title: Online Learning for Traffic Routing under Unknown Preferences

abstract:
  In transportation networks, users typically choose routes in a decentralized
and self-interested manner to minimize their individual travel costs, which, in
practice, often results in inefficient overall outcomes for society. As a
result, there has been a growing interest in designing road tolling schemes to
cope with these efficiency losses and steer users toward a system-efficient
traffic pattern. However, the efficacy of road tolling schemes often relies on
having access to complete information on users' trip attributes, such as their
origin-destination (O-D) travel information and their values of time, which may
not be available in practice.
  Motivated by this practical consideration, we propose an online learning
approach to set tolls in a traffic network to drive heterogeneous users with
different values of time toward a system-efficient traffic pattern. In
particular, we develop a simple yet effective algorithm that adjusts tolls at
each time period solely based on the observed aggregate flows on the roads of
the network without relying on any additional trip attributes of users, thereby
preserving user privacy. In the setting where the O-D pairs and values of time
of users are drawn i.i.d. at each period, we show that our approach obtains an
expected regret and road capacity violation of $O(\sqrt{T})$, where $T$ is the
number of periods over which tolls are updated. Our regret guarantee is
relative to an offline oracle that has complete information on users' trip
attributes. We further establish a $\Omega(\sqrt{T})$ lower bound on the regret
of any algorithm, which establishes that our algorithm is optimal up to
constants. Finally, we demonstrate the superior performance of our approach
relative to several benchmarks on a real-world transportation network, thereby
highlighting its practical applicability.


title:
Asymmetric Fraunhofer pattern in Josephson junctions from inversion
  symmetry broken V$_5$S$_8$

abstract:
  Introduction of spin orbit coupling (SOC) in a Josephson junction (JJ) gives
rise to unusual Josephson effects. We investigate JJs based on a newly
discovered heterodimensional superlattice V$_5$S$_8$ with broken inversion
symmetry and a special form of SOC. The unique homointerface of our JJs enables
elimination of extrinsic effects due to interfaces and disorder. We observe
asymmetric Fraunhofer patterns with respect to both the perpendicular magnetic
field and the current. The asymmetry is influenced by an in-plane magnetic
field. Analysis of the pattern points to a nontrivial spatial distribution of
the Josephson current that is intrinsic to the SOC in V$_5$S$_8$.


title: Perceptual Contrast Stretching on Target Feature for Speech Enhancement

abstract:
  Speech enhancement (SE) performance has improved considerably since the use
of deep learning (DL) models as a base function. In this study, we propose a
perceptual contrast stretching (PCS) approach to further improve SE
performance. PCS is derived based on the critical band importance function and
applied to modify the targets of the SE model. Specifically, PCS stretches the
contract of target features according to perceptual importance, thereby
improving the overall SE performance. Compared to post-processing based
implementations, incorporating PCS into the training phase preserves
performance and reduces online computation. It is also worth noting that PCS
can be suitably combined with different SE model architectures and training
criteria. Meanwhile, PCS does not affect the causality or convergence of the SE
model training. Experimental results on the VoiceBank-DEMAND dataset showed
that the proposed method can achieve state-of-the-art performance on both
causal (PESQ=3.07) and non-causal (PESQ=3.35) SE tasks.


title:
An energy-based deep splitting method for the nonlinear filtering
  problem

abstract:
  The purpose of this paper is to explore the use of deep learning for the
solution of the nonlinear filtering problem. This is achieved by solving the
Zakai equation by a deep splitting method, previously developed for approximate
solution of (stochastic) partial differential equations. This is combined with
an energy-based model for the approximation of functions by a deep neural
network. This results in a computationally fast filter that takes observations
as input and that does not require re-training when new observations are
received. The method is tested on three examples, one linear Gaussian and two
nonlinear. The method shows promising performance when benchmarked against the
Kalman filter and the bootstrap particle filter.


title: Solving the 3-SAT problem using network-based biocomputation

abstract:
  The 3-Satisfiability Problem (3-SAT) is a demanding combinatorial problem, of
central importance among the non-deterministic polynomial (NP) complete
problems, with applications in circuit design, artificial intelligence and
logistics. Even with optimized algorithms, the solution space that needs to be
explored grows exponentially with increasing size of 3-SAT instances. Thus,
large 3-SAT instances require excessive amounts of energy to solve with serial
electronic computers. Network-based biocomputation (NBC) is a multidisciplinary
parallel computation approach with drastically reduced energy consumption. NBC
uses biomolecular motors to propel cytoskeletal filaments through
nanofabricated networks that encode the mathematical problems. By
stochastically exploring possible paths through the networks, the cytoskeletal
filaments find possible solutions to the encoded problem instance. Here we
first report a novel algorithm that converts 3-SAT into NBC-compatible network
format. We demonstrate that this algorithm works in practice, by experimentally
solving four small 3-SAT instances (with up to 3 variables and 5 clauses) using
the actin-myosin biomolecular motor system. This is a key step towards the
broad general applicability of NBC because polynomial conversions to 3-SAT
exist for a wide set of important NP-complete problems.


title:
Predicting extreme events from data using deep machine learning: when
  and where

abstract:
  We develop a deep convolutional neural network (DCNN) based framework for
model-free prediction of the occurrence of extreme events both in time ("when")
and in space ("where") in nonlinear physical systems of spatial dimension two.
The measurements or data are a set of two-dimensional snapshots or images. For
a desired time horizon of prediction, a proper labeling scheme can be
designated to enable successful training of the DCNN and subsequent prediction
of extreme events in time. Given that an extreme event has been predicted to
occur within the time horizon, a space-based labeling scheme can be applied to
predict, within certain resolution, the location at which the event will occur.
We use synthetic data from the 2D complex Ginzburg-Landau equation and
empirical wind speed data of the North Atlantic ocean to demonstrate and
validate our machine-learning based prediction framework. The trade-offs among
the prediction horizon, spatial resolution, and accuracy are illustrated, and
the detrimental effect of spatially biased occurrence of extreme event on
prediction accuracy is discussed. The deep learning framework is viable for
predicting extreme events in the real world.


title: Adaptive Mean-Residue Loss for Robust Facial Age Estimation

abstract:
  Automated facial age estimation has diverse real-world applications in
multimedia analysis, e.g., video surveillance, and human-computer interaction.
However, due to the randomness and ambiguity of the aging process, age
assessment is challenging. Most research work over the topic regards the task
as one of age regression, classification, and ranking problems, and cannot well
leverage age distribution in representing labels with age ambiguity. In this
work, we propose a simple yet effective loss function for robust facial age
estimation via distribution learning, i.e., adaptive mean-residue loss, in
which, the mean loss penalizes the difference between the estimated age
distribution's mean and the ground-truth age, whereas the residue loss
penalizes the entropy of age probability out of dynamic top-K in the
distribution. Experimental results in the datasets FG-NET and CLAP2016 have
validated the effectiveness of the proposed loss. Our code is available at
https://github.com/jacobzhaoziyuan/AMR-Loss.


title: Broken R-symmetry and defect conformal manifolds

abstract:
  Just as exactly marginal operators allow to deform a conformal field theory
along the space of theories known as the conformal manifold, appropriate
operators on conformal defects allow for deformations of the defects. A setup
that guarantees exactly marginal defect operators in theories with extended
supersymmetry are defects that break the R-symmetry group. In that case the
conformal manifold is the symmetry breaking coset and its Zamolodchikov metric
is expressed as the two point function of the exactly marginal operator. As the
Riemann tensor on the conformal manifold can be expressed as an integrated
correlator of the marginal operators, we find an exact relation to the
curvature of the coset space.We examine in detail the case of the 1/2 BPS
Maldacena-Wilson loop in $\mathcal{N}=4$ SYM, which breaks $SO(6)\to SO(5)$ and
the 1/2 BPS surface operator of the 6d $\mathcal{N}=(2,0)$ theory with
$SO(5)\to SO(4)$ breaking. We verify this identity against known 4-point
functions, previously derived from AdS/CFT and the conformal bootstrap.


title: The maximum measure of non-trivial 3-wise intersecting families

abstract:
  Let $\mathcal G$ be a family of subsets of an $n$-element set. The family
$\mathcal G$ is called non-trivial $3$-wise intersecting if the intersection of
any three subsets in $\mathcal G$ is non-empty, but the intersection of all
subsets is empty. For a real number $p\in(0,1)$ we define the measure of the
family by the sum of $p^{|G|}(1-p)^{n-|G|}$ over all $G\in\mathcal G$. We
determine the maximum measure of non-trivial $3$-wise intersecting families. We
also discuss the uniqueness and stability of the corresponding optimal
structure. These results are obtained by solving linear programming problems.


title: Preventing Over-Smoothing for Hypergraph Neural Networks

abstract:
  In recent years, hypergraph learning has attracted great attention due to its
capacity in representing complex and high-order relationships. However, current
neural network approaches designed for hypergraphs are mostly shallow, thus
limiting their ability to extract information from high-order neighbors. In
this paper, we show both theoretically and empirically, that the performance of
hypergraph neural networks does not improve as the number of layers increases,
which is known as the over-smoothing problem. To tackle this issue, we develop
a new deep hypergraph convolutional network called Deep-HGCN, which can
maintain the heterogeneity of node representation in deep layers. Specifically,
we prove that a $k$-layer Deep-HGCN simulates a polynomial filter of order $k$
with arbitrary coefficients, which can relieve the problem of over-smoothing.
Experimental results on various datasets demonstrate the superior performance
of the proposed model comparing to the state-of-the-art hypergraph learning
approaches.


title:
Lack of total convexity of some two dimensional Busemann--Hausdorff
  densities

abstract:
  In this article, we prove that there exist finite dimensional normed vector
spaces, in which the two dimensional Busemann--Hausdroff densities are not
totally convex. This completes a result by D. Burago and S. Ivanov, who proved
that these densities are always convex in the two dimensional case. Moreover,
our main result gives partial answers to two questions posed by Ambrosio and
Schmidt and Busemann correspondingly.


title: Optical response of two-dimensional Dirac materials with a flat band

abstract:
  Two-dimensional Dirac materials with a flat band have been demonstrated to
possess a plethora of unusual electronic properties, but the optical properties
of these materials are less studied. Utilizing $\alpha$-$\mathcal{T}_3$ lattice
as a prototypical system, where $0\le \alpha \le 1$ is a tunable parameter and
a flat band through the conic intersection of two Dirac cones arises for
$\alpha > 0$, we investigate the conductivity of flat-band Dirac material
systems analytically and numerically. Motivated by the fact that the imaginary
part of the optical conductivity can have significant effects on the optical
response and is an important factor of consideration for developing
$\alpha$-$\mathcal{T}_3$ lattice based optical devices, we are led to derive a
complete conductivity formula with both the real and imaginary parts.
Scrutinizing the formula, we uncover two phenomena. First, for the value of
$\alpha$ in some range, two types of optical transitions coexist: one between
the two Dirac cones and another from the flat band to a cone, which generate
multi-frequency transverse electrical propagating waves. Second, for $\alpha=1$
so the quasiparticles become pseudospin-1, the flat-to-cone transition can
result in resonant scattering. These results pave the way to exploiting
$\alpha$-$T_3$ lattice for optical device applications in the terahertz
frequency domain.


title: Viscosity solutions for obstacle problems on Wasserstein space

abstract:
  This paper is a continuation of our accompanying paper [Talbi, Touzi and
Zhang (2021)], where we characterized the mean field optimal stopping problem
by an obstacle equation on the Wasserstein space of probability measures,
provided that the value function is smooth. Our purpose here is to establish
this characterization under weaker regularity requirements. We shall define a
notion of viscosity solutions for such equation, and prove existence,
stability, and comparison principle.


title:
Fully tunable longitudinal spin-photon interactions in Si and Ge quantum
  dots

abstract:
  Spin qubits in silicon and germanium quantum dots are promising platforms for
quantum computing, but entangling spin qubits over micrometer distances remains
a critical challenge. Current prototypical architectures maximize transversal
interactions between qubits and microwave resonators, where the spin state is
flipped by nearly resonant photons. However, these interactions cause
back-action on the qubit, that yield unavoidable residual qubit-qubit couplings
and significantly affect the gate fidelity. Strikingly, residual couplings
vanish when spin-photon interactions are longitudinal and photons couple to the
phase of the qubit. We show that large longitudinal interactions emerge
naturally in state-of-the-art hole spin qubits. These interactions are fully
tunable and can be parametrically modulated by external oscillating electric
fields. We propose realistic protocols to measure these interactions and to
implement fast and high-fidelity two-qubit entangling gates. These protocols
work also at high temperatures, paving the way towards the implementation of
large-scale quantum processors.


title:
Recovering models of open quantum systems from data via polynomial
  optimization: Towards globally convergent quantum system identification

abstract:
  Current quantum devices suffer imperfections as a result of fabrication, as
well as noise and dissipation as a result of coupling to their immediate
environments. Because of this, it is often difficult to obtain accurate models
of their dynamics from first principles. An alternative is to extract such
models from time-series measurements of their behavior. Here, we formulate this
system-identification problem as a polynomial optimization problem. Recent
advances in optimization have provided globally convergent solvers for this
class of problems, which using our formulation prove estimates of the Kraus map
or the Lindblad equation. We include an overview of the state-of-the-art
algorithms, bounds, and convergence rates, and illustrate the use of this
approach to modeling open quantum systems.


title: Policy Iteration for Multiplicative Noise Output Feedback Control

abstract:
  We propose a policy iteration algorithm for solving the multiplicative noise
linear quadratic output feedback design problem. The algorithm solves a set of
coupled Riccati equations for estimation and control arising from a partially
observable Markov decision process (POMDP) under a class of linear dynamic
control policies. We show in numerical experiments far faster convergence than
a value iteration algorithm, formerly the only known algorithm for solving this
class of problem. The results suggest promising future research directions for
policy optimization algorithms in more general POMDPs, including the potential
to develop novel approximate data-driven approaches when model parameters are
not available.


title: On the Evaluation of NLP-based Models for Software Engineering

abstract:
  NLP-based models have been increasingly incorporated to address SE problems.
These models are either employed in the SE domain with little to no change, or
they are greatly tailored to source code and its unique characteristics. Many
of these approaches are considered to be outperforming or complementing
existing solutions. However, an important question arises here: "Are these
models evaluated fairly and consistently in the SE community?". To answer this
question, we reviewed how NLP-based models for SE problems are being evaluated
by researchers. The findings indicate that currently there is no consistent and
widely-accepted protocol for the evaluation of these models. While different
aspects of the same task are being assessed in different studies, metrics are
defined based on custom choices, rather than a system, and finally, answers are
collected and interpreted case by case. Consequently, there is a dire need to
provide a methodological way of evaluating NLP-based models to have a
consistent assessment and preserve the possibility of fair and efficient
comparison.


title: The Legend of Zelda: The Complexity of Mechanics

abstract:
  We analyze some of the many game mechanics available to Link in the classic
Legend of Zelda series of video games. In each case, we prove that the
generalized game with that mechanic is polynomial, NP-complete, NP-hard and in
PSPACE, or PSPACE-complete. In the process we give an overview of many of the
hardness proof techniques developed for video games over the past decade: the
motion-planning-through-gadgets framework, the planar doors framework, the
doors-and-buttons framework, the "Nintendo" platform game / SAT framework, and
the collectible tokens and toll roads / Hamiltonicity framework.


title:
Lower bounds for uniform read-once threshold formulae in the randomized
  decision tree model

abstract:
  We investigate the randomized decision tree complexity of a specific class of
read-once threshold functions. A read-once threshold formula can be defined by
a rooted tree, every internal node of which is labeled by a threshold function
$T_k^n$ (with output 1 only when at least $k$ out of $n$ input bits are 1) and
each leaf by a distinct variable. Such a tree defines a Boolean function in a
natural way. We focus on the randomized decision tree complexity of such
functions, when the underlying tree is a uniform tree with all its internal
nodes labeled by the same threshold function. We prove lower bounds of the form
$c(k,n)^d$, where $d$ is the depth of the tree. We also treat trees with
alternating levels of AND and OR gates separately and show asymptotically
optimal bounds, extending the known bounds for the binary case.


title: Isovector helicity quark quasi-distributions inside a large Nc nucleon

abstract:
  In this letter, we report the results for the isovector polarized quark
quasi-distribution functions $\Delta u(x,v) - \Delta d(x,v)$ in the large
$N_c$, calculated within the chiral quark-soliton model. It is shown that the
polarized quark quasi-distributions present good convergence to the light-cone
PDFs in the limit of the nucleon boost momentum $P_N \to \infty$ (or the
velocity $v \to 1$), compared to the case of the unpolarized isosinglet
distributions.


title:
Vector boson pair production at one loop: analytic results for the
  process $q \bar{q} \ell \bar\ell \ell^\prime \bar{\ell}^\prime g$

abstract:
  We present compact analytic results for the one-loop amplitude for the
process $0 \rightarrow q \bar{q} \ell \bar\ell \ell^\prime \bar{\ell}^\prime
g$, relevant for both the production of a pair of $Z$ and $W$-bosons in
association with a jet. We focus on the gauge-invariant contribution mediated
by a loop of quarks. We explicitly include all effects of the loop-quark mass
$m$, appropriate for the production of a pair of $Z$-bosons. In the limit $m
\to 0$, our results are also applicable to the production of $W$-boson pairs,
mediated by a loop of massless quarks. Implemented in a numerical code, the
results are fast. The calculation uses novel advancements in spinor-helicity
simplification techniques, for the first time applied beyond five-point
massless kinematics. We make use of primary decompositions from
algebraic-geometry, which now involve non-radical ideals, and $p$-adic numbers
from number theory. We show how to infer whether numerator polynomials belong
to symbolic powers of non-radical ideals through numerical evaluations.


title: Thermodynamics of the Ramsey Zone

abstract:
  We carry out a study on thermodynamics properties as entropy and heat $J_{Q}$
and work $J_{W}$ fluxes involved in a Ramsey zone, i.e., a mode field inside a
low quality factor cavity that behaves classically and promotes rotations on
atomic states. Focusing on the atomic dynamic only, here we show that $J_{W}$
predominates when the atomic state evolves maintaining its maximum purity, as
computed by von Neumann entropy, in which case the rotation is successfully
applied. On the other hand, $J_{Q}$ is the quantity that stands out when the
atomic state ceases to be pure due to its entanglement with the cavity field
mode state. We describe those limits in terms of the driving strength, the
atom-field coupling and the cavity field dissipation rate, and interpret the
quantum-to-classical transition in light of the heat and work fluxes. Besides,
we show that for a driven-dissipative cavity mode to work out as a Ramsey zone
(classical field), a very large amount of photons, of the order of $10^{6}$,
need to cross the leaky cavity, which explains the classical behavior of the
intra-cavity mode field even though, on average, it has a number of photons of
the order of unity [Phys. Rev. Lett. 82, 4737 (1999)]


title:
Efficient Non-Autoregressive GAN Voice Conversion using VQWav2vec
  Features and Dynamic Convolution

abstract:
  It was shown recently that a combination of ASR and TTS models yield highly
competitive performance on standard voice conversion tasks such as the Voice
Conversion Challenge 2020 (VCC2020). To obtain good performance both models
require pretraining on large amounts of data, thereby obtaining large models
that are potentially inefficient in use. In this work we present a model that
is significantly smaller and thereby faster in processing while obtaining
equivalent performance. To achieve this the proposed model, Dynamic-GAN-VC
(DYGAN-VC), uses a non-autoregressive structure and makes use of vector
quantised embeddings obtained from a VQWav2vec model. Furthermore dynamic
convolution is introduced to improve speech content modeling while requiring a
small number of parameters. Objective and subjective evaluation was performed
using the VCC2020 task, yielding MOS scores of up to 3.86, and character error
rates as low as 4.3\%. This was achieved with approximately half the number of
model parameters, and up to 8 times faster decoding speed.


title:
The DESC Stellarator Code Suite Part I: Quick and accurate equilibria
  computations

abstract:
  3D equilibrium codes are vital for stellarator design and operation, and
high-accuracy equilibria are also necessary for stability studies. This paper
details comparisons of two 3D equilibrium codes, VMEC, which uses a
steepest-descent algorithm to reach a minimum-energy plasma state, and DESC,
which minimizes the MHD force error in real space directly. Accuracy as
measured by final plasma energy and satisfaction of MHD force balance, as well
as other metrics, will be presented for each code, along with the computation
time. It is shown that DESC is able to achieve more accurate solutions,
especially near-axis. DESC's global Fourier-Zernike basis also yields the
solution everywhere in the plasma volume, not just on discrete flux surfaces.
Further, DESC can compute the same accuracy solution as VMEC in an order of
magnitude less time.


title: Krylov techniques for low-rank ADI

abstract:
  One of the most computationally expensive steps of the low-rank ADI method
for large-scale Lyapunov equations is the solution of a shifted linear system
at each iteration. We propose the use of the extended Krylov subspace method
for this task. In particular, we illustrate how a single approximation space
can be constructed to solve all the shifted linear systems needed to achieve a
prescribed accuracy in terms of Lyapunov residual norm. Moreover, we show how
to fully merge the two iterative procedures in order to obtain a novel,
efficient implementation of the low-rank ADI method, for an important class of
equations. Many state-of-the-art algorithms for the shift computation can be
easily incorporated into our new scheme, as well. Several numerical results
illustrate the potential of our novel procedure when compared to an
implementation of the low-rank ADI method based on sparse direct solvers for
the shifted linear systems.


title:
The Solar-Cycle Temporal Variation of the Solar Wind Charge Exchange
  X-ray Lines

abstract:
  Solar wind charge exchange (SWCX) is the primary contamination to soft X-ray
emission lines from the Milky Way (MW) hot gas. We report a solar-cycle
($\approx 10$ yr) temporal variation of observed \ion{O}{7} and \ion{O}{8}
emission line measurements in the {\it XMM-Newton} archive, which is tightly
correlated with the solar cycle traced by the sunspot number (SSN). This
temporal variation is expected to be associated with the heliospheric SWCX.
Another observed correlation is that higher solar wind (SW) fluxes lead to
higher O VII or O VIII fluxes, which is due to the magnetospheric SWCX. We
construct an empirical model to reproduce the observed correlation between the
line measurements and the solar activity (i.e., the SW flux and the SSN). With
this model we discovered a lag of $0.91_{-0.22}^{+0.20}$ yr between the O VII
flux and the SSN. This time lag is a combination of the SW transit time within
the heliosphere, the lag of the neutral gas distribution responding to solar
activity, and the intrinsic lag between the SSN and the launch of a high-energy
SW (i.e., $\rm O^{7+}$ and $\rm O^{8+}$). MW O VII and O VIII fluxes have mean
values of 5.4 L.U. and 1.7 L.U., which are reduced by $50\%$ and $30\%$,
compared to studies where the SWCX contamination is not removed. This
correction also changes the determination of the density distribution and the
temperature profile of the MW hot gas.


title:
Visualizing the thermoelectric origin of photocurrent flow in
  anisotropic semimetals

abstract:
  Photocurrent measurements are incisive probes of crystal symmetry, electronic
band structure, and material interfaces. However, conventional scanning
photocurrent microscopy (SPCM) convolves the processes for photocurrent
generation and collection, which can obscure the intrinsic light-matter
interaction. Here, by using ac magnetometry with a nitrogen-vacancy center spin
ensemble, we demonstrate the high-sensitivity, sub-micron resolved imaging of
vector photocurrent flow. Our imaging reveals that in anisotropic semimetals
WTe2 and TaIrTe4, the photoexcited electron carriers propagate outward along
the zigzag chains and inward perpendicular to the chains. This circulating
pattern is explained by our theoretical modeling to emerge from an anisotropic
photothermoelectric effect (APTE) caused by a direction-dependent thermopower.
Through simultaneous SPCM and magnetic imaging, we directly visualize how local
APTE photocurrents stimulate long-range photocurrents at symmetry-breaking
edges and contacts. These results uniquely validate the Shockley-Ramo process
for photocurrent collection in gapless materials and identify the overlooked
APTE as the primary origin of robust photocurrents in anisotropic semimetal
devices. Our work highlights quantum-enabled photocurrent flow microscopy as a
clarifying perspective for complex optoelectronic phenomena.


title: Sample from copula: a COPPY module

abstract:
  The modeling of dependence between random variables is an important subject
in several applied fields of science. To this aim the copula function can be
used as a margin-free description of the dependence structure. Several copulae
belong to specific families such as Archimedean, Elliptical or Extreme. While
software implementation of copulae has been thoroughly explored in \textsf{R}
software, methods to work with copula in \textsf{Python} are still in their
infancy. To promote the dependence modeling with copula in \textsf{Python}, we
have developed \textbf{COPPY}, a library that provides a range of random vector
generation vector for copulae.


title: 3D Equivariant Graph Implicit Functions

abstract:
  In recent years, neural implicit representations have made remarkable
progress in modeling of 3D shapes with arbitrary topology. In this work, we
address two key limitations of such representations, in failing to capture
local 3D geometric fine details, and to learn from and generalize to shapes
with unseen 3D transformations. To this end, we introduce a novel family of
graph implicit functions with equivariant layers that facilitates modeling fine
local details and guaranteed robustness to various groups of geometric
transformations, through local $k$-NN graph embeddings with sparse point set
observations at multiple resolutions. Our method improves over the existing
rotation-equivariant implicit function from 0.69 to 0.89 (IoU) on the ShapeNet
reconstruction task. We also show that our equivariant implicit function can be
extended to other types of similarity transformations and generalizes to unseen
translations and scaling.


title: 4DL: a four-valued Dynamic logic and its proof-theory

abstract:
  Transition systems are often used to describe the behaviour of software
systems. If viewed as a graph then, at their most basic level, vertices
correspond to the states of a program and each edge represents a transition
between states via the (atomic) action labelled. In this setting, systems are
thought to be consistent so that at each state formulas are evaluated as either
True or False.
  On the other hand, when a structure of this sort - for example a map where
states represent locations, some local properties are known and labelled
transitions represent information available about different routes - is built
resorting to multiple sources of information, it is common to find inconsistent
or incomplete information regarding what holds at each state, both at the level
of propositional variables and transitions.
  This paper aims at bringing together Belnap's four values, Dynamic Logic and
hybrid machinery such as nominals and the satisfaction operator, so that
reasoning is still possible in face of contradicting evidence. Proof-theory for
this new logic is explored by means of a terminating, sound and complete
tableaux system.


title:
A Multi-Planet System's Sole Super-Puff: Exploring Allowable Physical
  Parameters for the Cold Super-Puff HIP 41378 f

abstract:
  The census of known exoplanets exhibits a variety of physical parameters,
including densities that are measured to span the range from less dense than
styrofoam to more dense than iron. These densities represent a large diversity
of interior structures. Despite this staggering diversity, recent analyses have
shown that the densities of planets that orbit a common star exhibit remarkable
uniformity. A fascinating exception to this is the system HIP 41378 (also known
as K2-93), which contains a super-puff planet, HIP 41378 f, as well as several
planets with more typical bulk densities. The range of densities in this system
begs the question of what physical processes are responsible for the disparate
planetary structures in this system. In this paper, we consider how the
densities of the planets in the HIP 41378 system would have changed over time
as the host star evolved and the planets' atmospheres were subsequently
affected by the evolving insolation level. We also present a range of allowable
core masses for HIP 41378 f based on the measured planet parameters, and
comment on the feasibility of the proposed existence of planetary rings around
HIP 41378 f as an explanation for its current low density.


title:
Quantum computing at the quantum advantage threshold: a down-to-business
  review

abstract:
  It is expected that quantum computers would enable solving various problems
that are beyond the capabilities of the most powerful current supercomputers,
which are based on classical technologies. In the last three decades, advances
in quantum computing stimulated significant interest in this field from
industry, investors, media, executives, and general public. However, the
understanding of this technology, its current capabilities and its potential
impact in these communities is still lacking. Closing this gap requires a
complete picture of how to assess quantum computing devices' performance and
estimate their potential, a task made harder by the variety of quantum
computing models and physical platforms. Here we review the state of the art in
quantum computing, promising computational models and the most developed
physical platforms. We also discuss potential applications, the requirements
posed by these applications and technological pathways towards addressing these
requirements. Finally, we summarize and analyze the arguments for the quantum
computing market's further exponential growth. The review is written in a
simple language without equations, and should be accessible to readers with no
advanced background in mathematics and physics.


title:
Current Challenges in Infinite-Domain Constraint Satisfaction: Dilemmas
  of the Infinite Sheep

abstract:
  A Constraint Satisfaction Problem (CSP) is a computational problem where we
are given variables and constraints about them; the question is whether the
variables can be assigned values such that all constraints are satisfied. We
give an overview of the current state of research on CSPs where values for the
variables and constraints are taken from a finitely bounded homogeneous
structure which is fixed beforehand. We explain the main mathematical ideas so
far, the three dilemmas they brought upon us, and what could be done to
overcome them in order to obtain a satisfactory understanding of the
computational complexity of such CSPs.


title: Ground state energy of dilute Bose gases in 1D

abstract:
  We study the ground state energy of a gas of 1D bosons with density $\rho$,
interacting through a general, repulsive 2-body potential with scattering
length $a$, in the dilute limit $\rho |a|\ll1$. The first terms in the
expansion of the thermodynamic energy density are $\pi^2\rho^3/3(1+2\rho a)$,
where the leading order is the 1D free Fermi gas. This result covers the
Tonks-Girardeau limit of the Lieb-Liniger model as a special case, but given
the possibility that $a>0$, it also applies to potentials that differ
significantly from a delta function. We include extensions to spinless fermions
and 1D anyonic symmetries, and discuss an application to confined 3D gases.


title: Stein-based preconditioners for weak-constraint 4D-var

abstract:
  Algorithms for data assimilation try to predict the most likely state of a
dynamical system by combining information from observations and prior models.
One of the most successful data assimilation frameworks is the linearized
weak-constraint four-dimensional variational assimilation problem (4D-Var),
that can be ultimately interpreted as a minimization problem. One of the main
challenges of such an approach is the solution of large saddle point linear
systems arising as an inner linear step within the adopted non-linear solver.
The linear algebraic problem can be solved by means of a Krylov method, like
MINRES or GMRES, that needs to be preconditioned to ensure fast convergence in
terms of the number of iterations. In this paper we illustrate novel, efficient
preconditioning operators which involve the solution of certain Stein matrix
equations. In addition to achieving better computational performance, the
latter machinery allows us to derive tighter bounds for the eigenvalue
distribution of the preconditioned saddle point linear system. A panel of
diverse numerical results displays the effectiveness of the proposed
methodology compared to current state-of-the-art approaches.


title:
First Detections of Exop(lan)ets: Observations and Follow-Ups of the
  Floofiest Transits on Zoom

abstract:
  With the proliferation of online Zoom meetings as a means of doing science in
the 2020s, astronomers have made new and unexpected Target of Opportunity (ToO)
observations. Chief among these ToOs are observations of exop(lan)ets, or
"exopets." Building on the work of Mayorga et al. (2021) - whose work
characterized the rotational variations of "floofy" objects - we model exopets
using methods similar to those used for exoplanetary transits. We present data
collected for such exopet Zoom transits through a citizen science program in
the month of February 2022. The dataset includes parameters like exopet color,
floofiness, transit duration, and percentage of Zoom screen covered during the
event. For some targets, we also present microlensing and direct imaging data.
Using results from our modelling of 62 exopet observations as transits,
microlensing, and direct imaging events, we discuss our inferences of exopet
characteristics like their masses, sizes, orbits, colors, and floofiness.


title: Down-conversion of a single photon as a probe of many-body localization

abstract:
  Decay of a particle into more particles is a ubiquitous phenomenon to
interacting quantum systems, taking place in colliders, nuclear reactors, or
solids. In a non-linear medium, even a single photon would decay by
down-converting (splitting) into lower frequency photons with the same total
energy, at a rate given by Fermi's Golden Rule. However, the energy
conservation condition cannot be matched precisely if the medium is finite and
only supports quantized modes. In this case, the photon's fate becomes the
long-standing question of many-body localization (MBL), originally formulated
as a gedanken experiment for the lifetime of a single Fermi-liquid
quasiparticle confined to a quantum dot. Here we implement such an experiment
using a superconducting multi-mode cavity, the non-linearity of which was
tailored to strongly violate the photon number conservation. The resulting
interaction attempts to convert a single photon excitation into a shower of
low-energy photons, but fails due to the MBL mechanism, which manifests as a
striking spectral fine structure of multi-particle resonances at the cavity's
standing wave mode frequencies. Each resonance was identified as a many-body
state of radiation composed of photons from a broad frequency range, and not
obeying the Fermi's Golden Rule theory. Our result introduces a new platform to
explore fundamentals of MBL without having to control many atoms or qubits.


title:
Nonprompt direct-photon production in Au$+$Au collisions at
  $\sqrt{s_{_{NN}}}=200$ GeV

abstract:
  The measurement of the direct-photon spectrum from Au$+$Au collisions at
$\sqrt{s_{_{NN}}}=200$ GeV is presented by the PHENIX collaboration using the
external-photon-conversion technique for 0\%--93\% central collisions in a
transverse-momentum ($p_T$) range of 0.8--10 GeV/$c$. An excess of direct
photons, above prompt-photon production from hard-scattering processes, is
observed for $p_T<6$ GeV/$c$. Nonprompt direct photons are measured by
subtracting the prompt component, which is estimated as $N_{\rm coll}$-scaled
direct photons from $p$$+$$p$ collisions at 200 GeV, from the direct-photon
spectrum. Results are obtained for $0.8<p_T<6.0$ GeV/$c$ and suggest that the
spectrum has an increasing inverse slope from ${\approx}0.2$ to 0.4 GeV/$c$
with increasing $p_T$, which indicates a possible sensitivity of the
measurement to photons from earlier stages of the evolution of the collision.
In addition, like the direct-photon production, the $p_T$-integrated nonprompt
direct-photon yields also follow a power-law scaling behavior as a function of
collision-system size. The exponent, $\alpha$, for the nonprompt component is
found to be consistent with 1.1 with no apparent $p_T$ dependence.


title:
Effects of a strong phase transition on supernova explosions, compact
  stars and their mergers

abstract:
  We outline a theoretical approach supporting strong phase transitions from
normal nuclear matter to the deconfined quark-gluon plasma, in the equation of
state (EOS) for compact star matter. Implications of this hypothesis are
discussed for astrophysical applications. Special emphasis is devoted to
potentially detectable signatures, which can be directly related with the
occurrence of a sufficiently strong phase transition. Therefore, simulations of
core-collapse supernovae and binary compact star mergers are considered,
including the subsequent emission of gravitational waves and, in the case of
supernova, in addition the neutrinos play the role of messengers.


title: Scaling Up Models and Data with $\texttt{t5x}$ and $\texttt{seqio}$

abstract:
  Recent neural network-based language models have benefited greatly from
scaling up the size of training datasets and the number of parameters in the
models themselves. Scaling can be complicated due to various factors including
the need to distribute computation on supercomputer clusters (e.g., TPUs),
prevent bottlenecks when infeeding data, and ensure reproducible results. In
this work, we present two software libraries that ease these issues:
$\texttt{t5x}$ simplifies the process of building and training large language
models at scale while maintaining ease of use, and $\texttt{seqio}$ provides a
task-based API for simple creation of fast and reproducible training data and
evaluation pipelines. These open-source libraries have been used to train
models with hundreds of billions of parameters on datasets with multiple
terabytes of training data.
  Along with the libraries, we release configurations and instructions for
T5-like encoder-decoder models as well as GPT-like decoder-only architectures.
  $\texttt{t5x}$ and $\texttt{seqio}$ are open source and available at
https://github.com/google-research/t5x and https://github.com/google/seqio,
respectively.


title:
Mixed-Phoneme BERT: Improving BERT with Mixed Phoneme and Sup-Phoneme
  Representations for Text to Speech

abstract:
  Recently, leveraging BERT pre-training to improve the phoneme encoder in text
to speech (TTS) has drawn increasing attention. However, the works apply
pre-training with character-based units to enhance the TTS phoneme encoder,
which is inconsistent with the TTS fine-tuning that takes phonemes as input.
Pre-training only with phonemes as input can alleviate the input mismatch but
lack the ability to model rich representations and semantic information due to
limited phoneme vocabulary. In this paper, we propose MixedPhoneme BERT, a
novel variant of the BERT model that uses mixed phoneme and sup-phoneme
representations to enhance the learning capability. Specifically, we merge the
adjacent phonemes into sup-phonemes and combine the phoneme sequence and the
merged sup-phoneme sequence as the model input, which can enhance the model
capacity to learn rich contextual representations. Experiment results
demonstrate that our proposed Mixed-Phoneme BERT significantly improves the TTS
performance with 0.30 CMOS gain compared with the FastSpeech 2 baseline. The
Mixed-Phoneme BERT achieves 3x inference speedup and similar voice quality to
the previous TTS pre-trained model PnG BERT


title:
Time Lens++: Event-based Frame Interpolation with Parametric Non-linear
  Flow and Multi-scale Fusion

abstract:
  Recently, video frame interpolation using a combination of frame- and
event-based cameras has surpassed traditional image-based methods both in terms
of performance and memory efficiency. However, current methods still suffer
from (i) brittle image-level fusion of complementary interpolation results,
that fails in the presence of artifacts in the fused image, (ii) potentially
temporally inconsistent and inefficient motion estimation procedures, that run
for every inserted frame and (iii) low contrast regions that do not trigger
events, and thus cause events-only motion estimation to generate artifacts.
Moreover, previous methods were only tested on datasets consisting of planar
and faraway scenes, which do not capture the full complexity of the real world.
In this work, we address the above problems by introducing multi-scale
feature-level fusion and computing one-shot non-linear inter-frame motion from
events and images, which can be efficiently sampled for image warping. We also
collect the first large-scale events and frames dataset consisting of more than
100 challenging scenes with depth variations, captured with a new experimental
setup based on a beamsplitter. We show that our method improves the
reconstruction quality by up to 0.2 dB in terms of PSNR and up to 15% in LPIPS
score.


title:
Primordial Stochastic Gravitational Wave Background Anisotropies: in-in
  Formalization and Applications

abstract:
  Primordial non-Gaussianities of the scalar(tensor)-tensor-tensor type
supporting a non-trivial squeezed component are known to induce anisotropies in
the stochastic gravitational wave background. We derive the explicit form of
such anisotropies by making use, for the first time in this context, of the
in-in formalism for cosmological correlation functions. After illustrating the
general method and using it for the minimal single-field slow-roll case, we
apply it to multi-field models, providing both a tree-level and a one-loop
example. First, we make contact with previous results on anisotropies due to
the presence of an extra spin-2 field during inflation. Secondly, we calculate
the 1-loop scalar-tensor-tensor three-point function in the context of
so-called supersolid inflation. The corresponding gravitational wave anisotropy
is induced atop a gravitational signal that may be sufficiently large for
detection.


title: Learning from many trajectories

abstract:
  We initiate a study of supervised learning from many independent sequences
("trajectories") of non-independent covariates, reflecting tasks in sequence
modeling, control, and reinforcement learning. Conceptually, our
multi-trajectory setup sits between two traditional settings in statistical
learning theory: learning from independent examples and learning from a single
auto-correlated sequence. Our conditions for efficient learning generalize the
former setting--trajectories must be non-degenerate in ways that extend
standard requirements for independent examples. They do not require that
trajectories be ergodic, long, nor strictly stable.
  For linear least-squares regression, given $n$-dimensional examples produced
by $m$ trajectories, each of length $T$, we observe a notable change in
statistical efficiency as the number of trajectories increases from a few
(namely $m \lesssim n$) to many (namely $m \gtrsim n$). Specifically, we
establish that the worst-case error rate this problem is $\Theta(n / m T)$
whenever $m \gtrsim n$. Meanwhile, when $m \lesssim n$, we establish a (sharp)
lower bound of $\Omega(n^2 / m^2 T)$ on the worst-case error rate, realized by
a simple, marginally unstable linear dynamical system. A key upshot is that, in
domains where trajectories regularly reset, the error rate eventually behaves
as if all of the examples were independent altogether, drawn from their
marginals. As a corollary of our analysis, we also improve guarantees for the
linear system identification problem.


title: Free Resolutions and Generalized Hamming Weights of binary linear codes

abstract:
  In this work, we explore the relationship between free resolution of some
monomial ideals and Generalized Hamming Weights (GHWs) of binary codes. More
precisely, we look for a structure smaller than the set of codewords of minimal
support that provides us some information about the GHWs. We prove that the
first and second generalized Hamming weight of a binary linear code can be
computed (by means of a graded free resolution) from a set of monomials
associated to a binomial ideal related with the code. Moreover, the remaining
weights are bounded by the Betti numbers for that set.


title:
Linear instability analysis on compressible Navier-Stokes equations with
  strong boundary layer

abstract:
  It is a classical problem in fluid dynamics about the stability and
instability of different hydrodynamic patterns in various physical settings, in
particular in the high Reynolds number limit of laminar flow with boundary
layer. However, there are very few mathematical results on the compressible
fluid despite the extensive studies when the fluid is governed by the
incompressible Navier-Stokes equations. This paper aims to introduce a new
approach to study the compressible Navier-Stokes equations in the subsonic and
high Reynolds number regime where a subtle quasi-compressible and Stokes
iteration is developed. As a byproduct, we show the spectral instability of
subsonic boundary layer.


title:
CatIss: An Intelligent Tool for Categorizing Issues Reports using
  Transformers

abstract:
  Users use Issue Tracking Systems to keep track and manage issue reports in
their repositories. An issue is a rich source of software information that
contains different reports including a problem, a request for new features, or
merely a question about the software product. As the number of these issues
increases, it becomes harder to manage them manually. Thus, automatic
approaches are proposed to help facilitate the management of issue reports.
  This paper describes CatIss, an automatic CATegorizer of ISSue reports which
is built upon the Transformer-based pre-trained RoBERTa model. CatIss
classifies issue reports into three main categories of Bug reports,
Enhancement/feature requests, and Questions. First, the datasets provided for
the NLBSE tool competition are cleaned and preprocessed. Then, the pre-trained
RoBERTa model is fine-tuned on the preprocessed dataset. Evaluating CatIss on
about 80 thousand issue reports from GitHub, indicates that it performs very
well surpassing the competition baseline, TicketTagger, and achieving 87.2%
F1-score (micro average). Additionally, as CatIss is trained on a wide set of
repositories, it is a generic prediction model, hence applicable for any unseen
software project or projects with little historical data. Scripts for cleaning
the datasets, training CatIss, and evaluating the model are publicly available.


title:
The collider landscape: which collider for establishing the SM
  instability?

abstract:
  Capabilities of future colliders are usually discussed assuming specific
hypothetical new physics. We consider the opposite possibility: that no new
physics is accessible, and we want to learn if the unnatural Standard Model is
part of a vast landscape. We quantify progress in this sense via the Shannon
entropy of the multiverse, and argue that a main point would be establishing
the possible instability scale of the Higgs potential. This primarily needs
reducing the uncertainty on the strong coupling and on the top quark mass. We
show that the top quark mass can be measured well enough via a $t \bar t$
threshold scan with low $10^{33}\,{\rm cm}^{-2}{\rm sec}^{-1}$ luminosity, that
seems achievable at a `small' $e^+ e^-$ collider in the LEP tunnel, or at a
muon collider demonstrator.


title:
Optical characterization of two cataclysmic variables: RBS 0490 and SDSS
  J075939.79+191417.3

abstract:
  We present optical photometric and spectroscopic observations of two
Cataclysmic Variables (CVs), namely RBS 0490 and SDSS J075939.79+191417.3. The
optical variations of RBS 0490 have been found to occur at the period of
1.689$\pm$0.001 hr which appears to be a probable orbital period of the system.
Present photometric observations of SDSS J075939.79+191417.3 confirm and refine
the previously determined orbital period as 3.14240928$\pm$0.00000096 hr. The
presence of long-duration eclipse features in the light curves of SDSS
J075939.79+191417.3 indicates eclipses might be due to an accretion disc and
bright spot. The orbital inclination of SDSS J075939.79+191417.3 is estimated
to be $\sim$ 78 $^\circ$ using the eclipse morphology. The phased-light curve
variations during the orbital cycle of RBS 0490 provide evidence of the
emission from an independent second accretion region or a second fainter pole.
Optical spectra of RBS 0490 and SDSS J075939.79+191417.3 show the presence of
strong Balmer, weak He II ($\lambda$4686) emission lines, along with the
detection of strong $H\beta$ emission lines with a large value of equivalent
width. The characteristic features of RBS 0490 seem to favour low-field polars,
while SDSS J075939.79+191417.3 appears to be similar to the non-magnetic
systems.


title: Quantum interference in asymmetric superconducting nanowire loops

abstract:
  Macroscopic phase coherence in superconductors enables quantum interference
and phase manipulation at realistic device length scales. Numerous
superconducting electronic devices are based on the modulation of the
supercurrent in superconducting loops. While the overall behavior of symmetric
superconducting loops have been studied, the effects of asymmetries in such
devices remain under-explored and poorly understood. Here we report on an
experimental and theoretical study of the flux modulation of the persistent
current in a doubly-connected asymmetric aluminum nanowire loop. A model
considering the length and electronic cross-section asymmetries in the loop
provides a quantitative account of the observations. Comparison with
experiments give essential parameters such as persistent and critical currents
as well as the amount of asymmetry which can provide feedback into the design
of superconducting quantum devices.


title:
Restoring Vision through Retinal Implants -- A Systematic Literature
  Review

abstract:
  This work presents a bunched of promising technologies to treat blind people:
the bionic eyes. The strategy is to combine a retina implant with software
capable to interpret the information received. Along this line of thinking,
projects such as Retinal Prosthetic Strategy with the Capacity to Restore
Normal Vision from Weill Medical College of Cornell University Project, Update
on Retinal Prosthetic Research from The Boston Retinal Implant Project, and
Restoration of Vision Using Wireless Cortical Implants from Monash Vision Group
Project, have shown in a different context the use of technologies that commits
to bring the vision through its use.


title: Freeness and $S$-arithmeticity of rational M\"{o}bius groups

abstract:
  We initiate a new, computational approach to a classical problem: certifying
non-freeness of ($2$-generator, parabolic) M\"{o}bius subgroups of
$\mathrm{SL}(2,\mathbb{Q})$. The main tools used are algorithms for Zariski
dense groups and algorithms to compute a presentation of $\mathrm{SL}(2, R)$
for a localization $R= \mathbb{Z}[\frac{1}{b}]$ of $\mathbb{Z}$. We prove that
a M\"{o}bius subgroup $G$ is not free by showing that it has finite index in
the relevant $\mathrm{SL}(2, R)$. Further information about the structure of
$G$ is obtained; for example, we compute the minimal subgroup of finite index
in $\mathrm{SL}(2,R)$ that contains $G$.


title:
Two-loop mixed QCD-EW corrections to $q \overline{q} \to H g$, $qg \to
  Hq$, and $\overline{q}g \to H\overline{q}$

abstract:
  We compute the two-loop mixed QCD-Electroweak corrections to $q \overline{q}
\to H g$ and its crossed channels $q g \to H q$, $\overline{q} g \to H
\overline{q}$, limiting ourselves to the contribution of light virtual quarks.
We compute the independent helicity amplitudes as well as the form factors for
this process, expressing them in terms of hyperlogarithms with algebraic
arguments. The Feynman integrals are computed by direct integration over
Feynman parameters and the results are expressed in terms of a basis of
rational prefactors.


title: Notes on 5d Partition Functions - II

abstract:
  We study the large $N$ limit of partition functions for 5d supersymmetric
gauge theories with fundamental matter. Depending on the matter content, we
find that the scaling behaviour at the leading order can be either $N^2$ or
$N^{\frac{3}{2}}$. The latter scaling reminds one of the 3d theories with
M-theory duals and we discuss how to extract this behaviour from the recently
proposed 3d theories associated with the compactification of 5d SCFTs on 2d
surfaces.


title: Dynamics of mean-field bosons at positive temperature

abstract:
  We study the time-evolution of an initially trapped weakly interacting Bose
gas at positive temperature, after the trapping potential has been switched
off. It has been recently shown in arXiv:2009.00992 that the one-particle
density matrix of Gibbs states of the interacting trapped gas is given, to
leading order in $N$, as $N \to \infty$, by the one of the ideal gas, with the
condensate wave function replaced by the minimizer of the Hartree energy
functional. We show that this structure is stable with respect to the many-body
evolution in the following sense: the dynamics can be approximated in terms of
the time-dependent Hartree equation for the condensate wave function and in
terms of the free evolution for the thermally excited particles. The main
technical novelty of our work is the use of the Hartree-Fock-Bogoliubov
equations to define a fluctuation dynamics.


title:
Leverage Your Local and Global Representations: A New Self-Supervised
  Learning Strategy

abstract:
  Self-supervised learning (SSL) methods aim to learn view-invariant
representations by maximizing the similarity between the features extracted
from different crops of the same image regardless of cropping size and content.
In essence, this strategy ignores the fact that two crops may truly contain
different image information, e.g., background and small objects, and thus tends
to restrain the diversity of the learned representations. In this work, we
address this issue by introducing a new self-supervised learning strategy,
LoGo, that explicitly reasons about Local and Global crops. To achieve view
invariance, LoGo encourages similarity between global crops from the same
image, as well as between a global and a local crop. However, to correctly
encode the fact that the content of smaller crops may differ entirely, LoGo
promotes two local crops to have dissimilar representations, while being close
to global crops. Our LoGo strategy can easily be applied to existing SSL
methods. Our extensive experiments on a variety of datasets and using different
self-supervised learning frameworks validate its superiority over existing
approaches. Noticeably, we achieve better results than supervised models on
transfer learning when using only 1/10 of the data.


title: Stochastic Calculus in Infinite Dimensions and SPDEs

abstract:
  These notes aim to take the reader from an elementary understanding of
functional analysis and probability theory to a robust construction of the
stochastic integral in Hilbert Spaces. We consider integrals driven at first by
real valued martingales and later by Cylindrical Brownian Motion, introducing
this concept and expanding into a basic set up for Stochastic Partial
Differential Equations (SPDEs). The framework that we establish facilitates an
exceedingly broad class of SPDEs and noise structures, in which we build upon
standard SDE theory and rigorously deduce a conversion between the Stratonovich
and It\^{o} Forms. The study of Stratonovich equations is largely motivated by
the stochastic variational principle of SALT [Holm,2015] for fluid dynamics,
and we discuss an application of the framework to a Navier-Stokes Equation with
Stochastic Lie Transport as seen in [Goodair,2022]. Moreover we prove a
fundamental existence and uniqueness result (which to the best of our
knowledge, is not present in the literature) for SPDEs evolving in a finite
dimensional Hilbert Space driven by Cylindrical Brownian Motion, assuming the
analogy to the Lipschitz and linear growth conditions for the standard
existence and uniqueness theory for SDEs.


title: A Proof of the Kahn-Kalai Conjecture

abstract: We prove the "expectation-threshold" conjecture of Kahn and Kalai.


title:
Controlled Discovery and Localization of Signals via Bayesian Linear
  Programming

abstract:
  Scientists often must simultaneously discover signals and localize them as
precisely as possible. For instance, in genetic fine-mapping, high correlations
between nearby genetic variants make it hard to identify the exact locations of
causal variants. So the statistical task is to output as many disjoint regions
containing a signal as possible, each as small as possible, while controlling
false positives. Similar problems arise in any application where signals cannot
be perfectly localized, such as locating stars in astronomical surveys and
change point detection in time series data. Our first contribution is to
propose a notion of resolution-adjusted power for such problems. Second, we
introduce Bayesian Linear Programming (BLiP), a Bayesian method for jointly
detecting and localizing signals. BLiP overcomes an extremely high-dimensional
and non-convex problem to verifiably nearly maximize expected power while
provably controlling false positives. BLiP is very computationally efficient
and can wrap around nearly any Bayesian model and algorithm. Applying BLiP to
existing state-of-the-art analyses of UK Biobank data (for genetic
fine-mapping) and the Sloan Digital Sky Survey (for astronomical point source
detection) increased resolution-adjusted power by 30-120% in just a few minutes
of computation. BLiP is implemented in the new packages pyblip (Python) and
blipr (R).


title: Adversarial Examples in Random Neural Networks with General Activations

abstract:
  A substantial body of empirical work documents the lack of robustness in deep
learning models to adversarial examples. Recent theoretical work proved that
adversarial examples are ubiquitous in two-layers networks with sub-exponential
width and ReLU or smooth activations, and multi-layer ReLU networks with
sub-exponential width. We present a result of the same type, with no
restriction on width and for general locally Lipschitz continuous activations.
  More precisely, given a neural network $f(\,\cdot\,;{\boldsymbol \theta})$
with random weights ${\boldsymbol \theta}$, and feature vector ${\boldsymbol
x}$, we show that an adversarial example ${\boldsymbol x}'$ can be found with
high probability along the direction of the gradient $\nabla_{{\boldsymbol
x}}f({\boldsymbol x};{\boldsymbol \theta})$. Our proof is based on a Gaussian
conditioning technique. Instead of proving that $f$ is approximately linear in
a neighborhood of ${\boldsymbol x}$, we characterize the joint distribution of
$f({\boldsymbol x};{\boldsymbol \theta})$ and $f({\boldsymbol x}';{\boldsymbol
\theta})$ for ${\boldsymbol x}' = {\boldsymbol x}-s({\boldsymbol
x})\nabla_{{\boldsymbol x}}f({\boldsymbol x};{\boldsymbol \theta})$.


title:
A Few Almost Trivial Notes on the Symplectic Radon Transform and the
  Tomographic Picture of Quantum Mechanics

abstract:
  We emphasize in these pedagogical notes the that the theory of the Radon
transform and its applications is best understood using the theory of the
metaplectic group and the quadratic Fourier transforms generating metaplectic
operator.. Doing this we hope that these notes will be useful to a larger
audience, including researchers in time-frequency analysis.


title:
ShapeFindAR: Exploring In-Situ Spatial Search for Physical Artifact
  Retrieval using Mixed Reality

abstract:
  Personal fabrication is made more accessible through repositories like
Thingiverse, as they replace modeling with retrieval. However, they require
users to translate spatial requirements to keywords, which paints an incomplete
picture of physical artifacts: proportions or morphology are non-trivially
encoded through text only. We explore a vision of in-situ spatial search for
(future) physical artifacts, and present ShapeFindAR, a mixed-reality tool to
search for 3D models using in-situ sketches blended with textual queries. With
ShapeFindAR, users search for geometry, and not necessarily precise labels,
while coupling the search process to the physical environment (e.g., by
sketching in-situ, extracting search terms from objects present, or tracing
them). We developed ShapeFindAR for HoloLens 2, connected to a database of
3D-printable artifacts. We specify in-situ spatial search, describe its
advantages, and present walkthroughs using ShapeFindAR, which highlight novel
ways for users to articulate their wishes, without requiring complex modeling
tools or profound domain knowledge.


title:
Anomaly-free axion dark matter in three Higgs doublet model and its
  phenomenological implications

abstract:
  We study phenomenological implications of an axion that arises as a pseudo
Nambu-Goldstone boson due to spontaneous breaking of anomaly-free global flavor
symmetry. One interesting possibility for such anomaly-free axion to explain
dark matter (DM) is when it has a mass of order keV and an intermediate scale
decay constant, it can be explored through direct search experiments, X-ray
observations, various stellar cooling processes, and the misalignment mechanism
naturally explains the DM abundance. As a concrete renormalizable model of such
axion, we consider an extended Higgs sector with global flavor symmetry, which
consists of three Higgs doublet fields and three singlet Higgs fields with
$U(1)_{\rm B-L}$ charges. We identify viable parameter regions that satisfy
theoretical bounds on the Higgs potential and various experimental limits on
this model, and evaluate the mass spectra of the axion and extra Higgs bosons.
We find that if the axion DM explains the XENON1T excess, the ratios of the
vacuum expectation values of the Higgs doublets are tightly constrained for the
case where the mass of the extra Higgs boson is below TeV. We also demonstrate
that the axion-electron coupling is correlated with the extra Higgs boson
masses and mixing angles for CP-even Higgs bosons. Thus, if the axion is
detected in the future observations, the extra Higgs boson masses and the
coupling of the standard model-like Higgs boson with the weak gauge bosons are
restricted. This is a good example of the synergy between searches for the
axion DM and the BSM around the electroweak scale.


title: Analyzing Wrap-Up Effects through an Information-Theoretic Lens

abstract:
  Numerous analyses of reading time (RT) data have been implemented -- all in
an effort to better understand the cognitive processes driving reading
comprehension. However, data measured on words at the end of a sentence -- or
even at the end of a clause -- is often omitted due to the confounding factors
introduced by so-called "wrap-up effects," which manifests as a skewed
distribution of RTs for these words. Consequently, the understanding of the
cognitive processes that might be involved in these wrap-up effects is limited.
In this work, we attempt to learn more about these processes by examining the
relationship between wrap-up effects and information-theoretic quantities, such
as word and context surprisals. We find that the distribution of information in
prior contexts is often predictive of sentence- and clause-final RTs (while not
of sentence-medial RTs). This lends support to several prior hypotheses about
the processes involved in wrap-up effects.


title: Constraining galaxy-halo connection with high-order statistics

abstract:
  We investigate using three-point statistics in constraining the galaxy-halo
connection. We show that for some galaxy samples, the constraints on the halo
occupation distribution parameters are dominated by the three-point function
signal (over its two-point counterpart). We demonstrate this on mock catalogs
corresponding to the Luminous Red Galaxies (LRGs), Emission-Line Galaxies
(ELG), and quasars (QSOs) targeted by the Dark Energy Spectroscopic Instrument
(DESI) Survey. The projected three-point function for triangle sides less up to
20$h^{-1}$ Mpc measured from a cubic Gpc of data can constrain the
characteristic minimum mass of the LRGs with a precision of $0.46$ %. For
comparison, similar constraints from the projected two-point function are
$1.55$ %. The improvements for the ELGs and QSOs targets are more modest. In
the case of the QSOs it is caused by the high shot-noise of the sample, and in
the case of the ELGs, this is caused by the range of halo masses of the host
halos. The most time-consuming part of our pipeline is the measurement of the
three-point functions. We adopt a tabulation method, proposed in earlier works
for the two-point function, to reduce significantly the required compute time
for the three-point analysis.


title:
A simplified nonsmooth nonconvex bundle method with applications to
  security-constrained ACOPF problems

abstract:
  An optimization algorithm for a group of nonsmooth nonconvex problems
inspired by two-stage stochastic programming problems is proposed. The main
challenges for these problems include (1) the problems lack the popular
lower-type properties such as prox-regularity assumed in many nonsmooth
nonconvex optimization algorithms, (2) the objective can not be analytically
expressed and (3) the evaluation of function values and subgradients are
computationally expensive. To address these challenges, this report first
examines the properties that exist in many two-stage problems, specifically
upper-C^2 objectives. Then, we show that quadratic penalty method for
security-constrained alternating current optimal power flow (SCACOPF)
contingency problems can make the contingency solution functions upper-C^2 .
Based on these observations, a simplified bundle algorithm that bears
similarity to sequential quadratic programming (SQP) method is proposed. It is
more efficient in implementation and computation compared to conventional
bundle methods. Global convergence analysis of the algorithm is presented under
novel and reasonable assumptions. The proposed algorithm therefore fills the
gap of theoretical convergence for some smoothed SCACOPF problems. The
inconsistency that might arise in our treatment of the constraints are
addressed through a penalty algorithm whose convergence analysis is also
provided. Finally, theoretical capabilities and numerical performance of the
algorithm are demonstrated through numerical examples.


title: Distance bounds for generalized bicycle codes

abstract:
  Generalized bicycle (GB) codes is a class of quantum error-correcting codes
constructed from a pair of binary circulant matrices. Unlike for other simple
quantum code ans\"atze, unrestricted GB codes may have linear distance scaling.
In addition, low-density parity-check GB codes have a naturally overcomplete
set of low-weight stabilizer generators, which is expected to improve their
performance in the presence of syndrome measurement errors. For such GB codes
with a given maximum generator weight $w$, we constructed upper distance bounds
by mapping them to codes local in $D\le w-1$ dimensions, and lower existence
bounds which give $d\ge {\cal O}({n}^{1/2})$. We have also done an exhaustive
enumeration of GB codes for certain prime circulant sizes in a family of
two-qubit encoding codes with row weights 4, 6, and 8; the observed distance
scaling is consistent with $A(w){n}^{1/2}+B(w)$, where $n$ is the code length
and $A(w)$ is increasing with $w$.


title: On the probability-quality paradox in language generation

abstract:
  When generating natural language from neural probabilistic models, high
probability does not always coincide with high quality: It has often been
observed that mode-seeking decoding methods, i.e., those that produce
high-probability text under the model, lead to unnatural language. On the other
hand, the lower-probability text generated by stochastic methods is perceived
as more human-like. In this note, we offer an explanation for this phenomenon
by analyzing language generation through an information-theoretic lens.
Specifically, we posit that human-like language should contain an amount of
information (quantified as negative log-probability) that is close to the
entropy of the distribution over natural strings. Further, we posit that
language with substantially more (or less) information is undesirable. We
provide preliminary empirical evidence in favor of this hypothesis; quality
ratings of both human and machine-generated text -- covering multiple tasks and
common decoding strategies -- suggest high-quality text has an information
content significantly closer to the entropy than we would expect by chance.


title:
Improved Relation Networks for End-to-End Speaker Verification and
  Identification

abstract:
  Speaker identification systems in a real-world scenario are tasked to
identify a speaker amongst a set of enrolled speakers given just a few samples
for each enrolled speaker. This paper demonstrates the effectiveness of
meta-learning and relation networks for this use case. We propose improved
relation networks for speaker verification and few-shot (unseen) speaker
identification. The use of relation networks facilitates joint training of the
frontend speaker encoder and the backend model. Inspired by the use of
prototypical networks in speaker verification and to increase the
discriminability of the speaker embeddings, we train the model to classify
samples in the current episode amongst all speakers present in the training
set. Furthermore, we propose a new training regime for faster model convergence
by extracting more information from a given meta-learning episode with
negligible extra computation. We evaluate the proposed techniques on VoxCeleb,
SITW and VCTK datasets on the tasks of speaker verification and unseen speaker
identification. The proposed approach outperforms the existing approaches
consistently on both tasks.


title: SimVQA: Exploring Simulated Environments for Visual Question Answering

abstract:
  Existing work on VQA explores data augmentation to achieve better
generalization by perturbing the images in the dataset or modifying the
existing questions and answers. While these methods exhibit good performance,
the diversity of the questions and answers are constrained by the available
image set. In this work we explore using synthetic computer-generated data to
fully control the visual and language space, allowing us to provide more
diverse scenarios. We quantify the effect of synthetic data in real-world VQA
benchmarks and to which extent it produces results that generalize to real
data. By exploiting 3D and physics simulation platforms, we provide a pipeline
to generate synthetic data to expand and replace type-specific questions and
answers without risking the exposure of sensitive or personal data that might
be present in real images. We offer a comprehensive analysis while expanding
existing hyper-realistic datasets to be used for VQA. We also propose Feature
Swapping (F-SWAP) -- where we randomly switch object-level features during
training to make a VQA model more domain invariant. We show that F-SWAP is
effective for enhancing a currently existing VQA dataset of real images without
compromising on the accuracy to answer existing questions in the dataset.


title:
Units of twisted group rings and their correlations to classical group
  rings

abstract:
  Given a central extension $\Gamma$ of some normal subgroup $N$ by a group
$G$, we study the group ring $R \Gamma$ over some domain $R$. We obtain a
direct sum decomposition in terms of various twisted group rings of $G$ and
concrete information on the kernel and cokernel of the projections. This allows
to kick-start the investigation of the unit group $\mathcal{U}( R\Gamma)$ via
the unit group of twisted group rings. Among others we construct a new generic
family of units therein. As an application hereof we are able to obtain a
complete description of the unit group of the integral group ring of $G \times
C_2^n$ from data of $G$.


title:
Singularity formation in the incompressible Euler equation in finite and
  infinite time

abstract:
  Some classical and recent results on the Euler equations governing perfect
(incompressible and inviscid) fluid motion are collected and reviewed, with
some small novelties scattered throughout. The perspective and emphasis will be
given through the lens of infinite-dimensional dynamical systems, and various
open problems are listed and discussed.


title:
Search for Coincident Gravitational Wave and Fast Radio Burst Events
  from 4-OGC and the First CHIME/FRB Catalog

abstract:
  Advanced LIGO and Virgo have reported ninety confident gravitational-wave
(GW) observations from compact-binary coalescences from their three observation
runs. In addition, numerous subthreshold gravitational-wave candidates have
been identified. Binary neutron star (BNS) mergers can produce gravitational
waves and short-gamma ray bursts, as confirmed by GW170817/GRB 170817A. There
may be electromagnetic counterparts recorded in archival observations
associated with subthreshold gravitational-wave candidates. The CHIME/FRB
collaboration has reported the first large sample of fast radio bursts (FRBs),
millisecond radio transients detected up to cosmological distances; a fraction
of these may be associated with BNS mergers. This work searches for coincident
gravitational waves and FRBs from BNS mergers using candidates from the
4th-Open Gravitational-wave Catalog (4-OGC) and the first CHIME/FRB catalog. We
use a ranking statistic for GW/FRB association which combines the
gravitational-wave detection statistic with the odds of temporal and spatial
association. We analyze gravitational-wave candidates and non-repeating FRBs
from 2019 April 1 to 2019 July 1, when both the Advanced LIGO/Virgo
gravitational-wave detectors and the CHIME radio telescope were observing. The
most significant coincident candidate has a false alarm rate of 0.29 per
observation time, which is consistent with a null observation. The null results
imply at most $\mathcal{O}(0.01)\%$ - $\mathcal{O}(1)\%$ of FRBs are produced
from the BNS mergers.


title: Cosmological Neutrinos

abstract:
  Within the context of hot big-bang cosmology, a cosmic background of
presently low energy neutrinos is predicted to exist in concert with the
photons of the cosmic background radiation. The number density of the
cosmological neutrinos is of the same order as that of the photons of the
cosmic background radiation. That makes neutrinos the second most abundant
particle species in the universe. In the early universe, when these neutrinos
were highly relativistic, their effects in determining the ultimate structure
and evolution of the universe were significant.


title: Gromov-Witten theory via roots and logarithms

abstract:
  Orbifold and logarithmic structures provide independent routes to the virtual
enumeration of curves with tangency orders for a simple normal crossings pair
$(X|D)$. The theories do not coincide and their relationship has remained
mysterious. We prove that the genus zero orbifold theories of multi-root stacks
of strata blowups of $(X|D)$ converge to the corresponding logarithmic theory
of $(X|D)$. With fixed numerical data, there is an explicit combinatorial
criterion that guarantees when a blowup is sufficiently refined for the
theories to coincide. There are two key ideas in the proof. The first is the
construction of a naive Gromov-Witten theory, which serves as an intermediary
between roots and logarithms. The second is a smoothing theorem for tropical
stable maps; the geometric theorem then follows via virtual intersection theory
relative to the universal target. The results import new computational tools
into logarithmic Gromov-Witten theory. As an application, we show that the
genus zero logarithmic Gromov-Witten theory of a pair is determined by the
absolute Gromov-Witten theories of its strata.


title: A Baseline Readability Model for Cebuano

abstract:
  In this study, we developed the first baseline readability model for the
Cebuano language. Cebuano is the second most-used native language in the
Philippines with about 27.5 million speakers. As the baseline, we extracted
traditional or surface-based features, syllable patterns based from Cebuano's
documented orthography, and neural embeddings from the multilingual BERT model.
Results show that the use of the first two handcrafted linguistic features
obtained the best performance trained on an optimized Random Forest model with
approximately 87% across all metrics. The feature sets and algorithm used also
is similar to previous results in readability assessment for the Filipino
language showing potential of crosslingual application. To encourage more work
for readability assessment in Philippine languages such as Cebuano, we
open-sourced both code and data.


title:
A Derivation of Nesterov's Accelerated Gradient Algorithm from Optimal
  Control Theory

abstract:
  Nesterov's accelerated gradient algorithm is derived from first principles.
The first principles are founded on the recently-developed optimal control
theory for optimization. This theory frames an optimization problem as an
optimal control problem whose trajectories generate various continuous-time
algorithms. The algorithmic trajectories satisfy the necessary conditions for
optimal control. The necessary conditions produce a controllable dynamical
system for accelerated optimization. Stabilizing this system via a quadratic
control Lyapunov function generates an ordinary differential equation. An Euler
discretization of the resulting differential equation produces Nesterov's
algorithm. In this context, this result solves the purported mystery
surrounding the algorithm.


title: Volume of Intersection of a Cone with a Sphere

abstract:
  The manuscript provides formulas for the volume of a body defined by the
intersection of a solid cone and a solid sphere as a function of the sphere
radius, of the distance between cone apex and sphere center, and of the cone
aperture angle. If the sphere center lies on the (extended) cone axis the
analysis may be based on cylinder coordinates fixed at the cone axis, and the
volume is the sum of the well-known volumes of finite cones and sphere caps. At
the general geometry the sphere center is not on the (extended) cone axis. Our
approach calculates the volume by slicing space perpendicular to the cone axis
and by integrating the lens areas defined by the sphere-cone intersection.
These volume integrals are rephrased with the aid of the Byrd-Friedmann tables
to Elliptic Integrals of the First, Second and Third Kind.


title: Exotic Cores with and without Dark-Matter Admixtures in Compact Stars

abstract:
  We parameterize the core of compact spherical star configurations by a mass
($m_x$) and a radius ($r_x$) and study the resulting admissible areas in the
total-mass vs. total-radius plane. The employed fiducial equation-of-state
models of the corona at radii $r \ge r_x$ and pressures $p \le p_x = p(r =
r_x)$ are that (i) of constant sound velocity and (ii) a proxy of DY$\Delta$
DD-ME2 provided by Buchdahl's exactly solvable ansatz. The core ($r < r_x$) may
contain any type of material, e.g. Standard-Model matter with unspecified
equation of state or/and an unspecified Dark-Matter admixture. Employing a toy
model for the cool equation of state with first-order phase transition we
discuss also the mass-radius relation of compact stars with an admixture of
Dark Matter in a Mirror-World scenario.


title:
Group decision making with q-rung orthopair hesitant fuzzy preference
  relations

abstract:
  This paper mainly studies group decision making (GDM) problem based on q-rung
orthopair hesitant fuzzy preference relations (q-ROHFPRs). First, the
definitions of q-ROHFPR and additive consistent q-ROHFPR are introduced. The
consistency index of q-ROHFPR is used to judge whether the matrix of q-ROHFPR
is acceptable. For the q-ROHFPR matrix that does not meet the acceptable
consistency, two optimization models are established for deriving the
acceptably additive consistent q-ROHFPRs. In order to make the q-ROHFPR matrix
of decision makers still satisfy the consistency after aggregation, this paper
extends the q-rung orthopair hesitant fuzzy weighted geometric average operator
(q-ROHFWGA). At the same time, in order to verify whether decision makers can
reach consensus after aggregation, a consensus index based on distance is
offered. Based on this consensus index, an optimization model that satisfies
consistency and consensus is constructed to solve the priority vector, and
develop a consistency and consensus-based approach for dealing with group
decision-making (GDM) with q-ROHFPRs. Finally, the case in this paper verifies
the validity and accuracy of the group decision-making model, and also verifies
that the q-ROHFPR consistency and consensus management model proposed in this
paper can solve the q-rung orthopair hesitant fuzzy preference group
decision-making problem.


title:
Multi-source data processing and fusion method for power distribution
  internet of things based on edge intelligence

abstract:
  With the rapid advancement of the Energy Internet strategy, the number of
sensors within the Power Distribution Internet of Things (PD-IoT) has increased
dramatically. In this paper, an edge intelligence-based PD-IoT multi-source
data processing and fusion method is proposed to solve the problems of
confusing storage and insufficient fusion computing performance of multi-source
heterogeneous distribution data. First, a PD-IoT multi-source data processing
and fusion architecture based on edge smart terminals is designed. Second, to
realize the uniform conversion of various sensor data sources in the
distribution network in terms of magnitude and order of magnitude. By
introducing the Box-Cox transform to improve the data offset problem in the
Zscore normalization process, a multi-source heterogeneous data processing
method for distribution networks based on the Box-Cox transform Zscore is
proposed. Then, the conflicting phenomena of DS inference methods in data
source fusion are optimally handled based on the PCA algorithm. A multi-source
data fusion model based on DS inference with conflict optimization is
constructed to ensure the effective fusion of distribution data sources from
different domains. Finally, the effectiveness of the proposed method is
verified by an experimental analysis of an IEEE39 node system in a regional
distribution network in China.


title:
Progressive approximation of bound states by finite series of
  square-integrable functions

abstract:
  We use the "tridiagonal representation approach" to solve the
time-independent Schr\"odinger equation for bound states in a basis set of
finite size. We obtain two classes of solutions written as finite series of
square integrable functions that support a tridiagonal matrix representation of
the wave operator. The differential wave equation becomes an algebraic
three-term recursion relation for the expansion coefficients of the series,
which is solved in terms of finite polynomials in the energy and/or potential
parameters. These orthogonal polynomials contain all physical information about
the system. The basis elements in configuration space are written in terms of
either the Romanovski-Bessel polynomial or the Romanovski-Jacobi polynomial.
The maximum degree of both polynomials is limited by the polynomial
parameter(s). This makes the size of the basis set finite but sufficient to
give a very good approximation of the bound states wavefunctions that improves
with an increase in the basis size.


title: Performative Power

abstract:
  We introduce the notion of performative power, which measures the ability of
a firm operating an algorithmic system, such as a digital content
recommendation platform, to steer a population. We relate performative power to
the economic theory of market power. Traditional economic concepts are well
known to struggle with identifying anti-competitive patterns in digital
platforms--a core challenge is the difficulty of defining the market, its
participants, products, and prices. Performative power sidesteps the problem of
market definition by focusing on a directly observable statistical measure
instead. High performative power enables a platform to profit from steering
participant behavior, whereas low performative power ensures that learning from
historical data is close to optimal.
  Our first general result shows that under low performative power, a firm
cannot do better than standard supervised learning on observed data. We draw an
analogy with a firm being a price-taker, an economic condition that arises
under perfect competition in classical market models. We then contrast this
with a market where performative power is concentrated and show that the
equilibrium state can differ significantly. We go on to study performative
power in a concrete setting of strategic classification where participants can
switch between competing firms. We show that monopolies maximize performative
power and disutility for the participant, while competition and outside options
decrease performative power. We end on a discussion of connections to measures
of market power in economics and of the relationship with ongoing antitrust
debates.


title:
Hall field-induced magneto-oscillations near charge neutrality point in
  graphene

abstract:
  We explore the non-equilibrium transport regime in graphene using a large dc
current in combination with a perpendicular magnetic field. The strong in-plane
Hall field that is generated in the bulk of the graphene channel results in
Landau levels that are tilted spatially. The energy of cyclotron orbits in the
bulk varies as a function of the spatial position of the guiding center,
enabling us to observe a series of compelling features. While Shubnikov-de Haas
oscillations are predictably suppressed in the presence of the Hall field, a
set of fresh magnetoresistance oscillations emerge near the charge neutrality
point as a function of dc current. Two branches of oscillations with linear
dispersions are evident as we vary carrier density and dc current, the velocity
of which closely resembles the TA and LA phonon modes, suggestive of
phonon-assisted intra-Landau level transitions between adjacent cyclotron
orbits. Our results offer unique possibilities to explore non-equilibrium
phenomena in two-dimensional materials and van der Waals heterostructures.


title:
Templates for 3D Object Pose Estimation Revisited: Generalization to New
  Objects and Robustness to Occlusions

abstract:
  We present a method that can recognize new objects and estimate their 3D pose
in RGB images even under partial occlusions. Our method requires neither a
training phase on these objects nor real images depicting them, only their CAD
models. It relies on a small set of training objects to learn local object
representations, which allow us to locally match the input image to a set of
"templates", rendered images of the CAD models for the new objects. In contrast
with the state-of-the-art methods, the new objects on which our method is
applied can be very different from the training objects. As a result, we are
the first to show generalization without retraining on the LINEMOD and
Occlusion-LINEMOD datasets. Our analysis of the failure modes of previous
template-based approaches further confirms the benefits of local features for
template matching. We outperform the state-of-the-art template matching methods
on the LINEMOD, Occlusion-LINEMOD and T-LESS datasets. Our source code and data
are publicly available at https://github.com/nv-nguyen/template-pose


title: Quantum Computing with $\mathbb{Z}_2$ Abelian anyon system

abstract:
  Topological quantum computers provide a fault-tolerant method for performing
quantum computation. Topological quantum computers manipulate topological
defects with exotic exchange statistics called anyons. The simplest anyon model
for universal topological quantum computation is the Fibonacci anyon model,
which is a non-abelian anyon system. In non-abelian anyon systems, exchanging
anyons always results a unitary operations instead of a simple phase changing
in abelian anyon systems. So, non-abelian anyon systems are of the interest to
topological quantum computation. Up till now, most people still hold the belief
that topological quantum computions can be implemented only on the non-abelian
anyon systems. But actually this is not true. Inspired by extrinsic
semiconductor technology, we suggest that abelian anyon systems with defects
also support topological quantum computing. In this letter, we report a
topological quantum computer prototype based on $\mathbb{Z}_2$ abelian anyon
system.


title: Mamba: a systematic software solution for beamline experiments at HEPS

abstract:
  To cater for the diverse experiment requirements at the High Energy Photon
Source (HEPS) with often limited human resources, Bluesky is chosen as the
basis for our software framework, Mamba. In our attempt to address Bluesky's
lack of integrated GUIs, command injection with feedback is chosen as the main
way for the GUIs to cooperate with the CLI; a RPC service is provided, which
also covers functionalities unsuitable for command injection, as well as
pushing of status updates. In order to fully support high-frequency
applications like fly scans, Bluesky's support for asynchronous control is
being improved; to support high-throughput experiments, Mamba Data Worker (MDW)
is being developed to cover the complexity in asynchronous online data
processing for these experiments. To systematically simplify the specification
of metadata, scan parameters and data-processing graphs for each type of
experiments, an experiment parameter generator (EPG) will be developed;
experiment-specific modules to automate preparation steps will also be made.
The integration of off-the-shelf code in Mamba for domain-specific needs is
under investigation, and Mamba GUI Studio (MGS) is being developed to simplify
the implementation and integration of GUIs.


title: Statistical description of galactic clusters in Finzi gravity model

abstract:
  We exploit a new theory of gravity proposed by Finzi, which gives stronger
interaction at large scales, to study the thermodynamic description of galaxy
clusters. We employ a statistical model to deduce various thermodynamics
equations of state. In addition, we analyze the behavior of clustering
parameter in comparison to its standard (Newtonian) counterpart. The general
distribution function and its behavior with varying strength of clustering
parameter are also studied. The possibility of phase transition is also
investigated and observed that a phase transition is possible though
hierarchically.


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

abstract:
  The recovery of the input signal covariance values from its one-bit sampled
counterpart has been deemed a challenging task in the literature. To deal with
its difficulties, some assumptions are typically made to find a relation
between the input covariance matrix and the autocorrelation values of the
one-bit sampled data. This includes the arcsine law and the modified arcsine
law that were discussed in Part I of this work [2]. We showed that by
facilitating the deployment of time-varying thresholds, the modified arcsine
law has a promising performance in covariance recovery. However, the modified
arcsine law also assumes input signals are stationary, which is typically a
simplifying assumption for real-world applications. In fact, in many signal
processing applications, the input signals are readily known to be
non-stationary with a non-Toeplitz covariance matrix. In this paper, we propose
an approach to extending the arcsine law to the case where one-bit ADCs apply
time-varying thresholds while dealing with input signals that originate from a
non-stationary process. In particular, the recovery methods are shown to
accurately recover the time-varying variance and autocorrelation values.
Furthermore, we extend the formulation of the Bussgang law to the case where
non-stationary input signals are considered.


title: Cite-seeing and Reviewing: A Study on Citation Bias in Peer Review

abstract:
  Citations play an important role in researchers' careers as a key factor in
evaluation of scientific impact. Many anecdotes advice authors to exploit this
fact and cite prospective reviewers to try obtaining a more positive evaluation
for their submission. In this work, we investigate if such a citation bias
actually exists: Does the citation of a reviewer's own work in a submission
cause them to be positively biased towards the submission? In conjunction with
the review process of two flagship conferences in machine learning and
algorithmic economics, we execute an observational study to test for citation
bias in peer review. In our analysis, we carefully account for various
confounding factors such as paper quality and reviewer expertise, and apply
different modeling techniques to alleviate concerns regarding the model
mismatch. Overall, our analysis involves 1,314 papers and 1,717 reviewers and
detects citation bias in both venues we consider. In terms of the effect size,
by citing a reviewer's work, a submission has a non-trivial chance of getting a
higher score from the reviewer: an expected increase in the score is
approximately 0.23 on a 5-point Likert item. For reference, a one-point
increase of a score by a single reviewer improves the position of a submission
by 11% on average.