title:
Three numerical approaches to find mutually unbiased bases using Bell
  inequalities

abstract:
  Mutually unbiased bases correspond to highly useful pairs of measurements in
quantum information theory. In the smallest composite dimension, six, it is
known that between three and seven mutually unbiased bases exist, with a
decades-old conjecture, known as Zauner's conjecture, stating that there exist
at most three. Here we tackle Zauner's conjecture numerically through the
construction of Bell inequalities for every pair of integers $n,d \ge 2$ that
can be maximally violated in dimension $d$ if and only if $n$ MUBs exist in
that dimension. Hence we turn Zauner's conjecture into an optimisation problem,
which we address by means of three numerical methods: see-saw optimisation,
non-linear semidefinite programming and Monte Carlo techniques. All three
methods correctly identify the known cases in low dimensions and all suggest
that there do not exist four mutually unbiased bases in dimension six, with all
finding the same bases that numerically optimise the corresponding Bell
inequality. Moreover, these numerical optimisers appear to coincide with the
"four most distant bases" in dimension six, found through numerically
optimising a distance measure in [P. Raynal, X. L\"u, B.-G. Englert, Phys. Rev.
A, 83 062303 (2011)]. Finally, the Monte Carlo results suggest that at most
three MUBs exist in dimension ten.


title:
Mutual Learning for Domain Adaptation: Self-distillation Image Dehazing
  Network with Sample-cycle

abstract:
  Deep learning-based methods have made significant achievements for image
dehazing. However, most of existing dehazing networks are concentrated on
training models using simulated hazy images, resulting in generalization
performance degradation when applied on real-world hazy images because of
domain shift. In this paper, we propose a mutual learning dehazing framework
for domain adaption. Specifically, we first devise two siamese networks: a
teacher network in the synthetic domain and a student network in the real
domain, and then optimize them in a mutual learning manner by leveraging EMA
and joint loss. Moreover, we design a sample-cycle strategy based on density
augmentation (HDA) module to introduce pseudo real-world image pairs provided
by the student network into training for further improving the generalization
performance. Extensive experiments on both synthetic and real-world dataset
demonstrate that the propose mutual learning framework outperforms
state-of-the-art dehazing techniques in terms of subjective and objective
evaluation.


title: On Bruhat-Tits theory over a higher dimensional base

abstract:
  Let $\mathcal O_{_n} := k[[z_{_1}, \ldots, z_{_n}]]$ over an algebraically
closed residue field $k$ of characteristic zero. Set $K_{_n} :=
\text{Fract}~\mathcal O_{_n}$. Let $G$ be an almost-simple, simply-connected
affine algebraic group over $k$ with a maximal torus $T$. Given a $n$-tuple
${\bf f} = (f_{_1}, \ldots, f_{_n})$ of concave functions on the root system of
$G$ as in Bruhat-Tits \cite{bruhattits1}, \cite{bruhattits}, we define {\it
{\tt n}-bounded subgroups ${\tt P}_{_{\bf f}}(k)\subset G(K_{_n})$} as a direct
generalization of Bruhat-Tits groups for the case $n=1$. We show that these
groups are {\it schematic}, i.e. they are valued points of smooth quasi-affine
group schemes with connected fibres. This provides a higher-dimensional
analogue of the Bruhat-Tits group schemes with natural specialization
properties. Under suitable {\em tameness assumptions}, we extend all these
results replacing $\mathcal O_{_n}$ by ${\mathcal O}[[x_{_1},\cdots,x_{_n}]]$
where $\mathcal O$ is a complete discrete valuation ring with residue field of
characteristic $p$. In the last part of the paper we give applications to
constructing certain natural group schemes on wonderful embeddings of groups
and also certain families of {\tt 2-parahoric} group schemes on minimal
resolutions of surface singularities.


title: Variants of the Selberg sieve, and almost prime k-tuples

abstract:
  Let $k\geq 2$ and $\mathcal{P} (n) = (A_1 n + B_1 ) \cdots (A_k n + B_k)$
where all the $A_i, B_i$ are integers. Suppose that $\mathcal{P} (n)$ has no
fixed prime divisors. For each choice of $k$ it is known that there exists an
integer $\varrho_k$ such that $\mathcal{P} (n)$ has at most $\varrho_k$ prime
factors infinitely often. We used a new weighted sieve set-up combined with a
device called an $\varepsilon$-trick to improve the possible values of
$\varrho_k$ for $k\geq 7$. As a by-product of our approach, we improve the
conditional possible values of $\varrho_k$ for $k\geq 4$, assuming the
generalized Elliott--Halberstam conjecture.


title: Diffeotopy groups of non-compact 4-manifolds

abstract:
  We provide information on diffeotopy groups of exotic smoothings of punctured
4-manifolds, extending previous results on diffeotopy groups of exotic
$\mathbb{R}^4$'s. In particular, we prove that for a smoothable 4-manifold $M$
and for a non-empty, discrete set of points $S \subsetneq \mathring{M}$, there
are uncountably many distinct smoothings of $M\smallsetminus S$ whose
diffeotopy groups are uncountable.
  We then prove that for a smoothable 4-manifold $M$ and for a non-empty,
discrete set of points $S \subsetneq \mathring{M}$, there exists a smoothing of
$M\smallsetminus S$ whose diffeotopy groups have similar properties as
$\mathcal{R}_U$, Freedman and Taylor's universal $\mathbb{R}^4$.
  Moreover, we prove that if $M$ is non-smoothable, both results still hold
under the assumption that $|S| \ge 2$.


title: $R=T$ theorems for weight one modular forms

abstract:
  We prove modularity of certain residually reducible ordinary 2-dimensional
$p$-adic Galois representations with determinant a finite order odd character
$\chi$. For certain non-quadratic $\chi$ we prove an $R=T$ result for $T$ the
weight 1 specialisation of the Hida Hecke algebra acting on non-classical
weight 1 forms, under the assumption that no two Hida families congruent to an
Eisenstein series cross in weight 1. For quadratic $\chi$ we prove that the
quotient of $R$ corresponding to deformations split at $p$ is isomorphic to the
Hecke algebra acting on classical CM weight 1 modular forms.


title:
Expanding Pretrained Models to Thousands More Languages via
  Lexicon-based Adaptation

abstract:
  The performance of multilingual pretrained models is highly dependent on the
availability of monolingual or parallel text present in a target language.
Thus, the majority of the world's languages cannot benefit from recent progress
in NLP as they have no or limited textual data. To expand possibilities of
using NLP technology in these under-represented languages, we systematically
study strategies that relax the reliance on conventional language resources
through the use of bilingual lexicons, an alternative resource with much better
language coverage. We analyze different strategies to synthesize textual or
labeled data using lexicons, and how this data can be combined with monolingual
or parallel text when available. For 19 under-represented languages across 3
tasks, our methods lead to consistent improvements of up to 5 and 15 points
with and without extra monolingual text respectively. Overall, our study
highlights how NLP methods can be adapted to thousands more languages that are
under-served by current technology


title:
A Stochastic Halpern Iteration with Variance Reduction for Stochastic
  Monotone Inclusion Problems

abstract:
  We study stochastic monotone inclusion problems, which widely appear in
machine learning applications, including robust regression and adversarial
learning. We propose novel variants of stochastic Halpern iteration with
recursive variance reduction. In the cocoercive -- and more generally
Lipschitz-monotone -- setup, our algorithm attains $\epsilon$ norm of the
operator with $\mathcal{O}(\frac{1}{\epsilon^3})$ stochastic operator
evaluations, which significantly improves over state of the art
$\mathcal{O}(\frac{1}{\epsilon^4})$ stochastic operator evaluations required
for existing monotone inclusion solvers applied to the same problem classes. We
further show how to couple one of the proposed variants of stochastic Halpern
iteration with a scheduled restart scheme to solve stochastic monotone
inclusion problems with ${\mathcal{O}}(\frac{\log(1/\epsilon)}{\epsilon^2})$
stochastic operator evaluations under additional sharpness or strong
monotonicity assumptions. Finally, we argue via reductions between different
problem classes that our stochastic oracle complexity bounds are tight up to
logarithmic factors in terms of their $\epsilon$-dependence.


title: Electron Spin in a Quantum Well

abstract:
  The wave nature of the electron spin proposed earlier [1][2] is studied by
solving the Dirac equation with a complete form of $4-$spinor. The result shows
a stable circulating total current density with a donut shaped topography for
the eigenstate electron inside a quantum well. The spin value inside the well
is modified by the confining geometry. Our analysis also shows that a free
electron Gaussian wave packet is unstable and experiences quick decoherence.
The impacts of the wave spin on quantum computing and quantum information
technologies are discussed in principle.


title:
An Explainable Stacked Ensemble Model for Static Route-Free Estimation
  of Time of Arrival

abstract:
  To compare alternative taxi schedules and to compute them, as well as to
provide insights into an upcoming taxi trip to drivers and passengers, the
duration of a trip or its Estimated Time of Arrival (ETA) is predicted. To
reach a high prediction precision, machine learning models for ETA are state of
the art. One yet unexploited option to further increase prediction precision is
to combine multiple ETA models into an ensemble. While an increase of
prediction precision is likely, the main drawback is that the predictions made
by such an ensemble become less transparent due to the sophisticated ensemble
architecture. One option to remedy this drawback is to apply eXplainable
Artificial Intelligence (XAI). The contribution of this paper is three-fold.
First, we combine multiple machine learning models from our previous work for
ETA into a two-level ensemble model - a stacked ensemble model - which on its
own is novel; therefore, we can outperform previous state-of-the-art static
route-free ETA approaches. Second, we apply existing XAI methods to explain the
first- and second-level models of the ensemble. Third, we propose three joining
methods for combining the first-level explanations with the second-level ones.
Those joining methods enable us to explain stacked ensembles for regression
tasks. An experimental evaluation shows that the ETA models correctly learned
the importance of those input features driving the prediction.


title:
Can decaying dark matter scenarios alleviate both $H_0$ and $\sigma_8$
  tensions?

abstract:
  Current tensions in cosmology, including $H_0$ and $\sigma_8$, provide one of
the strong reasons to suspect the existence of physics beyond the standard
model of cosmology ($\Lambda$CDM). In this paper, we investigate if there is a
relation between these tensions and beyond cold dark matter scenarios. To model
non-CDM, we assume a decaying dark matter (DDM) which is unstable and may decay
into two daughter particles, a combination of cold dark matter, warm dark
matter, and dark radiation, to explore a vast era of possibilities. We checked
our model against CMB data and could show that decaying dark matter seems not a
promising candidate to address the cosmological tensions.


title: TO-Scene: A Large-scale Dataset for Understanding 3D Tabletop Scenes

abstract:
  Many basic indoor activities such as eating or writing are always conducted
upon different tabletops (e.g., coffee tables, writing desks). It is
indispensable to understanding tabletop scenes in 3D indoor scene parsing
applications. Unfortunately, it is hard to meet this demand by directly
deploying data-driven algorithms, since 3D tabletop scenes are rarely available
in current datasets. To remedy this defect, we introduce TO-Scene, a
large-scale dataset focusing on tabletop scenes, which contains 20,740 scenes
with three variants. To acquire the data, we design an efficient and scalable
framework, where a crowdsourcing UI is developed to transfer CAD objects onto
tables from ScanNet, then the output tabletop scenes are simulated into real
scans and annotated automatically.
  Further, a tabletop-aware learning strategy is proposed for better perceiving
the small-sized tabletop instances. Notably, we also provide a real scanned
test set TO-Real to verify the practical value of TO-Scene. Experiments show
that the algorithms trained on TO-Scene indeed work on the realistic test data,
and our proposed tabletop-aware learning strategy greatly improves the
state-of-the-art results on both 3D semantic segmentation and object detection
tasks. TO-Scene and TO-Real, plus Web UI, will all be publicly available.


title: Learning of Structurally Unambiguous Probabilistic Grammars

abstract:
  The problem of identifying a probabilistic context free grammar has two
aspects: the first is determining the grammar's topology (the rules of the
grammar) and the second is estimating probabilistic weights for each rule.
Given the hardness results for learning context-free grammars in general, and
probabilistic grammars in particular, most of the literature has concentrated
on the second problem. In this work we address the first problem. We restrict
attention to structurally unambiguous weighted context-free grammars (SUWCFG)
and provide a query learning algorithm for \structurally unambiguous
probabilistic context-free grammars (SUPCFG). We show that SUWCFG can be
represented using \emph{co-linear multiplicity tree automata} (CMTA), and
provide a polynomial learning algorithm that learns CMTAs. We show that the
learned CMTA can be converted into a probabilistic grammar, thus providing a
complete algorithm for learning a structurally unambiguous probabilistic
context free grammar (both the grammar topology and the probabilistic weights)
using structured membership queries and structured equivalence queries. A
summarized version of this work was published at AAAI 21.


title: Anomalous localization and multifractality in a kicked quasicrystal

abstract:
  Multifractal states offer a "third way" for quantum matter, neither fully
localized nor ergodic, exhibiting singular continuous spectra, self-similar
wavefunctions, and transport and entanglement scaling exponents intermediate
between extended and localized states. While multifractality in equilibrium
systems generally requires fine-tuning to a critical point, externally driven
quantum matter can exhibit multifractal states with no equilibrium counterpart.
We report the experimental observation of multifractal matter and anomalous
localization in a kicked Aubry-Andr\'e-Harper quasicrystal. Our cold-atom
realization of this previously-unexplored model is enabled by apodized Floquet
engineering techniques which expand the accessible phase diagram by five orders
of magnitude. This kicked quantum quasicrystal exhibits a rich phase diagram
including not only fully localized and fully delocalized phases but also an
extended region comprising an intricate nested pattern of localized,
delocalized, and multifractal states. Mapping transport properties throughout
the phase diagram, we observe disorder-driven re-entrant delocalization and
sub-ballistic transport, and present a theoretical explanation of these
phenomena based on eigenstate multifractality. These results open up the
exploration of new states of matter characterized by an intricate interplay of
fractal structure and quantum dynamics.

categories: cond-mat.quant-gas cond-mat.stat-mech physics.atom-ph quant-ph


title: Spin-Electric Coupling in Lead-Halide Perovskites

abstract:
  Lead-halide perovskites enjoy a number of remarkable optoelectronic
properties. To explain their origin, it is necessary to question how
electromagnetic fields interact with these systems. We address this question
here by studying two classical quantities: Faraday rotation and the complex
refractive index in a paradigmatic perovskite CH$_3$NH$_3$PbBr$_3$ in a broad
wavelength range. We find that the minimal coupling of electromagnetic fields
to the k$\cdot$p Hamiltonian is insufficient to describe the observed data even
on the qualitative level. To amend this, we demonstrate that there exists a
relevant atomic-level coupling between electromagnetic fields and the spin
degree of freedom. This spin-electric coupling allows for quantitative
description of a number of previous as well as present experimental data. In
particular, we use it here to show that the Faraday effect in lead-halide
perovskites is dominated by the Zeeman splitting of the energy levels, and has
a substantial beyond-Becquerel contribution. Finally, we present general
symmetry-based phenomenological arguments that in the low-energy limit our
effective model includes all possible couplings to the electromagnetic field in
the linear order.


title: Interlayer Exciton Diode and Transistor

abstract:
  Interlayer excitons (IXs) are Coulomb-bound electron-hole pairs that occur in
2D semiconductor heterostructures. Controlling the flow of IXs can lead to
dissipationless excitonic circuits due to the bosonic nature of the IX
quasiparticle. Here, we report diode-like directed transport of IXs and an
optically gated IX transistor. Our results are enabled by a nanopatterned
graphene gate to create a smoothly varying potential slide within an MoSe2-WSe2
heterostructure. Using photoluminescence (PL) measurements, we measure shifting
IX energy with position and a drift velocity of 1,600 m/s. Additionally, IX
flow can be optically gated by saturating IX population with a second laser
pulse, demonstrating an excitonic transistor via IX-IX repulsion. Our work
paves the way towards low loss valleytronic circuits based on IXs in 2D
heterostructures.


title: Image Super-Resolution With Deep Variational Autoencoders

abstract:
  Image super-resolution (SR) techniques are used to generate a high-resolution
image from a low-resolution image. Until now, deep generative models such as
autoregressive models and Generative Adversarial Networks (GANs) have proven to
be effective at modelling high-resolution images. Models based on Variational
Autoencoders (VAEs) have often been criticized for their feeble generative
performance, but with new advancements such as VDVAE (very deep VAE), there is
now strong evidence that deep VAEs have the potential to outperform current
state-of-the-art models for high-resolution image generation. In this paper, we
introduce VDVAE-SR, a new model that aims to exploit the most recent deep VAE
methodologies to improve upon image super-resolution using transfer learning on
pretrained VDVAEs. Through qualitative and quantitative evaluations, we show
that the proposed model is competitive with other state-of-the-art methods.


title:
Vox2Cortex: Fast Explicit Reconstruction of Cortical Surfaces from 3D
  MRI Scans with Geometric Deep Neural Networks

abstract:
  The reconstruction of cortical surfaces from brain magnetic resonance imaging
(MRI) scans is essential for quantitative analyses of cortical thickness and
sulcal morphology. Although traditional and deep learning-based algorithmic
pipelines exist for this purpose, they have two major drawbacks: lengthy
runtimes of multiple hours (traditional) or intricate post-processing, such as
mesh extraction and topology correction (deep learning-based). In this work, we
address both of these issues and propose Vox2Cortex, a deep learning-based
algorithm that directly yields topologically correct, three-dimensional meshes
of the boundaries of the cortex. Vox2Cortex leverages convolutional and graph
convolutional neural networks to deform an initial template to the densely
folded geometry of the cortex represented by an input MRI scan. We show in
extensive experiments on three brain MRI datasets that our meshes are as
accurate as the ones reconstructed by state-of-the-art methods in the field,
without the need for time- and resource-intensive post-processing. To
accurately reconstruct the tightly folded cortex, we work with meshes
containing about 168,000 vertices at test time, scaling deep explicit
reconstruction methods to a new level.


title:
Existence of Chern Gaps in Kagome Magnets RMn$_6$Ge$_6$ (R = Nd, Sm, Tb,
  Dy, Ho, Er, Yb, Lu)

abstract:
  Kagome Chern Magnets are lattices which host a Chern state when they are
non-magnetic and a Chern Gap when they become magnetic. These Chern gaps can
host fractional Chern Fermions. There has been extensive research in the
RMn$_6$Sn$_6$ system which hosts a small Chern gap with many trivial bands
surrounding them. RMn$_6$Ge$_6$ is studied theoretically in order to find a
more insulating Kagome magnetic with fewer bands near the Chern gap.


title: A note on character sums over short moving intervals

abstract:
  We investigate the sums $(1/\sqrt{H}) \sum_{X < n \leq X+H} \chi(n)$, where
$\chi$ is a fixed non-principal Dirichlet character modulo a prime $q$, and $0
\leq X \leq q-1$ is uniformly random. Davenport and Erd\H{o}s, and more
recently Lamzouri, proved central limit theorems for these sums provided $H
\rightarrow \infty$ and $(\log H)/\log q \rightarrow 0$ as $q \rightarrow
\infty$, and Lamzouri conjectured these should hold subject to the much weaker
upper bound $H=o(q/\log q)$. We prove this is false for some $\chi$, even when
$H = q/\log^{A}q$ for any fixed $A > 0$. On the other hand, we show it is true
for "almost all" characters on the range $q^{1-o(1)} \leq H = o(q)$.
  Using P\'{o}lya's Fourier expansion, these results may be reformulated as
statements about the distribution of certain Fourier series with number
theoretic coefficients. Tools used in the proofs include the existence of
characters with large partial sums on short initial segments, and moment
estimates for trigonometric polynomials with random multiplicative
coefficients.


title:
Resolution of singularities of quasitoric orbifolds and equivariant
  cobordism of quasi-contact toric manifolds

abstract:
  We show that there is a resolution of singularities of a quasitoric orbifold.
Then we prove that a quasi-contact toric manifold is equivariantly a boundary.
This result implies that good contact toric manifolds and generalized lens
spaces are equivariantly boundaries.


title: Continual Learning Based on OOD Detection and Task Masking

abstract:
  Existing continual learning techniques focus on either task incremental
learning (TIL) or class incremental learning (CIL) problem, but not both. CIL
and TIL differ mainly in that the task-id is provided for each test sample
during testing for TIL, but not provided for CIL. Continual learning methods
intended for one problem have limitations on the other problem. This paper
proposes a novel unified approach based on out-of-distribution (OOD) detection
and task masking, called CLOM, to solve both problems. The key novelty is that
each task is trained as an OOD detection model rather than a traditional
supervised learning model, and a task mask is trained to protect each task to
prevent forgetting. Our evaluation shows that CLOM outperforms existing
state-of-the-art baselines by large margins. The average TIL/CIL accuracy of
CLOM over six experiments is 87.6/67.9% while that of the best baselines is
only 82.4/55.0%.


title: Snowmass2021 White Paper AF3-CEPC

abstract:
  The discovery of the Higgs boson at Large Hadron Collider (LHC) of CERN in
July 2012 raised new opportunities for large-scale accelerators. The Higgs
boson is the heart of the Standard Model (SM) and is at the center of many
biggest mysteries.In September 2012, Chinese scientists proposed a 240 GeV
Circular Electron Positron Collider (CEPC) as a Higgs Factory, having two large
detectors for Higgs studies and other topical researches. The 100 km tunnel of
CEPC could also host a Super proton proton Collider (SppC) to reach energies
well beyond the LHC. CEPC Conceptual Design Report (CDR) has been released in
Nov. 2018, and in this CEPC Accelerator White Paper to Snowmass21 AF3, CEPC
Technical Design Report (TDR) status with optimized design and key technology R
and D progresses have been reported. The energy range, luminosity, upgrade and
staging potentials, power consumption, cost, readiness assessment, timelines,
construction and operation plans, etc. are covered.


title:
Automated Transpilation of Imperative to Functional Code using
  Neural-Guided Program Synthesis (Extended Version)

abstract:
  While many mainstream languages such as Java, Python, and C# increasingly
incorporate functional APIs to simplify programming and improve
parallelization/performance, there are no effective techniques that can be used
to automatically translate existing imperative code to functional variants
using these APIs. Motivated by this problem, this paper presents a
transpilation approach based on inductive program synthesis for modernizing
existing code. Our method is based on the observation that the overwhelming
majority of source/target programs in this setting satisfy an assumption that
we call trace-compatibility: not only do the programs share syntactically
identical low-level expressions, but these expressions also take the same
values in corresponding execution traces. Our method leverages this observation
to design a new neural-guided synthesis algorithm that (1) uses a novel neural
architecture called cognate grammar network (CGN) and (2) leverages a form of
concolic execution to prune partial programs based on intermediate values that
arise during a computation. We have implemented our approach in a tool called
NGST2 and use it to translate imperative Java and Python code to functional
variants that use the Stream and functools APIs respectively. Our experiments
show that NGST2 significantly outperforms several baselines and that our
proposed neural architecture and pruning techniques are vital for achieving
good results.


title: Computing confined elasticae

abstract:
  A numerical scheme for computing arc-length parametrized curves of low
bending energy that are confined to convex domains is devised. The convergence
of the discrete formulations to a continuous model and the unconditional
stability of an iterative scheme are addressed. Numerical simulations confirm
the theoretical results and lead to a classification of observed optimal curves
within spheres.


title:
Synthetic-to-Real Domain Adaptation using Contrastive Unpaired
  Translation

abstract:
  The usefulness of deep learning models in robotics is largely dependent on
the availability of training data. Manual annotation of training data is often
infeasible. Synthetic data is a viable alternative, but suffers from domain
gap. We propose a multi-step method to obtain training data without manual
annotation effort: From 3D object meshes, we generate images using a modern
synthesis pipeline. We utilize a state-of-the-art image-to-image translation
method to adapt the synthetic images to the real domain, minimizing the domain
gap in a learned manner. The translation network is trained from unpaired
images, i.e. just requires an un-annotated collection of real images. The
generated and refined images can then be used to train deep learning models for
a particular task. We also propose and evaluate extensions to the translation
method that further increase performance, such as patch-based training, which
shortens training time and increases global consistency. We evaluate our method
and demonstrate its effectiveness on two robotic datasets. We finally give
insight into the learned refinement operations.


title:
Exotic $K(h)$-local Picard groups when $2p-1=h^2$ and the Vanishing
  Conjecture

abstract:
  In this paper, we study the exotic $K(h)$-local Picard group $\kappa_h$ when
$2p-1=h^2$. Using Gross-Hopkins duality, we relate it to certain Greek letter
element computations in chromatic homotopy theory. The classical computations
of Miller-Ravenel-Wilson then imply that an exotic element at height $3$ and
prime $5$ is not detected by the type-2 complex $V(1)$. The same method is
applied to study the Chromatic Vanishing Conjecture in the zeroth degree
homology when $p-1$ does not divide $h$. We show that this special case of the
Vanishing Conjecture implies the exotic Picard group is zero at height $3$ and
prime $5$.


title:
MolNet: A Chemically Intuitive Graph Neural Network for Prediction of
  Molecular Properties

abstract:
  The graph neural network (GNN) has been a powerful deep-learning tool in
chemistry domain, due to its close connection with molecular graphs. Most GNN
models collect and update atom and molecule features from the fed atom (and, in
some cases, bond) features, which are basically based on the two-dimensional
(2D) graph representation of 3D molecules. Correspondingly, the adjacency
matrix, containing the information on covalent bonds, or equivalent data
structures (e.g., lists) have been the main core in the feature-updating
processes, such as graph convolution. However, the 2D-based models do not
faithfully represent 3D molecules and their physicochemical properties,
exemplified by the overlooked field effect that is a "through-space" effect,
not a "through-bond" effect. The GNN model proposed herein, denoted as MolNet,
is chemically intuitive, accommodating the 3D non-bond information in a
molecule, with a noncovalent adjacency matrix $\bf{\bar A}$, and also
bond-strength information from a weighted bond matrix $\bf{B}$. The noncovalent
atoms, not directly bonded to a given atom in a molecule, are identified within
5 $\unicode{x212B}$ of cut-off range for the construction of $\bf{\bar A}$, and
$\bf{B}$ has edge weights of 1, 1.5, 2, and 3 for single, aromatic, double, and
triple bonds, respectively. Comparative studies show that MolNet outperforms
various baseline GNN models and gives a state-of-the-art performance in the
classification task of BACE dataset and regression task of ESOL dataset. This
work suggests a future direction of deep-learning chemistry in the construction
of deep-learning models that are chemically intuitive and comparable with the
existing chemistry concepts and tools.


title:
Look Outside the Room: Synthesizing A Consistent Long-Term 3D Scene
  Video from A Single Image

abstract:
  Novel view synthesis from a single image has recently attracted a lot of
attention, and it has been primarily advanced by 3D deep learning and rendering
techniques. However, most work is still limited by synthesizing new views
within relatively small camera motions. In this paper, we propose a novel
approach to synthesize a consistent long-term video given a single scene image
and a trajectory of large camera motions. Our approach utilizes an
autoregressive Transformer to perform sequential modeling of multiple frames,
which reasons the relations between multiple frames and the corresponding
cameras to predict the next frame. To facilitate learning and ensure
consistency among generated frames, we introduce a locality constraint based on
the input cameras to guide self-attention among a large number of patches
across space and time. Our method outperforms state-of-the-art view synthesis
approaches by a large margin, especially when synthesizing long-term future in
indoor 3D scenes. Project page at https://xrenaa.github.io/look-outside-room/.


title:
The SATCHEL pipeline: A general tool for data classified through citizen
  science

abstract:
  Citizen science is a powerful analysis tool, capable of processing large
amounts of data in a very short time. To bridge the gap between classification
data products from web-based citizen science platforms to statistically robust
signal significance scores, we present the Search Algorithm for Transits in the
Citizen science Hunt for Exoplanets in Lightcurves (SATCHEL) pipeline. This
open source, customizable pipeline was constructed to identify and assign
significance estimates to one-dimensional features marked by volunteers. We
describe the functional capabilities of the SATCHEL pipeline through
application to features in photometric time-series data from the Kepler Space
Telescope, classified by volunteers as part of the Planet Hunters citizen
science project hosted on the Zooniverse platform. We evaluate the SATCHEL
pipeline's overall performance based on recovery of known signals (both
simulations and signals corresponding to official Kepler Objects of Interest)
and relative contamination by spurious features. We find that, for a range of
pipeline hyperparameters and with a reasonable score cutoff, SATCHEL is able to
recover volunteer identifications of over 98% of signals from simulations
corresponding to exoplanets $>2~R_\oplus$ in radius and about 85% of signals
corresponding to the same size range of KOIs. SATCHEL is transparently
adaptable to other citizen science classification datasets, and available on
GitHub.


title:
Multipartite electron-nuclear gates in defect systems through dynamical
  decoupling sequences

abstract:
  Quantum networks play an indispensable role in quantum information tasks such
as secure communications, enhanced quantum sensing, and distributed computing.
Among the most mature and promising platforms for quantum networking are color
centers in solids. One of the challenges in using these systems for networking
applications is to controllably created and manipulate entanglement between the
electron and the nuclear spin register. Here, we develop a general formalism to
quantify and control the generation of entanglement in an arbitrarily large
nuclear spin register coupled to a defect electronic spin. We provide a
reliable measure of nuclear spin selectivity, taking into account the presence
of unwanted nuclei. We show how to realize direct multipartite gates through
the use of dynamical decoupling sequences, reducing drastically the total gate
time compared to protocols based on sequential entanglement with individual
nuclear spins. We quantify the performance of such gate operations in the
presence of unwanted residual entanglement links, capturing the dynamics of the
entire nuclear spin register.


title:
Covariance Recovery for One-Bit Sampled Data With Time-Varying Sampling
  Thresholds-Part I: Stationary Signals

abstract:
  One-bit quantization, which relies on comparing the signals of interest with
given threshold levels, has attracted considerable attention in signal
processing for communications and sensing. A useful tool for covariance
recovery in such settings is the arcsine law, that estimates the normalized
covariance matrix of zero-mean stationary input signals. This relation,
however, only considers a zero sampling threshold, which can cause a remarkable
information loss. In this paper, the idea of the arcsine law is extended to the
case where one-bit analog-to-digital converters (ADCs) apply time-varying
thresholds. Specifically, three distinct approaches are proposed, investigated,
and compared, to recover the autocorrelation sequence of the stationary signals
of interest. Additionally, we will study a modification of the Bussgang law, a
famous relation facilitating the recovery of the cross-correlation between the
one-bit sampled data and the zero-mean stationary input signal. Similar to the
case of the arcsine law, the Bussgang law only considers a zero sampling
threshold. This relation is also extended to accommodate the more general case
of time-varying thresholds for the stationary input signals.


title:
Beyond the Limitation of Pulse Width in Optical Time-domain
  Reflectometry

abstract:
  Optical time-domain reflectometry (OTDR) is the basis for distributed
time-domain optical fiber sensing techniques. By injecting pulse light into an
optical fiber, the distance information of an event can be obtained based on
the time of light flight. The minimum distinguishable event separation along
the fiber length is called the spatial resolution, which is determined by the
optical pulse width. By reducing the pulse width, the spatial resolution can be
improved. However, at the same time, the signal-to-noise ratio of the system is
degraded, and higher speed equipment is required. To solve this problem, data
processing methods such as iterative subdivision, deconvolution, and neural
networks have been proposed. However, they all have some shortcomings and thus
have not been widely applied. Here, we propose and experimentally demonstrate
an OTDR deconvolution neural network based on deep convolutional neural
networks. A simplified OTDR model is built to generate a large amount of
training data. By optimizing the network structure and training data, an
effective OTDR deconvolution is achieved. The simulation and experimental
results show that the proposed neural network can achieve more accurate
deconvolution than the conventional deconvolution algorithm with a higher
signal-to-noise ratio.


title: Derivative Portal Dark Matter

abstract:
  We propose a new kind of Dark Matter: Derivative Portal Dark Matter. This
kind of Dark Matter connects to the Standard Model through mediators which link
to Standard Model in the derivative form. Derivative of mediator in momentum
space corresponds to the mediated momentum, which vanishes in the zero momentum
transfer limit. As a result this kind of Dark Matter can evade stringent
constraint from the Dark Matter direct detection while fitting the Dark Matter
relic density observation naturally.


title:
FERV39k: A Large-Scale Multi-Scene Dataset for Facial Expression
  Recognition in Videos

abstract:
  Current benchmarks for facial expression recognition (FER) mainly focus on
static images, while there are limited datasets for FER in videos. It is still
ambiguous to evaluate whether performances of existing methods remain
satisfactory in real-world application-oriented scenes. For example, the
"Happy" expression with high intensity in Talk-Show is more discriminating than
the same expression with low intensity in Official-Event. To fill this gap, we
build a large-scale multi-scene dataset, coined as FERV39k. We analyze the
important ingredients of constructing such a novel dataset in three aspects:
(1) multi-scene hierarchy and expression class, (2) generation of candidate
video clips, (3) trusted manual labelling process. Based on these guidelines,
we select 4 scenarios subdivided into 22 scenes, annotate 86k samples
automatically obtained from 4k videos based on the well-designed workflow, and
finally build 38,935 video clips labeled with 7 classic expressions. Experiment
benchmarks on four kinds of baseline frameworks were also provided and further
analysis on their performance across different scenes and some challenges for
future research were given. Besides, we systematically investigate key
components of DFER by ablation studies. The baseline framework and our project
will be available.


title:
Calculation without IR divergence of the soft photon high energy limit
  of final state radiation for the process
  $e^{+}e^{-}\rightarrow\mu^{+}\mu^{-}\gamma$

abstract:
  In this paper it is shown that the final state radiation process
$e^{+}e^{-}\rightarrow\mu^{+}\mu^{-}\gamma$ at tree level is not associated
with any IR divergence in the soft photon high energy limit if the calculation
is done using a careful treatment of a certain distributional object (in fact,
a measure) arising from the Feynman amplitude for the process. Thus there need
be no "infrared catastrophe" associated with the process. It is shown that, in
fact, the cross section for the process vanishes in the soft photon high energy
limit.


title: Infinite series for $\pi/3$ and other identities

abstract:
  Using techniques from calculus, we combine classical identities for $\pi$,
$\operatorname{ln}2$, and harmonic numbers, to arrive at a nice infinite series
formula for $\pi/3$ that does not appear to be well known. In addition, we give
twenty-seven related identities involving $\pi$ and other irrational numbers.
  Posted in celebration of Pi Day 2022.


title: Self-consistent Maxwell-Pauli theory

abstract:
  We show that quantum mechanics can be constructed as a classical field theory
that correctly describes all basic quantum effects. We construct the
self-consistent Maxwell-Pauli theory, from which the correct spontaneous
emission spectrum of the hydrogen atom follows. It is shown that many
parameters, such as the spin and intrinsic magnetic moment of the electron,
which are considered purely quantum properties of the electron and do not have
a classical explanation, have a simple and clear physical meaning in the
framework of the classical self-consistent Maxwell-Pauli theory.


title:
Celeritas: GPU-accelerated particle transport for detector simulation in
  High Energy Physics experiments

abstract:
  Within the next decade, experimental High Energy Physics (HEP) will enter a
new era of scientific discovery through a set of targeted programs recommended
by the Particle Physics Project Prioritization Panel (P5), including the
upcoming High Luminosity Large Hadron Collider (LHC) HL-LHC upgrade and the
Deep Underground Neutrino Experiment (DUNE). These efforts in the Energy and
Intensity Frontiers will require an unprecedented amount of computational
capacity on many fronts including Monte Carlo (MC) detector simulation. In
order to alleviate this impending computational bottleneck, the Celeritas MC
particle transport code is designed to leverage the new generation of
heterogeneous computer architectures, including the exascale computing power of
U.S. Department of Energy (DOE) Leadership Computing Facilities (LCFs), to
model targeted HEP detector problems at the full fidelity of Geant4. This paper
presents the planned roadmap for Celeritas, including its proposed code
architecture, physics capabilities, and strategies for integrating it with
existing and future experimental HEP computing workflows.


title:
Markov chains with doubly stochastic transition matrices and application
  to a sequence of non-selective quantum measurements

abstract:
  A time-dependent finite-state Markov chain that uses doubly stochastic
transition matrices, is considered. Entropic quantities that describe the
randomness of the probability vectors, and also the randomness of the discrete
paths, are studied. Universal convex polytopes are introduced which contain all
future probability vectors, and which are based on the Birkhoff-von Neumann
expansion for doubly stochastic matrices. They are universal in the sense that
they depend only on the present probability vector, and are independent of the
doubly stochastic transition matrices that describe time evolution in the
future. It is shown that as the discrete time increases these convex polytopes
shrink, and the minimum entropy of the probability vectors in them increases.
These ideas are applied to a sequence of non-selective measurements (with
different projectors in each step) on a quantum system with $d$-dimensional
Hilbert space. The unitary time evolution in the intervals between the
measurements, is taken into account. The non-selective measurements destroy
stroboscopically the non-diagonal elements in the density matrix. This
`hermaphrodite' system is an interesting combination of a classical
probabilistic system (immediately after the measurements) and a quantum system
(in the intervals between the measurements). Various examples are discussed. In
the ergodic example, the system follows asymptotically all discrete paths with
the same probability. In the example of rapidly repeated non-selective
measurements, we get the well known quantum Zeno effect with `frozen discrete
paths' (presented here as a biproduct of our general methodology based on
Markov chains with doubly stochastic transition matrices).


title: Integrating Nijenhuis Structures

abstract:
  A Nijenhuis operator on a manifold $M$ is a $(1,1)$ tensor $\mathcal N$ whose
Nijenhuis-torsion vanishes. A Nijenhuis operator $\mathcal N$ on $M$ determines
a Lie algebroid structure $(TM)_{\mathcal N}$ on the tangent bundle $TM$. In
this sense a Nijenhuis operator can be seen as an infinitesimal object. In this
paper, we identify its "global counterpart". Namely, we show that when the Lie
algebroid $(TM)_{\mathcal N}$ is integrable, then it integrates to a Lie
groupoid equipped with appropriate additional structure responsible for
$\mathcal N$, and viceversa, the Lie algebroid of a Lie groupoid equipped with
such additional structure is of the type $(TM)_{\mathcal N}$ for some Nijenhuis
operator $\mathcal N$. We illustrate our integration result in various
examples.


title:
CURTAINs for your Sliding Window: Constructing Unobserved Regions by
  Transforming Adjacent Intervals

abstract:
  We propose a new model independent technique for constructing background data
templates for use in searches for new physics processes at the LHC. This
method, called CURTAINs, uses invertible neural networks to parametrise the
distribution of side band data as a function of the resonant observable. The
network learns a transformation to map any data point from its value of the
resonant observable to another chosen value. Using CURTAINs, a template for the
background data in the signal window is constructed by mapping the data from
the side-bands into the signal region. We perform anomaly detection using the
CURTAINs background template to enhance the sensitivity to new physics in a
bump hunt. We demonstrate its performance in a sliding window search across a
wide range of mass values. Using the LHC Olympics dataset, we demonstrate that
CURTAINs outperforms other leading approaches which aim to improve the
sensitivity of bump hunts. It can be trained on a much smaller range of the
invariant mass, and is fully data driven.


title:
Equivariant instanton Floer homology and calculations for the binary
  polyhedral spaces

abstract:
  We calculate the equivariant instanton Floer homology, in the sense of Miller
Eismeier, for the trivial $SO(3)$-bundle over the binary polyhedral spaces with
coefficients in a PID $R$ for which $2\in R$ is invertible. Along the way we
modify a part of the algebraic construction needed to define the equivariant
instanton Floer groups.


title: Electronic excited states in deep variational Monte Carlo

abstract:
  Obtaining accurate ground and low-lying excited states of electronic systems
is crucial in a multitude of important applications. One ab initio method for
solving the electronic Schr\"odinger equation that scales favorably for large
systems and whose accuracy is limited only by the choice of wavefunction ansatz
employed is variational quantum Monte Carlo (QMC). The recently introduced deep
QMC approach, using a new class of ansatzes represented by deep neural
networks, has been shown to generate nearly exact ground-state solutions for
molecules containing up to a few dozen electrons, with the potential to scale
to much larger systems where other highly accurate methods are not feasible. In
this paper, we advance one such ansatz (PauliNet) to compute electronic excited
states through a simple variational procedure. We demonstrate our method on a
variety of small atoms and molecules where we consistently achieve high
accuracy for low-lying states. To highlight the method's potential for larger
systems, we show that for the benzene molecule, PauliNet is on par with
significantly more expensive high-level electronic structure methods in terms
of the excitation energy and outperforms them in terms of absolute energies.


title: Product manifolds as realisations of general linear symmetries

abstract:
  This paper considers the relationship between geometry, symmetry and
fundamental interactions -- gravity and those mediated by gauge fields. We
explore product spacetimes which a) have the necessary symmetries for gauge
interactions and four-dimensional gravity and b) reduce to an N-dimensional
isotropic universe in their flat space limit. The key technique is looking at
orbits of the operator form of symmetric rank-two tensors under changes of
coordinate system. Orbits containing diagonal matrices are seen to correspond
to product manifolds. The GL(N, R) symmetry of the decompactified universe acts
non-linearly on such a product spacetime.
  We explore the resulting Kaluza-Klein theories, in which the internal
symmetries act indirectly on space of the extra dimensions, and give two
examples: a six-dimensional model in which the gauge symmetry is U (1) and a
seven-dimensional model in which it is SU (2). We identify constraints that can
be placed on any rank-two symmetric tensor to obtain such spacetimes:
relationships between polynomial invariants. The multiplicities of its
eigenvalues determine the dimensionalities of the factor spaces and hence the
gauge symmetries. If the tensor in question is the Ricci tensor, other than
two-dimensional factor spaces all the factor spaces are Einstein manifolds.
This situation represents the classical vacuum of the Kaluza-Klein theory


title: Self-Normalized Density Map (SNDM) for Counting Microbiological Objects

abstract:
  The statistical properties of the density map (DM) approach to counting
microbiological objects on images are studied in detail. The DM is given by
U$^2$-Net. Two statistical methods for deep neural networks are utilized: the
bootstrap and the Monte Carlo (MC) dropout. The detailed analysis of the
uncertainties for the DM predictions leads to a deeper understanding of the DM
model's deficiencies. Based on our investigation, we propose a
self-normalization module in the network. The improved network model, called
Self-Normalized Density Map (SNDM), can correct its output density map by
itself to accurately predict the total number of objects in the image. The SNDM
architecture outperforms the original model. Moreover, both statistical
frameworks -- bootstrap and MC dropout -- have consistent statistical results
for SNDM, which were not observed in the original model.


title:
CaRTS: Causality-driven Robot Tool Segmentation from Vision and
  Kinematics Data

abstract:
  Vision-based segmentation of the robotic tool during robot-assisted surgery
enables downstream applications, such as augmented reality feedback, while
allowing for inaccuracies in robot kinematics. With the introduction of deep
learning, many methods were presented to solve instrument segmentation directly
and solely from images. While these approaches made remarkable progress on
benchmark datasets, fundamental challenges pertaining to their robustness
remain. We present CaRTS, a causality-driven robot tool segmentation algorithm,
that is designed based on a complementary causal model of the robot tool
segmentation task. Rather than directly inferring segmentation masks from
observed images, CaRTS iteratively aligns tool models with image observations
by updating the initially incorrect robot kinematic parameters through forward
kinematics and differentiable rendering to optimize image feature similarity
end-to-end. We benchmark CaRTS with competing techniques on both synthetic as
well as real data from the dVRK, generated in precisely controlled scenarios to
allow for counterfactual synthesis. On training-domain test data, CaRTS
achieves a Dice score of 93.4 that is preserved well (Dice score of 91.8) when
tested on counterfactual altered test data, exhibiting low brightness, smoke,
blood, and altered background patterns. This compares favorably to Dice scores
of 95.0 and 62.8, respectively, of a purely image-based method trained and
tested on the same data. Future work will involve accelerating CaRTS to achieve
video framerate and estimating the impact occlusion has in practice. Despite
these limitations, our results are promising: In addition to achieving high
segmentation accuracy, CaRTS provides estimates of the true robot kinematics,
which may benefit applications such as force estimation.


title: Uncertainty with UAV Search of Multiple Goal-oriented Targets

abstract:
  This paper considers the complex problem of a team of UAVs searching targets
under uncertainty. The goal of the UAV team is to find all of the moving
targets as quickly as possible before they arrive at their selected goal. The
uncertainty considered is threefold: First, the UAVs do not know the targets'
locations and destinations. Second, the sensing capabilities of the UAVs are
not perfect. Third, the targets' movement model is unknown. We suggest a
real-time algorithmic framework for the UAVs, combining entropy and
stochastic-temporal belief, that aims at optimizing the probability of a quick
and successful detection of all of the targets. We have empirically evaluated
the algorithmic framework, and have shown its efficiency and significant
performance improvement compared to other solutions. Furthermore, we have
evaluated our framework using Peer Designed Agents (PDAs), which are computer
agents that simulate targets and show that our algorithmic framework
outperforms other solutions in this scenario.


title:
A Decomposition-Based Hybrid Ensemble CNN Framework for Improving
  Cross-Subject EEG Decoding Performance

abstract:
  Electroencephalogram (EEG) signals are complex, non-linear, and
non-stationary in nature. However, previous studies that applied decomposition
to minimize the complexity mainly exploited the hand-engineering features,
limiting the information learned in EEG decoding. Therefore, extracting
additional primary features from different disassembled components to improve
the EEG-based recognition performance remains challenging. On the other hand,
attempts have been made to use a single model to learn the hand-engineering
features. Less work has been done to improve the generalization ability through
ensemble learning. In this work, we propose a novel decomposition-based hybrid
ensemble convolutional neural network (CNN) framework to enhance the capability
of decoding EEG signals. CNNs, in particular, automatically learn the primary
features from raw disassembled components but not handcraft features. The first
option is to fuse the obtained score before the Softmax layer and execute
back-propagation on the entire ensemble network, whereas the other is to fuse
the probability output of the Softmax layer. Moreover, a component-specific
batch normalization (CSBN) layer is employed to reduce subject variability.
Against the challenging cross-subject driver fatigue-related situation
awareness (SA) recognition task, eight models are proposed under the framework,
which all showed superior performance than the strong baselines. The
performance of different decomposition methods and ensemble modes were further
compared. Results indicated that discrete wavelet transform (DWT)-based
ensemble CNN achieves the best 82.11% among the proposed models. Our framework
can be simply extended to any CNN architecture and applied in any EEG-related
sectors, opening the possibility of extracting more preliminary information
from complex EEG data.


title: A framework for quantum theory of elementary physical entities

abstract:
  A unified framework, which is directly established on the quantum ground, is
proposed for elementary physical entities, called \emph{modes} in this paper.
The framework is mainly built upon five basic assumptions, which loosely
speaking have the following contents. (i) The state space of each mode is given
by the direct product of a momentum-state space and a spinor-state space, the
latter of which is certain representation space of the $SL(2,C)$ group (a
covering group of the Lorentz group); (ii) spinor states of modes have a
layer-type structure and modes are either fermionic or bosonic, depending on
their helicity properties; (iii) there are three fundamental processes -- free
evolution, vacuum fluctuation (emergence or vanishing of a pair of fermionic
modes that possess exactly opposite physical properties), and two fundamental
interaction processes (change of two fermionic modes into one bosonic mode and
the reverse); (iv) vacuum fluctuation happens instantly; and (v) the time
evolution operator is constructed from operators, which map state spaces of
incoming modes of fundamental processes to those of outgoing modes. The time
evolution operator turns out to be a function of quantum fields that are
constructed from creation and annihilation operators for free-mode states,
whose interaction part has a local feature. As an example, a simple model of
modes is studied and is compared with the first-generation part of the standard
model (SM). Concerning electroweak interactions, the studied model has a time
evolution operator, whose main body is formally similar to that of the SM.
Besides, it predicts $\frac 13$ and $\frac 23$ electronic changes for
quark-type modes, gives an interpretation to the color degree of freedom, and
contains certain modes that behave like dark matters.


title:
Computer Vision Algorithm for Predicting the Welding Efficiency of
  Friction Stir Welded Copper Joints from its Microstructures

abstract:
  Friction Stir Welding is a robust joining process, and numerous AI-based
algorithms are being developed in this field to enhance mechanical and
microstructure properties. Convolutional Neural Networks (CNNs) are Artificial
Neural Networks that use image data as input. Identical to Artificial Neural
Networks, they are composed of weights that are determined throughout learning,
neurons (activated functions), and a goal (loss function). CNN is utilized in a
variety of applications, including image recognition, semantic segmentation,
image recognition, and localization. Utilizing training on 3000 microstructure
pictures and new tests on 300 microstructure photographs, the current work
investigates the predictions of Friction Stir Welded joint effectiveness using
microstructure images.


title:
Causality issue in the heat balance method for calculating the design
  heating and cooling load

abstract:
  Heating and cooling load calculation based on dynamic models is widely used
in simulation software and it is the method recommended by ASHRAE and CEN. The
principle is to make the heat balance for the air volume of a room space
considered at uniform temperature and to calculate from this equation the load,
i.e. the power needed to obtain the required indoor temperature. The problem is
that, by doing so, the physical causality is not respected. If the model is
approximated by a piecewise linear dynamical system, this procedure results in
an improper transfer function. In order to point out this problem, a method to
obtain state space and transfer function models from thermal networks is
introduced. Then, the transfer function representation is employed to show that
changing the physical causality results in an improper transfer function. The
practical consequence is that when the space temperature has a step variation,
the calculated load tends to infinity if the simulation time step tends to
zero. The issue of causality may be a problem in equation-based simulation
software, such as MODELICA, in which the equations do not represent causal
relations: a wrong choice of the causality in a balance equation may result in
improper transfer functions.


title: Diffusion Probabilistic Modeling for Video Generation

abstract:
  Denoising diffusion probabilistic models are a promising new class of
generative models that are competitive with GANs on perceptual metrics. In this
paper, we explore their potential for sequentially generating video. Inspired
by recent advances in neural video compression, we use denoising diffusion
models to stochastically generate a residual to a deterministic next-frame
prediction. We compare this approach to two sequential VAE and two GAN
baselines on four datasets, where we test the generated frames for perceptual
quality and forecasting accuracy against ground truth frames. We find
significant improvements in terms of perceptual quality on all data and
improvements in terms of frame forecasting for complex high-resolution videos.


title: Discussion of Foundation of Mathematics and Quantum Theory

abstract:
  Following the results of our recently published book (F. Lev, {\it Finite
mathematics as the foundation of classical mathematics and quantum theory. With
application to gravity and particle theory}. Springer (2020)), we discuss
different aspects of classical and finite mathematics and explain why finite
mathematics based on a finite ring of characteristic $p$ is more general
(fundamental) than classical mathematics: the former does not have foundational
problems, and the latter is a special degenerate case of the former in the
formal limit $p\to\infty$. In particular, quantum theory based on a finite ring
of characteristic $p$ is more general than standard quantum theory because the
latter is a special degenerate case of the former in the formal limit
$p\to\infty$.


title: Maximal acceleration and black hole evaporation

abstract:
  We discuss how in certain theories of spacetime admitting a maximal proper
acceleration Hawking radiation does not completely evaporate the black hole.
The black hole remnant's mass depends on the inverse of the maximal
acceleration. Furthermore, as a consequence of a duality between the minimum
mass that a Schwarzschild black hole can have in such spacetimes and the
maximal acceleration, we show that in certain theories with a maximal
acceleration there must be an upper uniform bounds on the value of the force
that can be exerted on test particles.


title: Distributed formation control of networked mechanical systems

abstract:
  This paper investigates a distributed formation tracking control law for
large-scale networks of mechanical systems. In particular, the formation
network is represented by a directed communication graph with leaders and
followers, where each agent is described as a port-Hamiltonian system with a
constant mass matrix. Moreover, we adopt a distributed parameter approach to
prove the scalable asymptotic stability of the network formation, i.e., the
scalability with respect to the network size and the specific formation
preservation. A simulation case illustrates the effectiveness of the proposed
control approach.


title: Physics in a diverse world or A Spherical Cow Model of Physics Talent

abstract: Talk at the KITP Conference: Snowmass Theory Frontier - Feb. 23, 2022

categories: physics.ed-ph hep-ph hep-th physics.soc-ph


title:
An Imitation Learning Curriculum for Text Editing with
  Non-Autoregressive Models

abstract:
  We propose a framework for training non-autoregressive sequence-to-sequence
models for editing tasks, where the original input sequence is iteratively
edited to produce the output. We show that the imitation learning algorithms
designed to train such models for machine translation introduces mismatches
between training and inference that lead to undertraining and poor
generalization in editing scenarios. We address this issue with two
complementary strategies: 1) a roll-in policy that exposes the model to
intermediate training sequences that it is more likely to encounter during
inference, 2) a curriculum that presents easy-to-learn edit operations first,
gradually increasing the difficulty of training samples as the model becomes
competent. We show the efficacy of these strategies on two challenging English
editing tasks: controllable text simplification and abstractive summarization.
Our approach significantly improves output quality on both tasks and controls
output complexity better on the simplification task.


title:
Defending Against Adversarial Attack in ECG Classification with
  Adversarial Distillation Training

abstract:
  In clinics, doctors rely on electrocardiograms (ECGs) to assess severe
cardiac disorders. Owing to the development of technology and the increase in
health awareness, ECG signals are currently obtained by using medical and
commercial devices. Deep neural networks (DNNs) can be used to analyze these
signals because of their high accuracy rate. However, researchers have found
that adversarial attacks can significantly reduce the accuracy of DNNs. Studies
have been conducted to defend ECG-based DNNs against traditional adversarial
attacks, such as projected gradient descent (PGD), and smooth adversarial
perturbation (SAP) which targets ECG classification; however, to the best of
our knowledge, no study has completely explored the defense against adversarial
attacks targeting ECG classification. Thus, we did different experiments to
explore the effects of defense methods against white-box adversarial attack and
black-box adversarial attack targeting ECG classification, and we found that
some common defense methods performed well against these attacks. Besides, we
proposed a new defense method called Adversarial Distillation Training (ADT)
which comes from defensive distillation and can effectively improve the
generalization performance of DNNs. The results show that our method performed
more effectively against adversarial attacks targeting on ECG classification
than the other baseline methods, namely, adversarial training, defensive
distillation, Jacob regularization, and noise-to-signal ratio regularization.
Furthermore, we found that our method performed better against PGD attacks with
low noise levels, which means that our method has stronger robustness.


title:
Quantum Sensors for High Precision Measurements of Spin-dependent
  Interactions

abstract:
  The applications of spin-based quantum sensors to measurements probing
fundamental physics are surveyed. Experimental methods and technologies
developed for quantum information science have rapidly advanced in recent
years, and these tools enable increasingly precise control and measurement of
spin dynamics. Theories of beyond-the-Standard-Model physics predict, for
example, symmetry violating electromagnetic moments aligned with particle
spins, exotic spin-dependent forces, coupling of spins to ultralight bosonic
dark matter fields, and changes to the local environment that affect spins.
Spin-based quantum sensors can be used to search for these myriad phenomena,
and offer a methodology for tests of fundamental physics that is complementary
to particle colliders and large scale particle detectors. Areas of
technological development that can significantly enhance the sensitivity of
spin-based quantum sensors to new physics are highlighted.

categories: quant-ph hep-ex hep-ph physics.atom-ph physics.ins-det


title: Solitary wave billiards

abstract:
  In the present work we introduce the concept of solitary wave billiards.
I.e., instead of a point particle, we consider a solitary wave in an enclosed
region and explore its collision with the boundaries and the resulting
trajectories in cases which for particle billiards are known to be integrable
and for cases that are known to be chaotic. A principal conclusion is that
solitary wave billiards are generically found to be chaotic even in cases where
the classical particle billiards are integrable. However, the degree of
resulting chaoticity depends on the particle speed and on the properties of the
potential. Furthermore, the nature of the scattering of the deformable solitary
wave particle is elucidated on the basis of a negative Goos-H\"anchen effect
which, in addition to a trajectory shift, also results in an effective
shrinkage of the billiard domain.


title: Prismatic $F$-crystals and crystalline local systems

abstract:
  We show that the category of integral crystalline local systems on the
generic fiber of a $p$-adic smooth formal scheme $X$ is equivalent to the
category of prismatic $F$-crystals over the analytic locus of the prismatic
site of $X$. This extends a recent result of Bhatt-Scholze to higher
dimensions.


title:
Accurate core-excited states via inclusion of core triple excitations in
  similarity-transformed EOM theory

abstract:
  The phenomenon of orbital relaxation upon excitation of core electrons is a
major problem in the linear-response treatment of core-hole spectroscopies.
Rather than addressing relaxation through direct dynamical correlation of the
excited state via equation-of-motion coupled cluster theory (EOMEE-CC), we
extend the alternative similarity-transformed equation-of-motion coupled
cluster theory (STEOMEE-CC) by including the core-valence separation (CVS) and
correlation of triple excitations only within the calculation of core
ionization energies. This new method, CVS-STEOMEE-CCSD+cT, significantly
improves on CVS-EOMEE-CCSD and unmodified CVS-STEOMEE-CCSD when compared to
full CVS-EOM-CCSDT for K-edge core-excitation energies of a set of small
molecules. The improvement in both absolute and relative (shifted) peak
positions is nearly as good as for transition-potential coupled cluster
(TP-CC), which includes an explicit treatment of orbital relaxation, and
CVS-EOMEE-CCSD*, which includes a perturbative treatment of triple excitations.


title: Positive Ricci Curvature and the Length of a shortest periodic geodesic

abstract:
  Let $M^n$ be a closed Riemannian manifold of dimension $n\geq 2$, with Ricci
curvature $Ric \geq n-1$. We will show that any sphere of dimension $m$ in the
space of closed loops on $M^n$ is homotopic to the sphere in the space of
closed loops of length at most $8 \pi m$. It follows that the length of a
shortest periodic geodesic on $M^n$ is bounded from above by $8 \pi (n-1)$.


title: Modellieren mit Heraklit

abstract:
  Heraklit is an infrastructure for modelling large, computer-integrated
systems. We discuss the central requirements for such models (hierarchies, user
view, transfer of informal into formal ideas, schematic models, equal treatment
of digital and person-bound processes) and explain how Heraklit supports these
requirements. A case study shows the usability of Heraklit in practice.


title: Transframer: Arbitrary Frame Prediction with Generative Models

abstract:
  We present a general-purpose framework for image modelling and vision tasks
based on probabilistic frame prediction. Our approach unifies a broad range of
tasks, from image segmentation, to novel view synthesis and video
interpolation. We pair this framework with an architecture we term Transframer,
which uses U-Net and Transformer components to condition on annotated context
frames, and outputs sequences of sparse, compressed image features. Transframer
is the state-of-the-art on a variety of video generation benchmarks, is
competitive with the strongest models on few-shot view synthesis, and can
generate coherent 30 second videos from a single image without any explicit
geometric information. A single generalist Transframer simultaneously produces
promising results on 8 tasks, including semantic segmentation, image
classification and optical flow prediction with no task-specific architectural
components, demonstrating that multi-task computer vision can be tackled using
probabilistic image models. Our approach can in principle be applied to a wide
range of applications that require learning the conditional structure of
annotated image-formatted data.


title:
Localization and melting of interfaces in the two-dimensional quantum
  Ising model

abstract:
  We study the non-equilibrium evolution of coexisting ferromagnetic domains in
the two-dimensional quantum Ising model -- a setup relevant in several
contexts, from quantum nucleation dynamics and false-vacuum decay scenarios to
recent experiments with Rydberg-atom arrays. We demonstrate that the
quantum-fluctuating interface delimiting a large bubble can be studied as an
effective one-dimensional system through a "holographic" mapping. For the
considered model, the emergent interface excitations map to an integrable chain
of fermionic particles. We discuss how this integrability is broken by
geometric features of the bubbles and by corrections in inverse powers of the
ferromagnetic coupling, and provide a lower bound to the timescale after which
the bubble is ultimately expected to melt. Remarkably, we demonstrate that a
symmetry-breaking longitudinal field gives rise to a robust ergodicity breaking
in two dimensions, a phenomenon underpinned by Stark many-body localization of
the emergent fermionic excitations of the interface.


title: Optimal schemes for combinatorial query problems with integer feedback

abstract:
  A query game is a pair of a set $Q$ of queries and a set $\mathcal{F}$ of
functions, or code words $f:Q\rightarrow\mathbb{Z}$. We think of this as a
two-player game. One player, Codemaker, picks a hidden code word
$f\in\mathcal{F}$. The other player, Codebreaker, then tries to determine $f$
by asking a sequence of queries $q\in Q$, after each of which Codemaker must
respond with the value $f(q)$. The goal of Codebreaker is to achieve this using
as few queries as possible. Two classical examples of such games are
coin-weighing with a spring scale, and Mastermind, which are of interest both
as recreational games and for their connection to information theory.
  In this paper, we will present a general framework for finding short
solutions to query games. As applications, we give new self-contained proofs of
the query complexity of variations of the coin-weighing problems, and prove new
results that the deterministic query complexity of Mastermind is $\Theta(n \log
k/ \log n + k)$ if only black-peg information is provided, and $\Theta(n \log k
/ \log n + k/n)$ if both black- and white-peg information is provided. In the
deterministic setting, these are the first up to constant factor optimal
solutions to Mastermind known for any $k\geq n^{1-o(1)}$.


title: Quantum Battery with Non-Hermitian Charging

abstract:
  We propose a design of a quantum battery exploiting the non-Hermitian
Hamiltonian as a charger. In particular, starting with the ground or the
thermal state of the interacting (non-interacting) Hamiltonian as the battery,
the charging of the battery is performed via PT- (RT)-symmetric Hamiltonian to
store or extract energy. We report that such a quenching with a non-Hermitian
Hamiltonian leads to an enhanced power output compared to a battery with a
Hermitian charger. We identify the region in the parameter space which provides
the gain in performance. We also demonstrate that the improvements persist with
the increase of system size both in the battery having PT- and RT-symmetric
charger. In the PT-symmetric case, although the anisotropy of the XY model does
not help in the performance, we show that the XXZ model as a battery with a
non-Hermitian charger performs better than that of the XX model having certain
interaction strengths. We also exhibit that the advantage of non-Hermiticity
remains valid even at finite temperatures in the initial states.


title:
The Frost Hollow Experiments: Pavlovian Signalling as a Path to
  Coordination and Communication Between Agents

abstract:
  Learned communication between agents is a powerful tool when approaching
decision-making problems that are hard to overcome by any single agent in
isolation. However, continual coordination and communication learning between
machine agents or human-machine partnerships remains a challenging open
problem. As a stepping stone toward solving the continual communication
learning problem, in this paper we contribute a multi-faceted study into what
we term Pavlovian signalling -- a process by which learned, temporally extended
predictions made by one agent inform decision-making by another agent with
different perceptual access to their shared environment. We seek to establish
how different temporal processes and representational choices impact Pavlovian
signalling between learning agents. To do so, we introduce a partially
observable decision-making domain we call the Frost Hollow. In this domain a
prediction learning agent and a reinforcement learning agent are coupled into a
two-part decision-making system that seeks to acquire sparse reward while
avoiding time-conditional hazards. We evaluate two domain variations: 1)
machine prediction and control learning in a linear walk, and 2) a prediction
learning machine interacting with a human participant in a virtual reality
environment. Our results showcase the speed of learning for Pavlovian
signalling, the impact that different temporal representations do (and do not)
have on agent-agent coordination, and how temporal aliasing impacts agent-agent
and human-agent interactions differently. As a main contribution, we establish
Pavlovian signalling as a natural bridge between fixed signalling paradigms and
fully adaptive communication learning. Our results therefore point to an
actionable, constructivist path towards continual communication learning
between reinforcement learning agents, with potential impact in a range of
real-world settings.


title: A 3D BEM algorithm for simulations of magnetic fluid droplet dynamics

abstract:
  This paper outlines a numerical algorithm that could be used for simulating
full 3D dynamics of magnetic fluid droplet shapes in external magnetic fields,
by solving boundary integral equations. The algorithm works with arbitrary
droplet and carrier fluid viscosity ratios. It is validated with known
theoretical relationships. Thus it may be used to evaluate various
approximations often used in description of ellipsoidal droplets by comparing
droplet dynamics calculated from them to the results obtained numerically from
first principles here. The algorithm may be used for investigations of droplet
configurations in arbitrary magnetic fields, as well as for indirectly
calculating its physical properties and predicting the magnetic field
thresholds above which droplet shape can develop instabilities in the form of
various spikes.


title:
Heterogeneous mean-field theory for two-species symbiotic processes on
  networks

abstract:
  A simple model to study cooperation is the two-species symbiotic contact
process (2SCP), in which two different species spread on a graph and interact
by a reduced death rate if both occupy the same vertex, representing a
symbiotic interaction. The 2SCP is known to exhibit a complex behavior with a
rich phase diagram, including continuous and discontinuous transitions between
the active phase and extinction. In this work, we advance the understanding of
the phase transition of the 2SCP on uncorrelated networks by developing a
heterogeneous mean-field (HMF) theory, in which the heterogeneity of contacts
is explicitly reckoned. The HMF theory for networks with power-law degree
distributions shows that the region of bistability (active and inactive phases)
in the phase diagram shrinks as the heterogeneity level is increased by
reducing the degree exponent. Finite-size analysis reveals a complex behavior
where a pseudo discontinuous transition at a finite-size can be converted into
a continuous one in the thermodynamic limit, depending on degree exponent and
symbiotic coupling. The theoretical results are supported by extensive
numerical simulations.


title:
A Coinductive Reformulation of Milner's Proof System for Regular
  Expressions Modulo Bisimilarity

abstract:
  Milner (1984) defined an operational semantics for regular expressions as
finite-state processes. In order to axiomatize bisimilarity of regular
expressions under this process semantics, he adapted Salomaa's proof system
that is complete for equality of regular expressions under the language
semantics. Apart from most equational axioms, Milner's system Mil inherits from
Salomaa's system a non-algebraic rule for solving single fixed-point equations.
Recognizing distinctive properties of the process semantics that render
Salomaa's proof strategy inapplicable, Milner posed completeness of the system
Mil as an open question.
  As a proof-theoretic approach to this problem we characterize the
derivational power that the fixed-point rule adds to the purely equational part
Mil$^-$ of Mil. We do so by means of a coinductive rule that permits cyclic
derivations that consist of a finite process graph with empty steps that
satisfies the layered loop existence and elimination property LLEE, and two of
its Mil$^{-}$-provable solutions. With this rule as replacement for the
fixed-point rule in Mil, we define the coinductive reformulation cMil as an
extension of Mil$^{-}$. In order to show that cMil and Mil are theorem
equivalent we develop effective proof transformations from Mil to cMil, and
vice versa. Since it is located half-way in between bisimulations and proofs in
Milner's system Mil, cMil may become a beachhead for a completeness proof of
Mil.
  This article extends our contribution to the CALCO 2022 proceedings. Here we
refine the proof transformations by framing them as eliminations of derivable
and admissible rules, and we link coinductive proofs to a coalgebraic
formulation of solutions of process graphs.


title: Optimization of vaccination for COVID-19 in the midst of a pandemic

abstract:
  During the Covid-19 pandemic a key role is played by vaccination to combat
the virus. There are many possible policies for prioritizing vaccines, and
different criteria for optimization: minimize death, time to herd immunity,
functioning of the health system. Using an age-structured population
compartmental finite-dimensional optimal control model, our results suggest
that the eldest to youngest vaccination policy is optimal to minimize deaths.
Our model includes the possible infection of vaccinated populations. We apply
our model to real-life data from the US Census for New Jersey and Florida,
which have a significantly different population structure. We also provide
various estimates of the number of lives saved by optimizing the vaccine
schedule and compared to no vaccination.


title:
Theory, phenomenology, and experimental avenues for dark showers: a
  Snowmass 2021 report

abstract:
  In this work, we consider the case of a strongly coupled dark/hidden sector,
which extends the Standard Model (SM) by adding an additional non-Abelian gauge
group. These extensions generally contain matter fields, much like the SM
quarks, and gauge fields similar to the SM gluons. We focus on the exploration
of such sectors where the dark particles are produced at the LHC through a
portal and undergo rapid hadronization within the dark sector before decaying
back, at least in part and potentially with sizeable lifetimes, to SM
particles, giving a range of possibly spectacular signatures such as emerging
or semi-visible jets. Other, non-QCD-like scenarios leading to soft unclustered
energy patterns or glueballs are also discussed. After a review of the theory,
existing benchmarks and constraints, this work addresses how to build
consistent benchmarks from the underlying physical parameters and present new
developments for the PYTHIA Hidden Valley module, along with jet substructure
studies. Finally, a series of improved search strategies is presented in order
to pave the way for a better exploration of the dark showers at the LHC.


title: A Type B analog of the Whitehouse representation

abstract:
  We give a Type $B$ analog of Whitehouse's lifts of the Eulerian
representations from $S_n$ to $S_{n+1}$ by introducing a family of
$B_{n}$-representations that lift to $B_{n+1}$. As in Type $A$, we interpret
these representations combinatorially via a family of orthogonal idempotents in
the Mantaci-Reutenauer algebra, and topologically as the graded pieces of the
cohomology of a certain $\mathbb{Z}_{2}$-orbit configuration space of
$\mathbb{R}^{3}$. We show that the lifted $B_{n+1}$-representations also have a
configuration space interpretation, and further parallel the Type $A$ story by
giving analogs of many of its notable properties, such as connections to
equivariant cohomology and the Varchenko-Gelfand ring.


title: Visualizing Global Explanations of Point Cloud DNNs

abstract:
  In the field of autonomous driving and robotics, point clouds are showing
their excellent real-time performance as raw data from most of the mainstream
3D sensors. Therefore, point cloud neural networks have become a popular
research direction in recent years. So far, however, there has been little
discussion about the explainability of deep neural networks for point clouds.
In this paper, we propose a point cloud-applicable explainability approach
based on a local surrogate model-based method to show which components
contribute to the classification. Moreover, we propose quantitative fidelity
validations for generated explanations that enhance the persuasive power of
explainability and compare the plausibility of different existing point
cloud-applicable explainability methods. Our new explainability approach
provides a fairly accurate, more semantically coherent and widely applicable
explanation for point cloud classification tasks. Our code is available at
https://github.com/Explain3D/LIME-3D


title: RDP del Pezzo surfaces with global vector fields in odd characteristic

abstract:
  We classify RDP del Pezzo surfaces with global vector fields over arbitrary
algebraically closed fields of characteristic $p \neq 2$. In characteristic
$0$, every RDP del Pezzo surface $X$ is equivariant, that is, ${\rm Aut}_X =
{\rm Aut}_{\widetilde{X}}$, where $\widetilde{X}$ is the minimal resolution of
$X$, hence the classification of RDP del Pezzo surfaces with global vector
fields is equivalent to the classification of weak del Pezzo surfaces with
global vector fields.
  In this article, we show that if $p \neq 2,3,5,7$, then it is still true that
every RDP del Pezzo surface is equivariant. We classify the non-equivariant RDP
del Pezzo surfaces in characteristic $p = 3,5,7$, giving explicit equations for
every such RDP del Pezzo surface in all possible degrees. As an application, we
construct regular non-smooth RDP del Pezzo surfaces over imperfect fields of
characteristic $7$, thereby showing that the known bound $p \leq 7$ for the
characteristics, where such a surface can exist, is sharp.


title: Towards Data-Efficient Detection Transformers

abstract:
  Detection Transformers have achieved competitive performance on the
sample-rich COCO dataset. However, we show most of them suffer from significant
performance drops on small-size datasets, like Cityscapes. In other words, the
detection transformers are generally data-hungry. To tackle this problem, we
empirically analyze the factors that affect data efficiency, through a
step-by-step transition from a data-efficient RCNN variant to the
representative DETR. The empirical results suggest that sparse feature sampling
from local image areas holds the key. Based on this observation, we alleviate
the data-hungry issue of existing detection transformers by simply alternating
how key and value sequences are constructed in the cross-attention layer, with
minimum modifications to the original models. Besides, we introduce a simple
yet effective label augmentation method to provide richer supervision and
improve data efficiency. Experiments show that our method can be readily
applied to different detection transformers and improve their performance on
both small-size and sample-rich datasets. Code will be made publicly available
at \url{https://github.com/encounter1997/DE-DETRs}.


title: DCarbonX Decentralised Application: Carbon Market Case Study

abstract:
  Decentralized applications developed using blockchain technology provide
innovative business models to serve the human race and solve existing
challenges. Climate change is one of the biggest problems humanity is facing
and there is a dearth of solutions in tackling this grave impediment to the
long-term sustainability of our planet. Accountability, greenwashing,
traceability, impact assessment and trading of carbon credits are unresolved
issues in the ESG sector. In this paper, we present a novel decentralized
application software, DCarbonX, that solves the enumerated problems using NFTs
on the blockchain platform, through smart contracts. The paper describes the
functional architecture of DCarbonX, while elaborating on its salient features
and utility in sustainable finance, in particular green sukuk. DCarbonX is a
pioneering software providing an exchange for trading of carbon credits. The
software facilitates logging of impact and traceable transactions in a carbon
market, that would help to prevent duplication of records and greenwashing. The
paper discusses the efforts being undertaken to achieve the climate goals as
per the Paris Agreement and also highlights the pivotal obstacles to achieving
carbon neutrality by 2050, as per COP26. The paper also encompasses a study on
the applications of dapps in DeFi, Web 3.0 and ESG, among other areas and gives
a comparative analysis of blockchain platforms for dapp development. The paper
is also a pioneer in highlighting the challenges that plague dapp development,
deployment and usage.


title:
ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and
  Implicit Hate Speech Detection

abstract:
  Toxic language detection systems often falsely flag text that contains
minority group mentions as toxic, as those groups are often the targets of
online hate. Such over-reliance on spurious correlations also causes systems to
struggle with detecting implicitly toxic language. To help mitigate these
issues, we create ToxiGen, a new large-scale and machine-generated dataset of
274k toxic and benign statements about 13 minority groups. We develop a
demonstration-based prompting framework and an adversarial
classifier-in-the-loop decoding method to generate subtly toxic and benign text
with a massive pretrained language model. Controlling machine generation in
this way allows ToxiGen to cover implicitly toxic text at a larger scale, and
about more demographic groups, than previous resources of human-written text.
We conduct a human evaluation on a challenging subset of ToxiGen and find that
annotators struggle to distinguish machine-generated text from human-written
language. We also find that 94.5% of toxic examples are labeled as hate speech
by human annotators. Using three publicly-available datasets, we show that
finetuning a toxicity classifier on our data improves its performance on
human-written data substantially. We also demonstrate that ToxiGen can be used
to fight machine-generated toxicity as finetuning improves the classifier
significantly on our evaluation subset.


title:
DetMatch: Two Teachers are Better Than One for Joint 2D and 3D
  Semi-Supervised Object Detection

abstract:
  While numerous 3D detection works leverage the complementary relationship
between RGB images and point clouds, developments in the broader framework of
semi-supervised object recognition remain uninfluenced by multi-modal fusion.
Current methods develop independent pipelines for 2D and 3D semi-supervised
learning despite the availability of paired image and point cloud frames.
Observing that the distinct characteristics of each sensor cause them to be
biased towards detecting different objects, we propose DetMatch, a flexible
framework for joint semi-supervised learning on 2D and 3D modalities. By
identifying objects detected in both sensors, our pipeline generates a cleaner,
more robust set of pseudo-labels that both demonstrates stronger performance
and stymies single-modality error propagation. Further, we leverage the richer
semantics of RGB images to rectify incorrect 3D class predictions and improve
localization of 3D boxes. Evaluating on the challenging KITTI and Waymo
datasets, we improve upon strong semi-supervised learning methods and observe
higher quality pseudo-labels. Code will be released at
https://github.com/Divadi/DetMatch


title: On Gromov's dihedral ridigidity conjecture and Stoker's conjecture

abstract:
  In this paper, we prove Gromov's strong dihedral rigidity conjecture for
convex Euclidean polyhedra in all dimensions. As a consequence, we answer
positively the Stoker conjecture for convex Euclidean polyhedra in all
dimensions.


title:
Snowmass 2021 White Paper: Higgs Coupling Sensitivities and
  Model-Independent Bounds on the Scale of New Physics

abstract:
  In this Snowmass white paper, we describe how unitarity bounds can convert
sensitivities for Higgs couplings at future colliders into sensitivities to the
scale of new physics. This gives a model-independent consequence of improving
these sensitivities and illustrate the impact they would have on constraining
new physics. Drawing upon past successful applications of unitarity as a guide
for future colliders (e.g. the Higgs mass bound and discovering it at the LHC),
we hope this data will be useful in the planning for next generation colliders.


title: On Multi-Domain Long-Tailed Recognition, Generalization and Beyond

abstract:
  Real-world data often exhibit imbalanced label distributions. Existing
studies on data imbalance focus on single-domain settings, i.e., samples are
from the same data distribution. However, natural data can originate from
distinct domains, where a minority class in one domain could have abundant
instances from other domains. We formalize the task of Multi-Domain Long-Tailed
Recognition (MDLT), which learns from multi-domain imbalanced data, addresses
label imbalance, domain shift, and divergent label distributions across
domains, and generalizes to all domain-class pairs. We first develop the
domain-class transferability graph, and show that such transferability governs
the success of learning in MDLT. We then propose BoDA, a theoretically grounded
learning strategy that tracks the upper bound of transferability statistics,
and ensures balanced alignment and calibration across imbalanced domain-class
distributions. We curate five MDLT benchmarks based on widely-used multi-domain
datasets, and compare BoDA to twenty algorithms that span different learning
strategies. Extensive and rigorous experiments verify the superior performance
of BoDA. Further, as a byproduct, BoDA establishes new state-of-the-art on
Domain Generalization benchmarks, improving generalization to unseen domains.
Code and data are available at
https://github.com/YyzHarry/multi-domain-imbalance.


title:
Emergence of isotropy and dynamic self-similarity in the birth of
  two-dimensional wave turbulence

abstract:
  Turbulence, characterised by cascades of excitations across lengthscales, is
ubiquitous in nature. Many of its statistical steady-state properties are
universal, but little is established experimentally about how they emerge,
starting from an equilibrium system. Here, we initiate a wave cascade in a
two-dimensional Bose gas, using a large-scale oscillating force, and probe it
on all lengthscales from the forcing one to the small one where energy is
eventually dissipated. We observe two key phenomena conceptually associated
with the development of turbulence -- the emergence of statistical isotropy
under anisotropic forcing, and the spatiotemporal scaling of the spectrum of
excitations at times before any energy is dissipated. This new microscopic view
on turbulence could enable further studies relevant for fields ranging from
mesoscopic physics to cosmology.

categories: cond-mat.quant-gas cond-mat.stat-mech physics.atom-ph physics.flu-dyn


title: Highly Uniform Prime Number Theorems

abstract:
  We prove a highly uniform version of the prime number theorem for a certain
class of $L$-functions. The range of $x$ depends polynomially on the analytic
conductor, and the error term is expressed in terms of an optimization problem
depending explicitly on the available zero-free region. The class contains the
Rankin-Selberg $L$-function $L(s,\pi \times \pi')$ associated to cuspidal
automorphic representations $\pi$ and $\pi'$ of $\mathrm{GL}_{m}$ and
$\mathrm{GL}_{m'}$, respectively. Our main result implies the first uniform
prime number theorems for such $L$-functions (with analytic conductor
uniformity) in complete generality.


title: AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generation

abstract:
  Powerful priors allow us to perform inference with insufficient information.
In this paper, we propose an autoregressive prior for 3D shapes to solve
multimodal 3D tasks such as shape completion, reconstruction, and generation.
We model the distribution over 3D shapes as a non-sequential autoregressive
distribution over a discretized, low-dimensional, symbolic grid-like latent
representation of 3D shapes. This enables us to represent distributions over 3D
shapes conditioned on information from an arbitrary set of spatially anchored
query locations and thus perform shape completion in such arbitrary settings
(e.g., generating a complete chair given only a view of the back leg). We also
show that the learned autoregressive prior can be leveraged for conditional
tasks such as single-view reconstruction and language-based generation. This is
achieved by learning task-specific naive conditionals which can be approximated
by light-weight models trained on minimal paired data. We validate the
effectiveness of the proposed method using both quantitative and qualitative
evaluation and show that the proposed method outperforms the specialized
state-of-the-art methods trained for individual tasks. The project page with
code and video visualizations can be found at
https://yccyenchicheng.github.io/AutoSDF/.


title: TensoRF: Tensorial Radiance Fields

abstract:
  We present TensoRF, a novel approach to model and reconstruct radiance
fields. Unlike NeRF that purely uses MLPs, we model the radiance field of a
scene as a 4D tensor, which represents a 3D voxel grid with per-voxel
multi-channel features. Our central idea is to factorize the 4D scene tensor
into multiple compact low-rank tensor components. We demonstrate that applying
traditional CP decomposition -- that factorizes tensors into rank-one
components with compact vectors -- in our framework leads to improvements over
vanilla NeRF. To further boost performance, we introduce a novel vector-matrix
(VM) decomposition that relaxes the low-rank constraints for two modes of a
tensor and factorizes tensors into compact vector and matrix factors. Beyond
superior rendering quality, our models with CP and VM decompositions lead to a
significantly lower memory footprint in comparison to previous and concurrent
works that directly optimize per-voxel features. Experimentally, we demonstrate
that TensoRF with CP decomposition achieves fast reconstruction (<30 min) with
better rendering quality and even a smaller model size (<4 MB) compared to
NeRF. Moreover, TensoRF with VM decomposition further boosts rendering quality
and outperforms previous state-of-the-art methods, while reducing the
reconstruction time (<10 min) and retaining a compact model size (<75 MB).