scenario was carried out. Consistent with historical
events, it was found that only 6 percent of the scenarios resulted in an
outcome that could be considered a Confederate victory.


title:
Fully-Quantum-Theoretic Numerical Study on Quantum Phase Sensing and
  Ghost Imaging Systems Operating with Multimode N00N States

abstract:
  We present a numerical study on the super-resolution of quantum phase sensing
and ghost imaging systems operating with multimode N00N states beyond the
Rayleigh diffraction limit. Our computational simulations are based on the
canonical quantization via numerical mode-decomposition (CQ-NMD) [1,2], in
which normal (eigen) modes of electromagnetic fields in inhomogeneous
dielectric media are numerically found using computational electromagnetics
methods. In the CQ-NMD framework and the Heisenberg picture, the expectation
value of arbitrary observables with respect to initial quantum states of
various non-classical lights can be evaluated with the use of Wick's theorem.
The present numerical framework has a great potential to deal with scattering
problems of entangled photons due to arbitrary dielectric objects.


title:
Stationary measures for the log-gamma polymer and KPZ equation in
  half-space

abstract:
  We construct explicit one-parameter families of stationary measures for the
Kardar-Parisi-Zhang equation in half-space with Neumann boundary conditions at
the origin, as well as for the log-gamma polymer model in a half-space. The
stationary measures are stochastic processes that depend on the boundary
condition as well as a parameter related to the drift at infinity. They are
expressed in terms of exponential functions of Brownian motions and gamma
random walks. We conjecture that these constitute all extremal stationary
measures for these models. The log-gamma polymer result is proved through a
symmetry argument related to half-space Whittaker processes which we expect may
be applicable to other integrable models. The KPZ result comes as an
intermediate disorder limit of the log-gamma polymer result and confirms the
conjectural description of these stationary measures from arXiv:2105.15178. To
prove the intermediate disorder limit, we provide a general half-space polymer
convergence framework that extends works of arXiv:1804.09815, arXiv:1901.09449,
arXiv:1202.4398.

categories: math.PR cond-mat.stat-mech math-ph math.MP


title:
Local Unitarity: cutting raised propagators and localising
  renormalisation

abstract:
  The Local Unitarity (LU) representation of differential cross-sections
locally realises the cancellations of infrared singularities predicted by the
Kinoshita-Lee-Nauenberg theorem. In this work we solve the two remaining
challenges to enable practical higher-loop computations within the LU
formalism. The first concerns the generalisation of the LU representation to
graphs with raised propagators. The solution to this problem results in a
generalisation of distributional Cutkosky rules. The second concerns the
regularisation of ultraviolet and spurious soft singularities, solved using a
fully automated and local renormalisation procedure based on Bogoliubov's
R-operation. We detail an all-order construction for the hybrid
$\overline{\text{MS}}$ and On-Shell scheme whose only analytic input is
single-scale vacuum diagrams. Using this novel technology, we provide
(semi-)inclusive results for two multi-leg processes at NLO, study limits of
individual supergraphs up to N3LO and present the first physical NNLO
cross-sections computed fully numerically in momentum-space, namely for the
processes $\gamma^* \rightarrow j j$ and $\gamma^* \rightarrow t \bar{t}$.


title:
Nested Open Quantum Systems Approach to Photonic Bose$-$Einstein
  Condensation

abstract:
  The photonic Bose$-$Einstein condensate is a recently observed collective
ground state of a coupled light-matter system. We describe this quantum state
based on macroscopic quantum electrodynamics in dispersing and absorbing
environments. To model the coupled photon$-$dye dynamics, we derive a master
equation using a nested open quantum systems approach yielding all parameters
essential to describe the condensation process. This approach allows us to
describe photon condensates of arbitrary shapes because all geometry-dependent
decay constants can be expressed in terms of the Green's tensor. In particular,
we obtain the cavity mode absorption and emission rates of the dye molecules.


title: A group-valued invariant of knots in the full torus

abstract:
  Knot concordance plays a crucial role in the low dimensional topology. We
propose a very elementary techniques which allows one to construct a lot of
sliceness obstructions for knots in the full torus. Our approach deals with
group theoretical techniques; it is completely combinatorial, and the groups
are very easy to deal with.


title: Warm deformed $R^{2}$ inflation

abstract:
  In this work, we study warm inflationary scenario based on a deformation of
$R^{2}$ gravity. We start considering $R^{p}$ and assume $p=2(1+\delta)$ with
$\delta\ll 1$ so that we simply obtain warm $R^{2}$ inflation when setting
$\delta=0$. We then derive the potential in the Einstein frame and consider a
dissipation parameter of the form $\Gamma = C_{1}T$ with $C_1$ being a coupling
parameter. We focus only on the strong regime of which the interaction between
inflaton and radiation fluid has been taken into account. We compute
inflationary observables and constrain the parameters of our model using latest
observational data reported by Planck. From our analysis, we discover that with
proper choices of parameters the derived $n_s$ and $r$ are in good agreement
with the Planck 2018 observational constraints. Particularly, we constrain the
potential scale $U_{0}$ of the models.


title:
Quantifying the Magnetic Structure of a Coronal Shock Producing a Type
  II Radio Burst

abstract:
  Type II radio bursts are thought to be produced by shock waves in the solar
atmosphere. However, what magnetic conditions are needed for the generation of
type II radio bursts is still a puzzling issue. Here, we quantify the magnetic
structure of a coronal shock associated with a type II radio burst. Based on
the multi-perspective extreme-ultraviolet observations, we reconstruct the
three-dimensional (3D) shock surface. By using a magnetic field extrapolation
model, we then derive the orientation of the magnetic field relative to the
normal of the shock front ($\theta_{\rm Bn}$) and Alfv\'{e}n Mach number
($M_A$) on the shock front. Combining the radio observations from Nancay Radio
Heliograph, we obtain the source region of the type II radio burst on the shock
front. It is found that the radio burst is generated by a shock with $M_A
\gtrsim 1.5$ and a bimodal distribution of $\theta_{Bn}$. We also use the
Rankine-Hugoniot relations to quantify the properties of the shock downstream.
Our results provide a quantitative 3D magnetic structure condition of a coronal
shock that produces a type II radio burst.


title:
G-CoReCCD: a GPU-based simulator of the charge transport in
  fully-depleted CCDs

abstract:
  We introduce a simulator of charge transport in fully-depleted, thick CCDs
that include Coulomb repulsion between carriers. The calculation of this
long-range interaction is highly intensive computationally, and only a few
thousands of carriers can be simulated in reasonable times using regular CPUs.
G-CoReCCD takes advantage of the high number of multiprocessors available in a
graphical processing unit (GPU) to parallelize the operations and thus achieve
a massive speedup. We can simulate the path inside the CCD bulk for up to
hundreds of thousands of carriers in only a few hours using modern GPUs.


title:
Feature-specific correlation of structural, optical, and chemical
  properties in the transmission electron microscope with hypermodal data
  fusion

abstract:
  Modern TEM instrumentation can probe a wide range of structural, optical, and
chemical properties with unprecedented resolution. However, each of these
properties must be recorded in independent datasets using different detector
modes with no unifying framework currently available for quantitatively mapping
their relationships onto chemically distinct features, particularly in complex
morphologies. Here, we tackle this challenge by proposing a data acquisition
and analysis workflow called "hypermodal data fusion," describing how to
directly couple an arbitrary number of highly disparate detector modes
including spectroscopy and scanning diffraction and jointly analyze them for
correlations. We demonstrate this concept on a random collection of anatase and
rutile nanoparticles, first detailing how to use core-loss EELS to unmix the
different polymorphs despite three-dimensional overlap along the beam direction
and then showing how this can be used to extract polymorph-specific
composition, bandgaps, and crystal structure. We conclude with a discussion on
the applicability of this workflow for a broad range of materials systems.


title:
First measurement of the correlation between cosmic voids and the
  Lyman-$\alpha$ forest

abstract:
  We report the first detection of large-scale matter flows around cosmic voids
at a median redshift z = 2.49. Voids are identified within a tomographic map of
the large-scale matter density built from eBOSS Lyman-$\alpha$ (Lya) forests in
SDSS Stripe 82. We measure the imprint of flows around voids, known as
redshift-space distortions (RSD), with a statistical significance of 10
$\sigma$. The observed quadrupole of the void-forest cross-correlation is
described by a linear RSD model. The derived RSD parameter is $\beta = 0.52 \pm
0.05$. Our model accounts for the tomographic effect induced by the Lya data
being located along parallel quasar lines of sight. This work paves the way
towards growth-rate measurements at redshifts currently inaccessible to galaxy
surveys.


title:
$\mathcal{A}$-caloric approximation and partial regularity for parabolic
  systems with Orlicz growth

abstract:
  We prove a new $\mathcal{A}$-caloric approximation lemma compatible with an
Orlicz setting. With this result, we establish a partial regularity result for
parabolic systems of the type $$ u_{t}- {\rm div} \,a(Du)=0. $$ Here the growth
of $a$ is bounded by the derivative of an $N$-function $\varphi$. The primary
assumption for $\varphi$ is that $t\varphi''(t)$ and $\varphi'(t)$ are
uniformly comparable on $(0,\infty)$.


title:
Transformative Technology for FLASH Radiation Therapy: A Snowmass 2021
  White Paper

abstract:
  Conventional cancer therapies include surgery, radiation therapy,
chemotherapy, and, more recently, immunotherapy. These modalities are often
combined to improve the therapeutic index. The general concept of radiation
therapy is to increase the therapeutic index by creating a physical dose
differential between tumors and normal tissues through precision dose
targeting, image guidance, and high radiation beams that deliver radiation dose
with high conformality, e.g., protons and ions. However, treatment and cure are
still limited by normal tissue radiation toxicity, with many patients
experiencing acute and long-term side effects. Recently, however, a
fundamentally different paradigm for increasing the therapeutic index of
radiation therapy has emerged, supported by preclinical research, and based on
the FLASH radiation effect. FLASH radiation therapy (FLASH-RT) is an ultra-high
dose-rate delivery of a therapeutic radiation dose within a fraction of a
second. Experimental studies have shown that normal tissues seem to be
universally spared at these high dose rates, whereas tumors are not. The dose
delivery conditions are not yet fully characterized. Still, it is currently
estimated that large doses of 10 Gy or more delivered in 200 ms or less produce
normal tissue sparing effects yet effectively kill tumor cells. There is a
great opportunity, but also many technical challenges, for the accelerator
community to create the required dose rates with novel and compact accelerators
to ensure the safe delivery of FLASH radiation beams.


title:
How Do We Answer Complex Questions: Discourse Structure of Long-form
  Answers

abstract:
  Long-form answers, consisting of multiple sentences, can provide nuanced and
comprehensive answers to a broader set of questions. To better understand this
complex and understudied task, we study the functional structure of long-form
answers collected from three datasets, ELI5, WebGPT and Natural Questions. Our
main goal is to understand how humans organize information to craft complex
answers. We develop an ontology of six sentence-level functional roles for
long-form answers, and annotate 3.9k sentences in 640 answer paragraphs.
Different answer collection methods manifest in different discourse structures.
We further analyze model-generated answers -- finding that annotators agree
less with each other when annotating model-generated answers compared to
annotating human-written answers. Our annotated data enables training a strong
classifier that can be used for automatic analysis. We hope our work can
inspire future research on discourse-level modeling and evaluation of long-form
QA systems.


title: Differentiable Duration Modeling for End-to-End Text-to-Speech

abstract:
  Parallel text-to-speech (TTS) models have recently enabled fast and
highly-natural speech synthesis. However, such models typically require
external alignment models, which are not necessarily optimized for the decoder
as they are not jointly trained. In this paper, we propose a differentiable
duration method for learning monotonic alignments between input and output
sequences. Our method is based on a soft-duration mechanism that optimizes a
stochastic process in expectation. Using this differentiable duration method, a
direct text to waveform TTS model is introduced to produce raw audio as output
instead of performing neural vocoding. Our model learns to perform
high-fidelity speech synthesis through a combination of adversarial training
and matching the total ground-truth duration. Experimental results show that
our model obtains competitive results while enjoying a much simpler training
pipeline. Audio samples are available online.


title: Subdiffraction Quantum Imaging with Undetected Photons

abstract:
  We propose a nonlinear imaging scheme with undetected photons that overcomes
the diffraction limit by transferring near-field information at one wavelength
to far-field information of a correlated photon with a different wavelength
generated through spontaneous photon-pair generation. At the same time, this
scheme allows for retrieval of high-contrast images with zero background,
making it a highly sensitive scheme for imaging of small objects at challenging
spectral ranges with subdiffraction resolutions.


title:
Hydrodynamic spin-orbit coupling in asynchronous optically driven
  micro-rotors

abstract:
  We investigate the hydrodynamic coupling between micron-sized optically
driven rotors. Driving particles with defocused circularly polarized light, we
find that birefringent colloids spin asynchronously, while freely diffusing in
plane. We characterize the coupling of the optical angular momentum to the
particles, and develop a hydrodynamic model for their flow profile using a
far-field approximation. Experimentally, we find that particles' spins and
orbit are coupled through a simple geometrical relation that depends only on
the power law of the flow.


title:
Signatures of anisotropic diffusion around PeVatrons in 100 TeV
  gamma-ray data

abstract:
  The Tibet AS$\gamma$ collaboration has reported a diffuse $\gamma$-ray
emission signal from the Galactic Plane. We consider the possibility that the
diffuse emission from the outer Galactic Plane at the highest energies is
produced by cosmic rays spreading from a single supernova-type source either in
the Local or Perseus arm of the Milky Way. We show that anisotropic diffusion
of multi-PeV cosmic rays along the Galactic magnetic field can produce an
extended source spanning ten(s) of degrees on the sky, with a
flux-per-unit-solid-angle consistent with Tibet AS$\gamma$ measurements.
Observations of this new type of very extended sources, and measurements of
their morphologies, can be used to characterize the anisotropic diffusion of
PeV cosmic rays in the Galactic magnetic field, and to constrain the locations
and properties of past PeVatrons.


title:
A generic framework of adiabatic approximation for nonlinear evolutions
  II

abstract:
  In this paper, we continue the development of a generic adiabatic scheme for
nonlinear evolutions. We consider an abstract gradient flow of some energy
functional, together with a given manifold of static solutions arising from
broken symmetries. First, we list a number of explicit and generic conditions
on the energy functional that ensures the validity of our adiabatic scheme.
Then, we construct some explicit low-energy but no necessarily static
configurations, which form a stable manifold with finite codimensions for the
given gradient flow. Thirdly, we show that the gradient flow is globally
well-posed with initial configuration from the stable manifold. Finally, we
show that any solution to the full gradient flow starting from the stable
manifold is essentially governed by an effective equation on the manifold of
static solutions, up to a uniformly small and dissipating error term.


title:
Targeted Extraction of Temporal Facts from Textual Resources for
  Improved Temporal Question Answering over Knowledge Bases

abstract:
  Knowledge Base Question Answering (KBQA) systems have the goal of answering
complex natural language questions by reasoning over relevant facts retrieved
from Knowledge Bases (KB). One of the major challenges faced by these systems
is their inability to retrieve all relevant facts due to factors such as
incomplete KB and entity/relation linking errors. In this paper, we address
this particular challenge for systems handling a specific category of questions
called temporal questions, where answer derivation involve reasoning over facts
asserting point/intervals of time for various events. We propose a novel
approach where a targeted temporal fact extraction technique is used to assist
KBQA whenever it fails to retrieve temporal facts from the KB. We use
$\lambda$-expressions of the questions to logically represent the component
facts and the reasoning steps needed to derive the answer. This allows us to
spot those facts that failed to get retrieved from the KB and generate textual
queries to extract them from the textual resources in an open-domain question
answering fashion. We evaluated our approach on a benchmark temporal question
answering dataset considering Wikidata and Wikipedia respectively as the KB and
textual resource. Experimental results show a significant $\sim$30\% relative
improvement in answer accuracy, demonstrating the effectiveness of our
approach.


title: backbone: An R package to extract network backbones

abstract:
  Networks are useful for representing phenomena in a broad range of domains.
Although their ability to represent complexity can be a virtue, it is sometimes
useful to focus on a simplified network that contains only the most important
edges: the backbone. This paper introduces and demonstrates the `backbone'
package for R, which implements methods for extracting the backbone from
weighted networks, weighted bipartite projections, and unweighted networks. For
each type of network, fully replicable code is presented first for small toy
examples, then for complete empirical examples using transportation, political,
and social networks. The paper also demonstrates the implications of several
issues of statistical inference that arise in backbone extraction. It concludes
by briefly reviewing existing applications of backbone extraction using the
`backbone' package, and future directions for research on network backbone
extraction.


title: Mass and width of an unstable particle

abstract:
  We show that the mass and width of an unstable particle are precisely defined
by the pole in the complex energy plane, $\mu = m - (i/2)\Gamma$, by using the
defining relationship between the width and the lifetime, $\Gamma = 1/\tau$. We
find that the physical $Z$ boson mass lies 26 MeV below its quoted value, while
the physical $W$ boson mass lies 20 MeV below. We also clarify the various
Breit-Wigner formulae that are used to describe a resonance.


title:
Constraint-Driven Optimal Control for Emergent Swarming and Predator
  Avoidance

abstract:
  In this letter, we present a constraint-driven optimal control framework that
achieves emergent cluster flocking within a constrained 2D environment. We
formulate a decentralized optimal control problem that includes safety,
flocking, and predator avoidance constraints. We explicitly derive conditions
for constraint compatibility and propose an event-driven constraint relaxation
scheme, which we map to an equivalent finite state machine that intuitively
describes the behavior of each agent in the system. Instead of minimizing
control effort, as it is common in the ecologically-inspired robotics
literature, in our approach, we minimize each agent's deviation from their most
efficient locomotion speed. Finally, we demonstrate our approach in simulation
both with and without the presence of a predator.


title: K-stability of Gorenstein Fano group compactifications with rank two

abstract:
  We give a classification of Gorenstein Fano bi-equivariant compactifications
of semisimple complex Lie groups with rank two, and determine which of them are
equivariant K-stable and admit (singular) K\"{a}hler-Einstein metrics. As a
consequence, we obtain several explicit examples of K-stable Fano varieties
admitting (singular) K\"{a}hler-Einstein metrics. We also compute the greatest
Ricci lower bounds, equivalently the delta invariants for K-unstable varieties.
This gives us three new examples on which each solution of the K\"{a}hler-Ricci
flow is of type II.


title: GRB 190919B: Rapid optical rise explained as a flaring activity

abstract:
  Following the detection of a long GRB 190919B by INTEGRAL (INTErnational
Gamma-Ray Astrophysics Laboratory), we obtained an optical photometric sequence
of its optical counterpart. The light curve of the optical emission exhibits an
unusually steep rise ~100 s after the initial trigger. This behaviour is not
expected from a 'canonical' GRB optical afterglow. As an explanation, we
propose a scenario consisting of two superimposed flares: an optical flare
originating from the inner engine activity followed by the hydrodynamic peak of
an external shock. The inner-engine nature of the first pulse is supported by a
marginal detection of flux in hard X-rays. The second pulse eventually
concludes in a slow constant decay, which, as we show, follows the closure
relations for a slow cooling plasma expanding into the constant interstellar
medium and can be seen as an optical afterglow sensu stricto.


title: Volumetric theory of intermittency in fully developed turbulence

abstract:
  This study introduces a new family of volumetric flatness factors which give
a rigorous parametric description of the phenomenon of intermittency in fully
developed turbulent flows. These quantities gather information about the most
"active" part of a velocity field at each scale $\ell$, and allows one to
define a dimension function $p \to D_p$ that recovers intermittency correction
to the structure exponents $\zeta_p$ in an explicit way. In particular, the
predictions of the Frisch-Parisi multifractal formalism can be recovered in a
systematic and rigorous way.
  Within this framework we identify active regions that carry the most
energetic part of a velocity field at a given scale $\ell$. A threshold for
what constitutes to be active is defined explicitly. Active regions have proven
to be experimentally observable in our previous joint work \cite{Ph-paper}, and
shown to capture concentration of the energy cascade as $\ell \to 0$, in
\cite{CS2014}.
  We present several examples of fields which exhibit arbitrary multifractal
spectrum within theoretically permitted limitations. At the same time we
demonstrate with the use of a probabilistic argument that a random field is
expected to produce the classical K41 spectrum in the limit $\ell\to 0$.
Intermittent deviations from K41 theory are estimated at any finite scale also.
  Lastly, we present a detailed information-theoretic analysis of the
introduced objects. In particular, we quantify concentration of a given
source-field in terms of the volume factors, thresholds, and active regions.


title: A comprehensive study of decay modes associated with Pb isotopes

abstract:
  Decay modes in Pb isotopes within the range 176$\leq$A$\leq$266 are
investigated by the calculation of half-lives using several empirical formulas.
These formulas along with various theoretical treatments are first tested to
reproduce experimental half-lives and known decay modes of Pb isotopes, which
are in consequence applied to estimate half-lives and decay modes of unknown Pb
isotopes. A comparison between $\alpha$-decay and weak-decay from the stable to
drip-line isotopes is canvassed which leads to the excellent match with
experimental data and applicability of applied empirical formulas. In addition,
the full chain of Pb isotopes is probed as potential daughter candidates of
cluster emission from superheavy nuclei which ensued the predominant role of
$^{208}$Pb and nearby isotopes in probable cluster emission.


title:
Inscribable Fans II: Inscribed zonotopes, simplicial arrangements, and
  reflection groups

abstract:
  An arrangement of hyperplanes is strongly inscribable if it has an inscribed
(or ideal hyperbolic) zonotope. We characterize inscribed zonotopes and prove
that the family of strongly inscribable arrangements is closed under
restriction and localization. Moreover, we show that (strongly) inscribable
arrangements are simplicial. We conjecture that only reflection arrangements
and their restrictions are strongly inscribable and we verify our conjecture in
rank-$3$ using the conjecturally complete list of irreducible simplicial
rank-$3$ arrangements.


title:
Non-Functional Requirements for Machine Learning: An Exploration of
  System Scope and Interest

abstract:
  Systems that rely on Machine Learning (ML systems) have differing demands on
system quality compared to traditional systems. Such quality demands, known as
non-functional requirements (NFRs), may differ in their definition, scope, and
importance from NFRs for traditional systems. Despite the importance of NFRs
for ML systems, our understanding of their definitions and scope -- and of the
extent of existing research in each NFR -- is lacking compared to our
understanding in traditional domains.
  Building on an investigation into importance and treatment of ML system NFRs
in industry, we make three contributions towards narrowing this gap: (1) we
present clusters of ML system NFRs based on shared characteristics, (2) we use
Scopus search results -- as well as inter-coder reliability on a sample of NFRs
-- to estimate the number of relevant studies on a subset of the NFRs, and (3),
we use our initial reading of titles and abstracts in each sample to define the
scope of NFRs over parts of the system (e.g., training data, ML model, or other
system elements). These initial findings form the groundwork for future
research in this emerging domain.


title: The Rational Homotopy Type of Homotopy Fibrations Over Connected Sums

abstract:
  We provide a simple condition on rational cohomology for the total space of a
pullback fibration over a connected sum to have the rational homotopy type of a
connected sum, after looping. This takes inspiration from recent work of
Jeffrey and Selick, in which they study pullback fibrations of this type, but
under stronger hypotheses compared to our result.


title:
Optimizing Revenue Maximization and Demand Learning in Airline Revenue
  Management

abstract:
  Correctly estimating how demand respond to prices is fundamental for airlines
willing to optimize their pricing policy. Under some conditions, these
policies, while aiming at maximizing short term revenue, can present too little
price variation which may decrease the overall quality of future demand
forecasting. This problem, known as earning while learning problem, is not
exclusive to airlines, and it has been investigated by academia and industry in
recent years. One of the most promising methods presented in literature
combines the revenue maximization and the demand model quality into one single
objective function. This method has shown great success in simulation studies
and real life benchmarks. Nevertheless, this work needs to be adapted to
certain constraints that arise in the airline revenue management (RM), such as
the need to control the prices of several active flights of a leg
simultaneously. In this paper, we adjust this method to airline RM while
assuming unconstrained capacity. Then, we show that our new algorithm
efficiently performs price experimentation in order to generate more revenue
over long horizons than classical methods that seek to maximize revenue only.


title:
Robust and generic properties for piecewise continuous maps on the
  interval

abstract:
  We construct an appropriate metric on the collection of piecewise $\mathcal
C^r$ maps defined on a compact interval. Although this metric space turns out
to be not complete, we show that it is indeed a Baire space. As an application,
we prove the robustness of non-degenerate critical points for this class of
systems and we show the genericity of piecewise Lipschitz maps that admit an
invariant Borel probability measure.


title: A nonlinear forward-backward problem

abstract:
  We prove existence and uniqueness of strong solutions of the equation $u u_x
- u_{yy} = f$ in the vicinity of the linear shear flow, subject to
perturbations of the source term and lateral boundary conditions. Since the
solutions we consider have opposite signs in the lower and upper half of the
domain, this is a forward-backward parabolic problem, which changes type across
a critical curved line within the domain. In particular, lateral boundary
conditions can be imposed only where the characteristics are inwards.
  There are several difficulties associated with this problem. First, the
forward-backward geometry depends on the solution itself. This requires to be
quite careful with the approximation procedure used to construct solutions.
Second, and maybe more importantly, the linearized equations solved at each
step of the iterative scheme admit a finite number of singular solutions. This
is similar to well-known phenomena in elliptic problems in nonsmooth domains.
Hence, the solutions of the equation are regular if and only if the source
terms satisfy a finite number of orthogonality conditions. A key difficulty of
this work is to cope with these orthogonality conditions during the nonlinear
fixed-point scheme. In particular, we are led to prove their stability with
respect to the underlying base flow.


title:
Learning Enriched Illuminants for Cross and Single Sensor Color
  Constancy

abstract:
  Color constancy aims to restore the constant colors of a scene under
different illuminants. However, due to the existence of camera spectral
sensitivity, the network trained on a certain sensor, cannot work well on
others. Also, since the training datasets are collected in certain
environments, the diversity of illuminants is limited for complex real world
prediction. In this paper, we tackle these problems via two aspects. First, we
propose cross-sensor self-supervised training to train the network. In detail,
we consider both the general sRGB images and the white-balanced RAW images from
current available datasets as the white-balanced agents. Then, we train the
network by randomly sampling the artificial illuminants in a sensor-independent
manner for scene relighting and supervision. Second, we analyze a previous
cascaded framework and present a more compact and accurate model by sharing the
backbone parameters with learning attention specifically. Experiments show that
our cross-sensor model and single-sensor model outperform other
state-of-the-art methods by a large margin on cross and single sensor
evaluations, respectively, with only 16% parameters of the previous best model.


title:
Model BOSS & eBOSS Luminous Red Galaxies at 0.2 < z < 1.0 using SubHalo
  Abundance Matching with 3 parameters

abstract:
  SubHalo Abundance Matching (SHAM) is an empirical method for constructing
galaxy catalogues based on high-resolution $N$-body simulations. We apply SHAM
on the UNIT simulation to simulate SDSS BOSS/eBOSS Luminous Red Galaxies (LRGs)
within a wide redshift range of $0.2 < z < 1.0$. Besides the typical SHAM
scatter parameter $\sigma$, we include $v_{\rm smear}$ and $V_{\rm ceil}$ to
take into account the redshift uncertainty and the galaxy incompleteness
respectively. These two additional parameters are critical for reproducing the
observed 2PCF multipoles on 5--25$\,h^{-1}\,{\rm Mpc}$. The redshift
uncertainties obtained from the best-fitting $v_{\rm smear}$ agree with those
measured from repeat observations for all SDSS LRGs except for the LOWZ sample.
We explore several potential systematics but none of them can explain the
discrepancy found in LOWZ. Our explanation is that the LOWZ galaxies might
contain another type of galaxies which needs to be treated differently. The
evolution of the measured $\sigma$ and $V_{\rm ceil}$ also reveals that the
incompleteness of eBOSS galaxies decreases with the redshift. This is the
consequence of the magnitude lower limit applied in eBOSS LRG target selection.
Our SHAM also set upper limits for the intrinsic scatter of the galaxy--halo
relation given a complete galaxy sample: $\sigma_{\rm int}<0.31$ for LOWZ at
$0.2<z<0.33$, $\sigma_{\rm int}<0.36$ for LOWZ at $0.33<z<0.43$, and
$\sigma_{\rm int}<0.46$ for CMASS at $0.43<z<0.51$. The projected 2PCFs of our
SHAM galaxies also agree with the observational ones on the 2PCF fitting range.


title:
From Concept Drift to Model Degradation: An Overview on
  Performance-Aware Drift Detectors

abstract:
  The dynamicity of real-world systems poses a significant challenge to
deployed predictive machine learning (ML) models. Changes in the system on
which the ML model has been trained may lead to performance degradation during
the system's life cycle. Recent advances that study non-stationary environments
have mainly focused on identifying and addressing such changes caused by a
phenomenon called concept drift. Different terms have been used in the
literature to refer to the same type of concept drift and the same term for
various types. This lack of unified terminology is set out to create confusion
on distinguishing between different concept drift variants. In this paper, we
start by grouping concept drift types by their mathematical definitions and
survey the different terms used in the literature to build a consolidated
taxonomy of the field. We also review and classify performance-based concept
drift detection methods proposed in the last decade. These methods utilize the
predictive model's performance degradation to signal substantial changes in the
systems. The classification is outlined in a hierarchical diagram to provide an
orderly navigation between the methods. We present a comprehensive analysis of
the main attributes and strategies for tracking and evaluating the model's
performance in the predictive system. The paper concludes by discussing open
research challenges and possible research directions.


title:
The near-core rotation of HD 112429: a gamma Doradus star with TESS
  photometry and legacy spectroscopy

abstract:
  The TESS space mission provides us with high-precision photometric
observations of bright stars over more than 70% of the entire sky, allowing us
to revisit and characterise well-known stars. We aim to conduct an
asteroseismic analysis of the gamma Doradus star HD112429 using both the
available ground-based spectroscopy and TESS photometry, and assess the
conditions required to measure the near-core rotation rate and buoyancy travel
time. We collect and reduce the available five sectors of short-cadence TESS
photometry of this star, as well as 672 legacy observations from six medium- to
high-resolution ground-based spectrographs. We determine the stellar pulsation
frequencies from both data sets using iterative prewhitening, do asymptotic g
mode modelling of the star and investigate the corresponding spectral line
profile variations using the pixel-by-pixel method. We validate the pulsation
frequencies from the TESS data up to $S/N \geq 5.6$, confirming recent reports
in the literature that the classical criterion $S/N \geq 4$ does not suffice
for space-based observations. We identify the pulsations as prograde dipole g
modes and r-mode pulsations, and measure a near-core rotation rate of $1.536(3)
d^{-1}$ and a buoyancy travel time $\Pi_0$ of 4190(50) s. These results are in
agreement with the observed spectral line profile variations, which were
qualitatively evaluated using a newly developed toy model. We establish a set
of conditions that have to be fulfilled for an asymptotic asteroseismic
analysis of g-mode pulsators. In the case of HD112429, two TESS sectors of
space photometry suffice. Although a detailed asteroseismic modelling analysis
is not viable for g-mode pulsators with only short or sparse light curves of
space photometry, we find that it is possible to determine global asteroseismic
quantities for a subset of these stars. (abbreviated.)


title:
Representation for martingales living after a random time with
  applications

abstract:
  Our financial setting consists of a market model with two flows of
information. The smallest flow F is the "public" flow of information which is
available to all agents, while the larger flow G has additional information
about the occurrence of a random time T. This random time can model the default
time in credit risk or death time in life insurance. Hence the filtration G is
the progressive enlargement of F with T. In this framework, under some mild
assumptions on the pair (F, T), we describe explicitly how G-local martingales
can be represented in terms of F-local martingale and parameters of T. This
representation complements Choulli, Daveloose and Vanmaele
\cite{ChoulliDavelooseVanmaele} to the case when martingales live "after T".
The application of these results to the explicit parametrization of all
deflators under G is fully elaborated. The results are illustrated on the case
of jump-diffusion model and the discrete-time market model.


title:
Questioning the 239Pu(n,2n)238Pu cross section shape above emission
  threshold

abstract:
  In the light of the JEF(F) European project longstanding story according to
the determination of the most exact shape of the 239Pu(n, 2n)238Pu reaction
cross section and a recent measurement by Meot et al., this paper aims to shed
another light on this topic by bringing new theoretical feedback. To achieve
this goal, the AVXSF-LNG computer program has been upgraded to model
second-chance reactions using its decay-probability module and, then chained to
the TALYS-ECIS06 nuclear reaction system of codes. Present diligent calculation
of the (n,2n) cross section over the energy range from the threshold to the
onset of third-chance fission at about 12 MeV, suggests that current
evaluations under-estimate the 239Pu(n,2n) cross section below 10 MeV;
under-estimation of the order of 7% relatively to the JEFF-3.1 evaluation. On
this ground, we propose an upward correction to the normalization of the
measurement by Meot et al. Correction factor of about 1.24 with a maximum
uncertainty on present fitted model estimated to 11.6%. Latter value is
extracted from a sensitivity analysis of the calculation route to the level
density model that is selected for the non-equilibrated residual nucleus and to
alternative choices we can make in terms of neutron fission cross section
measurement references for the 238Pu and 239Pu target nuclei.


title:
Distributed Stochastic Compositional Optimization Problems over Directed
  Networks

abstract:
  We study the distributed stochastic compositional optimization problems over
directed communication networks in which agents privately own a stochastic
compositional objective function and collaborate to minimize the sum of all
objective functions. We propose a distributed stochastic compositional gradient
descent method, where the gradient tracking and the stochastic correction
techniques are employed to adapt to the networks' directed structure and
increase the accuracy of inner function estimation. When the objective function
is smooth, the proposed method achieves the convergence rate
$\mathcal{O}\left(k^{-1/2}\right)$ and sample complexity
$\mathcal{O}\left(\frac{1}{\epsilon^2}\right)$ for finding the
($\epsilon$)-stationary point. When the objective function is strongly convex,
the convergence rate is improved to $\mathcal{O}\left(k^{-1}\right)$. Moreover,
the asymptotic normality of Polyak-Ruppert averaged iterates of the proposed
method is also presented. We demonstrate the empirical performance of the
proposed method on model-agnostic meta-learning problem and logistic regression
problem.


title: Dense Siamese Network

abstract:
  This paper presents Dense Siamese Network (DenseSiam), a simple unsupervised
learning framework for dense prediction tasks. It learns visual representations
by maximizing the similarity between two views of one image with two types of
consistency, i.e., pixel consistency and region consistency. Concretely,
DenseSiam first maximizes the pixel level spatial consistency according to the
exact location correspondence in the overlapped area. It also extracts a batch
of region embeddings that correspond to some sub-regions in the overlapped area
to be contrasted for region consistency. In contrast to previous methods that
require negative pixel pairs, momentum encoders, or heuristic masks, DenseSiam
benefits from the simple Siamese network and optimizes the consistency of
different granularities. It also proves that the simple location correspondence
and interacted region embeddings are effective enough to learn the similarity.
We apply DenseSiam on ImageNet and obtain competitive improvements on various
downstream tasks. We also show that only with some extra task-specific losses,
the simple framework can directly conduct dense prediction tasks. On an
existing unsupervised semantic segmentation benchmark, it surpasses
state-of-the-art segmentation methods by 2.1 mIoU with 28% training costs.


title: Collaborative Learning for Cyberattack Detection in Blockchain Networks

abstract:
  This article aims to study intrusion attacks and then develop a novel
cyberattack detection framework for blockchain networks. Specifically, we first
design and implement a blockchain network in our laboratory. This blockchain
network will serve two purposes, i.e., generate the real traffic data
(including both normal data and attack data) for our learning models and
implement real-time experiments to evaluate the performance of our proposed
intrusion detection framework. To the best of our knowledge, this is the first
dataset that is synthesized in a laboratory for cyberattacks in a blockchain
network. We then propose a novel collaborative learning model that allows
efficient deployment in the blockchain network to detect attacks. The main idea
of the proposed learning model is to enable blockchain nodes to actively
collect data, share the knowledge learned from its data, and then exchange the
knowledge with other blockchain nodes in the network. In this way, we can not
only leverage the knowledge from all the nodes in the network but also do not
need to gather all raw data for training at a centralized node like
conventional centralized learning solutions. Such a framework can also avoid
the risk of exposing local data's privacy as well as the excessive network
overhead/congestion. Both intensive simulations and real-time experiments
clearly show that our proposed collaborative learning-based intrusion detection
framework can achieve an accuracy of up to 97.7% in detecting attacks.


title:
Derivation of a statistical model for classical systems obeying
  fractional exclusion principle

abstract:
  The violation of the Pauli principle has been surmised in several models of
the Fractional Exclusion Statistics and successfully applied to several quantum
systems. In this paper, a classical alternative of the exclusion statistics is
studied using the maximum entropy methods. The difference between the
Bose-Einstein statistics and the Maxwell-Boltzmann statistics is understood in
terms of a separable quantity, namely the degree of indistinguishability.
Starting from the usual Maxwell-Boltzmann microstate counting formula, a
special restriction related to the degree of indistinguishability is
incorporated using Lagrange multipliers to derive the probability distribution
function at equilibrium under NVE conditions. It is found that the resulting
probability distribution function generates real positive values within the
permissible range of parameters. For a dilute system, the probability
distribution function is intermediate between the Fermi-Dirac and Bose-Einstein
statistics and follows the exclusion principle. Properties of various variables
of this novel statistical model are studied and possible application to
classical thermodynamics is discussed.


title:
MTBF-33: A multi-temporal building footprint dataset for 33 counties in
  the United States (1900-2015)

abstract:
  Despite abundant data on the spatial distribution of contemporary human
settlements, historical data on the long-term evolution of human settlements at
fine spatial and temporal granularity is scarce, limiting our quantitative
understanding of long-term changes of built-up areas. This is because commonly
used mapping methods (e.g., image classification) and suitable data sources
(i.e., aerial imagery, multi-spectral remote sensing data, LiDAR) have only
been available in recent decades. However, there are alternative data sources
such as cadastral records that are digitally available, containing relevant
information such as building age information, allowing for an approximate,
digital reconstruction of past building distributions. We conducted a
non-exhaustive search of open and publicly available data resources from
administrative institutions in the United States and gathered, integrated, and
harmonized cadastral parcel data, tax assessment data, and building footprint
data for 33 counties, wherever building footprint geometries and building
construction year information was available. The result of this effort is a
unique dataset which we call the Multi-Temporal Building Footprint Dataset for
33 U.S. Counties (MTBF-33). MTBF-33 contains over 6.2 million building
footprints including their construction year, and can be used to derive
retrospective depictions of built-up areas from 1900 to 2015, at fine spatial
and temporal grain and can be used for data validation purposes, or to train
statistical learning approaches aiming to extract historical information on
human settlements from remote sensing data, historical maps, or similar data
sources. MTBF-33 is available at http://doi.org/10.17632/w33vbvjtdy.


title:
Oracle with $\mathrm{P=NP\cap coNP}$, but no Many-One Completeness in
  UP, DisjNP, and DisjCoNP

abstract:
  We construct an oracle relative to which $\mathrm{P} = \mathrm{NP} \cap
\mathrm{coNP}$, but there are no many-one complete sets in $\mathrm{UP}$, no
many-one complete disjoint $\mathrm{NP}$-pairs, and no many-one complete
disjoint $\mathrm{coNP}$-pairs. This contributes to a research program
initiated by Pudl\'ak [Pud17], which studies incompleteness in the finite
domain and which mentions the construction of such oracles as open problem. The
oracle shows that $\mathsf{NP}\cap\mathsf{coNP}$ is indispensable in the list
of hypotheses studied by Pudl\'ak. Hence one should consider stronger
hypotheses, in order to find a universal one.


title: Nambu--Jona-Lasinio model with a fractal inspired coupling

abstract:
  The Nambu--Jona-Lasino model is modified by the inclusion of a
running-coupling that was obtained by a fractal approach to Quantum
Chromodynamics. The coupling follows a $q$-exponential function and, in the
context of high energy collisions, explains the origin of the Tsallis
non-extensive statistics distributions. The parameter $q$ is completely
determined in terms of the number of colours and the number of quark flavours.
We study several aspects of the extended model and compare our results to the
standard NJL model, where a constant coupling is used in combination with a
sharp cutoff to regularize the gap equation. We show that the modified coupling
regularizes the model like a smooth cutoff and reproduces the pion mass and
decay constant, providing an almost exact Gell-Mann-Oakes-Renner relation at
physical current quark masses.


title: Image Classification on Accelerated Neural Networks

abstract:
  For image classification problems, various neural network models are commonly
used due to their success in yielding high accuracies. Convolutional Neural
Network (CNN) is one of the most frequently used deep learning methods for
image classification applications. It may produce extraordinarily accurate
results with regard to its complexity. However, the more complex the model is
the longer it takes to train. In this paper, an acceleration design that uses
the power of FPGA is given for a basic CNN model which consists of one
convolutional layer and one fully connected layer for the training phase of the
fully connected layer. Nonetheless, inference phase is also accelerated
automatically due to the fact that training phase includes inference. In this
design, the convolutional layer is calculated by the host computer and the
fully connected layer is calculated by an FPGA board. It should be noted that
the training of convolutional layer is not taken into account in this design
and is left for future research. The results are quite encouraging as this FPGA
design tops the performance of some of the state-of-the-art deep learning
platforms such as Tensorflow on the host computer approximately 2 times in both
training and inference.


title: MixFormer: End-to-End Tracking with Iterative Mixed Attention

abstract:
  Tracking often uses a multi-stage pipeline of feature extraction, target
information integration, and bounding box estimation. To simplify this pipeline
and unify the process of feature extraction and target information integration,
we present a compact tracking framework, termed as MixFormer, built upon
transformers. Our core design is to utilize the flexibility of attention
operations, and propose a Mixed Attention Module (MAM) for simultaneous feature
extraction and target information integration. This synchronous modeling scheme
allows to extract target-specific discriminative features and perform extensive
communication between target and search area. Based on MAM, we build our
MixFormer tracking framework simply by stacking multiple MAMs with progressive
patch embedding and placing a localization head on top. In addition, to handle
multiple target templates during online tracking, we devise an asymmetric
attention scheme in MAM to reduce computational cost, and propose an effective
score prediction module to select high-quality templates. Our MixFormer sets a
new state-of-the-art performance on five tracking benchmarks, including LaSOT,
TrackingNet, VOT2020, GOT-10k, and UAV123. In particular, our MixFormer-L
achieves NP score of 79.9% on LaSOT, 88.9% on TrackingNet and EAO of 0.555 on
VOT2020. We also perform in-depth ablation studies to demonstrate the
effectiveness of simultaneous feature extraction and information integration.
Code and trained models are publicly available at
https://github.com/MCG-NJU/MixFormer.


title: Cutting rules and positivity in finite temperature many-body theory

abstract:
  For a given diagrammatic approximation in many-body perturbation theory it is
not guaranteed that positive observables, such as the density or the spectral
function, retain their positivity. For zero-temperature systems we developed a
method [Phys.Rev.B 90,115134 (2014)] based on so-called cutting rules for
Feynman diagrams that enforces these properties diagrammatically, thus solving
the problem of negative spectral densities observed for various vertex
approximations. In this work we extend this method to systems at finite
temperature by formulating the cutting rules in terms of retarded $N$-point
functions, thereby simplifying earlier approaches and simultaneously solving
the issue of non-vanishing vacuum diagrams that has plagued finite temperature
expansions. Our approach is moreover valid for nonequilibrium systems in
initial equilibrium and allows us to show that important commonly used
approximations, namely the $GW$, second Born and $T$-matrix approximation,
retain positive spectral functions at finite temperature. Finally we derive an
analytic continuation relation between the spectral forms of retarded $N$-point
functions and their Matsubara counterparts and a set of Feynman rules to
evaluate them.


title: Evolution of the Dust Trail of Comet 17P/Holmes

abstract:
  The massive outburst of the comet 17P/Holmes in October 2007 is the largest
known outburst by a comet thus far. We present a new comprehensive model
describing the evolution of the dust trail produced in this phenomenon. The
model comprises of multiparticle Monte Carlo approach including the solar
radiation pressure effects, gravitational disturbance caused by Venus, Earth
and Moon, Mars, Jupiter and Saturn, and gravitational interaction of the dust
particles with the parent comet itself. Good accuracy of computations is
achieved by its implementation in Orekit, which executes Dormad-Prince
numerical integration methods with higher precision. We demonstrate performance
of the model by simulating particle populations with sizes from 0.001 mm to 1
mm with corresponding spherically symmetric ejection speed distribution, and
towards the Sun outburst modelling. The model is supplemented with and
validated against the observations of the dust trail in common nodes for 0.5
and 1 revolutions. In all cases, the predicted trail position showed a good
match to the observations. Additionally, the hourglass pattern of the trail was
observed for the first time within this work. By using variations of the
outburst model in our simulations, we determine that the assumption of the
spherical symmetry of the ejected particles leads to the scenario compatible
with the observed hourglass pattern. Using these data, we make predictions for
the two-revolution dust trail behavior near the outburst point that should be
detectable by using ground-based telescopes in 2022.


title:
Telling Stories from Computational Notebooks: AI-Assisted Presentation
  Slides Creation for Presenting Data Science Work

abstract:
  Creating presentation slides is a critical but time-consuming task for data
scientists. While researchers have proposed many AI techniques to lift data
scientists' burden on data preparation and model selection, few have targeted
the presentation creation task. Based on the needs identified from a formative
study, this paper presents NB2Slides, an AI system that facilitates users to
compose presentations of their data science work. NB2Slides uses deep learning
methods as well as example-based prompts to generate slides from computational
notebooks, and take users' input (e.g., audience background) to structure the
slides. NB2Slides also provides an interactive visualization that links the
slides with the notebook to help users further edit the slides. A follow-up
user evaluation with 12 data scientists shows that participants believed
NB2Slides can improve efficiency and reduces the complexity of creating slides.
Yet, participants questioned the future of full automation and suggested a
human-AI collaboration paradigm.


title: Overcoming Oscillations in Quantization-Aware Training

abstract:
  When training neural networks with simulated quantization, we observe that
quantized weights can, rather unexpectedly, oscillate between two grid-points.
The importance of this effect and its impact on quantization-aware training are
not well-understood or investigated in literature. In this paper, we delve
deeper into the phenomenon of weight oscillations and show that it can lead to
a significant accuracy degradation due to wrongly estimated batch-normalization
statistics during inference and increased noise during training. These effects
are particularly pronounced in low-bit ($\leq$ 4-bits) quantization of
efficient networks with depth-wise separable layers, such as MobileNets and
EfficientNets. In our analysis we investigate several previously proposed
quantization-aware training (QAT) algorithms and show that most of these are
unable to overcome oscillations. Finally, we propose two novel QAT algorithms
to overcome oscillations during training: oscillation dampening and iterative
weight freezing. We demonstrate that our algorithms achieve state-of-the-art
accuracy for low-bit (3 & 4 bits) weight and activation quantization of
efficient architectures, such as MobileNetV2, MobileNetV3, and EfficentNet-lite
on ImageNet.


title:
Ovid: A Machine Learning Approach for Automated Vandalism Detection in
  OpenStreetMap

abstract:
  OpenStreetMap is a unique source of openly available worldwide map data,
increasingly adopted in real-world applications. Vandalism detection in
OpenStreetMap is critical and remarkably challenging due to the large scale of
the dataset, the sheer number of contributors, various vandalism forms, and the
lack of annotated data to train machine learning algorithms. This paper
presents Ovid - a novel machine learning method for vandalism detection in
OpenStreetMap. Ovid relies on a neural network architecture that adopts a
multi-head attention mechanism to effectively summarize information indicating
vandalism from OpenStreetMap changesets. To facilitate automated vandalism
detection, we introduce a set of original features that capture changeset,
user, and edit information. Our evaluation results on real-world vandalism data
demonstrate that the proposed Ovid method outperforms the baselines by 4.7
percentage points in F1 score.


title:
Stochastic Galerkin finite element method for nonlinear elasticity and
  application to reinforced concrete members

abstract:
  We develop a stochastic Galerkin finite element method for nonlinear
elasticity and apply it to reinforced concrete members with random material
properties. The strategy is based on the modified Newton-Raphson method, which
consists of an incremental loading process and a linearization scheme applied
at each load increment. We consider that the material properties are given by a
stochastic expansion in the so-called generalized polynomial chaos (gPC)
framework. We search the gPC expansion of the displacement, which is then used
to update the gPC expansions of the stress, strain and internal forces. The
proposed method is applied to a reinforced concrete beam with uncertain initial
concrete modulus of elasticity and a shear wall with uncertain maximum
compressive stress of concrete, and the results are compared to those of
stochastic collocation and Monte Carlo methods. Since the systems of equations
obtained in the linearization scheme using the stochastic Galerkin method are
very large, and there are typically many load increments, we also studied
iterative solution using preconditioned conjugate gradients. The efficiency of
the proposed method is illustrated by a set of numerical experiments.


title:
PersFormer: 3D Lane Detection via Perspective Transformer and the
  OpenLane Benchmark

abstract:
  Methods for 3D lane detection have been recently proposed to address the
issue of inaccurate lane layouts in many autonomous driving scenarios
(uphill/downhill, bump, etc.). Previous work struggled in complex cases due to
their simple designs of the spatial transformation between front view and
bird's eye view (BEV) and the lack of a realistic dataset. Towards these
issues, we present PersFormer: an end-to-end monocular 3D lane detector with a
novel Transformer-based spatial feature transformation module. Our model
generates BEV features by attending to related front-view local regions with
camera parameters as a reference. PersFormer adopts a unified 2D/3D anchor
design and an auxiliary task to detect 2D/3D lanes simultaneously, enhancing
the feature consistency and sharing the benefits of multi-task learning.
Moreover, we release one of the first large-scale real-world 3D lane datasets,
which is called OpenLane, with high-quality annotation and scenario diversity.
OpenLane contains 200,000 frames, over 880,000 instance-level lanes, 14 lane
categories, along with scene tags and the closed-in-path object annotations to
encourage the development of lane detection and more industrial-related
autonomous driving methods. We show that PersFormer significantly outperforms
competitive baselines in the 3D lane detection task on our new OpenLane dataset
as well as Apollo 3D Lane Synthetic dataset, and is also on par with
state-of-the-art algorithms in the 2D task on OpenLane. The project page is
available at https://github.com/OpenPerceptionX/PersFormer_3DLane and OpenLane
dataset is provided at https://github.com/OpenPerceptionX/OpenLane.


title: Substellar Hyades Candidates from the UKIRT Hemisphere Survey

abstract:
  We have used data from the UKIRT Hemisphere Survey (UHS) to search for
substellar members of the Hyades cluster. Our search recovered several known
substellar Hyades members, and two known brown dwarfs that we suggest may be
members based on a new kinematic analysis. We uncovered thirteen new substellar
Hyades candidates, and obtained near-infrared follow-up spectroscopy of each
with IRTF/SpeX. Six candidates with spectral types between M7 and L0 are ruled
out as potential members based on their photometric distances ($\gtrsim$100
pc). The remaining seven candidates, with spectral types between L5 and T4, are
all potential Hyades members, with five showing strong membership probabilities
based on BANYAN $\Sigma$ and a convergent point analysis. Distances and radial
velocities are still needed to confirm Hyades membership. If confirmed, these
would be some of the lowest mass free-floating members of the Hyades yet known,
with masses as low as $\sim$30 $M_{\rm Jup}$. An analysis of all known
substellar Hyades candidates shows evidence that the full extent of the Hyades
has yet to be probed for low-mass members, and more would likely be recovered
with deeper photometric and astrometric investigations.


title:
GCNET: graph-based prediction of stock price movement using graph
  convolutional network

abstract:
  The prediction of stocks' direction of movement using the historical price
information has attracted considerable attention as a challenging problem in
the field of machine learning. However, modeling and analyzing the hidden
relations among stock prices as an important source of information for the
prediction of their future behavior has not been explored well yet. The
existing methods in this domain suffer from the lack of generality and
flexibility and cannot be easily applied on any set of inter-related stocks.
The main challenges in this domain are to find a way for modeling the existing
relations among an arbitrary set of stocks and to exploit such a model for
improving the prediction performance for those stocks. In this paper, we
introduce a novel framework, called GCNET that models the relations among an
arbitrary set of stocks as a graph structure called influence network and uses
a set of history-based prediction models to infer plausible initial labels for
a subset of the stock nodes in the graph. Finally, GCNET uses the Graph
Convolutional Network algorithm to analyzes this partially labeled graph and
predicts the next price direction of movement for each stock in the graph.
GCNET is a general prediction framework that can be applied for the prediction
of the price fluctuations for any set of interacting stocks based on their
historical data. Our experiments and evaluations on sets of stocks from S\&P500
and NASDAQ show that GCNET significantly improves the performance of SOTA in
terms of accuracy and MCC measures.


title: Automated Clinical Coding: What, Why, and Where We Are?

abstract:
  Clinical coding is the task of transforming medical information in a
patient's health records into structured codes so that they can be used for
statistical analysis. This is a cognitive and time-consuming task that follows
a standard process in order to achieve a high level of consistency. Clinical
coding could potentially be supported by an automated system to improve the
efficiency and accuracy of the process. We introduce the idea of automated
clinical coding and summarise its challenges from the perspective of Artificial
Intelligence (AI) and Natural Language Processing (NLP), based on the
literature, our project experience over the past two and half years (late 2019
- early 2022), and discussions with clinical coding experts in Scotland and the
UK. Our research reveals the gaps between the current deep learning-based
approach applied to clinical coding and the need for explainability and
consistency in real-world practice. Knowledge-based methods that represent and
reason the standard, explainable process of a task may need to be incorporated
into deep learning-based methods for clinical coding. Automated clinical coding
is a promising task for AI, despite the technical and organisational
challenges. Coders are needed to be involved in the development process. There
is much to achieve to develop and deploy an AI-based automated system to
support coding in the next five years and beyond.


title: Small angle asymptotics for Robin Laplacians on infinite circular cones

abstract:
  For $\varepsilon>0$ and $n\in\mathbb{N}$ consider the infinite cone $
\Omega_{\varepsilon}:=\big\{(x_1,x')\in (0,\infty)\times\mathbb{R}^n: \, |x' |
< \varepsilon x_1\big\}\subset\mathbb{R}^{n+1}$, and the operator
$Q_{\varepsilon}^{\alpha}$ acting as the Laplacian $u\mapsto-\Delta u$ on
$\Omega_{\varepsilon}$ with the Robin boundary condition $\partial_\nu u=\alpha
u$ at $\partial\Omega_\varepsilon$, where $\partial_\nu$ is the outward normal
derivative and $\alpha>0$. It is known from numerous earlier works that the
essential spectrum of $Q^\alpha_\varepsilon$ is $[-\alpha^2,+\infty)$ and that
the discrete spectrum is finite for $n=1$ and infinite for $n\ge 2$, but the
behavior of individual eigenvalues with respect to the geometric parameter
$\varepsilon$ was only addressed for $n=1$ so far. In the present work we
consider arbitrary $n\ge2$ and look at the spectral asymptotics as
$\varepsilon$ becomes small, i.e. as the cone becomes "sharp" and collapses to
its central axis. Our main result is as follows: if $n\ge 2$, $\alpha>0$ and
$j\in \mathbb{N}$ are fixed, then the $j$th eigenvalue
$E_j(Q^\alpha_\varepsilon)$ of $Q^\alpha_\varepsilon$ behaves as\[
  E_j(Q^\alpha_\varepsilon)=-\frac{n^2\alpha^2}{(2j+n-2)^2\,\varepsilon^2}+O\left(\frac{1}{\varepsilon}\right)\]as
$\varepsilon\to 0^+$.


title: On the Jets and Resolutions of Codimension-$1$ Foliations

abstract:
  We introduce the jet schemes of a holomorphic foliation, and thereby prove an
alternate characterisation of simple singularities of codimension-$1$
foliations, independent of any normal form. This leads to an equivalent
condition for the existence of a desingularisation in the non-dicritical case.
We then give some results and conjectures to show when this condition is
satisfied.


title:
The multi-frequency angular power spectrum in parameter studies of the
  cosmic 21-cm signal

abstract:
  The light-cone effect breaks the periodicity and statistical homogeneity
(ergodicity) along the line-of-sight direction of cosmological
emission/absorption line surveys. The spherically averaged power spectrum
(SAPS), which by definition assumes ergodicity and periodicity in all
directions, can only quantify some of the second-order statistical information
in the 3D light-cone signals and therefore gives a biased estimate of the true
statistics. The multi-frequency angular power spectrum (MAPS), by extracting
more information from the data, does not rely on these assumptions. It is
therefore better aligned with the properties of the signal. We have compared
the performance of the MAPS and SAPS metrics for parameter estimation for a
mock 3D light-cone observation of the 21-cm signal from the Epoch of
Reionization. Our investigation is based on a simplified 3-parameter 21cmFAST
model. We find that the MAPS produces parameter constraints which are a factor
of $\sim 2$ more stringent than when the SAPS is used. The significance of this
result does not change much even in the presence of instrumental noise expected
for 128 hours of SKA-Low observations. Our results therefore suggest that a
parameter estimation framework based on the MAPS metric would yield superior
results over one using the SAPS metric.


title:
CLIP meets GamePhysics: Towards bug identification in gameplay videos
  using zero-shot transfer learning

abstract:
  Gameplay videos contain rich information about how players interact with the
game and how the game responds. Sharing gameplay videos on social media
platforms, such as Reddit, has become a common practice for many players.
Often, players will share gameplay videos that showcase video game bugs. Such
gameplay videos are software artifacts that can be utilized for game testing,
as they provide insight for bug analysis. Although large repositories of
gameplay videos exist, parsing and mining them in an effective and structured
fashion has still remained a big challenge. In this paper, we propose a search
method that accepts any English text query as input to retrieve relevant videos
from large repositories of gameplay videos. Our approach does not rely on any
external information (such as video metadata); it works solely based on the
content of the video. By leveraging the zero-shot transfer capabilities of the
Contrastive Language-Image Pre-Training (CLIP) model, our approach does not
require any data labeling or training. To evaluate our approach, we present the
$\texttt{GamePhysics}$ dataset consisting of 26,954 videos from 1,873 games,
that were collected from the GamePhysics section on the Reddit website. Our
approach shows promising results in our extensive analysis of simple queries,
compound queries, and bug queries, indicating that our approach is useful for
object and event detection in gameplay videos. An example application of our
approach is as a gameplay video search engine to aid in reproducing video game
bugs. Please visit the following link for the code and the data:
https://asgaardlab.github.io/CLIPxGamePhysics/


title: A splash in a one-dimensional cold gas

abstract:
  We consider a set of hard point particles distributed uniformly with a
specified density on the positive half-line and all initially at rest. The
particle masses alternate between two values, $m$ and $M$. The particles
interact via collisions that conserve energy and momentum. We study the cascade
of activity that results when the left-most particle is given a positive
velocity. At long times we find that this leads to two fascinating features in
the observed dynamics. First, in the bulk of the gas, a shock front develops
separating the cold gas from a thermalized region. The shock-front travels
sub-ballistically, with the bulk described by self-similar solutions of Euler
hydrodynamics. Second, there is a splash region formed by the recoiled
particles which move ballistically with negative velocities. The splash region
is completely non-hydrodynamic and we propose two conjectures for the long time
particle dynamics in this region. We provide a detailed analytic understanding
of these coexisting regimes. These are supported by the results of molecular
dynamics simulations.


title: A homogenized bending theory for prestrained plates

abstract:
  The presence of prestrain can have a tremendous effect on the mechanical
behavior of slender structures. Prestrained elastic plates show spontaneous
bending in equilibrium -- a property that makes such objects relevant for the
fabrication of active and functional materials. In this paper we study
microheterogeneous, prestrained plates that feature nonflat equilibrium shapes.
Our goal is to understand the relation between the properties of the
prestrained microstructure and the global shape of the plate in mechanical
equilibrium. To this end, we consider a three-dimensional, nonlinear elasticity
model that describes a periodic material that occupies a domain with small
thickness. We consider a spatially periodic prestrain described in the form of
a multiplicative decomposition of the deformation gradient. By simultaneous
homogenization and dimension reduction, we rigorously derive an effective plate
model as a {\Gamma}-limit for vanishing thickness and period. That limit has
the form of a nonlinear bending energy with an emergent spontaneous curvature
term. The homogenized properties of the bending model (bending stiffness and
spontaneous curvature) are characterized by corrector problems. For a model
composite -- a prestrained laminate composed of isotropic materials -- we
investigate the dependence of the homogenized properties on the parameters of
the model composite. Secondly, we investigate the relation between the
parameters of the model composite and the set of shapes with minimal bending
energy. Our study reveals a rather complex dependence of these shapes on the
composite parameters.


title: A quantitative Neumann lemma for finitely generated groups

abstract:
  We study the coset covering function $\mathfrak{C}(r)$ of a finitely
generated group: the number of cosets of infinite index subgroups needed to
cover the ball of radius $r$. We show that $\mathfrak{C}(r)$ is linear for
virtually nilpotent groups, exponential for property (T) groups, and is of
order at least $\sqrt{r}$ for all groups.


title: Migrating CUDA to oneAPI: A Smith-Waterman Case Study

abstract:
  To face the programming challenges related to heterogeneous computing, Intel
recently introduced oneAPI, a new programming environment that allows code
developed in Data Parallel C++ (DPC++) language to be run on different devices
such as CPUs, GPUs, FPGAs, among others. To tackle CUDA-based legacy codes,
oneAPI provides a compatibility tool (dpct) that facilitates the migration to
DPC++. Due to the large amount of existing CUDA-based software in the
bioinformatics context, this paper presents our experiences porting SW#db, a
well-known sequence alignment tool, to DPC++ using dpct. From the experimental
work, it was possible to prove the usefulness of dpct for SW#db code migration
and the cross-GPU vendor, cross-architecture portability of the migrated DPC++
code. In addition, the performance results showed that the migrated DPC++ code
reports similar efficiency rates to its CUDA-native counterpart or even better
in some tests (approximately +5%).


title:
Computation of eigenfrequency sensitivities using Riesz projections for
  efficient optimization of nanophotonic resonators

abstract:
  We present an approach for computing eigenfrequency sensitivities of
resonances. The theory is based on Riesz projections for Maxwell's equations.
Its numerical realization essentially relies on direct differentiation of
scattering problems. We use a numerical implementation to demonstrate the
performance of the approach compared to differentiation using finite
differences. The method is applied for the efficient optimization of the
quality factor of a nanophotonic resonator.