title: Exact $T\overline{T}$ deformation of two-dimensional Maxwell theory

abstract:
  $T\overline{T}$-deformed two-dimensional quantum Maxwell theory on the torus
is examined, taking into account nonperturbative effects in the deformation
parameter $\mu$. We study the deformed partition function solving the relevant
flow equation at the level of individual flux sectors. Summing exactly the
"instanton" series, we obtain a well-defined expression for the partition
function at arbitrary $\mu$. For $\mu>0$, the quantum spectrum of the theory
experiences a truncation, the partition function reducing to a sum over a
finite set of positive-energy states. For $\mu<0$ instead, the appearance of
nonperturbative contributions in $\mu$ drastically modifies the structure of
the partition function, regularizing its naive divergences through
instanton-like subtractions. For each flux sector, we show that the
semiclassical contribution is dominated by the deformed classical action. The
theory is observed to undergo infinite-order phase transitions for certain
values of $\mu$, associated with the vanishing of Polyakov-loop correlators.


title:
Solid immersion microlens arrays-based light-field camera for 3D in vivo
  imaging

abstract:
  Light-field imaging facilitates the miniaturization of 3D cameras while it
requires the extension of the depth-of-field (DoF) for practical applications
such as endoscopy and intraoral scanning. Here we report a light-field camera
(LFC) using solid immersion microlens arrays (siMLAs) for 3D biomedical
imaging. The experimental results show that the focal length of MLAs is
increased by 2.7 times and the transmittance is enhanced up to 6.9% by
immersion in PDMS film. In particular, the f-number of siMLAs exceeds the limit
of conventional MLAs fabricated by thermal reflow, resulting in a larger DoF.
The LFC based on siMLAs has successfully acquired the depth map of a dental
phantom as a hand-held scanner. This LFC suggests a new way for developing a
compact in vivo 3D imaging system.


title: Vibration Analysis of KAGRA Cryostat at Cryogenic Temperature

abstract:
  KAGRA uses cryogenics to cool its sapphire test masses down to 20 K to reduce
the thermal noise. However, cryocooler vibration and structural resonances of
the cryostat couple to test mass and can contaminate the detector sensitivity.
We performed vibration analysis of the cooling system at cryogenic temperature
to study its impact on detector sensitivity. Our measurement show shield
vibration below 1 Hz is not impacted by cryocooler operation or structural
resonances and follows ground motion. The noise floor of the shield in 1-100 Hz
was observed to be 2-3 order of magnitude larger than seismic motion even
without cryocooler operation. The operation of cryocoolers does not change the
noise floor, but 2.0 Hz peaks and their harmonics were observed over the entire
spectrum (1-100 Hz). These results were used to calculate the coupling of
cooling system vibration to the test mass. We conclude that vibration from the
cooling system does not limit KAGRA design sensitivity.


title: Non-thermal emission in hyper-velocity and semi-relativistic stars

abstract:
  Context. There is a population of runaway stars that move at extremely high
speeds with respect to their surroundings. The fast motion and the stellar wind
of these stars, plus the wind-medium interaction, can lead to particle
acceleration and non-thermal radiation.
  Aims. We characterise the interaction between the winds of fast runaway stars
and their environment, in particular to establish their potential as cosmic-ray
accelerators and non-thermal emitters.
  Methods. We model the hydrodynamics of the interaction between the stellar
wind and the surrounding material. We self-consistently calculate the injection
and transport of relativistic particles in the bow shock using a multi-zone
code, and compute their broadband emission from radio to $\gamma$-rays.
  Results. Both the forward and reverse shocks are favourable sites for
particle acceleration, although the radiative efficiency of particles is low
and therefore the expected fluxes are in general rather faint.
  Conclusions. We show that high-sensitivity observations in the radio band can
be used to detect the non-thermal radiation associated with bow shocks from
hypervelocity and semi-relativistic stars. Hypervelocity stars are expected to
be modest sources of sub-TeV cosmic rays, accounting perhaps for a $\sim 0.1$%
of that of galactic cosmic rays.


title: A Mass Transport Proof of the Ergodic Theorem

abstract:
  It is known that a gambler repeating a game with positive expected value has
a positive probability to never go broke. We use the mass transport method to
prove the generalization of this fact where the gains from the bets form a
stationary, rather than an i.i.d., sequence. Birkhoff's ergodic theorem follows
from this by a standard argument.


title:
Experimental Progress on the Emergent Infinite-Layer Ni-Based
  Superconductors

abstract:
  The emergence of the infinite-layer superconducting nickelate thin films
marks the Ni age of superconductivity, which has excited a huge surge of
studies since the first report in August of 2019. Despite of the tremendous
attention drawn from the entire material science community and a large body of
theoretical studies, the experimental progress has been relatively slow due to
the challenging sample fabrication, which may, in turn, be holding back the
fast development of theoretical research. Therefore, a timely and comprehensive
review on all the up-to-date experimental progress of the emergent
infinite-layer Ni-based superconductors is urgently needed. In this review, we
first introduce the history of more than 30-year-long Ni-based
superconductivity exploration, then summarize the sample fabrication processes,
later present the experimental electrical transport and magnetic properties,
and finally come up with several key issues deserving intensive studies. This
review is thus expected to be helpful for researchers with diverse research
background to readily capture the major progress of this emerging field.


title:
Surface temperature and emissivity measurement for materials exposed to
  a flame through two-color IR-thermography

abstract:
  Two-color (2C) pyrometry has long been used for flame temperature and soot
concentration studies and is now becoming more widely used to measure surface
temperatures of burning materials. With the obvious advantage of being a
contact-free method that requires only minimal optical access, 2C pyrometry
combined with high-speed acquisition is a promising diagnostic tool to obtain
exceptional temporal and spatial resolution of thermally degrading samples.
However, its conceptual simplicity relies on a set of basic assumptions that
when violated can result in large errors. In this work, we use an experimental
configuration representative for fire resistance testing for aerospace and
naval applications to analyze the impact of camera parameters and test setup on
the accuracy of the surface temperature results obtained. Two types of fibre
reinforced polymer composites and a steel plate are used to investigate
material specific aspects that effect the measurements. An improved workflow
for camera calibration is presented that takes the actual experimental setup
into account. The temperature and emissivity mapping obtained trough in-situ IR
measurements is compared against data acquired trough thermocouples and
post-fire hemispherical directional reflectance measurements at room
temperature. This comparison illustrates the necessity for proper
post-processing and demonstrates that emissivity values obtained from pristine
or burnt samples are not well suited to obtain accurate surface temperatures
through conventional (single color) IR thermography. We also present a detailed
error budget and suggestions for calibration measurements to keep the overall
error well below 50 K in a temperature range from 673 K - 1473 K.


title:
A$^3$T: Alignment-Aware Acoustic and Text Pretraining for Speech
  Synthesis and Editing

abstract:
  Recently, speech representation learning has improved many speech-related
tasks such as speech recognition, speech classification, and speech-to-text
translation. However, all the above tasks are in the direction of speech
understanding, but for the inverse direction, speech synthesis, the potential
of representation learning is yet to be realized, due to the challenging nature
of generating high-quality speech. To address this problem, we propose our
framework, Alignment-Aware Acoustic-Text Pretraining (A$^3$T), which
reconstructs masked acoustic signals with text input and acoustic-text
alignment during training. In this way, the pretrained model can generate high
quality of reconstructed spectrogram, which can be applied to the speech
editing and unseen speaker TTS directly. Experiments show A$^3$T outperforms
SOTA models on speech editing, and improves multi-speaker speech synthesis
without the external speaker verification model.


title:
Snowmass White Paper: Precision Studies of Spacetime Symmetries and
  Gravitational Physics

abstract:
  High-energy physics is primarily concerned with uncovering the laws and
principles that govern nature at the fundamental level. Research in this field
usually relies on probing the boundaries of established physics, an undertaking
typically associated with extreme energy and distance scales. It is therefore
unsurprising that particle physics has traditionally been dominated by
large-scale experimental methods often involving high energies, such as
colliders and storage rings, cosmological and astrophysical observations,
large-volume detector systems, etc. However, high-sensitivity measurements in
smaller experiments, often performed at lower energies, are presently
experiencing a surge in importance for particle physics for at least two
reasons. First, they exploit synergies to adjacent areas of physics with recent
advances in experimental techniques and technology. Together with intensified
phenomenological explorations, these advances have led to the realization that
challenges associated with weak couplings or the expected suppression factors
for new physics can be overcome with such methods while maintaining a large
degree of experimental control. Second, many of these measurements broaden the
range of particle-physics phenomena and observables relative to the above set
of more conventional methodologies. Combining such measurements with the
conventional efforts above therefore casts both a wider and tighter net for
possible effects originating from physics beyond the Standard Model (BSM). This
paper argues that this assessment points at a growing impact of such methods
and measurements on high-energy physics, and therefore warrants direct support
as particle-physics research. Leveraging the recent rapid progress and bright
outlook associated with such studies for high-energy physics, could yield high
returns, but requires substantial and sustained efforts by funding agencies.


title: Facial Geometric Detail Recovery via Implicit Representation

abstract:
  Learning a dense 3D model with fine-scale details from a single facial image
is highly challenging and ill-posed. To address this problem, many approaches
fit smooth geometries through facial prior while learning details as additional
displacement maps or personalized basis. However, these techniques typically
require vast datasets of paired multi-view data or 3D scans, whereas such
datasets are scarce and expensive. To alleviate heavy data dependency, we
present a robust texture-guided geometric detail recovery approach using only a
single in-the-wild facial image. More specifically, our method combines
high-quality texture completion with the powerful expressiveness of implicit
surfaces. Initially, we inpaint occluded facial parts, generate complete
textures, and build an accurate multi-view dataset of the same subject. In
order to estimate the detailed geometry, we define an implicit signed distance
function and employ a physically-based implicit renderer to reconstruct fine
geometric details from the generated multi-view images. Our method not only
recovers accurate facial details but also decomposes normals, albedos, and
shading parts in a self-supervised way. Finally, we register the implicit shape
details to a 3D Morphable Model template, which can be used in traditional
modeling and rendering pipelines. Extensive experiments demonstrate that the
proposed approach can reconstruct impressive facial details from a single
image, especially when compared with state-of-the-art methods trained on large
datasets.


title: Generative Principal Component Analysis

abstract:
  In this paper, we study the problem of principal component analysis with
generative modeling assumptions, adopting a general model for the observed
matrix that encompasses notable special cases, including spiked matrix recovery
and phase retrieval. The key assumption is that the underlying signal lies near
the range of an $L$-Lipschitz continuous generative model with bounded
$k$-dimensional inputs. We propose a quadratic estimator, and show that it
enjoys a statistical rate of order $\sqrt{\frac{k\log L}{m}}$, where $m$ is the
number of samples. We also provide a near-matching algorithm-independent lower
bound. Moreover, we provide a variant of the classic power method, which
projects the calculated data onto the range of the generative model during each
iteration. We show that under suitable conditions, this method converges
exponentially fast to a point achieving the above-mentioned statistical rate.
We perform experiments on various image datasets for spiked matrix and phase
retrieval models, and illustrate performance gains of our method to the classic
power method and the truncated power method devised for sparse principal
component analysis.

categories: stat.ML cs.IT cs.LG math.IT math.ST stat.TH


title: Group Contextualization for Video Recognition

abstract:
  Learning discriminative representation from the complex spatio-temporal
dynamic space is essential for video recognition. On top of those stylized
spatio-temporal computational units, further refining the learnt feature with
axial contexts is demonstrated to be promising in achieving this goal. However,
previous works generally focus on utilizing a single kind of contexts to
calibrate entire feature channels and could hardly apply to deal with diverse
video activities. The problem can be tackled by using pair-wise spatio-temporal
attentions to recompute feature response with cross-axis contexts at the
expense of heavy computations. In this paper, we propose an efficient feature
refinement method that decomposes the feature channels into several groups and
separately refines them with different axial contexts in parallel. We refer
this lightweight feature calibration as group contextualization (GC).
Specifically, we design a family of efficient element-wise calibrators, i.e.,
ECal-G/S/T/L, where their axial contexts are information dynamics aggregated
from other axes either globally or locally, to contextualize feature channel
groups. The GC module can be densely plugged into each residual layer of the
off-the-shelf video networks. With little computational overhead, consistent
improvement is observed when plugging in GC on different networks. By utilizing
calibrators to embed feature with four different kinds of contexts in parallel,
the learnt representation is expected to be more resilient to diverse types of
activities. On videos with rich temporal variations, empirically GC can boost
the performance of 2D-CNN (e.g., TSN and TSM) to a level comparable to the
state-of-the-art video networks. Code is available at
https://github.com/haoyanbin918/Group-Contextualization.


title: Self-protected adiabatic quantum computation

abstract:
  Recent experiments show the existence of collective decoherence in quantum
systems. We study the possibility of quantum computation in decoherence free
subspace which is robust against such kind of decoherence processes. This
passive protection protocol can be especially advantageous for continuous
quantum computation such as quantum annealers. As an example we propose to use
decoherence protected adiabatic quantum computation for the Grover search
problem. Our proposal contains only two-body interactions, making it feasible
in near-term quantum devices.


title:
Take-Away Impartial Combinatorial Games on Hypergraphs and Other Related
  Geometric and Discrete Structures

abstract:
  In a Take-Away Game on hypergraphs, two players take turns to remove the
vertices and the hyperedges of the hypergraphs. In each turn, a player must
remove either a single vertex or a hyperedge. When a player chooses to remove
one vertex, all of the hyperedges that contain the chosen vertex are also
removed. When a player chooses to remove one hyperedge, only that chosen
hyperedge is removed. Whoever removes the last vertex wins the game. Following
from the winning strategy for the Take-Away Impartial Combinatorial Games on
only Oddly Uniform or only Evenly Uniform Hypergraphs, this paper is about the
new winning strategy for Take-Away Games on neither Oddly nor Evenly Uniform
Hypergraphs. These neither Oddly nor Evenly Uniform Hypergraphs, however, have
to satisfy the specific given requirements.


title:
Towards Training Billion Parameter Graph Neural Networks for Atomic
  Simulations

abstract:
  Recent progress in Graph Neural Networks (GNNs) for modeling atomic
simulations has the potential to revolutionize catalyst discovery, which is a
key step in making progress towards the energy breakthroughs needed to combat
climate change. However, the GNNs that have proven most effective for this task
are memory intensive as they model higher-order interactions in the graphs such
as those between triplets or quadruplets of atoms, making it challenging to
scale these models. In this paper, we introduce Graph Parallelism, a method to
distribute input graphs across multiple GPUs, enabling us to train very large
GNNs with hundreds of millions or billions of parameters. We empirically
evaluate our method by scaling up the number of parameters of the recently
proposed DimeNet++ and GemNet models by over an order of magnitude. On the
large-scale Open Catalyst 2020 (OC20) dataset, these graph-parallelized models
lead to relative improvements of 1) 15% on the force MAE metric for the S2EF
task and 2) 21% on the AFbT metric for the IS2RS task, establishing new
state-of-the-art results.


title: Multi-chaotic inflation with and without spectator field

abstract:
  Motivated by the result of Planck+BICEP/Keck recently released, we
investigate the consistency of the multi-field inflation models in terms of the
spectral index $n_s$ and the tensor-to-scalar ratio $r$. In this study, we
focus on double-inflaton models with and without a spectator field. We find
that inflaton with a quadratic potential can become viable when three fields
with a specific hierarchical mass spectrum are realized such that two fields
act as inflatons and the other one is the spectator. We also discuss the
conditions to avoid the fine-tuning, by careful study of how the prediction
depends on the background trajectory in the inflaton-field space.


title:
Inverse scattering transform of the general coupled Hirota system with
  nonzero boundary conditions

abstract:
  The initial value problem for the general coupled Hirota system with nonzero
boundary conditions at infinity is solved by reporting a rigorous theory of the
inverse scattering transform. With the help of a suitable uniformization
variable, both the inverse and the direct problems are analyzed which allows us
to develop the inverse scattering transform on the complex $z$-plane. Firstly,
analyticity of the scattering eigenfunctions and scattering data, properties of
the discrete spectrum, symmetries, and asymptotics are discussed in detail.
Moreover, the inverse problem is posed as a Riemann-Hilbert problem for the
eigenfunctions, and the reconstruction formula of the potential in terms of
eigenfunctions and scattering data is presented. Finally, the main
characteristics of these obtained soliton solutions are graphically discussed
in the $2\times2$ self-focusing case. This family of solutions contains novel
Akhmediev breather and Kuznetsov-Ma soliton. These results would be of much
importance in understanding and enriching breather wave phenomena arising in
nonlinear and complex systems, especially in Bose-Einstein condensates.


title:
Rigorous derivation of the compressible Navier-Stokes equations from the
  two-fluid Navier-Stokes-Maxwell equations

abstract:
  In this paper, we rigorously derive the compressible one-fluid Navier-Stokes
equation from the scaled compressible two-fluid Navier-Stokes-Maxwell equations
locally in time under the assumption that the initial data are well prepared.
We justify the singular limit by proving the uniform decay of the error system,
which is obtained by elaborate energy estimates.


title: On multitype Branching Processes with Logistic Competition

abstract:
  Motivated by the stochastic Lotka-Volterra model, we introduce
continuous-time discrete-state interacting multitype branching processes (both
through intratype and intertype competition or cooperation). We show that these
processes can be obtained as the sum of a multidimensional random walk with a
Lamperti-type change proportional to the population size; and a
multidimensional Poisson process with a time-change proportional to the
pairwise interactions. Then, we define the analogous continuous-state process
as the unique strong solution of a multidimensional stochastic differential
equation. Finally, we prove that a large population scaling limits of the
discrete-state process correspond to its continuous counterpart. In addition,
we show that the continuous-state model can be constructed as a generalized
Lamperti-type transformation of multidimensional L\'evy processes.


title:
Federated Learning for Privacy Preservation in Smart Healthcare Systems:
  A Comprehensive Survey

abstract:
  Recent advances in electronic devices and communication infrastructure have
revolutionized the traditional healthcare system into a smart healthcare system
by using IoMT devices. However, due to the centralized training approach of
artificial intelligence (AI), the use of mobile and wearable IoMT devices
raises privacy concerns with respect to the information that has been
communicated between hospitals and end users. The information conveyed by the
IoMT devices is highly confidential and can be exposed to adversaries. In this
regard, federated learning (FL), a distributive AI paradigm has opened up new
opportunities for privacy-preservation in IoMT without accessing the
confidential data of the participants. Further, FL provides privacy to end
users as only gradients are shared during training. For these specific
properties of FL, in this paper we present privacy related issues in IoMT.
Afterwards, we present the role of FL in IoMT networks for privacy preservation
and introduce some advanced FL architectures incorporating deep reinforcement
learning (DRL), digital twin, and generative adversarial networks (GANs) for
detecting privacy threats. Subsequently, we present some practical
opportunities of FL in smart healthcare systems. At the end, we conclude this
survey by providing open research challenges for FL that can be used in future
smart healthcare systems


title: Cutting plane algorithms for nonlinear binary optimization

abstract:
  Current state-of-the-art methods for solving discrete optimization problems
are usually restricted to convex settings. In this paper, we propose a general
approach based on cutting planes for solving nonlinear, possibly nonconvex,
binary optimization problems. We provide a rigorous convergence analysis that
quantifies the number of iterations required under different conditions. This
is different to most other work in discrete optimization where only finite
convergence is proved. Moreover, using tools from variational analysis, we
provide necessary and sufficient dual optimality conditions.


title:
VISTA: Boosting 3D Object Detection via Dual Cross-VIew SpaTial
  Attention

abstract:
  Detecting objects from LiDAR point clouds is of tremendous significance in
autonomous driving. In spite of good progress, accurate and reliable 3D
detection is yet to be achieved due to the sparsity and irregularity of LiDAR
point clouds. Among existing strategies, multi-view methods have shown great
promise by leveraging the more comprehensive information from both bird's eye
view (BEV) and range view (RV). These multi-view methods either refine the
proposals predicted from single view via fused features, or fuse the features
without considering the global spatial context; their performance is limited
consequently. In this paper, we propose to adaptively fuse multi-view features
in a global spatial context via Dual Cross-VIew SpaTial Attention (VISTA). The
proposed VISTA is a novel plug-and-play fusion module, wherein the multi-layer
perceptron widely adopted in standard attention modules is replaced with a
convolutional one. Thanks to the learned attention mechanism, VISTA can produce
fused features of high quality for prediction of proposals. We decouple the
classification and regression tasks in VISTA, and an additional constraint of
attention variance is applied that enables the attention module to focus on
specific targets instead of generic points. We conduct thorough experiments on
the benchmarks of nuScenes and Waymo; results confirm the efficacy of our
designs. At the time of submission, our method achieves 63.0% in overall mAP
and 69.8% in NDS on the nuScenes benchmark, outperforming all published methods
by up to 24% in safety-crucial categories such as cyclist. The source code in
PyTorch is available at https://github.com/Gorilla-Lab-SCUT/VISTA


title:
Tailoring Dirac fermions by in-situ tunable high-order moire pattern in
  graphene-monolayer xenon heterostructure

abstract:
  A variety of novel quantum phases have been achieved in twist bilayer
graphene (tBLG) and other moire superlattices recently, including correlated
insulators, superconductivity, magnetism, and topological states. These
phenomena are very sensitive to the moire superlattices, which can hardly be
changed rapidly or intensely. Here, we report the experimental realization of a
high-order moire pattern (a high-order interference pattern) in
graphene-monolayer xenon heterostructure (G/mXe), with moire period in-situ
tuned from few nanometers to infinity by changing the lattice constant of Xe
through different annealing temperatures and pressures. We use angle-resolved
photoemission spectroscopy to directly observe that replicas of graphene Dirac
cone emerge and move close to each other in momentum-space as moire pattern
continuously expands in real-space. When the moire period approaches infinity,
the replicas finally overlap with each other and an energy gap is observed at
the Dirac point induced by intervalley coupling, which is a manifestation of
Kekule distortion. We construct a continuum moire Hamiltonian, which can
explain the experimental results well. The form of moire Hamiltonian in G/mXe
is similar to that in tBLG, and moire band with narrow bandwidth is predicted
in G/mXe. However, the moire Hamiltonian couples Dirac fermions from different
valleys in G/mXe, instead of ones from different layers in tBLG. Our work
demonstrates a novel platform to study the continuous evolution of moire
pattern and its modulation effect on electronic structure, and provides an
unprecedented approach for tailoring Dirac fermions with tunable intervalley
coupling.


title: Integrable 3D lattice model in M-theory

abstract:
  It is argued that the supersymmetric index of a certain system of branes in
M-theory is equal to the partition function of an integrable three-dimensional
lattice model. The local Boltzmann weights of the lattice model satisfy a
generalization of Zamolodchikov's tetrahedron equation. In a special case the
model is described by a solution of the tetrahedron equation discovered by
Kapranov and Voevodsky and by Bazhanov and Sergeev.


title:
M2TS: Multi-Scale Multi-Modal Approach Based on Transformer for Source
  Code Summarization

abstract:
  Source code summarization aims to generate natural language descriptions of
code snippets. Many existing studies learn the syntactic and semantic knowledge
of code snippets from their token sequences and Abstract Syntax Trees (ASTs).
They use the learned code representations as input to code summarization
models, which can accordingly generate summaries describing source code.
Traditional models traverse ASTs as sequences or split ASTs into paths as
input. However, the former loses the structural properties of ASTs, and the
latter destroys the overall structure of ASTs. Therefore, comprehensively
capturing the structural features of ASTs in learning code representations for
source code summarization remains a challenging problem to be solved. In this
paper, we propose M2TS, a Multi-scale Multi-modal approach based on Transformer
for source code Summarization. M2TS uses a multi-scale AST feature extraction
method, which can extract the structures of ASTs more completely and accurately
at multiple local and global levels. To complement missing semantic information
in ASTs, we also obtain code token features, and further combine them with the
extracted AST features using a cross modality fusion method that not only fuses
the syntactic and contextual semantic information of source code, but also
highlights the key features of each modality. We conduct experiments on two
Java and one Python datasets, and the experimental results demonstrate that
M2TS outperforms current state-of-the-art methods. We release our code at
https://github.com/TranSMS/M2TS.


title: Improve few-shot voice cloning using multi-modal learning

abstract:
  Recently, few-shot voice cloning has achieved a significant improvement.
However, most models for few-shot voice cloning are single-modal, and
multi-modal few-shot voice cloning has been understudied. In this paper, we
propose to use multi-modal learning to improve the few-shot voice cloning
performance. Inspired by the recent works on unsupervised speech
representation, the proposed multi-modal system is built by extending Tacotron2
with an unsupervised speech representation module. We evaluate our proposed
system in two few-shot voice cloning scenarios, namely few-shot
text-to-speech(TTS) and voice conversion(VC). Experimental results demonstrate
that the proposed multi-modal learning can significantly improve the few-shot
voice cloning performance over their counterpart single-modal systems.


title: Relativistic Nontopological Soliton Stars in a U(1) Gauge Higgs Model

abstract:
  We study spherically symmetric nontopological soliton stars (NTS stars)
numerically in the coupled system of a complex scalar field, a U(1) gauge
field, a complex Higgs scalar field, and Einstein gravity, where the symmetry
is broken spontaneously. The gravitational mass of NTS stars is limited by a
maximum mass for a fixed breaking scale, and the maximum mass increases steeply
as the breaking scale decreases. In the case of the breaking scale is much less
than the Planck scale, the maximum mass of NTS stars becomes the astrophysical
scale, and such a star is relativistically compact so that it has the innermost
stable circular orbit.
  The first author contributed with a part of numerical calculations. The
second did with planning and conducting the research, and the third did with
all numerical calculations and finding new properties of the system.


title: Learning Stabilizable Deep Dynamics Models

abstract:
  When neural networks are used to model dynamics, properties such as stability
of the dynamics are generally not guaranteed. In contrast, there is a recent
method for learning the dynamics of autonomous systems that guarantees global
exponential stability using neural networks. In this paper, we propose a new
method for learning the dynamics of input-affine control systems. An important
feature is that a stabilizing controller and control Lyapunov function of the
learned model are obtained as well. Moreover, the proposed method can also be
applied to solving Hamilton-Jacobi inequalities. The usefulness of the proposed
method is examined through numerical examples.


title:
DEAM: Dialogue Coherence Evaluation using AMR-based Semantic
  Manipulations

abstract:
  Automatic evaluation metrics are essential for the rapid development of
open-domain dialogue systems as they facilitate hyper-parameter tuning and
comparison between models. Although recently proposed trainable
conversation-level metrics have shown encouraging results, the quality of the
metrics is strongly dependent on the quality of training data. Prior works
mainly resort to heuristic text-level manipulations (e.g. utterances shuffling)
to bootstrap incoherent conversations (negative examples) from coherent
dialogues (positive examples). Such approaches are insufficient to
appropriately reflect the incoherence that occurs in interactions between
advanced dialogue models and humans. To tackle this problem, we propose DEAM, a
Dialogue coherence Evaluation metric that relies on Abstract Meaning
Representation (AMR) to apply semantic-level Manipulations for incoherent
(negative) data generation. AMRs naturally facilitate the injection of various
types of incoherence sources, such as coreference inconsistency, irrelevancy,
contradictions, and decrease engagement, at the semantic level, thus resulting
in more natural incoherent samples. Our experiments show that DEAM achieves
higher correlations with human judgments compared to baseline methods on
several dialog datasets by significant margins. We also show that DEAM can
distinguish between coherent and incoherent dialogues generated by baseline
manipulations, whereas those baseline models cannot detect incoherent examples
generated by DEAM. Our results demonstrate the potential of AMR-based semantic
manipulations for natural negative example generation.


title: Hypersurfaces with Constant Mean Curvatures on Finsler manifolds

abstract:
  In this paper, we give the geometric meaning of hypersurfaces with constant
mean curvature in Finsler geometry by using volume preserving variation. Then
we give the correspondence between principal curvatures of submanifolds by a
homothetic navigation, which means that hypersurfaces with constant mean
curvature are same, and from which we deduce a Heintze-Karcher type inequality
and prove an Alexandrov type theorem in a kind of special Finsler spaces


title: Lepton Asymmetric Universe

abstract:
  The recent observation of $^4$He implies that our universe has a large lepton
asymmetry. We consider the Affleck-Dine (AD) mechanism for lepton number
generation. In the AD mechanism, non-topological solitons called L-balls are
produced, and the generated lepton number is confined in them. The L-balls
protect the generated lepton number from being converted to baryon number
through the sphaleron processes. We study the formation and evolution of the
L-balls and find that the universe with large lepton asymmetry suggested by the
recent $^4$He measurement can be realized.


title: Decentralizing Permissioned Blockchain with Delay Towers

abstract:
  Growing excitement around permissionless blockchains is uncovering its latent
scalability concerns. Permissioned blockchains offer high transactional
throughput and low latencies while compromising decentralization. In the quest
for a decentralized, scalable blockchain fabric, i.e., to offer the scalability
of permissioned blockchain in a permissionless setting, we present L4L to
encourage decentralization over the permissioned Libra network without
compromising its sustainability. L4L employs delay towers, -- puzzle towers
that leverage verifiable delay functions -- for establishing identity in a
permissionless setting. Delay towers cannot be parallelized due to their
sequential execution, making them an eco-friendly alternative. We also discuss
methodologies to replace validators participating in consensus to promote
compliant behavior. Our evaluations found that the cost of enabling
decentralization over permissioned networks is almost negligible. Furthermore,
delay towers offer an alternative to existing permissionless consensus
mechanisms without requiring airdrops or pre-sale of tokens.


title:
Carnot machine-based massive MIMO communication capacity modeling and
  performance analysis

abstract:
  Similar to the energy flowing process in traditional heat engines,
information could be considered to flow in the communication systems with the
form of energy and entropy. Combining the thermodynamic Carnot machine and the
classical Shannon information theory, a generalized thermodynamic MIMO
(multiple input multiple outputs) communication system is established to
analyze the channel capacity using forward error correction codes. Based on the
concepts of freedom and entropy in the communication system, the generalized
channel capacity is proposed under the thermodynamic theory. Furthermore, the
relationships between the proposed channel capacity and the noise freedom and
coding overhead are derived and simulated. Simulation results verify the
proposed channel capacity is coincident with the classical channel capacity.


title: Singular vectors in affine subspaces and $\Psi$-Dirichlet numbers

abstract:
  We prove inheritance of measure zero property of the set of singular vectors
for affine subspaces and submanifolds inside them in both $\mathbb{R}$ and
function field over finite fields. Another result of this paper shows that the
only $\psi$-Dirichlet numbers in a function field over a finite field are
rational functions, unlike $\psi$-Dirichlet numbers in $\mathbb{R}$. We also
prove that there are uncountably many totally irrational singular vectors with
large uniform exponent in quadratic surfaces over a positive characteristic
field.


title:
Towards an AI-Driven Universal Anti-Jamming Solution with Convolutional
  Interference Cancellation Network

abstract:
  Wireless links are increasingly used to deliver critical services, while
intentional interference (jamming) remains a very serious threat to such
services. In this paper, we are concerned with the design and evaluation of a
universal anti-jamming building block, that is agnostic to the specifics of the
communication link and can therefore be combined with existing technologies. We
believe that such a block should not require explicit probes, sounding,
training sequences, channel estimation, or even the cooperation of the
transmitter. To meet these requirements, we propose an approach that relies on
advances in Machine Learning, and the promises of neural accelerators and
software defined radios. We identify and address multiple challenges, resulting
in a convolutional neural network architecture and models for a multi-antenna
system to infer the existence of interference, the number of interfering
emissions and their respective phases. This information is continuously fed
into an algorithm that cancels the interfering signal. We develop a two-antenna
prototype system and evaluate our jamming cancellation approach in various
environment settings and modulation schemes using Software Defined Radio
platforms. We demonstrate that the receiving node equipped with our approach
can detect a jammer with over 99% of accuracy and achieve a Bit Error Rate
(BER) as low as $10^{-6}$ even when the jammer power is nearly two orders of
magnitude (18 dB) higher than the legitimate signal, and without requiring
modifications to the link modulation. In non-adversarial settings, our approach
can have other advantages such as detecting and mitigating collisions.


title:
An Impartial Perspective for Superconducting Nb3Sn coated Copper RF
  Cavities for Future Accelerators

abstract:
  This Snowmass21 Contributed Paper encourages the Particle Physics community
in fostering R&D in Superconducting Nb3Sn coated Copper RF Cavities instead of
costly bulk Niobium. It describes the pressing need to devote effort in this
direction, which would deliver higher gradient and higher temperature of
operation and reduce the overall capital and operational costs of any future
collider. It is unlikely that an ILC will be built in the next ten years with
Nb as one of the main cost drivers of SRFs. This paper provides strong
arguments on the benefits of using this time for R&D on producing Nb3Sn on
inexpensive and thermally efficient metals such as Cu or bronze, while pursuing
in parallel the novel U.S. concept of parallel-feed RF accelerator structures.
A technology that synergistically uses both of these advanced tools would make
an ILC or equivalent machines more affordable and more likely to be built. Such
a successful enterprise would readily apply to other HEP accelerators, for
instance a Muon Collider, and to accelerators beyond HEP. We present and assess
current efforts in the U.S. on the novel concept of parallel-feed RF
accelerator structures, and in the U.S. and abroad in producing Nb3Sn films on
either Cu or bronze despite minimal funding.


title:
Evolution as Explanation: The Origins of Neural Codes and their
  Efficiencies

abstract:
  Neural codes appear efficient. Naturally, neuroscientists contend that an
efficient process is responsible for generating efficient codes. They argue
that natural selection is the efficient process that generates those codes.
Although natural selection is an adaptive process, evolution itself, is not.
Evolution consists of not only natural selection, but also neutral stochastic
forces that can generate biological inefficiencies. The explanatory power of
natural selection cannot be appealed to, without regards for the remaining
evolutionary forces. In this paper, we aim to reformulate the explanatory role
of evolutionary forces on neural coding, with special attention to neutral
forces. We propose a framework that argues for differing contributions of
adaptive and stochastic evolutionary forces, for different phenotypic `levels',
including those of neural codes. We assert that this framework is of special
interest to neuroscience, because the field has derived much progress from an
efficiency-based worldview. We advocate for a pluralistic neuroscience capable
of appealing to both adaptive and non-adaptive explanations.


title: Layer-by-Layer Epitaxy of Multilayer MoS2 Wafers

abstract:
  Two-dimensional (2D) semiconductor of MoS2 has great potential for advanced
electronics technologies beyond silicon1-9. So far, high-quality monolayer MoS2
wafers10-12 are already available and various demonstrations from individual
transistors to integrated circuits have also been shown13-15. In addition to
the monolayer, multilayers have narrower band gaps but improved carrier
mobilities and current capacities over the monolayer5,16-18. However, achieving
high-quality multilayer MoS2 wafers remains a challenge. Here we report the
growth of high quality multilayer MoS2 4-inch wafers via the layer-by-layer
epitaxy process. The epitaxy leads to well-defined stacking orders between
adjacent epitaxial layers and offers a delicate control of layer numbers up to
6. Systematic evaluations on the atomic structures and electronic properties
were carried out for achieved wafers with different layer numbers. Significant
improvements on device performances were found in thicker-layer field effect
transistors (FETs), as expected. For example, the average field-effect mobility
({\mu}FE) at room temperature (RT) can increase from ~80 cm2V-1s-1 for
monolayer to ~110/145 cm2V-1s-1 for bilayer/trilayer devices. The highest RT
{\mu}FE=234.7 cm2V-1s-1 and a record-high on-current densities of 1.704
mA{\mu}m-1 at Vds=2 V were also achieved in trilayer MoS2 FETs with a high
on/off ratio exceeding 107. Our work hence moves a step closer to practical
applications of 2D MoS2 in electronics.


title: Deterministic Bridge Regression for Compressive Classification

abstract:
  Pattern classification with compact representation is an important component
in machine intelligence. In this work, an analytic bridge solution is proposed
for compressive classification. The proposal has been based upon solving a
penalized error formulation utilizing an approximated $\ell_p$-norm. The
solution comes in a primal form for over-determined systems and in a dual form
for under-determined systems. While the primal form is suitable for problems of
low dimension with large data samples, the dual form is suitable for problems
of high dimension but with a small number of data samples. The solution has
also been extended for problems with multiple classification outputs. Numerical
studies based on simulated and real-world data validated the effectiveness of
the proposed solution.


title: DGC-vector: A new speaker embedding for zero-shot voice conversion

abstract:
  Recently, more and more zero-shot voice conversion algorithms have been
proposed. As a fundamental part of zero-shot voice conversion, speaker
embeddings are the key to improving the converted speech's speaker similarity.
In this paper, we study the impact of speaker embeddings on zero-shot voice
conversion performance. To better represent the characteristics of the target
speaker and improve the speaker similarity in zero-shot voice conversion, we
propose a novel speaker representation method in this paper. Our method
combines the advantages of D-vector, global style token (GST) based speaker
representation and auxiliary supervision. Objective and subjective evaluations
show that the proposed method achieves a decent performance on zero-shot voice
conversion and significantly improves speaker similarity over D-vector and
GST-based speaker embedding.


title:
Estimation of Consistent Time Delays in Subsample via
  Auxiliary-Function-Based Iterative Updates

abstract:
  In this paper, we propose a new algorithm for the estimation of multiple time
delays (TDs). Since a TD is a fundamental spatial cue for sensor array signal
processing techniques, many methods for estimating it have been studied. Most
of them, including generalized cross correlation (CC)-based methods, focus on
how to estimate a TD between two sensors. These methods can then be easily
adapted for multiple TDs by applying them to every pair of a reference sensor
and another one. However, these pairwise methods can use only the partial
information obtained by the selected sensors, resulting in inconsistent TD
estimates and limited estimation accuracy. In contrast, we propose joint
optimization of entire TD parameters, where spatial information obtained from
all sensors is taken into account. We also introduce a consistent constraint
regarding TD parameters to the observation model. We then consider a
multidimensional CC (MCC) as the objective function, which is derived on the
basis of maximum likelihood estimation. To maximize the MCC, which is a
nonconvex function, we derive the auxiliary function for the MCC and design
efficient update rules. We additionally estimate the amplitudes of the transfer
functions for supporting the TD estimation, where we maximize the Rayleigh
quotient under the non-negative constraint. We experimentally analyze essential
features of the proposed method and evaluate its effectiveness in TD
estimation. Code will be available at https://github.com/onolab-tmu/AuxTDE.


title:
Rethinking the optimization process for self-supervised model-driven MRI
  reconstruction

abstract:
  Recovering high-quality images from undersampled measurements is critical for
accelerated MRI reconstruction. Recently, various supervised deep
learning-based MRI reconstruction methods have been developed. Despite the
achieved promising performances, these methods require fully sampled reference
data, the acquisition of which is resource-intensive and time-consuming.
Self-supervised learning has emerged as a promising solution to alleviate the
reliance on fully sampled datasets. However, existing self-supervised methods
suffer from reconstruction errors due to the insufficient constraint enforced
on the non-sampled data points and the error accumulation happened alongside
the iterative image reconstruction process for model-driven deep learning
reconstrutions. To address these challenges, we propose K2Calibrate, a K-space
adaptation strategy for self-supervised model-driven MR reconstruction
optimization. By iteratively calibrating the learned measurements, K2Calibrate
can reduce the network's reconstruction deterioration caused by statistically
dependent noise. Extensive experiments have been conducted on the open-source
dataset FastMRI, and K2Calibrate achieves better results than five
state-of-the-art methods. The proposed K2Calibrate is plug-and-play and can be
easily integrated with different model-driven deep learning reconstruction
methods.


title:
Forward-Looking Dynamic Persuasion for Pipeline Stochastic Bayesian
  Game: A Fixed-Point Alignment Principle

abstract:
  This paper studies a general-sum two-player pipeline stochastic game where
each period is composed of two stages. The agents have uncertainty about the
transition of the state which is characterized by a variable known as shock.
The first stage is cognitive decision-making in which each agent selects one of
multiple information sources (ISs) and then acquires information about the
shock from his selected IS. In the second stage, each agent takes an action
based on the information provided by his selected IS. There is a rational
Bayesian persuader who controls one IS and aims to influence the agents'
actions to induce her desired dynamic equilibria by solely crafting the
information structure of her IS. We refer to such persuasion as forward-looking
dynamic persuasion. We restrict attention to a direct persuasion in which the
persuader incentivizes the agents to select her IS at the first stage and
consider an equilibrium concept known as pipelined perfect Markov Bayesian
equilibrium (PPME). We propose a novel design principle termed fixed-point
alignment that captures the observation that the agents' strategic interactions
in the second stage induce an indirect cognitive competition (i.e., IS
selection) in the first stage and formulate the direct Bayesian persuasion in
PPME as a constrained non-linear optimization problem. By decomposing the
optimization problem into a two-stage local process, we then provide a class of
verifiable necessary and sufficient condition referred to as local
admissibility of the direct Bayesian persuasion in PPME. A case study is
provided in which we obtain a myopic version of local admissibility, by solving
a series of static problems which is homogeneous with respect to the state
variable.


title:
Efficient Estimation of the Additive Risks Model for Interval-Censored
  Data

abstract:
  In contrast to the popular Cox model which presents a multiplicative
covariate effect specification on the time to event hazards, the semiparametric
additive risks model (ARM) offers an attractive additive specification,
allowing for direct assessment of the changes or the differences in the hazard
function for changing value of the covariates. The ARM is a flexible model,
allowing the estimation of both time-independent and time-varying covariates.
It has a nonparametric component and a regression component identified by a
finite-dimensional parameter. This chapter presents an efficient approach for
maximum-likelihood (ML) estimation of the nonparametric and the
finite-dimensional components of the model via the minorize-maximize (MM)
algorithm for case-II interval-censored data. The operating characteristics of
our proposed MM approach are assessed via simulation studies, with illustration
on a breast cancer dataset via the R package MMIntAdd. It is expected that the
proposed computational approach will not only provide scalability to the ML
estimation scenario but may also simplify the computational burden of other
complex likelihoods or models.


title: DEFORM: A Practical, Universal Deep Beamforming System

abstract:
  We introduce, design, and evaluate a set of universal receiver beamforming
techniques. Our approach and system DEFORM, a Deep Learning (DL) based RX
beamforming achieves significant gain for multi antenna RF receivers while
being agnostic to the transmitted signal features (e.g., modulation or
bandwidth). It is well known that combining coherent RF signals from multiple
antennas results in a beamforming gain proportional to the number of receiving
elements. However in practice, this approach heavily relies on explicit channel
estimation techniques, which are link specific and require significant
communication overhead to be transmitted to the receiver. DEFORM addresses this
challenge by leveraging Convolutional Neural Network to estimate the channel
characteristics in particular the relative phase to antenna elements. It is
specifically designed to address the unique features of wireless signals
complex samples, such as the ambiguous $2\pi$ phase discontinuity and the high
sensitivity of the link Bit Error Rate. The channel prediction is subsequently
used in the Maximum Ratio Combining algorithm to achieve an optimal combination
of the received signals. While being trained on a fixed, basic RF settings, we
show that DEFORM DL model is universal, achieving up to 3 dB of SNR gain for a
two antenna receiver in extensive experiments demonstrating various settings of
modulations, bandwidths, and channels. The universality of DEFORM is
demonstrated through joint beamforming relaying of LoRa (Chirp Spread Spectrum
modulation) and ZigBee signals, achieving significant improvements to Packet
Loss/Delivery Rates relatively to conventional Amplify and Forward (LoRa PLR
reduced by 23 times and ZigBee PDR increased by 8 times).


title: On the Problem of Undirected st-connectivity

abstract:
  In this paper, we discuss an algorithm for the problem of undirected
st-connectivity that is deterministic and log-space, namely that of Reingold
within his 2008 paper "Undirected Connectivity in Log-Space". We further
present a separate proof by Rozenman and Vadhan of $\text{USTCONN} \in L$ and
discuss its similarity with Reingold's proof. Undirected st-connectively is
known to be complete for the complexity class SL--problems solvable by
symmetric, non-deterministic, log-space algorithms. Likewise, by Aleliunas et.
al., it is known that undirected st-connectivity is within the RL complexity
class, problems solvable by randomized (probabilistic) Turing machines with
one-sided error in logarithmic space and polynomial time. Finally, our paper
also shows that undirected st-connectivity is within the L complexity class,
problems solvable by deterministic Turing machines in logarithmic space.
Leading from this result, we shall explain why SL = L and discuss why is it
believed that RL = L.


title: REALY: Rethinking the Evaluation of 3D Face Reconstruction

abstract:
  The evaluation of 3D face reconstruction results typically relies on a rigid
shape alignment between the estimated 3D model and the ground-truth scan. We
observe that aligning two shapes with different reference points can largely
affect the evaluation results. This poses difficulties for precisely diagnosing
and improving a 3D face reconstruction method. In this paper, we propose a
novel evaluation approach with a new benchmark REALY, consists of 100 globally
aligned face scans with accurate facial keypoints, high-quality region masks,
and topology-consistent meshes. Our approach performs region-wise shape
alignment and leads to more accurate, bidirectional correspondences during
computing the shape errors. The fine-grained, region-wise evaluation results
provide us detailed understandings about the performance of state-of-the-art 3D
face reconstruction methods. For example, our experiments on single-image based
reconstruction methods reveal that DECA performs the best on nose regions,
while GANFit performs better on cheek regions. Besides, a new and high-quality
3DMM basis, HIFI3D++, is further derived using the same procedure as we
construct REALY to align and retopologize several 3D face datasets. We will
release REALY, HIFI3D++, and our new evaluation pipeline at
https://realy3dface.com.


title: A Dual Weighting Label Assignment Scheme for Object Detection

abstract:
  Label assignment (LA), which aims to assign each training sample a positive
(pos) and a negative (neg) loss weight, plays an important role in object
detection. Existing LA methods mostly focus on the design of pos weighting
function, while the neg weight is directly derived from the pos weight. Such a
mechanism limits the learning capacity of detectors. In this paper, we explore
a new weighting paradigm, termed dual weighting (DW), to specify pos and neg
weights separately. We first identify the key influential factors of pos/neg
weights by analyzing the evaluation metrics in object detection, and then
design the pos and neg weighting functions based on them. Specifically, the pos
weight of a sample is determined by the consistency degree between its
classification and localization scores, while the neg weight is decomposed into
two terms: the probability that it is a neg sample and its importance
conditioned on being a neg sample. Such a weighting strategy offers greater
flexibility to distinguish between important and less important samples,
resulting in a more effective object detector. Equipped with the proposed DW
method, a single FCOS-ResNet-50 detector can reach 41.5% mAP on COCO under 1x
schedule, outperforming other existing LA methods. It consistently improves the
baselines on COCO by a large margin under various backbones without bells and
whistles. Code is available at https://github.com/strongwolf/DW.


title:
Bubbling solutions for mean field equations with variable intensities on
  compact Riemann surfaces

abstract:
  For an asymmetric sinh-Poisson problem arising as a mean field equation of
equilibrium turbulence vortices with variable intensities of interest in
hydrodynamic turbulence, we address the existence of bubbling solutions on
compact Riemann surfaces. By using a Lyapunov-Schmidt reduction, we find
sufficient conditions under which there exist bubbling solutions blowing up at
$m$ different points of $S$: positively at $m_1$ points and negatively at
$m-m_1$ points with $m\ge 1$ and $m_1\in\{0,1,...,m\}$. Several examples in
different situations illustrate our results in the sphere $\mathbb S^2$ and
flat two-torus $\mathbb T$ including non negative potentials with zero set non
empty.


title:
Nested Iterative Shift-invert Diagonalization for Many-body Localization
  in the Random-field Heisenberg Chain

abstract:
  We study the many-body localization of the random-field Heisenberg chain
using the nested shift-invert Lanczos method with an iterative linear solver.
We use the minimum residual method (MINRES) inside each Lanczos iteration. The
memory consumption of the proposed method is only proportional to the dimension
of the Hilbert space. We also introduce a preconditioner that takes into
account the effects of disorder and interaction in the random-field Heisenberg
chain. As a probe of many-body localization transition, we propose a unitary
operator called the twist operator, which has a clear interpretation in the
real space. We discuss its behavior for thermal and localized eigenstates. We
demonstrate the efficiency of the nested iterative shift-invert diagonalization
method with the proposed preconditioner for the many-body localization problem
and estimate the transition point of the random-field Heisenberg chain more
precisely based on the finite-size analysis of the expectation value of the
twist operator.


title:
Distortion-Tolerant Monocular Depth Estimation On Omnidirectional Images
  Using Dual-cubemap

abstract:
  Estimating the depth of omnidirectional images is more challenging than that
of normal field-of-view (NFoV) images because the varying distortion can
significantly twist an object's shape. The existing methods suffer from
troublesome distortion while estimating the depth of omnidirectional images,
leading to inferior performance. To reduce the negative impact of the
distortion influence, we propose a distortion-tolerant omnidirectional depth
estimation algorithm using a dual-cubemap. It comprises two modules:
Dual-Cubemap Depth Estimation (DCDE) module and Boundary Revision (BR) module.
In DCDE module, we present a rotation-based dual-cubemap model to estimate the
accurate NFoV depth, reducing the distortion at the cost of boundary
discontinuity on omnidirectional depths. Then a boundary revision module is
designed to smooth the discontinuous boundaries, which contributes to the
precise and visually continuous omnidirectional depths. Extensive experiments
demonstrate the superiority of our method over other state-of-the-art
solutions.


title: A Facility for Low-Radioactivity Underground Argon

abstract:
  The DarkSide-50 experiment demonstrated the ability to extract and purify
argon from deep underground sources and showed that the concentration of
$^{39}$Ar in that argon was greatly reduced from the level found in argon
derived from the atmosphere. That discovery broadened the physics reach of
argon-based detector and created a demand for low-radioactivity underground
argon (UAr) in high-energy physics, nuclear physics, and in environmental and
allied sciences. The Global Argon Dark Matter Collaboration (GADMC) is
preparing to produce UAr for DarkSide-20k, but a general UAr supply for the
community does not exist. With the proper resources, those plants could be
operated as a facility to supply UAr for most of the experiments after the
DarkSide 20k production. However, if the current source becomes unavailable, or
UAr masses greater than what is available from the current source is needed,
then a new source must be found. To find a new source will require
understanding the production of the radioactive argon isotopes underground in a
gas field, and the ability to measure $^{37}$Ar, $^{39}$Ar, and $^{42}$Ar to
ultra-low levels. The operation of a facility creates a need for ancillary
systems to monitor for $^{37}$Ar, $^{39}$Ar, or $^{42}$Ar infiltration either
directly or indirectly, which can also be used to vet the $^{37}$Ar, $^{39}$Ar,
and $^{42}$Ar levels in a new UAr source, but requires the ability to separate
UAr from the matrix well gas. Finding methods to work with industry to find gas
streams enriched in UAr, or to commercialize a UAr facility, are highly
desirable.


title:
PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive
  Weakly-Supervised Learning

abstract:
  Weakly-supervised learning (WSL) has shown promising results in addressing
label scarcity on many NLP tasks, but manually designing a comprehensive,
high-quality labeling rule set is tedious and difficult. We study interactive
weakly-supervised learning -- the problem of iteratively and automatically
discovering novel labeling rules from data to improve the WSL model. Our
proposed model, named PRBoost, achieves this goal via iterative prompt-based
rule discovery and model boosting. It uses boosting to identify large-error
instances and then discovers candidate rules from them by prompting pre-trained
LMs with rule templates. The candidate rules are judged by human experts, and
the accepted rules are used to generate complementary weak labels and
strengthen the current model. Experiments on four tasks show PRBoost
outperforms state-of-the-art WSL baselines up to 7.1% and bridges the gaps with
fully supervised models. Our Implementation is available at
\url{https://github.com/rz-zhang/PRBoost}.


title: Series Photo Selection via Multi-view Graph Learning

abstract:
  Series photo selection (SPS) is an important branch of the image aesthetics
quality assessment, which focuses on finding the best one from a series of
nearly identical photos. While a great progress has been observed, most of the
existing SPS approaches concentrate solely on extracting features from the
original image, neglecting that multiple views, e.g, saturation level, color
histogram and depth of field of the image, will be of benefit to successfully
reflecting the subtle aesthetic changes. Taken multi-view into consideration,
we leverage a graph neural network to construct the relationships between
multi-view features. Besides, multiple views are aggregated with an
adaptive-weight self-attention module to verify the significance of each view.
Finally, a siamese network is proposed to select the best one from a series of
nearly identical photos. Experimental results demonstrate that our model
accomplish the highest success rates compared with competitive methods.


title:
Semi-Supervised Learning with Mutual Distillation for Monocular Depth
  Estimation

abstract:
  We propose a semi-supervised learning framework for monocular depth
estimation. Compared to existing semi-supervised learning methods, which
inherit limitations of both sparse supervised and unsupervised loss functions,
we achieve the complementary advantages of both loss functions, by building two
separate network branches for each loss and distilling each other through the
mutual distillation loss function. We also present to apply different data
augmentation to each branch, which improves the robustness. We conduct
experiments to demonstrate the effectiveness of our framework over the latest
methods and provide extensive ablation studies.


title: Blockchain for the Metaverse: A Review

abstract:
  Since Facebook officially changed its name to Metaverse in Oct. 2021, the
metaverse has become a new norm of social networks and three-dimensional (3D)
virtual worlds. The metaverse aims to bring 3D immersive and personalized
experiences to users by leveraging many pertinent technologies. Despite great
attention and benefits, a natural question in the metaverse is how to secure
its users' digital content and data. In this regard, blockchain is a promising
solution owing to its distinct features of decentralization, immutability, and
transparency. To better understand the role of blockchain in the metaverse, we
aim to provide an extensive survey on the applications of blockchain for the
metaverse. We first present a preliminary to blockchain and the metaverse and
highlight the motivations behind the use of blockchain for the metaverse. Next,
we extensively discuss blockchain-based methods for the metaverse from
technical perspectives, such as data acquisition, data storage, data sharing,
data interoperability, and data privacy preservation. For each perspective, we
first discuss the technical challenges of the metaverse and then highlight how
blockchain can help. Moreover, we investigate the impact of blockchain on
key-enabling technologies in the metaverse, including Internet-of-Things,
digital twins, multi-sensory and immersive applications, artificial
intelligence, and big data. We also present some major projects to showcase the
role of blockchain in metaverse applications and services. Finally, we present
some promising directions to drive further research innovations and
developments towards the use of blockchain in the metaverse in the future.


title: Do Deep Networks Transfer Invariances Across Classes?

abstract:
  To generalize well, classifiers must learn to be invariant to nuisance
transformations that do not alter an input's class. Many problems have
"class-agnostic" nuisance transformations that apply similarly to all classes,
such as lighting and background changes for image classification. Neural
networks can learn these invariances given sufficient data, but many real-world
datasets are heavily class imbalanced and contain only a few examples for most
of the classes. We therefore pose the question: how well do neural networks
transfer class-agnostic invariances learned from the large classes to the small
ones? Through careful experimentation, we observe that invariance to
class-agnostic transformations is still heavily dependent on class size, with
the networks being much less invariant on smaller classes. This result holds
even when using data balancing techniques, and suggests poor invariance
transfer across classes. Our results provide one explanation for why
classifiers generalize poorly on unbalanced and long-tailed distributions.
Based on this analysis, we show how a generative approach for learning the
nuisance transformations can help transfer invariances across classes and
improve performance on a set of imbalanced image classification benchmarks.
Source code for our experiments is available at
https://github.com/AllanYangZhou/generative-invariance-transfer.


title: Simulations for the Development of Quantum Computational Devices for HEP

abstract:
  In this white paper, we describe characteristics of tools for classical
simulations of quantum computational devices appropriate for High Energy
Physics applications.


title:
Towards non-independence of modular additions in searching differential
  trails of ARX ciphers: new automatic methods with application to SPECK and
  Chaskey

abstract:
  ARX-based ciphers, constructed by the modular addition, rotation and XOR
operations, have been receiving a lot of attention in the design of lightweight
symmetric ciphers. For their differential cryptanalysis, most automatic search
methods of differential trails adopt the assumption of independence of modulo
additions. However, this assumption does not necessarily hold when the trail
includes consecutive modular additions (CMAs). It has already been found that
in this case some differential trails searched by automatic methods before are
actually impossible, but the study is not in depth yet, for example, few effort
has been paid to exploiting the root causes of non-independence between CMAs
and accurate calculation of probabilities of the valid trails. In this paper,
we devote to solving these two problems. By examing the differential equations
of single and consecutive modular additions, we find that the influence of
non-independence can be described by relationships between constraints on the
intermediate state of two additions. Specifically, constraints of the first
addition can make some of its output bits non-uniform, and when they meet the
constraints of the second addition, the differential probability of the whole
CMA may be different from the value calculated under the independence
assumption. As a result, we can build SAT models to verify the validity of a
given differential trail of ARX ciphers and #SAT models to calculate the exact
probabilities of the differential propagation through CMAs in the trail,
promising a more accurate evaluation of probability of the trail. Our automic
methods and searching tools are applied to search related-key differential
trails of SPECK and Chaskey including CMAs in the key schedule and the round
function respectively.


title:
GRS: Combining Generation and Revision in Unsupervised Sentence
  Simplification

abstract:
  We propose GRS: an unsupervised approach to sentence simplification that
combines text generation and text revision. We start with an iterative
framework in which an input sentence is revised using explicit edit operations,
and add paraphrasing as a new edit operation. This allows us to combine the
advantages of generative and revision-based approaches: paraphrasing captures
complex edit operations, and the use of explicit edit operations in an
iterative manner provides controllability and interpretability. We demonstrate
these advantages of GRS compared to existing methods on the Newsela and ASSET
datasets.


title:
The Visit of Prof. Hideki Yukawa to Kochi to Attend the Unveiling
  Ceremony of the Yukawa Bronze Statue: Turning Point in Life

abstract:
  We have discovered unknown very valuable old photos of Prof. Hideki Yukawa at
the unveiling ceremony of his bronze statue at an elementary school in Kochi in
1954. The statue was moved to a newly created garden in1982, which in the
subsequent decades accelerated the forgetting of its existence. The forgetting
of the statue by the local people is related by chance to a historic event in
the Marshall Islands. Prof. Yukawa's visit to attend the unveiling ceremony
occurred just after news that Japanese fishermen were heavily injured by atomic
testing at the Bikini Atoll in the Pacific Ocean. At the news conference when
he arrived at Kochi, he was unexpectedly asked about his attitude to the bomb
testing by the US. He was considered to be the foremost top scientist in
nuclear science in Japan at that time, having been awarded the Nobel Prize for
physics in 1949. People in Kochi were very anxious about the test since many
fishermen worked near Bikini Atoll. Contrary to the expectation of local
people, he refused to comment on the matter at the news conference and in
public lectures, which were held several times during his stay in Kochi. After
returning to Kyoto, he issued the famous statement titled Nuclear Era and the
Turning Point of Mankind in an influential national newspaper. This was a time
in Prof. Yukawa's life when he had to reflect and consult his conscience, and
he chose to take a principled stance that became a turning point in his life.
After the visit to Kochi, he devoted himself with his wife to the peace
movement against nuclear weapons. Looking back, it can be observed that the
visit in 1954 to attend the unveiling ceremony of his bronze statue in Kochi
marked the start of that change. The statue can be seen to symbolize not only
his Nobel Prize but also the turning point in his life that led him to support
the peace movement against nuclear weapons.


title:
Class-Balanced Pixel-Level Self-Labeling for Domain Adaptive Semantic
  Segmentation

abstract:
  Domain adaptive semantic segmentation aims to learn a model with the
supervision of source domain data, and produce satisfactory dense predictions
on unlabeled target domain. One popular solution to this challenging task is
self-training, which selects high-scoring predictions on target samples as
pseudo labels for training. However, the produced pseudo labels often contain
much noise because the model is biased to source domain as well as majority
categories. To address the above issues, we propose to directly explore the
intrinsic pixel distributions of target domain data, instead of heavily relying
on the source domain. Specifically, we simultaneously cluster pixels and
rectify pseudo labels with the obtained cluster assignments. This process is
done in an online fashion so that pseudo labels could co-evolve with the
segmentation model without extra training rounds. To overcome the class
imbalance problem on long-tailed categories, we employ a distribution alignment
technique to enforce the marginal class distribution of cluster assignments to
be close to that of pseudo labels. The proposed method, namely Class-balanced
Pixel-level Self-Labeling (CPSL), improves the segmentation performance on
target domain over state-of-the-arts by a large margin, especially on
long-tailed categories.


title: Probing Three-Dimensional Magnetic Fields: I -- Polarized Dust Emission

abstract:
  The plane-of-the-sky component of interstellar magnetic fields can be traced
in two dimensions using polarized dust emission. Its potential to access
three-dimensional magnetic fields, including the inclination of the magnetic
fields relative to the line-of-sight, is crucial for a variety of astrophysical
problems. Based on the statistical features of the polarization fraction and
the averaged POS Alfv\'en Mach number $\overline{M_{\rm A}}_{,\bot}$, we
present a new method for estimating the inclination angle. The magnetic field
fluctuations raised by MHD turbulence are taken into account in our method. By
using synthetic dust emission from 3D MHD turbulence simulation, we show that
the fluctuations are preferentially perpendicular to the mean magnetic field
and magnify the depolarization effect. We analytically and numerically derive
that the polarization fraction is characterized by both the inclination angle
and magnetic field fluctuations. We propose and demonstrate that the mean
inclination angle over a region of interest can be calculated from the
polarization fraction in a strongly magnetized reference position, where
$\overline{M_{\rm A}}_{,\bot}^2\ll1$. We test the new method in sub-Alfv\'enic,
trans-Alfv\'enic, and moderately super-Alfv\'enic situations ($0.4\lesssim
M_{\rm A}\lesssim1.2$) and show that it recovers the mean inclination angle
well. This method is extended to determine the inclination angle distribution
over the sub-regions of a cloud.


title:
Soft Smoothness for Audio Inpainting Using a Latent Matrix Model in
  Delay-embedded Space

abstract:
  Here, we propose a new reconstruction method of smooth time-series signals. A
key concept of this study is not considering the model in signal space, but in
delay-embedded space. In other words, we indirectly represent a time-series
signal as an output of inverse delay-embedding of a matrix, and the matrix is
constrained. Based on the model under inverse delay-embedding, we propose to
constrain the matrix to be rank-1 with smooth factor vectors. The proposed
model is closely related to the convolutional model, and quadratic variation
(QV) regularization. Especially, the proposed method can be characterized as a
generalization of QV regularization. In addition, we show that the proposed
method provides the softer smoothness than QV regularization. Experiments of
audio inpainting and declipping are conducted to show its advantages in
comparison with several existing interpolation methods and sparse modeling.


title:
Efficient Split-Mix Federated Learning for On-Demand and In-Situ
  Customization

abstract:
  Federated learning (FL) provides a distributed learning framework for
multiple participants to collaborate learning without sharing raw data. In many
practical FL scenarios, participants have heterogeneous resources due to
disparities in hardware and inference dynamics that require quickly loading
models of different sizes and levels of robustness. The heterogeneity and
dynamics together impose significant challenges to existing FL approaches and
thus greatly limit FL's applicability. In this paper, we propose a novel
Split-Mix FL strategy for heterogeneous participants that, once training is
done, provides in-situ customization of model sizes and robustness.
Specifically, we achieve customization by learning a set of base sub-networks
of different sizes and robustness levels, which are later aggregated on-demand
according to inference requirements. This split-mix strategy achieves
customization with high efficiency in communication, storage, and inference.
Extensive experiments demonstrate that our method provides better in-situ
customization than the existing heterogeneous-architecture FL methods. Codes
and pre-trained models are available: https://github.com/illidanlab/SplitMix.


title:
Convex Optimization-Based Structure-Preserving Filter For
  Multidimensional Finite Element Simulations

abstract:
  In simulation sciences, it is desirable to capture the real-world problem
features as accurately as possible. Methods popular for scientific simulations
such as the finite element method (FEM) and finite volume method (FVM) use
piecewise polynomials to approximate various characteristics of a problem, such
as the concentration profile and the temperature distribution across the
domain. Polynomials are prone to creating artifacts such as Gibbs oscillations
while capturing a complex profile. An efficient and accurate approach must be
applied to deal with such inconsistencies in order to obtain accurate
simulations. This often entails dealing with negative values for the
concentration of chemicals, exceeding a percentage value over 100, and other
such problems. We consider these inconsistencies in the context of partial
differential equations (PDEs). We propose an innovative filter based on convex
optimization to deal with the inconsistencies observed in polynomial-based
simulations. In two or three spatial dimensions, additional complexities are
involved in solving the problems related to structure preservation. We present
the construction and application of a structure-preserving filter with a focus
on multidimensional PDEs. Methods used such as the Barycentric interpolation
for polynomial evaluation at arbitrary points in the domain and an optimized
root-finder to identify points of interest improve the filter efficiency,
usability, and robustness. Lastly, we present numerical experiments in 2D and
3D using discontinuous Galerkin formulation and demonstrate the filter's
efficacy to preserve the desired structure. As a real-world application,
implementation of the mathematical biology model involving platelet aggregation
and blood coagulation has been reviewed and the issues around FEM
implementation of the model are resolved by applying the proposed
structure-preserving filter.


title:
Robot peels banana with goal-conditioned dual-action deep imitation
  learning

abstract:
  A long-horizon dexterous robot manipulation task of deformable objects, such
as banana peeling, is problematic because of difficulties in object modeling
and a lack of knowledge about stable and dexterous manipulation skills. This
paper presents a goal-conditioned dual-action deep imitation learning (DIL)
which can learn dexterous manipulation skills using human demonstration data.
Previous DIL methods map the current sensory input and reactive action, which
easily fails because of compounding errors in imitation learning caused by
recurrent computation of actions. The proposed method predicts reactive action
when the precise manipulation of the target object is required (local action)
and generates the entire trajectory when the precise manipulation is not
required. This dual-action formulation effectively prevents compounding error
with the trajectory-based global action while respond to unexpected changes in
the target object with the reactive local action. Furthermore, in this
formulation, both global/local actions are conditioned by a goal state which is
defined as the last step of each subtask, for robust policy prediction. The
proposed method was tested in the real dual-arm robot and successfully
accomplished the banana peeling task.


title:
Chiral and Steric Effects in Ethane: A Next Generation QTAIM
  Interpretation

abstract:
  We introduce a development of next generation quantum theory of atoms in
molecules (NG-QTAIM) for an investigation of the chirality of ethane and
discover a $Q_{\sigma}$ isomer in addition to $S_{\sigma}$ and $R_{\sigma}$
stereoisomers in the stress tensor trajectory $U_{\sigma}$-space. The
$Q_{\sigma}$ isomer is defined to be a 'null-isomer' since the value of the
chirality-helicity function $\approx 0$. The presence of chiral contributions
suggests that steric effects, rather than hyper-conjugation, explain the
staggered geometry of ethane. The steric effects, within the NG-QTAIM
interpretation, are reduced by a factor of two using an applied electric-field
directed down a C-H bond.


title: Look-Ahead Acquisition Functions for Bernoulli Level Set Estimation

abstract:
  Level set estimation (LSE) is the problem of identifying regions where an
unknown function takes values above or below a specified threshold. Active
sampling strategies for efficient LSE have primarily been studied in
continuous-valued functions. Motivated by applications in human psychophysics
where common experimental designs produce binary responses, we study LSE active
sampling with Bernoulli outcomes. With Gaussian process classification
surrogate models, the look-ahead model posteriors used by state-of-the-art
continuous-output methods are intractable. However, we derive analytic
expressions for look-ahead posteriors of sublevel set membership, and show how
these lead to analytic expressions for a class of look-ahead LSE acquisition
functions, including information-based methods. Benchmark experiments show the
importance of considering the global look-ahead impact on the entire posterior.
We demonstrate a clear benefit to using this new class of acquisition functions
on benchmark problems, and on a challenging real-world task of estimating a
high-dimensional contrast sensitivity function.


title:
Chaotic resonance modes in dielectric cavities: Product of conditionally
  invariant measure and universal fluctuations

abstract:
  We conjecture that chaotic resonance modes in scattering systems are a
product of a conditionally invariant measure from classical dynamics and
universal fluctuations. The first factor depends strongly on the lifetime of
the mode. Its multifractal structure describes the average of modes with
similar lifetime. The conjecture is supported for a dielectric cavity with
chaotic ray dynamics at small wavelengths, in particular for experimentally
relevant supersharp modes. We explain scarring of the vast majority of modes
along segments of rays based on multifractality and universal fluctuations.


title: Neutron skin systematics from microscopic equations of state

abstract:
  We present an analysis of neutron skins based on fully microscopic equations
of state, including chiral two- and three-nucleon forces consistently at N3LO.
We will also address other theoretical predictions and recent constraints, such
as those from the PREX II experiment and the latest parity-violating electron
scattering measurement of the 27Al neutron skin.


title:
Magnetic field-Temperature Phase Diagram of CeCoSi Constructed by
  Specific Heat, Magnetoresistivity, and Magnetization Measurements for a
  Single Crystal

abstract:
  A Ce-based metallic compound CeCoSi with a tetragonal structure exhibits
successive phase transitions: the one whose order parameter is unidentified at
T_0 = 12 K and the antiferromagnetic one at T_N = 9.4 K. We performed specific
heat, magnetoresistivity (MR), and magnetization measurements for a single
crystal CeCoSi at low temperatures in magnetic fields B of up to 14 T and
constructed detailed magnetic field--temperature phase diagrams for both B ||
[100] and [001]. The longitudinal MR measured for B || [100] shows a sign
change from negative to positive across T_0 = 13 K updated in the present
sample, indicating a clear change in an electronic state. In addition, the
constructed magnetic phase diagrams for both the field directions have a
B-induced region in each ordered state. The presence of the newly found regions
would be attributed to a change in the symmetry of the order parameter or
domain alignment by applying B.


title:
Distributed Sketching for Randomized Optimization: Exact
  Characterization, Concentration and Lower Bounds

abstract:
  We consider distributed optimization methods for problems where forming the
Hessian is computationally challenging and communication is a significant
bottleneck. We leverage randomized sketches for reducing the problem dimensions
as well as preserving privacy and improving straggler resilience in
asynchronous distributed systems. We derive novel approximation guarantees for
classical sketching methods and establish tight concentration results that
serve as both upper and lower bounds on the error. We then extend our analysis
to the accuracy of parameter averaging for distributed sketches. Furthermore,
we develop unbiased parameter averaging methods for randomized second order
optimization for regularized problems that employ sketching of the Hessian.
Existing works do not take the bias of the estimators into consideration, which
limits their application to massively parallel computation. We provide
closed-form formulas for regularization parameters and step sizes that provably
minimize the bias for sketched Newton directions. Additionally, we demonstrate
the implications of our theoretical findings via large scale experiments on a
serverless cloud computing platform.


title: AutoAdversary: A Pixel Pruning Method for Sparse Adversarial Attack

abstract:
  Deep neural networks (DNNs) have been proven to be vulnerable to adversarial
examples. A special branch of adversarial examples, namely sparse adversarial
examples, can fool the target DNNs by perturbing only a few pixels. However,
many existing sparse adversarial attacks use heuristic methods to select the
pixels to be perturbed, and regard the pixel selection and the adversarial
attack as two separate steps. From the perspective of neural network pruning,
we propose a novel end-to-end sparse adversarial attack method, namely
AutoAdversary, which can find the most important pixels automatically by
integrating the pixel selection into the adversarial attack. Specifically, our
method utilizes a trainable neural network to generate a binary mask for the
pixel selection. After jointly optimizing the adversarial perturbation and the
neural network, only the pixels corresponding to the value 1 in the mask are
perturbed. Experiments demonstrate the superiority of our proposed method over
several state-of-the-art methods. Furthermore, since AutoAdversary does not
require a heuristic pixel selection process, it does not slow down excessively
as other methods when the image size increases.


title:
Stable determination of unbounded potential by asymptotic boundary
  spectral data

abstract:
  We consider the Dirichlet Laplacian $A_q=-\Delta+q$ in a bounded domain
$\Omega \subset \mathbb{R}^d$, $d \ge 3$, with real-valued perturbation $q \in
L^{\max(2 , 3 d / 5)}(\Omega)$. We examine the stability issue in the inverse
problem of determining the electric potential $q$ from the asymptotic behavior
of the eigenvalues of $A_q$. Assuming that the boundary measurement of the
normal derivative of the eigenfunctions is a square summable sequence in
$L^2(\partial \Omega)$, we prove that $q$ can be H\"older stably retrieved
through knowledge of the asymptotics of the eigenvalues


title: Strict Hierarchy of Strategies for Non-asymptotic Quantum Metrology

abstract:
  One of the main quests in quantum metrology is to determine the ultimate
precision limit with given resources, where the resources are not only of the
number of queries, but more importantly of the allowed strategies. With the
same number of queries, the restrictions on the strategies constrain the
achievable precision. In this work, we establish a framework to systematically
determine the ultimate precision limit of different strategies, which include
the parallel, the sequential and the indefinite-causal-order strategies. With
this framework, we show there exists a hierarchy of the precision limits under
different families of strategies. An efficient algorithm to design an optimal
strategy compatible with given constraints is provided, which has immediate
practical applications.


title:
Planet Formation Theory in the Era of ALMA and Kepler: from Pebbles to
  Exoplanets

abstract:
  Our understanding of planet formation has been rapidly evolving in recent
years. The classical planet formation theory, developed when the only known
planetary system was our own Solar System, has been revised to account for the
observed diversity of the exoplanetary systems. At the same time, the
increasing observational capabilities of the young stars and their surrounding
disks bring new constraints on the planet formation process. In this chapter,
we summarize the new information derived from the exoplanets population and the
circumstellar disks observations. We present the new developments in planet
formation theory, from dust evolution to the growth of planetary cores by
accretion of planetesimals, pebbles, and gas. We review the state-of-the-art
models for the formation of diverse planetary systems, including the population
synthesis approach which is necessary to compare theoretical model outcomes to
the exoplanet population. We emphasize that the planet formation process may
not be spatially uniform in the disk and there are preferential locations for
the formation of planetesimals and planets. Outside of these locations, a
significant fraction of solids is not growing past the pebble-sizes. The
reservoir of pebbles plays an important role in the growth of planetary cores
in the pebble accretion process. The timescale of the emergence of massive
planetary cores is an important aspect of the present models and it is likely
that the cores within one disk form at different times. In addition, there is
growing evidence that the first planetary cores start forming early, during the
circumstellar disk buildup process.


title:
Second-Harmonic Generation in Bulk Diamond Based on Inversion Symmetry
  Breaking by Color Centers

abstract:
  Breaking inversion symmetry in solids plays a central role in nonlinear
optics because it can change material properties, such as producing even-order
nonlinear optical (NLO) effects. Although a centrosymmetric diamond has been
developed as a photonic platform, including waveguides and light sources, the
NLO effects in bulk diamonds are limited to the third-order. Thus, exploiting
more powerful second-order NLO effects, such as second harmonic generation
(SHG), are still challenging. Here we explore symmetry-breaking-induced
second-order NLO effects in bulk diamonds using the color center,
nitrogen-vacancy center. Exciting with ultrashort laser pulses, SHG and
third-harmonic generation (THG) are simultaneously observed at the same time,
exhibiting characteristic intensity patterns, depending on both the excitation
fluence and the angle of light polarization. We uncovered that SHG serves as
the source for THG by the cascading process. Our findings will offer NLO
effect-based quantum sensing by diamond color centers, such as imaging of the
electromagnetic field by electro-optic effects on the nanofemto scale.


title: radioactivedecay: A Python package for radioactive decay calculations

abstract:
  radioactivedecay is a Python package for radioactive decay modelling. It
contains functions to fetch decay data, define inventories of nuclides and
perform decay calculations. The default nuclear decay dataset supplied with
radioactivedecay is based on ICRP Publication 107, which covers 1252
radioisotopes of 97 elements. The code calculates an analytical solution to a
matrix form of the decay chain differential equations using double or higher
precision numerical operations. There are visualization functions for drawing
decay chain diagrams and plotting activity decay curves.


title:
Superlinear and Quadratic Convergence of Riemannian Interior Point
  Methods

abstract:
  We extend the classical primal-dual interior point algorithms from the
Euclidean setting to the Riemannian one. Our method, named the Riemannian
interior point (RIP) method, is for solving Riemannian constrained optimization
problems. Under the standard assumptions in the Riemannian setting, we
establish locally superlinear, quadratic convergence for the Newton version of
RIP and locally linear, superlinear convergence for the quasi-Newton version.
These are generalizations of the classical local convergence theory of
primal-dual interior point algorithms for nonlinear programming proposed by
El-Bakry et al. and Yamashita et al. in 1996.


title:
Conserved relativistic Ertel's current generating the vortical and
  thermodynamic aspects of spacetime

abstract:
  Motivated by Aoki et al.'s recent research on conserved charges and entropy
current, we investigated the conservation of relativistic Ertel's current,
which has received little attention outside the field of geophysical fluid
dynamics. Ertel's charge is an important indicator of the correlation between
vortex vectors and entropy gradient fields in Earth's meridional heat
transport. We first show that in the generalized Hamiltonian structure of
baroclinic fluids, the duality between the total energy and the Casimir as a
function of Ertel's charge plays an important role in the nonrelativistic case.
Then, by extending the result to relativistic cases, we show that this finding
has far-reaching implications not only for spacetime issues in cosmology but
also for the foundation of quantum field theory. An especially important
finding is that, as an unreported dual form of the Einstein field equation, we
identify a special equation satisfied not only by the vortex tensor field
generated by the conserved charge but also by the Weyl tensor in interpreting
the physical nature of the metric tensor $g^{\mu\nu}$, which appears in the
cosmological term $\Lambda g^{\mu\nu}$.


title: Smoothing algorithms for projected center-vortex gauge fields

abstract:
  We study the application of $\mathrm{SU}(3)$ gauge field smoothing methods to
$\mathrm{Z}(3)$-projected center-vortex gauge fields. Due to the
proportionality of the vortex links to the identity, naive applications of
these methods are either ineffectual or limited in scope, containing subtle
issues which are not obviously manifest. To overcome these issues we introduce
centrifuge preconditioning, a novel method applied prior to smoothing that
rotates the links away from the center while preserving the fundamental
structure of the vortex field. Additionally, the concept of vortex-preserved
annealed smoothing is formulated to ensure the smoothing procedure maintains
the underlying vortex structure. The application of these new methods in the
context of annealed smoothing applied to vortex fields is shown to successfully
achieve the desired smoothness condition required for the study of more
advanced operators.


title: Metric and topological homology. Projectivity, injectivity and flatness

abstract:
  In this book we study metric and topological versions of projectivity
injectivity and flatness of Banach modules over Banach algebras. These two
non-standard versions of Banach homology theories are studied in parallel under
unified approach.


title:
Dual optical frequency combs with ultra-low relative phase jitters from
  550 nm to 1020 nm for precision spectroscopy

abstract:
  Here, ultra-low relative phase jitters over a wide optical spectrum were
achieved for dual Ti:Sapphire optical frequency combs. The two optical
frequency combs were independently phase-locked to a Sr optical lattice clock
laser delivered through a commercial optical fiber network. We confirmed that
the relative phase jitters between the two combs integrated from 8.3 mHz to 200
kHz were below 1 rad, corresponding to a relative linewidth of below 8.3 mHz,
over the entire wavelength of the optical frequency combs ranging from 550 nm
to 1020 nm. Our work paves the way for ultrahigh-precision dual-comb
spectroscopy covering a wide optical spectral range with a simple setup, and
provides an absolute optical frequency reference with great stability over a
wide range of wavelengths.