title:
Thermodynamic Property of Schwarzschild Black Hole in Non-Commutative
  Gauge Theory of Gravity

abstract:
  In this paper, we constructed the deformed Schwarzschild black hole in the
non-commutative (NC) gauge theory of gravity. Where the singularity at the
origin is shifted by the non-commutativity to the finite radius $r_h=2m$ and
changed the spherical symmetry of the black hole to the ellipse symmetry
(deformed the NC event horizon). The thermodynamics property of the NC black
hole is analyzed. As a first step, we describe the Hawking temperature and the
entropy of the NC Schwarzschild black hole, where the result shows a difference
in the pole-equator temperature of the black hole, and give us a new scenario
of the black hole evaporation. Our estimation for the NC parameter is close to
the Planck scale $\Theta\approx2.257\times 10^{-35}m$. Then, the description of
the ADM mass, the heat capacity, and the Gibbs free energy of the deformed
black hole show the effect of the NC gauge theory on the thermodynamic
stability and the phase transitions. Finally, we investigated the influence of
the pressure of the black hole in the first law of thermodynamics on the
stability and the phase transition of the Schwarzschild black hole in the NC
spacetime, where the result shows that the NC parameter plays a similar role as
the thermodynamical function and defined the critical point as the pressure in
the modified first law of thermodynamics which means a second-order phase
transition or a continuous one. The stability and the phase transition of the
Schwarzschild black hole are affected by the non-commutativity.


title: Unexciting non-Abelian electric fields

abstract:
  Electric fields in QED are known to discharge due to Schwinger pair
production of charged particles. Corresponding electric fields in non-Abelian
theory are known to discharge due to the production of gluons. Yet electric
flux tubes in QCD ought to be stable to the production of charged gluons as
they confine quarks. We resolve this conundrum by finding electric field
configurations in pure non-Abelian gauge theory in which the Schwinger process
is absent and the electric field is protected against quantum dissipation.


title: An optical transition-edge sensor with high energy resolution

abstract:
  Optical transition-edge sensors have shown energy resolution for resolving
the number of incident photons at the telecommunication wavelength. Higher
energy resolution is required for biological imaging and microscope
spectroscopy. In this paper, we report on a Au/Ti (10/20 nm) bilayer TES that
showed high energy resolution. This was achieved by lowering the critical
temperature Tc to 115 mK and the resultant energy resolution was 67 meV full
width at half maximum (FWHM) at 0.8 eV. When Tc was lowered to 115 mK, the
theoretical resolution would scaled up to 30 meV FWHM, considering that the
typical energy resolution of optical TESs is 150 meV and Tc is 300 mK. To
investigate the gap between the theoretical expectation (30 meV) and the
measured value (67 meV), we measured its complex impedance and current noise.
We found excess Johnson noise in the TES and an excess Johnson term M was 1.5
at a bias point where the resistance was 10% of normal resistance. For
reference, the TES was compared with a TES showing typical energy resolution
(156 meV FWHM). We will discuss what improved the energy resolution and what
might have been the limiting factor on it.


title: Prediction Intervals for Simulation Metamodeling

abstract:
  Simulation metamodeling refers to the construction of lower-fidelity models
to represent input-output relations using few simulation runs. Stochastic
kriging, which is based on Gaussian process, is a versatile and common
technique for such a task. However, this approach relies on specific model
assumptions and could encounter scalability challenges. In this paper, we study
an alternative metamodeling approach using prediction intervals to capture the
uncertainty of simulation outputs. We cast the metamodeling task as an
empirical constrained optimization framework to train prediction intervals that
attain accurate prediction coverage and narrow width. We specifically use
neural network to represent these intervals and discuss procedures to
approximately solve this optimization problem. We also present an adaptation of
conformal prediction tools as another approach to construct prediction
intervals for metamodeling. Lastly, we present a validation machinery and show
how our method can enjoy a distribution-free finite-sample guarantee on the
prediction performance. We demonstrate and compare our proposed approaches with
existing methods including stochastic kriging through numerical examples.


title:
Learning to Adapt to Domain Shifts with Few-shot Samples in Anomalous
  Sound Detection

abstract:
  Anomaly detection has many important applications, such as monitoring
industrial equipment. Despite recent advances in anomaly detection with
deep-learning methods, it is unclear how existing solutions would perform under
out-of-distribution scenarios, e.g., due to shifts in machine load or
environmental noise. Grounded in the application of machine health monitoring,
we propose a framework that adapts to new conditions with few-shot samples.
Building upon prior work, we adopt a classification-based approach for anomaly
detection and show its equivalence to mixture density estimation of the normal
samples. We incorporate an episodic training procedure to match the few-shot
setting during inference. We define multiple auxiliary classification tasks
based on meta-information and leverage gradient-based meta-learning to improve
generalization to different shifts. We evaluate our proposed method on a
recently-released dataset of audio measurements from different machine types.
It improved upon two baselines by around 10% and is on par with best-performing
model reported on the dataset.


title: Dynatask: A Framework for Creating Dynamic AI Benchmark Tasks

abstract:
  We introduce Dynatask: an open source system for setting up custom NLP tasks
that aims to greatly lower the technical knowledge and effort required for
hosting and evaluating state-of-the-art NLP models, as well as for conducting
model in the loop data collection with crowdworkers. Dynatask is integrated
with Dynabench, a research platform for rethinking benchmarking in AI that
facilitates human and model in the loop data collection and evaluation. To
create a task, users only need to write a short task configuration file from
which the relevant web interfaces and model hosting infrastructure are
automatically generated. The system is available at https://dynabench.org/ and
the full library can be found at https://github.com/facebookresearch/dynabench.


title: Weyl double copy and massless free fields in curved spacetimes

abstract:
  Based on the fact that any massless free-field with spin higher than
$\frac{1}{2}$ can be constructed from scalar fields (spin-0) and Dirac-Weyl
fields (spin-$\frac{1}{2}$), we introduce a map between spin-$2$ massless
free-fields (gravity fields) and spin-$\frac{1}{2}$ massless free-fields
(Dirac-Weyl fields) in spinor formalism. The associated Dirac-Weyl spinors can
live in certain spacetime are identified. Regarding them as basic units, other
higher spin massless free-fields are then constructed. In this way, some hidden
fundamental features related to massless-free fields are revealed. In
particular, for type N vacuum spacetimes, we show that all higher spin massless
free-fields can be constructed by the same type of Dirac-Weyl spinors and an
auxiliary scalar field, which satisfies the wave equation not only on curved
spacetime but also on Minkowski spacetime. For type D vacuum spacetimes, we
show that there exist similar relationships among different spin massless
free-fields. Furthermore, we systematically rebuild the Weyl double copy for
non-twisting type N vacuum spacetimes and type D vacuum spacetimes. We show
that the zeroth copy not only connects the gravity fields with a single copy
but also connects those degenerate electromagnetic fields with the Dirac-Weyl
fields living in the curved spacetime, both for type N and type D cases.
Afterward, we extend the study to type III non-twisting vacuum solutions.
Independent of the proposed map, we find that there is a special Dirac-Weyl
scalar whose square is just proportional to the Weyl scalar. A degenerate
Maxwell field and an auxiliary scalar field are then solved. Both of them play
the similar roles as the double copy. The result further confirms that there
must exist a deep connection between gravity theory and gauge theory.


title: Firefighting with a Distance-Based Restriction

abstract:
  In the classic version of the game of firefighter, on the first turn a fire
breaks out on a vertex in a graph $G$ and then $k$ firefighters protect $k$
vertices. On each subsequent turn, the fire spreads to the collective unburnt
neighbourhood of all the burning vertices and the firefighters again protect
$k$ vertices. Once a vertex has been burnt or protected it remains that way for
the rest of the game. A common objective with respect to some infinite graph
$G$ is to determine how many firefighters are necessary to stop the fire from
spreading after a finite number of turns, commonly referred to as containing
the fire. We introduce the concept of distance-restricted firefighting where
the firefighters' movement is restricted so they can only move up to some fixed
distance $d$ per turn rather than being able to move without restriction. We
establish some general properties of this new game in contrast to properties of
the original game, and we investigate specific cases of the distance-restricted
game on the infinite square, strong, and hexagonal grids. We conjecture that
two firefighters are insufficient on the square grid when $d = 2$, and we pose
some questions about how many firefighters are required in general when $d =
1$.


title: Locality of vortex stretching for the 3D Euler equations

abstract:
  We consider the 3D incompressible Euler equations under the following
situation: small-scale vortex blob being stretched by a prescribed large-scale
stationary flow. More precisely, we clarify what kind of large-scale stationary
flows really stretch small-scale vortex blobs in alignment with the straining
direction. The key idea is constructing a Lagrangian coordinate so that the Lie
bracket is identically zero (c.f. the Frobenius theorem), and investigate the
locality of the pressure term by using it.


title: Quasi-2-Segal sets

abstract:
  We show that the 2-Segal spaces (also called decomposition spaces) of
Dyckerhoff-Kapranov and G\'alvez-Kock-Tonks have a natural analogue within
simplicial sets, which we call quasi-2-Segal sets, and that the two ideas enjoy
a similar relationship as the one Segal spaces have with quasi-categories. In
particular, we construct a model structure on the category of simplicial sets
whose fibrant objects are the quasi-2-Segal sets which is Quillen equivalent to
a model structure for complete 2-Segal spaces (where our notion of completeness
comes from one of the equivalent characterizations of completeness for Segal
spaces). We also prove a path space criterion, which says that a simplicial set
is a quasi-2-Segal set if and only if its path spaces (also called d\'ecalage)
are quasi-categories, as well as an edgewise subdivision criterion.


title: Almost-Linear Planted Cliques Elude the Metropolis Process

abstract:
  A seminal work of Jerrum (1992) showed that large cliques elude the
Metropolis process. More specifically, Jerrum showed that the Metropolis
algorithm cannot find a clique of size $k=\Theta(n^{\alpha}), \alpha \in
(0,1/2)$, which is planted in the Erd\H{o}s-R\'{e}nyi random graph $G(n,1/2)$,
in polynomial time. Information theoretically it is possible to find such
planted cliques as soon as $k \ge (2+\epsilon) \log n$.
  Since the work of Jerrum, the computational problem of finding a planted
clique in $G(n,1/2)$ was studied extensively and many polynomial time
algorithms were shown to find the planted clique if it is of size $k =
\Omega(\sqrt{n})$, while no polynomial-time algorithm is known to work when
$k=o(\sqrt{n})$. Notably, the first evidence of the problem's algorithmic
hardness is commonly attributed to the result of Jerrum from 1992.
  In this paper we revisit the original Metropolis algorithm suggested by
Jerrum. Interestingly, we find that the Metropolis algorithm actually fails to
recover a planted clique of size $k=\Theta(n^{\alpha})$ for any constant $0
\leq \alpha < 1$.
  Moreover, we strengthen Jerrum's results in a number of other ways including:
Like many results in the MCMC literature, the result of Jerrum shows that there
exists a starting state (which may depend on the instance) for which the
Metropolis algorithm fails. For a wide range of temperatures, we show that the
algorithm fails when started at the most natural initial state, which is the
empty clique. This answers an open problem stated in Jerrum (1992). We also
show that the simulated tempering version of the Metropolis algorithm, a more
sophisticated temperature-exchange variant of it, also fails at the same regime
of parameters.
  Finally, our results confirm recent predictions by Gamarnik and Zadik (2019)
and Angelini, Fachin, de Feo (2021).


title:
Phase dynamics of noise-induced coherent oscillations in excitable
  systems

abstract:
  Noise can induce coherent oscillations in excitable systems without periodic
orbits. Here, we establish a method to derive a hybrid system approximating the
noise-induced coherent oscillations in excitable systems and further perform
phase reduction of the hybrid system to derive an effective,
dimensionality-reduced phase equation. We apply the reduced phase model to a
periodically forced excitable system and two-coupled excitable systems, both
undergoing noise-induced oscillations. The reduced phase model can
quantitatively predict the entrainment of a single system to the periodic force
and the mutual synchronization of two coupled systems, including the phase
slipping behavior due to noise, as verified by Monte Carlo simulations. The
derived phase model gives a simple and efficient description of noise-induced
oscillations and can be applied to the analysis of more general cases.


title:
Pattern preservation during the decay and growth of localized wave
  packet in two-dimensional channel flow

abstract:
  In this paper, the decay and growth of localized wave packet (LWP) in
two-dimensional plane-Poiseuille flow are studied numerically and
theoretically. When the Reynolds number ($Re$) is less than a critical value
$Re_c$, the disturbance kinetic energy $E_k$ of LWP decreases monotonically
with time and experiences three decay periods, i.e. the initial and the final
steep descent periods, and the middle plateau period. Higher initial $E_k$ of a
decaying LWP corresponds to longer lifetime. According to the simulations, the
lifetime scales as $(Re_c-Re)^{-1/2}$, indicating a divergence of lifetime as
$Re$ approaches $Re_c$, a phenomenon known as "critical slowing-down". By
proposing a pattern preservation approximation, i.e. the integral kinematic
properties (e.g. the disturbance enstrophy) of an evolving LWP are independent
of $Re$ and single valued functions of $E_k$, the disturbance kinetic energy
equation can be transformed into the classical differential equation for
saddle-node bifurcation, by which the lifetimes of decaying LWPs can be
derived, supporting the $-1/2$ scaling law. Furthermore, by applying the
pattern preservation approximation and the integral kinematic properties
obtained as $Re<Re_c$, the Reynolds number and the corresponding $E_k$ of the
whole lower branch, the turning point, and the upper-branch LWPs with
$E_k<0.15$ are predicted successfully with the disturbance kinetic energy
equation, indicating that the pattern preservation is an intrinsic feature of
this localized transitional structure.


title: TASI Lectures on the Mathematics of String Dualities

abstract:
  In these lecture proceedings, we describe some of the fundamental
mathematical concepts that underlie supersymmetric string theory and field
theory, and their role in describing and testing dualities. In particular, we
provide a pedagogical introduction to topological and holomorphic twisting,
descent, and higher algebraic structures. Our primary examples are worldsheet
theories of topological strings, namely the A- and B-models, which we briefly
review. These proceedings are based on lectures given by the second author at
TASI 2021.


title: An Exploration of Active Learning for Affective Digital Phenotyping

abstract:
  Some of the most severe bottlenecks preventing widespread development of
machine learning models for human behavior include a dearth of labeled training
data and difficulty of acquiring high quality labels. Active learning is a
paradigm for using algorithms to computationally select a useful subset of data
points to label using metrics for model uncertainty and data similarity. We
explore active learning for naturalistic computer vision emotion data, a
particularly heterogeneous and complex data space due to inherently subjective
labels. Using frames collected from gameplay acquired from a therapeutic
smartphone game for children with autism, we run a simulation of active
learning using gameplay prompts as metadata to aid in the active learning
process. We find that active learning using information generated during
gameplay slightly outperforms random selection of the same number of labeled
frames. We next investigate a method to conduct active learning with subjective
data, such as in affective computing, and where multiple crowdsourced labels
can be acquired for each image. Using the Child Affective Facial Expression
(CAFE) dataset, we simulate an active learning process for crowdsourcing many
labels and find that prioritizing frames using the entropy of the crowdsourced
label distribution results in lower categorical cross-entropy loss compared to
random frame selection. Collectively, these results demonstrate pilot
evaluations of two novel active learning approaches for subjective affective
data collected in noisy settings.


title:
Domain-Aware Contrastive Knowledge Transfer for Multi-domain Imbalanced
  Data

abstract:
  In many real-world machine learning applications, samples belong to a set of
domains e.g., for product reviews each review belongs to a product category. In
this paper, we study multi-domain imbalanced learning (MIL), the scenario that
there is imbalance not only in classes but also in domains. In the MIL setting,
different domains exhibit different patterns and there is a varying degree of
similarity and divergence among domains posing opportunities and challenges for
transfer learning especially when faced with limited or insufficient training
data. We propose a novel domain-aware contrastive knowledge transfer method
called DCMI to (1) identify the shared domain knowledge to encourage positive
transfer among similar domains (in particular from head domains to tail
domains); (2) isolate the domain-specific knowledge to minimize the negative
transfer from dissimilar domains. We evaluated the performance of DCMI on three
different datasets showing significant improvements in different MIL scenarios.


title:
Structural parameters and possible association of the Ultra-Faint Dwarfs
  Pegasus III and Pisces II from deep Hubble Space Telescope photometry

abstract:
  We present deep Hubble Space Telescope (HST) photometry of the ultra-faint
dwarf (UFD) galaxies Pegasus III (Peg III) and Pisces II (Psc II), two of the
most distant satellites in the halo of the Milky Way (MW). We measure the
structure of both galaxies, derive mass-to-light ratios with newly determined
absolute magnitudes, and compare our findings to expectations from UFD-mass
simulations. For Peg III, we find an elliptical half-light radius of
$a_h{{=}}1.88^{+0.42}_{-0.33}$ arcminutes ($118^{+31}_{-30}$ pc) and
$M_V{=}{-4.17}^{+0.19}_{-0.22}$; for Psc II, we measure
$a_h{=}1.31^{+0.10}_{-0.09}$ arcminutes ($69\pm8$ pc) and
$M_V{=}{-4.28}^{+0.19}_{-0.16}$. We do not find any morphological features that
indicate a significant interaction between the two has occurred, despite their
close separation of only $\sim$40 kpc. Using proper motions (PMs) from Gaia
early Data Release 3, we investigate the possibility of any past association by
integrating orbits for the two UFDs in a MW-only and a combined MW and Large
Magellanic Cloud (LMC) potential. We find that including the gravitational
influence of the LMC is crucial, even for these outer-halo satellites, and that
a possible orbital history exists where Peg III and Psc II experienced a close
($\sim$10-20 kpc) passage about each other just over $\sim$1 Gyr ago, followed
by a collective passage around the LMC ($\sim$30-60 kpc) just under $\sim$1 Gyr
ago. Considering the large uncertainties on the PMs and the restrictive priors
imposed to derive them, improved PM measurements for Peg III and Psc II will be
necessary to clarify their relationship. This would add to the rare findings of
confirmed pairs of satellites within the Local Group.


title: Text Spotting Transformers

abstract:
  In this paper, we present TExt Spotting TRansformers (TESTR), a generic
end-to-end text spotting framework using Transformers for text detection and
recognition in the wild. TESTR builds upon a single encoder and dual decoders
for the joint text-box control point regression and character recognition.
Other than most existing literature, our method is free from Region-of-Interest
operations and heuristics-driven post-processing procedures; TESTR is
particularly effective when dealing with curved text-boxes where special cares
are needed for the adaptation of the traditional bounding-box representations.
We show our canonical representation of control points suitable for text
instances in both Bezier curve and polygon annotations. In addition, we design
a bounding-box guided polygon detection (box-to-polygon) process. Experiments
on curved and arbitrarily shaped datasets demonstrate state-of-the-art
performances of the proposed TESTR algorithm.


title:
Superconducting Nanowire Single-Photon Detectors and effect of
  accumulation and unsteady releases of excess energy in materials

abstract:
  Universal fault-tolerant quantum computers, which promise to revolutionize
computing, are currently limited by excessive noise in their constituent
superconducting qubits. Determining the dominant sources of this excess noise
will lead to a clearer understanding of how to mitigate it in future
superconducting systems. Superconducting Nanowire Single-Photon Detectors
(SNSPDs) are devices that do not appear to suffer from such effects and have
extremely low dark-count backgrounds. We propose to use SNSPDs as
low-background laboratories to study noise accumulation processes in
superconducting systems with the purpose of explaining and mitigating noise in
related quantum information systems. Through these studies we also aim to
increase the sensitive wavelengths of SNSPDs above the current limits of 10
microns, which would open new regimes for dark matter detection, biology, space
sciences, and quantum sensing.


title:
Conformal transformation of uniform domains under weights that depend on
  distance to the boundary

abstract:
  The sphericalization procedure converts a Euclidean space into a compact
sphere. In this note we propose a variant of this procedure for locally
compact, rectifiably path-connected, non-complete, unbounded metric spaces by
using conformal deformations that depend only on the distance to the boundary
of the metric space. This deformation does not distort the local metric
structure of the space near its boundary, but transforms the space into a
bounded domain. We will show that if the original metric space is a uniform
domain with respect to its completion, then the transformed space is also a
uniform domain.


title:
Potential high-$T_{c}$ superconductivity in YCeH$_{20}$ and LaCeH$_{20}$
  under pressure

abstract:
  Lanthanum, yttrium, and cerium hydrides are the three most well-known
superconducting binary hydrides, which have gained great attention in both
theoretical and experimental studies. Recent studies have shown that ternary
hydrides composed of lanthanum and yttrium can achieve high superconductivity
around 253 K. In this study, we employ the evolutionary-algorithm-based crystal
structure prediction (CSP) method and first-principles calculations to
investigate the stability and superconductivity of ternary hydrides composed of
(Y, Ce) and (La, Ce) under high pressure. Our calculations show that there are
multiple stable phases in Y-Ce-H and La-Ce-H hydrides, among which
$P4/mmm$-YCeH$_{8}$, $P4/mmm$-LaCeH$_{8}$, $R\bar{3}m$-YCeH$_{20}$, and
$R\bar{3}m$-LaCeH$_{20}$ possessing H$_{18}$ or H$_{32}$ clathrate structures
can maintain both of the thermodynamic and dynamic stabilities. In addition, we
also find that these phases also maintain a strong resistance to decomposition
at high temperature. Electron-phonon coupling calculations show that all of
these four phases can exhibit high-temperature superconductivity.
$R\bar{3}m$-YCeH$_{20}$ is predicted to have a superconducting transition
temperature ($T_{c}$) as high as 246 K at 350 GPa. The $T_{c}$ value of
$R\bar{3}m$-LaCeH$_{20}$ at 250 GPa is about 233 K, which is slightly smaller
than that of $R\bar{3}m$-YCeH$_{20}$. However, it is found that
$R\bar{3}m$-LaCeH$_{20}$ can be stabilized at 200 GPa, making the high-pressure
synthesis of LaCeH$_{20}$ easier.


title:
SHAIL: Safety-Aware Hierarchical Adversarial Imitation Learning for
  Autonomous Driving in Urban Environments

abstract:
  Designing a safe and human-like decision-making system for an autonomous
vehicle is a challenging task. Generative imitation learning is one possible
approach for automating policy-building by leveraging both real-world and
simulated decisions. Previous work that applies generative imitation learning
to autonomous driving policies focuses on learning a low-level controller for
simple settings. However, to scale to complex settings, many autonomous driving
systems combine fixed, safe, optimization-based low-level controllers with
high-level decision-making logic that selects the appropriate task and
associated controller. In this paper, we attempt to bridge this gap in
complexity by employing Safety-Aware Hierarchical Adversarial Imitation
Learning (SHAIL), a method for learning a high-level policy that selects from a
set of low-level controller instances in a way that imitates low-level driving
data on-policy. We introduce an urban roundabout simulator that controls
non-ego vehicles using real data from the Interaction dataset. We then show
empirically that our approach can produce better behavior than previous
approaches in driver imitation which have difficulty scaling to complex
environments. Our implementation is available at
https://github.com/sisl/InteractionImitation.


title: Algorithms for the ferromagnetic Potts model on expanders

abstract:
  We give algorithms for approximating the partition function of the
ferromagnetic Potts model on $d$-regular expanding graphs. We require much
weaker expansion than in previous works; for example, the expansion exhibited
by the hypercube suffices. The main improvements come from a significantly
sharper analysis of standard polymer models, using extremal graph theory and
applications of Karger's algorithm to counting cuts that may be of independent
interest. It is #BIS-hard to approximate the partition function at low
temperatures on bounded-degree graphs, so our algorithm can be seen as evidence
that hard instances of #BIS are rare. We believe that these methods can shed
more light on other important problems such as sub-exponential algorithms for
approximate counting problems.


title:
Spontaneously broken supersymmetric fracton phases with fermionic
  subsystem symmetries

abstract:
  We construct a purely fermionic system with spontaneously broken
supersymmetry that shares the common feature with a fracton phase of matter.
Our model is gapless due to the Nambu-Goldstone mechanism. It shows a
ground-state degeneracy with the "Area-law" entropy due to fermionic subsystem
symmetries. In the strongly coupled limit, it becomes a variant of the Nicolai
model, and the ground-state degeneracy shows the "Volume-law" entropy. Gauging
the fermionic subsystem symmetry has an t'Hooft anomaly by itself, but the
would-be gauged theory may possess a fermionic defect that is immobile in
certain spatial directions.


title: Online No-regret Model-Based Meta RL for Personalized Navigation

abstract:
  The interaction between a vehicle navigation system and the driver of the
vehicle can be formulated as a model-based reinforcement learning problem,
where the navigation systems (agent) must quickly adapt to the characteristics
of the driver (environmental dynamics) to provide the best sequence of
turn-by-turn driving instructions. Most modern day navigation systems (e.g,
Google maps, Waze, Garmin) are not designed to personalize their low-level
interactions for individual users across a wide range of driving styles (e.g.,
vehicle type, reaction time, level of expertise). Towards the development of
personalized navigation systems that adapt to a variety of driving styles, we
propose an online no-regret model-based RL method that quickly conforms to the
dynamics of the current user. As the user interacts with it, the navigation
system quickly builds a user-specific model, from which navigation commands are
optimized using model predictive control. By personalizing the policy in this
way, our method is able to give well-timed driving instructions that match the
user's dynamics. Our theoretical analysis shows that our method is a no-regret
algorithm and we provide the convergence rate in the agnostic setting. Our
empirical analysis with 60+ hours of real-world user data using a driving
simulator shows that our method can reduce the number of collisions by more
than 60%.


title: Affine surface area

abstract:
  We give an overview of the affine surface area, its properties and its
history.


title: Random Double Tensors Integrals

abstract:
  In this work, we try to build a theory for random double tensor integrals
(DTI). We begin with the definition of DTI and discuss how randomness structure
is built upon DTI. Then, the tail bound of the unitarily invariant norm for the
random DTI is established and this bound can help us to derive tail bounds of
the unitarily invariant norm for various types of two tensors means, e.g.,
arithmetic mean, geometric mean, harmonic mean, and general mean. By
associating DTI with perturbation formula, i.e., a formula to relate the
tensor-valued function difference with respect the difference of the function
input tensors, the tail bounds of the unitarily invariant norm for the
Lipschitz estimate of tensor-valued function with random tensors as arguments
are derived for vanilla case and quasi-commutator case, respectively. We also
establish the continuity property for random DTI in the sense of convergence in
the random tensor mean, and we apply this continuity property to obtain the
tail bound of the unitarily invariant norm for the derivative of the
tensor-valued function.


title:
Cosmological phase transitions, gravitational waves and self-interacting
  dark matter in the singlet extension of MSSM

abstract:
  In the minimal supersymmetric standard model (MSSM) extended by a singlet
superfield, when the coupling between the singlet sector and the MSSM sector is
tiny, the singlet sector can be a quasi dark sector with supersymmetry (SUSY).
We investigate the cosmological phenomena in this scenario and obtain the
following observations: (i) In the parameter space solving the small
cosmological scale anomalies via self-interacting singlino dark matter (SIDM),
a first-order phase transition (FOPT) can readily happen but requires rather
light dark matter below MeV; (ii) The corresponding parameter space indicated
by FOPT and SIDM can be partially covered by detecting the phase-transition
gravitational waves (GWs) at the near-future projects, such as LISA, TianQin
and Taiji. Therefore, the recently developed GW astronomy could be a novel
probe to such a SUSY scenario.


title:
Optically Excited Two-Band Amplified Spontaneous Emission from a
  High-Current-Density Quantum-Dot LED

abstract:
  Laser diodes based on solution-processable materials could benefit numerous
technologies including integrated electronics and photonics, telecommunication,
and medical diagnostics. An attractive system for implementing these devices is
colloidal semiconductor quantum dots (QDs). The primary challenge that hampered
progress towards a QD laser diode (QLD) has been fast nonradiative Auger decay
of optical-gain-active multicarrier states. Recently, this problem has been
resolved by employing continuously graded QDs (cg-QDs) wherein Auger
recombination is strongly suppressed. The use of these structures allowed for
demonstrations of optical gain with electrical pumping and optically-excited
lasing in multilayered LED-like devices. Here we report on achieving the next
critical milestone towards a QLD, which is the demonstration of optically
excited amplified spontaneous emission from a fully functional high-current
density electroluminescent device. This advance has become possible due to
excellent optical gain properties of novel 'compact' cg-QDs and a new LED
architecture, which allows for concerted optimization of its optical and
electrical properties. The results of this work strongly suggest the
feasibility of the final step towards a functional QLD, which is the
demonstration of lasing with electrical pumping.


title:
Control Barrier Function Based Design of Gradient Flows for Constrained
  Nonlinear Programming

abstract:
  This paper considers the problem of designing a continuous time dynamical
system to solve constrained nonlinear optimization problems such that the
feasible set is forward invariant and asymptotically stable. The invariance of
the feasible set makes the dynamics anytime, when viewed as an algorithm,
meaning that it is guaranteed to return a feasible solution regardless of when
it is terminated. The system is obtained by augmenting the gradient flow of the
objective function with inputs, then designing a feedback controller to keep
the state evolution within the feasible set using techniques from the theory of
control barrier functions. The equilibria of the system correspond exactly to
critical points of the optimization problem. Since the state of the system
corresponds to the primal optimizer, and the steady-state input at equilibria
corresponds to the dual optimizer, the method can be interpreted as a
primal-dual approach. The resulting closed-loop system is locally Lipschitz
continuous, so classical solutions to the system exist. We characterize
conditions under which local minimizers are Lyapunov stable, drawing
connections between various constraint qualification conditions and the
stability of the local minimizer. The algorithm is compared to other continuous
time methods for optimization.


title: High-Quality Pluralistic Image Completion via Code Shared VQGAN

abstract:
  PICNet pioneered the generation of multiple and diverse results for image
completion task, but it required a careful balance between $\mathcal{KL}$ loss
(diversity) and reconstruction loss (quality), resulting in a limited diversity
and quality . Separately, iGPT-based architecture has been employed to infer
distributions in a discrete space derived from a pixel-level pre-clustered
palette, which however cannot generate high-quality results directly. In this
work, we present a novel framework for pluralistic image completion that can
achieve both high quality and diversity at much faster inference speed. The
core of our design lies in a simple yet effective code sharing mechanism that
leads to a very compact yet expressive image representation in a discrete
latent domain. The compactness and the richness of the representation further
facilitate the subsequent deployment of a transformer to effectively learn how
to composite and complete a masked image at the discrete code domain. Based on
the global context well-captured by the transformer and the available visual
regions, we are able to sample all tokens simultaneously, which is completely
different from the prevailing autoregressive approach of iGPT-based works, and
leads to more than 100$\times$ faster inference speed. Experiments show that
our framework is able to learn semantically-rich discrete codes efficiently and
robustly, resulting in much better image reconstruction quality. Our diverse
image completion framework significantly outperforms the state-of-the-art both
quantitatively and qualitatively on multiple benchmark datasets.


title: On near-martingales and a class of anticipating linear SDEs

abstract:
  The primary goal of this paper is to prove a near-martingale optional
stopping theorem and establish solvability and large deviations for a class of
anticipating linear stochastic differential equations. We prove the existence
and uniqueness of solutions using two approaches: (1) Ayed-Kuo differential
formula using an ansatz, and (2) a novel braiding technique by interpreting the
integral in the Skorokhod sense. We establish a Freidlin-Wentzell type large
deviations result for solution of such equations.


title: You are what your parents expect: Height and local reference points

abstract:
  Recent estimates are that about 150 million children under five years of age
are stunted, with substantial negative consequences for their schooling,
cognitive skills, health, and economic productivity. Therefore, understanding
what determines such growth retardation is significant for designing public
policies that aim to address this issue. We build a model for nutritional
choices and health with reference-dependent preferences. Parents care about the
health of their children relative to some reference population. In our
empirical model, we use height as the health outcome that parents target.
Reference height is an equilibrium object determined by earlier cohorts'
parents' nutritional choices in the same village. We explore the exogenous
variation in reference height produced by a protein-supplementation experiment
in Guatemala to estimate our model's parameters. We use our model to decompose
the impact of the protein intervention on height into price and reference-point
effects. We find that the changes in reference points account for 65% of the
height difference between two-year-old children in experimental and control
villages in the sixth annual cohort born after the initiation of the
intervention.


title:
Attention Distraction: Watermark Removal Through Continual Learning with
  Selective Forgetting

abstract:
  Fine-tuning attacks are effective in removing the embedded watermarks in deep
learning models. However, when the source data is unavailable, it is
challenging to just erase the watermark without jeopardizing the model
performance. In this context, we introduce Attention Distraction (AD), a novel
source data-free watermark removal attack, to make the model selectively forget
the embedded watermarks by customizing continual learning. In particular, AD
first anchors the model's attention on the main task using some unlabeled data.
Then, through continual learning, a small number of \textit{lures} (randomly
selected natural images) that are assigned a new label distract the model's
attention away from the watermarks. Experimental results from different
datasets and networks corroborate that AD can thoroughly remove the watermark
with a small resource budget without compromising the model's performance on
the main task, which outperforms the state-of-the-art works.


title: Nonlocal optimization of binary neural networks

abstract:
  We explore training Binary Neural Networks (BNNs) as a discrete variable
inference problem over a factor graph. We study the behaviour of this
conversion in an under-parameterized BNN setting and propose stochastic
versions of Belief Propagation (BP) and Survey Propagation (SP) message passing
algorithms to overcome the intractability of their current formulation.
Compared to traditional gradient methods for BNNs, our results indicate that
both stochastic BP and SP find better configurations of the parameters in the
BNN.


title: Hall-type theorems for fast almost dynamic matching and applications

abstract:
  A dynamic set of size up to K is a set in which elements can be inserted and
deleted and which at any moment in its history has at most K elements. In
dynamic matching in a bipartite graph, each element, when it is inserted in a
dynamic subset of left nodes, makes a request to be matched with one of its
neighbors, and the request has to be satisfied on-the-fly without knowing
future insertions and deletions and without revoking past matchings. We
consider a relaxation of dynamic matching in which each matching can survive at
most T insertions, and a right node can be assigned to more than one node of
the dynamic set. We show that a bipartite graph satisfying the condition in
Hall Marriage Theorem up to K has fast T-surviving dynamic matching for dynamic
sets of size up to K, in which every right node can be assigned to at most
O(log(KT)) left nodes. Fast matching means that each matching is done in time
poly(log N, log T, D), where N is the number of left nodes, and D is the left
degree. We obtain a similar result for epsilon-rich matching, in which a left
node needs to be assigned (1-epsilon) fraction of its neighbors. By taking
O(log (KT)) clones of the right set, one obtains T-surviving dynamic standard
matching (with no sharing of right nodes). We construct explicit bipartite
graphs admitting T-surviving dynamic matching up to K with small left degree D
and small right set R, and similarly for $\epsilon$-rich matching.
Specifically, D and |R|/K are polynomial in log N and log T, and for
$\epsilon$-rich the dependency is quasipolynomial. Previous constructions, both
non-explicit and explicit, did not require the T-surviving restriction, but had
only slow matching algorithms running in time exponential in K log N. We give
two applications. The first one is in the area of non-blocking networks, and
the second one is about one-probe storage schemes for dynamic sets.


title:
$^{22}$Na Activation Level Measurements of Fused Silica Rods in the LHC
  Target Absorber for Neutrals (TAN) Compared to FLUKA Simulations

abstract:
  The Target Absorbers for Neutrals (TANs) are located in a high-intensity
radiation environment inside the tunnel of the Large Hadron Collider (LHC).
TANs are positioned about $140$ m downstream from the beam interaction points.
Seven $40$ cm long fused silica rods with different dopant specifications were
irradiated in the TAN by the Beam RAte of Neutrals (BRAN) detector group during
$p$+$p$ data taking from 2016 to 2018 at the LHC. The peak dose delivered to
the fused silica rods was $18$ MGy. We report measurements of the $^{22}$Na
activation of the fused silica rods carried out at the University of Illinois
at Urbana-Champaign and Argonne National Laboratory. At the end of the
irradiation campaign, the maximum $^{22}$Na activity observed was $A=21$
kBq$/{\rm cm^3}$ corresponding to a density, $\rho= 2.5\times 10^{12} /{\rm
cm^3}$, of $^{22}$Na nuclei. FLUKA Monte Carlo simulations have been performed
by the CERN FLUKA team to estimate $^{22}$Na activities for the irradiated BRAN
rod samples. The simulations reproduce the $^{22}$Na activity profile measured
along the rods, with a 35% underestimation of the experimental measurement
results.


title: Extremal results on feedback arc sets in digraphs

abstract:
  A directed graph is oriented if it can be obtained by orienting the edges of
a simple, undirected graph. For an oriented graph $G$, let $\beta(G)$ denote
the size of a minimum feedback arc set, a smallest subset of edges whose
deletion leaves an acyclic subgraph. A simple consequence of a result of Berger
and Shor is that any oriented graph $G$ with $m$ edges satisfies $\beta(G) =
m/2 - \Omega(m^{3/4})$.
  We observe that if an oriented graph $G$ has a fixed forbidden subgraph $B$,
the upper bound of $\beta(G) = m/2 - \Omega(m^{3/4})$ is best possible as a
function of the number of edges if $B$ is not bipartite, but the exponent $3/4$
in the lower order term can be improved if $B$ is bipartite. We also show that
for every rational number $r$ between $3/4$ and $1$, there is a finite
collection of digraphs $\mathcal{B}$ such that every $\mathcal{B}$-free digraph
$G$ with $m$ edges satisfies $\beta(G) = m/2 - \Omega(m^r)$, and this bound is
best possible up to the implied constant factor. The proof uses a connection to
Tur\'an numbers and a result of Bukh and Conlon. Both of our upper bounds come
equipped with randomized linear-time algorithms that construct feedback arc
sets achieving those bounds. Finally, we give a characterization of quasirandom
directed graphs via minimum feedback arc sets.


title:
Global existence and stability of time-periodic solution to isentropic
  compressible Euler equations with source term

abstract:
  In this paper, we study the initial-boundary value problem of one-dimensional
isentropic compressible Euler equations with the source term
$\beta\rho|u|^{\alpha}u$. By means of wave decomposition and the uniform
a-priori estimates, we prove the global existence of smooth solutions under
small perturbations around the supersonic Fanno flow. Then, by Gronwall's
inequality, we get the smooth solution is time-periodic.


title:
Planets Across Space and Time (PAST). III. Morphology of the Planetary
  Radius Valley as a Function of Stellar Age and Metallicity in the Galactic
  Context Revealed by the LAMOST-Gaia-Kepler Sample

abstract:
  The radius valley, a dip in the radius distribution of exoplanets at ~1.9
Earth radii separates compact rocky Super-Earths and Sub-Neptunes with lower
density. Various hypotheses have been put forward to explain the radius valley.
Characterizing the radius valley morphology and its correlation to stellar
properties will provide crucial observation constraints on its origin mechanism
and deepen the understanding of planet formation and evolution. In this paper,
the third part of the Planets Across the Space and Time (PAST) series, using
the LAMOST-Gaia-Kepler catalog, we perform a systematical investigation into
how the radius valley morphology varies in the Galactic context, i.e.,
thin/thick galactic disks, stellar age and metallicity abundance ([Fe/H] and
[alpha/Fe]). We find that (1) The valley becomes more prominent with the
increase of both age and [Fe/H]. (2) The number ratio of super-Earths to
sub-Neptunes monotonically increases with age but decreases with [Fe/H] and
[alpha/Fe]. (3) The average radius of planets above the valley (2.1-6 Earth
radii) decreases with age but increases with [Fe/H]. (4) In contrast, the
average radius of planets below the valley (R < 1.7 Earth radii) is broadly
independent on age and metallicity. Our results demonstrate that the valley
morphology as well as the whole planetary radius distribution evolves on a long
timescale of giga-years, and metallicities (not only Fe but also other metal
elements, e.g., Mg, Si, Ca, Ti) play important roles in planet formation and in
the long term planetary evolution.


title:
Randomized matrix-free quadrature for spectrum and spectral sum
  approximation

abstract:
  We study randomized matrix-free quadrature algorithms for spectrum and
spectral sum approximation. The algorithms studied are characterized by the use
of a Krylov subspace method to approximate independent and identically
distributed samples of $\mathbf{v}^{\sf H}f[\mathbf{A}]\mathbf{v}$, where
$\mathbf{v}$ is an isotropic random vector, $\mathbf{A}$ is a Hermitian matrix,
and $f[\mathbf{A}]$ is a matrix function. This class of algorithms includes the
kernel polynomial method and stochastic Lanczos quadrature, two widely used
methods for approximating spectra and spectral sums. Our analysis, discussion,
and numerical examples provide a unified framework for understanding randomized
matrix-free quadrature and shed light on the commonalities and tradeoffs
between them. Moreover, this framework provides new insights into the practical
implementation and use of these algorithms, particularly with regards to
parameter selection in the kernel polynomial method.


title: Fault-Tolerant Deep Learning: A Hierarchical Perspective

abstract:
  With the rapid advancements of deep learning in the past decade, it can be
foreseen that deep learning will be continuously deployed in more and more
safety-critical applications such as autonomous driving and robotics. In this
context, reliability turns out to be critical to the deployment of deep
learning in these applications and gradually becomes a first-class citizen
among the major design metrics like performance and energy efficiency.
Nevertheless, the back-box deep learning models combined with the diverse
underlying hardware faults make resilient deep learning extremely challenging.
In this special session, we conduct a comprehensive survey of fault-tolerant
deep learning design approaches with a hierarchical perspective and investigate
these approaches from model layer, architecture layer, circuit layer, and cross
layer respectively.


title: Unified Implicit Neural Stylization

abstract:
  Representing visual signals by implicit representation (e.g., a coordinate
based deep network) has prevailed among many vision tasks. This work explores a
new intriguing direction: training a stylized implicit representation, using a
generalized approach that can apply to various 2D and 3D scenarios. We conduct
a pilot study on a variety of implicit functions, including 2D coordinate-based
representation, neural radiance field, and signed distance function. Our
solution is a Unified Implicit Neural Stylization framework, dubbed INS. In
contrary to vanilla implicit representation, INS decouples the ordinary
implicit function into a style implicit module and a content implicit module,
in order to separately encode the representations from the style image and
input scenes. An amalgamation module is then applied to aggregate these
information and synthesize the stylized output. To regularize the geometry in
3D scenes, we propose a novel self-distillation geometry consistency loss which
preserves the geometry fidelity of the stylized scenes. Comprehensive
experiments are conducted on multiple task settings, including novel view
synthesis of complex scenes, stylization for implicit surfaces, and fitting
images using MLPs. We further demonstrate that the learned representation is
continuous not only spatially but also style-wise, leading to effortlessly
interpolating between different styles and generating images with new mixed
styles. Please refer to the video on our project page for more view synthesis
results: https://zhiwenfan.github.io/INS.


title:
Atomic structural characteristics and dynamical properties in monatomic
  metallic liquids via molecular dynamics simulations

abstract:
  Molecular dynamics simulations were performed for five monatomic metallic
liquids and the atomic structural characteristics and dynamical properties were
systematically investigated and compared for understanding the underlying
structural basis of liquid properties, such as glass-forming ability. All
simulated monatomic liquids exhibit similar structural characteristics and
temperature evolution. However, the degree varies significantly in different
liquids. It is found that the atomic structures in liquid Cu, Ta, and Fe are
quite similar. However, liquid Ta exhibits more ordered and more densely packed
structure features, with more populated icosahedral-like atomic ordering and
five-fold HA indexes, less Voronoi entropy, and more regular tetrahedral
configurations. Moreover, the increase of crystal-like clusters in liquid Ta
slows down with decreasing temperature, and the icosahedral-like clusters
increase more quickly, exceeding crystal-like ones, in contrast to other
liquids. On the contrary, the atomic structures of liquid Al and Zr are more
similar, much more disordered and more loosely packed. Furthermore, liquid Ta
exhibits more slow and heterogeneous dynamics, which could be facilitated by
the particular atomic structures of liquid Ta. Both the atomic structure and
dynamics features in liquid Ta favor its glass formation. Our findings provide
comprehensive structural and dynamical information for better understanding GFA
and crystallization in these typical monatomic liquids.


title: Mapping class groups of manifolds with boundary are of finite type

abstract:
  In this note we prove that the mapping class group of a compact topological
manifold $M$ with boundary is of finite type, under assumptions on its
dimension and connectivity.


title: Chain duality for categories over complexes

abstract:
  We show that the additive category of chain complexes parametrized by a
finite simplicial complex $K$ forms a category with chain duality. This fact,
never fully proven in the original reference, is fundamental for Ranicki's
algebraic formulation of the surgery exact sequence of Sullivan and Wall, and
his interpretation of the surgery obstruction map as the passage from local
Poincar\'e duality to global Poincar\'e duality. Our paper also gives a new,
conceptual, and geometric treatment of chain duality on $K$-based chain
complexes.


title: Tournaments and Even Graphs are Equinumerous

abstract:
  A graph is called \emph{odd} if there is an orientation of its edges and an
automorphism that reverses the sense of an odd number of its edges, and
\emph{even} otherwise. Pontus von Br\"omssen (n\'e Andersson) showed that the
existence of such an automorphism is independent of the orientation, and
considered the question of counting pairwise non-isomorphic even graphs. Based
on computational evidence, he made the rather surprising conjecture that the
number of pairwise non-isomorphic \emph{even graphs} on $n$ vertices is equal
to the number of pairwise non-isomorphic \emph{tournaments} on $n$ vertices. We
prove this conjecture using a counting argument with several applications of
the Cauchy-Frobenius Theorem.


title:
Large deviations and fluctuation theorems for cycle currents defined in
  the loop-erased and spanning tree manners: a comparative study

abstract:
  The cycle current is a crucial quantity in stochastic thermodynamics. The
absolute and net cycle currents of a Markovian system can be defined in the
loop-erased (LE) or the spanning tree (ST) manner. Here we make a comparative
study between the large deviations and fluctuation theorems for the LE and ST
currents, i.e. cycle currents defined in the LE and ST manners. First, we
derive the exact joint distribution and the large deviation rate function for
the LE currents of a system with a cyclic topology and also obtain the rate
function for the ST currents of a general system. The relationship between the
rate functions for the LE and ST currents is clarified. Furthermore, we examine
various types of fluctuation theorems satisfied by the LE and ST currents and
clarify their ranges of applicability. We show that both the absolute and net
LE currents satisfy the strong form of all types of fluctuation theorems. In
contrast, the absolute ST currents do not satisfy fluctuation theorems, while
the net ST currents only satisfy the weak form of fluctuation theorems under
the periodic boundary condition.


title:
Quantum spin Hall effect in two-dimensional metals without spin-orbit
  coupling

abstract:
  The quantum spin Hall effect has been observed in topological insulators
using spin-orbit coupling as the probe, but it has not yet been observed in a
metal. An experiment is proposed to measure the quantum spin Hall effect of an
electron or hole in a two-dimensional (2D) metal by using the previously
unexplored but relativistically generated 2D quantum spin Hall Hamiltonian, but
without using spin-orbit coupling. A long cylindrical solenoid lies normally
through the inner radius of a 2D metallic Corbino disk. The current $I_S$
surrounding the solenoid produces an azimuthal magnetic vector potential but no
magnetic field in the disk. In addition, a radial electric field is generated
across the disk by imposing either (a) a potential difference $\Delta v$ or (b)
a radial charge current ${\bm I}$ across its inner and outer radii. Combined
changes in $I_S$ and in either $\Delta v$ or ${\bm I}$ generate spontaneously
quantized azimuthal charge and spin currents. The experiment is designed to
measure these quantized azimuthal charge and spin currents in the disk
consistently. The quantum Hamiltonians for experiments (a) and (b) are both
solved exactly. A method to control the Joule heating is presented, which could
potentially allow the quantum spin Hall measurements to be made at room
temperature. Extensions of this design to an array of thermally-managed
solenoids, each surrounded by thermally-managed stacks of 2D metallic Corbino
disks, could function as a quantum computer that could potentially operate at
room temperature.


title:
Digital Twin Virtualization with Machine Learning for IoT and Beyond 5G
  Networks: Research Directions for Security and Optimal Control

abstract:
  Digital twin (DT) technologies have emerged as a solution for real-time
data-driven modeling of cyber physical systems (CPS) using the vast amount of
data available by Internet of Things (IoT) networks. In this position paper, we
elucidate unique characteristics and capabilities of a DT framework that
enables realization of such promises as online learning of a physical
environment, real-time monitoring of assets, Monte Carlo heuristic search for
predictive prevention, on-policy, and off-policy reinforcement learning in
real-time. We establish a conceptual layered architecture for a DT framework
with decentralized implementation on cloud computing and enabled by artificial
intelligence (AI) services for modeling, event detection, and decision-making
processes. The DT framework separates the control functions, deployed as a
system of logically centralized process, from the physical devices under
control, much like software-defined networking (SDN) in fifth generation (5G)
wireless networks. We discuss the moment of the DT framework in facilitating
implementation of network-based control processes and its implications for
critical infrastructure. To clarify the significance of DT in lowering the risk
of development and deployment of innovative technologies on existing system, we
discuss the application of implementing zero trust architecture (ZTA) as a
necessary security framework in future data-driven communication networks.


title:
Reduced models for electron magnetohydrodynamics: well-posedness and
  singularity formation

abstract:
  We propose some one-dimensional reduced models for the three-dimensional
electron magnetohydrodynamics which involves a highly nonlinear Hall term with
intricate structure. The models contain nonlocal nonlinear terms. Local
well-posedness is obtained in certain circumstances. In contrast, for a model
with transport nonlinear term, we show that finite time singularity may occur
for some initial data.


title: Towards On-Board Panoptic Segmentation of Multispectral Satellite Images

abstract:
  With tremendous advancements in low-power embedded computing devices and
remote sensing instruments, the traditional satellite image processing pipeline
which includes an expensive data transfer step prior to processing data on the
ground is being replaced by on-board processing of captured data. This paradigm
shift enables critical and time-sensitive analytic intelligence to be acquired
in a timely manner on-board the satellite itself. However, at present, the
on-board processing of multi-spectral satellite images is limited to
classification and segmentation tasks. Extending this processing to its next
logical level, in this paper we propose a lightweight pipeline for on-board
panoptic segmentation of multi-spectral satellite images. Panoptic segmentation
offers major economic and environmental insights, ranging from yield estimation
from agricultural lands to intelligence for complex military applications.
Nevertheless, the on-board intelligence extraction raises several challenges
due to the loss of temporal observations and the need to generate predictions
from a single image sample. To address this challenge, we propose a multimodal
teacher network based on a cross-modality attention-based fusion strategy to
improve the segmentation accuracy by exploiting data from multiple modes. We
also propose an online knowledge distillation framework to transfer the
knowledge learned by this multi-modal teacher network to a uni-modal student
which receives only a single frame input, and is more appropriate for an
on-board environment. We benchmark our approach against existing
state-of-the-art panoptic segmentation models using the PASTIS multi-spectral
panoptic segmentation dataset considering an on-board processing setting. Our
evaluations demonstrate a substantial increase in accuracy metrics compared to
the existing state-of-the-art models.


title:
A vindication of the RR Lyrae Fourier light curve decomposition for the
  calculation of metallicity and distance in globular clusters

abstract:
  We report the mean metallicity and absolute magnitude of RR Lyrae stars in a
sample of 37 globular clusters, calculated via the Fourier decomposition of
their light curves and ad hoc semiempirical calibrations, in an unprecedented
homogeneous approach. This enabled a new discussion of the metallicity
dependence of the horizontal branch (HB) luminosity, as a fundamental distance
indicator. The calibration for the RRab and RRc stars should be treated
separately. For the RRab the dispersion is larger and non-linear. For the RRc
stars the correlation is less steep, very tight and linear. The relevance of
the HB structural parameter L, is highlighted and offer a non-linear
calibration of the form MV ([Fe/H],L). Excellent agreement is found between
values of [Fe/H] and MV from the light curve decomposition with spectroscopic
values and distances obtained via Gaia-DR3 and HST. The variable stars census
in 35 clusters includes 326 stars found by our program.


title:
Application of a Spectral Method to Simulate Quasi-Three-Dimensional
  Underwater Acoustic Fields

abstract:
  The solution and synthesis of quasi-three-dimensional sound fields have
always been core issues in computational ocean acoustics. Traditionally, finite
difference algorithms have been employed to solve these problems. In this
paper, a novel numerical algorithm based on the spectral method is devised. The
quasi-three-dimensional problem is transformed into a problem resembling a
two-dimensional line source using an integral transformation strategy. Then, a
stair-step approximation is adopted to address the range dependence of the
two-dimensional problem; because this approximation is essentially a
discretization, the range-dependent two-dimensional problem is further
simplified into a one-dimensional problem. Finally, we apply the Chebyshev--Tau
spectral method to accurately solve the one-dimensional problem. We present the
corresponding numerical program for the proposed algorithm and describe some
representative numerical examples. The simulation results ultimately verify the
reliability and capability of the proposed algorithm.


title: Autoregressive 3D Shape Generation via Canonical Mapping

abstract:
  With the capacity of modeling long-range dependencies in sequential data,
transformers have shown remarkable performances in a variety of generative
tasks such as image, audio, and text generation. Yet, taming them in generating
less structured and voluminous data formats such as high-resolution point
clouds have seldom been explored due to ambiguous sequentialization processes
and infeasible computation burden. In this paper, we aim to further exploit the
power of transformers and employ them for the task of 3D point cloud
generation. The key idea is to decompose point clouds of one category into
semantically aligned sequences of shape compositions, via a learned canonical
space. These shape compositions can then be quantized and used to learn a
context-rich composition codebook for point cloud generation. Experimental
results on point cloud reconstruction and unconditional generation show that
our model performs favorably against state-of-the-art approaches. Furthermore,
our model can be easily extended to multi-modal shape completion as an
application for conditional shape generation.


title: PSDoodle: Fast App Screen Search via Partial Screen Doodle

abstract:
  Searching through existing repositories for a specific mobile app screen
design is currently either slow or tedious. Such searches are either limited to
basic keyword searches (Google Image Search) or require as input a complete
query screen image (SWIRE). A promising alternative is interactive partial
sketching, which is more structured than keyword search and faster than
complete-screen queries. PSDoodle is the first system to allow interactive
search of screens via interactive sketching. PSDoodle is built on top of a
combination of the Rico repository of some 58k Android app screens, the Google
QuickDraw dataset of icon-level doodles, and DoodleUINet, a curated corpus of
some 10k app icon doodles collected from hundreds of individuals. In our
evaluation with third-party software developers, PSDoodle provided similar
top-10 screen retrieval accuracy as the state of the art from the SWIRE line of
work, while cutting the average time required about in half.


title: Discussions on the nature of GLEAM-X J162759.5-523504.3

abstract:
  The nature of the long period radio transient GLEAM-X J162759.5-523504.3
(GLEAM-X J1627 for short) is discussed. We try to understand both its radio
emission and pulsation in the neutron star scenario. We think that: (1) From
the radio emission point of view, GLEAM-X J1627 can be a radio-loud magnetar.
(2) From the rotational evolution point of view, GLEAM-X J1627 is unlikely to
be an isolated magnetar. (3) The 1091s period is unlikely to be the precession
period. (4) GLEAM-X J1627 may be a radio-loud magnetar spun-down by a fallback
disk. The pulsar death line is modified due to the presence of a fallback disk.
This may explain why GLEAM-X J1627 is still radio active with such a long
pulsation period. General constraint on the neutron star magnetic field and
initial disk mass are given analytically. Possible ways to discriminate between
different modelings are also discussed.


title: Radio Transient Detection with Closure Products and Machine Learning

abstract:
  For transient sources with timescales of 1-100 seconds, standardized imaging
for all observations at each time step become impossible as large modern
interferometers produce significantly large data volumes in this observation
time frame. Here we propose a method based on machine learning and using
interferometric closure products as input features to detect transient source
candidates directly from the spatial frequency domain without imaging. We train
a simple neural network classifier on a synthetic dataset of
Noise/Transient/RFI events, which we construct to tackle the lack of labelled
data. We also use the hyper-parameter dropout rate of the model to allow the
model to approximate Bayesian inference, and select the optimal dropout rate to
match the posterior prediction to the actual underlying probability
distribution of the detected events. The overall F1-score of the classifier on
the simulated dataset is greater than 85\%, with the signal-to-noise at
7$\sigma$. The performance of the trained neural network with Monte Carlo
dropout is evaluated on semi-real data, which includes a simulated transient
source and real noise. This classifier accurately identifies the presence of
transient signals in the detectable signal-to-noise levels (above 4$\sigma$)
with the optimal variance. Our findings suggest that a feasible radio transient
classifier can be built up with only simulated data for applying to the
prediction of real observation, even in the absence of annotated real samples
for the purpose of training.


title:
Data Augmentation for Intent Classification with Off-the-shelf Large
  Language Models

abstract:
  Data augmentation is a widely employed technique to alleviate the problem of
data scarcity. In this work, we propose a prompting-based approach to generate
labelled training data for intent classification with off-the-shelf language
models (LMs) such as GPT-3. An advantage of this method is that no
task-specific LM-fine-tuning for data generation is required; hence the method
requires no hyper-parameter tuning and is applicable even when the available
training data is very scarce. We evaluate the proposed method in a few-shot
setting on four diverse intent classification tasks. We find that GPT-generated
data significantly boosts the performance of intent classifiers when intents in
consideration are sufficiently distinct from each other. In tasks with
semantically close intents, we observe that the generated data is less helpful.
Our analysis shows that this is because GPT often generates utterances that
belong to a closely-related intent instead of the desired one. We present
preliminary evidence that a prompting-based GPT classifier could be helpful in
filtering the generated data to enhance its quality.


title:
FaceSigns: Semi-Fragile Neural Watermarks for Media Authentication and
  Countering Deepfakes

abstract:
  Deepfakes and manipulated media are becoming a prominent threat due to the
recent advances in realistic image and video synthesis techniques. There have
been several attempts at combating Deepfakes using machine learning
classifiers. However, such classifiers do not generalize well to black-box
image synthesis techniques and have been shown to be vulnerable to adversarial
examples. To address these challenges, we introduce a deep learning based
semi-fragile watermarking technique that allows media authentication by
verifying an invisible secret message embedded in the image pixels. Instead of
identifying and detecting fake media using visual artifacts, we propose to
proactively embed a semi-fragile watermark into a real image so that we can
prove its authenticity when needed. Our watermarking framework is designed to
be fragile to facial manipulations or tampering while being robust to benign
image-processing operations such as image compression, scaling, saturation,
contrast adjustments etc. This allows images shared over the internet to retain
the verifiable watermark as long as face-swapping or any other Deepfake
modification technique is not applied. We demonstrate that FaceSigns can embed
a 128 bit secret as an imperceptible image watermark that can be recovered with
a high bit recovery accuracy at several compression levels, while being
non-recoverable when unseen Deepfake manipulations are applied. For a set of
unseen benign and Deepfake manipulations studied in our work, FaceSigns can
reliably detect manipulated content with an AUC score of 0.996 which is
significantly higher than prior image watermarking and steganography
techniques.


title: Molecular states from $\bar{B}N$ interactions

abstract:
  In 2019, two new structures $\Lambda_b(6146)^0$ and $\Lambda_b(6152)^0$ at
the invariant mass spectrum of $\Lambda_b^0\pi^{+}\pi^{-}$ observed by the LHCb
Collaboration aroused a hot discussion about their inner structure. Although
many works studies seem to indicate that these two states are conventional
three-quark states, $\Lambda_b(6146)^0$ and $\Lambda_b(6152)^0$ might still be
$\bar{B}N$ bound states because the mass of $\Lambda_b(6146)^0$ and
$\Lambda_b(6152)^0$ are close to the $\bar{B}N$ threshold. In this work, we
perform a systematical investigation of the possible heavy molecular states
from the interaction of $\bar{B}N$ in a one-boson-exchange approach. The
interaction of the system considered is described by the $t$-channel $\sigma$,
$\omega$, and $\rho$ mesons exchange. By solving the non-relativistic
Schr\"{o}dinger equation with the obtained one-boson-exchange potentials, the
$\bar{B}N$ bound states with different quantum number configurations are
searched. Our calculation suggests that recently observed $\Lambda_b(6146)^0$
can be assigned as a $\bar{B}N$ molecular state with spin parity $J^P=3/2^{+}$,
while the $F$-wave $\bar{B}N$ molecular explains for $\Lambda_b(6152)^0$ is
disfavored. The calculation also favors the existence of an $S$-wave $\bar{B}N$
state that can not be associated with the $\Lambda_b(5912)^0$. The results in
this work help understand the high wave-bound states and future experimental
search for new molecules.


title: Buy-Many Mechanisms for Many Unit-Demand Buyers

abstract:
  A recent line of research has established a novel desideratum for designing
approximately-revenue-optimal multi-item mechanisms, namely the buy-many
constraint. Under this constraint, prices for different allocations made by the
mechanism must be subadditive implying that the price of a bundle cannot exceed
the sum of prices of individual items it contains. This natural constraint has
enabled several positive results in multi-item mechanism design bypassing
well-established impossibility results.
  Our work addresses a main open question from this literature involving the
design of buy-many mechanisms for multiple buyers. Our main result is that a
simple sequential item pricing mechanism with buyer-specific prices can achieve
an $O(\log m)$ approximation to the revenue of any buy-many mechanism when all
buyers have unit-demand preferences over $m$ items. This is the best possible
as it directly matches the previous results for the single-buyer setting where
no simple mechanism can obtain a better approximation. Our result applies in
full generality: even though there are many alternative ways buy-many
mechanisms can be defined for multi-buyer settings, our result captures all of
them at the same time. We achieve this by directly competing with a more
permissive upper-bound on the buy-many revenue, obtained via an ex-ante
relaxation.


title: Lelong Numbers of $m$-Subharmonic Functions Along Submanifolds

abstract:
  We study the possible singularities of an $m$-subharmonic function $\varphi$
along a complex submanifold $V$ of a compact K\"ahler manifold, finding a
maximal rate of growth for $\varphi$ which depends only on $m$ and $k$, the
codimension of $V$. When $k < m$, we show that $\varphi$ has at worst log poles
along $V$, and that the strength of these poles is moveover constant along $V$.
This can be thought of as an analogue of Siu's theorem.


title: BcMON: Blockchain Middleware for Offline Networks

abstract:
  Blockchain is becoming a new generation of information infrastructures.
However, the current blockchain solutions rely on a continuous connectivity
network to query and modify the state of the blockchain. The emerging satellite
technology seems to be a good catalyst to forward offline transactions to the
blockchain. However, this approach suffers expensive costs, difficult
interoperability, and limited computation problems. Therefore, we propose
BcMON, the first blockchain middleware for offline networks. BcMON incorporates
three innovative designs: 1) it reduces the costs of offline transactions
accessing the blockchain through Short Message Service (SMS), 2) it validates
the authenticity of offline cross-chain transactions by two-phase consensus, 3)
it supports offline clients to perform complex queries and computations on the
blockchains. The prototype of BcMON has been implemented to evaluate the
performance of the proposed middleware, which can show its stability,
efficiency, and scalability.


title: Controllable Garment Transfer

abstract:
  Image-based garment transfer replaces the garment on the target human with
the desired garment; this enables users to virtually view themselves in the
desired garment. To this end, many approaches have been proposed using the
generative model and have shown promising results. However, most fail to
provide the user with on the fly garment modification functionality. We aim to
add this customizable option of "garment tweaking" to our model to control
garment attributes, such as sleeve length, waist width, and garment texture.


title:
Time Efficient Joint UAV-BS Deployment and User Association based on
  Machine Learning

abstract:
  This paper proposes a time-efficient mechanism to decrease the on-line
computing time of solving the joint unmanned aerial vehicle base station
(UAV-BS) deployment and user/sensor association (UDUA) problem aiming at
maximizing the downlink sum transmission throughput. The joint UDUA problem is
decoupled into two sub-problems: one is the user association sub-problem, which
gets the optimal matching strategy between aerial and ground nodes for certain
UAV-BS positions; and the other is the UAV-BS deployment sub-problem trying to
find the best position combination of the UAV-BSs that make the solution of the
first sub-problem optimal among all the possible position combinations of the
UAV-BSs. In the proposed mechanism, we transform the user association
sub-problem into an equivalent bipartite matching problem and solve it using
the Kuhn-Munkres algorithm. For the UAV-BS deployment sub-problem, we
theoretically prove that adopting the best UAV-BS deployment strategy of a
previous user distribution for each new user distribution will introduce little
performance decline compared with the new user distribution's ground true best
strategy if the two user distributions are similar enough. Based on our
mathematical analyses, the similarity level between user distributions is well
defined and becomes the key to solve the second sub-problem. Numerical results
indicate that the proposed UDUA mechanism can achieve near-optimal system
performance in terms of average downlink sum transmission throughput and
failure rate with enormously reduced computing time compared with benchmark
approaches.


title:
Angle-resolved polarized Raman spectroscopy for distinguishing stage-I
  graphite intercalation compounds with Thorium, Uranium and Plutonium

abstract:
  Graphite intercalation compounds (GICs) with the geometrical anisotropy and
strong electron-phonon coupling are in full swing and have shown their great
potential for applications in nanodevices. I selected representative three
elements in actinide group with valence electron arrangement: Thorium (Th)
([Rn]6d27s2), Uranium (U) ([Rn]5f36d17s2), Plutonium (Pu) ([Rn]5f67s2). I
calculated their phonon spectra and demonstrated the atomic-scale
microstructure identification of actinide graphite intercalation compounds by
angle-resolved polarized Raman spectroscopy.


title: PSDoodle: Searching for App Screens via Interactive Sketching

abstract:
  Keyword-based mobile screen search does not account for screen content and
fails to operate as a universal tool for all levels of users. Visual searching
(e.g., image, sketch) is structured and easy to adopt. Current visual search
approaches count on a complete screen and are therefore slow and tedious.
PSDoodle employs a deep neural network to recognize partial screen element
drawings instantly on a digital drawing interface and shows results in
real-time. PSDoodle is the first tool that utilizes partial sketches and
searches for screens in an interactive iterative way. PSDoodle supports
different drawing styles and retrieves search results that are relevant to the
user's sketch query. A short video demonstration is available online at:
https://youtu.be/3cVLHFm5pY4


title: Region Rebalance for Long-Tailed Semantic Segmentation

abstract:
  In this paper, we study the problem of class imbalance in semantic
segmentation. We first investigate and identify the main challenges of
addressing this issue through pixel rebalance. Then a simple and yet effective
region rebalance scheme is derived based on our analysis. In our solution,
pixel features belonging to the same class are grouped into region features,
and a rebalanced region classifier is applied via an auxiliary region rebalance
branch during training. To verify the flexibility and effectiveness of our
method, we apply the region rebalance module into various semantic segmentation
methods, such as Deeplabv3+, OCRNet, and Swin. Our strategy achieves consistent
improvement on the challenging ADE20K and COCO-Stuff benchmark. In particular,
with the proposed region rebalance scheme, state-of-the-art BEiT receives +0.7%
gain in terms of mIoU on the ADE20K val set.


title: Streaming Algorithms for Multitasking Scheduling with Shared Processing

abstract:
  In this paper, we design the first streaming algorithms for the problem of
multitasking scheduling on parallel machines with shared processing. In one
pass, our streaming approximation schemes can provide an approximate value of
the optimal makespan. If the jobs can be read in two passes, the algorithm can
find the schedule with the approximate value. This work not only provides an
algorithmic big data solution for the studied problem, but also gives an
insight into the design of streaming algorithms for other problems in the area
of scheduling.


title:
Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose
  Estimation

abstract:
  Available 3D human pose estimation approaches leverage different forms of
strong (2D/3D pose) or weak (multi-view or depth) paired supervision. Barring
synthetic or in-studio domains, acquiring such supervision for each new target
environment is highly inconvenient. To this end, we cast 3D pose learning as a
self-supervised adaptation problem that aims to transfer the task knowledge
from a labeled source domain to a completely unpaired target. We propose to
infer image-to-pose via two explicit mappings viz. image-to-latent and
latent-to-pose where the latter is a pre-learned decoder obtained from a
prior-enforcing generative adversarial auto-encoder. Next, we introduce
relation distillation as a means to align the unpaired cross-modal samples i.e.
the unpaired target videos and unpaired 3D pose sequences. To this end, we
propose a new set of non-local relations in order to characterize long-range
latent pose interactions unlike general contrastive relations where positive
couplings are limited to a local neighborhood structure. Further, we provide an
objective way to quantify non-localness in order to select the most effective
relation set. We evaluate different self-adaptation settings and demonstrate
state-of-the-art 3D human pose estimation performance on standard benchmarks.


title:
Surrogate model for gravitational wave signals from non-spinning,
  comparable- to large-mass-ratio black hole binaries built on black hole
  perturbation theory waveforms calibrated to numerical relativity

abstract:
  We present a reduced-order surrogate model of gravitational waveforms from
non-spinning binary black hole systems with comparable to large mass-ratio
configurations. This surrogate model, \texttt{BHPTNRSur1dq1e4}, is trained on
waveform data generated by point-particle black hole perturbation theory
(ppBHPT) with mass ratios varying from 2.5 to 10,000. \texttt{BHPTNRSur1dq1e4}
extends an earlier waveform model, \texttt{EMRISur1dq1e4}, by using an updated
transition-to-plunge model, covering longer durations up to 30,500 $m_1$ (where
$m_1$ is the mass of the primary black hole), includes several more spherical
harmonic modes up to $\ell=10$, and calibrates subdominant modes to numerical
relativity (NR) data. In the comparable mass-ratio regime, including mass
ratios as low as $2.5$, the gravitational waveforms generated through ppBHPT
agree surprisingly well with those from NR after this simple calibration step.
We also compare our model to recent SXS and RIT NR simulations at mass ratios
ranging from $15$ to $32$, and find the dominant quadrupolar modes agree to
better than $\approx 10^{-3}$. We expect our model to be useful to study
intermediate-mass-ratio binary systems in current and future gravitational-wave
detectors.


title: M2-branes and plane partitions

abstract:
  There is a correspondence between the protected local operators in the 3d
SCFTs describing the geometry $\mathbb{C}^2$ probed by a stack of $N$ M2-branes
and plane partitions of trace $N$. We give combinatorial expressions of the
indices which count the local operators parametrizing
$\mathbb{C}^2/\mathbb{Z}_k$ probed by $N$ M2-branes in the canonical and grand
canonical ensembles in terms of generating functions for plane partitions. We
derive the asymptotic behaviors of the grand potential in the high-temperature
limit and the scaling dimension in the large $N$ limit.


title:
Microtransit adoption in the wake of the COVID-19 pandemic: evidence
  from a choice experiment with transit and car commuters

abstract:
  On-demand mobility platforms play an increasingly important role in urban
mobility systems. Impacts are still debated, as these platforms supply
personalized and optimized services, while also contributing to existing
sustainability challenges. Recently, microtransit services have emerged,
promising to combine advantages of pooled on-demand rides with more sustainable
fixed-route public transit services. Understanding traveler behavior becomes a
primary focus to analyze adoption likelihood and perceptions of different
microtransit attributes. The COVID-19 pandemic context adds an additional layer
of complexity to analyzing mobility innovation acceptance. This study
investigates the potential demand for microtransit options against the
background of the pandemic. We use a stated choice experiment to study the
decision-making of Israeli public transit and car commuters when offered to use
novel microtransit options (sedan vs. passenger van). We investigate the
tradeoffs related to traditional fare and travel time attributes, along with
microtransit features; namely walking time to pickup location, vehicle sharing,
waiting time, minimum advanced reservation time, and shelter at designated
boarding locations. Additionally, we analyze two latent constructs: attitudes
towards sharing, as well as experiences and risk-perceptions related to the
COVID-19 pandemic. We develop Integrated Choice and Latent Variable models to
compare the two commuter groups in terms of the likelihood to switch to
microtransit, attribute trade-offs, sharing preferences and pandemic impacts.
The results reveal high elasticities of several time and COVID effects for car
commuters compared to relative insensitivity of transit commuters to the risk
of COVID contraction. Moreover, for car commuters, those with strong sharing
identities were more likely to be comfortable in COVID risk situations, and to
accept microtransit.


title:
GAIL-PT: A Generic Intelligent Penetration Testing Framework with
  Generative Adversarial Imitation Learning

abstract:
  Penetration testing (PT) is an efficient network testing and vulnerability
mining tool by simulating a hacker's attack for valuable information applied in
some areas. Compared with manual PT, intelligent PT has become a dominating
mainstream due to less time-consuming and lower labor costs. Unfortunately,
RL-based PT is still challenged in real exploitation scenarios because the
agent's action space is usually high-dimensional discrete, thus leading to
algorithm convergence difficulty. Besides, most PT methods still rely on the
decisions of security experts. Addressing the challenges, for the first time,
we introduce expert knowledge to guide the agent to make better decisions in
RL-based PT and propose a Generative Adversarial Imitation Learning-based
generic intelligent Penetration testing framework, denoted as GAIL-PT, to solve
the problems of higher labor costs due to the involvement of security experts
and high-dimensional discrete action space. Specifically, first, we manually
collect the state-action pairs to construct an expert knowledge base when the
pre-trained RL / DRL model executes successful penetration testings. Second, we
input the expert knowledge and the state-action pairs generated online by the
different RL / DRL models into the discriminator of GAIL for training. At last,
we apply the output reward of the discriminator to guide the agent to perform
the action with a higher penetration success rate to improve PT's performance.
Extensive experiments conducted on the real target host and simulated network
scenarios show that GAIL-PT achieves the SOTA penetration performance against
DeepExploit in exploiting actual target Metasploitable2 and Q-learning in
optimizing penetration path, not only in small-scale with or without honey-pot
network environments but also in the large-scale virtual network environment.


title:
Streaming Approximation Scheme for Minimizing Total Completion Time on
  Parallel Machines Subject to Varying Processing Capacity

abstract:
  We study the problem of minimizing total completion time on parallel machines
subject to varying processing capacity. In this paper, we develop an
approximation scheme for the problem under the data stream model where the
input data is massive and cannot fit into memory and thus can only be scanned
for a few passes. Our algorithm can compute the approximate value of the
optimal total completion time in one pass and output the schedule with the
approximate value in two passes.


title:
Audio-visual multi-channel speech separation, dereverberation and
  recognition

abstract:
  Despite the rapid advance of automatic speech recognition (ASR) technologies,
accurate recognition of cocktail party speech characterised by the interference
from overlapping speakers, background noise and room reverberation remains a
highly challenging task to date. Motivated by the invariance of visual modality
to acoustic signal corruption, audio-visual speech enhancement techniques have
been developed, although predominantly targeting overlapping speech separation
and recognition tasks. In this paper, an audio-visual multi-channel speech
separation, dereverberation and recognition approach featuring a full
incorporation of visual information into all three stages of the system is
proposed. The advantage of the additional visual modality over using audio only
is demonstrated on two neural dereverberation approaches based on DNN-WPE and
spectral mapping respectively. The learning cost function mismatch between the
separation and dereverberation models and their integration with the back-end
recognition system is minimised using fine-tuning on the MSE and LF-MMI
criteria. Experiments conducted on the LRS2 dataset suggest that the proposed
audio-visual multi-channel speech separation, dereverberation and recognition
system outperforms the baseline audio-visual multi-channel speech separation
and recognition system containing no dereverberation module by a statistically
significant word error rate (WER) reduction of 2.06% absolute (8.77% relative).


title:
Exploring vortex formation in rotating Bose-Einstein condensates beyond
  mean-field regime

abstract:
  The production of quantized vortices having diverse structures is a
remarkable effect of rotating Bose-Einstein condensates. Vortex formation
described by the mean-field theory is valid only in the regime of weak
interactions. The exploration of the rich and diverse physics of strongly
interacting BEC requires a more general approach. This study explores the
vortex formation of strongly interacting and rapidly rotating BEC from a
general ab initio many-body perspective. We demonstrate that the quantized
vortices form various structures that emerge from an intricate interplay
between the angular momentum and many-body interaction. We examine the distinct
impact of the angular velocity and interaction energy on the vortex formation.
Our analysis shows that, while the angular rotation generally augments the
vortex formation, the interactions can enhance as well as impede the vortices
production.


title:
Multi-Weight Respecification of Scan-specific Learning for Parallel
  Imaging

abstract:
  Parallel imaging is widely used in magnetic resonance imaging as an
acceleration technology. Traditional linear reconstruction methods in parallel
imaging often suffer from noise amplification. Recently, a non-linear robust
artificial-neural-network for k-space interpolation (RAKI) exhibits superior
noise resilience over other linear methods. However, RAKI performs poorly at
high acceleration rates, and needs a large amount of autocalibration signals as
the training samples. In order to tackle these issues, we propose a
multi-weight method that implements multiple weighting matrices on the
undersampled data, named as MW-RAKI. Enforcing multiple weighted matrices on
the measurements can effectively reduce the influence of noise and increase the
data constraints. Furthermore, we incorporate the strategy of multiple
weighting matrixes into a residual version of RAKI, and form
MW-rRAKI.Experimental compari-sons with the alternative methods demonstrated
noticeably better reconstruction performances, particularly at high
acceleration rates.


title: Some explicit estimates for the error term in the prime number theorem

abstract:
  By combining and improving recent techniques and results, we provide explicit
estimates for the error terms $|\pi(x)-\text{li}(x)|$, $|\theta(x)-x|$ and
$|\psi(x)-x|$ appearing in the prime number theorem. For example, we show for
all $x\geq 2$ that $|\psi(x)-x|\leq 9.39x(\log x)^{1.515}\exp(-0.8274\sqrt{\log
x})$. Our estimates rely heavily on explicit zero-free regions and zero-density
estimates for the Riemann zeta-function, and improve on existing bounds for
prime-counting functions for large values of $x$.


title: Unsupervised Data Selection via Discrete Speech Representation for ASR

abstract:
  Self-supervised learning of speech representations has achieved impressive
results in improving automatic speech recognition (ASR). In this paper, we show
that data selection is important for self-supervised learning. We propose a
simple and effective unsupervised data selection method which selects
acoustically similar speech to a target domain. It takes the discrete speech
representation available in common self-supervised learning frameworks as
input, and applies a contrastive data selection method on the discrete tokens.
Through extensive empirical studies we show that our proposed method reduces
the amount of required pre-training data and improves the downstream ASR
performance. Pre-training on a selected subset of 6% of the general data pool
results in 11.8% relative improvements in LibriSpeech test-other compared to
pre-training on the full set. On Multilingual LibriSpeech French, German, and
Spanish test sets, selecting 6% data for pre-training reduces word error rate
by more than 15% relatively compared to the full set, and achieves competitive
results compared to current state-of-the-art performances.


title: Gauging the R-symmetry of old-minimal $R+R^2$ supergravity

abstract:
  Old-minimal $R+R^2$ supergravity has a $U(1)$ R-symmetry which rotates the
chiral curvature superfield. We gauge this R-symmetry and study new
interactions involving the gauge multiplet in the context of inflation and
supersymmetry breaking. We construct models where supersymmetry and the
R-symmetry are spontaneously broken during and after Starobinsky inflation, and
one-loop gauge anomalies are cancelled by the Green--Schwarz mechanism which
can also generate Standard Model gaugino masses. The hierarchy between the
auxiliary fields, $\langle F\rangle\gtrsim\langle D\rangle$, leads to split
mass spectrum where the chiral multiplet masses are around the inflationary
scale ($10^{13}$ GeV), while the gauge multiplet masses can be arbitrarily
small.


title:
The Boundary Term in Huisken's Monotonicity Formula and the Entropy of
  Translators

abstract:
  For a manifold-with-boundary moving by mean curvature flow, the entropy at a
later time is bounded by the entropy at an earlier time plus a boundary term.
This paper controls the boundary term in a geometrically natural way. In
particular, it shows (under mild hypotheses)that the entropy of a compact
translator is less than or equal to the entropy of the boundary plus the
maximal cone density of the boundary.


title: Collective unitary evolution with linear optics by Cartan decomposition

abstract:
  Unitary operation is an essential step for quantum information processing. We
first propose an iterative procedure for decomposing a general unitary
operation without resorting to controlled-NOT gate and single-qubit rotation
library. Based on the results of decomposition, we design two compact
architectures to deterministically implement arbitrary two-qubit
polarization-spatial and spatial-polarization collective unitary operations,
respectively. The involved linear optical elements are reduced from 25 to 20
and 21 to 20, respectively. Moreover, the parameterized quantum computation can
be flexibly manipulated by wave plates and phase shifters. As an application,
we construct the specific quantum circuits to realize two-dimensional quantum
walk and quantum Fourier transformation. Our schemes are simple and feasible
with the current technology.


title: Mock-integrability and stable solitary vortices

abstract:
  Localized soliton-like solutions to a $(2+1)$-dimensional hydro-dynamical
evolution equation are studied numerically. The equation is so-called
Williams-Yamagata-Flierl equation, which governs geostrophic fluid in a certain
parameter range. Although the equation does not have an integrable structure in
the ordinary sense, we find there exist shape-keeping solutions with very long
life in a special background flow and an initial condition. The stability of
the localization at the fusion process of two soliton-like objects is also
investigated. As for the indicator of the long-term stability of localization,
we propose a concept of configurational entropy, which has been introduced in
analysis for non-topological solitons in field theories.

categories: math-ph hep-th math.MP nlin.SI physics.flu-dyn