title: Near infrared and optical emission of WASP-5 b

abstract:
  CONTEXT: Thermal emission from extrasolar planets makes it possible to study
important physical processes in their atmospheres and derive more precise
orbital elements. AIMS: By using new near infrared and optical data, we examine
how these data constrain the orbital eccentricity and the thermal properties of
the planet atmosphere. METHODS: The full light curves acquired by the TESS
satellite from two sectors are used to put upper limit on the amplitude of the
planet's phase variation and estimate the occultation depth. Two, already
published and one, yet unpublished followup observations in the 2MASS K (Ks)
band are employed to derive a more precise occultation light curve in this near
infrared waveband. RESULTS: The merged occultation light curve in the Ks band
comprises 4515 data points. The data confirm the results of the earlier
eccentricity estimates, suggesting circular orbit: e=0.005+/-0.015. The high
value of the flux depression of (2.70+/-0.14) ppt in the Ks band excludes
simple black body emission at the 10 sigma level and disagrees also with
current atmospheric models at the (4-7) sigma level. From the analysis of the
TESS data, in the visual band we found tentative evidence for a near noise
level detection of the secondary eclipse, and placed constraints on the
associated amplitude of the planet's phase variation. A formal box fit yields
an occultation depth of (0.157+/-0.056) ppt. This implies a relatively high
geometric albedo of Ag=0.43+/-0.15 for fully efficient atmospheric circulation
and Ag=0.29+/-0.15 for no circulation at all. No preference can be seen either
for the oxygen-enhanced, or for the carbon-enhanced atmosphere models.


title: Geometric Thermodynamics of Strain-Induced Crystallization in Polymers

abstract:
  Going beyond the classical Gaussian approximation of Einstein's fluctuation
theory, Ruppeiner gave it a Riemannian geometric structure with an entropic
metric. This yielded a fundamental quantity - the Riemannian curvature, which
was used to extract information on the nature of interactions between molecules
in fluids, ideal gases and other open systems. In this article, we examine the
implications of this curvature in a non-equilibrium thermodynamic system where
relaxation is sufficiently slow so as not to invalidate the local equilibrium
hypothesis. The non-equilibrium system comprises of a rubbery polymer
undergoing strain induced crystallization. The curvature is found to impart
information on a spurious isochoric energy arising from the conformational
stretching of already crystallized segments. This unphysical component perhaps
arises as the crystallized manifold is considered Euclidean with the stretch
measures defined via the Euclidean metric. The thermodynamic state associated
with curvature is the key to determine the isochoric stretch and hence the
spurious energy. We determine this stretch and propose a form for the spurious
free energy that must be removed from the total energy in order that the
correct stresses are recovered.


title: BNV-Fusion: Dense 3D Reconstruction using Bi-level Neural Volume Fusion

abstract:
  Dense 3D reconstruction from a stream of depth images is the key to many
mixed reality and robotic applications. Although methods based on Truncated
Signed Distance Function (TSDF) Fusion have advanced the field over the years,
the TSDF volume representation is confronted with striking a balance between
the robustness to noisy measurements and maintaining the level of detail. We
present Bi-level Neural Volume Fusion (BNV-Fusion), which leverages recent
advances in neural implicit representations and neural rendering for dense 3D
reconstruction. In order to incrementally integrate new depth maps into a
global neural implicit representation, we propose a novel bi-level fusion
strategy that considers both efficiency and reconstruction quality by design.
We evaluate the proposed method on multiple datasets quantitatively and
qualitatively, demonstrating a significant improvement over existing methods.


title: Deformations of Vaisman manifolds

abstract:
  We construct a type of transverse deformations of a Vaisman manifold, which
preserves the canonical foliation. For this construction we only need a basic
1-form with certain properties. We show that such basic 1-forms exist in
abundance.


title: Neutrino Condensate Dark Energy from TeV Scale Extra Dimensions

abstract:
  We propose a new scenario of incorporating neutrino masses in models of TeV
scale gravity and large extra dimensions, which can explain the dark energy
problem through formation of neutrino condensates. The smallness of the
neutrino masses and the scale of dark energy is protected to be small by the
lepton number symmetry, which is broken in a distant brane, such that all
lepton number violating effects are suppressed by the distance factor in the
extra dimensions. The interactions of any bulk scalar particles are also
suppressed by the distance factor. A lepton number violating soft-term is also
induced from the distant brane, providing us a light pseudo-Nambu-Goldstone
boson which becomes the mediator of the attractive force that forms the
condensates.


title:
Proceedings of TDA: Applications of Topological Data Analysis to Data
  Science, Artificial Intelligence, and Machine Learning Workshop at SDM 2022

abstract:
  Topological Data Analysis (TDA) is a rigorous framework that borrows
techniques from geometric and algebraic topology, category theory, and
combinatorics in order to study the "shape" of such complex high-dimensional
data. Research in this area has grown significantly over the last several years
bringing a deeply rooted theory to bear on practical applications in areas such
as genomics, natural language processing, medicine, cybersecurity, energy, and
climate change. Within some of these areas, TDA has also been used to augment
AI and ML techniques.
  We believe there is further utility to be gained in this space that can be
facilitated by a workshop bringing together experts (both theorists and
practitioners) and non-experts. Currently there is an active community of pure
mathematicians with research interests in developing and exploring the
theoretical and computational aspects of TDA. Applied mathematicians and other
practitioners are also present in community but do not represent a majority.
This speaks to the primary aim of this workshop which is to grow a wider
community of interest in TDA. By fostering meaningful exchanges between these
groups, from across the government, academia, and industry, we hope to create
new synergies that can only come through building a mutual comprehensive
awareness of the problem and solution spaces.


title: Periods And Real Numbers

abstract:
  The purpose of this book is to provide an introduction to period theory and
then to place it within the matrix of recursive function theory.


title: Spin and polarization: a new direction in relativistic heavy ion physics

abstract:
  Since the first evidence of a global polarization of $\Lambda$ hyperons in
relativistic nuclear collisions in 2017, spin has opened a new window in the
field, both at experimental and theoretical level, and an exciting perspective.
The current state of the field is reviewed with regard to the theoretical
understanding of the data, reporting on the most recent achievements and
envisioning possible developments. The intriguing connections of spin physics
in relativistic matter with fundamental questions in quantum field theory and
applications in the non-relativistic domain are discussed.


title:
Direct visualization of ultrafast lattice ordering triggered by an
  electron-hole plasma in 2D perovskites

abstract:
  Direct visualization of ultrafast coupling between charge carriers and
lattice degrees of freedom in photo-excited semiconductors has remained a
long-standing challenge and is critical for understanding the light-induced
physical behavior of materials under extreme non-equilibrium conditions. Here,
by monitoring the evolution of the wave-vector resolved ultrafast electron
diffraction intensity following above-bandgap photo-excitation, we obtain a
direct visual of the structural dynamics in monocrystalline 2D perovskites.
Analysis reveals a surprising, light-induced ultrafast lattice ordering
resulting from a strong interaction between hot-carriers and the perovskite
lattice, which induces an in-plane octahedra rotation, towards a more symmetric
phase. Correlated ultrafast spectroscopy performed at the same carrier density
as ultrafast electron diffraction reveals that the creation of a hot and dense
electron-hole plasma triggers lattice ordering at short timescales by
modulating the crystal cohesive energy. Finally, we show that the interaction
between the carrier gas and the lattice can be altered by tailoring the
rigidity of the 2D perovskite by choosing the appropriate organic spacer layer.


title:
Proactive Anomaly Detection for Robot Navigation with Multi-Sensor
  Fusion

abstract:
  Despite the rapid advancement of navigation algorithms, mobile robots often
produce anomalous behaviors that can lead to navigation failures. The ability
to detect such anomalous behaviors is a key component in modern robots to
achieve high-levels of autonomy. Reactive anomaly detection methods identify
anomalous task executions based on the current robot state and thus lack the
ability to alert the robot before an actual failure occurs. Such an alert delay
is undesirable due to the potential damage to both the robot and the
surrounding objects. We propose a proactive anomaly detection network (PAAD)
for robot navigation in unstructured and uncertain environments. PAAD predicts
the probability of future failure based on the planned motions from the
predictive controller and the current observation from the perception module.
Multi-sensor signals are fused effectively to provide robust anomaly detection
in the presence of sensor occlusion as seen in field environments. Our
experiments on field robot data demonstrates superior failure identification
performance than previous methods, and that our model can capture anomalous
behaviors in real-time while maintaining a low false detection rate in
cluttered fields. Code, dataset, and video are available at
https://github.com/tianchenji/PAAD


title:
Continuous Jumping for Legged Robots on Stepping Stones via Trajectory
  Optimization and Model Predictive Control

abstract:
  Performing highly agile dynamic motions, such as jumping or running, over
uneven stepping stones has remained a challenging problem in legged robot
locomotion. This paper presents a framework that combines trajectory
optimization and model predictive control to perform consecutive jumping on
stepping stones. In our approach, we firstly utilize trajectory optimization
for full-nonlinear dynamics to formulate periodic jumps for various jumping
distances. A jumping controller based on model predictive control is then
designed for dynamic jumping transitions, enabling the robot to achieve
continuous jumps on stepping stones. The proposed framework is also validated
to be robust to platforms with unknown height perturbations and model
uncertainty (e.g. unknown load). Moreover, experiments are also conducted to
show the robustness of our jumping controller on an unknown uneven platform.


title: Data-based Control of Feedback Linearizable Systems

abstract:
  We present an extension of Willems' Fundamental Lemma to the class of
multi-input multi-output discrete-time feedback linearizable nonlinear systems.
We study the effect of approximating the unknown nonlinearities with a choice
of basis functions that depend only on input and output data, then provide
error bounds on the results of the data-based simulation and output matching
control problems. Furthermore, we use this data-based approximation of the
trajectories of the nonlinear system to design a multi-step robust data-based
nonlinear predictive control scheme. We show that this control scheme is
recursively feasible and renders the closed-loop system practically
exponentially stable. Finally, we illustrate our results on a model of a
fully-actuated double inverted pendulum.


title:
Singular limit for the compressible Navier--Stokes equations with the
  hard sphere pressure law on expanding domains

abstract:
  The article is devoted to the asymptotic limit of the compressible
Navier-Stokes system with a pressure obeying a hard--sphere equation of state
on a domain expanding to the whole physical space $R^3$. Under the assumptions
that acoustic waves generated in the case of ill-prepared data do not reach the
boundary of the expanding domain in the given time interval and a certain
relation between the Reynolds and Mach numbers and the radius of the expanding
domain, we prove that the target system is the incompressible Euler system on
$R^3$. We also provide an estimate of the rate of convergence expressed in
terms of characteristic numbers and the radius of domains.


title:
Practical exponential stability of a robust data-driven nonlinear
  predictive control scheme

abstract:
  We provide theoretical guarantees for recursive feasibility and practical
exponential stability of the closed-loop system of a feedback linearizable
nonlinear system when controlled by a robust data-driven nonlinear predictive
control scheme. This technical report serves as a supplementary material to our
recent paper "Data-based control of feedback linearizable systems". The
arguments shown in this report follow similar steps to those for the LTI case,
since feedback linearizable systems are linear in transformed coordinates.
However, the proof was suitably adapted to match the nonlinear setting under
consideration, and the differences are emphasized throughout the proof.


title:
Quantum Euler class and virtual Tevelev degrees of Fano complete
  intersections

abstract:
  We compute the quantum Euler class of Fano complete intersections X in a
projective space. In particular, we prove a recent conjecture of A. Buch and R.
Pandharipande. Finally we apply our result to obtain formulas for the virtual
Tevelev degrees of X. An algorithm computing all genus 0 two-point Hyperplane
Gromov Witten invariants of X is illustrated along the way.


title: Intelligent Reflective Surface Deployment in 6G: A Comprehensive Survey

abstract:
  Intelligent reflecting surfaces (IRSs) are considered a promising technology
that can smartly reconfigure the wireless environment to enhance the
performance of future wireless networks. However, the deployment of IRSs still
faces challenges due to highly dynamic and mobile unmanned aerial vehicle (UAV)
enabled wireless environments to achieve higher capacity. This paper sheds
light on the different deployment strategies for IRSs in future terrestrial and
non-terrestrial networks. Specifically, in this paper, we introduce key
theoretical concepts underlying the IRS paradigm and discuss the design aspects
related to the deployment of IRSs in 6G networks. We also explore
optimization-based IRS deployment techniques to improve system performance in
terrestrial and aerial IRSs. Furthermore, we survey model-free reinforcement
learning (RL) techniques from the deployment aspect to address the challenges
of achieving higher capacity in complex and mobile IRS-assisted UAV wireless
systems. Finally, we highlight challenges and future research directions from
the deployment aspect of IRSs for improving system performance for the future
6G network.


title: On the sequence $n! \bmod p$

abstract:
  We prove, that the sequence $1!, 2!, 3!, \dots$ produces at least $(\sqrt{2}
+ o(1))\sqrt{p}$ distinct residues modulo prime $p$. Moreover, factorials on an
interval $\mathcal{I} \subseteq \{0, 1, \dots, p - 1\}$ of length $N > p^{7/8 +
\varepsilon}$ produce at least $(1 - o(1))\sqrt{p}$ distinct residues modulo
$p$. As a consequence, we obtain a polynomial improvement in the problem of
representing a given residue class as a product of seven small factorials.


title:
Indoor Navigation Assistance for Visually Impaired People via Dynamic
  SLAM and Panoptic Segmentation with an RGB-D Sensor

abstract:
  Exploring an unfamiliar indoor environment and avoiding obstacles is
challenging for visually impaired people. Currently, several approaches achieve
the avoidance of static obstacles based on the mapping of indoor scenes. To
solve the issue of distinguishing dynamic obstacles, we propose an assistive
system with an RGB-D sensor to detect dynamic information of a scene. Once the
system captures an image, panoptic segmentation is performed to obtain the
prior dynamic object information. With sparse feature points extracted from
images and the depth information, poses of the user can be estimated. After the
ego-motion estimation, the dynamic object can be identified and tracked. Then,
poses and speed of tracked dynamic objects can be estimated, which are passed
to the users through acoustic feedback.


title: Byzantine-Robust Federated Linear Bandits

abstract:
  In this paper, we study a linear bandit optimization problem in a federated
setting where a large collection of distributed agents collaboratively learn a
common linear bandit model. Standard federated learning algorithms applied to
this setting are vulnerable to Byzantine attacks on even a small fraction of
agents. We propose a novel algorithm with a robust aggregation oracle that
utilizes the geometric median. We prove that our proposed algorithm is robust
to Byzantine attacks on fewer than half of agents and achieves a sublinear
$\tilde{\mathcal{O}}({T^{3/4}})$ regret with $\mathcal{O}(\sqrt{T})$ steps of
communication in $T$ steps. Moreover, we make our algorithm differentially
private via a tree-based mechanism. Finally, if the level of corruption is
known to be small, we show that using the geometric median of mean oracle for
robust aggregation further improves the regret bound.


title:
Switched Max-Plus Linear-Dual Inequalities: Application in Scheduling of
  Multi-Product Processing Networks

abstract:
  P-time event graphs are discrete event systems suitable for modeling
processes in which tasks must be executed in predefined time windows. Their
dynamics can be represented by systems of linear dynamical inequalities in the
max-plus algebra and its dual, the min-plus algebra, referred to as max-plus
linear-dual inequalities (LDIs). We define a new class of models called
switched LDIs (SLDIs), which allow to switch between different modes of
operations, each corresponding to an LDI, according to an infinite sequence of
modes called schedule. In this paper, we focus on the analysis of SLDIs when
the schedule is fixed and periodic. We show that SLDIs can model single-robot
multi-product processing networks, in which every product has different
processing requirements and corresponds to a specific mode of operation. Based
on the analysis of SLDIs, we propose an algorithm to compute minimum and
maximum cycle times for these processes that improves the time complexity of
other existing approaches.


title: Scattering for the Equivariant U(1) Problem

abstract:
  Extending our previous works on the Cauchy problem for the $2+1$ equivariant
Einstein-wave map system, we prove that the linear part dominates the nonlinear
part of the wave maps equation coupled to the full set of the Einstein
equations, for small data. A key ingredient in the proof is a nonlinear
Morawetz estimate for the fully coupled equivariant Einstein-wave maps. The
$2+1$ dimensional Einstein-wave map system occurs naturally in the U(1)
symmetric $3+1$ dimensional vacuum Einstein equations of general relativity.


title: An Algebraic Hypergraph Regularity Lemma

abstract:
  Szemer\'edi's regularity lemma is a powerful tool in graph theory. It states
that for every large enough graph, there exists a partition of the edge set
with bounded size such that most induced subgraphs are quasirandom. When the
graph is a definable set $\phi(x, y)$ in a finite field $F_q$, Tao's algebraic
graph regularity lemma shows that there is a partition of the graph $\phi(x,
y)$ such that all induced subgraphs are quasirandom and the error bound on
quasirandomness is $O(q^{-1/4})$. In this work we prove an algebraic hypergraph
regularity lemma for definable sets in finite fields, thus answering a question
of Tao. We also extend the algebraic regularity lemma to definable sets in the
difference fields $(F_q^{alg}, x^q)$ and we offer a new point of view on the
geometric content of the algebraic regularity lemma.


title: Shape-Pose Disentanglement using SE(3)-equivariant Vector Neurons

abstract:
  We introduce an unsupervised technique for encoding point clouds into a
canonical shape representation, by disentangling shape and pose. Our encoder is
stable and consistent, meaning that the shape encoding is purely
pose-invariant, while the extracted rotation and translation are able to
semantically align different input shapes of the same class to a common
canonical pose. Specifically, we design an auto-encoder based on Vector Neuron
Networks, a rotation-equivariant neural network, whose layers we extend to
provide translation-equivariance in addition to rotation-equivariance only. The
resulting encoder produces pose-invariant shape encoding by construction,
enabling our approach to focus on learning a consistent canonical pose for a
class of objects. Quantitative and qualitative experiments validate the
superior stability and consistency of our approach.


title:
Best-Response Bayesian Reinforcement Learning with Bayes-adaptive POMDPs
  for Centaurs

abstract:
  Centaurs are half-human, half-AI decision-makers where the AI's goal is to
complement the human. To do so, the AI must be able to recognize the goals and
constraints of the human and have the means to help them. We present a novel
formulation of the interaction between the human and the AI as a sequential
game where the agents are modelled using Bayesian best-response models. We show
that in this case the AI's problem of helping bounded-rational humans make
better decisions reduces to a Bayes-adaptive POMDP. In our simulated
experiments, we consider an instantiation of our framework for humans who are
subjectively optimistic about the AI's future behaviour. Our results show that
when equipped with a model of the human, the AI can infer the human's bounds
and nudge them towards better decisions. We discuss ways in which the machine
can learn to improve upon its own limitations as well with the help of the
human. We identify a novel trade-off for centaurs in partially observable
tasks: for the AI's actions to be acceptable to the human, the machine must
make sure their beliefs are sufficiently aligned, but aligning beliefs might be
costly. We present a preliminary theoretical analysis of this trade-off and its
dependence on task structure.


title: A Modern Theory for High-dimensional Cox Regression Models

abstract:
  The proportional hazards model has been extensively used in many fields such
as biomedicine to estimate and perform statistical significance testing on the
effects of covariates influencing the survival time of patients. The classical
theory of maximum partial-likelihood estimation (MPLE) is used by most software
packages to produce inference, e.g., the coxph function in R and the PHREG
procedure in SAS. In this paper, we investigate the asymptotic behavior of the
MPLE in the regime in which the number of parameters p is of the same order as
the number of samples n. The main results are (i) existence of the MPLE
undergoes a sharp 'phase transition'; (ii) the classical MPLE theory leads to
invalid inference in the high-dimensional regime. We show that the asymptotic
behavior of the MPLE is governed by a new asymptotic theory. These findings are
further corroborated through numerical studies. The main technical tool in our
proofs is the Convex Gaussian Min-max Theorem (CGMT), which has not been
previously used in the analysis of partial likelihood. Our results thus extend
the scope of CGMT and shed new light on the use of CGMT for examining the
existence of MPLE and non-separable objective functions.


title: Amenable covers of right-angled Artin groups

abstract:
  Let $A_L$ be the right-angled Artin group associated to a finite flag complex
$L$. We show that the amenable category of $A_L$ equals the virtual
cohomological dimension of the right-angled Coxeter group $W_L$. In particular,
right-angled Artin groups satisfy a question of Capovilla--L\"oh--Moraschini
proposing an inequality between the amenable category and Farber's topological
complexity.


title: Inclusive Jet and Hadron Suppression in a Multi-Stage Approach

abstract:
  We present a new study of jet interactions in the Quark-Gluon Plasma created
in high-energy heavy-ion collisions, using a multi-stage event generator within
the JETSCAPE framework. We focus on medium-induced modifications in the rate of
inclusive jets and high transverse momentum (high-$p_{\mathrm{T}}$) hadrons.
Scattering-induced jet energy loss is calculated in two stages: A high
virtuality stage based on the MATTER model, in which scattering of highly
virtual partons modifies the vacuum radiation pattern, and a second stage at
lower jet virtuality based on the LBT model, in which leading partons gain and
lose virtuality by scattering and radiation. Coherence effects that reduce the
medium-induced emission rate in the MATTER phase are also included. The
\trento\ model is used for initial conditions, and the (2+1)D VISHNU model is
used for viscous hydrodynamic evolution. Jet interactions with the medium are
modeled via 2-to-2 scattering with Debye screened potentials, in which the
recoiling partons are tracked, hadronized, and included in the jet clustering.
Holes left in the medium are also tracked and subtracted to conserve transverse
momentum. Calculations of the nuclear modification factor ($R_{\mathrm{AA}}$)
for inclusive jets and high-$p_{\mathrm{T}}$ hadrons are compared to
experimental measurements at RHIC and the LHC. Within this framework, we find
that two parameters for energy-loss, the coupling in the medium and the
transition scale between the stages of jet modification, suffice to
successfully describe these data at all energies, for central and semi-central
collisions, without re-scaling the jet transport coefficient $\hat{q}$.


title:
Seemo: A new tool for early design window view satisfaction evaluation
  in residential buildings

abstract:
  People spend approximately 90% of their lives indoors, and thus arguably, the
indoor space design can significantly influence occupant well-being. Adequate
views to the outside are one of the most cited indoor qualities related to
occupant well-being. However, due to urbanization and densification trends,
designers may have difficulties in providing vistas and views to the outside
with an assortment of content, which can support the needs of their occupants.
To better understand occupant view satisfaction and provide reliable design
feedback to architects, existing view satisfaction data must be expanded to
capture a wider variety of view scenarios and occupants. Most related research
remains challenging in architectural practice due to a lack of easy-to-use
early-design analysis tools. However, early assessment of view can be
advantageous as design decisions in early design, such as building orientation,
plan layout, and facade design, can improve the view quality. This paper,
hence, presents results from a 181 participant view satisfaction survey with
590 window views. The survey data is used to train a tree-regression model to
predict view satisfaction. The prediction performance was compared to an
existing view assessment framework through case studies. The result showed that
the new prediction is more accurate to the surveyed result than the framework.
Further, the prediction performance was generally high for most responses,
verifying the reliability. To facilitate view analysis in early design, this
paper describes integrating the satisfaction prediction model and a ray-casting
tool to compute view parameters in the CAD environment.


title: Holomorphic Yukawa Couplings in Heterotic String Theory

abstract:
  This thesis is concerned with heterotic E8 x E8 string models that can
produce quasirealistic N = 1 supersymmetric extensions of the Standard Model in
the low-energy limit. We start rather generally by deriving the
four-dimensional spectrum and Lagrangian terms from the ten-dimensional theory,
through a process of compactification over six-dimensional Calabi-Yau
manifolds, upon which holomorphic poly-stable vector bundles are defined. We
then specialise to a class of heterotic string models for which the vector
bundle is split into a sum of line bundles and the Calabi-Yau manifold is
defined as a complete intersection in projective ambient spaces. We develop a
method for calculating holomorphic Yukawa couplings for such models, by
relating bundle-valued forms on the Calabi-Yau manifold to their ambient space
counterparts, so that the relevant integrals can be evaluated over projective
spaces. The method is applicable for any of the 7890 CICY manifolds known in
the literature, and we show that it can be related to earlier algebraic
techniques to compute holomorphic Yukawa couplings. We provide explicit
calculations of the holomorphic Yukawa couplings for models compactified on the
tetra-quadric and on a co-dimension two CICY. A vanishing theorem is
formulated, showing that in some cases, topology rather than symmetry is
responsible for the absence of certain trilinear couplings. In addition, some
Yukawa matrices are found to be dependent on the complex structure moduli and
their rank is reduced in certain regions of the moduli space. In the final
part, we focus on a method to evaluate the matter field Kahler potential
without knowing the Ricci-flat Calabi-Yau metric. This is possible for large
internal gauge fluxes, for which the normalisation integral localises around a
point on the compactification manifold.


title:
Pragmatic constraints and pronoun reference disambiguation: the possible
  and the impossible

abstract:
  Pronoun disambiguation in understanding text and discourse often requires the
application of both general pragmatic knowledge and context-specific
information. In AI and linguistics research, this has mostly been studied in
cases where the referent is explicitly stated in the preceding text nearby.
However, pronouns in natural text often refer to entities, collections, or
events that are only implicitly mentioned previously; in those cases the need
to use pragmatic knowledge to disambiguate becomes much more acute and the
characterization of the knowledge becomes much more difficult. Extended
literary texts at times employ both extremely complex patterns of reference and
extremely rich and subtle forms of knowledge. Indeed, it is occasionally
possible to have a pronoun that is far separated from its referent in a text.
In the opposite direction, pronoun use is affected by considerations of focus
of attention and by formal constraints such as a preference for parallel
syntactic structures; these can be so strong that no pragmatic knowledge
suffices to overrule them.


title: Neutrino electromagnetic properties in a Leptoquark model

abstract:
  The neutrino magnetic dipole moment is studied in a simple extension of the
SM augmented with a scalar Leptoquark with quantum numbers
$(\bar{\boldsymbol{3}},\boldsymbol{1},1/3)$. In order to reach a sizeable
neutrino magnetic moment, we include in the framework the contribution of
right-handed neutrinos and analytical result for the one-loop contribution of a
scalar LQ is presented. The allowed parameter space is found through recent
restrictions on the muon $(g-2)$, the $\tau \to \mu \gamma$ and the $B_c \to
\tau^- \bar \nu$ decay. It is shown that exist a region of the parameter space
where the recent anomaly reported by the XENON collaboration can be
accommodated.


title: Few Shot Protein Generation

abstract:
  We present the MSA-to-protein transformer, a generative model of protein
sequences conditioned on protein families represented by multiple sequence
alignments (MSAs). Unlike existing approaches to learning generative models of
protein families, the MSA-to-protein transformer conditions sequence generation
directly on a learned encoding of the multiple sequence alignment,
circumventing the need for fitting dedicated family models. By training on a
large set of well-curated multiple sequence alignments in Pfam, our
MSA-to-protein transformer generalizes well to protein families not observed
during training and outperforms conventional family modeling approaches,
especially when MSAs are small. Our generative approach accurately models
epistasis and indels and allows for exact inference and efficient sampling
unlike other approaches. We demonstrate the protein sequence modeling
capabilities of our MSA-to-protein transformer and compare it with alternative
sequence modeling approaches in comprehensive benchmark experiments.


title: Minor Invertible Products Assignment and sparse hyperdeterminants

abstract:
  We consider an extension of Minor Assignment Problems derived from the
determinantal expansion of matrix products, under the condition that the terms
of the expansion are units of C(t). This restriction places constraints on the
sparsity and the factorization properties of a family of hyperdeterminants
derived from Grassmann-Pl\"ucker relations. We find minimal conditions
guaranteeing that allowed assignments returning a determinantal expansion are
the trivial ones, i.e. those induced by the action of a diagonal matrix of
monomials on a pair of constant matrices. Counterexamples are provided when
such conditions do not hold.


title: The inviscid limit of viscous Burgers at nondegenerate shock formation

abstract:
  We study the vanishing viscosity limit of the one-dimensional Burgers
equation near nondegenerate shock formation. We develop a matched asymptotic
expansion that describes small-viscosity solutions to arbitrary order up to the
moment the first shock forms. The inner part of this expansion has a novel
structure based on a fractional spacetime Taylor series for the inviscid
solution. We obtain sharp vanishing viscosity rates in a variety of norms,
including $L^\infty$. Comparable prior results break down in the vicinity of
shock formation. We partially fill this gap.


title:
Why Exposure Bias Matters: An Imitation Learning Perspective of Error
  Accumulation in Language Generation

abstract:
  Current language generation models suffer from issues such as repetition,
incoherence, and hallucinations. An often-repeated hypothesis is that this
brittleness of generation models is caused by the training and the generation
procedure mismatch, also referred to as exposure bias. In this paper, we verify
this hypothesis by analyzing exposure bias from an imitation learning
perspective. We show that exposure bias leads to an accumulation of errors,
analyze why perplexity fails to capture this accumulation, and empirically show
that this accumulation results in poor generation quality. Source code to
reproduce these experiments is available at
https://github.com/kushalarora/quantifying_exposure_bias


title:
PERFECT: Prompt-free and Efficient Few-shot Learning with Language
  Models

abstract:
  Current methods for few-shot fine-tuning of pretrained masked language models
(PLMs) require carefully engineered prompts and verbalizers for each new task
to convert examples into a cloze-format that the PLM can score. In this work,
we propose PERFECT, a simple and efficient method for few-shot fine-tuning of
PLMs without relying on any such handcrafting, which is highly effective given
as few as 32 data points. PERFECT makes two key design choices: First, we show
that manually engineered task prompts can be replaced with task-specific
adapters that enable sample-efficient fine-tuning and reduce memory and storage
costs by roughly factors of 5 and 100, respectively. Second, instead of using
handcrafted verbalizers, we learn new multi-token label embeddings during
fine-tuning, which are not tied to the model vocabulary and which allow us to
avoid complex auto-regressive decoding. These embeddings are not only learnable
from limited data but also enable nearly 100x faster training and inference.
Experiments on a wide range of few-shot NLP tasks demonstrate that PERFECT,
while being simple and efficient, also outperforms existing state-of-the-art
few-shot learning methods. Our code is publicly available at
https://github.com/facebookresearch/perfect.git.


title:
Compact-to-Dendritic Transition in the Reactive Deposition of Brownian
  Particles

abstract:
  When Brownian particles (such as ions, colloids, or misfolded proteins)
deposit onto a reactive cluster, the cluster undergoes a transition from a
compact to a dendritic morphology. Continuum modeling reveals that the critical
radius for this compact-to-dendritic (CTD) transition should be proportional to
the particle diffusivity divided by the surface reaction rate. However,
previous studies have had limited success verifying that the same scaling
arises in the continuum limit of a particle-based deposition model. This
discrepancy suggests that the continuum model may be missing part of the
microscopic dendrite formation mechanism, a concerning hypothesis given that
similar models are commonly used to study dendritic growth in electrodeposition
and lithium metal batteries. To clarify the accuracy of such models, we
reexamine the particle-based CTD transition using larger system sizes, up to
hundreds of millions of particles in some cases, and an improved paradigm for
the surface reaction. Specifically, this paradigm allows us to converge our
simulations and to work in terms of experimentally accessible parameters. With
these methods, we show that in both two and three dimensions, the behavior of
the critical radius is consistent with the scaling of the continuum model. Our
results help unify the particle-based and continuum views of the CTD
transition. In each of these cases, dendrites emerge when particles can no
longer diffuse around the cluster within the characteristic reaction timescale.
Consequently, this work implies that continuum methods can effectively capture
the microscopic physics of dendritic deposition.


title: CR embeddings of CR manifolds

abstract:
  We improve results of Baouendi, Rothschild and Treves and of Hill and
Nacinovich by finding a much weaker sufficient condition for a CR manifold of
type $(n,k)$ to admit a local CR embedding into a CR manifold of type
$(n+\ell,k-\ell)$. While their results require the existence of a finite
dimensional solvable transverse Lie algebra of vector fields; we require only a
finite dimensional extension.


title:
A Part-of-Speech Tagger for Yiddish: First Steps in Tagging the Yiddish
  Book Center Corpus

abstract:
  We describe the construction and evaluation of a part-of-speech tagger for
Yiddish (the first one, to the best of our knowledge). This is the first step
in a larger project of automatically assigning part-of-speech tags and
syntactic structure to Yiddish text for purposes of linguistic research. We
combine two resources for the current work - an 80K word subset of the Penn
Parsed Corpus of Historical Yiddish (PPCHY) (Santorini, 2021) and 650 million
words of OCR'd Yiddish text from the Yiddish Book Center (YBC). We compute word
embeddings on the YBC corpus, and these embeddings are used with a tagger model
trained and evaluated on the PPCHY. Yiddish orthography in the YBC corpus has
many spelling inconsistencies, and we present some evidence that even simple
non-contextualized embeddings are able to capture the relationships among
spelling variants without the need to first "standardize" the corpus. We
evaluate the tagger performance on a 10-fold cross-validation split, with and
without the embeddings, showing that the embeddings improve tagger performance.
However, a great deal of work remains to be done, and we conclude by discussing
some next steps, including the need for additional annotated training and test
data.


title: Analyzing Voting Power in Decentralized Governance: Who controls DAOs?

abstract:
  We empirically study the state of three prominent DAO governance systems on
the Ethereum blockchain: Compound, Uniswap and ENS. In particular, we examine
how the voting power is distributed in these systems. Using a comprehensive
dataset of all governance token holders, delegates, proposals and votes, we
analyze who holds the voting rights and how they are used to influence
governance decisions.


title: A product picture for quantum electrodynamics

abstract:
  We present a short account of our work to provide quantum electrodynamics
with a 'product picture'. It aims to complement the longer exposition in a
recent paper in 'Foundations of Physics' and to help to make that work more
accessible. The product picture is a formulation of QED, equivalent to standard
Coulomb gauge QED, but in which the Hilbert space arises as (a certain physical
subspace of) a product of a Hilbert space for the electromagnetic field and a
Hilbert space for charged matter (i.e. the Dirac field) and in which the
Hamiltonian arises as the sum of an electromagnetic Hamiltonian and a charged
matter Hamiltonian and an interaction term. (The Coulomb gauge formulation of
QED is not a product picture because, in it, the longitudinal part of the
electromagnetic field is made out of charged matter operators.) We also recall
a 'Contradictory Commutator Theorem' for QED which exposes flaws in previous
attempts at temporal gauge quantization of QED and we explain how our product
picture appears to offer a way to overcome those flaws. Additionally, we
discuss the extent to which that theorem generalizes to Yang-Mills fields. We
also develop a product picture for nonrelativistic charged particles in
interaction with the electromagnetic field and point out how this leads to a
novel way of thinking about the theory of many nonrelativistic electrically
charged particles with Coulomb interactions. We explain how the provision of a
product picture for QED gives hope that one will be able likewise to have a
product picture for (Yang Mills and) quantum gravity -- the latter being needed
to make sense of the author's 'matter-gravity entanglement hypothesis'. In an
afterword, we briefly discuss similarities and differences between that
hypothesis and its predictions and ideas of Roger Penrose related to a possible
role of gravity in quantum state reduction and to cosmological entropy.


title:
Key directions for research and development of superconducting radio
  frequency cavities

abstract:
  Radio frequency superconductivity is a cornerstone technology for many future
HEP particle accelerators and experiments from colliders to proton drivers for
neutrino facilities to searches for dark matter. While the performance of
superconducting RF (SRF) cavities has improved significantly over the last
decades, and the SRF technology has enabled new applications, the proposed HEP
facilities and experiments pose new challenges. To address these challenges,
the field continues to generate new ideas and there seems to be a vast room for
improvements. In this paper we discuss the key research directions that are
aligned with and address the future HEP needs.


title:
Local existence and uniqueness of a quasilinear system of equations as
  the fixed point of a Fibonacci-contraction

abstract:
  The Cauchy problem for a quasilinear system of hyperbolic-parabolic equations
is addressed with the method of linearization and fixed point. Coupling between
the hyperbolic and parabolic variables is allowed and we do not assume the
Friedrich's symmetrizability of the system. This coupling results in weaker
linear energy estimates and Banach's fixed point theorem cannot be applied. A
metric fixed point theorem is developed in order to conclude the local
existence and uniqueness of solutions. We apply our results to the
Cattaneo-Christov system for viscous compressible fluid flow, a system of
equations whose inviscid part is not hyperbolic.


title:
Effective treatment of U(1) gauge field and charged particles in axion
  inflation

abstract:
  The axionic inflaton with the Chern-Simons coupling may generate U(1) gauge
fields and charged particles simultaneously. In order to incorporate the
backreaction from the charged particles on the gauge fields, we develop a
procedure to obtain an equilibrium solution for the gauge fields by treating
the induced current as effective electric and magnetic conductivities.
Introducing mean field approximation, and numerically solving self-consistency
equations, we find that the gauge field amplitudes are drastically suppressed.
Interestingly, as the production becomes more efficient, the charged particles
gain a larger part of the transferred energy from the inflaton and eventually
dominate it. Our formalism offers a basis to connect this class of inflationary
models to a rich phenomenology such as baryogenesis and magnetogenesis.


title:
Fabrication of high-quality PMMA/SiO$_x$ spaced planar microcavities for
  strong coupling of light with monolayer WS$_2$ excitons

abstract:
  Exciton polaritons in atomically-thin transition metal dichalcogenide
crystals (monolayer TMDCs) have emerged as highly promising to enable
topological transport, ultra-efficient laser technologies, and collective
quantum phenomena such as polariton condensation and superfluidity at room
temperature. However, integrating monolayer TMDCs into high-quality planar
microcavities to achieve the required strong coupling between the cavity
photons and the TMDC excitons (bound electron-hole pairs) has proven
challenging. Previous approaches had to compromise between the adverse effects
on the strength of light-matter interactions in the monolayer, the cavity
photon lifetime, and the lateral size of the microcavity. Here, we demonstrate
a scalable approach to fabricating high-quality planar microcavities with
integrated monolayer WS$_2$ layer-by-layer by using polymethyl
methacrylate/silicon oxide (PMMA/SiO$_x$) as cavity spacer. Because the exciton
oscillator strength is well protected by the PMMA layer against the required
processing steps, the microcavities investigated in this work, which have
quality factors of above $10^3$, can operate in the strong light-matter
coupling regime at room temperature. This is an important step towards
fabricating patterned microcavities for engineering the exciton-polariton
potential landscape, which is essential for enabling many proposed
technologies.


title: Curiosity as filling, compressing, and reconfiguring knowledge networks

abstract:
  Due to the significant role that curiosity plays in our lives, several
theoretical constructs, such as the information gap theory and compression
progress theory, have sought to explain how we engage in its practice.
According to the former, curiosity is the drive to acquire information that is
missing from our understanding of the world. According to the latter, curiosity
is the drive to construct an increasingly parsimonious mental model of the
world. To complement the densification processes inherent to these theories, we
propose the conformational change theory, wherein we posit that curiosity
results in mental models with marked conceptual flexibility. We formalize
curiosity as the process of building a growing knowledge network to
quantitatively investigate information gap theory, compression progress theory,
and the conformational change theory of curiosity. In knowledge networks, gaps
can be identified as topological cavities, compression progress can be
quantified using network compressibility, and flexibility can be measured as
the number of conformational degrees of freedom. We leverage data acquired from
the online encyclopedia Wikipedia to determine the degree to which each theory
explains the growth of knowledge networks built by individuals and by
collectives. Our findings lend support to a pluralistic view of curiosity,
wherein intrinsically motivated information acquisition fills knowledge gaps
and simultaneously leads to increasingly compressible and flexible knowledge
networks. Across individuals and collectives, we determine the contexts in
which each theoretical account may be explanatory, thereby clarifying their
complementary and distinct explanations of curiosity. Our findings offer a
novel network theoretical perspective on intrinsically motivated information
acquisition that may harmonize with or compel an expansion of the traditional
taxonomy of curiosity.


title:
Cluster Structures with Machine Learning Support in Neutron Star M-R
  relations

abstract:
  Neutron stars (NS) are compact objects with strong gravitational fields, and
a matter composition subject to extreme physical conditions. The properties of
strongly interacting matter at ultra-high densities and temperatures impose a
big challenge to our understanding and modelling tools. Some difficulties are
critical, since one cannot reproduce such conditions in our laboratories or
assess them purely from astronomical observations. The information we have
about neutron star interiors are often extracted indirectly, e.g., from the
star mass-radius relation. The mass and radius are global quantities and still
have a significant uncertainty, which leads to great variability in studying
the micro-physics of the neutron star interior. This leaves open many questions
in nuclear astrophysics and the suitable equation of state (EoS) of NS.
Recently, new observations appear to constrain the mass-radius and consequently
has helped to close some open questions. In this work, utilizing modern machine
learning techniques, we analyze the NS mass-radius (M-R) relationship for a set
of EoS containing a variety of physical models. Our objective is to determine
patterns through the M-R data analysis and develop tools to understand the EoS
of neutron stars in forthcoming works.


title: Exploiting Temporal Relations on Radar Perception for Autonomous Driving

abstract:
  We consider the object recognition problem in autonomous driving using
automotive radar sensors. Comparing to Lidar sensors, radar is cost-effective
and robust in all-weather conditions for perception in autonomous driving.
However, radar signals suffer from low angular resolution and precision in
recognizing surrounding objects. To enhance the capacity of automotive radar,
in this work, we exploit the temporal information from successive ego-centric
bird-eye-view radar image frames for radar object recognition. We leverage the
consistency of an object's existence and attributes (size, orientation, etc.),
and propose a temporal relational layer to explicitly model the relations
between objects within successive radar images. In both object detection and
multiple object tracking, we show the superiority of our method compared to
several baseline approaches.


title: Wasserstein Hamiltonian flow with common noise on graph

abstract:
  We study the Wasserstein Hamiltonian flow with a common noise on the density
manifold of a finite graph. Under the framework of stochastic variational
principle, we first develop the formulation of stochastic Wasserstein
Hamiltonian flow and show the local existence of a unique solution. We also
establish a sufficient condition for the global existence of the solution.
Consequently, we obtain the global well-posedness for the nonlinear
Schr\"odinger equations with common noise on graph. In addition, using
Wong-Zakai approximation of common noise, we prove the existence of the
minimizer for an optimal control problem with common noise. We show that its
minimizer satisfies the stochastic Wasserstein Hamiltonian flow on graph as
well.


title:
Revisiting a kNN-based Image Classification System with High-capacity
  Storage

abstract:
  In existing image classification systems that use deep neural networks, the
knowledge needed for image classification is implicitly stored in model
parameters. If users want to update this knowledge, then they need to fine-tune
the model parameters. Moreover, users cannot verify the validity of inference
results or evaluate the contribution of knowledge to the results. In this
paper, we investigate a system that stores knowledge for image classification,
such as image feature maps, labels, and original images, not in model
parameters but in external high-capacity storage. Our system refers to the
storage like a database when classifying input images. To increase knowledge,
our system updates the database instead of fine-tuning model parameters, which
avoids catastrophic forgetting in incremental learning scenarios. We revisit a
kNN (k-Nearest Neighbor) classifier and employ it in our system. By analyzing
the neighborhood samples referred by the kNN algorithm, we can interpret how
knowledge learned in the past is used for inference results. Our system
achieves 79.8% top-1 accuracy on the ImageNet dataset without fine-tuning model
parameters after pretraining, and 90.8% accuracy on the Split CIFAR-100 dataset
in the task incremental learning setting.


title: Ab Initio Nuclear Reaction Theory with Applications to Astrophysics

abstract:
  We present an introduction to ab initio nuclear theory with a focus on
nuclear reactions. After a high-level overview of ab initio approaches in
nuclear physics, we give a more detailed description of the no-core shell model
technique equivalent to a large extent to configuration-interaction methods
applied in quantum chemistry. We then introduce the no-core shell model with
continuum approach that provides a quantum many-body description of nuclear
reactions. After a brief review of nuclear reactions important for
astrophysics, we present examples of results of ab initio calculations of
radiative capture and transfer reactions in light nuclei.


title:
Revisiting Sliced Wasserstein on Images: From Vectorization to
  Convolution

abstract:
  The conventional sliced Wasserstein is defined between two probability
measures that have realizations as vectors. When comparing two probability
measures over images, practitioners first need to vectorize images and then
project them to one-dimensional space by using matrix multiplication between
the sample matrix and the projection matrix. After that, the sliced Wasserstein
is evaluated by averaging the two corresponding one-dimensional projected
probability measures. However, this approach has two limitations. The first
limitation is that the spatial structure of images is not captured efficiently
by the vectorization step; therefore, the later slicing process becomes harder
to gather the discrepancy information. The second limitation is memory
inefficiency since each slicing direction is a vector that has the same
dimension as the images. To address these limitations, we propose novel slicing
methods for sliced Wasserstein between probability measures over images that
are based on the convolution operators. We derive convolution sliced
Wasserstein (CSW) and its variants via incorporating stride, dilation, and
non-linear activation function into the convolution operators. We investigate
the metricity of CSW as well as its sample complexity, its computational
complexity, and its connection to conventional sliced Wasserstein distances.
Finally, we demonstrate the favorable performance of CSW over the conventional
sliced Wasserstein in comparing probability measures over images and in
training deep generative modeling on images.


title:
The moduli Space of nonnegatively curved metrics on quotients of
  $S^2\times S^3$ by involutions

abstract:
  We show that for an orientable non-spin manifold with fundamental group
$\mathbb{Z}_2$ and universal cover $S^2\times S^3,$ the moduli space of metrics
of nonnegative sectional curvature has infinitely many path components. The
representatives of the components are quotients of the standard metric on
$S^3\times S^3$ or metrics on Brieskorn varieties previously constructed using
cohomogeneity one actions. The components are distinguished using the relative
$\eta$ invariant of the spin$^c$ Dirac operator computed by means of a
Lefschetz fixed point theorem.


title:
Quantum spatial superpositions and the possibility of superluminal
  signaling

abstract:
  Mari et al. [Sci. Rep. 6, 22777 (2016)] and Belenchia et al. [Phys. Rev. D
98, 126009 (2018)] explore a gedankenexperiment in which the (gravitational or
electromagnetic) interaction between two objects, one placed in a state of
quantum superposition of two locations, seems to allow for faster-than-light
communication. Building on the analysis carried out in Mari et al. [Sci. Rep.
6, 22777 (2016).], Belenchia et al. [Phys. Rev. D 98, 126009 (2018)] argue
that, by considering the quantization of radiation and the existence of vacuum
quantum fluctuations of the fields, the apparent possibility of superluminal
signaling is completely avoided. Moreover, in the gravitational case, such a
conclusion is taken as providing support for the view that gravity must
necessarily have a quantum nature. In this work, we reconsider the situation
and find several limitations of these assessments.


title: First-order variational analysis of non-amenable composite functions

abstract:
  This paper is devoted to studying the first-order variational analysis of
non-convex and non-differentiable functions that may not be subdifferentially
regular. To achieve this goal, we entirely rely on two concepts of directional
derivatives known as subderivative and semi-derivative. We establish the exact
chain and sum rules for this class of functions via these directional
derivatives. These calculus rules provide an implementable auto-differentiation
process such as back-propagation in composite functions. The latter calculus
rules can be used to identify the directional stationary points defined by the
subderivative. We show that the distance function of a geometrically derivable
constraint set is semi-differentiable, which opens the door for designing
first-order algorithms for non-Clarke regular constrained optimization
problems. We propose a first-order algorithm to find a directional stationary
point of non-Clarke regular and perhaps non-Lipschitz functions. We introduce a
descent property under which we establish the non-asymptotic convergence of our
method with rate $O(\varepsilon^{-2})$, akin to gradient descent for smooth
minimization. We show that the latter descent property holds for free in some
interesting non-amenable composite functions, in particular, it holds for the
Moreau envelope of any bounded-below function.


title:
Large-scale asymmetry in galaxy spin directions -- analysis of galaxies
  with spectra in DES, SDSS, and DESI Legacy Survey

abstract:
  Multiple previous studies using several different probes have shown
considerable evidence for the existence of cosmological-scale anisotropy and a
Hubble-scale axis. One of the probes that show such evidence is the
distribution of the directions toward which galaxies spin. The advantage of the
analysis of the distribution of galaxy spin directions compared to the CMB
anisotropy is that the ratio of galaxy spin directions is a relative
measurement, and therefore less sensitive to background contamination such as
Milky Way obstruction. Another advantage is that many spiral galaxies have
spectra, and therefore allow to analyze the location of such axis relative to
Earth. This paper shows an analysis of the distribution of the spin directions
of over 90K galaxies with spectra. That analysis is also compared to previous
analyses using the Earth-based SDSS, Pan-STARRS, and DESI Legacy Survey, as
well as space-based data collected by HST. The results show very good agreement
between the distribution patterns observed with the different telescopes. The
dipole or quadrupole axes formed by the spin directions of the galaxies with
spectra do not necessarily go directly through Earth.


title:
Detecting In-vehicle Intrusion via Semi-supervised Learning-based
  Convolutional Adversarial Autoencoders

abstract:
  With the development of autonomous vehicle technology, the controller area
network (CAN) bus has become the de facto standard for an in-vehicle
communication system because of its simplicity and efficiency. However, without
any encryption and authentication mechanisms, the in-vehicle network using the
CAN protocol is susceptible to a wide range of attacks. Many studies, which are
mostly based on machine learning, have proposed installing an intrusion
detection system (IDS) for anomaly detection in the CAN bus system. Although
machine learning methods have many advantages for IDS, previous models usually
require a large amount of labeled data, which results in high time and labor
costs. To handle this problem, we propose a novel semi-supervised
learning-based convolutional adversarial autoencoder model in this paper. The
proposed model combines two popular deep learning models: autoencoder and
generative adversarial networks. First, the model is trained with unlabeled
data to learn the manifolds of normal and attack patterns. Then, only a small
number of labeled samples are used in supervised training. The proposed model
can detect various kinds of message injection attacks, such as DoS, fuzzy, and
spoofing, as well as unknown attacks. The experimental results show that the
proposed model achieves the highest F1 score of 0.99 and a low error rate of
0.1\% with limited labeled data compared to other supervised methods. In
addition, we show that the model can meet the real-time requirement by
analyzing the model complexity in terms of the number of trainable parameters
and inference time. This study successfully reduced the number of model
parameters by five times and the inference time by eight times, compared to a
state-of-the-art model.


title: Continuous Variable Quantum MNIST Classifiers

abstract:
  In this paper, classical and continuous variable (CV) quantum neural network
hybrid multiclassifiers are presented using the MNIST dataset. The combination
of cutoff dimension and probability measurement method in the CV model allows a
quantum circuit to produce output vectors of size equal to n raised to the
power of n where n represents cutoff dimension and m, the number of qumodes.
They are then translated as one-hot encoded labels, padded with an appropriate
number of zeros. The total of eight different classifiers are built using
2,3,...,8 qumodes, based on the binary classifier architecture proposed in
Continuous variable quantum neural networks. The displacement gate and the Kerr
gate in the CV model allow for the bias addition and nonlinear activation
components of classical neural networks to quantum. The classifiers are
composed of a classical feedforward neural network, a quantum data encoding
circuit, and a CV quantum neural network circuit. On a truncated MNIST dataset
of 600 samples, a 4 qumode hybrid classifier achieves 100% training accuracy.


title:
Development of a near-5-Kelvin, cryogen-free, pulse-tube
  refrigerator-based scanning probe microscope

abstract:
  We report the design and performance of a cryogen-free, pulse-tube
refrigerator (PTR)-based scanning probe microscopy (SPM) system capable of
operating at the base temperature of near 5K. We achieve this by combining a
home-made interface design between the PTR cold head and the SPM head, with an
automatic gas-handling system. The interface design isolates the PTR vibrations
by a combination of polytetrafluoroethylene and stainless-steel bellows, and by
placing the SPM head on a passive vibration isolation table via two cold stages
that are connected to thermal radiation shields using copper heat links. The
gas-handling system regulates the helium heat-exchange gas pressures,
facilitating both the cool down to and the maintenance of the base temperature.
We discuss the effects of each component using measured vibration,
current-noise, temperature, and pressure data. We demonstrate that our SPM
system performance is comparable to known liquid-helium-based systems with the
measurements of the superconducting gap spectrum of Pb, atomic-resolution
scanning tunneling microscopy image and quasiparticle interference pattern of
Au(111) surface, and non-contact atomic force microscopy image of NaCl(100)
surface. Without the need for cryogen refills, the current SPM system enables
uninterrupted low-temperature measurements.


title:
Fewer, better pathways for all? Intersectional impacts of rural school
  consolidation in China's minority regions

abstract:
  Primary school consolidation--the closure of small community schools or their
mergers into larger, better-resourced schools--is emerging as a significant
policy response to changing demographics in middle income countries with large
rural populations. In China, large-scale consolidation took place in the early
21st century. Because officially-recognized minority populations
disproportionately reside in rural and remote areas, minority students were
among those at elevated risk of experiencing school consolidation. We analyze
heterogeneous effects of consolidation on educational attainment and reported
national language ability in China by exploiting variations in closure timing
across villages and cohorts captured in a 2011 survey of provinces and
autonomous regions with substantial minority populations. We consider
heterogeneous treatment effects across groups defined at the intersections of
minority status, gender, and community ethnic composition and socioeconomic
status. Compared to villages with schools, villages whose schools had closed
reported that the schools students now attended were better resourced, less
likely to offer minority language of instruction, more likely to have Han
teachers, farther away, and more likely to require boarding. Much more than Han
youth, ethnic minority youth were negatively affected by closure, in terms of
its impact on both educational attainment and written Mandarin facility.
However, significant penalties accruing to minority youth occurred only in the
poorest villages. Penalties were generally heavier for girls, but in the most
ethnically segregated minority villages, boys from minority families were
highly vulnerable to closure effects on attainment and written Mandarin
facility. Results show that intersections of minority status, gender, and
community characteristics can delineate significant heterogeneities in policy
impacts.


title:
Field-induced easy-axis softening of quantum ferromagnet with cubic
  anisotropy

abstract:
  In this work, we study the two-dimensional $S=2$ model that can lead to the
well-known cubic single-ion anisotropy along with a ferromagnetic Heisenberg
interaction under a perpendicular magnetic field. When the Heisenberg term
($J$) is ignored, this model can be exactly solved with a reshuffled local
Hilbert space. A three-fold (two-fold) eigenstate degeneracy is found at $h=0$
($h=2K$), where $h$ stands for the field strength and $K$ is the strength of
cubic anisotropy. Around the second degeneracy point there is a huge gap that
separates two low-energy states from the rest three. As we turn on the $J$
term, its leading-order contribution boils down to an effective hard-core
bosonic model as $K,h\gg J$. By using the two-dimensional tensor network ansatz
we reveal that indeed various ground-state ansatz with different magnetic easy
axes possess competing energies. Our finding demonstrates that the easy axes
can be "softened" as we turn on the magnetic field for such quantum model with
cubic anisotropy, which goes beyond the mean-field analysis. Such easy-axis
softening might provide a new measure to control the magnetic orientation for
spinful semiconductors.


title: Antenna Impedance Estimation at MIMO Receivers

abstract:
  This paper considers antenna impedance estimation based on training sequences
at MIMO receivers. The goal is to firstly leverage extensive resources
available in most wireless systems for channel estimation to estimate antenna
impedance in real-time. We assume the receiver switches its impedance in a
predetermined fashion during each training sequence. Based on voltage
observation across the load, a classical estimation framework is developed
incorporating the Rayleigh fading assumption. We then derive in closed-form a
maximum-likelihood (ML) estimator under i.i.d. fading and show this same ML
estimator is a method of moments (MM) estimator in correlated channels.
Numerical results suggest a fast algorithm, i.e., MLE in i.i.d. fading and the
MM estimator in correlated fading, that estimates the unknown antenna impedance
in real-time for all Rayleigh fading channels.


title:
Asymptotic analysis of operator families and applications to resonant
  media

abstract:
  We give an overview of operator-theoretic tools that have recently proved
useful in the analysis of boundary-value and transmission problems for
second-order partial differential equations, with a view to addressing, in
particular, the asymptotic behaviour of resolvents of physically motivated
parameter-dependent operator families. We demonstrate the links of this rich
area, on the one hand, to functional frameworks developed by S. N. Naboko and
his students, and on the other hand, to concrete applications of current
interest in the physics and engineering communities.


title: Unsupervised Change Detection Based on Image Reconstruction Loss

abstract:
  To train the change detector, bi-temporal images taken at different times in
the same area are used. However, collecting labeled bi-temporal images is
expensive and time consuming. To solve this problem, various unsupervised
change detection methods have been proposed, but they still require unlabeled
bi-temporal images. In this paper, we propose unsupervised change detection
based on image reconstruction loss using only unlabeled single temporal single
image. The image reconstruction model is trained to reconstruct the original
source image by receiving the source image and the photometrically transformed
source image as a pair. During inference, the model receives bi-temporal images
as the input, and tries to reconstruct one of the inputs. The changed region
between bi-temporal images shows high reconstruction loss. Our change detector
showed significant performance in various change detection benchmark datasets
even though only a single temporal single source image was used. The code and
trained models will be publicly available for reproducibility.


title:
A Novel Mask R-CNN Model to Segment Heterogeneous Brain Tumors through
  Image Subtraction

abstract:
  The segmentation of diseases is a popular topic explored by researchers in
the field of machine learning. Brain tumors are extremely dangerous and require
the utmost precision to segment for a successful surgery. Patients with tumors
usually take 4 MRI scans, T1, T1gd, T2, and FLAIR, which are then sent to
radiologists to segment and analyze for possible future surgery. To create a
second segmentation, it would be beneficial to both radiologists and patients
in being more confident in their conclusions. We propose using a method
performed by radiologists called image segmentation and applying it to machine
learning models to prove a better segmentation. Using Mask R-CNN, its ResNet
backbone being pre-trained on the RSNA pneumonia detection challenge dataset,
we can train a model on the Brats2020 Brain Tumor dataset. Center for
Biomedical Image Computing & Analytics provides MRI data on patients with and
without brain tumors and the corresponding segmentations. We can see how well
the method of image subtraction works by comparing it to models without image
subtraction through DICE coefficient (F1 score), recall, and precision on the
untouched test set. Our model performed with a DICE coefficient of 0.75 in
comparison to 0.69 without image subtraction. To further emphasize the
usefulness of image subtraction, we compare our final model to current
state-of-the-art models to segment tumors from MRI scans.


title: ScaleSFL: A Sharding Solution for Blockchain-Based Federated Learning

abstract:
  Blockchain-based federated learning has gained significant interest over the
last few years with the increasing concern for data privacy, advances in
machine learning, and blockchain innovation. However, gaps in security and
scalability hinder the development of real-world applications. In this study,
we propose ScaleSFL, which is a scalable blockchain-based sharding solution for
federated learning. ScaleSFL supports interoperability by separating the
off-chain federated learning component in order to verify model updates instead
of controlling the entire federated learning flow. We implemented ScaleSFL as a
proof-of-concept prototype system using Hyperledger Fabric to demonstrate the
feasibility of the solution. We present a performance evaluation of results
collected through Hyperledger Caliper benchmarking tools conducted on model
creation. Our evaluation results show that sharding can improve validation
performance linearly while remaining efficient and secure.


title:
Violation of Luttinger's theorem in the simplest doped Mott insulator:
  Falicov-Kimball model in strong correlation limit

abstract:
  The Luttinger's theorem has long been taken as the key feature of Landau's
Fermi liquid, which signals the presence of quasiparticles. Here, by the
unbiased Monte Carlo method, violation of Luttinger's theorem is clearly
revealed in the Falicov-Kimball (FK) model, indicating the robust
correlation-driven non-Fermi liquid characteristic under any electron density.
Introducing hole carriers to the half-filled FK leads to Mott insulator-metal
transition, where the Mott quantum criticality manifests unconventional scaling
behavior in transport properties. Further insight on the violation of the
Luttinger's theorem is examined by combining Hubbard-I approximation with a
composite fermion picture, which emphasizes the importance of a mixed
excitation of the itinerant electron and the composite fermion. Interestingly,
when compared FK model with a binary disorder system, it suggests that the
two-peak band structure discovered by Monte Carlo and Hubbard-I approaches is
underlying the violation of Luttinger's theorem.


title: Unsteady aerodynamic theory for membrane wings

abstract:
  We study analytically the dynamic response of membrane aerofoils subject to
arbitrary, small-amplitude chord motions and transverse gusts in a
two-dimensional inviscid incompressible flow. The theoretical model assumes
linear deformations of an extensible membrane under constant tension, which are
coupled aeroelastically to external aerodynamic loads using unsteady thin
aerofoil theory. The structural and aerodynamic membrane responses are
investigated for harmonic heave oscillations, an instantaneous change in angle
of attack, sinusoidal transverse gusts, and a sharp-edged gust. The unsteady
lift responses for these scenarios produce aeroelastic extensions to the
Theodorsen, Wagner, Sears, and K\"{u}ssner functions, respectively, for a
membrane aerofoil. These extensions incorporate for the first time membrane
fluid-structure interaction into the expressions for the unsteady lift response
of a flexible aerofoil. Our results suggest that membrane aerofoils with
appropriately tuned pretension could possess substantial aerodynamic benefits
over rigid aerofoils in unsteady flow conditions.


title:
Towards Large-Scale Learned Solvers for Parametric PDEs with
  Model-Parallel Fourier Neural Operators

abstract:
  Fourier neural operators (FNOs) are a recently introduced neural network
architecture for learning solution operators of partial differential equations
(PDEs), which have been shown to perform significantly better than comparable
approaches based on convolutional networks. Once trained, FNOs can achieve
speed-ups of multiple orders of magnitude over conventional numerical PDE
solvers. However, due to the high dimensionality of their input data and
network weights, FNOs have so far only been applied to two-dimensional or small
three-dimensional problems. To remove this limited problem-size barrier, we
propose a model-parallel version of FNOs based on domain-decomposition of both
the input data and network weights. We demonstrate that our model-parallel FNO
is able to predict time-varying PDE solutions of over 3.2 billions variables on
Summit using up to 768 GPUs and show an example of training a distributed FNO
on the Azure cloud for simulating multiphase CO$_2$ dynamics in the Earth's
subsurface.


title:
Diffusion on a lattice: transition rates, interactions and memory
  effects

abstract:
  We analyze diffusion of particles on a two dimensional square lattice. Each
lattice site contains an arbitrary number of particles. Interactions affect
particles only in the same site, and are macroscopically represented by the
excess chemical potential. In a recent work, a general expression for
transition rates between neighboring cells as functions of the excess chemical
potential was derived. With transition rates, the mean field tracer
diffusivity, $D^\text{MF}$, is immediately obtained. The tracer diffusivity, $D
= D^\text{MF} f$, contains the correlation factor $f$, representing memory
effects. An analysis of the joint probability of having given numbers of
particles at different sites when a force is applied to a tagged particle
allows an approximate expression for $f$ to be derived. The expression is
applied to soft core interaction (different values for the maximum number of
particles in a site are considered) and extended hard core.


title:
Homogenization of evolutionary incompressible Navier-Stokes system in
  perforated domains

abstract:
  In this paper, we consider the homogenization problems for evolutionary
incompressible Navier-Stokes system in three dimensional domains perforated
with a large number of small holes which are periodically located. We first
establish certain uniform estimates for the weak solutions. To overcome the
extra difficulties coming from the time derivative, we use the idea of Temam
and consider the equations by integrating in time variable. After suitably
extending the weak solutions to the whole domain, we employ the generalized
cell problem to study the limit process.


title: Attribute Prototype Network for Any-Shot Learning

abstract:
  Any-shot image classification allows to recognize novel classes with only a
few or even zero samples. For the task of zero-shot learning, visual attributes
have been shown to play an important role, while in the few-shot regime, the
effect of attributes is under-explored. To better transfer attribute-based
knowledge from seen to unseen classes, we argue that an image representation
with integrated attribute localization ability would be beneficial for
any-shot, i.e. zero-shot and few-shot, image classification tasks. To this end,
we propose a novel representation learning framework that jointly learns
discriminative global and local features using only class-level attributes.
While a visual-semantic embedding layer learns global features, local features
are learned through an attribute prototype network that simultaneously
regresses and decorrelates attributes from intermediate features. Furthermore,
we introduce a zoom-in module that localizes and crops the informative regions
to encourage the network to learn informative features explicitly. We show that
our locality augmented image representations achieve a new state-of-the-art on
challenging benchmarks, i.e. CUB, AWA2, and SUN. As an additional benefit, our
model points to the visual evidence of the attributes in an image, confirming
the improved attribute localization ability of our image representation. The
attribute localization is evaluated quantitatively with ground truth part
annotations, qualitatively with visualizations, and through well-designed user
studies.


title:
Rediscovery of the Effectiveness of Standard Convolution for Lightweight
  Face Detection

abstract:
  This paper analyses the design choices of face detection architecture that
improve efficiency between computation cost and accuracy. Specifically, we
re-examine the effectiveness of the standard convolutional block as a
lightweight backbone architecture on face detection. Unlike the current
tendency of lightweight architecture design, which heavily utilizes depthwise
separable convolution layers, we show that heavily channel-pruned standard
convolution layer can achieve better accuracy and inference speed when using a
similar parameter size. This observation is supported by the analyses
concerning the characteristics of the target data domain, face. Based on our
observation, we propose to employ ResNet with a highly reduced channel, which
surprisingly allows high efficiency compared to other mobile-friendly networks
(e.g., MobileNet-V1,-V2,-V3). From the extensive experiments, we show that the
proposed backbone can replace that of the state-of-the-art face detector with a
faster inference speed. Also, we further propose a new feature aggregation
method maximizing the detection performance. Our proposed detector EResFD
obtained 80.4% mAP on WIDER FACE Hard subset which only takes 37.7 ms for VGA
image inference in on CPU. Code will be available at
https://github.com/clovaai/EResFD.


title: Co-Teaching for Unsupervised Domain Adaptation and Expansion

abstract:
  Unsupervised Domain Adaptation (UDA) is known to trade a model's performance
on a source domain for improving its performance on a target domain. To resolve
the issue, Unsupervised Domain Expansion (UDE) has been proposed recently to
adapt the model for the target domain as UDA does, and in the meantime maintain
its performance on the source domain. For both UDA and UDE, a model tailored to
a given domain, let it be the source or the target domain, is assumed to well
handle samples from the given domain. We question the assumption by reporting
the existence of cross-domain visual ambiguity: Due to the lack of a crystally
clear boundary between the two domains, samples from one domain can be visually
close to the other domain. We exploit this finding and accordingly propose in
this paper Co-Teaching (CT) that consists of knowledge distillation based CT
(kdCT) and mixup based CT (miCT). Specifically, kdCT transfers knowledge from a
leader-teacher network and an assistant-teacher network to a student network,
so the cross-domain visual ambiguity will be better handled by the student.
Meanwhile, miCT further enhances the generalization ability of the student.
Comprehensive experiments on two image-classification benchmarks and two
driving-scene-segmentation benchmarks justify the viability of the proposed
method.


title: $p_{T}$ dispersion of inclusive jets in high-energy nuclear collisions

abstract:
  In this paper, we investigate the medium modifications of $p_{T}$
dispersion($p_{T}D$) of inclusive jets with small radius ($R=0.2$) in Pb+Pb
collisions at $\sqrt{s}=2.76$~TeV. The partonic spectrum in the initial hard
scattering of elementary collisions are obtained by an event generator
POWHEG+PYTHIA, which matches the next-to-leading (NLO) matrix elements with
parton showering, and energy loss of fast parton traversing in hot/dense QCD
medium is calculated by Monte Carlo simulation within Higher-Twist formalism of
jet quenching in heavy-ion collisions. We present the model calculations of
normalized $p_{T}D$ distributions for inclusive jets in p+p and Pb+Pb
collisions at $\sqrt{s}=2.76$~TeV, which give nice descriptions of ALICE
measurements. It is shown that the $p_{T}D$ distributions of inclusive jets in
Pb+Pb are shifted to higher $p_{T}D$ region relative to that in p+p. Thus the
nuclear modification factor of $p_{T}D$ distributions for inclusive jets is
smaller than unity at small $p_{T}D$ region, while larger than one at large
$p_{T}D$ region. This behaviour results from more uneven $p_T$ of jet
constituents as well as the fraction alteration of gluon/quark initiated jets
in heavy-ion collisions. The difference of $p_{T}D$ distributions between
groomed and ungroomed jets in Pb+Pb collisions are also discussed.


title: The local Gross-Prasad conjecture over $\mathbb{R}$: Epsilon dichotomy

abstract:
  Following the work of Jean-Loup Waldspurger, we prove the epsilon dichotomy
part of the local Gross-Prasad conjecture over $\mathbb{R}$ for tempered local
$L$-parameters.


title: Learning Linear Symmetries in Data Using Moment Matching

abstract:
  It is common in machine learning and statistics to use symmetries derived
from expert knowledge to simplify problems or improve performance, using
methods like data augmentation or penalties. In this paper we consider the
unsupervised and semi-supervised problems of learning such symmetries in a
distribution directly from data in a model-free fashion. We show that in the
worst case this problem is as difficult as the graph automorphism problem.
However, if we restrict to the case where the covariance matrix has unique
eigenvalues, then the eigenvectors will also be eigenvectors of the symmetry
transformation. If we further restrict to finding orthogonal symmetries, then
the eigenvalues will be either be 1 or -1, and the problem reduces to
determining which eigenvectors are which. We develop and compare theoretically
and empirically the effectiveness of different methods of selecting which
eigenvectors should have eigenvalue -1 in the symmetry transformation, and
discuss how to extend this approach to non-orthogonal cases where we have
labels


title: Scotogenic top-quark FCNC decays

abstract:
  Flavor-changing neutral current (FCNC) top-quark decays are highly suppressed
due to the Glashow-Iliopoulos-Maiani mechanism in the standard model (SM). If
$t\to q h, qV$ with $V=g,\gamma, Z$ are all induced via quantum loop levels,
then we investigate the effect that can enhance the top-FCNC up to the
sensitivity designed at the high-luminosity (HL) LHC. Inspired by the mechanism
of the scotogenic neutrino mass, we extend the SM by including $Z_2$-odd
colored fermions when a $Z_2$ discrete is imposed. The results show that by
taking $BR(t\to u g) \lesssim 0.61 \times 10^{-4}$ recently measured by ATLAS
as an input, $t\to q \gamma$ can be indirectly bounded to be $BR(t\to q
\gamma)\lesssim 3.2 \times 10^{-6}$, which is below the expected sensitivity at
the HL LHC. After taking potential constraints from various experiments into
account, the obtained branching ratios for the loop-induced $t\to q h$ and
$t\to qZ$ decays can be $O(10^{-4})$, which falls within the sensitivity at the
HL LHC.


title:
Capturing positive utilities during the estimation of recursive logit
  models: A prism-based approach

abstract:
  Although the recursive logit (RL) model has been recently popular and has led
to many applications and extensions, an important numerical issue with respect
to the evaluation of value functions remains unsolved. This issue is
particularly significant for model estimation, during which the parameters are
updated every iteration and may violate the model feasible condition. To solve
this numerical issue, this paper proposes a prism-constrained RL (Prism-RL)
model that implicitly restricts the path set by the prism constraint defined
based upon a state-extended network representation. Providing a set of
numerical experiments, we show that the Prism-RL model succeeds in the stable
estimation regardless of the initial and true parameter values and is able to
capture positive utilities. In the real application to a pedestrian network, we
found the positive effect of street green presence on pedestrians. Moreover,
the Prism-RL model achieved higher goodness of fit than the RL model, implying
that the Prism-RL model can also describe more realistic route choice behavior.


title:
MLPro: A System for Hosting Crowdsourced Machine Learning Challenges for
  Open-Ended Research Problems

abstract:
  The task of developing a machine learning (ML) model for a particular problem
is inherently open-ended, and there is an unbounded set of possible solutions.
Steps of the ML development pipeline, such as feature engineering, loss
function specification, data imputation, and dimensionality reduction, require
the engineer to consider an extensive and often infinite array of
possibilities. Successfully identifying high-performing solutions for an
unfamiliar dataset or problem requires a mix of mathematical prowess and
creativity applied towards inventing and repurposing novel ML methods. Here, we
explore the feasibility of hosting crowdsourced ML challenges to facilitate a
breadth-first exploration of open-ended research problems, thereby expanding
the search space of problem solutions beyond what a typical ML team could
viably investigate. We develop MLPro, a system which combines the notion of
open-ended ML coding problems with the concept of an automatic online code
judging platform. To conduct a pilot evaluation of this paradigm, we
crowdsource several open-ended ML challenges to ML and data science
practitioners. We describe results from two separate challenges. We find that
for sufficiently unconstrained and complex problems, many experts submit
similar solutions, but some experts provide unique solutions which outperform
the "typical" solution class. We suggest that automated expert crowdsourcing
systems such as MLPro have the potential to accelerate ML engineering
creativity.


title: Multiplier Hermitian-Einstein metrics on Fano manifolds of KSM-type

abstract:
  In this article we focus on multiplier Hermitian-Einstein metrics introduced
by Mabuchi which include K\"ahler-Einstein metrics, K\"ahler-Ricci solitons and
Mabuchi solitons as special cases. We also focus on KSM-manifolds, which are
introduced by the first author as toric bundles, to establish a criterion for
the existence of multiplier Hermitian-Einstein metrics in terms of KSM-data. An
explicit example for a KSM-manifold admitting a family of multiplier
Hermitian-Einstein metrics is constructed by using a continuous path connecting
a K\"ahler-Ricci soliton and a Mabuchi soliton.


title: Animatable Neural Radiance Fields from Monocular RGB-D

abstract:
  This paper aims at representing animatable photo-realistic humans under novel
views and poses. Recent work has shown significant progress with dynamic scenes
by exploring shared canonical neural radiance fields. However learning a
user-controlled model for novel poses remains a challenging task. To tackle
this problem, we introduce a novel method to integrate observations across
frames and encode the appearance at each individual frame by utilizing the
human pose that models the body shape and point clouds which cover partial part
of the human as the input. Specifically, our method simultaneously learns a
shared set of latent codes anchored to the human pose among frames, and learns
an appearance-dependent code anchored to incomplete point clouds generated by
monocular RGB-D at each frame. A human pose-based code models the shape of the
performer whereas a point cloud based code predicts details and reasons about
missing structures at the unseen poses. To further recover non-visible regions
in query frames, we utilize a temporal transformer to integrate features of
points in query frames and tracked body points from automatically-selected key
frames. Experiments on various sequences of humans in motion show that our
method significantly outperforms existing works under unseen poses and novel
views given monocular RGB-D videos as input.


title:
Harvesting Entanglement by non-identical detectors with different energy
  gaps

abstract:
  It has been shown that the vacuum state of a free quantum field is entangled
and such vacuum entanglement can be harvested by a pair of initially
uncorrelated detectors interacting locally with the vacuum field for a finite
time. In this paper, we examine the entanglement harvesting phenomenon of two
non-identical inertial detectors with different energy gaps locally interacting
with massless scalar fields via a Gaussian switching function. We focus on how
entanglement harvesting depends on the energy gap difference from two
perspectives: the amount of entanglement harvested and the
harvesting-achievable separation between the two detectors. In the sense of the
amount of entanglement, we find that as long as the inter-detector separation
is not too small with respect to the interaction duration parameter, two
non-identical detectors could extract more entanglement from the vacuum state
than the identical detectors. There exists an optimal value of the energy gap
difference when the inter-detector separation is sufficiently large that
renders the harvested entanglement to peak. Regarding the harvesting-achievable
separation, we further find that the presence of an energy gap difference
generally enlarges the harvesting-achievable separation range. Our results
suggest that the non-identical detectors may be advantageous to extracting
entanglement from vacuum in certain circumstances as compared to identical
detectors.


title:
Wigner time delays and Goos-H\"{a}nchen shifts of 2D quantum vortices
  scattered by potential barriers

abstract:
  We consider reflection and transmission of 2D quantum wavepackets with phase
vortices (also known in optics as spatiotemporal vortex pulses) at potential
step-like, delta-function, and rectangular barriers. The presence of a vortex
significantly modifies the Wigner time delays and Goos-H\"{a}nchen shifts,
previously studied for Gaussian-like wavepackets. In particular, the scattered
wavepackets undergo non-zero time delays and lateral shifts even for purely
real scattering coefficients, when the standard Wigner and Artmann formulae
vanish. We derive analytical expressions for the vortex-induced times delays
and spatial shifts of 2D vortices and verify these with numerical calculations
of the Schr\"{o}dinger equation. The time delays and shifts are resonantly
enhanced in the vicinity of the critical-angle incidence for a step-like
potential and near transmission resonances for a rectangular barrier.


title:
Gradient estimates for Donaldson's equation on a compact K\"ahler
  manifold

abstract:
  We prove a gradient estimate for Donaldson's equation
\[\omega\wedge(\chi+\sqrt{-1}\partial\bar{\partial}\varphi)^{n-1}=e^F(\chi+\sqrt{-1}\partial\bar{\partial}\varphi)^n\]
(and its parabolic analog) on an $n$-dimensional compact K\"ahler manifold
$(M,\omega)$ with another Hermitian metric $\chi$ directly from the $C^2$
estimate and Alexandrov-Bakelman-Pucci (ABP) maximum principle.


title:
Accurate determination of quadrupole polarizabilities of the excited
  states of alkali-metal atoms

abstract:
  The scalar and tensor components of the electric quadrupole (E2)
polarizabilities of the first two excited states of all the alkali-metal atoms
are determined. To validate the calculations, we have evaluated the ground
state E2 polarizabilities of these atoms and compared them with the literature
values. We could not find the ground state E2 polarizability value for Fr in
the literature to compare with our result. The dominant parts of these
quantities are estimated by combining the precisely calculated E2 transition
matrix elements of many low-lying transitions with the experimental energies,
while the other contributions are estimated using lower-order methods. Our
estimated values for the ground states of the above atoms are in good agreement
with the literature values suggesting that our estimated E2 polarizabilities
for the excited states of the alkali atoms, which were not known earlier except
for the Li atom, are also quite accurate. These reported E2 polarizabilities
could be useful in guiding many precision measurements in the alkali atoms.


title:
Gate voltage dependent Rashba spin splitting in hole transverse magnetic
  focussing

abstract:
  Magnetic focussing of charge carriers in two-dimensional systems provides a
solid state version of a mass spectrometer. In the presence of a spin-orbit
interaction, the first focussing peak splits into two spin dependent peaks,
allowing focussing to be used to measure spin polarisation and the strength of
the spin-orbit interaction. In hole systems, the k^3 dependence of the Rashba
spin-orbit term allows the spatial separation of spins to be changed in-situ
using a voltage applied to an overall top gate. Here we demonstrate that this
can be used to control the splitting of the magnetic focussing peaks.
Additionally, we compare the focussing peak splitting to that predicted by
Shubnikov-de Haas oscillations and k.p bandstructure calculations. We find that
the focussing peak splitting is consistently larger than expected, suggesting
further work is needed on understanding spin dependent magnetic focussing.


title: An Optimal Algorithm for Certifying Monotone Functions

abstract:
  Given query access to a monotone function $f\colon\{0,1\}^n\to\{0,1\}$ with
certificate complexity $C(f)$ and an input $x^{\star}$, we design an algorithm
that outputs a size-$C(f)$ subset of $x^{\star}$ certifying the value of
$f(x^{\star})$. Our algorithm makes $O(C(f) \cdot \log n)$ queries to $f$,
which matches the information-theoretic lower bound for this problem and
resolves the concrete open question posed in the STOC '22 paper of Blanc, Koch,
Lange, and Tan [BKLT22].
  We extend this result to an algorithm that finds a size-$2C(f)$ certificate
for a real-valued monotone function with $O(C(f) \cdot \log n)$ queries. We
also complement our algorithms with a hardness result, in which we show that
finding the shortest possible certificate in $x^{\star}$ may require
$\Omega\left(\binom{n}{C(f)}\right)$ queries in the worst case.


title: The X-ray transform on a generic family of smooth curves

abstract:
  We study the X-ray transform over a generic family of smooth curves in
$\mathbb{R}^2$ with a Riemannian metric $g$. We show that the singularities
cannot be recovered from local data in the presence of conjugate points, and
therefore artifacts may arise in the reconstruction. We perform numerical
experiments to illustrate the results.


title: Stochastic filtering under model ambiguity

abstract:
  In this paper, we study a non-linear filtering problem when the signal model
is uncertain. The model ambiguity is characterized by a class of probability
measures from which the true probability measure is taken. The optimal filter
can be estimated by converting to a conditional mean field optimal control
problem. In the first part of this article, we develop a general form
stochastic maximum principle for a conditional mean-field type model driven by
a forward and backward control system. In the second part, we characterize the
ambiguity filter and prove its existence and uniqueness.